Emission Inventories for Ocean-Going
Vessels Using Category 3 Propulsion
Engines In or Near the United States
Draft Technical Support Document
United States
Environmental Protection
Agency
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Emission Inventories for Ocean-Going
Vessels Using Category 3 Propulsion
Engines In or Near the United States
Draft Technical Support Document
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments.
SER&
United States
Environmental Protection
Agency
EPA420-D-07-007
December 2007
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Table of Contents
1.0 Executive Summary 1
2.0 Introduction 7
3.0 Development of 2002 Baseline Inventory 8
3.1 Near Port Emissions 8
3.1.1 Selection of Individual Ports to be Analyzed 8
3.1.2 Methodology 9
3.1.3 Inputs for Port Emission Calculations 16
3.1.4 Stand Alone Ports 38
3.1.5 Domestic Traffic 39
3.1.6 2002 Near Port Inventories 40
3.2 2002 National and Regional Emissions 81
3.2.1 Overview of the Methodology 81
3.2.2 Modeling Domain and Geographic Regions 82
3.2.3 Interport Emissions Modeling 84
3.2.4 Combining the Near Port and Interport Inventories 91
3.2.5 2002 Regional and National Emission Inventories 95
4.0 2020 and 2030 National and Regional Inventories 106
4.1 Overview of Methodology 106
4.2 Growth Factors by Geographic Region 106
4.2.1 Summary of Regional Growth Rate Development 106
4.2.2 Trade Analysis 108
4.2.3 Ship Analysis by Vessel Type and Size 110
4.2.4 Trade Analysis by Commodity Type and Trade Route 115
4.2.5 Worldwide Estimates of Fuel Demand 117
4.2.6 Worldwide Bunker Fuel Consumption 118
4.2.7 Fuel Demand Used to Import and Export Cargo for the United
States 120
4.2.8 Bunker Fuel Consumption for the United States 122
4.2.9 2020 and 2030 Growth Factors for Eight Geographic Regions.... 124
4.3 Adjustments to Account for Current IMO NOx Standards 126
4.4 2020 and 2030 National and Regional Inventories 132
5.0 Estimated Category 3 Inventory Contribution 134
5.1 Contribution to National Level Inventory 134
5.2 Contribution to Mobile Source Inventories for Selected Cities 137
Appendix A: Commercial Marine Port Inventory Development A-1
Appendix B: Estimation, Validation, and Forecasts of Regional Commercial
Marine Vessel Inventories B-1
Appendix C: Global Trade and Fuels Assessment C-1
Appendix D: Estimates of Growth in Bunker Fuel D-1
Appendix E: Estimation, Validation, and Forecasts of Regional Commercial
Marine Vessel Inventories, Task 1 and 2 Baseline Inventory and
Ports Comparison E-1
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List of Tables
Table 1-1. 2002 Port Emissions Summary by Engine and Port Type 2
Table 1-2. 2002 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines 3
Table 1-3. 2002 Contribution of Near Ports and Interport Emissions to the 4
Table 1-4. 2002 Contribution of Near Ports and Interport Emissions to the Total
C3 Inventory (Continued) 5
Table 1-5. Growth and NOx Emission Adjustment Factors for 2020 and 2030 5
Table 1-6. 2020 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines 6
Table 1-7. 2030 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines 6
Table 3-1. Bins and Average Ship Characteristics for Deep Sea Ports 18
Table 3-2. Bins and Average Ship Characteristics for Great Lake Ports 22
Table 3-3. Auxiliary Engine Power Ratios (ARB Survey, except as noted) 25
Table 3-4. Auxiliary Engine Load Factor Assumptions 26
Table 3-5. Assumed Sulfur Levels in 26
Table 3-6. Estimated Mix of Fuel Types Used by Ships 27
Table 3-7. Emission Factors for OGV Main Engines using RO, g/kWh 27
Table 3-8. Auxiliary Engine Emission Factors by Fuel Type, g/kWh 28
Table 3-9. Auxiliary Engine Emission Factors by Ship Type, g/kWh 29
Table 3-10. Emission Factor Algorithm Coefficients for OGV Main Engines using
RO 30
Table 3-11. Calculated Low Load Multiplicative Adjustment Factors 31
Table 3-12. Deep Sea MEPA Vessel Movement and Shifting Details 32
Table 3-13. Great Lake MEPA movements and shifts 33
Table 3-14. Matched Ports for the Deep Sea Ports 34
Table 3-15. Great Lake Match Ports 37
Table 3-16. Total Emissions by Deep Sea Port in 2002 41
Table 3-17. Auxiliary Engine Emissions by Deep Sea Port in 2002 43
Table 3-18. Cruise Emissions by Deep Sea Port in 2002 45
Table 3-19. Reduced Speed Zone Emissions by Deep Sea Port in 2002 47
Table 3-20. Maneuvering Emissions by Deep Sea Port in 2002 49
Table 3-21. Hotelling Emissions by Deep Sea Port in 2002 51
Table 3-22. Auto Carrier Deep Sea Port Emissions in 2002 53
Table 3-23. Barge Carrier Deep Sea Port Emissions in 2002 54
Table 3-24. Bulk Carrier Deep Sea Port Emissions in 2002 55
Table 3-25. Container Ship Deep Sea Port Emissions in 2002 57
Table 3-26. General Cargo Ship Deep Sea Port Emissions in 2002 58
Table 3-27. Miscellaneous Ship Deep Sea Port Emissions in 2002 60
Table 3-28. Passenger Ship Deep Sea Port Emissions in 2002 61
Table 3-29. Refrigerated Cargo Ship Deep Sea Port Emissions in 2002 62
Table 3-30. Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002 63
Table 3-31. Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002 (Continued)
64
ii
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Table 3-32. Tanker Ship Deep Sea Port Emissions in 2002 65
Table 3-33. Ocean Going Tug Deep Sea Port Emissions in 2002 67
Table 3-34. Total Emissions by Great Lake Port in 2002 68
Table 3-35. Auxiliary Engine Emissions by Great Lake Port in 2002 69
Table 3-36. Cruise Emissions by Great Lake Port in 2002 70
Table 3-37. Reduced Speed Zone Emissions by Great Lake Port in 2002 71
Table 3-38. Maneuvering Emissions by Great Lake Port in 2002 72
Table 3-39. Hotelling Emissions by Great Lake Port in 2002 73
Table 3-40. Self-Unloading Bulk Carrier Emissions by Great Lake Port 74
Table 3-41. Bulk Carrier Emissions by Great Lake Port in 2002 75
Table 3-42. General Cargo Ship Emissions by Great Lake Port in 2002 75
Table 3-43. Tanker Ship Emissions by Great Lake Port in 2002 75
Table 3-44. Integrated Tug-Barge Emissions by Great Lake Port in 2002 76
Table 3-45. 2002 Port Emissions Summary by Engine and Port Type 76
Table 3-46. 2002 Port Emissions Summary by Engine and Port Type 77
Table 3-47. 2002 Port Emissions Summary by Mode and Port Type 77
Table 3-48. 2002 Port Emissions Summary by Mode and Port Type 78
Table 3-49. 2002 Port Emissions Summary by Ship Type and Port Type 79
Table 3-50. 2002 Port Emissions Summary by Ship Type and Port Type 80
Table 3-51. Average Vessel Cruise Speed by Ship Typea 87
Table 3-52. Auxiliary Engine Power Ratios 88
Table 3-53. Main and Auxiliary Engine Load Factors at Cruise Speed by Ship
Type 89
Table 3-54. Main Engine Emission Factors by Ship and Fuel Type 89
Table 3-55. Auxiliary Engine Emission Factors by Ship and Fuel Type 90
Table 3-56. Auxiliary Engine SCb Composite Emission Factors by Vessel
Type 90
Table 3-57. S02 Emission Factors Used to Adjust STEEM Emission Inventories 91
Table 3-58. 2002 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines 95
Table 3-59. 2002 Contribution of Near Ports and Interport Emissions to the Total
C3 Inventory 96
Table 3-60. 2002 Contribution of Near Ports and Interport Emissions to the Total
C3 Inventory (Cont'd) 97
Table 4-1. Aggregate Regions and Associated Countries 109
Table 4-2. Illustration of World Trade Estimates for Composite Commodities,
2005, 2012, and 2020 110
Table 4-3. Assignment of Commodities to Vessel Types Ill
Table 4-4. Fleet Characteristics 112
Table 4-5. Main and Auxiliary Engine Load Factors 114
Table 4-6. Vessel Speed by Type 116
Table 4-7. Day Length for Voyages for Non-Container Cargo Ship 121
Table 4-8. Day Length for Voyages for Container-Ship Trade Routes 122
Table 4-9. Association of the RTI Regions to the Eight Emission Inventory
Regions 125
Table 4-10. Regional Emission Inventory Growth Factors for 2020 and 2030 .. 126
in
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Table 4-11. International Maritime Organization NOx Emission Standards for
Marine Diesel Engines Greater Than 130 Kilowatts 126
Table 4-12. Vessel Age Distribution for Deep Sea Ports by Engine Type 128
Table 4-13. Vessel Age Distribution for Great Lake Ports by Engine Type 130
Table 4-14. Emission Reduction and Multiplicative Adjustment Factors for
Projecting the Effects of the IMO NOx Standards in Future Years. 132
Table 4-15. Growth and NOx Emission Multiplicative Adjustment Factors for 2020
and 2030 132
Table 4-16. 2020 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines 133
Table 4-17. 2030 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines 133
Table 5-1. 50 State Annual NOx Baseline Emission Levels for Mobile and Other
Source Categories 135
Table 5-2. 50 State Annual PM2.s Baseline Emission Levels for Mobile and Other
Source Categories 136
Table 5-3. 50 State Annual S02 Baseline Emission Levels for Mobile and Other
Source Categories 137
Table 5-4. Contribution of Commercial Marine Vessels to Mobile Source
Inventories for Selected Ports in 2002a 138
IV
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List of Figures
Figure 3-1. Regional Modeling Domains 98
Figure 3-2. Illustration of STEEM Modeling Domain and Spatial Distribution of
Shipping Lanes 99
Figure 3-3. Example of Gridded RSZ Lane (Hopewell, Virginia) 100
Figure 3-4. Example of Multiple RSZ Lanes (Brunswick, Georgia) 101
Figure 3-5. Example of Multiple Cruise Lanes 102
Figure 3-6. Example of Complete Replacement of STEEM Emissions (Panama
City, Florida) 103
Figure 3-7. Example of Partial Replacement of STEEM Emissions (Coos Bay,
Oregon) 104
Figure 3-8. Spatial Comparison of the Original STEEM and New Combined
Gridded Inventories—Southeast United States 105
Figure 4-1. Illustration of Method for Estimating Bunker Fuel Demand 108
Figure 4-2. Diesel Engine Specific Fuel Consumption 113
Figure 4-3. Worldwide Bunker Fuel Consumption 119
Figure 4-4. Annual Growth Rate in World-Wide Bunker Fuel Use by Commodity
Type 120
Figure 4-5 Bunker Fuel Used to Import and Export Cargo by Region of the United
States 123
Figure 4-6. Annual Growth Rates for Bunker Fuel Used to Import and Export
Cargo by Region of the United States 124
Figure 4-7. Installed Power by Main Engine Type for Deep Sea Ports 129
Figure 4-8. Installed Power by Main Engine Type for Great Lake Ports 131
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1 Executive Summary
This report describes the development of emission inventories for ocean-going
vessels using Category 3 propulsion engines in or near the United States (U.S.).
Category 3 engines are defined by EPA as having displacement greater than 30 liters
per cylinder. The inventories include emissions from both propulsion and auxiliary
engines on these vessels. Inventories are presented for the 2002, 2020, and 2030
calendar years.
Inventories are estimated for the following pollutants: oxides of nitrogen
(NOx), particulate matter (PIVh.s and PMio), hydrocarbons (HC), carbon monoxide
(CO), and sulfur dioxide (SO2). The PM inventories include directly emitted PM
only.
The inventories include both near port emissions as well as the interport
emissions from these vessels when operating away from port in U.S. waters. In this
analysis, the boundaries for vessels operating in the oceans generally extend from the
U.S. coastline to the 200 nautical mile limit of the Exclusive Economic Zone. For
ships operating in the Great Lakes, the boundary extends out to the international
boundary with Canada. The emissions results are presented for the total U.S., as well
as eight geographic regions within the U.S. (including Alaska and Hawaii).
The national and regional inventories for the 2002 base year are the result of
merging near port and interport inventories, which were derived independently (Table
1-2 identifies the geographic regions). For near port emissions, inventories for 2002
were developed for 89 deep water and 28 Great Lake ports in the U.S. This work was
done by ICF International under EPA contract.
Emissions from propulsion and auxiliary engines were calculated for each port
for four modes of operation: 1) hotelling, 2) maneuvering, 3) reduced speed zone
(RSZ), and 4) cruise. Hotelling, or dwelling, occurs while the vessel is docked, and
only the auxiliary engine(s) are being used to provide power to meet the ship's energy
needs. Maneuvering occurs within a very short distance of the docks. The RSZ
varies from port to port, though generally the RSZ would begin and end when the
pilots board or disembark vessels, or similarly when the near port shipping lanes
reach unconstrained ocean shipping lanes. The cruise mode emissions are defined to
extend 25 nautical miles beyond the end of the RSZ lanes for deep sea ports and 7
nautical miles for Great Lake ports.
U.S. Army Corps of Engineers (USAGE) entrance and clearance data for
2002, together with Lloyd's data for ship characteristics, were used to calculate
average ship characteristics and calls by ship type, engine type, and dead weight
tonnage (DWT) for most ports. RSZ distances and speeds were also determined for
each port. Maneuvering and hotelling time-in-mode data were the only inputs that
were not available for all 117 ports. When these inputs were available for selected
ports, they were used directly for the respective port. When they were unavailable,
the inputs for a selected port were matched to a remaining port by using various
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criteria such as the similarity in ship types calling on the ports and the maximum
vessel draft of the ports. The resulting total port emissions by engine and port type
are presented in Table 1-1.
Table 1-1. 2002 Port Emissions Summary by Engine and Port Type
Engine Type
Propulsion
Auxiliary
All
Port Type
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Grand Total
Metric Tonnes
NOx
64,378
247
64,625
56,259
302
56,561
120,637
549
121,186
PM,n
5,477
25
5,502
5,040
25
5,065
10,517
50
10,567
PM,s
5,034
23
5,057
4,585
23
4,608
9,619
46
9,665
HC
2,534
11
2,545
1,611
8
1,619
4,145
19
4,164
CO
6,342
22
6,364
4,292
23
4,315
10,634
45
10,679
SO,
52,556
188
52,744
41,133
201
41,334
93,689
389
94,078
Interport emissions were estimated using the Waterway Network Ship Traffic,
Energy, and Environmental Model (STEEM). This model was developed by the
University of Delaware as a comprehensive approach to quantify and geographically
represent ship traffic, emissions, and energy consumption from ocean-going vessels.
The model estimates emissions from propulsion and auxiliary marine engines used on
Category 3 vessels that engage in foreign commerce using historical North American
shipping activity, ship attributes, and activity-based emission factor information.
These inputs are assembled using a GIS platform that also contains an empirically
derived network of shipping lanes.
The spatially-defined waterway network is based on empirical shipping route
information from two global ship reporting databases: the International
Comprehensive Ocean-Atmosphere Data Set (ICOADS), and the Automated Mutual-
Assistance Vessel Rescue (AMVER) system. STEEM used 20 years of ICOADS
data (1983-2002) and approximately one year of AMVER data (2004-2005) to
spatially define the direction and width of each shipping lane in the waterway
network.
The ship movements and ship attributes information were primarily obtained
from the USAGE report on vessel entrances and clearances, combined with ship
attributes data from Lloyd's Maritime Intelligence Unit. For each ship, these data
sources provide information on the actual routes travelled, trips per route, and ship
characteristics such as ship type, propulsion engine power, and cruise speed. Each
trip is then assigned to the waterway network. Emissions for each ship are calculated
for each trip, and then all trips summed to calculate the total inventory.
The STEEM interport methodology represents a significant overall
improvement in quantifying and spatially representing the emissions from all large
ocean-going vessels. Nonetheless, the different assumptions, inputs, and methods
reflected in specific port-based analyses are often superior to the analyses produced
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by the STEEM model near ports. For example, the precision associated with the use
of ship positioning data may be poor in some locations, especially as the lanes
approach shorelines where ships would need to follow more prescribed paths for safe
navigation. Also, the model includes only one maneuvering operational mode at a
reduced speed to represent all activity near ports. This operation is generally assumed
to occur during 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. Further, the 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, and may or may not represent the actual reduced speeds in the prescribed
navigational channels near shore. Finally, the model does not include the emissions
from auxiliary engines during hotelling operations at the port.
For the above reasons, EPA concluded that the regional emission inventories
produced by the current STEEM model are most accurate for vessels while traveling
in unconstrained ocean or Great Lakes shipping lanes, and that the near port
inventories, which use more detailed local port information, were significantly more
accurate near the ports. Therefore, the regional and national inventories in this
analysis were derived by merging together: 1) the near port inventories, which extend
25 nautical miles and 7 nautical miles from the terminus of the RSZ for deep sea ports
and Great Lake ports, respectively: and 2) the remaining interport portion of the
STEEM inventory, which extends from the endpoint of the near port inventories to
the 200 nautical mile EEZ boundary or international border with Canada, as
appropriate. This work was conducted by ENVIRON International as a subcontractor
under the EPA contract with ICF.
The STEEM results were adjusted as appropriate to reflect a consistent set of
emission factors relative to those used in the near port analysis. The near port
inventories were then spatially merged into the STEEM gridded inventory to create a
comprehensive 2002 base year inventory for Category 3 vessels. The final emission
inventories for 2002 are shown in Table 1-2 below for eight geographic regions of the
U.S. and the entire nation.
Table 1-2. 2002 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines
Region
Alaska East ( AE)
Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Total U.S. Metric Tonnes
Total U.S. Short Tons
Metric Tonnes
NOx
18.231
60,625
220,844
174,454
54,935
26,278
105,380
15,171
675,918
745,224
PM1n
1.439
4,736
17,665
14,187
4,315
2,176
8,175
1,191
53,884
59,409
PM,.S
1.324
4,357
16,252
13,052
3,979
2,002
7,521
1,096
49,583
54,667
HC
603
2,009
7,345
5,817
1,820
946
3,503
503
22,546
24,858
CO
1.424
4,732
17,383
14,322
4,291
2,108
8,534
1,186
53,980
59,515
SO2
10.725
35,137
146,295
105,926
32,040
15,388
60,997
8,851
415,359
457,948
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The USAGE entrances and clearances data only represents foreign cargo
movements for all vessels, i.e., both U.S. and foreign flagged. As a result, domestic
cargo is not accounted for in that database. Therefore, the inventories presented
above exclude domestic traffic, except in several instances where near port emission
inventories were taken directly from other studies or were specially developed for this
work using additional sources of information on vessel movements. This does not
significantly affect the results, however, because domestic traffic is estimated to
account for less than 5 percent of the emissions from Category 3 ships in or around
the U.S.
The relative contributions of the near port and interport emission inventories
to total U.S. emissions are presented in Table 1-3 and Table 1-4. As expected, based
on the geographic scope of the two types of inventories, the interport and near port
inventories comprise about 80 percent and 20 percent of the total, respectively. The
deep sea ports account for about 97 to nearly 100 percent of the total inventories. The
Great Lake ports comprise about 3 percent to almost zero percent of the total
inventories, depending on the port region. This result is also expected given the small
number of Great Lake ports and more limited geographic area of the modeling
domain.
Table 1-3. 2002 Contribution of Near Ports and Interport Emissions to the
Total C3 Inventory
Region and
Port Type
Interport
Deep Sea
Great Lakes
Near Port
Deep Sea
Great Lakes
All Regions
Deep Sea
Great Lakes
All Region
Short Tons
Metric Tonnes
NOx
Total
554,732
540,110
14,622
121,186
120,637
549
675,918
660,747
15,171
745,224
%
Region
82.1
—
—
17.9
—
—
100.0
—
-
..
%
Type
100.0
97.4
2.6
100.0
99.5
0.5
—
97.8
2.2
..
PMio
Total
43,317
42,176
1,141
10,567
10,517
50
53,884
52,693
1,191
59,409
%
Region
80.4
—
—
19.6
—
—
100.0
—
~
..
%
Type
100.0
97.4
2.6
100.0
99.5
0.5
—
97.8
2.2
..
PM25
Total
39,918
38,868
1,050
9,665
9,619
46
49,583
48,487
1,096
54,667
%
Region
80.5
—
—
19.5
—
—
100.0
—
-
..
%
Type
100.0%
97.4%
2.6%
100.0%
99.5%
0.5%
—
97.8%
2.2%
..
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Table 1-4. 2002 Contribution of Near Ports and Interport Emissions to the Total
C3 Inventory (Continued)
Region and
Port Type
Interport
Deep Sea
Great Lakes
Near Port
Deep Sea
Great Lakes
All Regions
Deep Sea
Great Lakes
All Regions
Short Tons
Metric Tonnes
HC
Total
18,382
17,898
484
4,164
4,145
19
22,546
22,043
503
24,858
%
Region
81.5
—
-
18.5
—
-
100.0
—
—
%
Type
100.0
97.4
2.6
100.0
99.5
0.5
~
97.8
2.2
CO
Total
43,302
42,161
1,141
10,678
10,633
45
53,980
52,794
1,186
59,515
%
Region
80.2
~
-
19.8
~
~
100.0
—
—
%
Type
100.0
97.4
2.6
100.0
99.6
0.4
100.0
97.8
2.2
so,
Total
321,281
312,819
8,462
94,078
93,689
389
415,359
406,508
8,851
457,948
%
Region
77.4
~
~
22.6
~
~
100.0
—
—
%
Type
100.0
97.4
2.6
100.0
99.6
0.4
100.0
97.9
2.1
The 2002 total inventory was projected to 2020 and 2030 using regional
growth rates and adjustments in emissions to account for the International Maritime
Organization (EVIO) NOx standard, which was adopted in 2000. The regional growth
rates are based fuel demand forecasts that were developed by RTI International, under
contract to EPA. Fuel demand is an appropriate surrogate for emissions growth
because the amount of fuel used in Category 3 engines is highly correlated with the
emissions being emitted by those vessels. The growth factors and EVIO adjustment
factors are shown below.
Table 1-5. Growth and NOx Emission Adjustment Factors for 2020 and 2030
U.S. Region
Alaska East (AE)
Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Average
Annualized
Growth Ratio
(%)
3.3
3.3
4.5
2.9
5.0
3.3
5.0
1.7
Growth Factor
Relative to 2002
2020
1.79
1.79
2.21
1.67
2.41
1.79
2.41
1.35
2030
2.48
2.48
3.43
2.23
3.92
2.48
3.92
1.60
IMO NOx Standard
Adjustment Factor
Relative to 2002
2020
0.902
0.902
0.902
0.902
0.902
0.902
0.902
0.996
2030
0.891
0.891
0.891
0.891
0.891
0.891
0.891
0.918
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The resulting 2020 and 2030 estimated emission inventories for each region
and the nation are displayed below.
Table 1-6. 2020 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines
U.S. Region
Alaska East (AE)
Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Total U.S. Metric
Tonnes
Total U.S. Short Tons
Metric Tonnes
NOx
29,500
98,099
439,932
263,247
119,251
42,521
228,756
19,850
1,241,157
1,368,420
PMln
2,581
8,496
39,013
23,734
10,385
3,904
19,674
1,613
109,400
120,617
PM2,
2,375
7,816
35,892
21,835
9,576
3,591
18,100
1,485
100,670
110,993
HC
1,082
3,604
16,221
9,731
4,380
1,697
8,430
681
45,827
50,526
CO
2,555
8,489
38,390
23,960
10,327
3,782
20,538
1,606
109,646
120,889
SO,
19,240
63,033
323,089
177,206
77,108
27,605
146,797
11,989
846,067
932,820
Table 1-7. 2030 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines
U.S. Region
Alaska East (AE)
Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Total U.S. Metric
Tonnes
Total U.S. Short Tons
Metric Tonnes
NOx
40,318
134,072
674,869
346,090
191,879
58,114
368,075
22,328
1,835,744
2,023,974
PM1n
3,572
11,755
60,586
31,588
16,915
5,401
32,047
1,909
163,773
180,566
PM,.S
3,286
10,814
55,739
29,061
15,598
4,969
29,483
1,757
150,708
166,161
HC
1,497
4,986
25,191
12,952
7,135
2,348
13,732
806
68,647
75,686
CO
3,534
11,745
59,618
31,889
16,821
5,232
33,454
1,901
164,196
181,032
SO2
26,620
87,211
501,748
235,848
125,601
38,194
239,116
14,190
1,268,528
1,398,598
Category 3 vessels contribute to the formation of ground level ozone and
concentrations of fine particles in the ambient atmosphere. Based on this emission
inventory analysis, it is estimated that these engines contributed nearly 6 percent of mobile
source NOx, over 10 percent of mobile source PM2 5, and about 40 percent of mobile
source SO2 in 2002. Due to new emission controls in other mobile source sectors and the
growth in activity discussed above, this contribution is estimated to increase to about 34
percent of mobile source NOx, 45 percent of mobile source PMa.s, and 94 percent of
mobile source SO2 by 2030 without further controls on these engines. In addition, more
than 40 major U.S. deep sea ports are currently located in areas that are designated as
being in nonattainment for either or both the 8-hour ozone and PM2 5 National Ambient
Air Quality Standards (NAAQS). Many ports are located in areas rated as Class I federal
areas for visibility impairment and regional haze.
-------
2 Introduction
Marine diesel engines above 30 liters per cylinder, called Category 3 marine
diesel engines, are significant contributors to the total United States (U.S.) mobile
source emission inventory. Category 3 marine engines are predominantly used in
ocean going vessels.
The regional and national inventories for Category 3 vessels presented in this
study are based, in part, on the Waterway Network Ship Traffic, Energy and
Environment Model (STEEM). ' More specifically, this model was used to quantify
and geographically (i.e., spatially) represent inter-port vessel traffic and emissions for
vessels traveling in or near the U.S. The resulting inventory includes emissions from
both propulsion and auxiliary engines used on these vessels.
The STEEM model includes detailed information about ship routes and
destinations in order to provide spatially allocated emissions of ships in transit. This
model was developed as a comprehensive approach to estimating emissions from
large vessel ship traffic. The shipping lanes and directions were determined along
paths empirically derived from ship positioning data. However, the precision of this
positioning data may be poor in some locations, especially as ships approach
shorelines where they would need to follow more prescribed navigational channels.
For the majority of the ship transiting trips, the cruise mode (approximately -80
percent engine load) was used to estimate emissions. However within 20 km of each
trip origin and destination, STEEM used what was referred to as a maneuvering mode
load of 20 percent, but this represents vessel speeds considerably higher than actual
maneuvering speeds very near the docks. Also, STEEM does not account for
hotelling emissions by auxiliary engines while the vessels are docked.
While the STEEM model is applicable for most of the activity in the ocean, it
was decided that the STEEM data should not be used to characterize emissions near
ports. Instead, a near port inventory was created by using detailed individual port
estimates. Near port inventories were developed for 89 deep water and 28 Great Lake
ports in the U.S.3 These 117 ports covered most, but not all, of the ports identified by
the shipping lane paths evident in the STEEM data.
The detailed port inventories were then spatially merged into the STEEM
gridded inventory to create a comprehensive inventory for Category 3 vessels. This
was done for a base year of 2002. Inventories for 2020 and 2030 were then projected
using regional growth rates4'5 and adjustment factors to account for the International
Maritime Organization NOx Standard, which was adopted in 2000.3 This report
details the methodologies used to create the baseline and future year inventories and
presents the resulting inventories for the U.S. Section 3 describes the methodology
and results for the 2002 base year inventory. Section 4 follows with a discussion of
the growth rates and methodology used to create the 2020 and 2030 inventories.
Section 5 presents the estimated contribution of Category 3 vessels to U.S. national
and local inventories.
-------
3 Development of 2002 Baseline Inventory
This section describes the methodology and presents the resulting inventories
for the 2002 baseline calendar year. Section 3.1 describes the methodology and
results for near port emissions. Section 3.2 describes the analysis conducted to
determine emissions when operating away from port (also referred to as "interport"
emissions), as well as the method for merging the interport and near port portions of
the inventory. Resulting total emissions for the U.S., as well as eight geographic
regions within the U.S., are then presented.
3.1 Near Port Emissions
Near port inventories for calendar year 2002 were developed for ocean-going
vessels at 89 deep water and 28 Great Lake ports in the U.S. For the purposes of this
analysis, ocean-going vessels are those that use Category 3 engines for propulsion,
where Category 3 engines are defined by EPA as having displacement greater than 30
liters per cylinder. The inventories include emissions from both propulsion and
auxiliary engines on these vessels. Inventories are estimated for the following
pollutants: oxides of nitrogen (NOx), particulate matter (PIVb.s and PMio), total
hydrocarbons (HC),a carbon monoxide (CO), and sulfur dioxide (SCh).
This section first describes the selection of the ports for analysis and then
provides the methodology used to develop the near port inventories. This is followed
by a description of the key inputs. Total emissions by port and pollutant for 2002 are
then presented. The work summarized here was conducted by ICF International
under contract to EPA3 The ICF documentation provides more detailed information
and is included as Appendix A.
3.1.1 Selection of Individual Ports to be Analyzed
All 150 deep sea and Great Lake ports in the Principal Ports of the United
States dataset were used as a starting point. Thirty ports which had no foreign traffic
were eliminated because there is no information in the USAGE entrances and
clearances data about domestic traffic. (See Section 3.1.5 for a further discussion of
domestic traffic and how it is accounted for in this study). In addition, two U.S.
Territory ports in Puerto Rico were removed as these were outside the area of interest
for this study. Several California ports were added to the principle ports list because
ARE provided the necessary data and estimates for those ports. This is discussed in
Section 3.1.4.1. Also, a conglomerate port in the Puget Sound area was added as
discussed in Section 3.1.4.2. The final list of 117 deep sea and Great Lake ports,
along with their coordinates, is given in Appendix A, Table A-l.
1 Total hydrocarbons can be converted to volatile organic carbon compounds (VOC) by using a multiplicative
conversion factor of 1.053.
8
-------
3.1.2 Methodology
Near port emissions for each port are calculated for four modes of operation:
1) hotelling, 2) maneuvering, 3) reduced speed zone (RSZ), and 4) cruise. Hotelling,
or dwelling, occurs while the vessel is docked or anchored near a dock, and only the
auxiliary engine(s) are being used to provide power to meet the ship's energy needs.
Maneuvering occurs within a very short distance of the docks. The RSZ varies from
port to port, though generally the RSZ would begin and end when the pilots board or
disembark, and typically occurs when the near port shipping lanes reach
unconstrained ocean shipping lanes. The cruise mode emissions in the near ports
analysis extend 25 nautical miles beyond the end of the RSZ lanes.
Emissions are calculated separately for propulsion and auxiliary engines. This
section provides the equations used to calculate propulsion and auxiliary emissions
for each activity mode and describes the inputs that are required.
3.1.2.1 Cruise
Cruise emissions are calculated for both propulsion (main) and auxiliary
engines. The basic equation used to calculate cruise mode emissions for the main
engines is:
Equation 3-1
Emission$mis{ma^ = (calls) x (P[maiJ x (hrs/callcmj x (LFcmis{mai^) x (EF[mait^) x (1 a6 tanned g)
Where:
Emissionscruise [main] = Metric tonnes emitted from main engines in cruise mode
Calls = Round-trip visits (i.e., one entrance and one clearance is considered a call)
P[main] = Total main engine power, in kilowatts
hrs/callcraise = Hours per call for cruise mode
LFcmise [main] = Load factor for main engines in cruise mode (unitless)
EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-
hr (these vary as a function of engine type and fuel used, rather than activity
mode)
10 = Conversion factor from grams to metric tonnes
In addition, the time in cruise is calculated as follows:
Equation 3-2
Hrs I callcruise = Cruise Distance [nmiles ] I Cruise Speed [knots ] x 2 trips I call
Where:
Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7
nautical miles for Great Lake ports)
Cruise speed = vessel service speed, in knots
2 trips/call = Used to calculate round trip cruise distance
9
-------
Main engine load factors are calculated directly from the propeller curve
based upon the cube of actual speed divided by maximum speed (at 100% maximum
continuous rating [MCR]):
Equation 3-3
LoadF actor crui = (Cruise Speed[knots]/ Maximum Speed[knots]f
Since cruise speed is estimated at 94 percent of maximum speed7, the load
factor for main engines at cruise is 0.83.
Substituting Equation 3-2 for time in cruise into Equation 3-1, and using the
load factor of 0.83, the equation used to calculate cruise mode emissions for the main
engines becomes the following:
Equation 3^1. Cruise Mode Emissions for Main Engines
EmissioY\^uiiimaiAL = (calty x (P[mai$ ) x(Cruis
-------
EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in
g/kW-hr (these vary as a function of engine type and fuel used, rather than
activity mode)
10"6 = Conversion factor from grams to metric tonnes
The inputs of calls, cruise distance, and vessel speed are the same for main
and auxiliary engines. Relative to the main engines, auxiliary engines have separate
inputs for engine power, load factor, and emission factors. The activity-related
inputs, such as engine power, vessel speed, and calls, can be unique to each ship
calling on a port, if ship-specific information is available. For this analysis, as
discussed in section 3.1.3.1.1, these inputs were developed by port for bins that varied
by ship type, engine type, and dead weight tonnage (DWT) range.
3.1.2.2 Reduced Speed Zone (RSZ)
RSZ emissions are calculated for both propulsion (main) and auxiliary
engines. The basic equation used to calculate RSZ mode emissions for the main
engines is:
Equation 3-6
tonnetg)
Where:
EmissionsRsz[main] = Metric tonnes emitted from main engines in RSZ mode
calls = Round-trip visits (i.e., one entrance and one clearance is considered a call)
P[main] = Total main engine power, in kilowatts
hrs/callRSZ = Hours per call for RSZ mode
LFRSZ [main] = Load factor for main engines in RSZ mode, unitless
EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-
hr (these vary as a function of engine type and fuel used, rather than activity
mode)
Adj = Low load adjustment factor, unitless (used when the load factor is below
0.20)
10 = Conversion factor from grams to metric tonnes
In addition, the time in RSZ mode is calculated as follows:
Equation 3-7
Hrs I call^z = RSZ Distance [nmiles ] I RSZ Speed [knots ] x 2 trips I call
Load factor during the RSZ mode is calculated as follows:
Equation 3-8
LoadFactorRSZ[main] = (RSZ Speed I Maximum Speed}
In addition:
Equation 3-9
Maximum Speed = Cruise Speed/ 0.94
11
-------
Where:
0.94 = Fraction of cruise speed to maximum speed
Substituting Equation 3-9 into Equation 3-8, the equation to calculate load factor
becomes:
Equation 3- 10
LoadFactorRSZ [main } = (RSZ Speed x 0 . 94 / Cruise Speed)
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 3-7 for time in mode and Equation 3-10 for load factor
into Equation 3-6, the expression used to calculate RSZ mode emissions for the main
engines becomes:
Equation 3-11. RSZ Mode Emissions for Main Engines
iorj&^j = (ca//| x (f[a^ ) x (RSZDitancel RSZSpeed) x (Itripkalt) x (RSZSpeedx 0.94/ CruisSpeed] x (EF[alai) x (Adj) x (1 0* tonneig)
Where:
EmissionsRSz[main] = Metric tonnes emitted from main engines in RSZ mode
calls = Round-trip visits (i.e., one entrance and one clearance is considered a call)
P[main] = Total main engine power, in kilowatts
RSZ distance = one way distance, in nautical miles (specific to each port)
RSZ speed = speed, in knots (specific to each port)
2 trips/call = Used to calculate round trip RSZ distance
Cruise speed = vessel service speed, in knots
EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-
hr (these vary as a function of engine type and fuel used, rather than activity
mode)
Adj = Low load adjustment factor, unitless (used when the load factor is below
0.20)
10"6 = Conversion factor from grams to tons
0.94 = Fraction of cruise speed to maximum speed
Emission factors are considered to be relatively constant down to about 20
percent load. Below that threshold, emission factors tend to increase rapidly as the
load decreases. During the RSZ mode, load factors can fall below 20 percent. Low
load multiplicative adjustment factors were developed and applied when the load falls
below 20 percent (0.20). If the load factor is 0.20 or greater, the low load adjustment
factor is set to 1.0.
The equation used to calculate RSZ mode emissions for the auxiliary engines
is:
12
-------
Equation 3-12. RSZ Mode Emissions for Auxiliary Engines
= (calls) x (P[awc]) x (RSZDi&avce/RSZSpeed} x (2tripf/calt) x (LFKS2iala]) x (EF[au,c]) x (10" tonnes/g)
Where:
EmissionsRsz[aux] = Metric tonnes emitted from auxiliary engines in RSZ mode
calls = Round-trip visits (i.e., one entrance and one clearance is considered a call)
P[aux] = Total auxiliary engine power, in kilowatts
RSZ distance = one way distance, in nautical miles (specific to each port)
RSZ speed = speed, in knots (specific to each port)
2 trips/call = Used to calculate round trip cruise distance
LFRSZ [aux] = Load factor for auxiliary engines in RSZ mode, unitless (these vary
by ship type and activity mode)
EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in
g/kW-hr (these vary as a function of engine type and fuel used, rather than
activity mode)
10"6 = Conversion factor from grams to metric tonnes
Unlike main engines, there is no need for a low load adjustment factor for
auxiliary engines, because of the way they are generally operated. When only low
loads are needed, one or more engines are shut off, allowing the remaining engines to
operate at a more efficient level.
The inputs of calls, RSZ distance, and RSZ speed are the same for main and
auxiliary engines. Relative to the main engines, auxiliary engines have separate
inputs for engine power, load factor, and emission factors. The RSZ distances vary
by port rather than vessel or engine type. Some RSZ speeds vary by ship type, while
others vary by DWT. Mostly, however, RSZ speed is constant for all ships entering
the harbor area. All Great Lake ports have reduced speed zone distances of three
nautical miles occurring at halfway between cruise speed and maneuvering speed.
3.1.2.3 Maneuvering
Maneuvering emissions are calculated for both propulsion (main) and
auxiliary engines. The basic equation used to calculate maneuvering mode emissions
for the main engines is:
Equation 3-13
Emission^maili = (calty x (P[ma^) x (hrsl callmj x (LFma/imai^ x (EF[maiJ x (Adj) x (1 a6 tonnedg)
Where:
Emissionsman[main] = Metric tonnes emitted from main engines in maneuvering
mode
calls = Round-trip visits (i.e., one entrance and one clearance is considered a call)
P[main] = Total main engine power, in kilowatts
hrs/callman = Hours per call for maneuvering mode
LFman [main] = Load factor for main engines in maneuvering mode, unitless
13
-------
EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-
hr (these vary as a function of engine type and fuel used, rather than activity
mode)
Adj = Low load adjustment factor, unitless (used when the load factor is below
0.20)
10"6 = Conversion factor from grams to metric tonnes
Maneuvering time-in-mode is estimated based on the distance a ship travels
from the breakwater or port entrance to the pier/wharf/dock (PWD). Maneuvering
times also include shifts from one PWD to another or from one port within a greater
port area to another. Average maneuvering speeds vary from 3 to 8 knots depending
on direction and ship type. For consistency, maneuvering speeds were assumed to be
the dead slow setting of approximately 5.8 knots.
Load factor during maneuvering is calculated as follows:
Equation 3-14
LoadFac torman [ main] = (Man Speed[knots ] I Maximum Speed[knots ] )
In addition:
Equation 3-15
Maximum Speed = Cruise Speed[kno is] 70.94
Where:
0.94 = Fraction of cruise speed to maximum speed
Also, the maneuvering speed is 5.8 knots. Substituting Equation 3-15 into Equation
3-14, and using a maneuvering speed of 5.8 knots, the equation to calculate load
factor becomes:
Equation 3-16
LoadFactorman[mam] = (5 .45 / Cruise Speed)3
Load factors below 2 percent were set to 2 percent as a minimum.
Substituting Equation 3-16 for load factor into Equation 3-13, the expression
used to calculate maneuvering mode emissions for the main engines becomes:
Equation 3-17. Maneuvering Mode Emissions for Main Engines
ai^ =(call§ x(P[mai^)x (hrsl callman)x (5A5ICruis
-------
EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-
hr (these vary as a function of engine type and fuel used, rather than activity
mode)
Adj = Low load adjustment factor, unitless (used when the load factor is below
0.20)
10"6 = Conversion factor from grams to metric tonnes
Since the load factor during maneuvering usually falls below 20 percent, low
load adjustment factors are also applied accordingly. Maneuvering times are not
readily available for all 117 ports. For this analysis, maneuvering times and load
factors available for a subset of the ports were used to calculate maneuvering
emissions for the remaining ports. This is discussed in more detail in sections.1.3.8.
The equation used to calculate maneuvering mode emissions for the auxiliary
engines is:
Equation 3-18. Maneuvering Mode Emissions for Auxiliary Engines
Emissionsma4aux] = (calls) x (P[am]) x (hrslcallmj x (LFman[aux]) x (EF[aux]) x (1 (T6 tonnes/ g)
Where:
Emissionsman[aux] = Metric tonnes emitted from auxiliary engines in maneuvering
mode
calls = Round-trip visits (i.e., one entrance and one clearance is considered a call)
P[aux] = Total auxiliary engine power, in kilowatts
hrs/callman = Hours per call for maneuvering mode
LFman [aux] = Load factor for auxiliary engines in maneuvering mode, unitless
(these vary by ship type and activity mode)
EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in
g/kW-hr (these vary as a function of engine type and fuel used, rather than
activity mode)
10"6 = Conversion factor from grams to metric tonnes
Low load adjustment factors are not applied for auxiliary engines.
3.1.2.4 Hotelling
Hotelling emissions are calculated for auxiliary engines only, as main engines
are not operational during this mode. The equation used to calculate hotelling mode
emissions for the auxiliary engines is:
Equation 3-19. Hotelling Mode Emissions for Auxiliary Engines
Emission^^ = (calls) x (P[aux]) x (hrslcallhotel) x (LFhote[aux]) x (EF[aux]) x (1 (T6 tonnes/ g)
Where:
Emissionshotei[aux] = Metric tonnes emitted from auxiliary engines in hotelling
mode
calls = Round-trip visits (i.e., one entrance and one clearance is considered a call)
15
-------
P[aux] = Total auxiliary engine power, in kilowatts
hrs/callhotei = Hours per call for hotelling mode
LFhotei [aux] = Load factor for auxiliary engines in hotelling 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 = Conversion factor from grams to metric tonnes
Hotelling times are not readily available for all 117 ports. For this analysis,
hotelling times available for a subset of the ports were used to calculate hotelling
emissions for the remaining ports. This is discussed in more detail in section 3.1.3.8
3.1.3 Inputs for Port Emission Calculations
From a review of the equations described in section 3.1.2 above, the following
inputs are required to calculate emissions for the four modes of operation (cruise,
RSZ, maneuvering, and hotelling):
• Number of calls
• Main engine power
• Cruise (vessel service) speed
• Cruise distance
• RSZ distance for each port
• RSZ speed for each port
• Auxiliary engine power
• Auxiliary load factors
• Main and auxiliary emission factors
• Low load adjustment factors for main engines
• Maneuvering time-in-mode (hours/call)
• Hotelling time-in-mode (hours/call)
Note that load factors for main engines are not listed explicitly, since they are
calculated as a function of mode and/or cruise speed. This section describes the
inputs in more detail, as well as the sources for each input.
3.1.3.1 Calls and Ship Characteristics (Propulsion Engine Power and
Cruise Speed)
For this analysis, U.S. Army Corps of Engineers (USAGE) entrance and
clearance data for 2002,8 together with Lloyd's data for ship characteristics,9 were
used to calculate average ship characteristics and calls by ship type for each port.
Information for number of calls, propulsion engine power, and cruise speed were
obtained from these data.
16
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3.1.3.1.1 Bins by Ship Type, Engine Type, andDWTRange
The records from the USAGE entrances and clearances data base were
matched with Lloyd's data on ship characteristics for each port. Calls with vessels
that have either Category 1 or 2 propulsion engines were eliminated from the data set.
The data was then binned by ship type, engine type and dead weight tonnage (DWT)
range. The number of entrances and clearances in each bin are counted, summed
together and divided by two to determine the number of calls (i.e., one entrance and
one clearance was considered a call). For Great Lake ports, there is a larger
frequency of ships either entering the port loaded and leaving unloaded (light) or
entering the port light and leaving loaded. In these cases, there would only be one
record (the loaded trip into or out of the port) that would be present in the data. For
Great Lake ports, clearances were matched with entrances by ship name. If there was
not a reasonable match, the orphan entrance or clearance was treated as a call.
Propulsion power and vessel cruise speed are also averaged for each bin.
While each port is analyzed separately, the various bins and national average ship
characteristics are given in Table 3-1 for deep sea ports and Table 3-2 for Great Lake
ports. Auxiliary engine power was computed from the average propulsion power
using the auxiliary power to propulsion power ratios discussed in section 3.1.3.4.
17
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Table 3-1. Bins and Average Ship Characteristics for Deep Sea Ports
Ship Type
AUTO CARRIER
Main
Engine3
MSB
DWT Range
< 10,000
10,000-20,000
20,000 - 30,000
MSB Total
SSB
<10,000
10,000-20,000
20,000 - 30,000
SSB Total
AUTO CARRIER Total
BARGE CARRIER
MSB
< 25,000
MSB Total
SSB
< 25,000
35,000-45,000
45,000 - 90,000
SSB Total
ST
35,000-45,000
ST Total
BARGE CARRIER Total
BULK CARRIER
MSB
< 25,000
25,000-35,000
35,000-45,000
45,000 - 90,000
> 90,000
MSB Total
Calls
35
224
28
286
84
2,316
621
3,020
3,306
1
1
1
20
19
40
5
5
45
213
6
44
51
1
314
Engine Power (kW)
Main
6,527
10,499
6,620
9,640
7,927
10,899
13,239
11,298
11,155
4,461
4,461
3,916
19,463
25,041
21,724
24,196
24,196
21,779
4,867
8,948
9,148
9,705
16,109
6,360
Auxiliary
1,736
2,793
1,761
2,564
2,109
2,899
3,522
3,005
2,967
1,200
1,200
1,053
5,236
6,736
5,844
6,509
6,509
5,859
1,080
1,986
2,031
2,155
3,576
1,412
Cruise
Speed
(kts)
16.0
18.2
13.0
17.4
17.7
18.7
19.5
18.8
18.7
13.3
13.3
14.0
18.0
20.0
18.9
21.7
21.7
19.1
14.0
14.0
15.2
14.3
15.8
14.2
DWT
6,211
13,003
22,268
13,063
8,845
14,959
24,860
16,826
16,500
4,393
4,393
11,783
44,799
48,093
45,538
41,294
41,294
44,657
15,819
29,984
39,128
71,242
105,550
28,621
18
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Table 3-1. Bins and Average Ship Characteristics for Deep Sea Ports (continued)
Ship Type
BULK CARRIER
Main
Engine3
SSD
DWT Range
< 25,000
25,000-35,000
35,000-45,000
45,000 - 90,000
> 90,000
SSD Total
ST
< 25,000
25,000-35,000
ST Total
BULK CARRIER Total
CONTAINER SHIP
MSB
< 25,000
25,000-35,000
35,000-45,000
45,000 - 90,000
MSB Total
SSB
< 25,000
25,000-35,000
35,000-45,000
45,000 - 90,000
> 90,000
SSB Total
ST
< 25,000
25,000-35,000
35,000-45,000
ST Total
CONTAINER SHIP Total
GENERAL CARGO
MSB
< 25,000
25,000-35,000
35,000-45,000
45,000 - 90,000
MSB Total
SSB
< 25,000
25,000-35,000
35,000-45,000
45,000 - 90,000
> 90,000
SSB Total
ST
< 25,000
ST Total
GENERAL CARGO Total
Calls
1,194
2,192
1,742
3,733
352
9,212
72
3
75
9,600
1,005
53
59
248
1,365
2,054
2,360
2,443
6,209
98
13,163
46
89
41
176
14,703
2,937
38
1
9
2,984
2,357
500
1,122
405
6
4,389
18
18
7,391
Engine Power (kW)
Main
5,650
7,191
8,515
9,484
14,071
8,434
6,290
8,948
6,379
8,350
6,846
22,304
26,102
37,650
13,878
12,381
19,247
24,755
36,151
57,325
27,454
20,396
21,066
23,562
21,472
26,122
5,080
9,458
13,728
11,932
5,159
6,726
7,575
9,269
9,336
10,628
7,718
17,897
17,897
6,709
Auxiliary
1,254
1,596
1,890
2,105
3,124
1,872
1,396
1,986
1,416
1,854
1,506
4,907
5,742
8,283
3,053
2,724
4,234
5,446
7,953
12,612
6,040
4,487
4,635
5,184
4,724
5,747
1,316
2,450
3,556
3,090
1,336
1,742
1,962
2,401
2,418
2,753
1,999
4,635
4,635
1,738
Cruise
Speed
(kts)
14.2
14.6
14.7
14.4
14.5
14.5
15.0
15.0
15.0
14.5
17.2
20.6
22.3
24.0
18.8
19.1
20.5
21.8
23.3
25.0
21.9
20.8
21.0
21.0
21.0
21.6
15.1
15.4
14.3
16.0
15.1
15.4
14.9
15.2
15.1
14.5
15.3
21.0
21.0
15.2
DWT
19,913
29,323
39,875
62,573
112,396
46,746
18,314
33,373
18,819
45,936
8,638
28,500
39,932
56,264
19,419
18,776
31,205
40,765
58,604
105,231
44,513
19,963
30,804
40,949
30,334
42,014
8,268
30,746
40,910
50,250
8,688
14,409
29,713
41,568
47,712
134,981
26,326
22,548
22,548
19,196
19
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Table 3-1. Bins and Average Ship Characteristics for Deep Sea Ports (continued)
Ship Type
MISCELLANEOUS
Main
Engine3
MSB
DWT Range
All
MSB Total
MSB-EB
All
MSB-EB Total
SSB
All
SSB Total
ST
All
ST Total
MISCELLANEOUS Total
PASSENGER
MSB
<10,000
10,000-20,000
MSB Total
MSB-EB
<10,000
10,000-20,000
MSB-EB Total
SSB
<10,000
SSB Total
GT-EB
10,000-20,000
GT-EB Total
ST
<10,000
10,000-20,000
ST Total
PASSENGER Total
REEFER
MSB
<10,000
10,000-20,000
MSB Total
SSB
<10,000
10,000-20,000
SSB Total
REEFER Total
RORO
MSB
<10,000
10,000-20,000
> 30,000
MSB Total
SSB
<10,000
10,000-20,000
20,000 - 30,000
> 30,000
SSB Total
GT
> 30,000
GT Total
ST
10,000-20,000
20,000 - 30,000
ST Total
RORO Total
Calls
51
51
6
6
7
7
1
1
64
1,011
24
1,035
1,964
228
2,192
189
189
143
143
13
52
65
3,623
122
60
182
464
801
1,265
1,447
892
286
31
1,208
132
208
31
555
925
1
1
2
1
3
2,137
Engine Power (kW)
Main
9,405
9,405
16,968
16,968
4,659
4,659
12,871
12,871
9,564
22,024
96,945
23,762
39,095
53,236
40,566
23,595
23,595
44,428
44,428
16,858
29,982
27,357
34,800
4,829
12,506
7,360
6,539
12,711
10,449
10,060
7,840
9,312
22,386
8,561
7,240
9,062
12,781
20,362
15,702
47,076
47,076
22,373
22,373
22,373
11,687
Auxiliary
2,530
2,530
4,565
4,565
1,253
1,253
3,462
3,462
2,573
6,123
26,951
6,606
10,868
14,800
11,277
6,559
6,559
12,351
12,351
4,687
8,335
7,605
9,674
1,961
5,077
2,988
2,655
5,161
4,242
4,084
2,031
2,412
5,798
2,217
1,875
2,347
3,310
5,274
4,067
12,193
12,193
5,795
5,795
5,795
3,027
Cruise
Speed
(kts)
12.7
12.7
12.7
12.7
14.2
14.2
21.0
21.0
13.0
20.2
28.5
20.4
20.9
22.0
21.1
20.1
20.1
24.0
24.0
21.2
18.0
18.6
20.9
16.3
20.0
17.5
18.0
20.8
19.7
19.5
15.5
17.0
21.0
16.0
15.0
16.9
18.9
18.9
17.9
24.0
24.0
25.0
25.0
25.0
16.8
DWT
6,083
6,083
15,795
15,795
8,840
8,840
16,605
16,605
7,311
5,976
15,521
6,197
7,345
10,924
7,717
6,235
6,235
11,511
11,511
6,981
13,960
12,564
7,443
5,646
11,632
7,619
7,267
13,138
10,986
10,562
6,641
11,338
31,508
8,389
4,695
14,293
22,146
42,867
30,321
36,827
36,827
16,144
22,501
18,687
17,910
20
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Table 3-1. Bins and Average Ship Characteristics for Deep Sea Ports (continued)
Ship Type
TANKER
Main
Engine3
MSB
DWT Range
<30,000
30,000 - 60,000
60,000 - 90,000
90,000 - 120,000
MSB Total
SSB
<30,000
30,000 - 60,000
60,000 - 90,000
90,000 - 120,000
120,000- 150,000
> 150,000
SSB Total
GT-EB
30,000 - 60,000
GT-EB Total
ST
< 30,000
30,000 - 60,000
60,000 - 90,000
90,000 - 120,000
120,000- 150,000
> 150,000
ST Total
TANKER Total
TUG
MSB
All
MSB Total
TUG Total
Grand Total
Calls
650
181
148
3
981
3,050
3,752
1,766
2,835
258
487
12,147
13
13
2
87
73
4
3
2
170
13,310
48
48
48
55,672
Engine Power (kW)
Main
4,888
10,533
9,782
15,139
6,697
6,303
9,021
10,310
12,318
15,840
16,888
9,755
7,592
7,592
13,534
15,818
26,848
17,660
19,125
20,785
20,678
9,667
7,579
7,579
7,579
15,212
Auxiliary
1,031
2,222
2,064
3,194
1,413
1,330
1,903
2,175
2,599
3,342
3,563
2,058
1,602
1,602
2,856
3,338
5,665
3,726
4,035
4,386
4,363
2,040
2,039
2,039
2,039
3,593
Cruise
Speed
(kts)
14.3
15.3
14.7
14.1
14.6
14.6
14.9
14.6
14.6
14.7
15.2
14.7
14.5
14.5
18.0
17.9
18.9
16.3
16.0
14.3
18.2
14.8
14.5
14.5
14.5
17.4
DWT
11,415
42,153
74,245
113,957
26,847
17,145
41,677
74,595
101,116
144,405
166,394
61,353
39,839
39,839
27,235
43,982
70,108
91,868
122,409
190,111
58,616
58,754
626
626
626
38,083
Engine Types: MSB = medium speed engine; SSB = slow speed engine; ST = steam turbine; GT = gas turbine
21
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Table 3-2. Bins and Average Ship Characteristics for Great Lake Ports
Ship Type
BULK CARRIER
Main
Engine a
MSD
DWT Range
10,000-20,000
20,000 - 30,000
30,000 - 40,000
MSD Total
SSD
10,000-20,000
20,000 - 30,000
30,000 - 40,000
SSD Total
ST
20,000 - 30,000
ST Total
BULK CARRIER Total
SELF UNLOADING
BULK CARRIER
MSD
10,000-20,000
20,000 - 30,000
30,000 - 40,000
> 40,000
MSD Total
SSD
20,000 - 30,000
30,000 - 40,000
SSD Total
ST
< 10,000
10,000-20,000
20,000 - 30,000
ST Total
SELF UNLOADING BULK CARRIER Total
GENERAL CARGO
MSD
< 10,000
10,000-20,000
MSD Total
SSD
< 10,000
10,000-20,000
20,000 - 30,000
30,000 - 40,000
SSD Total
GENERAL CARGO Total
INTEGRATED
TUG-BARGE
MSD
All
MSD Total
INTEGRATED TUG-BARGE Total
TANKER
MSD
10,000-20,000
MSD Total
SSD
10,000-20,000
SSD Total
TANKER Total
Grand Total
Calls
9
4
11
24
18
208
223
449
23
23
496
5
12
771
67
855
275
122
397
26
93
79
198
1,450
87
6
93
3
7
1
6
17
110
24
24
24
42
42
5
5
47
2,127
Engine Power (kW)
Main
4,413
8,826
6,001
5,876
4,844
6,995
8,284
7,549
6,910
6,910
7,438
3,114
6,436
6,881
12,140
7,265
6,659
7,574
6,940
3,236
4,750
6,679
5,321
6,910
4,436
5,939
4,533
4,763
6,280
7,099
8,827
6,959
4,908
5,364
5,364
5,364
3,972
3,972
5,160
5,160
4,098
6,850
Auxiliary
980
1,959
1,332
1,305
1,075
1,553
1,839
1,676
1,534
1,534
1,651
691
1,429
1,528
2,695
1,613
1,478
1,681
1,541
718
1,055
1,483
1,181
1,534
847
1,134
866
910
1,199
1,356
1,686
1,329
937
1,443
1,443
1,443
838
838
1,089
1,089
865
1,515
Cruise
Speed
(kts)
15.3
14.0
13.5
14.2
13.6
14.6
14.1
14.3
15.5
15.5
14.4
10.5
15.0
13.2
13.5
13.3
15.0
14.9
14.9
12.3
13.6
16.6
14.6
13.9
15.1
16.5
15.2
16.4
14.1
16.0
15.0
14.9
15.1
13.8
13.8
13.8
13.5
13.5
14.3
14.3
13.6
14.1
DWT
11,693
28,481
32,713
24,125
14,392
27,486
34,172
30,282
26,513
26,513
29,809
12,513
28,591
33,531
65,089
35,812
26,504
34,476
28,954
4,538
16,830
28,847
20,011
31,776
6,755
12,497
7,125
6,708
16,993
24,432
30,900
20,524
9,196
672
672
672
10,475
10,475
13,735
13,735
10,822
29,336
a Engine Types: MSD = medium speed engine; SSD = slow speed engine; ST = steam turbine
22
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3.1.3.1.2 Removal of Category 1 and 2 Ships
Since these inventories were intended to cover Category 3 propulsion engine
ships only, the ships with Category 1 and 2 propulsion engines were eliminated. This
was accomplished by matching all ship calls with information from Lloyd's Data,
which is produced by Lloyd's Register-F airplay Ltd9 Over 99.9 percent of the calls
in the entrances and clearances data were directly matched with Lloyd's data. The
remaining 0.1 percent was estimated based upon ships of similar type and size.
Engine category was determined from engine make and model. Engine bore
and stroke were found in the Marine Engine 2005 Guide10 and displacement per
cylinder was calculated. Ships with Category 1 or 2 propulsion engines were
eliminated from the data.
3.1.3.1.3 Treatment of Electric-Drive Ships
Many passenger ships and tankers have either diesel-electric or gas turbine-
electric engine that are used for both propulsion and auxiliary purposes. Lloyds
clearly calls out these types of engines in their database and that information was used
to distinguish them from direct and geared drive systems. Generally the power
Lloyds lists is the total power. To separate out propulsion from auxiliary power for
purposes of calculating emissions, the total power listed in the Lloyds data was
divided by 1 plus the ratio of auxiliary to propulsion power (given in Table 3-3) to
obtain the propulsion power portion of the total. The remaining portion was
considered auxiliary engine power. In addition, no low load adjustment factor was
applied to diesel and gas turbine electric engines for loads below 20 percent MCR
because several engines are used to generate power, and some can be shut down to
allow others to operate at a more efficient setting.
3.1.3.2 Cruise Distance
Cruise mode emissions are calculated assuming a 25 nautical mile distance
into and out of the port for deep sea ports and 7 nautical miles into and out of the port
for Great Lake ports outside of the reduced speed and maneuvering zones.
3.1.3.3 RSZ Distances and Speeds by Port
Reduced speed zone (RSZ) distance and speed were determined for each port.
For deep sea ports, the RSZ distances were developed from shipping lane information
contained in the U.S. Army Corps of Engineers National Waterway Network.11 The
NWN is a geographic database of navigable waterways in and around the U.S. The
database defines waterways as links or line segments that, for the purposes of this
study, represent actual shipping lanes (i.e., channels, intracostal waterways, sea lanes,
and rivers). The geographic locations of the waterways that were directly associated
with each of the 117 ports were viewed using geographic information system
computer software. The sea-side endpoint for the RSZ was selected as the point
along the line segment that was judged to be far enough into the ocean where ship
movements were unconstrained by the coastline or other vessel traffic. These RSZ
23
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sea-side endpoints typically coincided with estimates provided by the pilots for the
major ports as reported in earlier work. The resulting RSZ distance was then
measured for each deep sea port. The final RSZ distances for each deep sea port are
listed in Appendix A, Table 2-33. The RSZ endpoints for each of these ports are
shown in Table A-2 of the Appendix. The RSZ for each Great Lake port was fixed at
three nautical miles, as previously discussed in Section 3.1.2.2.
The RSZ speeds were primarily taken from previous studies by ICF ' or
from an ENVIRON report based upon discussions with pilots. A few of the RSZ
speeds were also modified based upon newer information obtained from
conversations with pilots. The final RSZ speeds for each port are listed in Appendix
A, Table 2-33. The speeds that were updated due to recent conversations with pilots
are bolded in the Table.
3.1.3.4 Auxiliary Engine Power and Load Factors
In the methodology used in this analysis, auxiliary engine maximum
continuous rating power and load factors were calculated separately from propulsion
engines and different emission factors applied. Most propulsion engines are
considered slow speed (SSD) Category 3 engines, while most auxiliary engines are
considered medium speed (MSD) Category 2 engines. Since hotelling emissions are
a large part of port inventories, it is important to distinguish propulsion engine
emissions from auxiliary engine emissions. All auxiliary engines were treated as
Category 2 MSD engines for purposes of this analysis.
Auxiliary engine power is not contained in the USAGE database and is only
sparsely populated in the Lloyd's database; as a result, it must be estimated. In 2005,
the California Air Resources Board (ARB) conducted an Oceangoing Ship Survey of
327 ships in January 2005.15 Table 3-3 shows average auxiliary engine power
compared to propulsion power obtained from the ARB survey and other sources.
While it would be more accurate to determine proper ratios for each port, these ratios
by ship type were used for all ports to derive auxiliary power from propulsion power.
24
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Table 3-3. Auxiliary Engine Power Ratios (ARE Survey, except as noted)
Ship Type
Auto Carrier
Bulk Carrier
Container Ship
Passenger Ship3
General Cargo
Miscellaneous15
RORO
Reefer
Tanker
Average
Propulsion
Engine (kW)
10,700
8,000
30,900
39,600
9,300
6,250
11,000
9,600
9,400
Average Auxiliary Engines
Number
2.9
2.9
3.6
4.7
2.9
2.9
2.9
4.0
2.7
Power
Each
(kW)
983
612
1,889
2,340
612
580
983
975
735
Total
Power
(kW)
2,850
1,776
6,800
11,000
1,776
1,680
2,850
3,900
1,985
Engine Speed
Medium
Medium
Medium
Medium
Medium
Medium
Medium
Medium
Medium
Auxiliary to
Propulsion
Ratio
0.266
0.222
0.220
0.278
0.191
0.269
0.259
0.406
0.211
a Many passenger ships typically use a different engine configuration known as diesel-electric. These vessels use
large generator sets for both propulsion and ship-board electricity. The figures for passenger ships above are
estimates taken from the Starcrest Vessel Boarding Program.
b Miscellaneous ship types were not provided in the ARE methodology, so values from the Starcrest Vessel
Boarding Program were used.
Load factors for auxiliary engines vary by ship type and operating mode. It
was previously thought that power generation was provided by propulsion engines in
all modes but hotelling. Starcrest's Vessel Boarding Program showed that auxiliary
engines are on all of the time, with the largest loads during hotelling (except when
cold ironing). Table 3-4 shows the auxiliary engine load factors by ship type
determined by Starcrest, through interviews conducted with ship captains, chief
engineers, and pilots during its vessel boarding programs. Auxiliary load factors
were used in conjunction with total auxiliary power. Auxiliary load factors listed in
Table 3-4 , , . , , , . ,, .
are used together with the total auxiliary engine power (determined trom
total propulsion power and the ratios from Table 3-3) to calculate auxiliary engine
emissions.
3 Cold ironing is a process where shore power is provided to a vessel, allowing it to shut down its auxiliary
generators.
25
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Table 3-4. Auxiliary Engine Load Factor Assumptions
Ship- Type
Auto Carrier
Bulk Carrier
Container Ship
Passenger Ship
General Cargo
Miscellaneous
RORO
Reefer
Tanker
Cruise
0.13
0.17
0.13
0.80
0.17
0.17
0.15
0.20
0.13
RSZ
0.30
0.27
0.25
0.80
0.27
0.27
0.30
0.34
0.27
Maneuver
0.67
0.45
0.50
0.80
0.45
0.45
0.45
0.67
0.45
Hotel
0.24
0.22
0.17
0.64
0.22
0.22
0.30
0.34
0.67
3.1.3.5 Fuel Types and Fuel Sulfur Levels
There are primarily three types of fuel used by marine engines: residual oil
(RO), marine diesel oil (MDO), and marine gas oil (MGO), with varying levels of
fuel sulfur. MDO and MGO are generally described as distillate fuels. For this
analysis, RO and MDO fuels are assumed to be used. Since PM and SO2 emission
factors are dependent on the fuel sulfur level, calculation of port inventories requires
information about the fuel sulfur levels associated with each fuel type, as well as
which fuel types are used by propulsion and auxiliary engines. The assumptions used
for this analysis are described in this section.
15
The ARE survey found the average fuel sulfur level for residual oil (RO) is
2.5 percent. One suspects these are more common for West Coast ports than
nationwide ports and a more reasonable value for the rest of the country should be 2.7
percent for RO. MDO has been shown to have sulfur levels from zero to 2.28
percent. For purposes of this analysis, a sulfur content of 1.5 percent was used,
which compares to 1.0 percent used by Entec.17 Sulfur levels in other areas of the
world can be significantly higher for RO. To calculate the U.S. inventory of ports,
the sulfur levels listed in Table 3-5 were used for marine fuels.
Table 3-5. Assumed Sulfur Levels in
Marine Diesel Fuels for U.S. Inventories
Fuel
RO
MDOa
Fuel Sulfur Levels
West Coast (%)
2.5
1.5
Other Areas (%)
2.7
1.5
MDO used to represent distillate fuel in this analysis
26
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The ARB survey also found that almost all ships used RO in their main
propulsion engines, and that only 29 percent of all ships (except passenger ships) used
distillate in their auxiliary engines, with the remaining 71 percent using RO.
However, only 8 percent of passenger ships used distillate in their auxiliary engines,
while the other 92 percent used RO. We used the results of this survey as reasonable
approximations for calculations of emission factors. However, their accuracy for
years other than those of the ARB survey may be affected by fuel prices, since as fuel
prices increase, more ships will use RO in their auxiliary engines. Table 3-6 provides
the assumed mix of fuel types used for propulsion and auxiliary engines by ship type.
Table 3-6. Estimated Mix of Fuel Types Used by Ships
Ship Type
Passenger
Other
Fuel Used
Propulsion
100% RO
100% RO
Auxiliary
92% RO/8% MDO
71%RO/29%MDO
3.1.3.6 Propulsion and Auxiliary Engine Emission Factors
17
The most recent analysis of emission data was published in 2002 by Entec
The factors from this study are generally accepted as the most current set available.
The Entec analysis included emissions data from 142 propulsion engines and 2 of the
most recent research programs: Lloyd's Register Engineering Services in 1995 and
IVL Swedish Environmental Research Institute in 2002. The resulting Entec
emission factors include individual factors for three speeds of diesel engines (slow-
speed diesel (SSD), medium-speed diesel (MSD), and high-speed diesel (HSD)),
steam turbines (ST), gas turbines (GT), and two types of fuel used here (RO and
MDO). Table 3-7 lists the propulsion engine emission factors for NOx and HC that
were used for the 2002 port inventory development, based on the Entec study using
RO and other data sources as discussed below. The CO, PM, SO2 and CO2 emission
factors shown in the table come from other data sources as explained below.
Table 3-7. Emission Factors for OGV Main Engines using RO, g/kWh
Engine
SSD
MSD
ST
GT
All Ports
NOx
18.1
14.0
2.1
6.1
CO
1.40
1.10
0.20
0.20
HC
0.60
0.50
0.10
0.10
CO2
620.62
668.36
970.71
970.71
West Coast Ports
PM10
1.4
1.4
1.4
1.4
PM25
1.3
1.3
1.3
1.3
SO2
9.53
10.26
14.91
14.91
Other Ports
PM10
1.4
1.4
1.5
1.5
PM25
1.3
1.3
1.4
1.4
SO2
10.29
11.09
16.10
16.10
27
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CO emission factors were developed from information provided in the Entec
appendices because they are not explicitly stated in the text. They were confirmed
with IVS Swedish Environmental Research Institute Ltd.
PM10 values were determined by EPA based on existing engine test data in
consultation with ARB.18 GT PM10 emission factors were not part of the EPA
analysis but assumed here to be equivalent to ST PM10 emission factors. The PM10 to
PM2.5 conversion factor used here is 0.92. While the NONROAD model uses 0.97
for such conversion based upon low sulfur fuels, a higher value of 0.80 was suggested
in a report from the Journal of Aerosol Science19. A reasonable value seems to be
closer to 0.92 because higher sulfur fuels in medium and slow speed engines would
tend to produce larger particulates than high speed engines on low sulfur fuels.
SO2 emission factors were based upon a fuel sulfur to SO2 conversion factor
from ENVIRON.20 Emission factors for SO2 emissions were calculated using the
below formula assuming that 97.753 percent of the fuel sulfur was converted to SO2
and taking into account the molecular weight difference between SO2 and sulfur
(molecular weight 2 times sulfur). The brake specific fuel consumption (BSFC)C
that was used for SSDs was 195 g/kWh, while the BSFC that was used for MSDs was
210 g/kWh based upon Lloyds 1995.21 The BSFC that was used for STs and GTs
was 305 g/kWh based upon Entec.17
Equation 3-20. Calculation of SO2 Emission Factors, g/kWh
SO2 EF = BSFC x 2 x 0.97753 x Fuel Sulfur Fraction
CO2 emission factors were calculated from the BSFC assuming a fuel carbon
content of 86.7 percent by weight and a ratio of molecular weights of CO2 and C at
3.667.
As with propulsion engines, the most current set of auxiliary engine emission
factors comes from Entec except as noted below. Table 3-8 provides these auxiliary
engine emission factors.
Table 3-8. Auxiliary Engine Emission Factors by Fuel Type, g/kWh
Engine
MSD
Fuel
RO
MDO
All Ports
NOx
14.70
13.90
CO
1.10
1.10
HC
0.40
0.40
CO2
668.36
668.36
West Coast Ports
PMio
1.4
0.6
PM25
1.3
0.55
SO:
10.26
6.16
Other Ports
PMio
1.4
0.6
PM25
1.3
0.55
SO2
11.09
6.16
: Brake specific fuel consumption is sometimes called specific fuel oil consumption (SFOC).
28
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It should be noted that Entec used 2.7 percent fuel sulfur content for RO, and
1.0 percent for MDO which is consistent with the RO assumptions made in this
analysis for other than West Coast ports. For MDO, there is a slight discrepancy
between the 1.0 percent used by Entec versus the 1.5 percent estimate used for this
analysis. SO2 emission factors were calculated based upon the sulfur levels listed in
Table 3-5 and the methodology suggested by ENVIRON20 while PM emissions were
determined by EPA based on existing engine test data in consultation with ARB.18
Using the ratios of RO versus MDO use determined by the ARB study as
given in Table 3-6 together with the emission factors shown in Table 3-8, the
auxiliary engine emission factor averages by ship type are listed in Table 3-9. If the
fuel sulfur level for MDO is correctly adjusted from 1.5 percent to 1.0 percent, the
effect on SO2 emissions is still less than 7 percent, due to the high percentage of RO
fuel used in auxiliary engines. The difference for PM is within the round off error of
the emission factor.
Table 3-9. Auxiliary Engine Emission Factors by Ship Type, g/kWh
Ship Type
Passenger
Others
All Ports
NOx
14.64
14.47
CO
1.10
1.10
HC
0.40
0.40
CO2
668.36
668.36
West Coast Ports
PM10
1.3
1.1
PM25
1.2
1.0
SOi
9.93
9.07
Other Ports
PM10
1.4
1.2
PM25
1.3
1.1
SO2
10.70
9.66
3.1.3.7 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 BSFC tends
to increase. Thus, while mass emissions (grams per hour) decrease with low loads,
the engine power tends to decrease more quickly, thereby increasing the emission
factor (grams per engine power) as load decreases. Energy and Environmental
Analysis Inc. (EEA) demonstrated this effect in a study prepared for EPA in 2000.22
In the EEA report, various equations have been developed for the various emissions.
The low-load emission factor adjustment factors were developed based upon the
concept that the BSFC increases as load decreases below about 20 percent load. For
fuel consumption, EEA developed the following equation:
Equation 3-21
Fuel Consumption (g/kWh) = 14.1205 (I/Fractional Load) + 205.7169
In addition, based upon test data, they developed algorithms to calculate emission
factors at reduced load. These equations are noted below:
Equation 3-22
Emission Rate (g/kWh) = a (Fractional Load)x + b
29
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For 862 emissions, however, EEA developed a slightly different equation:
Equation 3-23
Emission Rate (g/kWh) = a (Fuel Consumption x Fuel Sulfur Fraction) + b
The coefficients for the above equations are given in Table 3-10 below.
Table 3-10. Emission Factor Algorithm Coefficients for OGV Main Engines
using RO
Coefficient
a
X
b
NOx
0.1255
1.5
10.4496
HC
0.0667
1.5
0.3859
CO
0.8378
1.0
0.1548
PM
0.0059
1.5
0.2551
S02
2.3735
n/a
-0.4792
C02
44.1
1.0
648.6
The underlying database used to calculate these coefficients includes
primarily tests on engines rated below 10,000 kW, using diesel fuel. This introduces
uncertainty regarding the use of these coefficients for Category 3 engines using
residual fuel; however, these are the best estimates currently available.
Using these algorithms, fuel consumption and emission factors versus load
were calculated. By normalizing these emission factors to 20% load, the low-load
multiplicative adjustment factors presented in Table 3-11 are calculated. SO2
adjustment factors were calculated using 2.7% sulfur. The SO2 multiplicative
adjustment factors at 2.5 percent sulfur are not significantly different
There is no need for a low load adjustment factor for auxiliary engines,
because they are generally operated in banks. When only low loads are needed, one
or more engines are shut off, allowing the remaining engines to operate at a more
efficient level.
30
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Table 3-11. Calculated Low Load Multiplicative Adjustment Factors
Load(%)
1
2
o
6
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
NOx
11.47
4.63
2.92
2.21
.83
.60
.45
.35
.27
.22
.17
.14
.11
.08
.06
.05
.03
.02
.01
.00
HC
59.28
21.18
11.68
7.71
5.61
4.35
3.52
2.95
2.52
2.20
1.96
1.76
1.60
1.47
1.36
1.26
1.18
1.11
1.05
1.00
CO
19.32
9.68
6.46
4.86
3.89
3.25
2.79
2.45
2.18
.96
.79
.64
.52
.41
.32
.24
.17
.11
.05
.00
PM
19.17
7.29
4.33
3.09
2.44
2.04
1.79
1.61
1.48
1.38
1.30
1.24
1.19
1.15
1.11
1.08
1.06
1.04
1.02
1.00
SO2
5.99
3.36
2.49
2.05
.79
.61
.49
.39
.32
.26
.21
.18
.14
.11
.09
.07
.05
.03
.01
.00
CO2
5.82
3.28
2.44
2.01
1.76
1.59
1.47
1.38
1.31
1.25
1.21
1.17
1.14
1.11
1.08
1.06
1.04
1.03
1.01
1.00
3.1.3.8 Use of Detailed Typical Port Data for Other Inputs
There is currently not enough information to readily calculate time-in-mode
(hours/call) for all 117 ports during the maneuvering and hotelling modes of
operation. As a result, it was necessary to review and select available detailed
emission inventories that have been estimated for selected ports to date. These ports
are referred to as typical ports. The typical port information for maneuvering and
hotelling time-in-mode (as well as maneuvering load factors for the propulsion
engines) was then used for the typical ports and also assigned to the other modeled
ports. A modeled port is the port in which emissions are to be estimated. The
methodology that was used to select the typical ports and match these ports to the
other modeled ports is described in this section.
31
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3.1.3.8.1 Selection of Typical Ports
In 1999, EPA published two guidance documents!2,13 to calculate marine
vessel activity at ports. These documents contained detailed port inventories of 8
deep sea ports, 2 Great Lake ports and 2 inland river ports. The detailed inventories
were developed by obtaining ship call data from Marine Exchanges/Port Authorities
(MEPA) at the various ports for 1996 and matching the various ship calls to data from
Lloyds Maritime Information Services to provide ship characteristics. The ports for
which detailed inventories were developed are shown in Table 3-12 for deep sea ports
and Table 3-13 for Great Lake ports along with the level of detail of shifts for each
port. Most ports provided the ship name, Lloyd's number, the vessel type, the date
and time the vessel entered and left the port, and the vessel flag. Inland river ports
were developed from US Army Corps of Engineers (USAGE) Waterborne Commerce
Statistics Center data.
Table 3-12. Deep Sea MEPA Vessel Movement and Shifting Details
MEPA Area and Ports
Lower Mississippi River
including the ports of New
Orleans, South Louisiana,
Plaquemines, and Baton Rouge
Consolidated Port of New York
and New Jersey and other ports
on the Hudson and Elizabeth
Rivers
Delaware River Ports including
the ports of Philadelphia,
Camden, Wilmington and others
Puget Sound Area Ports including
the ports of Seattle, Tacoma,
Olympia, Bellingham, Anacortes,
and Grays Harbor
The Port of Corpus Christi, TX
The Port of Coos Bay, OR
Patapsco River Ports including
the port of Baltimore Harbor, MD
The Port of Tampa, FL
MEPA Data Includes
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 fro m the River.
All PWDs or anchorages for shifting are named. Shifting arrival
and departure times are not given. Maneuvering and hotelling
times are estimated from average speed and distance rather than
calculated from date and time fields.
All PWDs or anchorages for shifting are named. Shifting arrival
and departure times are not given. Maneuvering and hotelling
times are estimated from average speed and distance rather than
calculated from date and time fields.
All PWDs or anchorages for shifting are named. Arrival and
departure dates and times are noted for all movements, allowing
calculation of maneuvering and hotelling both for individual shifts
and the overall call on port.
Only has information on destination PWD and date and time in
and out of the port area. No shifting details.
Only has information on destination PWD and date and time in
and out of the port area. No shifting details.
All PWDs or anchorages for shifting are named. Shifting arrival
and departure times are not given. Maneuvering and hotelling
times are estimated from average speed and distance rather than
calculated from date and time fields.
All PWDs or anchorages for shifting are named. Arrival and
departure dates and times are noted for all movements, allowing
calculation of maneuvering and hotelling both for individual shifts
and the overall call.
32
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Table 3-13. Great Lake MEPA movements and shifts
MEPA Area and Ports
Port of Cleveland, OH
Port of Burns Harbor, IN
MEPA Data Includes
Information on the first and last PWD for the vessel (gives
information for at most one shift per vessel). No information on
intermediate PWDs..
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 Angeles7'23
• Puget Sound Ports24
9 S
• Port of New York/New Jersey
• Port of Houston/Galveston26
• Port of Beaumont/Port Arthur
98
• Port of Corpus Christi
• Port of Portland29
30
Ports of Cleveland, OH and Duluth-Superior, MN&WI
Based on the review of these newer studies, some of the previous typical ports
were replaced with newer data and an additional typical port was added. Data
developed for Cleveland and Duluth-Superior for LADCO was used in lieu of the
previous typical port data for Cleveland and Burns Harbor because it provided more
detailed information and better engine category definitions. The Port of
Houston/Galveston inventory provided enough data to add an additional typical port.
All three port inventories were adjusted to reflect the current methodology used in
this study.
The information provided in the current inventory for Puget Sound Ports24
was used to calculate RSZ speeds, load factors, and times for all Puget Sound ports.
As described in Section 3.1.4.2, an additional modeled port was also added to account
for the considerable amount of Jones Act31 tanker ship activity in the Puget Sound
area that is not contained in the original inventory.
The newer Port of New York/New Jersey inventory provided a check against
estimates made using the 1996 data. All other new inventory information was found
to lack sufficient detail to prepare the detailed typical port inventories needed for this
project.
The final list of nine deep sea and two Great Lake typical ports used in this
analysis and their data year is as follows:
33
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• Lower Mi ssi ssippi 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 Section 3.1.3.1.
3.1.3.8.2 Matching Typical Ports to Modeled Ports
The next step in the process was to match the ports to be modeled with the
typical port which was most like it. Three criteria were used for matching a given
port to a typical port: regional differences'1, maximum vessel draft, and the ship types
that call on a specific port. One container port, for instance, may have much smaller
bulk cargo and reefer ships number of calls on that port than another. Using these
three criteria and the eleven typical ports that are suitable for port matching, the 89
deep sea ports and 28 Great Lake ports were matched to the typical ports. For a
typical port, the modeled and typical port is the same (i.e., the port simply represents
itself). For California ports, we used data provided by ARB as discussed in Section
3.1.4.1. The matched ports for the deep seaports are provided in Table 3-14.
Table 3-14. Matched Ports for the Deep Sea Ports
Modeled Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Typical Like Port
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
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.
34
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Modeled Port Name
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christi, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Marcus Hook, PA
Morehead City, NC
Typical Like Port
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Coos Bay
Coos Bay
Coos Bay
Coos Bay
Coos Bay
Coos Bay
Houston
Houston
Houston
Houston
Houston
Houston
Corpus Christi
Corpus Christi
Corpus Christi
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Lower Mississippi
Lower Mississippi
Lower Mississippi
Lower Mississippi
New York/New Jersey
New York/New Jersey
New York/New Jersey
Delaware River
Delaware River
Delaware River
Delaware River
35
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Modeled Port Name
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Perm Manor, PA
Providence, RI
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, CT
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Typical Like Port
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Patapsco River
Patapsco River
Patapsco River
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
ARE Supplied
Great Lake ports were matched to either Cleveland or Duluth as shown in Table 3-15.
36
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Table 3-15. Great Lake Match Ports
Port Name
Alpena, MI
Buffalo, NY
Burns Waterway, IN
Calcite, MI
Cleveland, OH
Dolomite, MI
Erie, PA
Escanaba, MI
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, WI
Muskegon, MI
Presque Isle, MI
St Clair, MI
Stoneport, MI
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, MI
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, MI
Manistee, MI
Sandusky, OH
Toledo, OH
Typical Like Port
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Duluth - Superior
Duluth-Superior
Duluth-Superior
Duluth- Superior
Duluth - Superior
Duluth-Superior
Duluth-Superior
Duluth - Superior
Duluth-Superior
Duluth-Superior
Once a modeled port was matched to a typical port, the maneuvering and
hotelling time-in-mode values, as well as the maneuvering load factors by bin for the
typical ports, were used directly for the modeled ports, with no adjustments. The
other inputs used for both the typical and modeled ports are as described in Section
3.1.3.
3.1.3.8.3 Bin Mismatches
In some cases, the specific DWT range bin at the modeled port was not in the
typical like port data. In those cases, the next nearest DWT range bin was used for
the calculations. In a few cases, the engine type for a given ship type might not be in
the typical like port data. In these cases, the closest engine type at the typical like
port was used. Also in a few cases, a specific ship type in the modeled port data was
37
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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.
3.1.4 Stand Alone Ports
In a few cases, the USAGE entrances and clearances data was not used to
calculate emissions at the modeled port. These include the California ports for which
we received data from ARB, the Port of Valdez, Alaska, and a conglomerate port
within the Puget Sound area, as described below.
3.1.4.1 California Ports
The California Air Resources Board (ARB) supplied inventories for 14
California ports for 2002. The data received from ARB for the California ports were
modified to provide consistent PM and SO2 emissions to those calculated in this
report. In addition, cruise and RSZ emissions were calculated directly based upon
average ship power provided in the ARB methodology document 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 in Section 3.1.3.1.3 for use in
computing cruise and RSZ emissions. For maneuvering and hotelling emissions, the
ARB values were used and adjusted as discussed below. The data supplied by ARB
included domestic traffic as well as foreign cargo traffic.
For PM emission calculations, ARB used an emission factor of 1.5 g/kWh to
calculate total PM emissions and factors of 0.96 and 0.937 to convert total PM to
PMio and PM2.5 respectively. Since an emission factor of 1.4 g/kWh was used in our
calculations for PM10 and an emission factor of 1.3 g/kWh for PM2.5, ARB PM10 and
PM2.s emissions were multiplied by factors of 0.972 and 0.925, respectively to get
consistent PMio and PM2.5 emissions for propulsion engines.
For auxiliary engines, ARB used the same emission factors as above, while
we used PM10 and PM2.5 emission factors of 1.3 and 1.2 g/kWh, respectively for
passenger ships and 1.1 and 1.0 g/kWh, respectively for all other ships. In the ARB
inventory, all passenger ships are treated as electric drive and all emissions are
allocated to auxiliary engines. ARB auxiliary engine emissions were thus multiplied
by factors of 0.903 and 0.854 respectively for passenger ships and 0.764 and 0.711
respectively for other ships to provide consistent PM emission calculations.
SCh emissions were also different between the ARB and these analyses. ARB
used a composite6 propulsion engine SCh emission factor of 10.55 g/kWh while we
used a composite SO2 emission factor of 9.57 g/kWh. Thus, ARB SO2 propulsion
emissions were multiplied by a factor of 0.907 to be consistent with our emission
e Based upon ARB assuming 95 percent of the engines were SSD and 5 percent were MSB. The composite SC>2
EF of 9.57 g/kW-hr was calculated using this weighting, along with the SSD and MSB SO2 EFs for the West
Coast ports reported in Table 3-7.
38
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calculations. For auxiliary engines, ARB used SO2 emission factors of 11.48 and
9.34 g/kWh, respectively for passenger and other ships, while we use emission factors
of 9.93 and 9.07 g/kWh, respectively. Thus, ARB auxiliary SO2 emissions were
multiplied by factors of 0.865 and 0.971, respectively for passenger and other ships to
provide consistent SO2 emissions.
3.1.4.2 Port in Puget Sound
In the newest Puget Sound inventory24, it was found that a considerable
amount of tanker ships stop at Cherry Point, Ferndale, March Point and other areas
which are not within the top 89 US deep sea ports analyzed in this analysis. In
addition, since they are ships carrying US cargo (oil from Alaska) from one US port
to another, they are not documented in the USAGE entrances and clearances data. To
compensate for this anomaly, an additional port was added which encompassed these
tanker ships stopping within the Puget Sound area but not at one of the Puget Sound
ports analyzed in this analysis. Ship calls in the 1996 typical port data to ports other
than those in the top 89 US deep sea ports were analyzed separately. There were 363
ship calls by tankers to those areas in 1996. In the inventory report for 2005, there
were 468 calls. For 2002, it was estimated there were 432 calls. The same ship types
and ship characteristics were used as in the 1996 data, but the number of calls was
proportionally increased to 432 calls to represent these ships. The location of the
"Other Puget Sound" port was approximately at Cherry Point near Aberdeen.
3.1.4.3 PortofValdez
In a recent Alaska port inventory, it was found that significant Category 3
domestic tanker traffic enters and leaves the Port of Valdez on destination to West
Coast ports. Since the USAGE entrances and clearances data did not contain any
tanker calls at Valdez in 2002, the recent Alaska inventory data was used to calculate
emissions at that port. In this case, the number of calls and ship characteristics for
2002 were taken directly from the Alaska inventory and used in determining
emissions for the modeled port with the Puget Sound area typical port being used as
the like port.
3.1.5 Domestic Traffic
The one drawback of using USAGE entrances and clearances data is that it
only represents foreign cargo movements. The Maritime Administration (MARAD)
maintains the Foreign Traffic Vessel Entrances and Clearances database, which
contains statistics on U.S. foreign maritime trade. Data are compiled during the
regular processing of statistics on foreign imports and exports. The database contains
information on the type of vessel, commodities, weight, customs districts and ports,
and origins and destinations of goods. Thus domestic traffic, i.e., U.S. ships
delivering cargo from one U.S. port to another U.S. port, is covered under the Jones
Act31 and is not accounted for in the database. However, U.S. flagged ships carrying
cargo from a foreign port to a U.S. port or from a U.S. port to a foreign port are
accounted for in the USAGE entrances and clearances database, as these are
39
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considered foreign cargo movements. While at most ports, domestic commerce is
carried out by Category 2 ships, there are a few exceptions as discussed in Section
3.1.4 above. Unfortunately, there is little or no readily available information on
domestic trips, so these were ignored in the analysis except as stated above for the
Puget Sound area, California ports, and the port of Valdez. USAGE staff estimate
that the entrances and clearances data accounts for over 95 percent of the emissions
from Category 3 ships calling on US ports.34 This estimate is supported by an
independent estimate of 97 to 98 percent.
3.1.6 2002 Near Port Inventories
This section presents summaries of the baseline near port inventories for 2002.
Individual port inventories are presented separately for deep sea ports and Great Lake
ports because of the difference in ship types between the two. This is followed by
totals for the summed port inventories, provided by engine type (propulsion and
auxiliary), mode of operation, and ship type.
3.1.6.1 Deep Sea Ports
Emission inventories for the 89 deep sea ports are presented here. Total
emissions (propulsion and auxiliary) by ports are given in Table 3-16. Auxiliary only
emissions by ports are given in Table 3-17. Emissions by mode are given in Table
3-18 for cruise, Table 3-19 for reduced speed zone, Table 3-20 for maneuvering, and
Table 3-21 for hotelling. Emissions by ship type by port are given in Table 3-22
through Table 3-33. Ports that are missing from those lists had no emissions related
to that ship type during 2002.
For deep sea ports, auxiliary emissions are responsible for roughly 47% of the
NOX and PM emissions, primarily due to emissions during the hotelling mode.
Container and Tanker ships combined are responsible for approximately half the total
emissions, followed by Passenger ships and Bulk Carrier ships.
40
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Table 3-16. Total Emissions by Deep Sea Port in 2002
Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christi, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Installed
Power
(MW)
545
472
186
360
8,037
1,190
1,619
97
556
11,198
26,292
19,130
1,946
6,676
5,678
537
399
4,516
2,348
591
1,110
12,699
7,411
6,572
47,147
3,531
7,382
11,452
6,355
8,302
1,213
3,556
2,903
2,504
662
3,566
351
10,941
39,325
27,575
4,627
18,366
4,230
396
86,980
3,968
609
185
Metric Tonnes
NOx
403
115
82
50
1,268
359
413
56
151
2,304
6,646
5,742
439
343
2,111
219
34
929
470
122
270
2,106
709
1,011
4,597
436
954
1,733
842
1,181
143
603
655
389
58
530
39
1,504
4,331
6,535
1,966
6,428
1,045
103
7,287
722
89
42
PM,n
32
9
7
4
116
30
34
4
13
206
572
477
37
37
219
18
4
77
38
10
26
261
92
118
546
52
127
143
79
99
14
51
56
32
6
44
3
129
407
554
160
519
85
9
624
60
7
4
PM,<
29
8
6
4
102
26
30
4
11
182
513
428
33
33
197
16
3
70
35
9
24
240
85
102
491
47
117
132
73
91
13
46
49
28
5
40
3
109
376
512
148
479
78
8
576
55
7
3
HC
14
4
3
2
47
13
15
2
5
117
264
217
17
11
71
7
2
27
14
4
8
91
25
35
158
17
33
59
35
40
6
20
22
14
2
17
1
50
136
221
63
203
33
4
274
23
3
2
CO
32
9
7
4
102
30
35
4
12
223
549
464
38
27
169
17
4
72
37
9
21
189
54
69
346
37
74
401
239
313
14
48
53
33
6
43
3
121
337
535
155
502
82
9
620
57
7
4
SO,
225
66
46
30
800
210
239
31
89
1,319
3,792
3,211
254
299
1,745
133
27
626
309
83
209
1,972
716
873
4,136
388
986
1,090
590
754
108
414
450
239
44
345
27
988
3,157
4,234
1,223
3,976
658
65
4,634
466
152
211
41
-------
Table 3-16 Total Emissions by Deep See Port in 2002 (continued)
Port Name
Marcus Hook, PA
Morehead City, NC
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Perm Manor, PA
Providence, RI
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, CT
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Total Port Emissions
Total Port Emissions (short
tons)
Installed
Power
(MW)
2,754
967
3,272
1,467
290
765
721
1,097
5,184
17,794
46,233
1,801
2,544
1,452
4,209
7,963
4,444
4,888
596
13,985
57,682
543
12,417
181
24,500
5,529
37,523
928
3,442
1,685
409
3,334
56,935
50,489
48,762
456
3,956
455
8,255
6,260
1,210
863,667
Metric Tonnes
NOx
965
116
653
196
35
199
171
198
665
3,010
3,833
287
226
238
994
1,476
611
569
86
1,370
7,038
110
1,386
39
6,304
509
3,667
88
537
192
83
319
5,367
4,852
3,024
107
484
138
891
708
332
120,637
133,007
PM,n
79
10
55
16
3
16
14
16
54
281
318
23
20
20
82
126
52
53
7
125
651
9
131
3
512
41
296
7
39
14
6
22
389
352
222
8
35
10
68
53
24
10,517
11,595
PM,<
73
9
50
15
3
15
13
15
50
261
293
22
19
19
76
116
48
49
7
116
603
8
121
3
475
38
274
7
36
13
5
21
357
324
205
7
33
9
63
49
22
9,619
10,605
HC
30
4
22
7
1
6
6
6
22
89
133
9
7
8
34
51
23
22
3
51
218
3
48
1
209
18
128
3
17
6
2
10
167
152
100
3
15
4
27
22
10
4,145
4,570
CO
76
10
54
16
3
16
14
16
53
233
312
22
18
19
83
128
54
52
8
122
551
9
117
3
494
41
296
7
42
15
6
280
422
383
239
8
37
11
68
55
26
10,633
11,724
SO,
2,462
94
2,103
411
52
394
656
334
1,302
2,280
4,553
207
169
164
1,625
3,236
1,011
956
206
1,651
5,345
124
1,393
33
3,870
319
2,242
51
309
108
51
190
3,130
2,829
1,638
64
277
81
527
415
192
93,689
103J95
42
-------
Table 3-17. Auxiliary Engine Emissions by Deep Sea Port in 2002
Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christi, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Installed
Power
(MW)
115
101
40
73
2,043
260
346
21
111
2,560
5,947
4,305
427
1,411
1,198
158
78
1,252
649
164
235
2,415
1,342
1,645
8,410
640
1,414
2,486
1,341
1,840
260
878
902
535
130
795
87
2,639
10,037
6,374
988
3,988
919
85
20,036
883
129
40
Metric Tonnes
NOx
147
74
21
25
793
172
183
9
42
924
1,468
1,280
180
256
951
98
9
815
408
108
132
873
316
672
1,800
173
402
745
448
449
53
411
478
202
16
277
19
774
3,054
3,360
793
2,969
607
46
3,412
477
42
13
PM10
11
6
2
2
67
13
14
1
3
70
116
97
14
20
72
8
2
64
32
8
10
149
58
89
305
29
78
64
38
38
7
34
41
17
2
23
2
67
279
293
67
246
50
4
295
40
3
1
PM2.5
10
5
1
2
61
12
13
1
3
64
106
88
12
18
66
7
2
58
29
8
9
135
53
75
268
25
71
59
34
35
6
30
35
14
2
20
1
51
258
270
62
226
46
3
271
37
3
1
HC
4
2
1
1
22
5
5
0
1
26
40
35
5
7
26
3
1
23
12
3
4
31
11
24
64
6
15
21
13
13
2
11
13
6
1
8
1
21
84
94
22
82
17
1
96
13
1
0
CO
11
6
2
2
60
13
14
1
3
70
111
97
14
19
72
8
2
64
32
8
10
63
24
42
129
12
31
59
34
35
6
31
37
15
2
21
1
59
232
259
62
226
46
3
264
36
3
1
S02
92
46
13
16
522
108
115
6
26
579
937
802
113
161
596
63
14
529
265
70
83
1,188
461
660
2,352
220
626
514
303
303
56
292
343
131
19
190
14
534
2,173
2,332
543
1,982
406
31
2,350
320
28
11
43
-------
Table 3-17. Auxiliary Engine Emissions by Deep Sea Port in 2002 (continued)
Port Name
Marcus Hook, PA
Morehead City, NC
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Perm Manor, PA
Providence, RI
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, CT
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Total Auxiliary Emissions
Total Auxiliary Emissions
(short tons)
Installed
Power
(MW)
583
203
701
318
61
164
159
236
1,299
4,911
10,277
379
564
522
1,286
1,910
1,155
1,045
130
3,300
14,563
116
2,913
53
5,924
1,216
8,297
257
111
355
88
1,010
79,720
11,535
10,759
101
866
95
2,164
1,480
259
264,478
Metric Tonnes
NOx
617
69
280
63
17
120
66
118
258
2,436
1,574
188
133
178
579
747
287
262
26
645
5,058
73
889
28
1,656
174
1,129
45
193
47
59
177
2,634
2,359
862
59
164
61
483
345
126
56,259
62,028
PM,n
51
6
25
5
2
10
6
10
22
225
135
16
11
15
48
65
25
28
2
64
463
6
84
2
139
14
91
4
13
3
4
11
178
160
57
4
11
4
37
25
8
5,040
5,557
PM,S
47
6
23
5
2
9
5
9
20
209
124
14
10
14
44
59
23
25
2
59
429
6
77
2
128
13
83
4
11
3
4
10
162
145
52
3
10
4
34
23
7
4,585
5,055
HC
17
2
8
2
1
3
2
3
7
68
45
5
4
5
16
22
8
9
1
22
142
2
27
1
46
5
31
1
5
1
2
5
72
65
24
2
5
2
13
9
3
1,611
1,776
CO
47
6
22
5
2
9
5
9
20
187
123
14
10
14
44
59
23
25
2
59
390
6
74
2
52
14
86
3
15
4
5
47
205
184
67
5
13
5
37
27
10
4,292
4,732
SO2
412
49
198
42
15
80
46
79
176
1,805
1,089
125
91
125
387
523
202
223
18
520
3,715
49
675
19
1,127
125
754
28
128
32
38
115
1,704
1,525
551
39
109
40
311
224
82
41,133
45,351
44
-------
Table 3-18. Cruise Emissions by Deep Sea Port in 2002
Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christi, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Installed
Power
(MW)
545
472
186
360
8,037
1,190
1,619
97
556
11,198
26,292
19,130
1,946
6,676
5,678
537
399
4,516
2,348
591
1,110
12,699
7,411
6,572
47,147
3,531
7,382
11,452
6,355
8,302
1,213
3,556
2,903
2,504
662
3,566
351
10,941
39,325
27,575
4,627
18,366
4,230
396
86,980
3,968
609
185
Metric Tonnes
NOx
50
25
9
15
300
72
89
5
27
423
775
622
85
45
197
22
21
107
57
14
32
665
362
283
2,178
184
386
584
266
406
69
148
132
143
35
189
16
539
1,369
1,249
238
961
221
20
3,247
195
31
10
PM10
4
2
1
1
28
6
8
0
2
40
74
59
8
8
24
2
2
14
7
2
4
52
28
23
173
15
30
46
24
33
5
13
11
11
3
15
1
45
133
102
19
75
17
2
261
16
3
1
PM2.5
4
2
1
1
26
6
7
0
2
37
69
55
7
8
22
2
2
13
6
2
4
48
26
22
161
13
28
43
23
31
5
12
10
10
3
14
1
42
123
94
17
70
16
2
242
15
2
1
HC
2
1
0
1
10
2
3
0
1
15
27
22
3
2
7
1
1
4
2
1
1
22
12
9
72
6
13
19
9
14
2
5
4
5
1
6
1
18
46
41
8
32
7
1
108
6
1
0
CO
4
2
1
1
23
6
7
0
2
33
59
49
7
4
15
2
2
9
5
1
3
51
28
22
169
14
30
45
21
31
5
12
10
11
3
15
1
42
105
97
18
74
17
2
251
15
2
1
S02
29
14
6
10
206
45
55
3
17
290
544
428
54
75
202
14
12
109
50
15
34
384
209
175
1,290
108
224
341
194
251
40
95
82
83
20
116
10
341
1,056
761
139
557
128
12
1,945
118
19
6
45
-------
Table 3-18. Cruise Emissions by Deep Sea Port in 2002 (continued)
Port Name
Marcus Hook, PA
Morehead City, NC
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Perm Manor, PA
Providence, RI
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, C T
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Total Cruise Emissions
Total Cruise Emissions (short
tons)
Installed
Power
(MW)
2,754
967
3,272
1,467
290
765
721
1,097
5,184
17,794
46,233
1,801
2,544
1,452
4,209
7,963
4,444
4,888
596
13,985
57,682
543
12,417
181
24,500
5,529
37,523
928
3,442
1,685
409
3,334
56,935
50,489
48,762
456
3,956
455
8,255
6,260
1,210
863,667
Metric Tonnes
NOx
143
44
166
63
13
41
38
58
222
665
1,758
92
89
58
191
337
178
213
25
567
2,065
27
430
8
1,003
213
1,387
44
171
87
20
138
2,142
1,900
1,676
24
198
23
380
292
63
34,370
37,894
PM,n
11
4
13
5
1
3
3
4
17
54
138
7
8
4
15
27
14
17
2
46
174
2
38
1
80
17
110
4
13
7
2
11
168
149
131
2
15
2
30
23
5
2,831
3,121
PM,<
10
3
12
5
1
3
3
4
16
50
128
7
8
4
14
25
13
16
2
43
162
2
35
1
74
16
102
3
12
6
1
10
156
138
122
2
14
2
28
21
5
2,627
2,896
HC
5
1
5
2
0
1
1
2
7
22
58
3
3
2
6
11
6
7
1
19
70
1
14
0
33
7
46
1
6
3
1
5
71
63
55
1
7
1
12
10
2
1,146
1,264
CO
11
3
13
5
1
3
3
4
17
52
136
7
7
5
15
26
14
16
2
44
160
2
33
1
78
17
107
3
13
7
2
11
166
147
130
2
15
2
29
23
5
2,663
2,936
SO,
82
28
97
37
9
23
22
33
129
501
1,029
54
64
34
113
202
104
125
15
345
1,503
17
313
5
597
125
817
25
92
47
11
74
1,157
1,026
900
13
106
13
210
159
34
21,207
23,381
46
-------
Table 3-19. Reduced Speed Zone Emissions by Deep Sea Port in 2002
Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christi, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Installed
Power
(MW)
545
472
186
360
8,037
1,190
1,619
97
556
11,198
26,292
19,130
1,946
6,676
5,678
537
399
4,516
2,348
591
1,110
12,699
7,411
6,572
47,147
3,531
7,382
11,452
6,355
8,302
1,213
3,556
2,903
2,504
662
3,566
351
10,941
39,325
27,575
4,627
18,366
4,230
396
86,980
3,968
609
185
Metric Tonnes
NOx
191
3
49
3
75
101
125
43
77
967
4,273
3,685
171
33
963
121
5
27
14
4
117
771
28
101
656
97
181
419
175
358
23
50
78
55
7
71
5
329
73
2,669
1,091
2,897
244
48
879
48
16
22
PM10
15
0
4
0
7
8
10
3
6
86
349
290
14
5
112
10
0
2
1
0
12
81
2
10
57
10
16
33
20
30
2
4
7
5
1
6
0
29
7
224
87
229
19
4
83
4
1
2
PM2.5
14
0
4
0
6
7
9
3
6
79
323
269
13
5
104
9
0
2
1
0
12
75
2
9
53
9
14
31
19
28
1
3
4
3
0
5
0
16
7
208
80
212
18
4
77
4
1
2
HC
6
0
2
0
3
4
5
1
3
58
150
121
7
1
32
4
0
1
0
0
4
45
1
4
22
6
6
14
13
12
1
2
3
3
0
2
0
12
3
98
36
95
8
2
54
2
1
1
CO
15
0
4
0
6
9
11
3
6
108
345
285
16
3
75
10
1
2
1
0
9
88
2
8
50
11
14
293
185
247
2
5
7
6
1
6
0
28
7
228
85
225
19
5
105
4
1
2
S02
103
2
27
2
48
57
70
23
45
538
2,406
2,024
96
46
942
71
3
18
9
2
99
574
18
73
429
71
117
250
124
225
12
27
36
27
4
39
3
159
53
1,678
648
1,712
144
30
549
30
105
196
47
-------
Table 3-19. Reduced Speed Zone Emissions by Deep Sea Port in
(continued)
2002
Port Name
Marcus Hook, PA
Morehead City, NC
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Perm Manor, PA
Providence, RI
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, CT
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Total RSZ Emissions
Total RSZ Emissions (short
tons)
Installed
Power
(MW)
2,754
967
3,272
1,467
290
765
721
1,097
5,184
17,794
46,233
1,801
2,544
1,452
4,209
7,963
4,444
4,888
596
13,985
57,682
543
12,417
181
24,500
5,529
37,523
928
3,442
1,685
409
3,334
56,935
50,489
48,762
456
3,956
455
8,255
6,260
1,210
863,667
Metric Tonnes
NOx
245
2
254
86
5
45
82
26
215
73
569
4
6
2
346
525
206
110
44
204
182
11
125
4
4,205
130
1,325
13
183
58
5
8
764
770
526
25
123
58
106
106
156
34,369
37,893
PM,n
20
0
21
7
0
4
7
2
17
7
46
0
1
0
29
45
17
9
4
17
17
1
12
0
338
11
107
1
14
5
0
1
62
63
43
2
10
5
9
8
12
2,882
3,178
PM,<
18
0
19
7
0
3
6
2
16
7
43
0
1
0
27
41
16
9
3
16
16
1
11
0
314
10
99
1
13
4
0
1
58
58
40
2
9
4
8
8
11
2,653
2,925
HC
9
0
9
3
0
1
3
1
7
2
21
0
0
0
14
20
10
5
2
9
6
0
6
0
139
5
46
0
6
2
0
0
30
30
23
1
4
2
3
3
5
1,277
1,408
CO
20
0
21
8
0
4
7
2
17
6
49
0
1
0
32
50
20
10
4
19
15
1
12
0
328
11
109
1
14
5
0
256
70
70
53
2
10
4
8
8
12
3,803
4,193
so.
1,996
16
1,841
343
29
295
598
225
1,019
94
2,502
27
15
6
1,208
2,604
747
620
180
818
325
59
446
10
2,535
86
803
8
100
32
2
5
433
437
272
14
67
32
62
60
85
35,016
38,606
48
-------
Table 3-20. Maneuvering Emissions by Deep Sea Port in 2002
Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christi, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Installed
Power
(MW)
545
472
186
360
8,037
1,190
1,619
97
556
11,198
26,292
19,130
1,946
6,676
5,678
537
399
4,516
2,348
591
1,110
12,699
7,411
6,572
47,147
3,531
7,382
11,452
6,355
8,302
1,213
3,556
2,903
2,504
662
3,566
351
10,941
39,325
27,575
4,627
18,366
4,230
396
86,980
3,968
609
185
Metric Tonnes
NOx
50
23
9
12
360
63
72
3
19
501
976
810
75
55
252
1
1
12
6
1
2
49
23
40
169
17
28
112
54
70
8
27
33
16
4
21
2
66
237
190
35
143
33
3
454
37
3
1
PM,n
5
2
1
1
36
6
7
0
2
49
100
81
7
8
29
0
0
1
1
0
0
14
7
12
47
5
8
11
6
7
1
3
3
2
0
2
0
7
25
19
3
14
3
0
46
4
0
0
PM,<
3
2
1
1
28
4
5
0
1
37
76
62
5
6
22
0
0
1
1
0
0
12
6
5
31
3
7
10
5
6
1
2
3
1
0
2
0
6
23
17
3
12
3
0
42
3
0
0
HC
3
1
1
1
19
4
4
0
1
33
70
57
4
3
13
0
0
1
0
0
0
2
1
1
6
1
1
8
4
5
1
2
2
1
0
2
0
4
13
13
2
10
2
0
35
2
0
0
CO
5
2
1
1
32
6
7
0
2
50
97
82
8
5
25
0
0
1
1
0
0
4
2
3
13
1
2
14
6
8
1
3
4
2
0
3
0
8
23
22
4
18
4
0
53
4
0
0
SO,
23
10
4
6
194
31
35
2
10
232
445
368
36
46
163
1
0
8
4
1
1
95
45
38
255
25
59
68
38
44
7
20
25
13
3
18
2
95
166
117
21
87
20
2
266
23
2
1
49
-------
Table 3-20. Maneuvering Emissions by Deep Sea Port in 2002 (continued)
Port Name
Marcus Hook, PA
Morehead City, NC
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Perm Manor, PA
Providence, RI
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, CT
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Total Maneuver Emissions
Total Maneuver Emissions
(short tons)
Installed
Power
(MW)
2,754
967
3,272
1,467
290
765
721
1,097
5,184
17,794
46,233
1,801
2,544
1,452
4,209
7,963
4,444
4,888
596
13,985
57,682
543
12,417
181
24,500
5,529
37,523
928
3,442
1,685
409
3,334
56,935
50,489
48,762
456
3,956
455
8,255
6,260
1,210
863,667
Metric Tonnes
NOx
22
5
24
5
1
5
4
7
25
70
198
11
10
10
27
48
22
24
2
67
241
4
59
1
129
25
163
10
23
9
4
9
272
242
241
3
26
3
80
54
7
7,374
8,130
PM,n
2
0
2
1
0
0
0
1
2
7
20
1
1
1
3
5
2
2
0
7
25
0
6
0
13
3
17
1
1
1
0
0
15
13
10
0
2
0
6
4
0
758
836
PM,<
2
0
2
1
0
0
0
1
2
6
18
1
1
1
2
4
2
2
0
6
24
0
6
0
12
2
15
1
1
1
0
0
13
12
9
0
1
0
6
4
0
625
689
HC
2
0
2
0
0
0
0
0
2
3
17
1
1
1
2
3
2
2
0
5
14
0
4
0
10
2
14
0
1
0
0
0
6
5
5
0
1
0
2
1
0
439
484
CO
3
1
3
1
0
1
0
1
3
6
24
1
1
1
3
6
3
3
0
8
24
0
7
0
15
3
20
1
1
1
0
1
15
13
11
0
2
0
6
4
0
723
797
SO,
14
3
15
3
1
3
2
4
15
50
112
7
7
6
17
29
13
14
1
40
165
2
40
1
76
14
92
6
11
4
2
4
120
106
89
1
12
1
46
29
3
4,355
4,802
50
-------
Table 3-21. Hotelling Emissions by Deep Sea Port in 2002
Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christi, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Installed
Power
(MW)
545
472
186
360
8,037
1,190
1,619
97
556
11,198
26,292
19,130
1,946
6,676
5,678
537
399
4,516
2,348
591
1,110
12,699
7,411
6,572
47,147
3,531
7,382
11,452
6,355
8,302
1,213
3,556
2,903
2,504
662
3,566
351
10,941
39,325
27,575
4,627
18,366
4,230
396
86,980
3,968
609
185
Metric Tonnes
NOx
113
64
14
20
533
123
126
5
29
413
622
624
107
210
699
75
8
783
392
103
119
622
296
588
1,594
138
359
618
346
347
42
378
413
175
12
249
16
570
2,653
2,427
601
2,427
547
32
2,707
442
38
9
PM,n
9
5
1
2
45
9
10
0
2
31
49
47
8
16
53
6
1
60
30
8
9
114
55
73
269
23
73
53
29
29
6
31
35
15
2
21
1
49
241
210
51
201
45
3
235
37
3
1
PM,<
8
4
1
1
41
9
9
0
2
29
45
43
7
15
48
5
1
54
27
7
8
105
51
67
246
21
67
49
27
27
6
29
32
13
2
19
1
45
223
193
47
185
42
2
215
34
3
1
HC
3
2
0
1
15
3
3
0
1
11
17
17
3
6
19
2
1
22
11
3
3
22
11
21
57
5
13
18
10
10
2
10
12
5
1
7
0
16
73
68
17
67
15
1
77
12
1
0
CO
9
5
1
2
40
9
10
0
2
31
47
47
8
16
53
6
1
60
30
8
9
46
22
36
115
10
28
49
27
27
6
29
32
13
2
19
1
43
202
188
47
185
42
2
210
34
3
1
SO,
71
40
9
12
352
77
79
3
18
259
397
391
67
132
438
47
12
491
246
65
75
919
445
587
2,162
184
585
430
235
235
49
272
307
117
16
171
12
392
1,883
1,678
414
1,620
365
21
1,874
296
26
9
51
-------
Table 3-21. Hotelling Emissions by Deep Sea Port in 2002 (continued)
Port Name
Marcus Hook, PA
Morehead City, NC
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Perm Manor, PA
Providence, RI
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, CT
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Total Hotel Emissions
Total Hotel Emissions (short
tons)
Installed
Power
(MW)
2,754
967
3,272
1,467
290
765
721
1,097
5,184
17,794
46,233
1,801
2,544
1,452
4,209
7,963
4,444
4,888
596
13,985
57,682
543
12,417
181
24,500
5,529
37,523
928
3,442
1,685
409
3,334
56,935
50,489
48,762
456
3,956
455
8,255
6,260
1,210
863,667
Metric Tonnes
NOx
555
64
209
41
15
108
47
108
203
2,202
1,308
179
122
167
430
566
205
222
15
533
4,551
68
772
25
967
140
793
21
159
37
55
164
2,189
1,941
581
55
137
54
326
257
107
44,525
49,090
PM,n
46
6
19
3
2
9
4
9
17
213
113
15
10
15
36
50
18
24
1
55
434
6
75
2
81
11
63
2
10
2
4
11
144
127
37
4
9
3
23
18
7
4,047
4,462
PM,<
42
5
17
3
2
8
4
8
16
198
104
14
10
13
33
46
17
22
1
51
402
5
69
2
75
10
58
2
9
2
3
10
130
116
34
3
8
3
21
16
6
3,714
4,095
HC
15
2
6
1
1
3
1
3
6
62
38
5
3
5
12
17
6
8
0
18
128
2
24
1
27
4
22
1
4
1
2
5
60
53
16
2
4
1
9
7
3
1,283
1,415
CO
42
5
17
3
2
8
4
8
16
169
103
14
9
13
33
46
17
22
1
51
352
5
65
2
74
11
60
2
13
3
4
13
172
152
46
4
11
4
25
20
8
3,444
3,797
SO,
371
47
150
27
13
72
34
72
140
1,635
911
120
84
117
287
402
147
196
10
447
3,351
46
594
17
662
93
529
13
107
25
36
107
1,420
1,259
376
36
92
35
209
167
70
33,112
36,507
52
-------
Table 3-22. Auto Carrier Deep Sea Port Emissions in 2002
Port Name
Baltimore, MD
New York/New Jersey
Jacksonville, FL
Brunswick, GA
Portland, OR
Tacoma, WA
Hueneme, CA
Charleston, SC
San Diego, CA
Houston, TX
Long Beach, CA
Wilmington, DE
Los Angeles, CA
Boston, MA
Carquinez, CA
Savannah, GA
Galveston, TX
Honolulu, HI
Richmond, CA
Tampa, FL
Vancouver, WA
Newport News, VA
Mobile, AL
Pensacola, FL
Everglades, FL
Miami, FL
Philadelphia, PA
Bridgeport, CT
Morehead City, NC
Beaumont, TX
San Francisco, CA
Matagorda Ship
South Louisiana, LA
Oakland, CA
Seattle, WA
Chester, PA
Port Angeles, WA
Manatee, FL
Total Auto Carrier
Total Auto Carrier (short tons)
Installed
Power
(MW)
5,458
4,588
4,420
3,313
2,331
2,123
2,036
1,922
1,374
1,141
1,068
1,012
947
744
682
644
560
539
468
284
278
270
182
169
136
131
111
40
35
31
20
16
16
10
9
9
6
4
37,129
Metric Tonnes
NOx
1,290
329
337
364
416
733
125
148
132
122
96
126
87
62
84
76
59
59
51
23
48
27
26
12
22
10
16
3
3
4
2
1
3
1
3
2
1
0
4,901
5,404
PM,n
103
30
32
29
38
61
9
15
9
12
7
10
6
5
6
6
6
5
4
2
4
2
2
1
2
1
1
0
0
1
0
0
0
0
0
0
0
0
412
454
PM,<
95
28
29
27
33
55
8
14
9
11
6
10
6
5
6
6
5
5
3
2
4
2
2
1
2
1
1
0
0
1
0
0
0
0
0
0
0
0
377
416
HC
43
15
14
12
21
27
4
6
4
4
3
5
3
2
3
3
2
3
2
1
2
1
1
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
181
200
CO
101
32
32
29
42
59
157
14
10
8
7
11
7
5
6
6
4
5
4
2
5
2
7
1
2
1
1
0
0
0
0
0
0
0
0
0
0
0
563
621
SO,
768
218
362
494
246
414
71
169
77
92
55
180
50
54
49
46
43
35
30
15
28
20
19
8
14
7
27
2
2
4
1
1
2
1
2
4
1
0
3,608
3,978
53
-------
Table 3-23. Barge Carrier Deep Sea Port Emissions in 2002
Port Name
New Orleans, LA
Charleston, SC
Morehead City, NC
Mobile, AL
Total Barge Carrier
Total Barge Carrier (short
tons)
Installed
Power
(MW)
472
420
73
2
967
Metric Tonnes
NOx
72
55
6
0
133
147
PM,n
8
4
1
0
13
14
PM,<
7
4
1
0
12
13
HC
3
2
0
0
5
6
CO
8
4
0
0
12
13
SO,
57
78
5
0
141
755
54
-------
Table 3-24. Bulk Carrier Deep Sea Port Emissions in 2002
Port Name
South Louisiana, LA
New Orleans, LA
Houston, TX
Tampa, FL
Corpus Christi, TX
New York/New Jersey
Baltimore, MD
Mobile, AL
Plaquemines, LA
Portland, OR
Long Beach, CA
Los Angeles, CA
Baton Rouge, LA
Charleston, SC
Savannah, GA
Jacksonville, FL
Longview, WA
Lake Charles, LA
Galveston, TX
Beaumont, TX
Kalama, WA
Vancouver, WA
Port Arthur, TX
Tacoma, WA
Camden, NJ
Carquinez, CA
Newport News, VA
Brownsville, TX
Perm Manor, PA
Stockton, CA
Everglades, FL
Pascagoula, MS
Matagorda Ship
Seattle, WA
Providence, RI
San Francisco, CA
Texas City, TX
Philadelphia, PA
Installed
Power
(MW)
11,606
8,311
5,996
3,380
3,359
3,168
2,851
2,752
2,714
2,351
2,297
2,037
1,668
1,589
1,474
1,394
1,142
1,116
1,063
1,055
1,007
1,003
890
872
775
717
692
685
659
638
626
586
586
523
511
498
481
473
Metric Tonnes
NOx
4,014
2,511
655
602
460
482
1,160
401
665
633
468
423
722
238
334
203
265
147
114
185
233
256
106
445
176
172
118
74
160
198
109
116
118
244
78
101
60
105
PM,n
323
202
66
49
37
41
94
32
54
51
33
29
58
19
27
17
22
13
11
19
19
21
11
35
14
12
10
9
13
14
9
9
10
19
6
7
6
8
PM,<
298
187
54
43
34
37
87
30
50
46
30
27
53
18
25
15
19
12
9
16
17
19
9
32
13
11
9
8
12
13
8
8
9
18
6
6
5
8
HC
127
79
22
20
16
16
38
14
21
23
14
13
23
8
11
6
10
6
4
9
8
9
4
15
6
5
4
3
5
6
3
4
4
8
3
3
2
3
CO
313
196
48
48
121
39
90
115
52
53
36
33
56
19
26
16
22
46
8
18
20
22
9
35
14
13
9
8
13
15
9
9
10
19
6
8
4
8
SO,
2,470
1,550
446
365
278
317
711
241
417
364
283
255
439
449
205
337
154
91
78
129
136
147
74
247
714
103
78
65
637
116
70
70
71
135
154
61
40
296
55
-------
Table 3-24. Bulk Carrier Deep Sea Port Emissions in 2002 (continued)
Port Name
Portland, ME
Boston, MA
Canaveral, FL
Redwood City, CA
New Haven, CT
Wilmington, NC
Georgetown, SC
Freeport, TX
Richmond, CA
Brunswick, GA
San Diego, CA
Wilmington, DE
Manatee, FL
Albany, NY
Oakland, CA
Nikishka, AK
Marcus Hook, PA
New Castle, DE
Sacramento, CA
Paulsboro, NJ
Honolulu, HI
Grays Harbor, WA
Morehead City, NC
Hopewell, VA
Fall River, MA
Miami, FL
Gulfport, MS
Eureka, CA
Bridgeport, CT
Coos Bay, OR
Palm Beach, FL
Barbers Point, HI
Panama City, FL
Olympia, WA
Port Angeles, WA
Everett, WA
Anacortes, WA
Anchorage, AK
Searsport, ME
Chester, PA
Kahului, HI
Hilo, HI
Pensacola, FL
Richmond, VA
Valdez, AK
Total Bulk Carrier
Total Bulk Carrier (short tons)
Installed
Power
(MW)
470
468
464
437
424
422
408
392
385
370
350
345
322
280
280
246
243
240
218
168
158
140
130
127
127
122
120
114
98
87
83
82
79
73
72
71
67
52
37
35
34
31
25
11
7
82,455
Metric Tonnes
NOx
62
60
59
103
55
68
63
35
83
75
64
66
60
79
40
74
54
51
72
30
29
24
11
26
13
9
17
28
13
6
8
14
13
45
22
33
28
20
6
7
4
3
4
3
1
19,189
27,757
PM,n
5
5
5
7
4
5
5
4
6
6
4
5
5
6
3
6
4
4
5
3
2
2
1
2
2
1
2
2
1
1
1
1
1
4
2
3
2
2
0
1
0
0
0
0
0
1,562
1,722
PM,<
5
5
4
7
4
5
5
3
5
6
4
5
4
6
3
5
4
4
5
3
2
2
1
2
1
1
2
2
1
1
1
1
1
3
2
2
2
2
0
1
0
0
0
0
0
1,423
1,569
HC
2
2
2
3
2
2
2
1
2
2
2
2
2
3
1
2
2
2
2
1
1
1
1
1
1
1
1
1
0
0
0
1
0
2
1
1
1
1
0
0
0
0
0
0
0
629
693
CO
5
5
5
8
4
5
5
3
6
6
5
5
5
7
3
6
4
4
6
3
2
2
1
2
1
1
2
2
1
1
1
1
1
4
2
3
2
2
0
1
0
0
0
0
0
1,723
1,900
SO,
38
91
54
61
43
160
116
25
50
276
39
215
36
49
23
41
192
37
42
57
17
14
13
144
30
17
13
18
10
6
9
9
8
24
12
18
15
12
9
26
2
2
3
18
1
14,885
16,411
56
-------
Table 3-25. Container Ship Deep Sea Port Emissions in 2002
Port Name
New York/New Jersey
Oakland, CA
Long Beach, CA
Charleston, SC
Los Angeles, CA
Savannah, GA
Seattle, WA
Miami, FL
Tacoma, WA
Houston, TX
Baltimore, MD
Everglades, FL
New Orleans, LA
Portland, OR
Boston, MA
Jacksonville, FL
Newport News, VA
Philadelphia, PA
Honolulu, HI
Wilmington, DE
Wilmington, NC
Freeport, TX
Gulfport, MS
San Francisco, CA
Chester, PA
Palm Beach, FL
Richmond, VA
Galveston, TX
San Diego, CA
Richmond, CA
Hueneme, CA
Eureka, CA
Mobile, AL
South Louisiana, LA
Lake Charles, LA
Carquinez, CA
Everett, WA
Corpus Christi, TX
Morehead City, NC
Port Angeles, WA
New Haven, CT
Plaquemines, LA
Vancouver, WA
Total Container Ship
Total Container Ship (short
tons)
Installed
Power
(MW)
56,253
47,109
42,292
37,982
37,505
28,209
21,749
21,100
15,446
13,441
9,224
8,753
5,756
5,227
5,016
4,219
3,797
2,696
2,190
1,999
1,779
1,575
1,538
1,209
1,140
1,018
539
427
385
165
82
55
39
36
36
27
24
24
24
14
14
12
7
380,131
Metric Tonnes
NOx
3,236
2,835
3,436
2,767
3,099
2,165
5,207
1,312
3,108
696
1,422
703
788
879
274
274
254
303
308
197
130
74
181
102
139
54
74
21
30
15
6
6
4
5
4
3
6
2
2
2
1
1
4
34,125
37,624
PM,n
269
208
244
227
221
176
444
108
264
59
114
60
65
85
27
24
21
25
30
16
11
6
15
7
11
5
6
2
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
2,757
3,040
PM,<
249
192
225
210
203
163
396
100
236
55
105
56
60
74
25
22
19
23
25
15
10
6
14
7
10
4
6
2
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
2,516
2,774
HC
130
94
109
98
99
79
217
46
124
23
50
25
35
59
12
11
9
13
15
8
5
2
7
3
5
2
3
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1,288
1,420
CO
280
224
272
226
247
179
439
106
253
52
114
57
76
96
27
24
21
28
27
18
12
6
15
8
11
4
7
2
2
1
6
0
1
1
1
0
0
0
0
0
0
0
0
2,845
3,137
SO,
1,949
1,532
1,986
3,175
1,791
1,298
2,859
963
1,741
447
843
460
482
486
295
275
151
657
181
379
162
46
110
59
306
40
182
14
17
8
4
4
2
3
2
2
3
1
1
1
1
1
2
22,920
25,270
57
-------
Table 3-26. General Cargo Ship Deep Sea Port Emissions in 2002
Port Name
Houston, TX
Miami, FL
New Orleans, LA
Mobile, AL
Savannah, GA
Baltimore, MD
New York/New Jersey
Charleston, SC
Everglades, FL
Jacksonville, FL
Wilmington, NC
Brunswick, GA
Long Beach, CA
Tampa, FL
Camden, NJ
Philadelphia, PA
Port Arthur, TX
Los Angeles, CA
San Diego, CA
Seattle, WA
South Louisiana, LA
Portland, OR
Beaumont, TX
Palm Beach, FL
Lake Charles, LA
Canaveral, FL
Newport News, VA
Panama City, FL
Vancouver, WA
Gulfport, MS
Pascagoula, MS
Oakland, CA
San Francisco, CA
Longview, WA
Port Angeles, WA
Morehead City, NC
New Haven, CT
Portland, ME
Baton Rouge, LA
Coos Bay, OR
Manatee, FL
Tacoma, WA
Freeport, TX
Chester, PA
Grays Harbor, WA
Brownsville, TX
Sacramento, CA
Stockton, CA
Installed
Power
(MW)
5,806
2,941
2,925
2,529
2,521
2,275
1,841
1,814
1,813
1,237
1,178
1,102
996
986
974
960
890
883
867
841
810
771
744
722
670
596
568
545
514
496
466
462
453
441
390
387
382
380
356
312
301
264
238
237
220
206
202
202
Metric Tonnes
NOx
560
354
601
297
415
673
153
214
197
152
155
173
158
118
180
129
100
143
144
261
216
121
113
76
71
42
74
40
66
51
45
43
82
61
90
40
43
31
111
28
36
106
17
40
26
23
58
55
PM,n
59
31
50
25
34
56
13
17
18
13
13
14
11
10
15
14
11
10
10
21
18
10
12
7
7
5
6
4
6
4
4
3
6
5
7
3
4
3
10
3
3
9
2
3
2
2
4
4
PM,<
52
29
46
23
32
52
12
16
16
12
12
13
10
9
14
13
9
9
9
19
16
9
12
6
6
5
6
4
5
4
4
3
5
5
7
3
3
3
9
3
3
8
2
3
2
2
4
4
HC
19
11
20
10
14
22
6
7
6
5
5
6
5
4
6
6
4
4
4
9
7
5
5
2
3
2
2
2
3
2
2
1
2
2
3
1
1
1
4
1
1
4
1
1
1
1
2
2
CO
42
28
48
85
32
52
13
17
16
12
12
14
12
9
15
14
9
11
11
21
17
11
11
6
22
5
6
4
6
4
4
3
6
5
7
3
3
2
9
3
3
8
2
3
2
2
5
4
SO,
439
272
384
190
261
430
95
306
138
160
237
486
94
75
349
315
77
85
87
145
134
69
89
54
49
55
47
33
38
32
30
23
50
35
49
30
30
25
73
21
22
62
16
71
16
15
34
32
58
-------
Table 3-26. General Cargo Ship Deep Sea Port Emissions in 2002 (continued)
Port Name
Georgetown, SC
Corpus Christi, TX
Wilmington, DE
Eureka, CA
Plaquemines, LA
Fall River, MA
Boston, MA
Kalama, WA
Galveston, TX
Albany, NY
Hueneme, CA
Pensacola, FL
Richmond, CA
Everett, WA
Perm Manor, PA
Texas City, TX
Hopewell, VA
Honolulu, HI
Marcus Hook, PA
Carquinez, CA
Richmond, VA
Providence, RI
Matagorda Ship
Nikishka, AK
Olympia, WA
Anacortes, WA
Paulsboro, NJ
Redwood City, CA
Kahului, HI
Valdez, AK
Hilo, HI
Anchorage, AK
Searsport, ME
Total General Cargo
Total General Cargo (short
tons)
Installed
Power
(MW)
202
188
185
183
178
139
122
116
111
83
77
71
67
58
56
46
44
43
39
39
38
32
27
24
24
23
22
19
7
6
5
4
3
49,992
Metric Tonnes
NOx
26
20
28
42
29
17
14
15
10
15
7
7
13
19
10
5
12
6
7
8
7
4
2
7
11
5
3
4
1
1
1
1
0
7,335
8,087
PM,n
2
2
2
3
2
2
1
1
1
1
0
1
1
2
1
1
1
1
1
1
1
0
0
1
1
0
0
0
0
0
0
0
0
632
697
PM,<
2
2
2
3
2
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
0
0
0
1
0
0
0
0
0
0
0
0
579
638
HC
1
1
1
1
1
1
0
1
0
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
252
278
CO
2
5
2
3
2
1
1
1
1
1
10
1
1
1
1
0
1
1
1
1
1
0
0
1
1
0
0
0
0
0
0
0
0
686
756
SO,
35
14
43
26
19
16
13
8
9
10
4
5
8
11
18
4
42
4
16
5
5
7
2
4
6
3
2
2
0
1
0
1
1
6,203
6,839
59
-------
Table 3-27. Miscellaneous Ship Deep Sea Port Emissions in 2002
Port Name
Mobile, AL
Corpus Christi, TX
Pensacola, FL
Anchorage, AK
New York/New Jersey
Baltimore, MD
Portland, OR
Honolulu, HI
Houston, TX
New Orleans, LA
Seattle, WA
Newport News, VA
Lake Charles, LA
Kahului, HI
Total Miscellaneous
Total Miscellaneous (short
tons)
Installed
Power
(MW)
709
119
65
58
26
23
21
16
13
12
9
6
3
1
1,081
Metric Tonnes
NOx
99
16
11
22
7
14
7
4
1
7
5
2
0
0
195
275
PM10
9
2
1
2
1
1
1
0
0
1
0
0
0
0
18
20
PM2.5
9
1
1
2
1
1
0
0
0
1
0
0
0
0
17
19
HC
3
1
0
1
0
0
0
0
0
0
0
0
0
0
7
8
CO
30
5
1
2
1
1
0
0
0
1
0
0
0
0
41
45
S02
74
12
8
15
5
10
4
2
1
4
3
2
0
0
140
154
60
-------
Table 3-28. Passenger Ship Deep Sea Port Emissions in 2002
Port Name
Miami, FL
Everglades, FL
Canaveral, FL
New York/New Jersey
Long Beach, CA
New Orleans, LA
San Diego, CA
Los Angeles, CA
Hilo, HI
Honolulu, HI
Tampa, FL
Galveston, TX
Seattle, WA
Boston, MA
Kahului, HI
San Francisco, CA
Catalina, CA
Charleston, SC
Houston, TX
Manatee, FL
Nawiliwili, HI
Baltimore, MD
Mobile, AL
Anchorage, AK
Palm Beach, FL
Paulsboro, NJ
Corpus Christi, TX
Portland, OR
Eureka, CA
Philadelphia, PA
Valdez, AK
Hueneme, CA
New Bedford/Fairhaven, MA
Savannah, GA
Fall River, MA
Total Passenger
Total Passenger (short tons)
Installed
Power
(MW)
28,808
22,083
15,756
6,841
5,756
5,401
5,172
5,105
4,467
4,359
3,599
3,248
3,017
2,874
2,281
2,241
919
758
751
634
583
359
330
200
146
126
113
60
57
44
31
29
16
16
11
126,193
Metric Tonnes
NOx
4,919
2,447
2,758
745
629
1,133
507
573
923
637
352
644
739
430
462
237
87
113
143
66
120
427
80
66
15
30
21
12
7
11
2
2
2
5
1
19,345
21,329
PM10
463
244
256
74
52
110
42
47
76
58
34
76
72
41
38
19
7
10
19
7
10
42
7
5
1
3
2
1
1
1
0
0
0
1
0
1,816
2,002
PM2.5
430
227
238
68
48
102
38
43
70
53
25
64
66
38
34
18
7
9
15
5
9
39
6
5
1
3
2
1
1
1
0
0
0
0
0
1,665
1,836
HC
142
73
80
25
19
37
15
17
27
19
12
23
23
13
14
7
3
3
5
2
4
13
2
2
0
1
1
1
0
0
0
0
0
0
0
583
643
CO
373
187
209
59
48
91
39
44
72
48
28
42
54
33
36
18
7
9
9
5
9
33
11
5
1
2
3
1
1
1
0
4
0
0
0
1,482
1,634
S02
3,712
1,897
2,044
551
371
835
298
338
622
427
271
559
540
326
304
140
51
83
131
52
82
320
52
43
11
23
14
8
4
8
2
1
2
4
1
14,127
15,576
61
-------
Table 3-29. Refrigerated Cargo Ship Deep Sea Port Emissions in 2002
Port Name
Camden, NJ
New York/New Jersey
Manatee, FL
Bridgeport, CT
Philadelphia, PA
Hueneme, CA
Miami, FL
Wilmington, DE
Long Beach, CA
Los Angeles, CA
Galveston, TX
Canaveral, FL
Gulfport, MS
Tampa, FL
Pascagoula, MS
Jacksonville, FL
New Orleans, LA
Brunswick, GA
Anchorage, AK
Everglades, FL
Corpus Christi, TX
Charleston, SC
Houston, TX
New Bedford/Fairhaven, MA
Seattle, WA
San Diego, CA
Baltimore, MD
Mobile, AL
Honolulu, HI
Morehead City, NC
Searsport, ME
Paulsboro, NJ
Port Angeles, WA
Pensacola, FL
Total Reefer
Total Reefer (short tons)
Installed
Power
(MW)
2,088
1,575
1,277
1,086
994
963
742
733
662
587
532
525
374
245
232
173
163
158
140
116
97
82
78
69
55
48
47
22
6
6
5
4
3
2
13,889
Metric Tonnes
NOx
531
195
453
188
246
161
130
171
94
85
87
96
56
38
54
34
109
32
62
71
21
16
13
15
30
9
58
5
3
1
1
1
1
0
3,068
3,383
PM10
44
16
37
15
21
11
11
14
6
6
9
8
5
3
5
3
9
3
5
6
2
1
1
1
2
1
5
0
0
0
0
0
0
0
251
277
PM2.5
41
15
33
14
19
10
10
13
6
5
8
7
4
3
4
3
8
2
4
5
2
1
1
1
2
1
4
0
0
0
0
0
0
0
229
252
HC
19
7
14
6
9
5
4
6
3
2
3
3
2
1
2
1
3
1
2
2
1
0
0
0
1
0
2
0
0
0
0
0
0
0
100
110
CO
45
16
36
15
21
81
10
14
7
7
6
7
4
3
4
3
9
2
5
5
3
1
1
1
2
1
4
1
0
0
0
0
0
0
317
350
S02
341
123
307
121
158
99
84
110
56
51
70
63
37
25
38
22
72
20
36
47
13
10
11
10
17
5
38
3
2
1
1
1
1
0
1,994
2,198
62
-------
Table 3-30. Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002
Port Name
Everglades, FL
Miami, FL
New York/New Jersey
Baltimore, MD
Savannah, GA
Gulfport, MS
New Orleans, LA
Oakland, CA
Jacksonville, FL
Houston, TX
Mobile, AL
Long Beach, CA
Charleston, SC
Los Angeles, CA
Palm Beach, FL
Wilmington, NC
Philadelphia, PA
Portland, ME
Boston, MA
Brunswick, GA
Tampa, FL
Tacoma, WA
Portland, OR
Newport News, VA
Canaveral, FL
Beaumont, TX
Galveston, TX
Hueneme, CA
Chester, PA
Honolulu, HI
New Haven, CT
Everett, WA
Morehead City, NC
Bridgeport, CT
Camden, NJ
Pensacola, FL
Vancouver, WA
Seattle, WA
Wilmington, DE
Richmond, VA
Corpus Christi, TX
Lake Charles, LA
Albany, NY
Perm Manor, PA
Longview, WA
Anchorage, AK
South Louisiana, LA
Installed
Power
(MW)
3,734
3,646
3,323
3,284
2,578
1,028
1,008
901
892
810
576
483
455
428
423
342
333
305
235
219
166
148
110
77
75
62
59
52
47
39
32
27
27
23
22
18
11
11
10
8
6
6
6
6
5
5
4
Metric Tonnes
NOx
285
268
289
836
277
299
161
104
105
72
82
67
45
61
49
33
58
28
29
17
48
45
8
12
9
13
7
5
8
3
3
2
2
3
4
5
2
4
1
2
1
1
1
1
1
2
1
PM,n
27
33
24
65
22
26
14
8
9
10
7
5
4
4
4
3
5
3
3
2
4
4
1
1
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PM,,
25
31
22
60
20
23
13
7
9
9
6
4
3
4
4
3
5
2
2
2
4
3
1
1
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HC
10
12
11
28
9
9
6
3
3
3
3
2
1
2
2
1
2
1
1
1
1
2
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
CO
23
30
25
65
22
23
15
8
8
7
16
5
3
5
4
3
5
2
2
2
4
4
1
1
1
1
0
8
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
SO,
209
261
176
494
169
222
102
59
73
75
53
39
29
36
35
22
42
20
21
14
36
25
5
8
6
9
5
3
5
2
2
1
2
2
3
4
1
2
1
1
1
1
1
1
1
1
1
63
-------
Table 3-31. Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002 (Continued)
Port Name
Barbers Point, HI
Manatee, FL
Total RoRo
Total RoRo (short tons)
Installed
Power
(MW)
4
3
26,071
Metric Tonnes
NOx
0
1
3,360
3,705
PM,n
0
0
292
322
PM,S
0
0
269
297
HC
0
0
118
130
CO
0
0
296
326
SO2
0
1
2,279
2,513
64
-------
Table 3-32. Tanker Ship Deep Sea Port Emissions in 2002
Port Name
Houston, TX
Beaumont, TX
New York/New Jersey
Corpus Christi, TX
Texas City, TX
Valdez, AK
South Louisiana, LA
Other Puget Sound
Freeport, TX
Lake Charles, LA
New Orleans, LA
Long Beach, CA
Los Angeles, CA
Boston, MA
Paulsboro, NJ
Richmond, CA
Portland, ME
Baton Rouge, LA
Marcus Hook, PA
Philadelphia, PA
Tampa, FL
Pascagoula, MS
Savannah, GA
Everglades, FL
Carquinez, CA
Matagorda Ship
San Francisco, CA
Port Arthur, TX
El Segundo, CA
Jacksonville, FL
Plaquemines, LA
Charleston, SC
Wilmington, NC
Mobile, AL
Baltimore, MD
New Haven, CT
Nikishka, AK
Honolulu, HI
Providence, RI
Galveston, TX
New Castle, DE
Searsport, ME
Anacortes, WA
Installed
Power
(MW)
19,096
10,807
9,361
7,498
6,856
6,632
5,886
5,678
5,206
4,517
3,506
3,380
2,998
2,954
2,952
2,871
2,813
2,603
2,472
2,352
2,282
2,275
2,083
2,036
1,977
1,875
1,839
1,751
1,685
1,633
1,322
1,213
1,167
1,114
979
951
840
687
554
552
524
498
455
Metric Tonnes
NOx
2,334
1,791
1,851
1,188
889
338
2,187
2,111
584
619
1,149
419
383
516
589
323
601
1,133
904
609
323
313
395
495
270
268
184
230
192
263
349
237
183
182
424
184
189
221
116
68
147
103
370
PM10
319
228
157
99
121
36
177
219
80
60
96
31
28
50
48
24
49
93
74
50
27
27
31
41
20
22
14
31
14
28
28
20
21
15
33
15
19
18
10
12
12
9
29
PM2.5
294
210
144
91
111
32
164
197
74
55
89
28
26
46
45
22
45
86
68
46
24
24
29
37
19
20
13
28
13
26
26
18
19
14
31
14
18
16
9
11
11
8
26
HC
80
76
65
40
31
11
68
71
20
26
37
13
12
17
20
10
19
36
28
17
11
10
13
15
9
9
6
9
6
11
11
7
8
6
14
6
6
8
4
2
5
3
13
CO
178
159
156
263
69
27
170
169
45
169
92
33
30
44
48
25
47
90
71
49
26
25
31
39
21
22
14
19
15
27
27
19
20
46
33
14
15
18
9
5
12
8
29
S02
2,494
1,742
1,202
752
942
296
1,365
1,745
630
446
744
245
223
592
2,021
181
383
711
2,255
1,733
202
206
258
320
151
166
104
237
108
419
221
255
375
115
256
131
165
130
173
93
357
114
207
65
-------
Table 3-32. Tanker Ship Deep Sea Port Emissions in 2002 (continued)
Port Name
Barbers Point, HI
Stockton, CA
Manatee, FL
Canaveral, FL
Camden, NJ
Brownsville, TX
Portland, OR
Morehead City, NC
Tacoma, WA
Bridgeport, CT
Miami, FL
Wilmington, DE
Vancouver, WA
Palm Beach, FL
Newport News, VA
New Bedford/Fairhaven, MA
Hueneme, CA
Anchorage, AK
Seattle, WA
Port Angeles, WA
Kalama, WA
San Diego, CA
Panama City, FL
Sacramento, CA
Longview, WA
Albany, NY
Brunswick, GA
Hopewell, VA
Fall River, MA
Hilo, HI
Kahului, HI
Catalina, CA
Nawiliwili, HI
Everett, WA
Total Tanker
Total Tanker (short tons)
Installed
Power
(MW)
387
370
355
351
349
320
309
286
111
206
161
159
133
124
118
96
95
78
74
72
67
60
38
34
30
28
21
14
13
13
9
9
8
6
145,399
Metric Tonnes
NOx
101
79
39
44
103
45
222
51
1,306
31
33
22
64
22
21
22
13
45
152
34
112
6
5
8
86
8
5
4
3
2
2
1
2
23
28,938
31,905
PM,n
8
6
4
6
8
4
19
4
104
3
3
3
5
2
2
2
1
4
12
3
9
0
0
1
7
1
0
0
0
0
0
0
0
2
2,760
3,043
PM,<
7
6
4
5
8
3
17
4
94
3
3
3
5
2
1
2
1
3
11
3
8
0
0
1
6
1
0
0
0
0
0
0
0
2
2,529
2,788
HC
3
3
2
2
3
1
8
2
45
1
1
1
2
1
1
1
0
2
5
1
4
0
0
0
3
0
0
0
0
0
0
0
0
1
982
1,083
CO
8
6
4
6
8
4
18
4
105
3
3
3
5
2
2
2
14
4
12
3
9
0
0
1
7
1
0
0
0
0
0
0
0
2
2,664
2,937
SO,
58
44
32
55
218
28
133
41
723
29
26
83
37
19
13
22
8
25
87
25
66
3
3
4
49
5
13
25
5
1
1
0
1
13
27,359
30,164
66
-------
Table 3-33. Ocean Going Tug Deep Sea Port Emissions in 2002
Port Name
Corpus Christi, TX
Mobile, AL
Miami, FL
Everglades, FL
Canaveral, FL
Palm Beach, FL
New Orleans, LA
Galveston, TX
Portland, OR
Jacksonville, FL
Houston, TX
Kahului, HI
Lake Charles, LA
Manatee, FL
South Louisiana, LA
Pascagoula, MS
Plaquemines, LA
Seattle, WA
Boston, MA
New York/New Jersey
Brownsville, TX
Total Ocean Going Tug
Toted Ocean Going Tug (short
tons)
Installed
Power
(MW)
47
46
31
28
28
28
21
19
18
17
16
16
7
7
7
7
4
4
4
3
3
360
Metric Tonnes
NOx
5
6
3
3
3
3
4
2
6
2
1
2
1
1
2
1
1
2
0
0
0
48
53
PM,n
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
5
6
PM,<
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
4
HC
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
CO
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
7
SO,
4
4
2
2
2
2
3
1
3
2
1
1
1
1
1
1
1
1
0
0
0
34
37
67
-------
3.1.6.2 Great Lake Ports
Emissions inventories for 28 Great Lake ports were developed and are
presented here. Great Lake ships include self-unloading bulk carriers (Bulk Carrier,
SU) which tend to operate within the Great Lakes only. Other ships travel down the
St. Lawrence River from the open ocean. Ships which operate in the Great Lakes
only are known as Lakers while ships that come into the Lakes from the open ocean
are called Salties. Integrated tug-barges (ITB) are also used on the Great Lakes.
Total emissions by port for Great Lakes Ports are shown in Table 3-34
Auxiliary engine emissions by port for Great Lakes Ports are shown in Table 3-35.
Emissions by mode for Great Lake ports are shown in Table 3-36 for cruise, Table 3-
37 for reduced speed zone, Table 3-38 for maneuvering and Table 3-39 for hotelling.
Emissions by ship type are shown in Table 3-40 through Table 3-44.
Table 3-34. Total Emissions by Great Lake Port in 2002
Port Name
Alpena, MI
Buffalo, NY
Bums Waterway, IN
Calcite, MI
Cleveland, OH
Dolomite, MI
Erie, PA
Escanaba, MI
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, WI
Muskegon, MI
Presque Isle, MI
St Clair, MI
Stoneport, MI
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, MI
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, MI
Manistee, MI
Sandusky, OH
Toledo, OH
Total Emissions
Total Emissions (short tons)
Installed
Power
(MW)
88.6
83.6
818.8
125.5
559.9
66.9
55.3
117.7
114.0
84.1
64.1
26.0
494.8
36.9
562.4
155.9
22.3
48.2
1,179.2
491.5
1,863.1
1,359.3
3,440.6
139.7
55.6
163.9
741.8
1,516.6
14,476.4
Metric Tonnes
NOx
1.5
2.9
45.5
3.4
32.6
1.9
2.2
3.1
3.0
3.2
1.5
0.5
26.1
0.9
16.2
4.2
0.7
1.2
36.8
22.0
52.6
51.4
131.8
5.9
1.5
17.8
21.0
57.5
548.9
605.2
PM,n
0.3
0.3
3.9
0.3
2.8
0.2
0.2
0.3
0.3
0.3
0.2
0.1
2.3
0.1
1.4
0.4
0.1
0.1
3.4
1.9
5.0
4.7
12.0
0.5
0.1
1.5
2.0
5.0
49.6
54.7
PM,<
0.2
0.3
3.6
0.3
2.5
0.1
0.2
0.3
0.3
0.3
0.2
0.1
2.1
0.1
1.3
0.4
0.1
0.1
3.1
1.8
4.7
4.4
11.1
0.5
0.1
1.4
1.8
4.6
45.7
50.4
HC
0.0
0.1
1.5
0.1
1.0
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.8
0.0
0.7
0.2
0.0
0.0
1.3
0.7
1.9
1.7
4.5
0.2
0.1
0.5
0.8
2.0
18.9
20. 8
CO
0.1
0.2
3.7
0.3
2.6
0.2
0.2
0.3
0.3
0.3
0.1
0.0
2.1
0.1
1.4
0.4
0.1
0.1
3.1
1.8
4.4
4.2
10.7
0.5
0.1
1.4
1.8
4.7
44.9
49.5
SO,
2.5
2.3
30.0
2.5
21.8
1.1
1.7
2.3
2.5
2.2
1.3
0.5
17.8
0.7
10.0
3.0
0.4
0.9
26.4
15.3
39.5
37.5
94.5
4.1
1.1
12.2
15.2
39.2
388.8
428.7
68
-------
Table 3-35. Auxiliary Engine Emissions by Great Lake Port in 2002
Port Name
Alpena, MI
Buffalo, NY
Burns Waterway, IN
Calcite, MI
Cleveland, OH
Dolomite, MI
Erie, PA
Escanaba, MI
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, WI
Muskegon, MI
Presque Isle, MI
St Clair, MI
Stoneport, MI
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, MI
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, MI
Manistee, MI
Sandusky, OH
Toledo, OH
Total Auxiliary Emissions
Total Auxiliary Emissions
(short tons)
Installed
Power
(MW)
19.7
18.6
181.1
27.9
122.5
14.9
11.9
26.1
25.3
18.5
14.2
5.8
108.8
8.2
124.9
34.6
4.9
10.7
261.6
107.9
413.6
302.6
759.9
31.0
12.3
34.7
164.7
335.5
3,202.4
Metric Tonnes
NOx
1.2
1.5
29.6
1.4
22.5
0.6
1.5
1.2
1.3
1.5
0.7
0.3
17.5
0.4
5.6
1.6
0.2
0.5
16.3
13.6
20.9
29.6
74.0
3.7
0.6
15.1
8.1
30.6
301.6
332.5
PM,n
0.1
0.1
2.5
0.1
1.9
0.1
0.1
0.1
0.1
0.1
0.1
0.0
1.4
0.0
0.5
0.1
0.0
0.0
1.3
1.1
1.7
2.5
6.1
0.3
0.0
1.3
0.7
2.5
25.0
27.6
PM,<
0.1
0.1
2.2
0.1
1.7
0.0
0.1
0.1
0.1
0.1
0.1
0.0
1.3
0.0
0.4
0.1
0.0
0.0
1.2
1.0
1.6
2.2
5.6
0.3
0.0
1.2
0.6
2.3
22.9
25.2
HC
0.0
0.0
0.8
0.0
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.5
0.0
0.2
0.0
0.0
0.0
0.4
0.4
0.6
0.8
2.0
0.1
0.0
0.4
0.2
0.8
8.3
9.2
CO
0.1
0.1
2.2
0.1
1.7
0.0
0.1
0.1
0.1
0.1
0.1
0.0
1.3
0.0
0.4
0.1
0.0
0.0
1.2
1.0
1.6
2.2
5.6
0.3
0.0
1.2
0.6
2.3
22.9
25.2
SO,
0.8
1.0
19.7
0.9
15.0
0.4
1.0
0.8
0.9
1.0
0.5
0.2
11.7
0.3
3.7
1.1
0.2
0.3
10.9
9.1
13.9
19.8
49.4
2.5
0.4
10.1
5.4
20.4
201.3
221.9
69
-------
Table 3-36. Cruise Emissions by Great Lake Port in 2002
Port Name
Alpena, MI
Buffalo, NY
Burns Waterway, IN
Calcite, MI
Cleveland, OH
Dolomite, MI
Erie, PA
Escanaba, MI
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, WI
Muskegon, MI
Presque Isle, MI
St Clair, MI
Stoneport, MI
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, MI
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, MI
Manistee, MI
Sandusky, OH
Toledo, OH
Total Cruise Emissions
Total Cruise Emissions (short
tons)
Installed
Power
(MW)
88.6
83.6
818.8
125.5
559.9
66.9
55.3
117.7
114.0
84.1
64.1
26.0
494.8
36.9
562.4
155.9
22.3
48.2
1,179.2
491.5
1,863.1
1,359.3
3,440.6
139.7
55.6
163.9
741.8
1,516.6
14,476.4
Metric Tonnes
NOx
0.3
0.9
11.7
1.4
7.7
0.8
0.6
1.5
1.2
1.1
0.6
0.1
6.4
0.4
7.3
1.7
0.3
0.6
15.1
6.3
23.1
16.5
43.3
1.7
0.7
2.0
9.4
20.1
182.8
201.5
PM,n
0.1
0.1
1.0
0.1
0.7
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.6
0.0
0.6
0.2
0.0
0.1
1.4
0.6
2.3
1.6
4.2
0.2
0.1
0.2
0.9
1.8
17.3
19.1
PM,<
0.1
0.1
0.9
0.1
0.6
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.5
0.0
0.5
0.2
0.0
0.0
1.3
0.5
2.1
1.5
3.9
0.2
0.1
0.2
0.8
1.7
16.1
17.8
HC
0.0
0.0
0.4
0.0
0.3
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.2
0.0
0.2
0.1
0.0
0.0
0.5
0.2
0.8
0.6
1.5
0.1
0.0
0.1
0.3
0.7
6.3
6.9
CO
0.0
0.1
0.9
0.1
0.6
0.1
0.0
0.1
0.1
0.1
0.0
0.0
0.5
0.0
0.6
0.1
0.0
0.0
1.2
0.5
1.8
1.3
3.4
0.1
0.1
0.2
0.7
1.6
14.3
15.8
SO,
1.2
0.9
7.5
1.1
5.2
0.5
0.5
1.2
1.1
0.8
0.6
0.3
4.5
0.3
4.4
1.4
0.2
0.4
11.3
4.6
18.4
13.1
33.3
1.3
0.5
1.6
7.1
14.0
137.2
151.3
70
-------
Table 3-37. Reduced Speed Zone Emissions by Great Lake Port in 2002
Port Name
Alpena, MI
Buffalo, NY
Burns Waterway, IN
Calcite, MI
Cleveland, OH
Dolomite, MI
Erie, PA
Escanaba, MI
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, WI
Muskegon, MI
Presque Isle, MI
St Clair, MI
Stoneport, MI
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, MI
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, MI
Manistee, MI
Sandusky, OH
Toledo, OH
Total RSZ Emissions
Total RSZ Emissions (short
tons)
Installed
Power
(MW)
88.6
83.6
818.8
125.5
559.9
66.9
55.3
117.7
114.0
84.1
64.1
26.0
494.8
36.9
562.4
155.9
22.3
48.2
1,179.2
491.5
1,863.1
1,359.3
3,440.6
139.7
55.6
163.9
741.8
1,516.6
14,476.4
Metric Tonnes
NOx
0.1
0.2
2.8
0.3
1.9
0.2
0.1
0.4
0.3
0.3
0.1
0.0
1.6
0.1
1.7
0.4
0.1
0.1
3.7
1.5
5.8
4.1
10.8
0.4
0.2
0.5
2.3
4.9
45.0
49.6
PM,n
0.0
0.0
0.2
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.3
0.1
0.6
0.4
1.0
0.0
0.0
0.0
0.2
0.4
4.2
4.6
PM,<
0.0
0.0
0.2
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.3
0.1
0.5
0.4
0.9
0.0
0.0
0.0
0.2
0.4
3.9
4.3
HC
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.1
0.1
0.2
0.1
0.4
0.0
0.0
0.0
0.1
0.2
1.5
1.7
CO
0.0
0.0
0.2
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.1
0.0
0.0
0.0
0.3
0.1
0.5
0.3
0.8
0.0
0.0
0.0
0.2
0.4
3.5
3.9
SO,
0.3
0.2
1.8
0.3
1.3
0.1
0.1
0.3
0.3
0.2
0.1
0.1
1.1
0.1
1.0
0.3
0.0
0.1
2.8
1.1
4.5
3.1
8.1
0.3
0.1
0.4
1.7
3.4
33.3
36.7
71
-------
Table 3-38. Maneuvering Emissions by Great Lake Port in 2002
Port Name
Alpena, MI
Buffalo, NY
Burns Waterway, IN
Calcite, MI
Cleveland, OH
Dolomite, MI
Erie, PA
Escanaba, MI
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, WI
Muskegon, MI
Presque Isle, MI
St Clair, MI
Stoneport, MI
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, MI
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, MI
Manistee, MI
Sandusky, OH
Toledo, OH
Total Maneuver Emissions
Total Maneuver Emissions
(short tons)
Installed
Power
(MW)
88.6
83.6
818.8
125.5
559.9
66.9
55.3
117.7
114.0
84.1
64.1
26.0
494.8
36.9
562.4
155.9
22.3
48.2
1,179.2
491.5
1,863.1
1,359.3
3,440.6
139.7
55.6
163.9
741.8
1,516.6
14,476.4
Metric Tonnes
NOx
0.2
0.6
4.4
0.9
2.0
0.5
0.2
0.3
0.8
0.7
0.4
0.1
2.3
0.2
4.2
1.1
0.2
0.2
6.0
1.9
9.7
5.7
15.2
0.5
0.3
0.6
3.8
6.6
69.6
76.7
PM,n
0.0
0.1
0.4
0.1
0.2
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.2
0.0
0.4
0.1
0.0
0.0
0.6
0.2
1.0
0.6
1.6
0.1
0.0
0.1
0.4
0.7
7.2
7.9
PM,<
0.0
0.1
0.4
0.1
0.2
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.2
0.0
0.4
0.1
0.0
0.0
0.6
0.2
0.9
0.6
1.5
0.0
0.0
0.1
0.4
0.6
6.7
7.4
HC
0.0
0.0
0.3
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.3
0.1
0.0
0.0
0.3
0.1
0.6
0.3
0.9
0.0
0.0
0.0
0.2
0.4
4.2
4.6
CO
0.0
0.1
0.5
0.1
0.2
0.1
0.0
0.0
0.1
0.1
0.0
0.0
0.3
0.0
0.5
0.1
0.0
0.0
0.7
0.2
1.1
0.6
1.7
0.1
0.0
0.1
0.4
0.8
8.0
8.8
SO,
0.3
0.4
3.0
0.6
1.4
0.3
0.2
0.2
0.6
0.5
0.3
0.1
1.6
0.2
2.6
0.7
0.1
0.2
4.4
1.4
7.3
4.5
11.3
0.4
0.2
0.4
2.7
4.6
50.4
55.6
72
-------
Table 3-39. Hotelling Emissions by Great Lake Port in 2002
Port Name
Alpena, MI
Buffalo, NY
Burns Waterway, IN
Calcite, MI
Cleveland, OH
Dolomite, MI
Erie, PA
Escanaba, MI
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, WI
Muskegon, MI
Presque Isle, MI
St Clair, MI
Stoneport, MI
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, MI
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, MI
Manistee, MI
Sandusky, OH
Toledo, OH
Total Hotel Emissions
Total Hotel Emissions (short
tons)
Installed
Power
(MW)
88.6
83.6
818.8
125.5
559.9
66.9
55.3
117.7
114.0
84.1
64.1
26.0
494.8
36.9
562.4
155.9
22.3
48.2
1,179.2
491.5
1,863.1
1,359.3
3,440.6
139.7
55.6
163.9
741.8
1,516.6
14,476.4
Metric Tonnes
NOx
1.0
1.2
26.6
0.8
20.9
0.4
1.3
0.9
0.8
1.1
0.4
0.2
15.8
0.2
3.0
1.0
0.1
0.3
12.1
12.2
14.0
25.1
62.6
3.3
0.4
14.7
5.5
25.8
251.5
277.3
PM,n
0.1
0.1
2.2
0.1
1.7
0.0
0.1
0.1
0.1
0.1
0.0
0.0
1.3
0.0
0.3
0.1
0.0
0.0
1.0
1.0
1.2
2.1
5.2
0.3
0.0
1.2
0.5
2.1
20.9
23.0
PM,<
0.1
0.1
2.0
0.1
1.6
0.0
0.1
0.1
0.1
0.1
0.0
0.0
1.2
0.0
0.2
0.1
0.0
0.0
0.9
0.9
1.1
1.9
4.8
0.2
0.0
1.1
0.4
2.0
19.1
21.1
HC
0.0
0.0
0.7
0.0
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
0.0
0.1
0.0
0.0
0.0
0.3
0.3
0.4
0.7
1.7
0.1
0.0
0.4
0.2
0.7
7.0
7.7
CO
0.1
0.1
2.0
0.1
1.6
0.0
0.1
0.1
0.1
0.1
0.0
0.0
1.2
0.0
0.2
0.1
0.0
0.0
0.9
0.9
1.1
1.9
4.8
0.2
0.0
1.1
0.4
2.0
19.1
21.1
SO,
0.7
0.8
17.7
0.5
14.0
0.2
0.9
0.6
0.5
0.7
0.3
0.1
10.5
0.2
2.0
0.6
0.1
0.2
8.0
8.1
9.3
16.8
41.8
2.2
0.3
9.8
3.7
17.2
167.9
185.1
73
-------
Table 3-40. Self-Unloading Bulk Carrier Emissions by Great Lake Port
in 2002
Port Name
Duluth-Superior, MN&WI
Conneaut, OH
Ashtabula, OH
Toledo, OH
Detroit, MI
Sandusky, OH
Presque Isle, MI
Burns Waterway, IN
Chicago, IL
Milwaukee, WI
St Clair, MI
Calcite, MI
Escanaba, MI
Fairport, OH
Alpena, MI
Cleveland, OH
Gary, IN
Buffalo, NY
Dolomite, MI
Lorain, OH
Inland Harbor, MI
Indiana, IN
Two Harbors, MN
Muskegon, MI
Erie, PA
Marblehead, OH
Stoneport, MI
Manistee, MI
Total SU Bulk Carrier
Total SU Bulk Carrier (short
tons)
Installed
Power
(MW)
2,201.1
1,842.7
1,047.0
987.0
801.9
734.5
562.4
236.1
207.5
168.5
155.9
125.5
117.7
114.0
88.6
74.7
71.3
70.7
66.9
64.1
55.6
52.1
48.2
36.9
27.0
26.0
22.3
9.0
10,015.2
Metric Tonnes
NOx
61.8
51.3
29.2
28.1
20.3
20.6
16.2
7.6
5.5
4.3
4.2
3.4
3.1
3.0
1.5
1.3
2.3
2.0
1.9
1.5
1.5
1.3
1.2
0.9
0.5
0.5
0.7
0.2
275.9
304.2
PM,n
6.0
4.9
2.8
2.5
2.1
1.9
1.4
0.7
0.5
0.5
0.4
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
26.7
29.4
PM,<
5.6
4.6
2.6
2.3
2.0
1.8
1.3
0.7
0.5
0.4
0.4
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.1
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
24.7
27.2
HC
2.3
1.9
1.1
1.0
0.7
0.8
0.7
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0.0
0.0
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
10.4
11.5
CO
5.2
4.3
2.5
2.4
1.7
1.7
1.4
0.7
0.5
0.4
0.4
0.3
0.3
0.3
0.1
0.1
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.1
0.0
23.3
25.7
so.
47.9
38.7
21.6
19.3
17.5
14.8
10.0
5.7
4.4
3.8
3.0
2.5
2.3
2.5
2.5
1.7
1.6
1.8
1.1
1.3
1.1
1.1
0.9
0.7
0.6
0.5
0.4
0.2
209.7
231.2
74
-------
Table 3-41. Bulk Carrier Emissions by Great Lake Port in 2002
Port Name
Duluth-Superior, MN&WI
Burns Waterway, IN
Detroit, MI
Cleveland, OH
Toledo, OH
Milwaukee, WI
Chicago, IL
Ashtabula, OH
Indiana, IN
Conneaut, OH
Erie, PA
Buffalo, NY
Sandusky, OH
Gary, IN
Total Bulk Carrier
Total Bulk Carrier (short tons)
Installed
Power
(MW)
1,031.6
562.0
457.9
427.0
421.4
292.3
219.0
126.0
87.6
20.4
17.2
12.9
7.3
6.9
3,689.4
Metric Tonnes
NOx
61.1
36.8
27.2
27.7
25.1
19.9
12.8
7.4
4.6
1.2
1.1
0.9
0.4
0.5
226.7
249.9
PM,n
5.2
3.0
2.2
2.3
2.1
1.6
1.1
0.6
0.4
0.1
0.1
0.1
0.0
0.0
18.8
20.7
PM,<
4.7
2.8
2.1
2.1
2.0
1.5
1.0
0.6
0.4
0.1
0.1
0.1
0.0
0.0
17.4
19.2
HC
1.9
1.1
0.9
0.9
0.8
0.6
0.4
0.2
0.1
0.0
0.0
0.0
0.0
0.0
7.1
7.8
CO
4.8
2.9
2.2
2.2
2.0
1.6
1.0
0.6
0.4
0.1
0.1
0.1
0.0
0.0
17.9
79.7
SO,
40.5
23.5
17.3
17.7
16.8
12.7
8.3
4.7
3.1
0.8
0.7
0.6
0.4
0.3
147.3
162.4
Table 3-42. General Cargo Ship Emissions by Great Lake Port in 2002
Port Name
Duluth-Superior, MN&WI
Toledo, OH
Cleveland, OH
Detroit, MI
Chicago, IL
Milwaukee, WI
Burns Waterway, IN
Erie, PA
Ashtabula, OH
Total General Cargo
Total General Cargo (short
tons)
Installed
Power
(MW)
166.6
76.8
58.2
44.5
43.6
34.0
20.8
11.1
6.2
461.9
Metric Tonnes
NOx
6.7
3.1
3.5
2.0
1.8
1.9
1.2
0.6
0.2
21.1
23.3
PM,n
0.6
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.0
1.9
2.1
PM,<
0.5
0.3
0.3
0.2
0.1
0.2
0.1
0.1
0.0
1.7
1.9
HC
0.2
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.7
0.8
CO
0.5
0.2
0.3
0.2
0.1
0.2
0.1
0.0
0.0
1.7
1.9
SO,
4.7
2.2
2.4
1.3
1.2
1.3
0.8
0.4
0.2
14.5
15.0
Table 3-43. Tanker Ship Emissions by Great Lake Port in 2002
Port Name
Manistee, MI
Chicago, IL
Duluth-Superior, MN&WI
Detroit, MI
Toledo, OH
Total Tanker
Total Tanker (short tons)
Installed
Power
(MW)
154.9
15.4
11.9
5.8
4.6
192.6
Metric Tonnes
NOx
17.6
1.8
1.4
0.7
0.5
22.0
24.3
PM,n
1.5
0.1
0.1
0.1
0.0
1.9
2.1
PM,<
1.4
0.1
0.1
0.1
0.0
1.7
1.9
HC
0.5
0.1
0.0
0.0
0.0
0.6
0.7
CO
1.4
0.1
0.1
0.1
0.0
1.7
1.9
SO,
12.1
1.2
0.9
0.4
0.3
15.0
16.5
75
-------
Table 3-44. Integrated Tug-Barge Emissions by Great Lake Port in 2002
Port Name
Detroit, MI
Duluth-Superior, MN&WI
Toledo, OH
Gary, IN
Chicago, IL
Total ITB
Total ITE (short tons)
Installed
Power
(MW)
49.2
29.4
26.9
5.9
5.9
117.3
Metric Tonnes
NOx
1.2
0.7
0.7
0.3
0.1
3.1
3.4
PM,n
0.1
0.1
0.1
0.0
0.0
0.3
0.3
PM,<
0.1
0.1
0.1
0.0
0.0
0.3
0.3
HC
0.0
0.0
0.0
0.0
0.0
0.1
0.1
CO
0.1
0.1
0.1
0.0
0.0
0.3
0.3
so,
0.9
0.6
0.5
0.2
0.1
2.3
2.5
For Great Lake ports, auxiliary emissions are responsible for roughly 50% of
the NOX and PM emissions, primarily due to emissions during the hotelling mode.
Bulk Carrier ships are responsible for the vast majority of the emissions.
3.1.6.3 Summary
This section provides a summary of the total port emissions for 2002. Table
3^15 and Table 3-46 provide a breakout of the total port emissions by auxiliary and
propulsion engines, in units of metric tonnes and short tons, respectively. Table 3-47
and Table 3-48 provide the breakout by mode of operation, while Table 3-49 and
Table 3-50 provide a summary of port emissions by ship type.
Auxiliary emissions at ports are responsible for 39-48% of the total inventory,
depending on the pollutant. Hotelling, cruise, and RSZ modes of operation are all
important contributors to emissions. Container and Tanker ships are the largest
contributors to port emissions.
Table 3-45. 2002 Port Emissions Summary by Engine and Port Type
(metric tonnes)
Engine Type
Propulsion
Auxiliary
All
Port Type
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Grand Total
Metric Tonnes
NOx
64,378
247
64,625
56,259
302
56,561
120,637
549
121,186
PM,n
5,477
25
5,502
5,040
25
5,065
10,517
50
10,567
PM,,
5,034
23
5,057
4,585
23
4,608
9,619
46
9,665
HC
2,534
11
2,545
1,611
8
1,619
4,145
19
4,164
CO
6,342
22
6,364
4,292
23
4,315
10,634
45
10,679
SO,
52,556
188
52,744
41,133
201
41,334
93,689
389
94,078
76
-------
Table 3-46. 2002 Port Emissions Summary by Engine and Port Type
(short tons)
Engine Type
Propulsion
Auxiliary
All
Port Type
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Grand Total
Short Tons
NOx
70,979
273
71,252
62,028
333
62,360
133,007
605
133,612
PM,o
6,039
27
6,066
5,557
28
5,584
11,595
55
11,650
PM25
5,550
25
5,575
5,055
25
5,080
10,605
50
10,656
HC
2,794
12
2,806
1,776
9
1,785
4,570
21
4,591
CO
6,992
24
7,017
4,732
25
4,757
11,724
50
11,774
SO2
57,945
207
58,152
45,351
222
45,573
103,295
429
103,724
Table 3-47. 2002 Port Emissions Summary by Mode and Port Type
(metric tonnes)
Mode
Cruise
RSZ
Maneuvering
Hotelling
All
Port Type
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Grand Total
Metric Tonnes
NOx
34,370
183
34,553
34,369
45
34,414
7,374
70
7,444
44,525
252
44,777
120,638
549
121,187
PM,o
2,831
17
2,848
2,882
4
2,886
758
7
765
4,047
21
4,068
10,518
50
10,568
PM25
2,627
16
2,643
2,653
4
2,657
625
7
632
3,714
19
3,733
9,619
46
9,665
HC
1,146
6
1,152
1,277
2
1,279
439
4
443
1,283
7
1,290
4,145
19
4,164
CO
2,663
14
2,677
3,803
4
3,807
723
8
731
3,444
19
3,463
10,633
45
10,678
S02
21,207
137
21,344
35,016
33
35,049
4,355
50
4,405
33,112
168
33,280
93,690
389
94,079
77
-------
Table 3-48. 2002 Port Emissions Summary by Mode and Port Type
(short tons)
Mode
Cruise
RSZ
Maneuvering
Hotelling
All
Port Type
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Grand Total
Short Tons
NOx
37,894
202
38,096
37,893
50
37,943
8,130
77
8,207
49,090
277
49,368
133,008
605
133,613
PM,n
3,121
19
3,140
3,178
5
3,182
836
8
844
4,462
23
4,485
11,596
55
11,651
PM,5
2,896
18
2,914
2,925
4
2,929
689
7
696
4,095
21
4,116
10,605
50
10,656
HC
1,264
7
1,270
1,408
2
1,410
484
5
489
1,415
8
1,422
4,570
21
4,591
CO
2,936
16
2,952
4,193
4
4,197
797
9
806
3,797
21
3,818
11,723
50
11,773
S02
23,381
151
23,533
38,606
37
38,643
4,802
56
4,857
36,507
185
36,692
103,297
429
103,725
78
-------
Table 3-49. 2002 Port Emissions Summary by Ship Type and Port Type
(metric tonnes)
Ship Type
Auto Carrier
Barge Carrier
Self-Unloading
Bulk Carrier
Other Bulk
Carrier
Container
General Cargo
Miscellaneous
Passenger
Refrigerated
Cargo
Roll-On/Roll-
Off
Tanker
Ocean Going
Tug
Integrated Tug-
Barge
All
Port Type
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Grand Total
Metric Tonnes
NOx
4,901
0
4,901
133
0
133
0
276
276
19,189
227
19,416
34,125
0
34,125
7,335
21
7,356
195
0
195
19,345
0
19,345
3,068
0
3,068
3,360
0
3,360
28,938
22
28,960
48
0
48
0
3
3
120,637
549
121,186
PMln
412
0
412
13
0
13
0
27
27
1,562
19
1,581
2,757
0
2,757
632
2
634
18
0
18
1,816
0
1,816
251
0
251
292
0
292
2,760
2
2,762
5
0
5
0
0
0
10,518
50
10,568
PM,S
377
0
377
12
0
12
0
25
25
1,423
17
1,440
2,516
0
2,516
579
2
581
17
0
17
1,665
0
1,665
229
0
229
269
0
269
2,529
2
2,531
4
0
4
0
0
0
9,620
46
9,666
HC
181
0
181
5
0
5
0
10
10
629
7
636
1,288
0
1,288
252
1
253
7
0
7
583
0
583
100
0
100
118
0
118
982
1
983
2
0
2
0
0
0
4,147
19
4,166
CO
563
0
563
12
0
12
0
23
23
1,723
18
1,741
2,845
0
2,845
686
2
688
41
0
41
1,482
0
1,482
317
0
317
296
0
296
2,664
2
2,666
6
0
6
0
0
0
10,635
45
10,680
S02
3,608
0
3,608
141
0
141
0
210
210
14,885
147
15,032
22,920
0
22,920
6,203
15
6,218
140
0
140
14,127
0
14,127
1,994
0
1,994
2,279
0
2,279
27,359
15
27,374
34
0
34
0
2
2
93,690
389
94,079
79
-------
Table 3-50. 2002 Port Emissions Summary by Ship Type and Port Type
(short tons)
Ship Type
Auto Carrier
Barge Carrier
Self-Unloading
Bulk Carrier
Other Bulk
Carrier
Container
General Cargo
Miscellaneous
Passenger
Refrigerated
Cargo
Roll-On/Roll-
Off
Tanker
Ocean Going
Tug
Integrated Tug-
Barge
All
Port Type
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Total
Deep Sea
Great Lakes
Grand Total
Short Tons
NOx
5,404
0
5,404
147
0
147
0
304
304
21,157
250
21,407
37,624
0
37,624
8,087
23
8,110
215
0
215
21,329
0
21,329
3,383
0
3,383
3,705
0
3,705
31,905
24
31,929
53
0
53
0
3
3
133,007
605
133,612
PMln
454
0
454
14
0
14
0
29
29
1,722
21
1,743
3,040
0
3,040
697
2
699
20
0
20
2,002
0
2,002
277
0
277
322
0
322
3,043
2
3,045
6
0
6
0
0
0
11,596
55
11,651
PM,S
416
0
416
13
0
13
0
27
27
1,569
19
1,588
2,774
0
2,774
638
2
640
19
0
19
1,836
0
1,836
252
0
252
297
0
297
2,788
2
2,790
4
0
4
0
0
0
10,606
50
10,657
HC
200
0
200
6
0
6
0
11
11
693
8
701
1,420
0
1,420
278
1
279
8
0
8
643
0
643
110
0
110
130
0
130
1,083
1
1,083
2
0
2
0
0
0
4,572
21
4,593
CO
621
0
621
13
0
13
0
26
26
1,900
20
1,919
3,137
0
3,137
756
2
758
45
0
45
1,634
0
1,634
350
0
350
326
0
326
2,937
2
2,939
7
0
7
0
0
0
11,725
50
11,775
S02
3,978
0
3,978
155
0
155
0
231
231
16,411
162
16,574
25,270
0
25,270
6,839
16
6,855
154
0
154
15,576
0
15,576
2,198
0
2,198
2,513
0
2,513
30,164
17
30,181
37
0
37
0
3
3
103,297
429
103,725
80
-------
3.2 2002 National and Regional Emissions
This section presents EPA's nationwide analysis of the emissions from main
propulsion and auxiliary engines used by Category 3 ocean-going vessels for the 2002
calendar year. The geographic scope of the inventory modeling domain includes both
near port emissions and the emissions from these vessels when operating away from
port into either the ocean or the Great Lakes. More specifically, the modeling domain
for vessels operating in the ocean extends from the U.S. coastline to the 200 nautical
mile limit of the Exclusive Economic Zone. For ships operating in the Great Lakes, it
extends out to the international boundary with Canada. The emission results are
divided into eight geographic regions of the U.S. (including Alaska and Hawaii), and
then totaled to provide a national inventory.
Emission inventories are presented for the following pollutants: oxides of
nitrogen (NOX), particulate matter (PM2.5 and PM10), total hydrocarbons (HC),f
carbon monoxide (CO), and sulfur dioxide (SO2). The PM inventories include
directly emitted PM only.
3.2.1 Overview of the Methodology
The regional and national inventories for Category 3 vessels presented in this
study are the result of merging the results from work performed primarily by the
University of Delaware, with the near port inventories developed in Section 3.1.
More specifically, the University of Delaware analysis, which is included as
Appendix B, was funded by the California Air Resources Board (ARE) and the
Commission for Environmental Cooperation in North America to develop state,
regional, and national emission results using the Waterway Network Ship Traffic,
Energy, and Environmental Model (STEEM). ' The model geographically
characterizes emissions from ships traveling along shipping lanes to and from
individual ports, in addition to the emissions from vessels transiting near the ports.
The shipping lanes were identified from actual ship positioning reports. Also,
STEEM captures the emissions from ships while maneuvering in a port. The model
then uses detailed information about ship destinations, ship attributes (e.g., vessel
speed and engine horsepower), and emission factors to produce spatially allocated
(i.e., gridded) emission estimates for ships engaged in foreign commerce. As
described below, STEEM does not include the emissions from hotelling while a ship
is docked or anchored in port. For that reason, STEEM is referred to as an "interport"
model in the remainder of this report, to easily distinguish it from the near ports
analysis.
The STEEM interport methodology represents a significant overall
improvement in quantifying and spatially representing the emissions from all large
ocean-going vessels. Nonetheless, as noted by University of Delaware, the different
assumptions, inputs, and methods reflected in specific port-based analyses are often
f Total hydrocarbons can be converted to volatile organic carbon compounds (VOC) by using a multiplicative
adjustment factor of 1.053.
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superior to the analyses produced by the current STEEM model near ports.1'2 EPA's
assessment of the current STEEM model identified some specific weaknesses when
estimating emissions closer to ports. First, the precision associated with the use of
ship positioning data may be poor in some locations, especially as the lanes approach
shorelines where ships would need to follow more prescribed paths. Second, the
model includes a maneuvering operational mode (i.e., reduced speed) that is generally
assumed to occur for the first and last 20 kilometers of each trip when a ship is
leaving or entering a port. If a trip was shorter than 20 kilometers, then it was
assumed that the ship was at maneuvering speed for the entire distance. 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 model assumes that the maneuvering
distance occurs at an engine load of 20 percent, which represents a vessel speed of
approximately 60 percent of cruise speed. This is considerably faster than ships
would maneuver near the docks. The single maneuvering speed assumed by STEEM
also does not reflect the fact that the reduced speed zone, and therefore emissions,
may vary by port. Fourth, and finally, the model does not include the emissions from
auxiliary engines during hotelling operations at the port.
For the above reasons, EPA concluded that the regional emission inventories
produced by the current STEEM interport model are most accurate for vessels while
traveling in unconstrained ocean or Great Lakes shipping lanes, and that the near port
inventories described in Section 3.1, which use more detailed local port information,
were significantly more accurate near the ports. Therefore, the regional and national
inventories in this analysis are derived by merging together: 1) the near port
inventories, which extend 25 nautical miles and 7 nautical miles from the terminus of
the RSZ for deep sea ports and Great Lake ports, respectively: and 2) the remaining
interport portion of the STEEM inventory, which extends from the endpoint of the
near port inventories to the 200 nautical mile EEZ boundary or international border
with Canada, as appropriate.
The remainder of this section describes the geographic modeling domain and
identifies the eight individual regions (Section 3.2.2), the STEEM model and inputs
that were used to develop the interport inventories (Section 3.2.4), the method used to
merge the near port and interport inventories (Section 3.2.4), and the final emission
inventory results for 2002 (Section 3.2.5). See Section 3.1 for a complete description
of the near ports methodology and results that are incorporated here.
3.2.2 Modeling Domain and Geographic Regions
The modeling domain was defined using an integrated suite of geographic
information system software called ArcGIS. This software allows the user to define,
view, and analyze a variety of geospatial data. For the ocean domain, the boundaries
of the EEZ for the U.S. were obtained from the National Oceanic and Atmospheric
Administration, Office of Coast Survey as latitude/longitude shapefiles that can be
viewed in ArcMAP35 The inner maritime boundary is the official U.S. baseline,
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which is recognized as the low-water line along the coast as marked on the official
U.S. nautical charts in accordance with the articles of the Law of the Sea. The outer
boundary of the zone is drawn as a line in such a manner that each point on it is 200
nautical miles from the inner baseline. NOAA provides these shapefiles for five
regions associated with the U.S. ocean shoreline that are of interest in this analysis:
East Coast, West Coast, Gulf of Mexico, Hawaii, and Alaska. The accuracy of the
NOAA shapefiles was verified with images obtained from the U.S. Geological
Survey. The confirmed NOAA shapefiles were then combined with a shapefile of the
U.S. international border from the National Atlas. The inner boundaries of these
shapefiles were then extended an arbitrary distance on shore to ensure full coverage
of all ports and relevant inland waterways represented in the ports inventory.
The resulting EEZ was further subdivided for this analysis to create regions
that were compatible with the geographic scope of the regional growth rates, which
are used to project emission inventories for the years 2020 and 2030, as described
later in this report. The method for further subdividing the EEZ is described below.
• The Pacific Coast region was split into separate North Pacific and South
Pacific regions along a horizontal line originating from the
Washington/Oregon border (Latitude 46° 15' North).
• The East Coast and Gulf of Mexico regions were divided along a vertical line
roughly drawn through Key Largo (Longitude 80° 26' West).
The Alaska region was divided into separate Alaska Southeast and Alaska
West regions along a straight line intersecting the cities of Naknek and Kodiak. The
Alaska Southeast region includes most of the State's population, and the Alaska West
region includes the emissions from ships on a great circle route along the Aleutian
Islands between Asia and the U.S. West Coast.
For the Great Lakes domain, a similar approach was used to create shapefiles
containing all the ports and inland waterways in the near port inventory and extending
out into the lakes to the international border with Canada. The modeling domain
spanned from Lake Superior on the west to the point eastward in the State of New
York where the St. Lawrence River parts from U.S. soil. Again, the shapefiles could
be viewed in ArcMap.
This methodology resulted in eight separate regional modeling domains that
are identified below and shown in Figure 3-1.
• South Pacific (SP)
• North Pacific (NP)
• East Coast (EC)
• Gulf Coast (GC)
• Alaska Southeast (AE)
• Alaska West (AW)
• Hawaii (HI)
• Great Lakes (GL)
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3.2.3 Interport Emissions Modeling
As described above, the interport portion of the national and regional
inventory is taken from an analysis using the STEEM model. The following two
sections describe the model and its general methodology, and the inputs that are used
to calculate the interport inventory.
3.2.3.1 STEEM Interport Model Description
As more generally described above, The Waterway Network Ship Traffic,
Energy, and Environmental Model (STEEM) was developed by the University of
Delaware as a comprehensive approach to quantify and geographically represent
interport ship traffic, emissions, and energy consumption from large vessels calling
on U.S. ports or transiting the U.S. coastline to other destinations, and shipping
activity in Canada and Mexico. The model estimates emissions from main propulsion
and auxiliary marine engines used on Category 3 vessels that engage in foreign
commerce using historical North American shipping activity, ship attributes (i.e.,
characteristics), and activity-based emission factor information. These inputs are
assembled using a GIS platform that also contains an empirically derived network of
shipping lanes. It includes the emissions for all ship operational modes from cruise in
unconstrained shipping lanes to maneuvering in a port. The model, however,
excludes hotelling operations while the vessel is docked or anchored, and very low
speed maneuvering close to a dock.
As illustrated in Figure 3 -2, the waterway network represented by STEEM
resembles a highway network on land. It is composed of ports, which are origins and
destinations of shipping routes: junctions where shipping routes intersect, and
segments that are shipping lanes between two connected junctions. Each segment can
have only two junctions or ports, and ship traffic flow can enter and leave a segment
only through a junction or at a port. The figure represents only a sample of the many
routes contained in the model.
STEEM uses advanced ArcGIS tools and develops emission inventories in the
following way. The model begins by building a spatially-defined waterway network
based on empirical shipping route information from two global ship reporting
databases. The first is the International Comprehensive Ocean-Atmosphere Data Set
(ICOADS), which contains reports on marine surface and atmospheric conditions
from the Voluntary Observing Ships (VOS) fleet.37 There are approximately 4,000
vessels worldwide in the VOS system. The ICOADS project is sponsored by the
National Oceanic and Atmospheric Administration and National Science
Foundation's National Center for Atmospheric Research (NCAR). The second
TO
database is the Automated Mutual-Assistance Vessel Rescue (AMVER) system.
The AMVER data set is based on a ship search and rescue reporting network
sponsored by the U.S. Coast Guard. The AMVER system is also voluntary, but is
generally limited to ships over 1,000 gross tons on voyages of 24 hours or longer.
About 8,600 vessels reported to AMVER in 2004.
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The latitude and longitude coordinates for the ship reports in the above
databases are used to spatially define the direction and width of each shipping lane in
the waterway network (ocean shipping lanes can be hundreds of nautical miles wide).
Each lane (route and segment) is given a unique identification number for
computational purposes. For the current analysis, STEEM used 20 years of ICOADS
data (1983-2002) and about one year of AMVER data (part of 2004 and part of 2005).
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 U.S. Army Corps of Engineers report on vessel
entrances and clearances (subsequently referred to as USAGE).8 There are 251 U.S.
ports in the entrances and clearances report. Each port also has a unique
identification number for computational purposes.
The STEEM interport model also employs a number of databases to identify
the movements for each vessel (e.g., trips), individual ship attributes (e.g., vessel size
and horsepower), and related emission factor information (e.g., emission rates) that
are subsequently used in the inventory calculations. (Each of these databases is
described in the following section.)
Once the waterway network and various databases are constructed, STEEM
uses ArcGIS Network Analyst tools along with specific information on each
individual ship movement to solve the most probable path on the network between
each pair of ports (i.e., a trip) for a certain ship size. This is assumed to represent the
least-energy path, which in most cases is the shortest distance unless prevented by
weather or sea conditions, water depth, channel width, navigational regulations, or
other constraints that are beyond the model's capability to forecast.
After identifying the shipping route and resulting distance associated with
each unique trip, the emissions are simply calculated for each operational mode using
the following generalized equation along with information from the ship attributes
and emission factor databases:
Emissions per trip = distance (nautical miles) / speed (nautical
miles/hour) x horsepower (kW) x fractional
load factor x emission factor (g/kW-hour)
Emissions are calculated separately for distances representing cruise
and maneuvering operational modes. Maneuvering occurs at slower speeds
and load factors than during cruise conditions. In STEEM, maneuvering is
assumed to occur for the first and last 20 kilometers of each trip when a ship is
entering or leaving a port. A ship is assumed to move at maneuvering speed
for an entire trip if the distance is less than 20 kilometers.
Finally, the emissions along each shipping route (i.e., segment) for all trips are
proportioned among the respective cells that are represented by the gridded modeling
domain. For this work, emissions estimates were produced at a cell resolution of 4
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kilometers by 4 kilometers, which is appropriate for most atmospheric air quality
models. The results for each cell are then summed, as appropriate, to produce
emission inventories for the various geographic regions of interest in this analysis.
3.2.3.2 Emission Inputs
The STEEM waterway network model relies on a number of inputs to identify
the movements for each vessel, individual ship attributes, and related emission factor
information. Each of these databases are described separately below.
3.2.3.2.1 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 USAGE report for the U.S. and the Lloyd's Maritime Intelligence Unit
(LMIU) for Canada and Mexico.8'39'40 These sources contain information for each
vessel carrying foreign cargo at each major port or waterway that, most importantly
for this analysis, includes:
• Vessel name
• Last port of call (entrance record) or next port of call (clearance record)
The database then establishes unique identification numbers for each ship,
each port pair, and each resulting trip.
3.2.3.2.2 Ship Attributes
The ship attributes data set contains the important characteristics of each ship
that are necessary for the STEEM interport model to calculate the emissions
associated with each trip. The information in this data set is matched to each
previously assigned ship identification number. The following information comes
from the USAGE entrances and clearances report for each ship identification number:
• Ship type
• Gross registered tonnage (GRT)
• Net registered tonnage (NRT)
The ship attributes data set contains the following information from Lloyd's
Register-Fairplay for each ship identification number.9
• Main propulsion engine installed power (horsepower)
• Service speed (cruise speed)
• Ship size (length, wide, and draft)
Sometimes data was lacking from the above references for ship speed. In
these instances, the missing information was developed for each of nine vessel types
and the appropriate value was applied to each individual ship of that type.
Specifically, the missing ship speeds for each ship category were obtained from the
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average speeds used in a Lloyd's Register study of the Baltic Sea and from an Entec
UK Limited study for the European Commission.41'17 The resulting vessel cruise
speeds for ships with missing data are shown in Table 3-51.
Table 3-51. Average Vessel Cruise Speed by Ship Typea
Ship Type Average Cruise Speed (knots)
Bulk Carrier
Container Ship
General Cargo
Passenger Ship
Refrigerated Cargo
Roll On-Roll Off
Tanker
Fishing
Miscellaneous
14.1
19.9
12.3
22.4
16.4
16.9
13.2
11.7
12.7
aU sed only when ship specific data were missing from the
commercial database references.
The average speed during maneuvering is approximately 60 percent of a
ship's cruise speed based on using the propeller law described in Section 3.1 above
and the engine load factor for maneuvering that is presented later in this section.
As with vessel cruise speed, main engine installed power was sometimes
lacking in the Lloyd's Register-Fairplay data set. Here again, the missing information
was developed for nine different vessel types and the appropriate value was applied to
each individual ship of that type when the data were lacking. In this case, the missing
main engine horsepower was estimated by regressing the relationships between GRT
and NRT, and between installed power and GRT for each category. This operation is
performed internally in the model and the result applied to each individual ship, as
appropriate.
The ship attributes database also contains information on the installed power
of engines used for auxiliary purposes. However, this information is usually lacking
in the Lloyds data set, so an alternative technique was employed to estimate the
required values. Basically, the STEEM model calculates a ratio of main engine
horsepower to auxiliary engine horsepower was determined for eight different vessel
types using information primarily from ICF International.42 (The ICF report
attributed these power values to a study for the Port of Los Angeles by Starcrest
Consulting.7) The auxiliary engine power for each individual vessel of a given ship
type is then estimated by multiplying the appropriate main power to auxiliary power
ratio and the main engine horsepower rating for that individual ship. The main and
auxiliary power values and the resulting auxiliary engine to main engine ratios are
shown in Table 3-52.
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Table 3-52. Auxiliary Engine Power Ratios
Vessel Type
Bulk Carrier
Container Ship
General Cargo
Passenger Ship
Refrigerated Cargo
Roll On-Roll Off
Tanker
Miscellaneous
Average Main
Engine Power
(kW)
7,954
30,885
9,331
39,563
9,567
10,696 c
9,409
6,252
Average Auxiliary
Engine Power (kW)
1,169
5,746
1,777
39,563 a
3,900 b
2,156 c
1,985
1,680
Auxiliary to Main
Engine Power
Ratio
0.147
0.186
0.190
1.000
0.136
0.202
0.211
0.269
The ICF reference reported a value of 11,000 for auxiliary engines used on passenger
i 42
vessels.
b The STEEM used auxiliary engine power as reported in the ARE methodology
32
document.
0 The STEEM purportedly used values for Roll On-Roll Off main and auxiliary engines
that represent a trip weighted average of the Auto Carrier and Cruise Ship power values
from the ICF reference.
Finally, the ship attributes database provides information on the load factors
for main engines during cruise and maneuvering operation, in addition to load factors
for auxiliary marine engines. Main engine load factors for cruise operation were
taken from a study of international shipping for all ship types, except passenger
vessels43 For this analysis, the STEEM model used a propulsion engine load factor
for passenger ship engines at cruise speed of 55 percent of the total installed power.
This is based on engine manufacturer data contained in two global shipping
studies.43'44 During maneuvering, it was assumed that all main engines, including
those for passenger ships, operate at 20 percent of the installed power. This is
consistent with a study done by Entec UK for the European Commission.17 The main
engine load factors at cruise speed by ship type are shown in Table 3-53.
Auxiliary engine load factors, except for passenger ships, were obtained from
the ICF International study referenced above. These values are also shown in Table
3-53.
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Table 3-53. Main and Auxiliary Engine Load Factors at Cruise Speed
by Ship Type
Ship Type
Bulk Carrier
Container Ship
General Cargo
Passenger Ship
Refrigerated Cargo
Roll On-Roll Off
Tanker
Miscellaneous
Average Main Engine
Load Factor (%)
75
80
80
55
80
80
75
70
Average Auxiliary Engine
Load Factor (%)
17
13
17
25
20
15
13
17
3.2.3.2.3 Emission Factor Information
The emission factor data set contains emission rates for the various pollutants
in terms of grams of pollutant per kilowatt-hour (g/kW-hr). The main engine
emission factors are shown in Table 3-54. The speed specific factors for NOx, HC,
and SO2 were taken from several recent analyses of ship emissions in the U.S.,
Canada, and Europe.l7'3^42'43'45 The PM factor was based on discussions with the
California Air Resources Board (ARE) staff. The fuel specific CO emission factor
was taken from a report by ENVIRON International.14 The STEEM study used the
composite emission factors shown in the table because the voyage data used in the
model do not explicitly identify main engine speed ratings, i.e., slow or medium, or
the auxiliary engine fuel type, i.e., marine distillate or residual oil. The composite
factor for each pollutant is determined by weighting individual emission factors by
vessel engine population data from a 2005 survey of ocean-going vessels that was
performed by ARB.15
Table 3-54. Main Engine Emission Factors by Ship and Fuel Type
Engine Type
Slow Speed
Medium Speed
Composite EF
Main Engine Emission Factors (g/kW-hr)
Fuel Type
Residual Oil
Residual Oil
Residual Oil
NOx
18.1
14
17.9
PMio
1.5
1.5
1.5
PM25a
1.4
1.4
1.4
HC
0.6
0.5
0.6
CO
1.4
1.1
1.4
SO2
10.5
11.5
10.6
Estimated from PM10 using a multiplicative adjustment factor of 0.92.
The emission factors for auxiliary engines are shown in Table 3-55. The fuel
specific main emission factore for NOx and HC were taken from several recent
analyses of ship emissions in the U.S., Canada, and Europe, as referenced above for
the main engine load factors. The PM factor for marine distillate was taken from a
report by ENVIRON International, which was also referenced above. The PM factor
for residual oil was based on discussions with the California Air Resources Board
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(ARB) staff. The CO factors are from the Starcrest Consulting study of the Port of
Los Angeles.7 For SO2, the fuel specific emission factors were obtained from Entec
and Corbett and Koehler. ' The composite emission factors displayed in the table
are discussed below.
Table 3-55. Auxiliary Engine Emission Factors by Ship and Fuel Type
Engine Type
Medium Speed
Medium Speed
Composite EF
Auxiliary Engine Emission Factors (2/kW-hr)
Fuel Type
Marine
Distillate
Residual Oil
Residual Oil
NOx
13.9
14.7
14.5
PM10
0.3
1.5
1.2
PM25a
0.3
1.4
1.1
HC
0.4
0.4
0.4
CO
1.1
1.1
1.1
S02
4.3
12.3
**
Estimated from PMio using a multiplicative adjustment factor of 0.92.
b See Table 3-56 for composite SO2 emission factors by vessel type.
As for main engines, the STEEM study used the composite emission factors
for auxiliary engines. For all pollutants other than SO2, underlying data used in the
model do not explicitly identify auxiliary engine voyages by fuel type, i.e., marine
distillate or residual oil. Again, the composite factor for those pollutants was
determined by weighting individual emission factors by vessel engine population data
from a 2005 survey of ocean-going vessels that was performed by ARB.15
For SCh, 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 3-55 and weighting them with an estimate of the
amount of marine distillate and residual oil that is used by these engines. The relative
amount of each fuel type consumed was taken from the 2005 ARB survey. The
relative amounts of each fuel type for each vessel type and the resulting 862 emission
factors are shown in Table 3-56.
Table 3-56. Auxiliary Engine SO2 Composite Emission Factors by
Vessel Type
Vessel Type
Bulk Carrier
Container Ship
General Cargo
Passenger Ship
Refrigerated Cargo
Roll On-Roll Off
Tanker
Miscellaneous
Residual Oil
(%)
71
71
71
92
71
71
71
0
Marine Distillate
(%)
29
29
29
8
29
29
29
100
Composite
Emission Factor
(g/kW-hr)
9.98
9.98
9.98
11.66
9.98
9.98
9.98
4.3
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3.2.3.2.4 EPA A djustments to STEEM PM and SO2 Emission Inventories
The interport emission results contained in this study for PMio and 862 were
taken from the STEEM inventories and then adjusted to reflect EPA's recent review
of available engine test data and fuel sulfur levels as described Section 3.1.3.6 for the
near port analysis. In the near ports work, a PM emission factor of 1.4 g/kW-hr was
used for most main engines, e.g., slow speed diesel and medium speed diesel engines,
all of which are assumed to use residual oil. A slightly higher value was used for
steam turbine and gas turbine engines, and a slightly lower value was used for most
auxiliary engines. However, these engines represent only a small fraction of the total
emissions inventory. As shown in Section 3.2.4.2.3, the STEEM study used an
emission factor of 1.5 g/kW-hr for all main engines and a slightly lower value for
auxiliary engines. Here again, the auxiliary engines comprise only a small fraction of
the total emissions from these ships. Therefore, for simplicity, EPA adjusted the
interport PM inventories by multiplying the STEEM results by the ratio of the two
primary emission factors, i.e., 1.4/1.5 or 0.933, to approximate the difference in fuel
effects.
The STEEM SCh emission inventories were similarly adjusted using SCh
emission factors from the interport analysis (Section 3.2.4.2.3.) and the near ports
analysis (Section 3.1.3.6). This information is displayed in Table 3-57. The
composite values in the table are calculated by mathematically weighting the slow
speed and medium speed emission factors from each study by their individual
population fraction from the 2005 ARB shipping survey, i.e., 95 percent and 5
percent, respectively.15 Therefore, the interport SO2 inventories that appear in this
report are the result of multiplying the STEEM inventories by the ratio of the two
composite g/kW-hr emission factors shown in table, i.e., 10.33 /10.6 or 0.975.
Table 3-57. SO2 Emission Factors Used to Adjust STEEM
Emission Inventories
Engine Type
Slow Speed
Medium Speed
Composite
Fuel Type
Residual Oil
Residual Oil
Residual Oil
STEEM
(g/ kW -hr)
10.50
11.50
10.6 a
Near Ports
(g/kW-hr)
10.29
11.09
10.33 a
Composite
(g/ kW -hr)
n/a
n/a
0.975
Weighted by ship populations from 2005 ARB survey: 95 percent slow speed and
5 percent medium speed.
3.2.4 Combining the Near Port and Interport Inventories
The national and regional inventories in this study are a combination of the
results from the near ports analysis described in Section 3.1 and the STEEM interport
modeling described in this section. The two inventories are quite different in form.
As previously presented in Figure 3-1, the STEEM modeling domain spans the
Atlantic and Pacific Oceans in the northern hemisphere. The model characterizes
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emissions from vessels while traveling between ports. That includes when a vessel is
maneuvering a distance of 20 kilometers to enter or exist a port, cruising near a port
as it traverses the area, or moving in a shipping lane across the open sea. For the
U.S., STEEM includes the emissions associated with 251 ports. These ports are
described in Appendix B, Table A-2. The results are spatially reported in a gridded
format that is resolved to a cell dimension of 4 kilometers by 4 kilometers.
The near port results, however, are much more geographically limited and are
not reported in a gridded format. The analysis includes the emissions associated with
ship movements when entering or exiting each of 117 major U.S. ports. For deep sea
ports that includes when a vessel is hotelling and maneuvering in the port, operating
in the RSZ that varies in length for each port, and cruising 25 nautical miles between
the end of the RSZ and an unconstrained shipping lane. For Great Lakes ports that
includes hotelling and maneuvering, three nautical miles of RSZ operation, and
cruising 7 nautical miles between the end of the RSZ and open water. The results are
reported for each port and mode of operation.
To precisely replace only the portion of the STEEM interport inventory that is
represented in the near port inventory results, it is necessary to spatially allocate the
emissions in a format that is compatible with the STEEM 4 kilometers by 4
kilometers gridded output. Once that has been accomplished, the two inventories can
be blended together. Both of these processes are described below. This work was
conducted by ENVIRON International as a subcontractor under the EPA contract
with ICF3
3.2.4.1 Spatial Location of the Near Port Inventories
The hotelling, maneuvering, RSZ, and cruise emissions from the near port
inventories were spatially located by their respective latitude and longitude
coordinates using ArcGIS software. For this study, shapefiles were created that
depicted the emission locations as described above. Additional shapefiles were also
obtained to locate other geographic features such as the coastline and rivers of the
U.S. These shapefiles and the STEEM output can be layered upon each other, viewed
in ArcMap, and analyzed together. The following sections provide a more detailed
description of how the shapefiles representing the ports, RSZ lanes, and cruise lanes
were developed.
3.2.4.1.1 Ports
Each port, and thus the designated location for hotelling and maneuvering
emissions, is modeled as a single latitude/longitude coordinate point using the port
center as defined by the Army Corp of Engineers in the Principal Ports of the United
States dataset6 One additional port, "Other Puget Sound," which was specially
created in the near ports analysis, was added to the list of ports. Some port locations
were inspected by consulting Google Earth satellite images to ensure that the point
that defined the port's location was physically reasonable for the purposes of this
analysis. This resulted in slightly modifying the locations of five ports: Gray's
Harbor, Washington; Freeport and Houston, Texas; Jacksonville, Florida; and
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Moreshead City, North Carolina. In all five cases the change was very small. The
hotelling and maneuvering emissions represented by the latitude/longitude coordinate
for each port were subsequently assigned to a single cell in the gridded inventory
where that point was located. It should be noted that modeling a port as a point will
over specify the location of the emissions associated with that port if it occupies an
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 A, Table A-l.
3.2.4.1.2 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 3.1 and the Army Corp of Engineers National Waterway Network (NWN)
geographic database of navigable waterways in and around the U.S. The
coordinates of RSZ endpoints for all of the 117 ports used in this work are shown in
Appendix A, Table A-2.
The RSZ emissions were distributed evenly along the length of the line. The
latitude/longitude coordinates for each point along the line were subsequently used to
assign the emissions to a grid cell based on the proportion of the line segment that
occurred in the respective cell. Figure 3-3 illustrates how the length of the RSZ line
can vary in any grid cell.
In several instances the NWN links and STEEM data indicated there were two
RSZs. These ports are: Honolulu, Hawaii; Los Angeles, Long Beach and El
Segundo, California; Brunswick, Georgia; and Baton Rouge, New Orleans, Port of
South Louisiana, and Plaquemines, Louisiana. The lengths of the two lines were
similar in every case, so the RSZ emissions from the near ports analysis were divided
equally between both branches. Figure 3-4 shows an example of a port with multiple
RSZs.
3.2.4.1.3 Cruise Operations
The cruise mode links that extend 25 nautical miles for deep sea ports or 7
nautical miles for Great Lake ports from the end of the RSZ end point were also
modeled with line shapefiles. These links were spatially described for each port
following the direction of the shipping lane evident in the STEEM data. Again, as
with RSZ emissions, the latitude/longitude coordinates for each point along the line
were subsequently used to assign the emissions to a grid cell based on the proportion
of the line segment that occurred in the respective cell.
The STEEM data sometimes indicated there were two or three cruise mode
links associated with a port. In these cases, the underlying STEEM ship movement
data was evaluated to determine whether any particular route should be assigned
larger emissions than the others. That information was judged to be inadequate to
justify such differential treatment, so the near port cruise emissions for ports with
multiple cruise lanes were assigned equally to each link. Figure 3-5 provides an
example of multiple cruise lanes.
93
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3.2.4.2 Combining the Near Port and STEEM Emission Inventories
After spatially defining the geographic location of the near port emissions, but
before actually inserting them into the gridded STEEM inventory, it was necessary to
determine if all of the STEEM emissions within an affected cell should be replaced,
or if some of the emissions should be retained. In this latter case, ships would be
traversing the area near a port, but not actually entering or exiting the port.
This evaluation was performed for each port by first overlaying the RSZ and
cruise shapefiles on the STEEM gridded inventory, and then using ArcGIS tools to
create a series of circular buffers with a radius of 25 nautical miles around each of the
points that represented an RSZ line. A single elongated buffer was then made from
the intersection or outer boundaries of all the individual circular buffers. As
illustrated in Figure 3-5, the resulting RSZ buffer encloses the port, RSZ links and
cruise mode links. The STEEM emissions underneath the buffer were then evaluated.
In cases where the STEEM data showed that ships were routed directly to an isolated
port, the STEEM emissions were completely replaced by near port emissions (Figure
3-6). Conversely, when the examination revealed that the underlying STEEM
emissions included some ship passages that were simply traversing near the port, the
emissions associated with those vessel movements were retained, i.e., not completely
replaced with the near port emission results (Figure 3-6). The methodology for
determining the emissions from transient ship operation is described below.
The percentage of STEEM emissions that are attributable to a port, and should
be replaced, were approximated by dividing the STEEM emissions in the isolated
portion of the route that lead only to the port, with the STEEM emissions in the major
shipping lane. As an example, the STEEM emissions in the portion of the buffer in
Figure 3-7 that only went to the port were approximately 347 kg/cell/year. The
emissions within the buffer for just the major shipping lane were 6996 kg/cell/year.
Therefore, the emissions in the grid cells that comprised the portion of the buffer
overlaying the major shipping lane were reduced by the fraction 347/6996, or 5
percent before the near port emissions were added to the gridded inventory.
The actual merging of the two inventories was performed by creating a
number of databases that identified the fraction of the near port inventory for each
pollutant species and operating mode that should be added to the grid cells for each
port. A similar database was also created that identified how much of the original
STEEM emissions should be reduced to account for ship movements associated
directly with a port, while preserving those that represented transient vessel traffic.
These databases were subsequently used to calculate the new emission results for
each affected cell in the original STEEM gridded inventory, resulting in the combined
inventory results for this study.
Figure 3-8 provides side-by-side comparisons of the original STEEM
emissions inventory and the new merged inventory. The results indicate that the
spatial allocation of the near port emissions conducted in this study provides a more
precise assessment of vessel travel near a port than the STEEM methodology. As
previously described, the near port ship emissions may be over specified, but this
94
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approach generally provides a more reasonable placement of emissions near the
coastline than the wide shipping lanes in the STEEM model, which in some cases
show shipping emissions over land.
3.2.5 2002 Regional and National Emission Inventories
The modeling domain of the new combined emission inventory described
above is the same as the original STEEM domain, i.e., the Atlantic and Pacific
Oceans, the Gulf of Mexico, and the Great Lakes. Inventories for the eight
geographic regions of the U.S. specified in Section 3.2.3 were created using ArcGIS
software to intersect the regional shapefiles with the 4 kilometers by 4 kilometers
gridded domain. Any grid cell split by a regional boundary was considered to be within
a region if over 50 percent of its area was within the region. The emissions in each of
the cells defined within a region were then summed. The final emission inventories for
2002 are shown in Table 3-58 for each of the eight geographic regions and the nation.
The geographic scope of these regions was previously displayed in Figure 3-1.
Table 3-58. 2002 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines
Region
Alaska East ( AE)
Alaska West (AW)
East Coast (EC)
Gulf Coast (GQ
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Total Metric Tonnes
Total Short Tons0
Metric Tonnes
NOx
18,231
60,625
220,844
174,454
54,935
26,278
105,380
15,171
675,918
745,224
PM10
1,439
4,736
17,665
14,187
4,315
2,176
8,175
1,191
53,884
59,409
PM25a
1,324
4,357
16,252
13,052
3,979
2,002
7,521
1,096
49,583
54,667
HC
603
2,009
7,345
5,817
1,820
946
3,503
503
22,546
24,858
CO
1,424
4,732
17,383
14,322
4,291
2,108
8,534
1,186
53,980
59,515
SO2
10,725
35,137
146,295
105,926
32,040
15,388
60,997
8,851
415,359
457,948
a Estimated from PMio using a
b Converted from metric tonnes
tonne.
multiplicative adjustment factor of 0.92.
using a multiplicative conversion factor of 1.102 short tons per metric
As previously noted, the inventories in the above table reflect the emissions
associated with Category 3 ocean-going vessels that are engaged in foreign
commerce. The STEEM interport analysis also roughly estimated the emissions
associated with these ships that are engaged solely in domestic commerce.' These
vessels are sometimes referred to as Jones Act ships, as explained in Section 3.1.
Specifically, the interport analysis estimated that the emissions from large ocean-
going vessels carrying only domestic cargo represent approximately 2-3 percent of
the total values presented in Table 3-58.
95
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The relative contributions of the near port and interport emission inventories
to the total U.S. emissions are presented in Table 3-59 and Table 3-60. As expected,
based on the geographic scope of the two types of inventories, the interport and near
port inventories are about 80 percent and 20 percent of the total, respectively. The
deep sea ports are about 97 to nearly 100 percent and the Great Lake ports are about 3
to almost zero percent of the total inventories, depending on the port region. This
result is also expected given the small number of Great Lake ports and more limited
geographic area of the modeling domain.
Table 3-59. 2002 Contribution of Near Ports and Interport Emissions to the
Total C3 Inventory
Region and
Port Type
Interport
Deep Sea
Great Lakes
Near Port
Deep Sea
Great Lakes
All Regions
Deep Sea
Great Lakes
A 11 Region
Short Tons
Metric Tonnes
NOx
Total
554,732
540,110
14,622
121,186
120,637
549
675,918
660,747
15,171
745,224
%
Region
82.1
—
—
17.9
—
—
100.0
—
-
—
%
Type
100.0
97.4
2.6
100.0
99.5
0.5
—
97.8
2.2
—
PM10
Total
43,317
42,176
1,141
10,567
10,517
50
53,884
52,693
1,191
59,409
%
Region
80.4
—
—
19.6
—
—
100.0
—
~
—
%
Type
100.0
97.4
2.6
100.0
99.5
0.5
—
97.8
2.2
—
PM25a
Total
39,918
38,868
1,050
9,665
9,619
46
49,583
48,487
1,096
54,667
%
Region
80.5
—
—
19.5
—
—
100.0%
—
-
—
%
Type
100.0
97.4
2.6
100.0
99.5
0.5
—
97.8
2.2
—
a Estimated from PMio using a multiplicative adjustment factor of 0.92.
b Converted from metric tonnes using a multiplicative adjustment factor of 1.102 short tons per metric
tonne.
96
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Table 3-60. 2002 Contribution of Near Ports and Interport Emissions to the
Total C3 Inventory (Cont'd)
Region and
Port Type
Interport
Deep Sea
Great Lakes
Near Port
Deep Sea
Great Lakes
All Regions
Deep Sea
Great Lakes
All Region Short
Tons'"
Metric Tonnes
HC
Total
18,382
17,898
484
4,164
4,145
19
22,546
22,043
503
24,858
%
Region
81.5
—
—
18.5
—
—
100.0
—
—
%
Type
100.0
97.4
2.6
100.0
99.5
0.5
—
97.8
2.2
CO
Total
43,302
42,161
1,141
10,678
10,633
45
53,980
52,794
1,186
59,515
%
Region
80.2
—
—
19.8
—
—
100.0
—
—
%
Type
100.0
97.4
2.6
100.0
99.6
0.4
100.0
97.8
2.2
so,
Total
321,281
312,819
8,462
94,078
93,689
389
415,359
406,508
8,851
457,948
%
Region
77.4
—
—
22.6
—
—
100.0
—
—
%
Type
100.0
97.4
2.6
100.0
99.6
0.4
100.0
97.9
2.1
a Converted from metric tonnes using a multiplicative adjustment factor of 1.102 short tons per metric
tonne.
97
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Figure 3-1. Regional Modeling Domains
98
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Figure 3-2 Illustration of STEEM Modeling Domain and Spatial Distribution of
Shipping Lanes
0-10,000
'I 110.000 - 50.000
060,000-100,000
• 100,000-11.735,908
99
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Figure 3-3. Example of Gridded RSZ Lane (Hopewell, Virginia)
100
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Figure 3-4. Example of Multiple RSZ Lanes (Brunswick, Georgia)
Brunswick. GA
Jacksonville, F
101
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Figure 3-5. Example of Multiple Cruise Lanes
(Tampa and Port Manatee, Flordia)
102
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Figure 3-6. Example of Complete Replacement of
STEEM Emissions (Panama City, Florida)
.
103
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Figure 3-7. Example of Partial Replacement of
STEEM Emissions (Coos Bay, Oregon)
104
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Figure 3-8. Spatial Comparison of the Original STEEM and New
Combined Gridded Inventories—Southeast United States
Original
x
- *:
• ..
T-j
I. sava*rfd& '
^
New
105
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4 2020 and 2030 National and Regional Inventories
4.1 Overview of Methodology
The emissions from Category 3 ocean-going vessels (main propulsion and
auxiliary engines) are projected to 2020 and 2030 by applying certain adjustment
factors to the 2002 emission inventories that were presented in Section 3.2. These
factors account for the change in ship traffic over these time periods, i.e., growth, and
the effect of the International Marine Organization's (EVIO) NOx standard for marine
diesel engines that became effective in 2000.
The remaining sections describe the derivation of the growth adjustment
factors for each of the modeling regions described in Section 3.2, the EVIO NOx
emission adjustment factors for deep sea and Great Lake ports, and the resulting 2020
and 2030 emission inventories.
4.2 Growth Factors by Geographic Region
This section describes the growth factors that are used to project the emissions
to 2020 and 2030 for each of the eight geographic regions evaluated in this analysis.
These factors are based on the expected demand for marine bunker fuels that is
associated with shipping goods, i.e., commodities, into and out of the U.S. The use of
bunker fuel as a surrogate for estimating future emissions is appropriate because the
quantity of fuel consumed by C3 engines is highly correlated with the amount of
combustion products, i.e., pollutants, that are emitted from those vessels. The term
bunker fuel in this report also includes marine distillate oil and marine gas oil that are
used in some auxiliary power engines.
The remainder of this section first summarizes the development of growth
rates by RTI International (RTI) for five geographic regions of the U.S., as performed
under contract to EPA (Section 4.2.1). ' The RTI reports that more thoroughly
documents this work, and other associated information, are contained in Appendices
C and D. This is followed by the derivation of the growth factors that are used in this
study for the eight geographic regions of interest (Section 4.2.2).
4.2.1 Summary of Regional Growth Rate Development
RTI developed fuel consumption growth rates for five geographic
regions of the U.S. These regions are the East Coast, Gulf Coast, North Pacific,
South Pacific, and Great Lakes. The amount of bunker fuel required in any region
and year is based on the demand for transporting various types of cargo by Category
3 vessels. This transportation demand is in turn driven by the demand for
commodities that are produced in one location and consumed in another, as predicted
by an econometric model. The flow of commodities is matched with typical vessels
for trade routes (characterized according to cargo capacity, engine horsepower, age,
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specific fuel consumption, and engine load factors). Typical voyage parameters are
then assigned to the trade routes that include average ship speed, round trip mileage,
tons of cargo shipped, and days in port. Fuel consumption for each trade route and
commodity type thus depends on commodity projections, ship characteristics, and
voyage characteristics. Figure 4-1 from RTI illustrates the approach to developing
baseline projections of marine fuel consumption.
107
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Figure 4-1. Illustration of Method for Estimating Bunker Fuel Demand
Ship Analysis: by Vessel Type and Size Category
Inputs Outputs
Deadweight for all Vessels of
Given Type & Size3
Horsepower, Year of Build
for all Vessels of Given
Type & Size3
Specific Fuel Consumption
(g/SHP-HR) by Year of Build"
Engine Load Factors0
Average Cargo f~~/\*\
Carried (Tons) V )
Average Daily Fuel
>>• Consumption
(Tons/Day)
Average Daily Fuel
Consumption (Tons/Day) ^cT^
H - Main, Aux. Engine at Sea v _J
-Aux. Engine in Port
Trade Analysis: by Commodity and Trade Route
Inputs
Average Ship Speed0
Round Trip Mileaged
Tons of Cargo Shipped6
Average Cargo Carried/"^
per Ship Voyage \/\j
Outputs
Days at Sea and in
Port, per Voyage
Total Days at J^r~\
Sea and in Port v_y
Number of Voyages
Total Estimated Bunker Fuel Demand
f N
Average Daily Fuel Consumption
(Tons/Day) x Total Days at Sea _ Bunker Fuel
- Main, Aux. Engine at Sea (^\ and in Port (~r-~"\ Demand
- Aux. Engine in Port \. ) \ )
^ J
Driven by changes in engine efficiency. Driven ^f °^h in
commodity flows.
a - Clarksons Ship Register Database
b- Engine Manufacturers' Data, Technical Papers
c-Corbett and Wang (2005) "Emission Inventory Review: SECA Inventory Progress Discussion"
d - Combined trade routes and heavy leg analysis
e - Global Insight Inc. (Gil) Trade Flow Projections
4.2.2 Trade Analysis
The trade flows between geographic regions of the world, as illustrated by the
middle portion of Figure 4-1, 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)
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liquid bulk -gas (including LNG and LPG)
- dry bulk (e.g., grain, coal, steel, ores and scrap)
general cargo (e.g., lumber/forest products)
- containerized cargo
The analysis specifically evaluated trade flows between 21 regions of the
, , Table 4-1 , . .,.,,.
world. shows the countries associated with each region.
Table 4-1. Aggregate Regions and Associated Countries
Aggregate Regions
U.S. Atlantic Coast
U.S. Great Lakes
U.S. Gulf Coast
E. Canada3
W. Canada3
U.S. Pacific North
U.S. Pacific South
Greater Caribbean
South America
Africa - West
Africa- North/East-
Mediterranean
Africa- East/South
Europe-North
Europe-South
Europe-East
Caspian Region
Russia/FSU
Middle East Gulf
Australia/NZ
Japan
Pacific -High Growth
China
Rest of Asia
Base Countries / Regions
U.S. Atlantic Coast
U.S. Great Lakes
U.S. Gulf Coast
Canada3
Canada3
U.S. Pacific North
U.S. Pacific South
Colombia, Mexico, Venezuela, Caribbean Basin, Central America
Argentina, Brazil, Chile, Peru, Other East Coast of S. America, Other
West Coast of S. America
Western Africa
Mediterranean Northern Africa, Egypt, Israel,
Kenya, Other Eastern Africa, South Africa, Other Southern Africa
Austria, Belgium, Denmark, Finland, France, Germany, Ireland,
Netherlands, Norway, Sweden, Switzerland, United Kingdom
Greece, Italy, Portugal, Spain, Turkey, Other Europe
Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic
Southeast CIS
The Baltic States, Russia Federation, Other Western CIS
Jordan, Saudi Arabia, UAE, Other Persian Gulf
Australia, New Zealand
Japan
Hong Kong S.A.R., Indonesia, Malaysia, Philippines, Singapore, South
Korea, Taiwan, Thailand
China
Viet Nam, India, Pakistan, Other Indian Subcontinent
3 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.46
The overall forecast of demand for shipping services and bunker fuel was
determined for each of the areas using information on commodity flows from Global
Insight's (GI) World Trade Service. Specifically, GI provided a specialized forecast
that reports the flow of each commodity type for the period 1995-2024, based on a
109
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proprietary econometric model. The general structure of the GI model for calculating
trade flows assumes a country's imports from another country are driven by the
importing country's demand forces (given that the exporting country possesses
enough supply capacity), and affected by exporting the country's export price and
importing country's import cost for the commodity. The model then estimates
demand forces, country-specific exporting capacities, export prices, and import costs.
The GI model included detailed annual region-to-region trade flows for eight
composite commodities from 1995 to 2024, in addition to the total trade represented
by the commodities. Table 4-2 illustrates the projections for 2012 and 2020, along
with baseline data for 2005. In 2005, dry bulk accounted for 41 percent of the total
trade volume, crude oil accounted for 28 percent, and containers accounted for 12
percent. Dry bulk and crude oil shipments are expected to grow more slowly over the
forecast period than container shipments. By 2020, dry bulk represents 39 percent of
the total, crude oil is 26 percent, and containers rise to 17 percent.
Table 4-2. Illustration of World Trade Estimates for Composite
Commodities, 2005, 2012, and 2020
Commodity Type
Dry Bulk
Grade Oil
Container
Refined Petroleum
General Cargo
Residual Petroleum and Other Liquids
Chemicals
Natural Gas
Total International Cargo Demand
Cargo (millions of tons)
2005
2,473
1,703
714
416
281
190
122
79
5,979
2012
3,051
2,011
1,048
471
363
213
175
91
7,426
2020
3,453
2,243
1,517
510
452
223
228
105
8,737
4.2.3 Ship Analysis by Vessel Type and Size
Different types of vessels are required to transport the different commodities
to the various regions of the world. As shown at the top of Figure 4-1, profiles of
these ships were developed to identify the various vessel types and size categories
that are assigned to transport commodities of each type along each route. These
profiles include attributes such as ship size, engine horsepower, engine load factors,
age, and engine fuel efficiency. This information was subsequently used to estimate
average daily fuel consumption for each typical ship type and size category,
The eight GI commodity categories were mapped to the type of vessel that
would be used to transport that type of cargo using information from Clarksons
Shipping Database.47 These assignments are shown in Table 4-3.
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Table 4-3. Assignment of Commodities to Vessel Types
Commodity Ship Category Vessel Type
Liquid bulk - crude oil
Liquid bulk - refined
petroleum products
Liquid bulk - residual
petroleum products
Liquid bulk - chemicals
(organic and inorganic)
Liquid bulk - natural gas
(including LNG and LPG)
Dry bulk (e.g. grain, coal,
steel, ores and scrap)
General cargo (including
neobulk, lumber/forest
products)
Containerizable cargo
Crude Oil Tankers
Product Tankers
Product Tankers
Chemical Tankers
Gas Carriers
Dry Bulk Carriers
General Cargo
Container Ships
Tanker
Product Carrier
Product Carrier
Chemical & Oil Carrier
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
Bulk Carrier
General Cargo Liner, Reefer, General Cargo Tramp, Reefer
Fish Carrier, Ro-Ro, Reefer/Container, Ro-Ro
Freight/Passenger, Reefer/Fleet Replen., Ro-Ro/Container,
Reefer/General Cargo, Ro-Ro/Lo-Lo, Reefer/Pallets
Carrier, Reefer/Pass./Ro-Ro, Reefer/Ro-Ro Cargo
Fully Cellular Container
Each of the vessel types were classified by their cargo carrying capacity or
deadweight tons (DWT). The size categories were identified based on both industry
definitions and natural size breaks within the data. Table 4-4 summarizes the size
categories that were used in the analysis and provides other information on the
general attributes of the vessels from Clarksons Shipping Database. The vessel size
descriptions are also used to define shipping routes based on physical limitations that
are represented by canals or straits through which ships can pass. Very large crude
oil tankers are the largest by DWT rating, and the biggest container ships (Suezmax)
are also very large.
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Table 4-4. Fleet Characteristics
Ship Type
Container
General Cargo
Dry Bulk
Crude Oil Tanker
Chemical Tanker
Petroleum Product
Tanker
Natural Gas
Carrier
Other
Total
Size by DWT
Suezmax
PostPanamax
Panamax
Intermediate
Feeder
All
Capesize
Panamax
Handymax
Handy
VLCC
Suezmax
AFRAmax
Panamax
Handymax
Coastal
All
AFRAmax
Panamax
Handy
Coastal
VLGC
LGC
Midsize
All
~
Minimum
Size
(DWT)
83,000
56,500
42,100
14,000
0
Maximum
Size
(DWT)
140,000
83,000
56,500
42,100
14,000
All
79,000
54,000
40,000
0
180,000
120,000
75,000
43,000
27,000
0
0
79,000
54,000
40,000
0
180,000
120,000
75,000
43,000
27,000
All
68,000
40,000
27,000
0
60,000
35,000
0
0
68,000
40,000
27,000
0
60,000
35,000
All
-
-
Number
of Ships
101
465
375
1,507
1,100
3,214
715
1,287
991
2,155
470
268
511
164
100
377
2,391
226
352
236
349
157
140
863
7,675
26,189
Total
DWT
(millions)
9.83
30.96
18.04
39.80
8.84
26.65
114.22
90.17
46.50
58.09
136.75
40.63
51.83
10.32
3.45
3.85
38.80
19.94
16.92
7.90
3.15
11.57
6.88
4.79
88.51
888.40
Total
Horse
Power
(millions)
8.56
29.30
15.04
32.38
7.91
27.07
13.81
16.71
10.69
19.58
15.29
5.82
8.58
2.17
1.13
1.98
15.54
3.60
4.19
2.56
1.54
5.63
2.55
3.74
53.60
308.96
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. Clarksons' Shipping Database Register provides each ship's total
installed horsepower (HP), type of propulsion (diesel or steam), and year of build.
These characteristics are then matched to information on typical specific fuel
consumption (SFC), which is expressed in terms of grams of bunker fuel burned per
horsepower-hour (g/HP-hr).
112
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The specific SFC values are based on historical data from Wartsila Sulzer, a
popular manufacturer of diesel engines for marine vessels. RTI added an additional
10 percent to the reported "test bed" or "catalogue" numbers to account for the
guaranteed tolerance level and an in-service SFC differential. Overall, the 10 percent
estimate is consistent with other analyses that show some variation between the "test
bed" SFC values reported in the manufacturer product catalogues and those observed
in actual service. This difference is explained by the fact that old, used engines
consume more fuel than brand new engines and in-service fuels may be different than
the test bed fuels.
48
Figure 4-2 shows SFC values that were used in the model regarding the
evolution of specific fuel oil consumption rates for diesel engines over time. Engine
efficiency in terms of SFC has improved over time, most noticeably in the early
1980s in response to rising fuel prices. However, there is a tradeoff between
improving fuel efficiency and reducing emissions. Conversations with engine
manufacturers indicate that it is reasonable to assume SFC will remain constant for
the projection period of this study, particularly as they focus on meeting NOx
emission standard as required by MARPOL Annex VI, or other potential pollution
control requirements.
Figure 4-2. Diesel Engine Specific Fuel Consumption
200
180
/g 160
140
fl 120
.s
1 100
80
60
40
20
0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
RTI assumed a fixed SFC of 220 g/HP-hr for steam engines operating on
bunker fuel.
Using the above information, the average daily fuel consumption (AFC),
expressed in metric tons of fuel at full engine load, for each vessel type and size
category is found using the following equation:
113
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Equation 4-1
Fleet AFCV>, =^[SFCvsxHPvs xl(T6 tonnes Ig]
Where:
Fleet AFC = Average daily fuel consumption in metric tonnes at full engine
load
v = Vessel type
s = Vessel size category
N = Number of vessels in the fleet
SFC = Specific fuel consumption in grams of bunker fuel burned per
horsepower- hour (g/FIP-hr)
FTP = Total installed engine power, in horsepower (FTP)
106 tonnes/g = Conversion from grams to metric tonnes
As previously noted, AFC values calculated in the above equation are based
on total horsepower; therefore, they must be scaled down to reflect typical operation
using less than 100 percent of the horsepower rating, i.e., actual engine load. Table
4-5 shows the engine load factors that were used to estimate the typical average daily
fuel consumption (tons/day) for the main propulsion engine and the auxiliary engines
when operated at sea and in port.49
Table 4-5. Main and Auxiliary Engine Load Factors
Vessel Type
Container Vessels
General Cargo Carriers
Dry Bulk Carriers
Crude Oil Tankers
Chemical Tankers
Petroleum Product Tankers
Natural Gas Carrier
Other
Main
Engine
Load Factor
(%)
80
80
75
75
75
75
75
70
Auxiliary Engine as
Percent of Main
Engine
22.0
19.1
22.2
21.1
21.1
21.1
21.1
20.0
Auxiliary Engine as
Percent of Main Engine at
Sea
11.0
9.5
11.1
10.6
10.6
10.6
10.6
10.0
The RTI analysis also assumed that the shipping fleet changes over time as
older vessels are scrapped and replaced with newer ships. Specifically, vessels over
25 years of age are retired and replaced by new ships of the most up-to-date
configuration. This assumption leads to the following change in fleet characteristics
over the projection period:
• New ships have engines rated at the current SFC, so even though there are
no further improvements in specific fuel consumption, the fuel efficiency
114
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of the fleet as a whole will improve over time through retirement and
replacement.
• New ships will weigh as much as the average ship built in 2005, so the
total cargo capacity of the fleet will increase over time as smaller ships
retire and are replaced.
• Container ships will increase in size over time on the trade routes between
Asia to either North America or Europe.
4.2.4 Trade Analysis by Commodity Type and Trade Route
Determining the total number of days at sea and in port, as shown in the
middle portion of Figure 4-1, requires information on the relative amount of each
commodity that is carried by the different ship type size categories on each of the
trade routes. For example, to serve the large crude oil trade from the Middle East
Gulf region to the Gulf Coast of the U.S., 98 percent of the deadweight tonnage is
carried on very large oil tankers, while the remaining 2 percent is carried on smaller
Suezmax vessels. After the vessel type size distribution was found, voyage
parameters were estimated. Specifically, these are days at sea and in port for each
voyage (based on ports called, distance between ports, and ship speed), and the
number of voyages (based on cargo volume projected by GI and the DTW from
Clarksons Shipping Database). The length of each voyage and number of voyages
were used to estimate the total number of days at sea and at port, which is a parameter
used later to calculate total fuel consumption for each vessel type and size category
over each route and for each commodity type. (More information on determining the
round trip distance for each voyage that is associated with cargo demand for the U.S.
is provided in Section 4.2.5)
The days at sea were calculated by dividing the round trip distance by the
average vessel speed:
Equation 4-2
round trip distance route
Days at Sea Per Voyagev
speedv s x 24 hrs
Where:
v = Vessel type
s = Vessel size category
route = Unique trip itinerary
round trip route distance = Trip length in nautical miles
speed = Vessel speed in knots or nautical miles per hour
24 hrs = Number of hours in one day
115
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Table 4-6 . 49
presents the speeds by vessel type that were used in the analysis.
These values are the same for all size categories, and are assumed to remain constant
over the forecast period.
Table 4-6. Vessel Speed by Type
Vessel Type
Grade Oil Tankers
Petroleum Product Tankers
Chemical Tankers
Natural Gas Carriers
Dry Bulk Carriers
General Cargo Vessels
Container Vessels
Other
Speed (knots)
13.2
13.2
13.2
13.2
14.1
12.3
19.9
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:
Number of Voyages,, Me =
Equation 4-3
total metric tonnes of cargo moved
fleet average DWTv s x utilization rate
Where:
v = Vessel type
s = Vessel size category
trade = Commodity type
Fleet average DWT = Median dead weight tonnage carrying capacity in
metric tons
Utilization rate = Fraction of total ship DWT capacity used
The cargo per voyage is based on the fleet average ship size from the vessel
profile analysis. For most cargo, a utilization rate of 0.9 is assumed to be constant
throughout the forecast period. Lowering this factor would increase the estimated
number of voyages required to move the forecasted cargo volumes, which would lead
to an increase in estimated fuel demand.
In addition to calculating the average days at sea per voyage, the average days
in port per voyage was also estimated by assuming that most types of cargo vessels
spend four days in port per voyage. RTI notes, however, that this can vary somewhat
by commodity and port.
116
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4.2.5 Worldwide Estimates of Fuel Demand
This section describes how the information from the vessel and trade analyses
were used to calculate the total annual fuel demand associated with international
cargo trade. Specifically, for each year y of the analysis, the total bunker fuel demand
is the sum of the fuel consumed on each route of each trade (commodity). The fuel
consumed on each route of each trade is in turn the sum of the fuel consumed for each
route and trade for that year by propulsion main engines and auxiliary engines when
operated at sea and in port. These steps are illustrated by the described by the
following equations:
Equation 4-4
— y y pr
y trade,route, year
trade route
= Z Z LAFCtrade)routejyatseaxDaysatSeatade)routeiy+AFCtradeiroutejyatportxDaysatPorttadejroute)y
trade route
Where:
FC = Fuel consumed in metric tonnes
y = calendar year
trade = Commodity type
route = Unique trip itinerary
AFC = Average daily fuel consumption in metric tonnes
yatsea = Calendar year main and auxiliary engines are operated at sea
yatport = Calendar year main and auxiliary engines are operated in port
Equations 4-5
rade.route.yatsea = 2 (Percent of trade alongroute)Vj, [Fleet AFCV>, x(MELF+AE at sea LF)]
rade.mute.yatport = ^ (Percent of trade alongroute)v s [Fleet AFCV s x AE import LF]
Days at Seatade route v = 2 (Percent of trade along route)v s [Days at sea per voyagev s x Number of voyagesv s
'3 v,s,t,r ' L
^y = 2 (Percentof trade along route)vs [Days at port per voyage xNumberof voyages]
Where:
AFC = Average daily fuel consumption in metric tonnes
trade = Commodity type
route = Unique trip itinerary
yatsea = Calendar year main and auxiliary engines are operated at sea
yatport = Calendar year main and auxiliary engines are operated in port
y = calendar year
v = Vessel type
s = Vessel size category
117
-------
t = Trade
r = Route
Fleet AFC = Average daily fuel consumption in metric tonnes at full engine
load
MELF = main engine load factor, unitless
AE at sea LF = auxiliary engine at-sea load factor, unitless
AE in port LF = auxiliary engine in-port load factor, unitless
The inputs for these last four equations are all derived from the vessel analysis
in Section 4.2.2 and the trade analysis in Section 4.2.3.
4.2.6 Worldwide Bunker Fuel Consumption
Based on the methodology outlined above, estimates of global fuel
consumption over time were computed, and growth rates determined from these
projections. Figure 4-3 shows estimated world-wide bunker fuel consumption by
vessel type.
118
-------
Figure 4-3. Worldwide Bunker Fuel Consumption
600
5 Container 01 General Cargo D Dry Bulk S Crude Oil
D Chemicals D Petroleum D Natural Gas D Other
B Fishing Vessels B Passenger Ships D Military Vessels
Figure 4-4 shows the annual growth rates by vessel-type/cargo that are used in
the projections shown in Figure 4-3. Total annual growth is generally between 2.5
percent and 3.5 percent over the time period between 2006 and 2020 and generally
declines over time, resulting in an average annual growth of around 2.6 percent.
119
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Figure 4-4. Annual Growth Rate in World-Wide Bunker Fuel Use by
Commodity Type
10%
-0-Total
-*-Crude Oil
Other
•Container ~*~General Cargo "•"Dry Bulk
Chemicals -*- Petroleum -^-Natural Gas
Fishing Vessels Passenger Ships Military Vessels
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 as previously described, i.e.,
equations 4.1-4.5 above, but were limited to routes that carried cargo between
specific cities in Asia, Europe and Middle East to the various ports in the specific
regions of the U.S.
The trip distances for non-container vessel types were developed from
information from Worldscale Association and Maritime Chain.50'51 The data from
120
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Worldscale is considered to be the industry standard for measuring port-to-port
distances, particularly for tanker traffic. The reported distances account for common
routes through channels, canals, or straits. This distance information was
supplemented by data from Maritime Chain, a web service that provides port-to-port
distances along with some information about which channels, canals, or straits must
be passed on the voyage.
Voyage distances for container vessels are based on information from
Containerization International Yearbook (CIY) and calculations by RTI. That
reference provides voyage information for all major container services. Based on the
frequency of the service, number of vessels assigned to that service, and the number
of days in operation per year, RTI estimated the average length of voyages for the
particular bilateral trade routes in the Global Insights trade forecasts.
The distance information developed above was combined with the vessel
speeds previously shown in Table 4-6 to find the length of a voyage in days. Table
4-7 presents the day lengths for non-containerized vessel types and Table 4-8 shows
the same information for container vessels.
Table 4-7. Day Length for Voyages for Non-Container Cargo Ship
(approximate average)
Days per Voyage
Global Insights Trade Regions
Africa East-South
Africa North-Mediterranean
Africa West
Australia-New Zealand
Canada East
Canada West
Caspian Region
China
Europe Eastern
Europe Western-North
Europe Western-South
Greater Caribbean
Japan
Middle East Gulf
Pacific High Growth
Rest of Asia
Russia-FSU
Rest of South America
US South
Pacific
68
49
56
48
37
11
95
41
61
53
54
26
35
77
52
68
64
51
US North
Pacific
75
56
63
47
46
5
89
36
68
60
61
33
31
72
48
64
71
30
US East
Coast
57
37
36
65
7
40
41
73
38
24
30
16
65
56
67
66
38
41
US Great
Lakes
62
43
46
81
18
58
46
87
45
32
37
29
81
65
76
64
46
46
US Gulf
54
47
43
63
19
39
48
69
46
34
37
17
62
83
88
73
48
44
121
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Table 4-8. 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
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
4.2.8 Bunker Fuel Consumption for the United States
Figure 4-5 and Figure 4-6 present the estimates of fuel use for delivering trade
goods to and from the U.S. The results in Figure 4-5 show estimated historical
bunker fuel use in year 2001 of around 47 million tons (note: while this fuel is used to
carry trade goods to and from the U.S., it is not necessarily all purchased in the U.S.
and is not all burned in U.S. waters). This amount grows to over 90 million tons by
2020 with the most growth occurring on trade routes from the East Coast and the
"South Pacific" region of the West Coast.
122
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Figure 4-5 Bunker Fuel Used to Import and Export Cargo by Region of the
United States
BUS North Pacific EUS Great Lakes DUS Gulf HUS East Coast 1 US South Pacific
Figure 4-6 shows the estimated annual growth rates for the fuel consumption
that are used in the projections shown in Figure 4-5. Overall, the average annual
growth rate in marine bunkers associated with future U.S. trade flows is 3.4 percent
between 2005 and 2020.
123
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Figure 4-6. Annual Growth Rates for Bunker Fuel Used to Import and Export
Cargo by Region of the United States
10%
•United States
•US Great Lakes
•US South Pacific
•US Gulf
•US North Pacific
•US East Coast
4.2.9 2020 and 2030 Growth Factors for Eight Geographic
Regions
The results of the RTI analysis described above are used to develop the
growth factors that are necessary to project the 2002 base year emissions inventory to
2020 and 2030. The next two sections describe how the five RTI regions were
associated with the eight regions analyzed in this report, and how the specific growth
rates for each of the eight regions were developed.
4.2.9.1 Mapping the RTI Regional Results to the Eight Region Analysis
As described in Section 3.2, the eight geographic regions analyzed in this
study were designed to be consistent with the five RTI regional modeling domains.
More specifically, three of the eight geographic areas in this study, i.e., Alaska East,
Alaska West, and Hawaii, are actually subsets of two broader regional areas that were
analyzed by RTI, i.e., the North Pacific for both Alaska regions and South Pacific for
Hawaii. Therefore, the growth rate information from the related larger region was
assumed to be representative for that state.
124
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The eight geographic regions represented in the emission inventory study are
presented in Figure 3-1. The association of the RTI regions to the emission inventory
regions is shown in Table 4-9.
Table 4-9. Association of the RTI Regions to the
Eight Emission Inventory Regions
Consumption Region
North Pacific
North Pacific
North Pacific
South Pacific
South Pacific
Gulf
East Coast
Great Lakes
Corresponding Emission
Inventory Region
North Pacific (NP)
Alaska East (AE)
Alaska West (AW)
South Pacific (SP)
Hawaii (HI)
Gulf Coast (GC)
East Coast (EC)
Great Lakes (GL)
4.2.9.2 Growth Factors for the Emission Inventory Analysis
Emission inventories for 2020 and 2030 are estimated in Section 4.4 by
multiplying the 2002 baseline inventory for each region by a corresponding growth
factor that was developed from the RTI regional results. Specifically, the average
annual growth rate from 2002-2020 was calculated for each of the five regions. Each
regional growth rate was then compounded over the inventory projection time period
for 2020 and 2030, i.e., 18 and 28 years, respectively. The resulting multiplicative
growth factors for each emission inventory region and the associated RTI average
annual growth rate are presented in Table 4-10 for each projection year.
125
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Table 4-10. Regional Emission Inventory Growth Factors
or 2020 and 2030
Emission Inventory Region
Alaska East (AE)
Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
2002-2020
Average
Annualized
Growth Rate
(%)
3.3
3.3
4.5
2.9
5.0
3.3
5.0
1.7
Multiplicative Growth
Factor Relative to 2002
2020
1.79
1.79
2.21
1.67
2.41
1.79
2.41
1.35
2030
2.48
2.48
3.43
2.23
3.92
2.48
3.92
1.60
4.3 Adjustments to Account for Current IMO NOx
Standards
This section describes how the effects of the International Maritime
Organization's NOx emission standards are accounted for when the Category 3
ocean-going vessel emission inventories are projected from the base year of 2002
(Section 3.2) to 2020 and 2030 (Section 4.4). The NOx requirement was adopted in
Annex VI to the International Convention for Prevention of Pollution from Ships
(MARPOL) in 1997. The standard applies to all marine diesel engines over 130
kilowatts (kW) that were built on or after January 1, 2000. It also applies to older
engines that are substantially rebuilt on or after that date. The required number of
countries ratified Annex VI in May 2004 and it went into effect for those countries in
May 2005. The annex has not yet been ratified by the U.S.; however, most new
marine diesel manufacturers are already building compliant engines. The Annex VI
NOx emission standard is presented in Table 4-11.
Table 4-11. International Maritime Organization NOx Emission
Standards for Marine Diesel Engines Greater Than 130 Kilowatts
Engine Speed (n)
n > 2000 rpm
9.8
2000 >n> 130 rpm
45.0 xn"02
n< 130 rpm
17.0
The University of Delaware STEEM modeling analysis described in Section
3.2 assumed that the IMO requirements would reduce NOx emissions from slow
speed diesel engines (SSD) and medium speed diesel engines (MSD) by 11 percent
from uncontrolled levels based on an analysis of industry data. That estimate is
126
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consistent with data presented by MAN Diesel, a major producer of large-bore
powerplants used in marine applications, for its pre- and post-2000 model year diesel
engines. Based on that information, this analysis assumes that the main propulsion
and auxiliary diesel engines used on Category 3 SSD and MSD equipped vessels will
emit 11 percent less NOx than their predecessors beginning in 2000. This is
somewhat conservative because the EVIO standards also apply to older engines that
are substantially rebuilt starting in 2000, which are unaccounted for in the percent
reduction estimate.
The effects of the EVIO NOx standard are reflected in the 2020 and 2030
emission inventories by applying an appropriate adjustment factor that reflects the
percentage of the vessel fleet in those years that are estimated to comply with the
EVIO standard. These factors are calculated by weighting together the following
information for each engine type reflected in the near port inventories prepared in
Section 3.1, i.e., SSD, MSD, gas turbine (GT), and steam turbine (ST):g
• Age distribution of the vessels (by name) calling on U.S. ports (fraction of
ships represented by build year)
• Installed power (number of ship port calls x main engine power (kW))
• NOx emission factor for main propulsion engines by model year
(uncontrolled or controlled as appropriate)
The number of each vessel and port calls comes from the U.S. Corp of Army
Engineers entrances and clearances report, and the vessel age and main engine power
(kW) are taken from Lloyd's Fairplay, as discussed in Section 3.1. The uncontrolled
emission factors for main propulsion engines are also presented in that section. Even
though the EVIO NOx standards apply to auxiliary engines, the power for main
engines was used in the adjustment factor calculation as the best indicator of total
emissions for simplicity.
Adjustment factors are developed separately for deep sea ports and Great
Lake ports because of differences in their respective fleet characteristics, e.g., types
and age distribution of engines, installed power, etc. The age distribution (fraction of
the fleet in each age group) by engine type for deep sea ports is shown in Table 4-12,
along with the final age-based fractions of all engine types that are used in the
adjustment factor calculation. These latter values are the result of multiplying the age
fraction for each vessel type in an age group by the fraction of total installed power
represented by that engine type. The installed power by engine type for deep sea
ports is presented in Figure 4-7. The same information for Great Lake ports is shown
in Table 4-13 and Figure 4-8.
The resulting fleet average emission reductions from the EVIO NOx standard
and the respective adjustment factors for 2020 and 2030 are shown in Table 4-14.h
9 The IMO NOx standards do not apply to GT or ST engines. There are included in the adjustment factor
calculations so a single factor can be multiplied by the 2002 inventories that include all engine types.
Even though the IMO NOx standards take effect in 2000, no adjustment factor was applied to the 2002 emission
inventories presented in Sections 3.1 and 3.2 because the fleet turnover is very small and the NOx reduction
estimate is uncertain in that timeframe.
127
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Table 4-12. Vessel Age Distribution for Deep Sea Ports by Engine Type
Age Group
(years old)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35+
Propulsion Engine Type a (Fraction of Total)
MSB
0.04091
0.12022
0.09333
0.10243
0.08757
0.06583
0.04447
0.04924
0.03866
0.05233
0.03511
0.03958
0.04034
0.00257
0.02228
0.01855
0.01296
0.00842
0.01677
0.00689
0.01007
0.01209
0.02073
0.00985
0.01311
0.00404
0.00462
0.00155
0.00093
0.00041
0.00158
0.00368
0.00010
0.01200
0.00085
0.00592
SSD
0.03215
0.07968
0.05950
0.05313
0.07517
0.06985
0.04853
0.05291
0.04459
0.05051
0.03512
0.04022
0.02865
0.02881
0.03082
0.02847
0.02577
0.03813
0.03460
0.02320
0.01738
0.02088
0.01746
0.01455
0.01273
0.01153
0.00579
0.00246
0.00432
0.00124
0.00825
0.00343
0.00000
0.00000
0.00018
0.00000
GT
0.10315
0.61727
0.26516
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00277
0.01165
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
ST
0.01349
0.00000
0.00607
0.00304
0.00000
0.00000
0.00277
0.00000
0.00276
0.05421
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00228
0.00000
0.00408
0.20769
0.14144
0.02240
0.04356
0.03448
0.06194
0.02886
0.09228
0.01376
0.01235
0.00000
0.06769
0.00000
0.18486
Allb
0.03459
0.09302
0.06848
0.06317
0.07640
0.06749
0.04664
0.05098
0.04236
0.05049
0.03439
0.03924
0.03069
0.02230
0.02826
0.02564
0.02235
0.03064
0.02986
0.01907
0.01537
0.01851
0.02017
0.01478
0.01280
0.01009
0.00582
0.00301
0.00379
0.00206
0.00673
0.00355
0.00002
0.00347
0.00033
0.00341
aMSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine.
b Fleet average weighted by installed power (ship port calls x main propulsion engine power).
128
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Figure 4-7. Installed Power by Main Engine Type for Deep Sea Ports'
ST, 1.1%
GT, 0.9%
MSD, 22.5%
SSD, 75.4%
a Installed power is main propulsion engine power (kW) multiplied by ship port calls by engine type.
MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine.
129
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Table 4-13. Vessel Age Distribution for Great Lake Ports by
Engine Type
Age Group
(years old)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35+
Propulsion Engine Type" (Fraction of Total)
MSB
0.00244
0.01047
0.00965
0.00171
0.00459
0.00152
0.00170
0.00118
0.00000
0.00000
0.00000
0.00072
0.00000
0.00048
0.00128
0.00000
0.00000
0.00946
0.00049
0.04711
0.05184
0.12474
0.02368
0.12340
0.06341
0.22059
0.06471
0.00190
0.04553
0.05975
0.04932
0.00506
0.00000
0.00000
0.00000
0.07328
SSD
0.01102
0.02450
0.00079
0.02116
0.00000
0.01875
0.01270
0.00092
0.00184
0.00000
0.00553
0.00000
0.00395
0.00451
0.00229
0.04216
0.02501
0.10376
0.09568
0.06781
0.02124
0.05185
0.09478
0.00398
0.00237
0.00222
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.02807
0.16168
0.19141
ST
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
1.00000
Allb
0.00596
0.01569
0.00500
0.01002
0.00221
0.00888
0.00634
0.00097
0.00080
0.00000
0.00240
0.00035
0.00172
0.00219
0.00161
0.01831
0.01086
0.04962
0.04179
0.05216
0.03421
0.08264
0.05257
0.06121
0.03159
0.10729
0.03119
0.00091
0.02194
0.02880
0.02377
0.00244
0.00000
0.01219
0.07022
0.20217
MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is
steam turbine.
b Fleet average weighted by installed power (ship port calls x main propulsion engine
power).
130
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Figure 4-8. Installed Power by Main Engine Type for Great Lake Portsl
ST, 8.4%
SSD, 43.4%
MSD, 48.2%
a Installed power is main propulsion engine power (kW) multiplied by ship port calls by engine type.
MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine.
131
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Table 4-14. Emission Reduction and Multiplicative Adjustment Factors for
Year
2020
2030
Fleet Average Reduction (%)
Deep Sea
9.8
10.9
Great Lakes
3.4
8.2
Multiplicative Adjustment Factor
(1 -Reduction Fraction)
Deep Sea
0.902
0.891
Great Lakes
0.966
0.918
4.4 2020 and 2030 National and Regional Inventories
The emission inventories for future years are estimated by multiplying the
2002 regional emission results from Table 3-58 by their respective growth factors and
EVIO NOx standard adjustment factors from Table 4-10 and Table 4-11. For
convenience, the growth factors and EVIO adjustment factors are duplicated below in
Table 4-15. The resulting 2020 and 2030 estimated emission inventories by region
and the nation are shown in Table 4-16 and Table 4-17.
Table 4-15. Growth and NOx Emission Multiplicative Adjustment Factors for
2020 and 2030
U.S. Region
Alaska East (AE)
(Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Average
Annualized
Growth Rate
(%)
3.3
3.3
4.5
2.9
5.0
3.3
5.0
1.7
Growth Factor
Relative to 2002
2020
1.79
1.79
2.21
1.67
2.41
1.79
2.41
1.35
2030
2.48
2.48
3.43
2.23
3.92
2.48
3.92
1.60
IMO NOx Standard
Adjustment Factor
Relative to 2002
2020
0.902
0.902
0.902
0.902
0.902
0.902
0.902
0.996
2030
0.891
0.891
0.891
0.891
0.891
0.891
0.891
0.918
132
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Table 4-16. 2020 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines
U.S. Region
Alaska East (AE)
(Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Total U.S. Metric
Tonnes
Total U.S. Short Tons0
Metric Tonnes per Year
NOx
29,500
98,099
439,932
263,247
119,251
42,521
228,756
19,850
1,241,157
1,368,420
PM10
2,581
8,496
39,013
23,734
10,385
3,904
19,674
1,613
109,400
120,617
PM2.5a
2,375
7,816
35,892
21,835
9,576
3,591
18,100
1,485
100,670
110,993
HC
1,082
3,604
16,221
9,731
4,380
1,697
8,430
681
45,827
50,526
CO
2,555
8,489
38,390
23,960
10,327
3,782
20,538
1,606
109,646
120,889
SOi
19,240
63,033
323,089
177,206
77,108
27,605
146,797
11,989
846,067
932,820
a Estimated from PM10 using a multiplicative conversion factor of 0.92.
b Converted from metric tonnes using a multiplicative conversion factor of 1.102 short tons per metric
tonne.
Table 4-17. 2030 Regional and National Emissions from Category 3 Vessel Main
and Auxiliary Engines
U.S. Region
Alaska East (AE)
(Alaska West (AW)
East Coast (EC)
Gulf Coast (GC)
Hawaii (HI)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Total U.S. Metric
Tonnes
Total U.S. Short Tons'0
Metric Tonnes per Year
NOx
40,318
134,072
674,869
346,090
191,879
58,114
368,075
22,328
1,835,744
2,023,974
PM10
3,572
11,755
60,586
31,588
16,915
5,401
32,047
1,909
163,773
180,566
PM2.5 a
3,286
10,814
55,739
29,061
15,598
4,969
29,483
1,757
150,708
166,161
HC
1,497
4,986
25,191
12,952
7,135
2,348
13,732
806
68,647
75,686
CO
3,534
11,745
59,618
31,889
16,821
5,232
33,454
1,901
164,196
181,032
SOi
26,620
87,211
501,748
235,848
125,601
38,194
239,116
14,190
1,268,528
1,398,598
a Estimated from PM10 using a multiplicative conversion factor of 0.92.
b Converted from metric tonnes using a multiplicative conversion factor of 1.102 short tons per metric
tonne.
133
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5 Estimated Category 3 Inventory Contribution
This section describes the contribution of Category 3 marine engines to
national and selected local emission inventories in 2002, 2020, and 2030. The
pollutants analyzed are NOX, directly emitted PM2 5, and SO2. All weight units in the
following tables are short tons.
5.1 Contribution to National Level Inventory
Category 3 marine engines contribute to the formation of ground level ozone
and concentrations of fine particles in the ambient atmosphere. Based on our current
emission inventory analysis, we estimate that these engines contributed nearly 6
percent of mobile source NOx, over 10 percent of mobile source PM2.5, and about 40
percent of mobile source SCh in 2001. We estimate that their contribution will
increase to about 34 percent of mobile source NOx, 45 percent of mobile source
PM2.5, and 94 percent of mobile source SO2 by 2030 without further controls on these
engines. Our current estimates for NOx, PM2.5, and SO2 inventories are set out in
Table 5-1 through Table 5-3. The inventory projections for 2020 and 2030 include
the effect of existing emission mobile source and stationary source control programs
previously adopted by EPA (excluding the recently adopted MSAT regulations,
signed on February 9, 2007 which will have an effect on future highway non-diesel
PM2.5 levels).
134
-------
Table 5-1. 50 State Annual NOx Baseline Emission Levels for Mobile and
Other Source Categories
Category
Commercial Marine (C3)b
Locomotive
Recreational Marine Diesel
Commercial Marine (Cl & C2)
Land -Based Nonroad Diesel
Small Nonroad SI
Recreational Marine SI
SI Recreational Vehicles
Large Nonroad SI (>25hp)
Aircraft
Total Off Highway
Highway Diesel
Highway non-diesel
Total Highway
Total Mobile Sources
Stationary Point & Area
Sources
Total Man-Made Sources
2001a
short tons0
745,224
1,118,786
40,437
834,025
1,548,236
114,319
44,732
5,488
321,098
83,764
4,856,109
3,750,886
4,354,430
8,105,316
12,961,425
9,355,659
22,317,084
%of
mobile
source
5.7
8.6
0.3
6.4
11.9
0.9
0.3
0.0
2.5
0.6
37.5
28.9
33.6
62.5
100
-
%of
total
3.3
5.0
0.2
3.7
6.9
0.5
0.2
0.0
1.4
0.4
21.8
16.8
19.5
36.3
58.1
41.9
100
2020
short tons0
1,368,420
860,474
45,477
676,154
678,377
114,881
86,908
17,496
46,319
105,133
3,999,639
646,961
1,361,276
2,008,237
6,007,876
6,111,866
12,119,742
%of
mobile
source
22.8
14.3
0.8
11.3
11.3
1.9
1.4
0.3
0.8
1.7
66.6
10.8
111
33.4
100
-
%of
total
11.3
7.1
0.4
5.6
5.6
0.9
0.7
0.1
0.4
0.9
33.0
5.3
11.2
16.6
49.6
50.4
100
2030
short tons0
2,023,974
854,226
48,102
680,025
434,466
133,197
96,143
20,136
46,253
118,740
4,455,262
260,915
1,289,780
1,550,695
6,005,957
6,111,866
12,117,823
%of
mobile
source
33.7
14.2
0.8
11.3
7.2
2.2
1.6
0.3
0.8
2.0
74.2
4.3
21.5
25.8
100
-
%of
total
16.7
7.0
0.4
5.6
3.6
1.1
0.8
0.2
0.4
1.0
36.8
2.2
10.6
12.8
49.6
50.4
100
The locomotive, commercial marine (Cl & C2), and recreational marine diesel estimates are for calendar year
2002.
This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
on ocean-going vessels.
° Short tons can be converted to metric tonnes using the multiplicative factor of 0.907 metric tonne per short
ton
135
-------
Table 5-2. 50 State Annual PMi.s Baseline Emission Levels for Mobile and
Other Source Categories
Category
Commercial Marine (C3)b
Locomotive
Recreational Marine Diesel
Commercial Marine (Cl & C2)
Land-Based Nonroad Diesel
Small Nonroad SI
Recreational Marine SI
SI Recreational Vehicles
Large Nonroad SI (>25hp)
Aircraft
Total Off Highway
Highway Diesel
Highway non-diesel
Total Highway
Total Mobile Sources
Stationary Point & Area Sources
Total Man-Made Sources
2001a
short tons0
54,667
29,660
1,096
28,730
164,180
25,466
16,837
12,301
1,610
5,664
340,211
109,952
50,277
160,229
500,440
1,963,264
2,463,704
%of
mobile
source
10.9
5.9
0.2
5.7
32.8
5.1
3.4
2.5
0.3
1.1
68.0
22.0
10.0
32.0
100
-
-
%of
total
2.2
1.2
0.0
1.2
6.7
1.0
0.7
0.5
0.1
0.2
13.8
4.5
2.0
6.5
20.3
79.7
100
2020
short tons0
110,993
26,301
1,006
22,236
46,075
32,904
6,367
11,773
2,421
7,044
267,120
15,800
47,354
63,154
330,274
1,817,722
2,147,996
%of
mobile
source
33.6
8.0
0.3
6.7
14.0
10.0
1.9
3.6
0.7
2.1
80.9
4.8
14.3
19.1
100
-
-
%of
total
5.2
1.2
0.0
1.0
2.1
1.5
0.3
0.5
0.1
0.3
12.4
0.7
2.2
2.9
15.4
84.6
100
2030
short tons0
166,161
25,109
1,140
23,760
17,934
37,878
6,163
9,953
2,844
8,569
299,511
10,072
56,734
66,806
366,317
1,817,722
2,184,039
%of
mobile
source
45.4
6.9
0.3
6.5
4.9
10.3
1.7
2.7
0.8
2.3
81.8
2.7
15.5
18.2
100
-
-
%of
total
7.6
1.1
0.1
1.1
0.8
1.7
0.3
0.5
0.1
0.4
13.7
0.5
2.6
3.1%
16.8
83.2
100
The locomotive, commercial marine (Cl & C2), and recreational marine diesel estimates are for calendar year
2002.
This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
on ocean-going vessels.
Short tons can be converted to metric tonnes using the multiplicative factor of 0.907 metric tonne per short
ton
136
-------
Table 5-3. 50 State Annual SCh Baseline Emission Levels for Mobile and Other
Source Categories
Category
Commercial Marine (C3)b
Locomotive
Recreational Marine Diesel
Commercial Marine (Cl & C2)
Land-Based Nonroad Diesel
Small Nonroad SI
Recreational Marine SI
SI Recreational Vehicles
Large Nonroad SI (>25hp)
Aircraft
Total Off Highway
Highway Diesel
Highway non-diesel
Total Highway
Total Mobile Sources
Stationary Point & Area
Sources
Total Man-Made Sources
2001 a
short tons0
457,948
76,727
5,145
80,353
167,615
6,710
2,739
1,241
925
7,890
807,293
103,632
169,125
272,757
1,080,050
15,057,420
16,137,470
%of
mobile
source
42.4
7.1
0.5
7.4
15.5
0.6
0.3
0.1
0.1
0.7
74.7
9.6
15.7
25.3
100
-
%of
total
2.8
0.5
0.0
0.5
1.0
0.0
0.0
0.0
0.0
0.0
5.0
0.6
1.0
1.7
6.7
93.3
100
2020
short tons0
932,820
400
162
3,104
999
8,797
2,963
2,643
905
9,907
962,700
3,443
35,195
38,638
1,001,338
8,215,016
9,216,354
%of
mobile
source
93.2
0.0
0.0
0.3
0.1
0.9
0.3
0.3
0.1
1.0
96.1
0.3
3.5
3.9
100
-
%of
total
10.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.1
10.4
0.0
0.4
0.4
10.9
89.1
100
2030
short tons0
1,398,598
468
192
3,586
1,078
10,196
3,142
2,784
1,020
11,137
1,432,201
4,453
42,709
47,162
1,479,363
8,215,016
9,694,379
%of
mobile
source
94.5
0.0
0.0
0.2
0.1
0.7
0.2
0.2
0.1
0.8
96.8
0.3
2.9
3.2
100
-
%of
total
14.4
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.1
14.8
0.0
0.4
0.5
15.3
84.7
100
The locomotive, commercial marine (Cl & C2), and recreational marine diesel estimates are for calendar year
2002.
This category includes emissions from Category 3 (C3) propulsion engines and C2/3 auxiliary engines used
on ocean-going vessels.
° Short tons can be converted to metric tonnes using the multiplicative factor of 0.907 metric tonne per short
ton
5.2 Contribution to Mobile Source Inventories for
Selected Cities
Commercial marine vessels, powered by Category 3 marine engines,
contribute significantly to the emissions inventory for many U.S. ports. This is
illustrated in Table 5-4 which presents the mobile source inventory contributions of
these vessels for several ports. The ports in this table were selected to present a
sampling over a wide geographic area along the U.S. coasts. In 2005, these twenty
ports received approximately 60 percent of the vessel calls to the U.S. from ships of
10,000 dead weight tons (DWT) or greater.55
137
-------
Table 5-4. Contribution of Commercial Marine Vessels to Mobile Source
Inventories for Selected Ports in 2002a
Port Area
Valdez, AK
Seattle, WA
Tacoma, WA
San Francisco, CA
Oakland, CA
LA/Long Beach, CA
Beaumont, TX
Galveston, TX
Houston, TX
New Orleans, LA
South Louisiana, LA
Miami, FL
Port Everglades, FL
Jacksonville, FL
Savannah, GA
Charleston, SC
Wilmington, NC
Baltimore, MD
New York/New Jersey
Boston, MA
% of total
NOx
4
10
20
1
8
5
6
5
3
14
12
13
9
5
24
22
7
12
4
4
% of total
PM25
10
20
38
1
14
10
20
12
10
24
24
25
20
11
39
33
16
27
9
5
% of total
S02
43
56
74
31
80
71
55
47
41
59
58
66
56
52
80
87
73
69
39
30
This category includes emissions from Category 3 (C3) propulsion engines and
C2/3 auxiliary engines used on ocean-going vessels.
Currently, more than 40 major U.S. deep sea ports are located in areas that are
designated as being in nonattainment for either or both the 8-hour ozone NAAQS and
PM2.5 NAAQS. Many ports are located in areas rated as class I federal areas for
visibility impairment and regional haze. It should be noted that emissions from
ocean-going vessels are not simply a localized problem related only to cities that have
commercial ports. Virtually all U.S. coastal areas are affected by emissions from
ships that transit between those ports, using shipping lanes that are close to land.
Many of these coastal areas also have high population densities. For example, Santa
Barbara, which has no commercial port, estimates that engines on ocean-going
marine vessels currently contribute about 37 percent of total NOx in their area.
These emissions are from ships that transit the area, and "are comparable to (even
slightly larger than) the amount of NOx produced onshore by cars and truck." By
2015 these emissions are expected to increase 67 percent, contributing 61 percent of
Santa Barbara's total NOx emissions. This mix of emission sources led Santa Barbara
to point out that they will be unable to meet air quality standards for ozone without
significant emission reductions from these vessels, even if they completely eliminate
all other sources of pollution. Interport emissions from OGV also contribute to other
environmental problems, affecting sensitive marine and land ecosystems.
56
138
-------
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Years 1995 Through 2005, Various 1996-2004, London, United Kingdom.
40 Lloyds Maritime Intelligence Unit, Commercial Marine Vessel Activity Data in Mexico for the Years
1996, 1998, 2000, and 2004, Various 1996-2004, London, United Kingdom.
41 Lloyd's Register and International Maritime Organization, Marine Exhaust Emission Quantification
Study-Baltic Sea, in MEPC 45/INF.7. 1998.
42 ICF International, Best Practices in Preparing Port Emision Inventories: Draft for Review, 2005,
prepared for the U.S. Environmental Protection Agency, June 23, 2005.
43 Corbett, J.J. andH.W. Koehler, Updated Emissions from Ocean Shipping, Journal of Geophysical
Research, 2003, 108(D20); p. 4650.
44 Corbett, J.J. andH.W. Koehler, Considering Alternative Input Parameters in an Activity-Based Ship
Fuel Consumption and Emissions Model: Reply to Comment by Oyvind Endresen et al. on
"Updated Emissions from Ocean Shipping," Journal of Geophysical Research, 2004. 109(D23303).
Levelton Consultants Ltd., Marine Emission Inventory Study Eastern Canada and Great Lakes-
Interim Report 4: Gridding Results, 2006, prepared for Transportation Development Centre,
Transport Canada.
46 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.
140
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47 Clarksons, Clarksons Shipping Database (purchased electronic resource). 2005. London, UK:
Clarkson Research Studies, http://www.clarksons.com
48 Koehler (2003): Koehler, H.W. NOx Emissions from Oceangoing Ships: Calculation and
Evaluation, Proceedings of ICESO3, 2003 Spring Technical Conference of the ASME Internal
49
Combustion Engine Division. Paper No. ICES2003-689. 2003.
Corbett, James and Chengfeng Wang, Emission Inventory Review SECA Inventory Progress
Discussion, p 11, memorandum to California Air Resources Board, October 26, 2005.
50 Worldscale Association; New Worldwide Tanker Nominal Freight Scale, 2002, London.
51 Maritime Chain. 2005. http://www.maritimechain.com/port/pot_distance.asp
Degerlund, J. (ed). Containerization International Yearbook 2005. London: TF Informa UK Ltd.
53 Wartsila NSD and K. Heim, Forward Look at Future Emissions Control Measures for Sulzer RTA
Engines, Marine News, 2001, Wartsila NSD: Helsinki, Finland.
54 Henningsen, S., 2007 Panel Discussion on Emission Reduction Solutions for Marine Vessels; Engine
Technologies 0, MAN B&W, Clean Ships: Advanced Technology for Clean Air Conference,
February 7-9, 2007.
U.S. Maritime Administration, Office of Statistical and Economic Analysis, Vessel Calls at U.S. &
WorldPorts, 2005, April 2006, Docket ID EPA-HQ-OAR-2007-0121 -0040.
56 Memorandum to Docket A-2001-11 from Jean-Marie Revelt, Santa Barbara County Air Quality
News, Issue 62, July-August 2001 and other materials provided to EPA by Santa Barbara County,
March 14, 2002.
141
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Appendix A for EPA420-D-07-007
Commercial Marine Port Inventory
Development
2002 and 2005 Inventories
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
Prepared for EPA by
ICF International
San Francisco, CA
EPA Contract No. EP-C-06-094
Work Assignment No. 0-02
NOTICE
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments.
SER&
United States
Environmental Protection
Agency
EPA420-R-07-012
October 2007
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Table of Contents
1. Introduction 1-1
2. Near Port Inventory Methodology 2-1
2.1. Original Typical Ports 2-1
2.2. Review of New Inventories and Selection of Typical Ports 2-3
2.3. Updates to Typical Port Inventories 2-4
2.3.1. Propulsion Engine Load Factors 2-5
2.3.2. Treatment of Auxiliary Engines 2-5
2.3.3. Consistent Fuel Sulfur Levels 2-7
2.3.4. Propulsion and Auxiliary Engine Emission Factors 2-8
2.3.5. Treatment of Electric-Drive Ships 2-11
2.3.6. Maneuvering Assumptions 2-11
2.3.7. Removal of Category 1 and 2 Ships 2-11
2.4. Revised Typical Port Inventories 2-12
2.4.1. Lower Mississippi River Ports 2-12
2.4.2. Ports of New York and New Jersey 2-13
2.4.3. Delaware River Ports 2-13
2.4.4. Puget Sound Ports 2-14
2.4.5. Corpus Christ! 2-15
2.4.6. Houston 2-16
2.4.7. Tampa 2-16
2.4.8. Baltimore 2-17
2.4.9. Coos Bay 2-18
2.4.10. Cleveland 2-18
2.4.11. Duluth-Superior 2-19
2.5. Updated Modeled Port Methodology 2-20
2.5.1. Selection of Individual Ports to be Analyzed 2-20
2.5.2. Matching Typical Ports to Modeled Ports 2-21
2.5.3. Extrapolation from Typical Port to Modeled Port 2-24
2.6. Stand Alone Ports 2-31
2.6.1 California Ports 2-31
2.6.2. Puget Sound Ports 2-32
2.6.3. PortofValdez 2-33
2.7. Domestic Traffic 2-33
2.8. Calculation of Fuel Consumption 2-33
2.9. IMO NOx Reductions 2-33
3. 2002 Baseline Near Port Inventories 3-1
3.1. Deep Sea Ports 3-1
3.2. Great Lake Ports 3-29
4. Spatial Allocation of Near Port Inventory and Blending of Near Port and STEEM
Inventories 4-1
4.1. STEEM Modeling Domain 4-1
4.2. Near Port Emissions Types 4-1
4.3. Spatial Allocation Methodology 4-2
4.3.1. Port Locations 4-2
4.3.2. Reduced Speed Zone Transit 4-2
4.3.3. Multiple RSZ paths 4-5
4.3.4. Cruise Mode Transit 4-6
4.3.5. Regions of Overlapping Near Port and STEEM Emissions 4-7
4.3.6. Partial Replacement of STEEM Emissions 4-7
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4.4. Scaling of STEEM PM and SOx emissions 4-8
4.5. Geographic Projection Specification 4-8
4.6. Results of Blending Emissions 4-9
5. Total Emissions and Fuel Consumption by Region 5-1
5.1. Definition of Regions 5-1
5.2. STEEM Fuel Consumption Estimate 5-2
5.3. Summing by Region Methodology 5-2
5.4. 2005 Growth Factors 5-3
5.5. 2002 and 2005 Blended Emission Totals by Region 5-3
Appendix A A-1
List of Figures
Figure 3-1. Installed Power by Engine Type for Deep Sea Ports 3-3
Figure 3-2. Deep Sea Ship Registry Breakdown 3-3
Figure 3-3. Installed Power by Engine Type at Great Lake Ports 3-31
Figure 3-4. Great Lake Ship Registry Breakdown 3-31
Figure 4-1. Extent of STEEM data 4-1
Figure 4-2. Demonstration of creation of RSZ shapefile in the San Francisco Bay Area. Top left, NWN
links; top right, circular buffer around Stockton; middle left, intersection of circular buffers with
NWN; middle right, discard irrelevant NWN links; bottom left, refined RSZ links; bottom right,
more detail showing final RSZ shapefile. 4-4
Figure 4-3 . Gridded RSZ emissions from near port inventory 4-5
Figure 4-4. Branched RSZ link (pink lines) and CM links (green). 4-6
Figure 4-5. Three Cruise Mode links (dark green) extending 25 nautical miles out from RSZ links
(magenta), and RSZ buffer region enclosing all near port emission types. 4-7
Figure 4-6. Example of complete replacement of STEEM data with near port data. 4-8
Figure 4-7. Example of partial replacement of STEEM data with near port data. Only some of the
emissions within the circular region are attributable to that port. 4-8
Figure 4-8. Graphical comparison of original STEEM emissions (left) and final merged STEEM and near
port inventory emissions (right) for the Chesapeake Bay and Southeast US 4-10
Figure 5-1. Nine regions defined by the U.S. Exclusive Economic Zone and guidance from EPA 5-2
List of Tables
Table 2-1. Deep Sea MEPA vessel movement and shifting details 2-2
Table 2-2. Great Lake MEPA movements and shifts 2-2
Table 2-3. Auxiliary Engine Power Ratios (ARB Survey, except as noted) 2-6
Table 2-4. Auxiliary Engine Load Factor Assumptions 2-7
Table 2-5. Assumed sulfur levels in marine diesel fuels for U.S. Inventories 2-7
Table 2-6. Emission Factors for OGV Main Engines using RO, g/kWh 2-8
Table 2-7. Emission Factor Algorithm Coefficients for OGV Main Engines using RO 2-9
Table 2-8. Calculated Low Load Multiplicative Adjustment Factors 2-10
Table 2-9. Auxiliary Engine Emission Factors, g/kWh 2-10
Table 2-10. Auxiliary Engine Emission Factors for use in this study, g/kWh 2-11
Table 2-11. Lower Mississippi Port Emissions by Ship Type for 1996 2-12
Table 2-12. Lower Mississippi Port Emissions by Mode for 1996 2-12
Table 2-13. New York/New Jersey Emissions by Ship Type for 1996 2-13
Table 2-14. New York/New Jersey Emissions by Mode for 1996 2-13
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Table 2-15. Delaware River Emissions by Ship Type for 1996 2-14
Table 2-16. Delaware River Emissions by Mode for 1996 2-14
Table 2-17. Puget Sound Emissions by Ship Type for 1996 2-15
Table 2-18. Puget Sound Emissions by Mode for 1996 2-15
Table 2-19. Corpus Christ! Emissions by Ship Type for 1996 2-15
Table 2-20. Corpus Christ! Emissions by Mode for 1996 2-16
Table 2-21. Houston/Galveston Emissions by Ship Type for 1997 2-16
Table 2-22. Houston/Galveston Emissions by Mode for 1997 2-16
Table 2-23. Tampa Emissions by Ship Type for 1996 2-17
Table 2-24. Tampa Emissions by Mode for 1996 2-17
Table 2-25. Baltimore Emissions by Ship Type for 1996 2-17
Table 2-26. Baltimore Emissions by Mode for 1996 2-18
Table 2-27. Coos Bay Emissions by Ship Type for 1996 2-18
Table 2-28. Coos Bay Emissions by Mode for 1996 2-18
Table 2-29. Cleveland Emissions by Ship Type for 2005 2-19
Table 2-30. Cleveland Emissions by Mode for 2005 2-19
Table 2-31. Duluth-Superior Emissions by Ship Type for 2005 2-19
Table 2-32. Duluth-Superior Emissions by Mode for 2005 2-19
Table 2-33. Matched ports and Reduced Speed Zone Information 2-21
Table 2-34. Speed definitions 2-23
Table 2-35. Regional definitions 2-24
Table 2-36. Great Lake Match Ports 2-24
Table 2-37. Bins and Average Ship Characteristics for Deep Sea Ports 2-25
Table 2-38. Bins and Average Ship Characteristics for Great Lake Ports 2-29
Table 2-39. Annex VI NOx emission standards (g/kWh) 2-34
Table 2-40. NOx Adjustment Factors for Annex VI NOx Standards 2-34
Table 3-1. Deep Sea Ship Age Fractions by Engine Type 3-2
Table 3-2. Match Port File Codes and File Names 3-4
Table 3-3. Total Emissions by Port (Deep Sea Ports) 3-5
Table 3-4. Auxiliary Engine Emissions by Port (Deep Sea Ports) 3-7
Table 3-5. Cruise Emissions by Port (Deep Sea Ports) 3-9
Table 3-6. Reduced Speed Zone Emissions by Port (Deep Sea Ports) 3-11
Table 3-7. Maneuvering Emissions by Port (Deep Sea Ports) 3-13
Table 3-8. Hotelling Emissions by Port (Deep Sea Ports) 3-15
Table 3-9. Auto Carrier Deep Sea Port Emissions 3-17
Table 3-10. Barge Carrier Deep Sea Port Emissions 3-18
Table 3-11. Bulk Carrier Deep Sea Port Emissions 3-18
Table 3-12. Container Ship Deep Sea Port Emissions 3-20
Table 3-13. General Cargo Ship Deep Sea Port Emissions 3-21
Table 3-14. Miscellaneous Ship Deep Sea Port Emissions 3-23
Table 3-15. Passenger Ship Deep Sea Port Emissions 3-24
Table 3-16. Refrigerated Cargo Ship Deep Sea Port Emissions 3-25
Table 3-17. Roll-On/Roll-Off Ship Deep Sea Port Emissions 3-25
Table 3-18. Tanker Ship Deep Sea Port Emissions 3-27
Table 3-19. Ocean Going Tug Deep Sea Port Emissions 3-29
Table 3-20. Great Lake Ship Age Fractions by Engine Type 3-30
Table 3-21. Match Port File Codes and File Names 3-32
Table 3-22. Total Emissions by Port (Great Lake Ports) 3-32
Table 3-23. Auxiliary Engine Emissions by Port (Great Lake Ports) 3-33
Table 3-24. Cruise Emissions by Port (Great Lake Ports) 3-34
Table 3-25. Reduced Speed Zone Emissions by Port (Great Lake Ports) 3-35
Table 3-26. Maneuvering Emissions by Port (Great Lake Ports) 3-36
Table 3-27. Hotelling Emissions by Port (Great Lake Ports) 3-37
Table 3-28. Self-Unloading Bulk Carrier Emissions by Port 3-38
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Table 3-29. Bulk Carrier Emissions by Port 3-39
Table 3-30. General Cargo Ship Emissions by Port 3-39
Table 3-31. Tanker Ship Emissions by Port 3-39
Table 3-32. Integrated Tug-Barge Emissions by Port 3-40
Table 4-1. Map projection specifications 4-9
Table 5-1. Growth and Emission Adjustment Factors 5-3
Table 5-2. 2002 Regional U.S. emissions 5-3
Table 5-3. 2005 Regional U.S. emissions 5-4
Table A-1. Port Coordinates A-1
Table A-2. RSZ Distances and End Point Coordinates A-4
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1. Introduction
Individual port inventories were developed for the ocean going Category 3 commercial marine
vessels (CMV) during final rulemaking for the Category 3 Marine Diesel Rule1. Port activity was
developed by Dr. Louis Browning of ARCADIS Geraghty & Miller Inc. (now with ICF
International).2'3 Detailed activity profiles were determined for selected typical deep sea ports,
Great Lake and inland river ports within the U.S. A methodology was also developed and
implemented to extrapolate these typical port activity profiles to other ports within the U.S.
where only general vessel activity was available. Activity profiles were estimated for the top 95
deep sea ports and the top 60 Great Lakes and inland river ports.
These activity profiles were then used in conjunction with emission factors and load factors to
calculate individual port emission inventories for Category 3 vessels in calendar year 1996.
This work was done by Dr. Chris Lindhjem of ENVIRON under EPA contract4. Intraport
inventories were also developed as part of this work. The port and intraport inventories were
then summed and used to estimate nationwide inventories which were used for the Category 3
rulemaking.
More recently, Dr. James Corbett of University of Delaware, under contract with the California
Air Resources Board, developed a regional gridded inventory for Category 3 vessels to support
development of a SECA (SOx Emission Control Area) application for the U.S.5 Corbett used the
Waterway Network Ship Traffic, Energy and Environment Model (STEEM) to quantify and
geographically represent inter-port vessel traffic and emission. Emissions for North America,
including the United States, Canada, and Mexico were graphically characterized.
The Delaware STEEM model approach included detailed information about ship routes and
destinations in order to provide spatially allocated emissions of ships in transit. This model was
developed as a comprehensive approach to estimating emissions from large vessel ship traffic.
The shipping lanes and directions were accurately determined along paths empirically derived
from ship positioning data. However, the precision of this positioning data may be poor in some
locations, especially as the lanes approach shorelines where ships would need to follow more
prescribed paths. For the majority of the ship transiting trips, the cruise mode (approximately
-80% engine load) was used to estimate emissions. However within 20 km of each trip origin
and destination, STEEM used what was referred to as a maneuvering mode load of 20%, but
20% represents vessel speeds approximately 60% of cruise speed or at considerably higher
speed than actual maneuvering speeds very near the docks. Due to this approximation near
port, the STEEM model did not represent emission rates specific to each port.
When vessels reduce speed, the engine load can be predicted through a cubic proportionality,
while the time along a distance is inversely proportional to the speed. Therefore, the emissions
along a shipping lane would be generally proportional to the square of the vessel speed. When
1 40 CFR Parts 9 and 94, Control of Emissions From New Marine Compression-Ignition Engines at or Above 30 Liters
Per Cylinder; Final Rule, February 28, 2003.
2 ARCADIS Geraghty & Miller, Commercial Marine Activity for Deep Sea Ports in the United States, EPA Report
EPA420-R-99-020, September 1999.
ARCADIS Geraghty & Miller, Commerc
EPA Report EPA420-R-99-019, September 1999.
ENVIRON Internation
for EPA, April 2002.
J. Corbett, Estimation
Baseline Inventory and Ports Comparison, Final Report, May 2006.
3 ARCADIS Geraghty & Miller, Commercial Marine Activity for Great Lake and Inland River Ports in the United States,
4 ENVIRON International Corporation, Commercial Marine Emission Inventory Development, Final Report, Prepared
5 J. Corbett, Estimation, Validation and Forecasts of Regional Commercial Marine Vessel Inventories, Task 1 and 2:
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the actual speed for a given port's reduced speed zone is known more accurately, the load, and
thus emissions, will likely be different than those modeled by STEEM. Furthermore, due to its
inherent approximate methodology, STEEM could not predict actual maneuvering (transiting
near dock) and hotelling activity.
For these reasons, it was decided the STEEM data should not be used near the ports in the
final gridded emissions inventory. While the STEEM model is applicable for most of the activity
in the ocean, it was decided that the near port inventory, which was created by using detailed
individual port estimates, was more appropriate near ports.
ICF International was asked to update the 1996 inventories developed for the Category 3 diesel
engine rulemaking and added new ports based upon recent studies. ICF International updated
the typical ports used for the Category 3 diesel engine rulemaking based upon current
methodologies and provided a detailed assessment of port traffic and emissions for 117 ports as
a part of this work. These 117 ports covered most, but not all, of the ports identified by the
shipping lane paths evident in the STEEM data. Also in contrast to the Delaware STEEM work,
this near port inventory focused on hotelling, maneuvering (within a very short distance of the
docks, compared with the 'maneuvering' activity 20 km from each port used in the STEEM
model), and reduced speed zone (RSZ) modes. The outer range of the RSZ varies port to port,
though generally the RSZ would begin and end when the pilots board or disembark, and occurs
generally when the near port shipping lanes reach unconstrained ocean shipping lanes.
ENVIRON then inserted the near port inventories into the University of Delaware gridded
inventory. This report details the work described in this paragraph.
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2. Near Port Inventory Methodology
This section describes the methodology used to develop the near-port inventories. ICF
International updated port-specific inventories for Category 3 ocean-going vessels at 89 deep-
water and 28 Great Lake ports. Pollutants included in the inventories were HC, CO, NOx, PM10,
PM2.s, and SO2 as well as the amount of fuel consumed.
The Category 3 Commercial Marine Vessel (CMV) port inventories originally developed for the
Category 3 Marine Diesel Engine Rulemaking were updated as follows:
• New detailed port inventories that have been developed since the Category 3 Rulemaking
were reviewed and used to the extent possible to add to or refine the typical port inventory
set
• Typical port emission inventories were updated using new activity assumptions, emission
factors and load factors as well as separating out auxiliary engine emissions from
propulsion emissions
• The methodology for updating and extrapolating to other ports was updated using U.S.
Army Corp of Engineers Entrances and Clearances data
• Category 1 and 2 ships were eliminated from the analysis
• Fuel consumed was calculated
2.1. Original Typical Ports
In 1999, EPA published two guidance documents2'3 to calculate marine vessel activity at ports.
These documents contained detailed port inventories of 8 deep sea ports, 2 Great Lake ports
and 2 inland river ports. The detailed inventories were developed by obtaining ship call data
from Marine Exchanges/Port Authorities (MEPA) at the various ports for 1996 and matching the
various ship calls to data from Lloyds Maritime Information Services to provide ship
characteristics. The ports for which detailed inventories were developed are shown in Table 2-1
for deep sea ports and Table 2-2 for Great Lake ports along with the level of detail of shifts for
each port. Most all ports provided the ship name, Lloyd's number, the vessel type, the date and
time the vessel entered and left the port, and the vessel flag. Inland river ports were developed
from US Army Corps of Engineers (USAGE) Waterborne Commerce Statistics Center data.
In addition to the detailed port inventories of ship activity, the two EPA guidance documents laid
out a methodology to extrapolate the detailed activity data from one of the typical ports which is
"like" the port being modeled. This involved:
• Determining which typical port is "like" the port to be modeled
• Determining the speed and distance of the reduced speed zone for the modeled port to
use in determining the time in mode and load factor for the modeled port while operating
in the reduced speed zone.
• Determining the number of calls in the calendar year which is to be modeled for the like
port and the modeled port using USAGE data.
In the guidance document methodology, emissions calculations for a given activity mode
(cruise, reduced speed zone, maneuvering, or hotelling) and ship type involves forming ratios of
the number of calls at the modeled port in the year to be analyzed divided by the number of
calls for the "like" port (typical port like the modeled port) in 1996 and multiplying the ratio by the
typical port's 1996 emissions. All data such as time in mode per call, engine power, vessel
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speed, number of shifts per call and load factors are considered the same between the modeled
port in the year to be analyzed and the typical like port in 1996 with the exception of calculating
the reduced speed zone (RSZ).
Table 2-1. Deep Sea MEPA vessel movement and shifting details
MEPA Area and Ports
MEPA Data Includes
Lower Mississippi River including
the ports of New Orleans, South
Louisiana, Plaquemines, and
Baton Rouge
Information on the first and last pier/wharf/dock (PWD) for the
vessel (gives information for at most one shift per vessel). No
information on intermediate PWDs, the time of arrival at the first
destination PWD, or the time of departure from the River.
Consolidated Port of New York
and New Jersey and other ports
on the Hudson and Elizabeth
Rivers
All PWDs or anchorages for shifting are named. Shifting arrival
and departure times are not given. Maneuvering and hotelling
times are estimated from average speed and distance rather than
calculated from date and time fields.
Delaware River Ports including the
ports of Philadelphia, Camden,
Wilmington and others
All PWDs or anchorages for shifting are named. Shifting arrival
and departure times are not given. Maneuvering and hotelling
times are estimated from average speed and distance rather than
calculated from date and time fields.
Puget Sound Area Ports including
the ports of Seattle, Tacoma,
Olympia, Bellingham, Anacortes,
and Grays Harbor
All PWDs or anchorages for shifting are named. Arrival and
departure dates and times are noted for all movements, allowing
calculation of maneuvering and hotelling both for individual shifts
and the overall call on port.
The Port of Corpus Christi, TX
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.
Only has information on destination PWD and date and time in
and out of the port area. No shifting details.
Patapsco River Ports including the
port of Baltimore Harbor, MD
All PWDs or anchorages for shifting are named. Shifting arrival
and departure times are not given. Maneuvering and hotelling
times are estimated from average speed and distance rather than
calculated from date and time fields.
The Port of Tampa, FL
All PWDs or anchorages for shifting are named. Arrival and
departure dates and times are noted for all movements, allowing
calculation of maneuvering and hotelling both for individual shifts
and the overall call.
Table 2-2. Great Lake MEPA movements and shifts
MEPA Area and Ports
MEPA Data Includes
Port of Cleveland, OH
Information on the first and last PWD for the vessel (gives
information for at most one shift per vessel). No information on
intermediate PWDs..
Port of Burns Harbor, IN
No shifting details, No PWDs listed..
For the RSZ, the modeled port RSZ distance and average speed are used in the calculations.
The time-in-mode for the RSZ for the modeled port is calculated directly from the modeled port's
RSZ speed and distance. In addition, the load factor is determined based upon the ratio of the
modeled port's RSZ speed to the typical port's cruise speed. These new values are used to
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determine emissions at the modeled port in the year to be analyzed from the typical port in 1996
when operating in the RSZ.
In 2002, EPA published a national Category 3 engine emission inventory4 to support
rulemaking. The approach taken in this inventory development was to apply emission factors to
the deep sea and Great Lake port activity data as described above. Detailed emission
inventories for the 10 typical ports were developed and extrapolated as discussed above to the
top 100 US deep sea ports and the top 36 US Great Lake ports based upon the ratio of calls
and RSZ information for 1996.
2.2. Review of New Inventories and Selection of Typical Ports
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 Angeles6,7
• Puget Sound Ports8
• Port of New York/New Jersey9
• Port of Houston/Galveston10
• Port of Beaumont/Port Arthur11
• Port of Corpus Christi12
Port of Portland13
Ports of Cleveland, OH and Duluth-Superior, MN&WI
14
In most cases, the inventories did not provide enough detail to replace the typical ports
described in the Commercial Marine Activity guidance documents.2'3 Several modifications to
Starcrest Consulting Group, Port-Wide Baseline Air Emissions Inventory, prepared for the Port of Los Angeles, June
2004.
7 Starcrest Consulting Group, Draft Port of Los Angeles Air Emissions Inventory for Calendar Year 2005, January
2007
8 Starcrest Consulting Group, Puget Sound Maritime Air Forum Maritime Air Emissions Inventory, April 2007
9 Starcrest Consulting Group, LLC, The New York, Northern New Jersey, Long Island Nonattainment Area
Commercial Marine Vessel Emission Inventory, Vol 1 - Report, Prepared for the Port Authority of New York & New
Jersey, United States and the Army Corps of Engineers, New York District, April 2003.
10 Starcrest Consulting Group, LLC, Houston-Galveston Area Vessel Emissions Inventory, Prepared for the Port of
Houston Authority and the Texas Natural Resource Conservation Commission, November 2000.
11 Eastern Research Group and Starcrest Consulting Group, LLC, Update To The Commercial Marine Inventory For
Texas To Review Emissions Factors, Consider A Ton-Mile El Method, And Revise Emissions For The Beaumont-
Port Arthur Non-Attainment Area Final Report, Submitted to the Houston Advanced Research Center, January
2004.
12 Zuber M. Farooqui and Kuruvilla John, Refinement of the Marine Emissions Inventory for the Corpus Christi Urban
Airshed, Department of Environmental Engineering, Texas A&M University - Kingsville, Proceedings of the 97th
Annual A&WMA Conf. & Exhibition, June 2004.
13 Bridgewater Group and CH2M Hill, Inc., Port Of Portland Calendar Year 2000 Baseline Air Emission Inventory,
Prepared for the Port of Portland, April, 2005.
14 ENVIRON International Corporation, LADCO 2005 Commercial Marine Emissions, March 2007.
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the typical port data calculations were made as described in Section 2.3 to bring the
methodology up to date. Additionally some of the previous typical ports were replaced with
newer data and an additional typical port was added. Data developed for Cleveland and Duluth-
Superior for LADCO was used in lieu of the previous typical port data for Cleveland and Burns
Harbor because it provided more detailed information and better engine category definitions.
Port of Houston/Galveston provided enough data to add an additional typical port but needed to
be modified using new emission factors. All three port inventories were adjusted to reflect the
current methodology used in this study. The newer Port of New York/New Jersey inventory
provided a check against estimates made using the 1996 data. All other new inventory
information was found to lack sufficient detail to prepare the detailed typical port inventories
needed for this project.
The final list of typical ports used in this analysis and their data year is as follows:
• Lower Mississippi River Ports [1996]
• Consolidated Ports of New York and New Jersey and Hudson River [1996]
• Delaware River Ports [1996]
• Puget Sound Area Ports [1996]
• Corpus Christi, TX [1996]
• Houston/Galveston Area Ports [1997]
• Ports on the Patapsco River [1996]
• Port of Coos Bay, OR [1996]
• Port of Tampa, FL [1996]
• Port of Cleveland, OH on Lake Erie [2005]
• Duluth-Superior, MN & Wl on Lake Michigan [2005]
2.3. Updates to Typical Port Inventories
Since the development of the original methodology in the Commercial Marine Activity guidance
documents, several newer inventories have advanced significant changes in estimates of load
factors at lower speeds and of Lloyd's service speed versus cruise speed. In addition, newer
values of emission factors now available are considered more representatives of Category 3 ship
emissions and the treatment of auxiliary engine emissions has greatly improved. Therefore, the
typical port emissions inventories were updated as follows:
• Load factors for propulsion engines were calculated using the propeller law
• Auxiliary engine power and load factors were calculated
• Consistent sulfur levels in fuels were used
• Emission factors were updated
• Low load adjustment factors were applied to emission factors when the propulsion
engine load factor was below 20% maximum continuous rating.
• Electric drive vessels were identified and a new methodology was used
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• Maneuvering assumptions were updated
• A more accurate method for removing Category 1 and 2 ships from the typical port data
was used
• Carbon dioxide (CO2) emissions were calculated and used to determine fuel
consumption
Using the newest methodology, typical port inventories were recalculated as discussed in the
following subsections.
2.3.1. Propulsion Engine Load Factors
In the previous inventory, propulsion engine load factors were estimated from a modification of
the Propeller curve as such:
Load Factor = 0.1 + 0.7*(actual speed/cruise speed)3 (1)
The 10% minimum for zero actual speed was considered to include auxiliary engines and could
be used to estimate auxiliary power when hotelling. In newer methodologies, propulsion engine
load factors are calculated directly from the propeller curve based upon the cube of actual
speed divided by maximum speed (at 100% maximum continuous rating [MCR]) or:
Load Factor = (actual speed/maximum speed)3 (2)
Since cruise speed, which is listed in the Lloyds data, is estimated at 94 percent of maximum
speed6, the load factor at cruise is 0.83. While this is very close to the 0.8 used previously in
the inventory development, auxiliary engines are not included in the new calculations but
handled separately.
In Starcrest's two most recent inventories, they found that load factors as low as 2 percent were
possible.6'9 These lower factors are possible, because ships often cycle their propulsion engine
on and off during maneuvering to reduce speeds below the dead slow setting of approximately
5.8 knots. In fact, during its vessel boarding program at the Port of Los Angeles, Starcrest
found container ships had engines stopped 25 to 50 percent of their time during maneuvering
near dock. While load factors should be calculated using the above propeller law for each call,
load factors below 2 percent were set to 2 percent as a minimum.
2.3.2. Treatment of A uxiliary Engines
As stated above, auxiliary engines were combined with propulsion engines in the previous
inventory. In the methodology used in this analysis, auxiliary engine maximum continuous
rating power and load factors were calculated separately from propulsion engines and different
emission factors applied. Most propulsion engines are considered slow speed Category 3
engines, while most auxiliary engines are considered medium speed Category 2 engines. Since
hotelling emissions are a large part of port inventories, it is important to distinguish propulsion
engine emissions from auxiliary engine emissions. All auxiliary engines were treated as
Category 2 MSD engines for purposes of this analysis.
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In 2005, California Air Resources Board (ARE) conducted an Oceangoing Ship Survey of 327
ships in January 2005.15 Table 2-3 shows average auxiliary engine power compared to
propulsion power obtained from the ARE survey and other sources. While it would be more
accurate to determine proper ratios for each port, these ratios were used in this analysis to
determine auxiliary power from propulsion power.
Table 2-3. Auxiliary Engine Power Ratios (ARB Survey, except as noted)
Ship Type
Auto Carrier
Bulk Carrier
Container Ship
Passenger Ship3
General Cargo
Miscellaneous13
RORO
Reefer
Tanker
Average
Propulsion
Engine (kW)
10,700
8,000
30,900
39,600
9,300
6,250
11,000
9,600
9,400
Average Auxiliary Engines
Number
2.9
2.9
3.6
4.7
2.9
2.9
2.9
4.0
2.7
Power
Each
(kW)
983
612
1,889
2,340
612
580
983
975
735
Total
Power
(kW)
2,850
1,776
6,800
11,000
1,776
1,680
2,850
3,900
1,985
Engine
Speed
Medium
Medium
Medium
Medium
Medium
Medium
Medium
Medium
Medium
Auxiliary
to
Propulsion
Ratio
0.266
0.222
0.220
0.278
0.191
0.269
0.259
0.406
0.211
a Passenger ships typically use a different engine configuration known as diesel-electric. These vessels use large
generator sets for both propulsion and ship-board electricity. The figures for passenger ships above are estimates
taken from the Starcrest Vessel Boarding Program.
b Miscellaneous ship types were not provided in the ARB methodology, so values from the Starcrest Vessel Boarding
Program were used.
Load factors for auxiliary engines vary by ship type and operating mode. It was previously
thought that power generation was provided by propulsion engines in all modes but hotelling.
Starcrest's Vessel Boarding Program6 showed that auxiliary engines are on all of the time, with
the largest loads during hotelling (except when cold ironing16). Table 2-4 shows the auxiliary
engine load factors determined by Starcrest, through interviews conducted with ship captains,
chief engineers, and pilots during its vessel boarding programs. Auxiliary load factors should be
used in conjunction with total auxiliary power. Auxiliary load factors listed in Table 2-4 are used
together with the total auxiliary engine power (determined from total propulsion power and the
ratios from Table 2-3) to calculate auxiliary engine emissions.
16
' California Air Resources Board, 2005 Oceangoing Ship Survey, Summary of Results, September 2005.
Cold ironing is a process where shore power is provided to a vessel, allowing it to shut down its auxiliary
generators.
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Table 2-4. Auxiliary Engine Load Factor Assumptions
Ship-Type
Auto Carrier
Bulk Carrier
Container Ship
Passenger Ship
General Cargo
Miscellaneous
RORO
Reefer
Tanker
Cruise
0.13
0.17
0.13
0.80
0.17
0.17
0.15
0.20
0.13
RSZ
0.30
0.27
0.25
0.80
0.27
0.27
0.30
0.34
0.27
Maneuver
0.67
0.45
0.50
0.80
0.45
0.45
0.45
0.67
0.45
Hotel
0.24
0.22
0.17
0.64
0.22
0.22
0.30
0.34
0.67
2.3.3. Consistent Fuel Sulfur L e vels
In the previous inventory development for Category 3 rulemaking, an average sulfur level of 3%
sulfur was used for propulsion engines, and an average fuel sulfur level of 0.33% was used for
auxiliary engines. It was assumed that while at dock, auxiliary engines were fueled on EPA
non-road diesel fuel. The ARE survey15, however, found the average fuel sulfur level for
residual oil (RO) to be 2.5 percent and the average fuel sulfur level for distillate to be 0.5
percent. One suspects these are more common for West Coast ports than nationwide ports and
a more reasonable value for the rest of the country should be 2.7 percent for RO. Generally,
distillate fuels are described as marine diesel oil (MDO) and marine gas oil (MGO). MDO has
been shown to have sulfur levels from zero to 2.28 percent.17 For purposes of this analysis, a
sulfur content of 1.5 percent. MGO is estimated here as having a sulfur content of 0.5 percent
based upon the ARE survey and also consistent with Entec. Sulfur levels in other areas of the
world can be significantly higher for RO. For purposes of the U.S. inventory of ports, the sulfur
levels listed in Table 2-5 were used for marine fuels.
Table 2-5. Assumed sulfur levels in marine diesel fuels for U.S. Inventories
Fuel
Fuel Sulfur Levels
West Coast
RO 2.5%
MDO 1 .5%
MGO 0.5%
Other Areas
2.7%
1 .5%
0.5%
The ARE survey also found that almost all ships used RO in their main propulsion engines, and
that only 29 percent of all ships (except passenger ships) used distillate in their auxiliary
engines, with the remaining 71 percent using RO. However, only 8 percent of passenger ships
used distillate in their auxiliary engines, while the other 92 percent used RO. We used the
ICF Consulting Group, In-Use Marine Diesel Fuel, EPA report No, EPA420-R-99-027, August 1999.
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results of this survey as reasonable approximations for calculations of emission factors.
However, their accuracy for years other than those of the ARE survey may be affected by fuel
prices, since as fuel prices increase, more ships will use RO in their auxiliary engines.
2.3.4. Propulsion and A uxiliary Engine Emission Factors
The emission factors used previously for Category 3 engine rulemaking came from an analysis
of Lloyds Marine Exhaust Emissions Research Programme and Environment Canada ship
emission data. The most recent analysis of emission data was published in 2002 by Entec20..
The factors from this study are generally accepted as the most current set available. The Entec
analysis included emissions data from 142 propulsion engines and 2 of the most recent
research programs: Lloyd's Register Engineering Services in 1995 and IVL Swedish
Environmental Research Institute in 2002. The resulting Entec emission factors include
individual factors for three speeds of diesel engines (slow-speed diesel (SSD), medium-speed
diesel (MSD), and high-speed diesel (HSD)), steam turbines (ST), gas turbines (GT), and three
types of fuel (RO, MDO and MGO). Table 2-6 lists the propulsion engine emission factors for
NOx and HC that were used for the 2002 port inventory development, based on the Entec study
using RO and other data sources as discussed below. The CO, PM, SO2 and CO2 emission
factors shown in the table come from other data sources as explained below.
Table 2-6. Emission Factors for OGV Main Engines using RO, g/kWh
Engine
SSD
MSD
ST
GT
All Ports
NOx
18.1
14.0
2.1
6.1
CO
1.40
1.10
0.20
0.20
HC
0.60
0.50
0.10
0.10
CO2
620.62
668.36
970.71
970.71
West Coast Ports
PM10
1.4
1.4
1.4
1.4
PM2.5
1.3
1.3
1.3
1.3
SO2
9.53
10.26
14.91
14.91
Other Ports
PM10
1.4
1.4
1.5
1.5
PM2.5
1.3
1.3
1.4
1.4
SO2
10.29
11.09
16.10
16.10
CO emission factors were developed from information provided in the Entec appendices
because they are not explicitly stated in the text. They were confirmed with IVS Swedish
Environmental Research Institute Ltd. Entec also does not list PM factors for either PM10 or
PM2.5. The PM10 to PM2.5 conversion factor used here is 0.92. While the NONROAD model
uses 0.97 for such conversion based upon low sulfur fuels, a higher value of 0.80 was
suggested in a report from the Journal of Aerosol Science21. A reasonable value seems to be
closer to 0.92 because higher sulfur fuels in medium and slow speed engines would tend to
produce larger particulates than high speed engines on low sulfur fuels.
Lloyds, Marine Exhaust Emissions Research Programme, Lloyds Register Engineering Services, Croyden, Lloyds
Register of Shipping, London, 1995.
19 Environment Canada, Port of Vancouver Marine Vessel Emissions Test Program, Final Report, ERMD Report #97-
04, 1997.
20 Entec UK Limited, Quantification of Emissions from Ships Associated with Ship Movements between Ports in the
European Community, prepared for the European Commission, July 2002.
21 Lyyranen, J., Jokiniemi, J., Kauppinen, E. and Joutsensaari, J., Aerosol characterisation in medium-speed diesel
engines operating with heavy fuel oils, published in the Journal of Aerosol Science, Vol. 30., No. 6. pp. 771-784,
1999.
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PM10 values were determined by EPA based on existing engine test data in consultation with
ARE.22 GT PM10 emission factors were not part of the EPA analysis but assumed here to be
equivalent to ST PM10 emission factors. SO2 emission factors were based upon a fuel sulfur to
SO2 conversion factor from ENVIRON.23 Emission factors for SO2 emissions were calculated
using the below formula assuming that 97.753% of the fuel sulfur was converted to SO2 and
taking into account the molecular weight difference between SO2 and sulfur (molecular weight 2
times sulfur). Brake specific fuel consumption (BSFC) used for SSDs was 195 g/kWh, while
BSFC used for MSDs was 210 g/kWh based upon Lloyds 199518. BSFC used for STs and GTs
was 305 g/kWh based upon Entec.20
SO2 EF = BSFC x 2 x 0.97753 x Fuel Sulfur Fraction (3)
CO2 emission factors were calculated from the BSFC assuming a fuel carbon content of 86.7
percent by weight20 and a ratio of molecular weights of CO2 and C at 3.667.
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 BSFC tends to increase. Thus, while mass
emissions (grams per hour) decrease with low loads, the engine power tends to decrease more
quickly, thereby increasing the emission factor (grams per engine power) as load decreases.
Energy and Environmental Analysis Inc. (EEA) demonstrated this effect in a study prepared for
EPA in 2000.24 In the EEA report, various equations have been developed for the various
emissions. The low-load emission factor adjustment factors were developed based upon the
concept that the BSFC increases as load decreases below about 20 percent load. For fuel
consumption, EEA developed the following equation:
Fuel Consumption (g/kWh) = 14.1205 (1/Fractional Load) + 205.7169 (4)
In addition, based upon test data, they developed algorithms to calculate emission factors at
reduced load. These equations are noted below:
Emission Rate (g/kWh) = a (Fractional Load)"x + b (5)
For SO2 emissions, however, EEA developed a slightly different equation:
Emission Rate (g/kWh) = a (Fuel Consumption x Fuel Sulfur Fraction) + b (6)
The coefficients for the above equations are given in Table 2-7 below.
Table 2-7. Emission Factor Algorithm Coefficients for OGV Main Engines using RO
Coefficient
a
x
b
NOx
0.1255
1.5
10.4496
HC
0.0667
1.5
0.3859
CO
0.8378
1.0
0.1548
PM
0.0059
1.5
0.2551
S02
2.3735
n/a
-0.4792
C02
44.1
1.0
648.6
22 Email between Barry Garelick of EPA and Dongmin Luo of ARB, May 10, 2007.
23 Memo from Chris Lindhjem of ENVIRON, PM Emission Factors, December 15, 2005.
24 Energy and Environmental Analysis Inc., Analysis of Commercial Marine Vessels Emissions and Fuel Consumption
Data, EPA420-R-00-002, February 2000.
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Using these algorithms, fuel consumption and emission factors versus load were calculated and
normalizing these emission factors to 20% load, the low-load multiplicative adjustment factors
presented in Table 2-8 are calculated. SO2 adjustment factors were calculated using 2.7%
sulfur. The SO2 multiplicative adjustment factors at 2.5 percent sulfur are not significantly
different.
Table 2-8. Calculated Low Load Multiplicative Adjustment Factors
| Load
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
11%
12%
13%
14%
15%
16%
17%
18%
19%
20%
NOx
11.47
4.63
2.92
2.21
1.83
1.60
1.45
1.35
1.27
1.22
1.17
1.14
1.11
1.08
1.06
1.05
1.03
1.02
1.01
1.00
HC
59.28
21.18
11.68
7.71
5.61
4.35
3.52
2.95
2.52
2.20
1.96
1.76
1.60
1.47
1.36
1.26
1.18
1.11
1.05
1.00
CO
19.32
9.68
6.46
4.86
3.89
3.25
2.79
2.45
2.18
1.96
1.79
1.64
1.52
1.41
1.32
1.24
1.17
1.11
1.05
1.00
PM
19.17
7.29
4.33
3.09
2.44
2.04
1.79
1.61
1.48
1.38
1.30
1.24
1.19
1.15
1.11
1.08
1.06
1.04
1.02
1.00
S02
5.99
3.36
2.49
2.05
1.79
1.61
1.49
1.39
1.32
1.26
1.21
1.18
1.14
1.11
1.09
1.07
1.05
1.03
1.01
1.00
C02
5.82
3.28
2.44
2.01
1.76
1.59
1.47
1.38
1.31
1.25
1.21
1.17
1.14
1.11
1.08
1.06
1.04
1.03
1.01
1.00
As with propulsion engines, the most current set of auxiliary engine emission factors comes
from Entec except as noted below. Table 2-9 provides these auxiliary engine emission factors.
There is no need for a low load adjustment factor for auxiliary engines, because they are
generally operated in banks. When only low loads are needed, one or more engines are shut
off, allowing the remaining engines to operate at a more efficient level.
Table 2-9. Auxiliary Engine Emission Factors, g/kWh
Engine
Fuel
All Ports
NOx
CO
HC
CO2
West Coast Ports
PM10
PM2.5
SO2
Other Ports
PM10
PM2.5
SO2
MSD
RO
MDO
14.70
13.90
1.10
1.10
0.40
0.40
668.36
668.36
1.4
0.6
1.3
0.55
10.26
6.16
1.4
0.6
1.3
0.55
11.09
6.16
It should be noted that Entec used 2.7 percent fuel sulfur content for RO, and 1.0 percent for
MDO. Therefore, SO2 emission factors were recalculated based upon the sulfur levels listed in
Table 2-5 and the methodology suggested by ENVIRON23 while PM emission were determined
by EPA based on existing engine test data in consultation with ARE.22
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Using the ratios of RO versus MDO use determined by the ARE study15 as described in Section
2.3.3 together with the emission factors shown in Table 2-9, the auxiliary engine emission factor
averages by ship type are listed in Table 2-10.
Table 2-10. Auxiliary Engine Emission Factors for use in this study, g/kWh
Ship Type
All Ports
NOx
Passenger 14.64
Others 14.47
CO
1.10
1.10
HC
0.40
0.40
CO2
West Coast Ports
PM10
668.36 1.3
668.36 1.1
PM2.5
1.2
1.0
SO2
Other Ports
PM10
9.93 1.4
9.07 1.2
PM2.5
1.3
1.1
SO2
10.70
9.66
2.3.5. Treatment of Electric-Drive Ships
Many passenger ships and tankers have either diesel-electric or gas turbine-electric engine that
are used for both propulsion and auxiliary purposes. Lloyds clearly calls out these types of
engines in their database and that information was used to distinguish them from direct and
geared drive systems. Generally the power Lloyds lists is the total power. To separate out
propulsion from auxiliary power for purposes of calculating emissions, the total power listed in
the Lloyds data was divided by 1 plus the ratio of auxiliary to propulsion power given in Table
2-3 to give the propulsion power portion and the remain portion was considered auxiliary engine
power. In addition, no low load adjustment factor was applied to diesel and gas turbine electric
engines for loads below 20% MCR because several engines are used to generate power, and
some can be shut down to allow others to operate at a more efficient setting.
2.3.6. Maneuvering Assumptions
Maneuvering time-in-mode is estimated based on the distance a ship travels from the
breakwater or port entrance to the pier/wharf/dock (PWD). Average maneuvering speeds vary
from 3 to 8 knots depending on direction and ship type. For consistency, maneuvering speeds
were assumed to be the vessel stall speed or 5.8 knots. Previous calculations assumed the
maneuvering speed was 4 knots which is below the stall speed of most ships. Maneuvering
times also included shifts from one PWD to another or from one port within a greater port area
to another.
2.3.7. Re mo val of Category 1 and 2 Ships
Since the SECA inventories were intended to cover Category 3 propulsion engine ships only,
the Category 1 and 2 ships in the typical and modeled port inventories needed to be eliminated.
To accomplish this task, all ship calls were matched with Lloyd's Data produced by Lloyd's
Register-Fairplay Ltd.25 Over 99.9% of the calls in the entrances and clearances data were
directly matched with Lloyd's data. The remaining 0.1% was estimated based upon ships of
similar type and size.
Lloyds Register Fairplay, Internet Ship Register, available at http://www.ships-reqister.com/.
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Engine category was determined from engine make and model. Engine bore and stroke were
found in the Marine Engine 2005 Guide26 and displacement per cylinder was calculated. Ships
with Category 1 or 2 propulsion engines were eliminated from the data.
2.4. Revised Typical Port Inventories
The typical port inventories discussed in Section 2.2 were recalculated using the updated
methodology discussed in Section 2.3. The revised typical port inventories are discussed
below. In addition, revised typical port inventories are compared against those developed by
ENVIRON in 2002.4
2.4.1. Lo wer Mississippi River Ports
The lower Mississippi River ports include the Port of Plaquemines, the Port of New Orleans, the
Port of South Louisiana, and the Port of Baton Rouge. Table 2-11 shows the revised emissions
profiles by ship type. Table 2-12 shows the revised emission profiles by mode. The previous
inventory emission levels for this port are different due to elimination of Category 1 and 2 ships
and new emission and load factors.
Table 2-11. Lower Mississippi Port Emissions by Ship Type for 1996
Ship Type
Auto Carrier
Barge Carrier
Bulk Carrier
Container
General Cargo
Miscellaneous
Passenger
Reefer
RORO
Tanker
Ocean Going Tug
Total
Installed
Power
(kW)
36
819
26,315
5,843
4,822
96
2,947
112
1,115
13,642
32
55,780
Metric Tonnes per Year
NOx
9
123
7,580
489
1,022
35
531
75
172
4,715
7
14,757
PM10
1
13
616
52
84
3
55
6
15
385
1
1,231
PM2.5
1
12
568
48
78
3
51
6
14
355
1
1,136
HC
0
4
239
20
34
1
18
2
7
147
0
473
CO
1
10
592
44
82
3
45
6
15
367
1
1,165
SOx
6
106
4,742
410
645
24
421
49
110
2,978
5
9,496
Fuel
116
2,168
96,975
8,119
12,929
490
8,149
1,054
2,218
61,008
96
193,321
Table 2-12. Lower Mississippi Port Emissions by Mode for 1996
nil -j
Mode
Cruise
Reduced Speed Zone
Maneuver
Hotel
Metric Tonnes per Year
MOx PM10
2,599 216
5,631 465
398 39
6,130 510
Total 14,757 1,231
Prior Inventory 21,051 1,213
PM2.5
201
431
36
468
1,136
1,116
HC
86
190
27
169
473
396
CO
201
449
48
466
1,165
2,322
SOx
1,642
3,500
251
4,103
9,496
8,385
Fuel
31,284
68,034
5,052
88,951
193,321
~
26
Nexus Media Communications, The Motor Ship's Guide to Marine Diesel Engines 2005, available at
http://www.motorship.com/
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2.4.2. Ports of New York and New Jersey
The consolidated ports of New York, New Jersey and Albany are included in this typical port.
Emissions by ship type are shown in Table 2-13, while emissions by mode are shown in Table
2-14. Again the prior inventory levels are higher generally because of the inclusion of Category
1 and 2 ships.
Table 2-13. New York/New Jersey Emissions by Ship Type for 1996
Ship Type
Auto Carrier
Barge Carrier
Bulk Carrier
Container
General Cargo
Miscellaneous
Passenger
Reefer
RORO
Tanker
Ocean Going Tug
Total
Installed
Power
(kW)
3,108
143
3,113
40,407
2,855
8
6,061
795
3,304
10,908
1,324
72,027
Metric Tonnes per Year
NOx
253
3
549
2,303
274
3
485
98
315
2,014
138
6,436
PM10
22
1
46
210
23
0
55
8
26
176
13
580
PM2.5
20
1
42
195
22
0
51
8
24
162
12
536
HC
10
0
18
92
10
0
18
4
12
69
5
240
CO
22
0
45
201
23
0
40
8
27
167
12
546
SOx
159
7
355
1,593
175
2
408
62
194
1,369
103
4,426
Fuel
3,192
137
7,287
31,468
3,497
40
8,008
1,286
3,936
28,485
2,074
89,409
Table 2-14. New York/New Jersey Emissions by Mode for 1996
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes per Year
NOx
2,535
994
388
2,518
6,436
7,933
PM10
229
100
41
211
580
621
PM2.5
212
92
38
193
536
572
HC
85
57
29
70
240
171
CO
197
113
45
191
546
850
SOx
1,799
689
248
1,691
4,426
4,493
Fuel
34,254
13,668
4,971
36,516
89,409
2.4.3. Del a ware River Ports
The Delaware River Ports include the ports of Philadelphia Harbor, PA; Marcus Hook, PA;
Paulsboro, NJ; New Castle, DE; Camden, NJ; Wilmington, DE; Chester, PA; Trenton,
NJ; and Penn Manor, PA. Emissions by ship type are shown in Table 2-15, while emissions
by mode are shown in Table 2-16. Again the prior inventory levels are higher generally because
of the inclusion of Category 1 and 2 ships.
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Table 2-15. Delaware River Emissions by Ship Type for 1996
Ship Type
Auto Carrier
Bulk Carrier
Container
General Cargo
Miscellaneous
Passenger
Reefer
RORO
Tanker
Ocean Going Tug
Total
Installed
Power
(kW)
723
3,201
4,712
2,093
10
551
2,491
287
9,501
157
23,726
Metric Tonnes per Year
NOx
81
627
493
346
2
75
558
48
2,944
22
5,197
PM10
7
51
42
29
0
9
46
4
251
2
440
PM2.5
6
47
39
27
0
8
43
4
231
2
405
HC
3
20
18
12
0
2
19
2
91
1
167
CO
7
50
41
28
0
6
46
4
232
2
415
SOx
103
1,856
889
678
2
71
358
33
6,048
16
10,054
Fuel
1,017
7,872
6,189
4,450
31
1,380
7,505
663
41,386
324
70,818
Table 2-16. Delaware River Emissions by Mode for 1996
Mode
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes per Year
NOx
1,046
1,289
144
2,718
5,197
5,701
PM10
88
111
14
226
440
366
PM2.5
82
103
13
207
405
336
HC
35
47
10
75
167
112
CO
81
110
17
207
415
611
SOx
678
7,467
92
1,817
10,054
2,574
Fuel
12,915
16,600
1,865
39,439
70,818
-
2.4.4. Puget Sound Ports
Puget Sound ports include the ports of Seattle Harbor, WA; Tacoma Harbor, WA; Anacortes
Harbor, WA; Everett Harbor, WA; Port Angeles Harbor, WA; Grays Harbor, WA; Bellingham Bay
and Harbor, WA; and Olympia Harbor, WA. Emissions by ship type are shown in Table 2-17,
while emissions by mode are shown in Table 2-18.
In reviewing the current Starcrest inventory for Puget Sound ports8, a significant difference in
RSZ emissions was noticed. This was because in the detailed port inventory developed as part
of the deep sea commercial marine guidance document2, an RSZ speed of 15 knots was
assumed through the Strait of Juan de Fuca and 12 knots from pilot pick-up at Port Angeles to
the final destination port. According to Captain McKerty of the Puget Sound Pilots27, ships enter
the Strait of Juan de Fuca at service speed and continue at service speed until they reach Port
Angeles where the pilot boards. All ships except tankers continue at service speed or 20 knots,
whichever is less, until they are about 12 nautical miles from port. At that point they begin
slowing to maneuvering speed. Tankers on the other hand travel at service speed to Port
Angeles and then travel at 12 knots until 12 nautical miles before the port. At that point they
slow from 12 knots to maneuvering speed. This new information was used to calculate RSZ
27
Conversation with Capt McKerty of the Puget Sound Pilots, May 31, 2007.
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speeds, load factors and times for all Puget Sound ports and thus resulted in higher emissions
than the prior inventory.
Table 2-17. Puget Sound Emissions by Ship Type for 1996
Ship Type
Auto Carrier
Bulk Carrier
Container
General Cargo
Miscellaneous
Passenger
Reefer
RORO
Tanker
Total
Installed
Power
(kW)
1,478
195
29,932
18
273
47
254
2,864
7,221
42,281
Metric Tonnes per Year
NOx
488
2,193
6,409
950
79
77
154
379
1,729
12,459
PM10
41
179
605
80
7
7
12
70
203
1,206
PM2.5
37
162
543
72
7
7
11
63
182
1,083
HC
18
75
251
34
3
3
5
14
56
459
CO
39
175
520
76
6
6
12
31
137
1,003
SOx
279
1,269
4,241
550
53
55
91
611
1,701
8,851
Fuel
5,882
26,993
88,834
11,550
1,123
1,159
2,024
12,810
36,642
187,017
Table 2-18. Puget Sound Emissions by Mode for 1996
Mode
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes per Year
NOx
1,537
7,118
1,676
2,128
12,459
10,137
PM10
160
690
193
162
1,206
1,030
PM2.5
149
640
147
147
1,083
947
HC
54
237
109
59
459
210
CO
121
554
166
162
1,003
894
SOx
1,222
5,366
927
1,335
8,851
7,721
Fuel
25,113
110,874
20,158
30,872
187,017
-
2.4.5. Corpus Christ!
Emissions for the Port of Corpus Christi, TX by ship type are shown in Table 2-19, while
emissions by mode are shown in Table 2-20. In this case, the inventory is different from the
prior one due to the change in emission factors.
Table 2-19. Corpus Christi Emissions by Ship Type for 1996
Ship Type
Barge Carrier
Bulk Carrier
Container
General Cargo
Tanker
Total
Installed
Power
(kW)
47
1,962
50
117
13,042
15,217
Metric Tonnes per Year
NOx
3
267
4
13
1,702
1,989
PM10
0
22
0
1
171
195
PM2.5
0
20
0
1
158
180
HC
0
9
0
0
55
65
CO
0
71
1
4
343
419
SOx
4
163
2
9
1,422
1,600
Fuel
84
3,240
50
178
28,681
32,233
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Table 2-20. Corpus Christi Emissions by Mode for 1996
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes per Year
NOx
552
430
113
893
1,989
1,689
PM10
61
47
13
74
195
190
PM2.5
57
43
12
68
180
174
HC
19
14
7
25
65
40
CO
43
295
13
68
419
173
SOx
522
396
85
596
1,600
1,427
Fuel
9,924
7,632
1,712
12,966
32,233
-
2.4.6. Houston
Emissions for the Houston/Galveston Area Ports by ship type are shown in Table 2-21, while
emissions by mode are shown in Table 2-22. The Houston/Galveston area port emissions in
the prior inventory were estimated from the Starcrest inventory, however, it did not include
cruise emissions and the emission factors used in that inventory were considerably lower than
used in the analysis done for this report. The emissions from the Starcrest inventory were
adjusted as per the methodology used for the other typical ports.
Table 2-21. Houston/Galveston Emissions by Ship Type for 1997
Ship Type
Bulk Carrier
Container
General Cargo
Miscellaneous
Tanker
Total
Installed
Power
(kW)
7,697
11,747
15,101
2,906
63,626
101,077
Metric Tonnes per Year
NOx
1,055
780
1,840
351
8,777
12,803
PM10
102
67
179
36
1,154
1,539
PM2.5
86
62
162
32
1,063
1,405
HC
36
26
62
12
300
435
CO
78
58
137
25
669
968
SOx
706
504
1,344
272
9,003
11,828
Fuel
12,388
8,932
22,751
4,715
110,429
159,216
Table 2-22. Houston/Galveston Emissions by Mode for 1997
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes per Year
NOx
5,089
3,158
392
4,164
12,803
7,992
PM10
405
273
107
753
1,539
520
PM2.5
376
253
86
690
1,405
478
HC
169
106
13
147
435
190
CO
394
240
29
305
968
787
SOx
3,016
2,057
695
6,061
11,828
3,118
Fuel
57,383
36,564
4,842
60,427
159,216
~
2.4.7. Tampa
Emissions for the Port of Tampa, FL by ship type are shown in Table 2-23, while emissions by
mode are shown in Table 2-24. The prior inventory levels are higher generally because of the
inclusion of Category 1 and 2 ships.
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Table 2-23. Tampa Emissions by Ship Type for 1996
Installed
Ship Type Power
(kW)
Auto Carrier 17
Bulk Carrier 3,864
Container 87
General Cargo 798
Miscellaneous 4
Passenger 2,421
Reefer 874
RORO 23
Tanker 3,695
Ocean Going Tug 557
Total 12,340
Metric Tonnes per Year
NOx PM10 PM2.5 HC CO
10000
584 48 43 19 47
81101
83 8 7 37
10000
258 24 18 9 22
133 11 9 4 11
71101
434 43 39 14 35
76 7 6 36
1,585 142 123 53 129
SOx
1
360
5
60
0
183
88
5
354
55
1,111
Fuel
18
7,392
100
1,196
10
3,379
1,740
101
7,271
1,127
22,334
Table 2-24. Tampa Emissions by Mode for 1996
Mode N(
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total 1
Prior Inventory 1
Metric Tonnes per Year
Dx PM10 PM2.5 HC CO
558 51 47 19 44
284 27 17 11 25
73 875 8
670 56 52 18 51
,585 142 123 53 129
,864 127 117 37 219
SOx
402
160
93
456
1,111
895
Fuel
7,654
3,994
985
9,701
22,334
-
2.4.8. Baltimore
Emissions for the Port of Baltimore, MD by ship type are shown in Table 2-25, while emissions
by mode are shown in Table 2-26. Again the prior inventory levels are higher generally because
of the inclusion of Category 1 and 2 ships.
Table 2-25. Baltimore Emissions by Ship Type for 1996
Ship Type
Auto Carrier
Bulk Carrier
Container
General Cargo
Miscellaneous
Passenger
Reefer
RORO
Tanker
Ocean Going Tug
Total
Installed
Power
(kW)
3,447
4,458
10,970
1,713
19
327
19
2,478
1,140
290
24,860
Metric Tonnes
NOx
636
1,625
1,527
456
10
93
24
586
367
61
5,386
PM10
52
134
123
38
1
10
2
46
31
6
442
PM2.5
48
124
114
35
1
9
2
42
29
5
409
HC
21
53
53
15
0
3
1
19
12
2
179
per Year
CO
50
126
122
36
1
7
2
46
29
5
423
SOx
391
1,021
912
291
8
81
16
351
252
47
3,369
Fuel
7,704
20,030
17,808
5,705
153
1,569
340
6,908
4,964
907
66,088
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Table 2-26. Baltimore Emissions by Mode for 1996
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes
NOx
1,015
3,438
120
812
5,386
6,753
PM10
83
281
12
66
442
481
PM2.5
77
261
11
60
409
442
HC
34
114
9
22
179
145
per Year
CO
79
268
14
62
423
532
SOx
623
2,128
71
546
3,369
3,487
Fuel
11,876
40,967
1,455
11,790
66,088
-
2.4.9. Coos Bay
Emissions for the Port of Coos Bay, OR by ship type are shown in Table 2-27, while emissions
by mode are shown in Table 2-28. Again the prior inventory levels are higher generally because
of the inclusion of Category 1 and 2 ships.
Table 2-27. Coos Bay Emissions by Ship Type for 1996
Ship Type
Installed
Power
(kW)
Metric Tonnes per Year
NOx PM10
Bulk Carrier 752 87 7
General Cargo 755 77 6
Miscellaneous 300
PM2.5 HC
6 3
6 3
0 0
CO
7
6
0
SOx
51
45
0
Fuel
1,088
948
6
Total
1,510 165
13
12
14
96 2,043
Table 2-28. Coos Bay Emissions by Mode for 1996
nil -j
Mode
NOx
Metric Tonnes per Year
PM10 PM2.5
Cruise 78 6 6
Reduced Speed Zone 13 1 1
Maneuver
300
Hotel 71 5 5
Total 165 13 12
Prior Inventory 260 13 12
HC
3
1
0
2
6
5
CO
6
2
0
5
14
33
SOx
42
8
2
45
96
81
Fuel
873
103
33
1,033
2,043
~
2.4.10. Cleveland
Emissions for the Port of Cleveland, OH by ship type are shown in Table 2-29, while emissions
by mode are shown in Table 2-30. Generally a significant amount of Great Lake traffic is
Category 1 and 2 ships and these have been eliminated in the new inventory.
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Table 2-29. Cleveland Emissions by Ship Type for 2005
Ship Type
Installed
Power
(kW)
Metric Tonnes per Year
NOx PM10
Bulk Carrier 434 29 2
Self-Unloading Bulk Carrier 612 9 2
General Cargo 90 6 0
PM2.5
2
1
0
HC
1
0
0
CO
2
1
0
SOx
18
14
4
Fuel
1,219
896
248
Total
1,136
43
36 2,363
Table 2-30. Cleveland Emissions by Mode for 2005
[
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes per Year
\IOx PM10
11 1
2 0
4 0
27 2
43 4
220 15
PM2.5
1
0
0
2
4
13
HC
0
0
0
1
1
8
CO
1
0
0
2
3
33
SOx
13
3
3
18
36
101
Fuel
759
162
200
1,242
2,363
-
2.4.11. Duluth-Superior
Emissions for the Port of Duluth-Superior, MN & Wl by ship type are shown in Table 2-31, while
emissions by mode are shown in Table 2-32. Generally a significant amount of Great Lake
traffic is Category 1 and 2 ships and these have been eliminated in the new inventory.
Table 2-31. Duluth-Superior Emissions by Ship Type for 2005
Installed
Ship Type Power ..„
(kW) N0
Bulk Carrier 777
Self-Unloading Bulk Carrier 4,289 1
General Cargo 175
Tanker 3
Integrated Tug-Barge 60
Metric Tonnes per Year
x PM10 PM2.5 HC
45 4 4 1
13 12 11 4
7110
0000
2000
Total 5,305 167 17 15 6
CO
4
9
1
0
0
14
SOx
30
98
5
0
1
135
Fuel
2,001
6,248
325
16
77
8,668
Table 2-32. Duluth-Superior Emissions by Mode for 2005
Mode
Cruise
Reduced Speed Zone
Maneuver
Hotel
Total
Prior Inventory
Metric Tonnes per Year
NOx PM10
58 7
13 1
23 3
73 6
167 17
220 15
PM2.5 HC
6 2
1 0
2 1
6 2
15 6
13 8
CO
5
1
3
6
14
33
SOx
55
11
20
49
135
101
Fuel
3,315
707
1,257
3,388
8,668
~
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2.5. Updated Modeled Port Methodology
In order to extrapolate the detailed port inventory data28 from the 11 typical ports to the 117
modeled ports with a base year of 2002, a new methodology needed to be used. ENVIRON4
originally used the streamlined method discussed in the two EPA guidance documents2'3 to
extrapolate the detailed data from the original 10 typical ports to the modeled ports29. As
discussed above, the modeled port was first matched to a typical port and then the emissions by
ship type were determined through a ratio of ship trips between the modeled port and the typical
port along with RSZ differences. However, there are several problems with that methodology
which made it less desirable for this application. They include:
1. The number of ship trips by ship type does not differentiate between Category 3 ships,
which are the ships of interest for this work, and other Category ships.
2. The number of ship trips also does not account for changes in propulsion power
between ports and over the six years between most typical port inventories and 2002.
This is very important in obtaining an accurate picture of emissions at different ports
several years later.
3. Ship speed has also increased over the last six years thereby changing the time in mode
at cruise as well as the load factors at lower speeds.
A more accurate method is to use a method similar to the mid-tier methodology described in the
Best Practices and Current Methodology document30. This uses average ship characteristics as
well as calls at the port to be modeled to estimate emissions using average ship emissions per
call and ship type from a detailed typical port that is most like the port to be modeled. In the
methodology used for this analysis, U.S. Army Corps of Engineers (USAGE) entrance and
clearance data for 200231 together with from Lloyd's data25 were used to calculate average ship
characteristics and calls by ship type for the port to be modeled and then use a detailed typical
port which is most like the port to be modeled to estimate emissions per call based upon the
ratio of average ship characteristics between the two ports. This methodology allows for
calculation of increased ship power and speeds as well as being able to determine which ships
have Category 3 propulsion engines and which do not. Further discussion of the methodology
follows.
2.5.1. Selection of Individual Ports to be Analyzed
All the deep sea and Great Lake ports in the Principal Ports of the United States dataset32 were
used as a starting point. These are the top US ports in terms of tonnage of cargo. Ports which
had no foreign traffic were eliminated because there is no information in the USAGE entrances
28 Most of the typical port data had an inventory year of 1996. However, Houston had an inventory year of 1997 and
the two Great Lake ports had an inventory year of 2005.
' A modeled port is the port in which emissions are to be e
emissions which is like the modeled port based upon a ratio of calls and RSZ information.
ICF Consulting, Best Practices and Current Methodologies in Preparing Port Emission Invt
Prepared for U.S. Environmental Protection Agency Sector Strategies Program, April 2006.
U.S. Army Corps of Engineers Navigation Data Center, Vess©
http://www.iwr.usace.army.mil/ndc/db/entclrn/data/entrclrn02/
U.S. Army Corps of Engineers Navigation Data Center, Print.
http://www.iwr.usace.armv.mil/ndc/db/pport/dbf/pport02.dbf.
29 A modeled port is the port in which emissions are to be estimated. Emissions are calculated from a typical port's
i
30 ICF Consulting, Best Practices and Current Methodologies in Preparing Port Emission Inventories, Final Report,
31 U.S. Army Corps of Engineers Navigation Data Center, Vessel Entrances and Clearances, 2002, available at
J
32 U.S. Army Corps of Engineers Navigation Data Center, Principal Ports of the United States, 2002, available at
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and clearances data about domestic traffic. (See Section 2.7 for a further discussion of domestic
traffic). In addition, U.S. Territory ports such as those in Puerto Rico were removed as these
were outside the area of interest for this study. Several California ports which the ARE provided
data and were not on the principal ports list were added to the list. This is discussed in Section
2.6.1. Also a conglomerate port in the Puget Sound area was added as discussed in Section
2.6.2.
2.5.2. Matching Typical Ports to Modeled Ports
The next step in the process was to match the ports to be modeled with the typical port which
was most like it. In the 2002 Environ report4, two criteria were used for matching a given port to
a typical port: regional differences33 and maximum vessel draft. A third consideration that was
taken into account is the ship types that call on a specific port. One container port, for instance,
may have much smaller bulk cargo and reefer ships call on that port than another. Using these
three criteria and the new port inventories that have been recently prepared and are suitable for
port matching, the 89 deep sea ports and 28 Great Lake ports were matched to the typical and
new ports which a detailed emission inventory was recalculated. For California ports, we used
data provided by ARE as discussed in Section 2.6.1.
In addition to which typical "like" port a modeled port was matched to, reduced speed zone
distance and speed were determined for each port. Originally this data came from either the
original Commercial Marine Activity guidance document2 for the ports within a typical port or
from the ENVIRON report4 based upon discussions with pilots. As discussed later in Section
4.3.2, the RSZ distance was modified by ENVIRON so that the end-point of the RSZ ended at a
proper place. ICF recalculated RSZ emissions based upon the new distances. ICF also
modified a few of the RSZ speeds based upon newer information obtained from conversations
with pilots. The final typical "like" port and the RSZ distance and speed are listed in Table 2-33.
The speeds that were updated due to recent conversations with pilots are bolded in Table 2-33.
Speed notes are provided in Table 2-34 . Region abbreviations are defined in Table 2-35.
Table 2-33. Matched ports and Reduced Speed Zone Information
Modeled Port Name
Anacortes, WA
Barbers Point, HI
Everett, WA
Grays Harbor, WA
Honolulu, HI
Kalama, WA
Longview, WA
Olympia, WA
Port Angeles, WA
Portland, OR
Seattle, WA
Tacoma, WA
Typical Like Port
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
Puget Sound
RSZ
Distance
(nt mi)
108.3
5.1
123.3
4.9
10.0
68.2
67.3
185.9
65.0
105.1
133.3
150.5
RSZ
Speed
(knts)
a
10.0
a
a
10.0
b
b
a
a
b
a
a
Region
NP
SPH
NP
NP
SPH
SP
SP
NP
NP
SP
NP
NP
33
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.
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Table 2-33. Matched ports and Reduced Speed Zone Information (continued)
Port Name
Vancouver, WA
Valdez, AK
Other Puget Sound
Anchorage, AK
Coos Bay, OR
Hilo, HI
Kahului, HI
Nawiliwili, HI
Nikishka, AK
Beaumont, TX
Freeport, TX
Galveston, TX
Houston, TX
Port Arthur, TX
Texas City, TX
Corpus Christ!, TX
Lake Charles, LA
Mobile, AL
Brownsville, TX
Gulfport, MS
Manatee, FL
Matagorda Ship
Panama City, FL
Pascagoula, MS
Pensacola, FL
Tampa, FL
Everglades, FL
New Orleans, LA
Baton Rouge, LA
South Louisiana, LA
Plaquemines, LA
Albany, NY
New York/New Jersey
Portland, ME
Georgetown, SC
Hopewell, VA
Marcus Hook, PA
Morehead City, NC
Paulsboro, NJ
Chester, PA
Fall River, MA
New Castle, DE
Penn Manor, PA
Typical Like Port
Puget Sound
Puget Sound
Puget Sound
Coos Bay
Coos Bay
Coos Bay
Coos Bay
Coos Bay
Coos Bay
Houston
Houston
Houston
Houston
Houston
Houston
Corpus Christ!
Corpus Christ!
Corpus Christ!
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Lower Mississippi
Lower Mississippi
Lower Mississippi
Lower Mississippi
New York/New Jersey
New York/New Jersey
New York/New Jersey
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
RSZ
Distance
(nt mi)
95.7
27.2
106.0
143.6
13.0
7.1
7.5
7.3
90.7
53.5
2.6
9.3
49.6
21.0
15.1
30.1
38.0
36.1
18.7
17.4
27.4
24.0
10.0
17.5
12.7
30.0
2.1
104.2
219.8
142.8
52.4
142.5
15.7
11.4
17.6
91.8
94.7
2.2
83.5
78.2
22.7
60.5
114.5
RSZ
Speed
(knts)
b
10.0
a
14.5
6.5
10.0
10.0
10.0
14.5
7.0
c
c
c
7.0
c
d
6.0
11.0
8.8
10.0
9.0
7.3
10.0
10.0
12.0
9.0
7.5
10.0
10.0
10.0
10.0
c
c
10.0
12.0
10.0
c
10.0
c
c
9.0
c
c
Region
NP
AE
NP
AE
SP
HI
HI
HI
AE
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
GC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
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Table 2-33. Matched ports and Reduced Speed Zone Information (continued)
Port Name
Providence, Rl
Brunswick, GA
Canaveral, FL
Charleston, SC
New Haven, CT
Palm Beach, FL
Bridgeport, CT
Camden, NJ
Philadelphia, PA
Wilmington, DE
Wilmington, NC
Richmond, VA
Jacksonville, FL
Miami, FL
Searsport, ME
Boston, MA
New Bedford/Fairhaven, MA
Baltimore, MD
Newport News, VA
Savannah, GA
Catalina, CA
Carquinez, CA
El Segundo, CA
Eureka, CA
Hueneme, CA
Long Beach, CA
Los Angeles, CA
Oakland, CA
Redwood City, CA
Richmond, CA
Sacramento, CA
San Diego, CA
San Francisco, CA
Stockton, CA
Typical Like Port
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Delaware River
Patapsco River
Patapsco River
Patapsco River
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
ARB Supplied
RSZ
Distance
(nt mi)
24.9
38.8
4.4
17.3
2.1
3.1
2.0
94.0
88.1
65.3
27.6
106.4
18.6
3.8
22.2
14.3
22.4
157.1
24.3
45.5
11.9
39.0
23.3
9.0
2.8
18.1
20.6
18.4
36.0
22.6
90.5
11.7
14.4
86.9
RSZ
Speed
(knts)
9.0
13.0
10.0
12.0
10.0
3.0
10.0
c
c
c
10.0
10.0
10.0
12.0
9.0
10.0
9.0
c
14.0
13.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
12.0
Region
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
EC
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
Bolded speeds were determined through recent conversations with pilots. RSZ speeds and distances for
the ARB supplied ports were obtained from ARB.
Table 2-34. Speed definitions
Speed
Definition
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
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Table 2-35. Regional definitions
Region
SP
HI
NP
AE
EC
GC
Definition
South Pacific
Hawaii
North Pacific
Alaska East
East Coast
Gulf Coast
Great Lake ports were matched to either Cleveland or Duluth as shown in Table 2-36. All Great
Lake ports are considered in the Great Lake Region and have reduced speed zone distances of
3 nautical miles occurring at halfway between service speed and maneuvering speed.
Table 2-36. Great Lake Match Ports
| Port Name
Typical Like Port
Alpena, Ml
Buffalo, NY
Burns Waterway, IN
Calcite, Ml
Cleveland, OH
Dolomite, Ml
Erie, PA
Escanaba, Ml
Fairport, OH
Gary, IN
Lorain, OH
Marblehead, OH
Milwaukee, Wl
Muskegon, Ml
Presque Isle, Ml
StClair, Ml
Stoneport, Ml
Two Harbors, MN
Ashtabula, OH
Chicago, IL
Conneaut, OH
Detroit, Ml
Duluth-Superior, MN&WI
Indiana, IN
Inland Harbor, Ml
Manistee, Ml
Sandusky, OH
Toledo, OH
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Duluth-Superior
Duluth-Superior
Duluth-Superior
Duluth-Superior
Duluth-Superior
Duluth-Superior
Duluth-Superior
Duluth-Superior
Duluth-Superior
Duluth-Superior
2.5.3. Extrapolation from Typical Port to Modeled Port
The first step in the process of extrapolation of typical port data to a modeled port was to parse
the records from the USAGE entrances and clearances data for a given port. These records
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were then matched with Lloyd's data to obtain ship characteristics. Calls with 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 DWT range. The number of entrances and clearances in
each bin are counted, summed together and divided by two to determine the number of calls
(i.e., one entrance and one clearance was considered a call). For Great Lake ports, there is a
larger frequency of ships either entering the port loaded and leaving unloaded (light) or entering
the port light and leaving loaded. In these cases, there would only be one record (the loaded
trip into or out of the port) that would be present in the data. For Great Lake ports, clearances
were matched with entrances by ship name. If there was not a reasonable match, the orphan
entrance or clearance was treated as a call.
Propulsion power and vessel service speed are averaged for each bin. While each port is
analyzed separately, the various bins and national average ship characteristics are given in
Table 2-37 for deep sea ports and Table 2-38 for Great Lake ports. These bins are identical to
the ones used to bin the typical port data. Auxiliary engine power was computed from the
average propulsion power using the auxiliary power to propulsion power ratios listed in Table
2-3.
Table 2-37. Bins and Average Ship Characteristics for Deep Sea Ports
Ship Type
AUTO CARRIER
Main
Engine
MSD
DWT Range
< 10,000
10,000-20,000
20,000-30,000
MSD Total
SSD
<10,000
10,000-20,000
20,000-30,000
SSD Total
AUTO CARRIER Total
BARGE CARRIER
MSD
< 25,000
MSD Total
SSD
< 25,000
35,000-45,000
45,000-90,000
SSD Total
ST
35,000-45,000
ST Total
BARGE CARRIER Total
BULK CARRIER
MSD
< 25,000
25,000-35,000
35,000-45,000
45,000-90,000
> 90,000
MSD Total
Calls
35
224
28
286
84
2,316
621
3,020
3,306
1
1
1
20
19
40
5
5
45
213
6
44
51
1
314
Engine Power (kW)
Main
6,527
10,499
6,620
9,640
7,927
10,899
13,239
11,298
11,155
4,461
4,461
3,916
19,463
25,041
21,724
24,196
24,196
21,779
4,867
8,948
9,148
9,705
16,109
6,360
Auxiliary
1,736
2,793
1,761
2,564
2,109
2,899
3,522
3,005
2,967
1,200
1,200
1,053
5,236
6,736
5,844
6,509
6,509
5,859
1,080
1,986
2,031
2,155
3,576
1,412
Vessel
Speed
(kts)
16.0
18.2
13.0
17.4
17.7
18.7
19.5
18.8
18.7
13.3
13.3
14.0
18.0
20.0
18.9
21.7
21.7
19.1
14.0
14.0
15.2
14.3
15.8
14.2
Build
Year
1980
1981
1982
1981
1987
1988
1990
1988
1988
1983
1983
1986
1972
1984
1978
1973
1973
1978
1980
1973
1981
1981
1974
1980
DWT
6,211
13,003
22,268
13,063
8,845
14,959
24,860
16,826
16,500
4,393
4,393
11,783
44,799
48,093
45,538
41,294
41,294
44,657
15,819
29,984
39,128
71,242
105,550
28,621
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Table 2-37. Bins and Average Ship Characteristics for Deep Sea Ports (continued)
Ship Type
BULK CARRIER
Main
Engine
SSD
SSD Total
ST
DWT Range
< 25,000
25,000-35,000
35,000-45,000
45,000-90,000
> 90,000
< 25,000
25,000-35,000
ST Total
BULK CARRIER Total
CONTAINER SHIP
MSD
< 25,000
25,000-35,000
35,000-45,000
45,000-90,000
MSD Total
SSD
< 25,000
25,000-35,000
35,000-45,000
45,000-90,000
> 90,000
SSD Total
ST
< 25,000
25,000-35,000
35,000-45,000
ST Total
CONTAINER SHIP Total
GENERAL CARGO
MSD
< 25,000
25,000-35,000
35,000-45,000
45,000-90,000
MSD Total
SSD
< 25,000
25,000-35,000
35,000-45,000
45,000-90,000
> 90,000
SSD Total
ST
< 25,000
ST Total
GENERAL CARGO Total
Calls
1,194
2,192
1,742
3,733
352
9,212
72
3
75
9,600
1,005
53
59
248
1,365
2,054
2,360
2,443
6,209
98
13,163
46
89
41
176
14,703
2,937
38
1
9
2,984
2,357
500
1,122
405
6
4,389
18
18
7,391
Engine Power (kW)
Main
5,650
7,191
8,515
9,484
14,071
8,434
6,290
8,948
6,379
8,350
6,846
22,304
26,102
37,650
13,878
12,381
19,247
24,755
36,151
57,325
27,454
20,396
21,066
23,562
21,472
26,122
5,080
9,458
13,728
11,932
5,159
6,726
7,575
9,269
9,336
10,628
7,718
17,897
17,897
6,709
Auxiliary
1,254
1,596
1,890
2,105
3,124
1,872
1,396
1,986
1,416
1,854
1,506
4,907
5,742
8,283
3,053
2,724
4,234
5,446
7,953
12,612
6,040
4,487
4,635
5,184
4,724
5,747
1,316
2,450
3,556
3,090
1,336
1,742
1,962
2,401
2,418
2,753
1,999
4,635
4,635
1,738
Vessel
Speed
(kts)
14.2
14.6
14.7
14.4
14.5
14.5
15.0
15.0
15.0
14.5
17.2
20.6
22.3
24.0
18.8
19.1
20.5
21.8
23.3
25.0
21.9
20.8
21.0
21.0
21.0
21.6
15.1
15.4
14.3
16.0
15.1
15.4
14.9
15.2
15.1
14.5
15.3
21.0
21.0
15.2
Build
Year
1990
1987
1985
1992
1992
1989
1975
1983
1975
1989
1994
1979
1988
1995
1993
1993
1993
1990
1994
1999
1993
1979
1977
1979
1978
1993
1991
1975
1983
1976
1991
1988
1990
1987
1993
1990
1988
1969
1969
1989
DWT
19,913
29,323
39,875
62,573
112,396
46,746
18,314
33,373
18,819
45,936
8,638
28,500
39,932
56,264
19,419
18,776
31,205
40,765
58,604
105,231
44,513
19,963
30,804
40,949
30,334
42,014
8,268
30,746
40,910
50,250
8,688
14,409
29,713
41,568
47,712
134,981
26,326
22,548
22,548
19,196
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Table 2-37. Bins and Average Ship Characteristics for Deep Sea Ports (continued)
Ship Type
MISCELLANEOUS
Main
Engine
MSD
DWT Range
All
MSD Total
MSD-ED
All
MSD-ED Total
SSD
All
SSD Total
ST
All
ST Total
MISCELLANEOUS Total
PASSENGER
MSD
<10,000
10,000-20,000
MSD Total
MSD-ED
<10,000
10,000-20,000
MSD-ED Total
SSD
<10,000
SSD Total
GT-ED
10,000-20,000
GT-ED Total
ST
<10,000
10,000-20,000
ST Total
PASSENGER Total
REEFER
MSD
<10,000
10,000-20,000
MSD Total
SSD
<10,000
10,000-20,000
SSD Total
REEFER Total
RORO
MSD
<10,000
10,000-20,000
> 30,000
MSD Total
SSD
<10,000
10,000-20,000
20,000-30,000
> 30,000
SSD Total
GT
> 30,000
GT Total
Calls
51
51
6
6
7
7
1
1
64
1,011
24
1,035
1,964
228
2,192
189
189
143
143
13
52
65
3,623
122
60
182
464
801
1,265
1,447
892
286
31
1,208
132
208
31
555
925
1
1
Engine Power (kW)
Main
9,405
9,405
16,968
16,968
4,659
4,659
12,871
12,871
9,564
22,024
96,945
23,762
39,095
53,236
40,566
23,595
23,595
44,428
44,428
16,858
29,982
27,357
34,800
4,829
12,506
7,360
6,539
12,711
10,449
10,060
7,840
9,312
22,386
8,561
7,240
9,062
12,781
20,362
15,702
47,076
47,076
Auxiliary
2,530
2,530
4,565
4,565
1,253
1,253
3,462
3,462
2,573
6,123
26,951
6,606
10,868
14,800
11,277
6,559
6,559
12,351
12,351
4,687
8,335
7,605
9,674
1,961
5,077
2,988
2,655
5,161
4,242
4,084
2,031
2,412
5,798
2,217
1,875
2,347
3,310
5,274
4,067
12,193
12,193
Vessel
Speed
(kts)
12.7
12.7
12.7
12.7
14.2
14.2
21.0
21.0
13.0
20.2
28.5
20.4
20.9
22.0
21.1
20.1
20.1
24.0
24.0
21.2
18.0
18.6
20.9
16.3
20.0
17.5
18.0
20.8
19.7
19.5
15.5
17.0
21.0
16.0
15.0
16.9
18.9
18.9
17.9
24.0
24.0
Build
Year
1990
1990
1994
1994
1980
1980
1969
1969
1989
1986
1969
1986
1996
1999
1996
1985
1985
2000
2000
1960
1961
1961
1,992
1987
1993
1989
1989
1988
1988
1988
1985
1982
1978
1984
1980
1988
1984
1986
1986
2000
2000
DWT
6,083
6,083
15,795
15,795
8,840
8,840
16,605
16,605
7,311
5,976
15,521
6,197
7,345
10,924
7,717
6,235
6,235
11,511
11,511
6,981
13,960
12,564
7,443
5,646
11,632
7,619
7,267
13,138
10,986
10,562
6,641
11,338
31,508
8,389
4,695
14,293
22,146
42,867
30,321
36,827
36,827
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Table 2-37. Bins and Average Ship Characteristics for Deep Sea Ports (continued)
Ship Type
RORO
Main
Engine
ST
DWT Range
10,000-20,000
20,000-30,000
ST Total
RORO Total
TANKER
MSD
<30,000
30,000-60,000
60,000-90,000
90,000-120,000
MSD Total
SSD
<30,000
30,000-60,000
60,000-90,000
90,000-120,000
120,000-150,000
> 150,000
SSD Total
GT-ED
30,000-60,000
GT-ED Total
ST
< 30,000
30,000-60,000
60,000-90,000
90,000-120,000
120,000-150,000
> 150,000
ST Total
TANKER Total
TUG
MSD
All
MSD Total
TUG Total
Grand Total
Calls
2
1
3
2,137
650
181
148
3
981
3,050
3,752
1,766
2,835
258
487
12,147
13
13
2
87
73
4
3
2
170
13,310
48
48
48
55,672
Engine Power (kW)
Main
22,373
22,373
22,373
11,687
4,888
10,533
9,782
15,139
6,697
6,303
9,021
10,310
12,318
15,840
16,888
9,755
7,592
7,592
13,534
15,818
26,848
17,660
19,125
20,785
20,678
9,667
7,579
7,579
7,579
15,212
Auxiliary
5,795
5,795
5,795
3,027
1,031
2,222
2,064
3,194
1,413
1,330
1,903
2,175
2,599
3,342
3,563
2,058
1,602
1,602
2,856
3,338
5,665
3,726
4,035
4,386
4,363
2,040
2,039
2,039
2,039
3,593
Vessel
Speed
(kts)
25.0
25.0
25.0
16.8
14.3
15.3
14.7
14.1
14.6
14.6
14.9
14.6
14.6
14.7
15.2
14.7
14.5
14.5
18.0
17.9
18.9
16.3
16.0
14.3
18.2
14.8
14.5
14.5
14.5
17.4
Build
Year
1976
1973
1975
1985
1988
1981
1984
1987
1986
1990
1991
1988
1994
1993
1996
1991
1975
1975
1975
1978
1979
1976
1973
1978
1978
1991
1981
1981
1981
1990
DWT
16,144
22,501
18,687
17,910
11,415
42,153
74,245
113,957
26,847
17,145
41,677
74,595
101,116
144,405
166,394
61,353
39,839
39,839
27,235
43,982
70,108
91,868
122,409
190,111
58,616
58,754
626
626
626
38,083
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Table 2-38. Bins and Average Ship Characteristics for Great Lake Ports
Ship Type
BULK CARRIER
Main
Engine
MSD
DWT Range
10,000-20,000
20,000-30,000
30,000-40,000
MSD Total
SSD
10,000-20,000
20,000-30,000
30,000-40,000
SSD Total
ST
20,000-30,000
ST Total
BULK CARRIER Total
SELF UNLOADING
BULK CARRIER
MSD
10,000-20,000
20,000-30,000
30,000-40,000
> 40,000
MSD Total
SSD
20,000-30,000
30,000-40,000
SSD Total
ST
< 10,000
10,000-20,000
20,000-30,000
ST Total
SELF UNLOADING BULK CARRIER Total
GENERAL CARGO
MSD
< 10,000
10,000-20,000
MSD Total
SSD
< 10,000
10,000-20,000
20,000-30,000
30,000-40,000
SSD Total
GENERAL CARGO Total
INTEGRATED
TUG-BARGE
MSD
All
MSD Total
INTEGRATED TUG-BARGE Total
TANKER
MSD
10,000-20,000
MSD Total
SSD
10,000-20,000
SSD Total
TANKER Total
Grand Total
Calls
9
4
11
24
18
208
223
449
23
23
496
5
12
771
67
855
275
122
397
26
93
79
198
1,450
87
6
93
3
7
1
6
17
110
24
24
24
42
42
5
5
47
2,127
Engine Power
(kW)
Main
4,413
8,826
6,001
5,876
4,844
6,995
8,284
7,549
6,910
6,910
7,438
3,114
6,436
6,881
12,140
7,265
6,659
7,574
6,940
3,236
4,750
6,679
5,321
6,910
4,436
5,939
4,533
4,763
6,280
7,099
8,827
6,959
4,908
5,364
5,364
5,364
3,972
3,972
5,160
5,160
4,098
6,850
Auxiliary
980
1,959
1,332
1,305
1,075
1,553
1,839
1,676
1,534
1,534
1,651
691
1,429
1,528
2,695
1,613
1,478
1,681
1,541
718
1,055
1,483
1,181
1,534
847
1,134
866
910
1,199
1,356
1,686
1,329
937
1,443
1,443
1,443
838
838
1,089
1,089
865
1,515
Vessel
Speed
(kts)
15.3
14.0
13.5
14.2
13.6
14.6
14.1
14.3
15.5
15.5
14.4
10.5
15.0
13.2
13.5
13.3
15.0
14.9
14.9
12.3
13.6
16.6
14.6
13.9
15.1
16.5
15.2
16.4
14.1
16.0
15.0
14.9
15.1
13.8
13.8
13.8
13.5
13.5
14.3
14.3
13.6
14.1
Build
Year
1976
1971
1985
1979
1982
1974
1986
1980
1961
1961
1979
1929
1959
1977
1978
1977
1966
1975
1969
1942
1946
1961
1951
1971
1994
1967
1992
1992
1997
1981
1982
1990
1992
1978
1978
1978
1966
1966
1986
1986
1968
1974
DWT
11,693
28,481
32,713
24,125
14,392
27,486
34,172
30,282
26,513
26,513
29,809
12,513
28,591
33,531
65,089
35,812
26,504
34,476
28,954
4,538
16,830
28,847
20,011
31,776
6,755
12,497
7,125
6,708
16,993
24,432
30,900
20,524
9,196
672
672
672
10,475
10,475
13,735
13,735
10,822
29,336
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Emissions for the modeled port were determined by mode, ship type, engine type, and DWT
range from similar categories at the typical like port. A discussion of each time in mode
calculation is discussed below.
Cruise Mode
Average time in mode for the modeled port (mp) was determined using the average service
speed assuming a 25 nautical mile distance into and out of the port for deep sea ports and 7
nautical miles into and out of the port for Great Lake ports. Emissions for propulsion (main)
engines at the typical like port (tp) were multiplied by the ratio of calls, propulsion power, and
time in mode differences between the two ports. Auxiliary engine emissions were determined
from the typical like port auxiliary engine emissions based upon the ratio of auxiliary power,
number of calls and time in mode between the two ports.
Timemp [hrs/call] = Cruise Distance [miles]/Cruise Speedmp [knots] x 2 trips/call (7)
Emissions [main engine]mp = Emissions [main engine]tp x (callsmp/callstp)
x (Main Powermp/Main Powertp) x (timemp/timetp) (8)
Emissions [aux engine]mp = Emissions [aux engine]mp x (callsmp/callstp)
x (Aux Powermp/Aux Powertp) x (timemp/timetp) (9)
Reduced Speed Zone Mode
Average time in mode for the modeled port was determined using the reduced speed zone
speed and distance for the modeled port. Load factors were also calculated based upon the
reduced speed zone speed at the modeled port and the average maximum speed at the
modeled port for the given ship type, engine type, and DWT range bin. The cruise speed listed
in Lloyds data is considered to be 94 percent of the maximum speed. Once load factors are
calculated for the modeled port, if either of the modeled port or typical like port load factors are
below 20 percent, low-load multiplicative adjustment factors are also calculated. The main
engine emissions for the modeled port are then estimated from the typical like port emissions
times the ratio of calls, main propulsion power, the load factors, the time in mode and the low-
load multiplicative adjustment factors between the two ports. If the load factor at either port is
20 percent or greater, the low load adjustment factor is set to 1.00. Auxiliary engine emissions
were determined from the typical like port based upon the ratio of auxiliary power, number of
calls and time in mode between the two ports.
Timemp [hrs/call] = RSZ distancemp (nm)/RSZ Speedmp [knots] x 2 trips/call (10)
Maximum Speedmp [knots] = Cruise Speedmp [knots]/0.94 (11)
Load Factormp = (RSZ Speedmp/Maximum Speedmp)3 (12)
Emissions [main engine]mp = Emissions [main engine]tp x (callsmp/callstp)
x (Main Powermp/Main Powertp) x (timemp/timetp)
x (Load Factormp/Load Factortp)
x (Low Load Adjustment Factormp/ Low Load Adjustment Factortp) (13)
Emissions [aux engine]mp = Emissions [aux engine]mp x (callsmp/callstp)
x (Aux Powermp/Aux Powertp) x (timemp/timetp) (14)
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Maneuvering Mode
In determining emissions for the modeled port, it was assumed that the maneuvering times and
load factors at the modeled port were the same as the time in mode and load factor for the
typical like port for the given ship type, engine type and DWT range. This also assumes that the
number of shifts per call at the modeled port were the same as at the typical like port. While it
would be more accurate to calculate actual maneuvering times at the modeled port, the USAGE
entrances and clearances data provided no detail on either the number of shifts or the final
PWD at which the vessel berthed. Therefore emissions at the modeled port were determined
directly from the emissions at the typical like port using the ratio of the number of calls and main
or auxiliary power.
Emissions [main engine]mp = Emissions [main engine]tp x (callsmp/callstp)
x (Main Powermp/Main Powertp) (15)
Emissions [aux engine]mp = Emissions [aux engine]mp x (callsmp/callStp)
x (Aux Powermp/Aux Powertp) (16)
Hotelling Mode
Again due to lack of information as to actual hotelling times at the various modeled ports, it was
assumed that the hotelling time at the modeled port was the same as the hotelling time at the
typical like port for the same ship type, engine type and DWT range. Thus emissions at the
modeled port were determined directly from the emissions at the typical like port using the ratio
of the number of calls and auxiliary power.
Emissions [aux engine]mp = Emissions [aux engine]mp x (callsmp/callStp)
x (Aux Powermp/Aux Powertp) (17)
Bin Mismatches
In some cases, the specific DWT range bin at the modeled port was not in the typical like port
data. In those cases, the next nearest DWT range bin was used for the calculations. In a few
cases, the engine type for a given ship type might not be in the typical like port data. In these
cases, the closest engine type at the typical like port was used and ratios of emission factors
were also used in calculation of emissions for the specific engine type at the modeled port. A
ratio of low load adjustment factors was also used if one of the engine types was an electric
drive as discussed in Section 2.3.5. 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. Stand Alone Ports
In a few cases, the USAGE 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 ARE,
the Port of Valdez, AK and a conglomerate port within the Puget Sound area.
2.6.1. California Ports
ARE supplied inventories for 14 California ports for 2002. The data received from ARE for the
California ports was modified to provide consistent PM and SOx emissions to those calculated
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Commercial Marine Port Inventory Development
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in this report. In addition, cruise and RSZ emissions were calculated directly based upon
average ship power provided in the ARE methodology document34 and number of calls,
because ARE did not calculate cruise emissions and transit (RSZ) emissions were allocated to
counties instead of ports. Andy Alexis of ARE provided transit distances for each port to
calculate the RSZ emissions. In addition, in the ARE inventory all passenger ships are treated
as electric drive and all emissions are allocated to auxiliary engines. In the ICF calculations,
ship propulsion and auxiliary engine power was calculated based upon Section 2.3.5 discussed
above for use in computing cruise and RSZ emissions. For maneuvering and hotelling
emissions ARE values were used and adjusted as discussed below. The data supplied by ARE
included domestic traffic as well as foreign cargo traffic.
For PM emission calculations, ARE used an emission factor of 1.5 g/kWh to calculate total PM
emissions and factors of 0.96 and 0.937 to convert total PM to PM10 and PM2.s respectively.
Since an emission factor of 1.4 g/kWh was used in the ICF calculations for PM10 and an
emission factor of 1.3 g/kWh was used in the ICF calculations for PM2.s, ARE PM10 and PM2.s
emissions were multiplied by factors of 0.972 and 0.925 respectively to get consistent PM10 and
PM2.s emissions for propulsion engines. For auxiliary engines, ARE used the same emission
factors as above, while ICF used emission factors of 1.3 and 1.2 g/kWh respectively for
passenger ships and 1.1 and 1.0 g/kWh respectively for other ships. ARE auxiliary engine
emissions were thus multiplied by factors of 0.903 and 0.854 respectively for passenger ships
and 0.764 and 0.711 respectively for other ships to provide consistent PM emission
calculations. SOx emissions were also different between the ARE and ICF analyses. ARE
used a composite35 SOx emission factor of 10.55 g/kWh while ICF used a composite SOx
emission factor of 9.57 g/kWh. Thus ARE SOx emissions were multiplied by a factor of 0.907 to
be consistent with ICF emission calculations. For auxiliary engines, ARE used SOx emission
factors of 11.48 and 9.34 g/kWh respectively for passenger and other ships, while ICF use
emission factors of 9.93 and 9.07 g/kWh respectively. Thus ARE auxiliary SOx emissions were
multiplied by factors of 0.865 and 0.971 respectively for passenger and other ships to provide
consistent SOx emissions.
2.6.2. Puget Sound Ports
In the newest Starcrest inventory8, it was found that a considerable amount of Jones Act tanker
ships stop at Cherry Point, Ferndale, March Point and other areas which are not within the top
89 US deep sea ports analyzed in this analysis. In addition, since they are Jones Act ships
carrying US cargo (oil from Alaska) from one US port to another, they are not documented in the
USAGE entrances and clearances data. To compensate for this anomaly, an additional port
was added which encompassed Jones Act tanker ships stopping within the Puget Sound area
but not at one of the Puget Sound ports analyzed in this analysis. Ship calls in the 1996 typical
port data to ports other than those in the top 89 US deep sea ports were analyzed separately.
There were 363 ship calls by tankers to those areas in 1996. In the Starcrest inventory report
for 2005, there were 468 calls. For 2002, it was estimated there were 432 calls. The same ship
types and ship characteristics were used as in the 1996 data, but the number of calls was
proportionally increased to 432 calls to represent these Jones Act ships. The location of the
"Other Puget Sound" port was approximately at Cherry Point near Aberdeen.
34 California Air Resources Board, Emissions Estimation Methodology for Ocean-Going Vessels, October 2005.
35 Based upon ARE assuming 95% of the engines were SSD and 5% were MSD.
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2.6.3. PortofValdez
In a recent Alaska port inventory36, it was found that significant Category 3 domestic tanker
traffic enters and leaves the port on destination to West Coast ports. Since the USAGE
entrances and clearances data did not contain any tanker calls at Valdez in 2002, the recent
Alaska inventory data was used to in calculation of emissions at that port. In this case, the
number of calls and ship characteristics for 2002 were taken directly from the Alaska inventory
and used in determining emissions for the modeled port with the Puget Sound area typical port
being used as the like port.
2.7. Domestic Traffic
The one drawback of using USAGE entrances and clearances data is that it only represents
foreign cargo movements. The Maritime Administration (MARAD) maintains the Foreign Traffic
Vessel Entrances and Clearances database, which contains statistics on U.S. foreign maritime
trade. Data are compiled during the regular processing of statistics on foreign imports and
exports. The database contains information on the type of vessel, commodities, weight, customs
districts and ports, and origins and destinations of goods. Thus domestic traffic, U.S. ships
delivering cargo from one U.S. port to another U.S. port is covered under the Jones Act37, is not
accounted for in the database. However, U.S. flagged ships carrying cargo from a foreign port
to a U.S. port or from a U.S. port to a foreign port are accounted for in the USAGE entrances
and clearances database as these are considered foreign cargo movements. While at most
ports, domestic commerce is carried out by Category 2 ships, there are a few exceptions as
discussed in Section 2.6 above. Unfortunately, there is little or no readily available information
on domestic trips, so these were ignored in the analysis except as stated above. The USAGE
entrances and clearances data accounts for over 95 percent of the emissions from Category 3
ships calling on US ports.38
2.8. Calculation of Fuel Consumption
Instead of using a ratio of fuel consumption to NOx emissions, fuel consumption was calculated
directly from CO2 emissions. For every 3.183 metric tonnes of CO2, one metric tonne of fuel
was consumed. This is based upon a carbon weight content of 86.7% and the ratio of
molecular weights of CO2 and elemental carbon.
2.9. IMO NOx Reductions
The International Maritime Organization (IMO) adopted NOx limits in Annex VI to the
International Convention for Prevention of Pollution from Ships in 1997. These NOx limits apply
for all marine engines over 130 kilowatts (kW) for engines built on or after January 1, 2000,
including those that underwent a major rebuild after January 1, 2000. The required number of
countries ratified Annex VI in May 2004 and it went into force for those countries in May 2005.
The Annex has not yet been ratified by the United States; however, most ship engine
manufacturers are building engines compliant with Annex VI. Annex VI emission standards are
given in Table 2-39.
36 E.H. Pechan & Associates Inc., Commercial Marine Inventories for Select Alaskan Ports, Final Report, Prepared
for the Alaska Department of Conservation, June 2005.
37 Merchant Seaman Protection and Relief 46 USCS Appx § 688 (2002) Title 46. Appendix. Shipping Chapter 18.
38 Conversation with Bruce Lambert of US Army Corps of Engineers Institute of Water Resources, June 2007.
ICF International 2-33 U.S. Environmental Protection Agency
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Commercial Marine Port Inventory Development
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Table 2-39. Annex VI NOx emission standards (g/kWh)
Engine Speed (n)
n > 2000 rpm
2000 > n> 130 rpm
n < 130 rpm
9.8
45.0 xn
-0.2
17.0
Most manufacturers build engines to emit well below the standard. EPA determined the effect
of the I MO standard to be a reduction in NOx emissions of 11 percent below engines built
before 2000.39 Therefore for engines built in 2000 and later, a NOx factor of 0.89 is applied to
the calculation of NOx emissions for both propulsion and auxiliary engines. Since this standard
only applies to diesel engines, the factor is not applied to either steam turbines or gas turbines.
Average NOx emission factors (applying the correct emission factor by engine type) were
determined by model year for the ships calling on U.S. ports using installed power by model
year determined from the USAGE call data used for calculating the inventories in this report.
The average NOx emission factors for the various calendar years applying the 0.89 emission
reduction factor were normalized to one where no reduction factors were applied. The
normalized NOx adjustment factors for various calendar years are shown in Table 2-40. Since
the Great Lakes fleet represents vessels that are generally older than the normal deep sea fleet,
the NOx emission factors and adjustment factors were calculated separated and are also given
in the table below.
Table 2-40. NOx Adjustment Factors for Annex VI NOx Standards
I Analysis
Year
Deep
Sea
Great
Lakes
2005
2010
2015
2020
2025
2030
0.957
0.932
0.915
0.902
0.893
0.891
0.994
0.993
0.989
0.966
0.929
0.918
Conversation with Michael Samulski of EPA, May 2007.
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3. 2002 Baseline Near Port Inventories
This section presents summaries of the baseline near port inventories for 2002. Detailed results
can be found in the spreadsheet files available with this document. Near port inventories are
divided in this section between deep sea ports and Great Lake ports because of the difference
in ship types between the two.
3.1. Deep Sea Ports
Emissions inventories for 89 deep sea ports were developed and are presented here. National
Ship age distributions were determined using the USAGE entrances and clearances data
married with Lloyds data to determine ship engine characteristics and build date. All ships with
Category 1 or 2 propulsion engines were eliminated from the database. Age profiles were
determined separately for deep sea and Great Lake ports. Build dates were weighted by
installed power (calls times total propulsion power) as this is the best indicator of ship
emissions. Table 3-1 presents the age profiles by engine type for deep sea ports. These can
be used to determine the effects of new emission regulations.
Figure 3-1 Shows installed power by engine type. Over 75 percent of installed power comes
from ships with slow speed engines. Medium speed diesel engines account for 22.5 percent of
the installed power at deep sea ports. Installed power is the ship's propulsion power times the
number of calls. The breakdown of ship registry is shown in Figure 3-2. Slightly more than 3
percent of the deep sea ship calls were made by US flagged ships. Spreadsheet which detail
emission calculations are listed in Table 3-2. Total emissions by ports are given in Table 3-3.
Auxiliary only emissions by ports are given in Table 3-4. Emissions by mode are given in Table
3-5 for cruise, Table 3-6 for reduced speed zone, Table 3-7 for maneuvering, and Table 3-8 for
hotelling. Emissions by ship type by port are given in Tables 3-8 through 3-19. Ports that are
missing from those lists had no emissions related to that ship type during 2002. By far the
highest emissions come from Container ships, followed by Passenger ships and Tanker ships.
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Commercial Marine Port Inventory Development
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Table 3-1. Deep Sea Ship Age Fractions by Engine Type
Age
(yrs)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35+
Propulsion Engine Type
MSD
0.04091
0.12022
0.09333
0.10243
0.08757
0.06583
0.04447
0.04924
0.03866
0.05233
0.03511
0.03958
0.04034
0.00257
0.02228
0.01855
0.01296
0.00842
0.01677
0.00689
0.01007
0.01209
0.02073
0.00985
0.01311
0.00404
0.00462
0.00155
0.00093
0.00041
0.00158
0.00368
0.00010
0.01200
0.00085
0.00592
SSD
0.03215
0.07968
0.05950
0.05313
0.07517
0.06985
0.04853
0.05291
0.04459
0.05051
0.03512
0.04022
0.02865
0.02881
0.03082
0.02847
0.02577
0.03813
0.03460
0.02320
0.01738
0.02088
0.01746
0.01455
0.01273
0.01153
0.00579
0.00246
0.00432
0.00124
0.00825
0.00343
0.00000
0.00000
0.00018
0.00000
GT
0.10315
0.61727
0.26516
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00277
0.01165
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
ST
0.01349
0.00000
0.00607
0.00304
0.00000
0.00000
0.00277
0.00000
0.00276
0.05421
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00228
0.00000
0.00408
0.20769
0.14144
0.02240
0.04356
0.03448
0.06194
0.02886
0.09228
0.01376
0.01235
0.00000
0.06769
0.00000
0.18486
All
0.03459
0.09302
0.06848
0.06317
0.07640
0.06749
0.04664
0.05098
0.04236
0.05049
0.03439
0.03924
0.03069
0.02230
0.02826
0.02564
0.02235
0.03064
0.02986
0.01907
0.01537
0.01851
0.02017
0.01478
0.01280
0.01009
0.00582
0.00301
0.00379
0.00206
0.00673
0.00355
0.00002
0.00347
0.00033
0.00341
MSD = Medium speed diesel, SSD = Slow speed diesel
GT = Gas turbine, ST = Steam turbine
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Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Figure 3-1. Installed Power by Engine Type for Deep Sea Ports
ST, 1.1%
GT, 0.9%^
Figure 3-2. Deep Sea Ship Registry Breakdown
Other, 19.56%
Netherlands, 2.02%
Marshall Islands, 3.03%^
USA, 3.22%
Germany, 3.41%^
Greece, 3.45%^
Antigua and Barbuda, /
3.47%
Malta and Gozo, 3.72%^
-Panama, 19.14%
-Liberia, 13.27%
-Bahamas, 10.32%
^Norway, 5.62%
Singapore, 4.96%
Cyprus, 4.79%
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-3
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-2. Match Port File Codes and File Names
Code
PS
CB
HU
CC
TA
LM
NY
D1
D2
D3
D4
D5
BA
CA
Typical Like Port
Puget Sounds
Coos Bay
Houston
Corpus Christ!
Tampa
Lower Mississippi
New York/New Jersey
Delaware River
Baltimore
California Ports
Detailed Inventory Spreadsheet File
Puget Sound Ports New.xls
Coos Bay Ports New.xls
Houston Ports New.xls
Corpus Christ! Ports New.xls
Tampa Ports New.xls
Lower Mississippi Ports New.xls
New York Ports Final.xls
Delaware Ports 1 New.xls
Delaware Ports 2 New.xls
Delaware Ports 3 New.xls
Delaware Ports 4 New.xls
Delaware Ports 5 New.xls
Baltimore Ports New.xls
ARB 2002 New.xls
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-4
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-3. Total Emissions by Port (Deep Sea Ports)
Installed
Power
Port Name (MW) NOx p
Anacortes, WA 545 403
Barbers Point, HI 472 115
Everett, WA 186 82
Grays Harbor, WA 360 50
Honolulu, HI 8,037 1,268
Kalama, WA 1,190 359
Longview, WA 1,619 413
Olympia, WA 97 56
Port Angeles, WA 556 151
Portland, OR 11,198 2,304
Seattle, WA 26,292 6,646
Tacoma, WA 19,130 5,742
Vancouver, WA 1,946 439
Valdez, AK 6,676 343
Other Puget Sound 5,678 2,111
Anchorage, AK 537 219
Coos Bay, OR 399 34
Hilo, HI 4,516 929
Kahului, HI 2,348 470
Nawiliwili, HI 591 122
Nikishka, AK 1,110 270
Beaumont, TX 12,699 2,106
Freeport, TX 7,411 709
Galveston, TX 6,572 1,011
Houston, TX 47,147 4,597
Port Arthur, TX 3,531 436
Texas City, TX 7,382 954
Corpus Christi, TX 1 1 ,452 1 ,733
Lake Charles, LA 6,355 842
Mobile, AL 8,302 1,181
Brownsville, TX 1,213 143
Gulfport, MS 3,556 603
Manatee, FL 2,903 655
Matagorda Ship 2,504 389
Panama City, FL 662 58
Pascagoula, MS 3,566 530
Pensacola, FL 351 39
Tampa, FL 10,941 1,504
Everglades, FL 39,325 4,331
New Orleans, LA 27,575 6,535
Baton Rouge, LA 4,627 1,966
South Louisiana, LA 18,366 6,428
Plaquemines, LA 4,230 1,045
Albany, NY 396 103
New York/New Jersey 86,980 7,287
Portland, ME 3,968 722
Georgetown, SC 609 89
Hopewell, VA 185 42
Metric tonnes per year
Mio PM2.5 HC CO SOx
32 29 14 32 225
9 84 9 66
7 63 7 46
4 42 4 30
116 102 47 102 800
30 26 13 30 210
34 30 15 35 239
4 42 4 31
13 11 5 12 89
206 182 117 223 1,319
572 513 264 549 3,792
477 428 217 464 3,211
37 33 17 38 254
37 33 11 27 299
219 197 71 169 1,745
18 16 7 17 133
4 32 4 27
77 70 27 72 626
38 35 14 37 309
10 94 9 83
26 24 8 21 209
261 240 91 189 1,972
92 85 25 54 716
118 102 35 69 873
546 491 158 346 4,136
52 47 17 37 388
127 117 33 74 986
143 132 59 401 1,090
79 73 35 239 590
99 91 40 313 754
14 13 6 14 108
51 46 20 48 414
56 49 22 53 450
32 28 14 33 239
6 52 6 44
44 40 17 43 345
3 31 3 27
129 109 50 121 988
407 376 136 337 3,157
554 512 221 535 4,234
160 148 63 155 1,223
519 479 203 502 3,976
85 78 33 82 658
9 84 9 65
624 576 274 620 4,634
60 55 23 57 466
7 73 7 152
4 3 2 4 211
Fuel
4,858
1,476
982
649
17,086
4,573
5,182
643
1,898
28,412
79,587
67,802
5,485
6,531
37,269
2,588
569
13,932
6,878
1,849
4,334
26,307
8,929
13,711
57,796
5,448
12,119
22,067
11,986
15,125
2,217
8,288
9,081
4,972
876
7,007
542
19,803
62,928
85,467
24,683
80,850
13,590
1,309
93,529
9,688
1,152
554
Match
Port
File
PS
PS
PS
PS
PS
PS
PS
PS
PS
PS
PS
PS
PS
PS
PS
CB
CB
CB
CB
CB
CB
HU
HU
HU
HU
HU
HU
CC
CC
CC
TA
TA
TA
TA
TA
TA
TA
TA
LM
LM
LM
LM
LM
NY
NY
NY
D5
D5
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-5
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-3.
Installed
Power
Port Name (MW)
Marcus Hook, PA 2,754
Morehead City, NC 967
Paulsboro, NJ 3,272
Chester, PA 1,467
Fall River, MA 290
New Castle, DE 765
Penn Manor, PA 721
Providence, Rl 1,097
Brunswick, GA 5,184
Canaveral, FL 17,794
Charleston, SC 46,233
New Haven, CT 1,801
Palm Beach, FL 2,544
Bridgeport, CT 1,452
Camden, NJ 4,209
Philadelphia, PA 7,963
Wilmington, DE 4,444
Wilmington, NC 4,888
Richmond, VA 596
Jacksonville, FL 13,985
Miami, FL 57,682
Searsport, ME 543
Boston, MA 12,417
New Bedford/Fairhaven, MA 181
Baltimore, MD 24,500
Newport News, VA 5,529
Savannah, GA 37,523
Catalina, CA 928
Carquinez, CA 3,442
El Segundo, CA 1,685
Eureka, CA 409
Hueneme, CA 3,334
Long Beach, CA 56,935
Los Angeles, CA 50,489
Oakland, CA 48,762
Redwood City, CA 456
Richmond, CA 3,956
Sacramento, CA 455
San Diego, CA 8,255
San Francisco, CA 6,260
Stockton, CA 1,210
Total Emissions by Port (continued)
Metric
NOx PM10 PM2.5
965 79 73
116 10 9
653 55 50
196 16 15
35 3 3
199 16 15
171 14 13
198 16 15
665 54 50
3,010 281 261
3,833 318 293
287 23 22
226 20 19
238 20 19
994 82 76
1,476 126 116
611 52 48
569 53 49
86 7 7
1,370 125 116
7,038 651 603
110 9 8
1,386 131 121
39 3 3
6,304 512 475
509 41 38
3,667 296 274
88 7 7
537 39 36
192 14 13
83 6 5
319 22 21
5,367 389 357
4,852 352 324
3,024 222 205
107 8 7
484 35 33
138 10 9
891 68 63
708 53 49
332 24 22
Total Port Emissions 863,667 120,637 10,517 9,619
tonnes
HC
30
4
22
7
1
6
6
6
22
89
133
9
7
8
34
51
23
22
3
51
218
3
48
1
209
18
128
3
17
6
2
10
167
152
100
3
15
4
27
22
10
4,145
per year
CO
76
10
54
16
3
16
14
16
53
233
312
22
18
19
83
128
54
52
8
122
551
9
117
3
494
41
296
7
42
15
6
280
422
383
239
8
37
11
68
55
26
10,634
SOx
2,462
94
2,103
411
52
394
656
334
1,302
2,280
4,553
207
169
164
1,625
3,236
1,011
956
206
1,651
5,345
124
1,393
33
3,870
319
2,242
51
309
108
51
190
3,130
2,829
1,638
64
277
81
527
415
192
93,689
Fuel
12,744
1,632
8,381
2,403
549
2,594
2,161
2,583
8,269
43,926
48,248
3,807
3,244
3,359
13,051
19,649
7,954
8,251
1,047
19,600
101,648
1,498
20,969
534
75,965
6,271
44,514
1,144
6,636
2,272
1,156
4,213
69,284
62,546
35,329
1,418
5,929
1,762
11,749
9,149
4,151
1,574,197
Match
Port
File
D5
D5
D5
D4
D4
D4
D4
D4
D3
D3
D3
D3
D3
D2
D2
D2
D2
D2
D2
D1
D1
D1
D1
D1
BA
BA
BA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-6
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-4. Auxiliary Engine Emissions
Auxiliary
Power
Port Name (MW) NOx PMl°
Anacortes, WA 115 147 11
Barbers Point, HI 101 74 6
Everett, WA 40 21 2
Grays Harbor, WA 73 25 2
Honolulu, HI 2,043 793 67
Kalama, WA 260 172 13
Longview, WA 346 183 14
Olympia, WA 21 9 1
Port Angeles, WA 111 42 3
Portland, OR 2,560 924 70
Seattle, WA 5,947 1,468 116
Tacoma, WA 4,305 1,280 97
Vancouver, WA 427 180 14
Valdez, AK 1,411 256 20
Other Puget Sound 1,198 951 72
Anchorage, AK 158 98 8
Coos Bay, OR 78 9 2
Hilo, HI 1,252 815 64
Kahului, HI 649 408 32
Nawiliwili, HI 164 108 8
Nikishka, AK 235 132 10
Beaumont, TX 2,415 873 149
Freeport, TX 1,342 316 58
Galveston, TX 1,645 672 89
Houston, TX 8,410 1,800 305
Port Arthur, TX 640 173 29
Texas City, TX 1,414 402 78
Corpus Christi, TX 2,486 745 64
Lake Charles, LA 1,341 448 38
Mobile, AL 1,840 449 38
Brownsville, TX 260 53 7
Gulfport, MS 878 411 34
Manatee, FL 902 478 41
Matagorda Ship 535 202 17
Panama City, FL 130 16 2
Pascagoula, MS 795 277 23
Pensacola, FL 87 19 2
Tampa, FL 2,639 774 67
Everglades, FL 10,037 3,054 279
New Orleans, LA 6,374 3,360 293
Baton Rouge, LA 988 793 67
South Louisiana, LA 3,988 2,969 246
Plaquemines, LA 919 607 50
Albany, NY 85 46 4
New York/New Jersey 20,036 3,412 295
Portland, ME 883 477 40
Georgetown, SC 129 42 3
Hopewell, VA 40 13 1
by Port (Deep Sea Ports)
Metric tonnes per year
PM2.5 HC CO SOx Fuel
10 4 11 92 2,136
5 2 6 46 1,073
1 1 2 13 307
2 1 2 16 370
61 22 60 522 11,425
12 5 13 108 2,491
13 5 14 115 2,653
10 1 6 129
3 1 3 26 604
64 26 70 579 13,400
106 40 111 937 21,251
88 35 97 802 18,568
12 5 14 113 2,615
18 7 19 161 3,719
66 26 72 596 13,800
7 3 8 63 1,157
2 1 2 14 298
58 23 64 529 11,949
29 12 32 265 5,971
8 3 8 70 1,578
9 4 10 83 1,767
135 31 63 1,188 12,672
53 11 24 461 4,656
75 24 42 660 9,782
268 64 129 2,352 26,509
25 6 12 220 2,514
71 15 31 626 6,064
59 21 59 514 11,173
34 13 34 303 6,584
35 13 35 303 6,589
6 2 6 56 1,225
30 11 31 292 5,975
35 13 37 343 7,053
14 6 15 131 2,927
2 1 2 19 413
20 8 21 190 4,080
1 1 1 14 285
51 21 59 534 11,227
258 84 232 2,173 44,318
270 94 259 2,332 49,472
62 22 62 543 11,795
226 82 226 1,982 43,089
46 17 46 406 8,815
3 1 3 31 663
271 96 264 2,350 50,383
37 13 36 320 6,922
3 1 3 28 616
1 0 1 11 238
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-7
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-4. Auxiliary Engine Emissions by Port (continued)
Auxiliary
Power
Port Name (MW) NOx PMl°
Marcus Hook, PA 583 617 51
Morehead City, NC 203 69 6
Paulsboro, NJ 701 280 25
Chester, PA 318 63 5
Fall River, MA 61 17 2
Newcastle, DE 164 120 10
Penn Manor, PA 159 66 6
Providence, Rl 236 118 10
Brunswick, GA 1,299 258 22
Canaveral, FL 4,911 2,436 225
Charleston, SC 10,277 1,574 135
New Haven, CT 379 188 16
Palm Beach, FL 564 133 11
Bridgeport, CT 522 178 15
Camden, NJ 1,286 579 48
Philadelphia, PA 1,910 747 65
Wilmington, DE 1,155 287 25
Wilmington, NC 1,045 262 28
Richmond, VA 130 26 2
Jacksonville, FL 3,300 645 64
Miami, FL 14,563 5,058 463
Searsport, ME 116 73 6
Boston, MA 2,913 889 84
New Bedford/Fairhaven, MA 53 28 2
Baltimore, MD 5,924 1,656 139
Newport News, VA 1,216 174 14
Savannah, GA 8,297 1,129 91
Catalina, CA 257 45 4
Carquinez, CA 772 193 13
El Segundo, CA 355 47 3
Eureka, CA 88 59 4
Hueneme, CA 1,010 177 11
Long Beach, CA 79,720 2,634 178
Los Angeles, CA 11,535 2,359 160
Oakland, CA 10,759 862 57
Redwood City, CA 101 59 4
Richmond, CA 866 164 11
Sacramento, CA 95 61 4
San Diego, CA 2,164 483 37
San Francisco, CA 1,480 345 25
Stockton, CA 259 126 8
Total Auxiliary Emissions 264,478 56,259 5,040
Metric tonnes per year
PM2.5 HC CO SOx Fuel
47 17 47 412 8,960
6 2 6 49 1,075
23 8 22 198 4,267.6
5 2 5 42 910
2 1 2 15 325
9 3 9 80 1,738
5 2 5 46 1,007
9 3 9 79 1,708
20 7 20 176 3,834
209 68 187 1,805 35,699
124 45 123 1,089 23,561
14 5 14 125 2,722
10 4 10 91 1,962
14 5 14 125 2,710
44 16 44 387 8,405
59 22 59 523 11,348
23 8 23 202 4,393
25 9 25 223 4,837
2 1 2 18 382
59 22 59 520 11,293
429 142 390 3,715 74,442
6 2 6 49 1,062
77 27 74 675 14,151
2 1 2 19 402
128 46 52 1,127 24,007
13 5 14 125 2,595
83 31 86 754 16,390
41 3 28 676
11 5 15 128 2,920
31 4 32 708
4 2 5 38 899
10 5 47 115 2,686
162 72 205 1,704 40,083
145 65 184 1,525 35,869
52 24 67 551 13,054
3 2 5 39 899
10 5 13 109 2,486
4 2 5 40 925
34 13 37 311 7,329
23 9 27 224 5,235
7 3 10 82 1,902
4,585 1,611 4,292 41,133 833,151
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-8
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-5. Cruise Emissions by Port (Deep Sea Ports)
Installed
Power
Port Name (MW) NOx PMl°
Anacortes, WA 545 50 4
Barbers Point, HI 472 25 2
Everett, WA 186 9 1
Grays Harbor, WA 360 15 1
Honolulu, HI 8,037 300 28
Kalama, WA 1,190 72 6
Longview, WA 1,619 89 8
Olympia, WA 97 5 0
Port Angeles, WA 556 27 2
Portland, OR 11,198 423 40
Seattle, WA 26,292 775 74
Tacoma, WA 19,130 622 59
Vancouver, WA 1,946 85 8
Valdez, AK 6,676 45 8
Other Puget Sound 5,678 197 24
Anchorage, AK 537 22 2
Coos Bay, OR 399 21 2
Hilo, HI 4,516 107 14
Kahului, HI 2,348 57 7
Nawiliwili, HI 591 14 2
Nikishka, AK 1,110 32 4
Beaumont, TX 12,699 665 52
Freeport, TX 7,411 362 28
Galveston, TX 6,572 283 23
Houston, TX 47,147 2,178 173
Port Arthur, TX 3,531 184 15
Texas City, TX 7,382 386 30
Corpus Christi, TX 11,452 584 46
Lake Charles, LA 6,355 266 24
Mobile, AL 8,302 406 33
Brownsville, TX 1,213 69 5
Gulfport, MS 3,556 148 13
Manatee, FL 2,903 132 11
Matagorda Ship 2,504 143 11
Panama City, FL 662 35 3
Pascagoula, MS 3,566 189 15
Pensacola, FL 351 16 1
Tampa, FL 10,941 539 45
Everglades, FL 39,325 1,369 133
New Orleans, LA 27,575 1,249 102
Baton Rouge, LA 4,627 238 19
South Louisiana, LA 18,366 961 75
Plaquemines, LA 4,230 221 17
Albany, NY 396 20 2
New York/New Jersey 86,980 3,247 261
Portland, ME 3,968 195 16
Georgetown, SC 609 31 3
Hopewell, VA 185 10 1
Metric tonnes per year
PM2.5 HC CO SOx Fuel
4 2 4 29 588
2 1 2 14 287
10 1 6 121
1 1 1 10 197
26 10 23 206 4,232
6 2 6 45 926
7 3 7 55 1,130
00 0 3 58
2 1 2 17 356
37 15 33 290 5,969
69 27 59 544 11,186
55 22 49 428 8,801
7 3 7 54 1,106
8 2 4 75 1,541
22 7 15 202 4,153
2 1 2 14 294
2 1 2 12 238
13 4 9 109 2,285
6 2 5 50 1,052
2 1 1 15 309
4 1 3 34 697
48 22 51 384 7,305
26 12 28 209 3,966
22 9 22 175 3,375
161 72 169 1,290 24,544
13 6 14 108 2,049
28 13 30 224 4,266
43 19 45 341 6,504
23 9 21 194 3,686
31 14 31 251 4,780
5 2 5 40 771
12 5 12 95 1,811
10 4 10 82 1,568
10 5 11 83 1,577
3 1 3 20 389
14 6 15 116 2,218
1 1 1 10 199
42 18 42 341 6,505
123 46 105 1,056 20,121
94 41 97 761 14,505
17 8 18 139 2,651
70 32 74 557 10,616
16 7 17 128 2,440
2 1 2 12 231
242 108 251 1,945 37,048
15 6 15 118 2,240
2 1 2 19 362
10 1 6 112
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-9
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-5. Cruise Emissions by Port (continued)
Installed
Power
Port Name (MW)
Marcus Hook, PA 2,754
Morehead City, NC 967
Paulsboro, NJ 3,272
Chester, PA 1,467
Fall River, MA 290
New Castle, DE 765
Penn Manor, PA 721
Providence, Rl 1,097
Brunswick, GA 5,184
Canaveral, FL 17,794
Charleston, SC 46,233
New Haven, CT 1,801
Palm Beach, FL 2,544
Bridgeport, CT 1,452
Camden, NJ 4,209
Philadelphia, PA 7,963
Wilmington, DE 4,444
Wilmington, NC 4,888
Richmond, VA 596
Jacksonville, FL 13,985
Miami, FL 57,682
Searsport, ME 543
Boston, MA 12,417
New Bedford/Fairhaven, MA 181
Baltimore, MD 24,500
Newport News, VA 5,529
Savannah, GA 37,523
Catalina, CA 928
Carquinez, CA 3,442
El Segundo, CA 1,685
Eureka, CA 409
Hueneme, CA 3,334
Long Beach, CA 56,935
Los Angeles, CA 50,489
Oakland, CA 48,762
Redwood City, CA 456
Richmond, CA 3,956
Sacramento, CA 455
San Diego, CA 8,255
San Francisco, CA 6,260
Stockton, CA 1,210
Total Cruise Emissions 863,667
Metric
NOx PMio PM2.5
143 11 10
44 4 3
166 13 12
63 5 5
13 1 1
41 3 3
38 3 3
58 4 4
222 17 16
665 54 50
1,758 138 128
92 7 7
89 8 8
58 4 4
191 15 14
337 27 25
178 14 13
213 17 16
25 2 2
567 46 43
2,065 174 162
27 2 2
430 38 35
8 1 1
1,003 80 74
213 17 16
1,387 110 102
44 4 3
171 13 12
87 7 6
20 2 1
138 11 10
2,142 168 156
1,900 149 138
1,676 131 122
24 2 2
198 15 14
23 2 2
380 30 28
292 23 21
63 5 5
34,370 2,831 2,627
tonnes
HC
5
1
5
2
0
1
1
2
7
22
58
3
3
2
6
11
6
7
1
19
70
1
14
0
33
7
46
1
6
3
1
5
71
63
55
1
7
1
12
10
2
1,146
per year
CO
11
3
13
5
1
3
3
4
17
52
136
7
7
5
15
26
14
16
2
44
160
2
33
1
78
17
107
3
13
7
2
11
166
147
130
2
15
2
29
23
5
2,663
SOx
82
28
97
37
9
23
22
33
129
501
1,029
54
64
34
113
202
104
125
15
345
1,503
17
313
5
597
125
817
25
92
47
11
74
1,157
1,026
900
13
106
13
210
159
34
21,207
Fuel
1,563
530
1,850
710
170
444
424
631
2,456
9,557
19,583
1,024
1,226
651
2,160
3,850
1,974
2,387
280
6,574
28,653
320
5,957
104
11,383
2,377
15,555
506
1,893
964
218
1,527
23,779
21,087
18,494
267
2,179
258
4,316
3,271
696
413,213
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-10
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-6. Reduced Speed Zone Emissions by Port (Deep Sea Ports)
Installed
Power
Port Name (MW) NC
Metric tonnes per year
f
)x PMio PM2.5 HC | CO
Anacortes, WA 545 191 15 14 6 15
Barbers Point, HI 472
Everett, WA 186
Grays Harbor, WA 360
Honolulu, HI 8,037
30000
49 4 4 2 4
30000
75 7 6 3 6
Kalama, WA 1,190 101 8 7 4 9
Longview, WA 1,619 125 10 9 5 11
Olympia, WA 97
Port Angeles, WA 556
43 3 3 1 3
77 6 6 3 6
Portland, OR 11,198 967 86 79 58 108
Seattle, WA 26,292 4,273 349 323 150 345
Tacoma, WA 19,130 3,685 290 269 121 285
Vancouver, WA 1,946 171 14 13 7 16
Valdez, AK 6,676
33 5 5 1 3
Other Puget Sound 5,678 963 112 104 32 75
Anchorage, AK 537 121 10 9 4 10
Coos Bay, OR 399
Hilo, HI 4,516
Kahului, HI 2,348
Nawiliwili, HI 591
50001
27 2 2 1 2
14 1 1 0 1
40000
Nikishka, AK 1,110 117 12 12 4 9
Beaumont, TX 12,699 771 81 75 45 88
Freeport, TX 7,411
28 2 2 1 2
Galveston, TX 6,572 101 10 9 4 8
Houston, TX 47,147 656 57 53 22 50
Port Arthur, TX 3,531
97 10 96 11
Texas City, TX 7,382 181 16 14 6 14
Corpus Christi, TX 11,452 419 33 31 14 293
Lake Charles, LA 6,355 175 20 19 13 185
Mobile, AL 8,302 358 30 28 12 247
Brownsville, TX 1,213
Gulfport, MS 3,556
Manatee, FL 2,903
Matagorda Ship 2,504
Panama City, FL 662
Pascagoula, MS 3,566
Pensacola, FL 351
23 2 1 1 2
50 4 3 2 5
78 7 4 3 7
55 5 3 3 6
71001
71 6 5 2 6
50000
Tampa, FL 10,941 329 29 16 12 28
Everglades, FL 39,325
73 7 7 3 7
New Orleans, LA 27,575 2,669 224 208 98 228
Baton Rouge, LA 4,627 1,091 87 80 36 85
South Louisiana, LA 18,366 2,897 229 212 95 225
Plaquemines, LA 4,230 244 19 18 8 19
Albany, NY 396
48 4 4 2 5
New York/New Jersey 86,980 879 83 77 54 105
Portland, ME 3,968
Georgetown, SC 609
Hopewell, VA 185
48 4 4 2 4
16 1 1 1 1
22 2 2 1 2
SOx
103
2
27
2
48
57
70
23
45
538
2,406
2,024
96
46
942
71
3
18
9
2
99
574
18
73
429
71
117
250
124
225
12
27
36
27
4
39
3
159
53
1,678
648
1,712
144
30
549
30
105
196
Fuel
2,128
33
561
34
1,013
1,194
1,459
474
918
11,377
49,723
41,884
2,026
956
19,456
1,169
39
106
48
15
1,878
9,383
319
1,244
7,613
1,181
2,067
4,848
2,439
4,356
276
650
1,000
665
83
862
71
4,185
1,028
32,674
12,592
33,254
2,799
580
10,935
578
193
245
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-11
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-6. Reduced Speed Zone Emissions by Port (continued)
Installed
Power
Port Name (MW) NOx p
Marcus Hook, PA 2,754 245
Morehead City, NC 967 2
Paulsboro, NJ 3,272 254
Chester, PA 1,467 86
Fall River, MA 290 5
New Castle, DE 765 45
Penn Manor, PA 721 82
Providence, Rl 1,097 26
Brunswick, GA 5,184 215
Canaveral, FL 17,794 73
Charleston, SC 46,233 569
New Haven, CT 1,801 4
Palm Beach, FL 2,544 6
Bridgeport, CT 1,452 2
Camden, NJ 4,209 346
Philadelphia, PA 7,963 525
Wilmington, DE 4,444 206
Wilmington, NC 4,888 110
Richmond, VA 596 44
Jacksonville, FL 13,985 204
Miami, FL 57,682 182
Searsport, ME 543 11
Boston, MA 12,417 125
New Bedford/Fairhaven, MA 181 4
Metric tonnes per year
Mio PM2.5 HC CO SOx
20 18 9 20 1,996
0 0 0 0 16
21 19 9 21 1,841
773 8 343
000 0 29
431 4 295
763 7 598
221 2 225
17 16 7 17 1,019
7 7 2 6 94
46 43 21 49 2,502
000 0 27
1 1 0 1 15
00006
29 27 14 32 1,208
45 41 20 50 2,604
17 16 10 20 747
9 9 5 10 620
432 4 180
17 16 9 19 818
17 16 6 15 325
1 1 0 1 59
12 11 6 12 446
0 0 0 0 10
Baltimore, MD 24,500 4,205 338 314 139 328 2,535
Newport News, VA 5,529 130
Savannah, GA 37,523 1,325
Catalina, CA 928 13
Carquinez, CA 3,442 183
El Segundo, CA 1,685 58
Eureka, CA 409 5
Hueneme, CA 3,334 8
Long Beach, CA 56,935 764
Los Angeles, CA 50,489 770
Oakland, CA 48,762 526
Redwood City, CA 456 25
Richmond, CA 3,956 123
Sacramento, CA 455 58
San Diego, CA 8,255 106
San Francisco, CA 6,260 106
Stockton, CA 1,210 156
11 10 5 11 86
107 99 46 109 803
11018
14 13 6 14 100
5 4 2 5 32
00002
1 1 0 256 5
62 58 30 70 433
63 58 30 70 437
43 40 23 53 272
221 2 14
10 9 4 10 67
542 4 32
9 8 3 8 62
883 8 60
12 11 5 12 85
Total RSZ Emissions 863,667 34,369 2,882 2,653 1,277 3,803 35,016
Fuel
2,846
24
2,993
1,034
72
525
957
305
2,471
1,041
6,675
46
79
31
4,302
6,492
2,512
1,310
530
2,494
2,577
139
1,742
50
48,993
1,569
15,582
159
2,071
658
52
79
9,078
9,156
5,774
284
1,391
656
1,290
1,246
1,755
413,653
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-12
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-7. Maneuvering Emissions by Port (Deep Sea Ports)
Installed
Power
Port Name (MW) NOx PMl°
Anacortes, WA 545 50 5
Barbers Point, HI 472 23 2
Everett, WA 186 9 1
Grays Harbor, WA 360 12 1
Honolulu, HI 8,037 360 36
Kalama, WA 1,190 63 6
Longview, WA 1,619 72 7
Olympia, WA 97 3 0
Port Angeles, WA 556 19 2
Portland, OR 11,198 501 49
Seattle, WA 26,292 976 100
Tacoma, WA 19,130 810 81
Vancouver, WA 1,946 75 7
Valdez, AK 6,676 55 8
Other Puget Sound 5,678 252 29
Anchorage, AK 537 1 0
Coos Bay, OR 399 1 0
Hilo, HI 4,516 12 1
Kahului, HI 2,348 6 1
Nawiliwili, HI 591 1 0
Nikishka, AK 1,110 2 0
Beaumont, TX 12,699 49 14
Freeport, TX 7,411 23 7
Galveston, TX 6,572 40 12
Houston, TX 47,147 169 47
Port Arthur, TX 3,531 17 5
Texas City, TX 7,382 28 8
Corpus Christi, TX 11,452 112 11
Lake Charles, LA 6,355 54 6
Mobile, AL 8,302 70 7
Brownsville, TX 1,213 8 1
Gulfport, MS 3,556 27 3
Manatee, FL 2,903 33 3
Matagorda Ship 2,504 16 2
Panama City, FL 662 4 0
Pascagoula, MS 3,566 21 2
Pensacola, FL 351 2 0
Tampa, FL 10,941 66 7
Everglades, FL 39,325 237 25
New Orleans, LA 27,575 190 19
Baton Rouge, LA 4,627 35 3
South Louisiana, LA 18,366 143 14
Plaquemines, LA 4,230 33 3
Albany, NY 396 3 0
New York/New Jersey 86,980 454 46
Portland, ME 3,968 37 4
Georgetown, SC 609 3 0
Hopewell, VA 185 1 0
Metric tonnes per year
PM2.5 HC CO SOx Fuel
3 3 5 23 506
2 1 2 10 225
11 1 4 95
11 1 6 129
28 19 32 194 4,162
4 4 6 31 667
5 4 7 35 757
00 0 2 34
1 1 2 10 209
37 33 50 232 5,080
76 70 97 445 9,679
62 57 82 368 8,058
5 4 8 36 795
6 3 5 46 992
22 13 25 163 3,513
000 1 17
00008
11 1 8 175
101 4 88
000 1 23
000 1 28
12 2 4 95 600
6 1 2 45 282
5 1 3 38 526
31 6 13 255 2,122
3 1 1 25 214
7 1 2 59 334
10 8 14 68 1,378
5 4 6 38 756
6 5 8 44 883
11 1 7 102
2 2 3 20 322
3 2 4 25 409
1 1 2 13 191
00 0 3 45
2 2 3 18 264
00 0 2 21
6 4 8 95 828
23 13 23 166 3,282
17 13 22 117 2,353
3 2 4 21 431
12 10 18 87 1,761
3 2 4 20 407
00 0 2 38
42 35 53 266 5,367
3 2 4 23 463
00 0 2 40
000 1 12
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-13
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-7. Maneuvering Emissions by Port (continued)
Installed
Power
Port Name (MW) NOx PMl°
Marcus Hook, PA 2,754 22 2
Morehead City, NC 967 5 0
Paulsboro, NJ 3,272 24 2
Chester, PA 1,467 5 1
Fall River, MA 290 1 0
New Castle, DE 765 5 0
Penn Manor, PA 721 4 0
Providence, Rl 1,097 7 1
Brunswick, GA 5,184 25 2
Canaveral, FL 17,794 70 7
Charleston.SC 46,233 198 20
New Haven, CT 1,801 11 1
Palm Beach, FL 2,544 10 1
Bridgeport, CT 1,452 10 1
Camden, NJ 4,209 27 3
Philadelphia, PA 7,963 48 5
Wilmington, DE 4,444 22 2
Wilmington, NC 4,888 24 2
Richmond, VA 596 2 0
Jacksonville, FL 13,985 67 7
Miami, FL 57,682 241 25
Searsport, ME 543 4 0
Boston, MA 12,417 59 6
New Bedford/Fairhaven, MA 181 1 0
Baltimore, MD 24,500 129 13
Newport News, VA 5,529 25 3
Savannah, GA 37,523 163 17
Catalina, CA 928 10 1
Carquinez, CA 3,442 23 1
ElSegundo, CA 1,685 9 1
Eureka, CA 409 4 0
Hueneme, CA 3,334 9 0
Long Beach, CA 56,935 272 15
Los Angeles, CA 50,489 242 13
Oakland, CA 48,762 241 10
Redwood City, CA 456 3 0
Richmond, CA 3,956 26 2
Sacramento, CA 455 3 0
San Diego, CA 8,255 80 6
San Francisco, CA 6,260 54 4
Stockton, CA 1,210 7 0
Total Maneuver Emissions 863,667 7,374 758
Metric tonnes per year
PM2.5 HC CO SOx Fuel
2 2 3 14 274
0013 64
2 2 3 15 299
1013 64
0001 19
0013 62
0002 50
1014 85
2 2 3 15 306
6 3 6 50 979
18 17 24 112 2,275
1117 137
1117 131
1116 132
2 2 3 17 343
4 3 6 29 585
2 2 3 13 270
2 2 3 14 290
0001 25
6 5 8 40 822
24 14 24 165 3,273
0002 46
6 4 7 40 802
0001 16
12 10 15 76 1,551
2 2 3 14 292
15 14 20 92 1,873
1016 154
11 1 11 242
1014 93
0002 44
0014 98
13 6 15 120 2,897
12 5 13 106 2,569
9 5 11 89 2,160
0001 27
1 1 2 12 273
0001 27
6 2 6 46 1,149
41 4 29 703
0003 71
625 439 723 4,355 84,244
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-14
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-8. Hotelling Emissions by Port (Deep Sea Ports)
Installed
Power
Port Name (MW) NOx PMl°
Anacortes, WA 545 113 9
Barbers Point, HI 472 64 5
Everett, WA 186 14 1
Grays Harbor, WA 360 20 2
Honolulu, HI 8,037 533 45
Kalama, WA 1,190 123 9
Longview, WA 1,619 126 10
Olympia, WA 97 5 0
Port Angeles, WA 556 29 2
Portland, OR 11,198 413 31
Seattle, WA 26,292 622 49
Tacoma, WA 19,130 624 47
Vancouver, WA 1,946 107 8
Valdez, AK 6,676 210 16
Other Puget Sound 5,678 699 53
Anchorage, AK 537 75 6
Coos Bay, OR 399 8 1
Hilo, HI 4,516 783 60
Kahului, HI 2,348 392 30
Nawiliwili, HI 591 103 8
Nikishka, AK 1,110 119 9
Beaumont, TX 12,699 622 114
Freeport, TX 7,411 296 55
Galveston, TX 6,572 588 73
Houston, TX 47,147 1,594 269
Port Arthur, TX 3,531 138 23
Texas City, TX 7,382 359 73
Corpus Christi, TX 11,452 618 53
Lake Charles, LA 6,355 346 29
Mobile, AL 8,302 347 29
Brownsville, TX 1,213 42 6
Gulfport, MS 3,556 378 31
Manatee, FL 2,903 413 35
Matagorda Ship 2,504 175 15
Panama City, FL 662 12 2
Pascagoula, MS 3,566 249 21
Pensacola, FL 351 16 1
Tampa, FL 10,941 570 49
Everglades, FL 39,325 2,653 241
New Orleans, LA 27,575 2,427 210
Baton Rouge, LA 4,627 601 51
South Louisiana, LA 18,366 2,427 201
Plaquemines, LA 4,230 547 45
Albany, NY 396 32 3
New York/New Jersey 86,980 2,707 235
Portland, ME 3,968 442 37
Georgetown, SC 609 38 3
Hopewell, VA 185 9 1
Metric tonnes per year
PM2.5 HC CO SOx Fuel
8 3 9 71 1,636
4 2 5 40 932
10 1 9 205
1 1 2 12 288
41 15 40 352 7,680
9 3 9 77 1,786
9 3 10 79 1,835
00 0 3 77
2 1 2 18 414
29 11 31 259 5,986
45 17 47 397 8,999
43 17 47 391 9,058
7 3 8 67 1,558
15 6 16 132 3,043
48 19 53 438 10,147
5 2 6 47 1,108
1 1 1 12 284
54 22 60 491 11,366
27 11 30 246 5,690
7 3 8 65 1,502
8 3 9 75 1,730
105 22 46 919 9,019
51 11 22 445 4,362
67 21 36 587 8,567
246 57 115 2,162 23,517
21 5 10 184 2,004
67 13 28 585 5,452
49 18 49 430 9,337
27 10 27 235 5,105
27 10 27 235 5,106
6 2 6 49 1,069
29 10 29 272 5,505
32 12 32 307 6,104
13 5 13 117 2,539
2 1 2 16 359
19 7 19 171 3,663
1 0 1 12 251
45 16 43 392 8,285
223 73 202 1,883 38,497
193 68 188 1,678 35,934
47 17 47 414 9,009
185 67 185 1,620 35,220
42 15 42 365 7,944
2 1 2 21 461
215 77 210 1,874 40,179
34 12 34 296 6,407
3 1 3 26 557
10 1 9 185
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-15
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-8. Hotelling Emissions by Port (continued)
Installed
Power
Port Name (MW)
Marcus Hook, PA 2,754
Morehead City, NC 967
Paulsboro, NJ 3,272
Chester, PA 1,467
Fall River, MA 290
New Castle, DE 765
Penn Manor, PA 721
Providence, Rl 1,097
Brunswick, GA 5,184
Canaveral, FL 17,794
Charleston, SC 46,233
New Haven, CT 1,801
Palm Beach, FL 2,544
Bridgeport, CT 1,452
Camden, NJ 4,209
Philadelphia, PA 7,963
Wilmington, DE 4,444
Wilmington, NC 4,888
Richmond, VA 596
Jacksonville, FL 13,985
Miami, FL 57,682
Searsport, ME 543
Boston, MA 12,417
New Bedford/Fairhaven, MA 181
Baltimore, MD 24,500
Newport News, VA 5,529
Savannah, GA 37,523
Catalina, CA 928
Carquinez, CA 3,442
El Segundo, CA 1,685
Eureka, CA 409
Hueneme, CA 3,334
Long Beach, CA 56,935
Los Angeles, CA 50,489
Oakland, CA 48,762
Redwood City, CA 456
Richmond, CA 3,956
Sacramento, CA 455
San Diego, CA 8,255
San Francisco, CA 6,260
Stockton, CA 1,210
Total Hotel Emissions 863,667
Metric
NOx PM10 PM2.5
555 46 42
64 6 5
209 19 17
41 3 3
15 2 2
108 9 8
47 4 4
108 9 8
203 17 16
2,202 213 198
1,308 113 104
179 15 14
122 10 10
167 15 13
430 36 33
566 50 46
205 18 17
222 24 22
15 1 1
533 55 51
4,551 434 402
68 6 5
772 75 69
25 2 2
967 81 75
140 11 10
793 63 58
21 2 2
159 10 9
37 2 2
55 4 3
164 11 10
2,189 144 130
1,941 127 116
581 37 34
55 4 3
137 9 8
54 3 3
326 23 21
257 18 16
107 7 6
44,525 4,047 3,714
tonnes
HC
15
2
6
1
1
3
1
3
6
62
38
5
3
5
12
17
6
8
0
18
128
2
24
1
27
4
22
1
4
1
2
5
60
53
16
2
4
1
9
7
3
1,283
per year
CO
42
5
17
3
2
8
4
8
16
169
103
14
9
13
33
46
17
22
1
51
352
5
65
2
74
11
60
2
13
3
4
13
172
152
46
4
11
4
25
20
8
3,444
SOx
371
47
150
27
13
72
34
72
140
1,635
911
120
84
117
287
402
147
196
10
447
3,351
46
594
17
662
93
529
13
107
25
36
107
1,420
1,259
376
36
92
35
209
167
70
33,112
Fuel
8,061
1,015
3,239
594
288
1,563
730
1,563
3,036
32,348
19,715
2,600
1,808
2,545
6,246
8,721
3,198
4,265
212
9,710
67,146
994
12,467
364
14,037
2,032
11,503
325
2,431
558
843
2,510
33,530
29,734
8,901
840
2,086
821
4,995
3,928
1,628
663,087
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-16
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-9. Auto Carrier Deep Sea Port Emissions
Installed
Power
Port Name (MW) NOx p
Baltimore, MD 5,458 1,290
New York/New Jersey 4,588 329
Jacksonville, FL 4,420 337
Brunswick, GA 3,313 364
Portland, OR 2,331 416
Tacoma, WA 2,123 733
Hueneme, CA 2,036 125
Charleston, SC 1,922 148
San Diego, CA 1,374 132
Houston, TX 1,141 122
Long Beach, CA 1,068 96
Wilmington, DE 1,012 126
Los Angeles, CA 947 87
Boston, MA 744 62
Carquinez, CA 682 84
Savannah, GA 644 76
Galveston, TX 560 59
Honolulu, HI 539 59
Richmond, CA 468 51
Tampa, FL 284 23
Vancouver, WA 278 48
Newport News, VA 270 27
Mobile, AL 182 26
Pensacola, FL 169 12
Everglades, FL 136 22
Miami, FL 131 10
Philadelphia, PA 111 16
Bridgeport, CT 40 3
Morehead City, NC 35 3
Beaumont, TX 31 4
San Francisco, CA 20 2
Matagorda Ship 16 1
South Louisiana, LA 16 3
Oakland, CA 101
Seattle, WA 93
Chester, PA 92
Port Angeles, WA 6 1
Manatee, FL 40
Total Auto Carrier 37,129 4,901
Metric tonnes per year
Mio PM2.5 HC CO SOx
103 95 43 101 768
30 28 15 32 218
32 29 14 32 362
29 27 12 29 494
38 33 21 42 246
61 55 27 59 414
9 8 4 157 71
15 14 6 14 169
9 9 4 10 77
12 11 4 8 92
7 63 7 55
10 10 5 11 180
6 63 7 50
5 52 5 54
6 63 6 49
6 63 6 46
6 52 4 43
5 53 5 35
4 32 4 30
2 21 2 15
4 42 5 28
2 21 2 20
2 21 7 19
11018
2 21 2 14
11017
1 11 1 27
00002
00002
11004
00001
00001
00002
00001
00002
00004
00001
00000
412 377 181 563 3,608
Fuel
15,128
4,374
4,848
4,461
5,313
8,699
1,498
2,273
1,672
1,577
1,192
1,575
1,081
784
1,053
910
745
753
647
312
611
354
382
163
295
124
190
42
32
61
26
15
40
14
34
20
15
4
37,129
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-17
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-10. Barge
Installed
Power
Port Name (MW)
New Orleans, LA 472
Charleston, SC 420
Morehead City, NC 73
Mobile, AL 2
Total Barge Carrier 967
Carrier Deep Sea Port Emissions
Metric tonnes per year
NOx PMio PM2.5
72 8 7
55 4 4
6 1 1
000
133 13 12
HC
3
2
0
0
5
CO
8
4
0
0
12
SOx
57
78
5
0
141
Fuel
1,174
716
104
5
1,999
Table 3-11. Bulk Carrier Deep Sea Port Emissions
Installed
Power
Port Name (MW)
South Louisiana, LA 11,606
New Orleans, LA 8,311
Houston, TX 5,996
Tampa, FL 3,380
Corpus Christi, TX 3,359
New York/New Jersey 3,168
Baltimore, MD 2,851
Mobile, AL 2,752
Plaquemines, LA 2,714
Portland, OR 2,351
Long Beach, CA 2,297
Los Angeles, CA 2,037
Baton Rouge, LA 1,668
Charleston, SC 1,589
Savannah, GA 1,474
Jacksonville, FL 1,394
Longview, WA 1,142
Lake Charles, LA 1,116
Galveston, TX 1,063
Beaumont, TX 1,055
Kalama, WA 1,007
Vancouver, WA 1,003
Port Arthur, TX 890
Tacoma, WA 872
Camden, NJ 775
Carquinez, CA 717
Newport News, VA 692
Brownsville, TX 685
Penn Manor, PA 659
Stockton, CA 638
Everglades, FL 626
Pascagoula, MS 586
Matagorda Ship 586
Seattle, WA 523
Providence, Rl 511
San Francisco, CA 498
Texas City, TX 481
Philadelphia, PA 473
Metric tonnes per year
NOx PMio PM2.5
4,014 323 298
2,511 202 187
655 66 54
602 49 43
460 37 34
482 41 37
1,160 94 87
401 32 30
665 54 50
633 51 46
468 33 30
423 29 27
722 58 53
238 19 18
334 27 25
203 17 15
265 22 19
147 13 12
114 11 9
185 19 16
233 19 17
256 21 19
106 11 9
445 35 32
176 14 13
172 12 11
118 10 9
74 9 8
160 13 12
198 14 13
109 9 8
116 9 8
118 10 9
244 19 18
78 6 6
101 7 6
60 6 5
105 8 8
HC
127
79
22
20
16
16
38
14
21
23
14
13
23
8
11
6
10
6
4
9
8
9
4
15
6
5
4
3
5
6
3
4
4
8
3
3
2
3
CO
313
196
48
48
121
39
90
115
52
53
36
33
56
19
26
16
22
46
8
18
20
22
9
35
14
13
9
8
13
15
9
9
10
19
6
8
4
8
SOx
2,470
1,550
446
365
278
317
711
241
417
364
283
255
439
449
205
337
154
91
78
129
136
147
74
247
714
103
78
65
637
116
70
70
71
135
154
61
40
296
Fuel
50,129
31,598
7,741
7,530
5,550
6,532
13,937
4,793
8,603
7,873
6,331
5,702
8,819
3,057
4,159
2,650
3,349
1,844
1,346
2,198
2,956
3,182
1,265
5,221
2,173
2,275
1,513
1,330
1,987
2,528
1,461
1,441
1,481
2,860
992
1,368
698
1,307
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-18
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-11. Bulk Carrier Deep Sea Port Emissions (continued)
Installed
Power
Port Name (MW) NOx PMl°
Portland, ME 470 62 5
Boston, MA 468 60 5
Canaveral, FL 464 59 5
Redwood City, CA 437 103 7
New Haven, CT 424 55 4
Wilmington, NC 422 68 5
Georgetown, SC 408 63 5
Freeport, TX 392 35 4
Richmond, CA 385 83 6
Brunswick, GA 370 75 6
San Diego, CA 350 64 4
Wilmington, DE 345 66 5
Manatee, FL 322 60 5
Albany, NY 280 79 6
Oakland, CA 280 40 3
Nikishka, AK 246 74 6
Marcus Hook, PA 243 54 4
New Castle, DE 240 51 4
Sacramento, CA 218 72 5
Paulsboro, NJ 168 30 3
Honolulu, HI 158 29 2
Grays Harbor, WA 140 24 2
Morehead City, NC 130 11 1
Hopewell, VA 127 26 2
Fall River, MA 127 13 2
Miami, FL 122 9 1
Gulfport, MS 120 17 2
Eureka, CA 114 28 2
Bridgeport, CT 98 13 1
Coos Bay, OR 87 6 1
Palm Beach, FL 83 8 1
Barbers Point, HI 82 14 1
Panama City, FL 79 13 1
Olympia, WA 73 45 4
Port Angeles, WA 72 22 2
Everett, WA 71 33 3
Anacortes, WA 67 28 2
Anchorage, AK 52 20 2
Searsport, ME 37 6 0
Chester, PA 35 7 1
Kahului, HI 34 4 0
Hilo, HI 31 3 0
Pensacola, FL 25 4 0
Richmond, VA 11 3 0
Valdez, AK 7 1 0
Total Bulk Carrier 82,455 19,189 1,562
Metric tonnes per year
PM2.5 HC CO SOx Fuel
5 2 5 38 772
5 2 5 91 871
4 2 5 54 771
7 3 8 61 1,364
4 2 4 43 717
52 5 160 854
52 5 116 819
3 1 3 25 423
5 2 6 50 1,111
62 6 276 922
4 2 5 39 871
52 5 215 820
4 2 5 36 743
6 3 7 49 990
3 1 3 23 511
5 2 6 41 820
42 4 192 674
4 2 4 37 654
5 2 6 42 925
3 1 3 57 478
2 1 2 17 373
2 1 2 14 306
1 1 1 13 217
21 2 144 367
1 1 1 30 249
1 1 1 17 205
2 1 2 13 262
2 1 2 18 400
1 0 1 10 172
101 6 122
101 9 139
111 9 188
101 8 162
3 2 4 24 512
2 1 2 12 266
2 1 3 18 379
2 1 2 15 324
2 1 2 12 240
000 9 71
1 0 1 26 91
000 2 45
000 2 39
000 3 56
0 0 0 18 37
000 1 18
1,423 629 1,723 14,885 241,128
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-19
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-12. Container Ship Deep Sea Port Emissions
Installed
Power
Port Name (MW) NOx p
New York/New Jersey 56,253 3,236
Oakland, CA 47,109 2,835
Long Beach, CA 42,292 3,436
Charleston, SC 37,982 2,767
Los Angeles, CA 37,505 3,099
Savannah, GA 28,209 2,165
Seattle, WA 21,749 5,207
Miami, FL 21,100 1,312
Tacoma, WA 15,446 3,108
Houston, TX 13,441 696
Baltimore, MD 9,224 1,422
Everglades, FL 8,753 703
New Orleans, LA 5,756 788
Portland, OR 5,227 879
Boston, MA 5,016 274
Jacksonville, FL 4,219 274
Newport News, VA 3,797 254
Philadelphia, PA 2,696 303
Honolulu, HI 2,190 308
Wilmington, DE 1,999 197
Wilmington, NC 1,779 130
Freeport, TX 1,575 74
Gulfport, MS 1,538 181
San Francisco, CA 1,209 102
Chester, PA 1,140 139
Palm Beach, FL 1,018 54
Richmond, VA 539 74
Galveston, TX 427 21
San Diego, CA 385 30
Richmond, CA 165 15
Hueneme, CA 82 6
Eureka, CA 55 6
Mobile, AL 39 4
South Louisiana, LA 36 5
Lake Charles, LA 36 4
Carquinez, CA 27 3
Everett, WA 24 6
Corpus Christi, TX 24 2
Morehead City, NC 24 2
Port Angeles, WA 14 2
New Haven, CT 14 1
Plaquemines, LA 12 1
Vancouver, WA 7 4
Total Container Ship 380,131 34,125 2
Metric tonnes per year
Mio PM2.5 HC CO SOx
269 249 130 280 1,949
208 192 94 224 1,532
244 225 109 272 1,986
227 210 98 226 3,175
221 203 99 247 1,791
176 163 79 179 1,298
444 396 217 439 2,859
108 100 46 106 963
264 236 124 253 1,741
59 55 23 52 447
114 105 50 114 843
60 56 25 57 460
65 60 35 76 482
85 74 59 96 486
27 25 12 27 295
24 22 11 24 275
21 19 9 21 151
25 23 13 28 657
30 25 15 27 181
16 15 8 18 379
11 10 5 12 162
6 62 6 46
15 14 7 15 110
7 73 8 59
11 10 5 11 306
5 42 4 40
6 63 7 182
221 2 14
221 2 17
11018
00064
00004
00012
00013
00012
00002
00003
00001
00001
00001
00001
00001
00002
757 2,516 1,288 2,845 22,920
Fuel
38,624
33,033
44,035
33,850
39,671
25,599
60,078
16,260
36,626
8,056
16,499
9,424
9,720
10,412
3,931
3,516
2,988
3,668
3,897
2,374
1,607
842
2,328
1,311
1,669
728
882
249
382
187
82
81
49
62
42
34
66
26
18
25
11
13
45
413,000
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-20
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-13. General Cargo Ship Deep Sea Port Emissions
Installed
Power
Port Name (MW) NOx PMl°
Houston, TX 5,806 560 59
Miami, FL 2,941 354 31
New Orleans, LA 2,925 601 50
Mobile, AL 2,529 297 25
Savannah, GA 2,521 415 34
Baltimore, MD 2,275 673 56
New York/New Jersey 1,841 153 13
Charleston.SC 1,814 214 17
Everglades, FL 1,813 197 18
Jacksonville, FL 1,237 152 13
Wilmington, NC 1,178 155 13
Brunswick, GA 1,102 173 14
Long Beach, CA 996 158 11
Tampa, FL 986 118 10
Camden, NJ 974 180 15
Philadelphia, PA 960 129 14
Port Arthur, TX 890 100 11
Los Angeles, CA 883 143 10
San Diego, CA 867 144 10
Seattle, WA 841 261 21
South Louisiana, LA 810 216 18
Portland, OR 771 121 10
Beaumont, TX 744 113 12
Palm Beach, FL 722 76 7
Lake Charles, LA 670 71 7
Canaveral, FL 596 42 5
Newport News, VA 568 74 6
Panama City, FL 545 40 4
Vancouver, WA 514 66 6
Gulfport, MS 496 51 4
Pascagoula, MS 466 45 4
Oakland, CA 462 43 3
San Francisco, CA 453 82 6
Longview, WA 441 61 5
Port Angeles, WA 390 90 7
Morehead City, NC 387 40 3
New Haven, CT 382 43 4
Portland, ME 380 31 3
Baton Rouge, LA 356 111 10
Coos Bay, OR 312 28 3
Manatee, FL 301 36 3
Tacoma, WA 264 106 9
Freeport, TX 238 17 2
Chester, PA 237 40 3
Grays Harbor, WA 220 26 2
Brownsville, TX 206 23 2
Sacramento, CA 202 58 4
Stockton, CA 202 55 4
Metric tonnes per year
PM2.5 HC CO SOx Fuel
52 19 42 439 7,370
29 11 28 272 5,034
46 20 48 384 7,709
23 10 85 190 3,747
32 14 32 261 5,197
52 22 52 430 8,419
12 6 13 95 1,872
16 7 17 306 2,733
16 6 16 138 2,845
12 5 12 160 2,018
12 5 12 237 1,975
13 6 14 486 2,115
10 5 12 94 2,092
9 4 9 75 1,503
14 6 15 349 2,347
13 6 14 315 2,170
9 4 9 77 1,283
9 4 11 85 1,886
9 4 11 87 1,949
19 9 21 145 3,033
16 7 17 134 2,671
9 5 11 69 1,473
12 5 11 89 1,474
6 2 6 54 1,107
6 3 22 49 990
5 2 5 55 851
6 2 6 47 953
4 2 4 33 650
5 3 6 38 805
4 2 4 32 643
4 2 4 30 602
3 1 3 23 505
5 2 6 50 1,116
5 2 5 35 747
7 3 7 49 1,034
3 1 3 30 529
3 1 3 30 563
3 1 2 25 479
9 4 9 73 1,453
3 1 3 21 446
3 1 3 22 449
8 4 8 62 1,297
2 1 2 16 265
3 1 3 71 527
2 1 2 16 343
2 1 2 15 298
4 2 5 34 747
4 2 4 32 699
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-21
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-13. General Cargo Ship Deep Sea Port Emissions (continued)
Installed Metri
Power
Port Name (MW) NOx PMl° PM2
Georgetown, SC 202 26 2
Corpus Christi, TX 188 20 2
Wilmington, DE 185 28 2
Eureka, CA 183 42 3
Plaquemines, LA 178 29 2
Fall River, MA 139 17 2
Boston, MA 122 14 1
Kalama, WA 116 15 1
Galveston, TX 111 10 1
Albany, NY 83 15 1
Hueneme, CA 77 7 0
Pensacola, FL 71 7 1
Richmond, CA 67 13 1
Everett, WA 58 19 2
Penn Manor, PA 56 10 1
Texas City, TX 46 5 1
Hopewell, VA 44 12 1
Honolulu, HI 43 6 1
Marcus Hook, PA 39 7 1
Carquinez, CA 39 8 1
Richmond, VA 38 7 1
Providence, Rl 32 4 0
Matagorda Ship 27 2 0
Nikishka, AK 24 7 1
Olympia, WA 24 1 1 1
Anacortes, WA 23 5 0
Paulsboro, NJ 22 3 0
Redwood City, CA 19 4 0
Kahului, HI 710
Valdez, AK 6 1 0
Hilo, HI 510
Anchorage, AK 410
Searsport, ME 300
c tonnes per year
.5 HC CO SOx Fuel
2 1 2 35 334
2 1 5 14 275
2 1 2 43 366
3 1 3 26 583
2 1 2 19 374
1 1 1 16 243
1 0 1 13 190
11 1 8 178
10 1 9 153
1 1 1 10 201
0 0 10 4 85
101 5 90
10 1 8 175
1 1 1 11 240
1 0 1 18 157
1 0 0 4 73
1 0 1 42 140
101 4 92
10 1 16 94
10 1 5 109
10 1 5 101
00 0 7 50
00 0 2 33
001 4 78
10 1 6 132
00 0 3 69
00 0 2 46
00 0 2 54
00009
000 1 12
00007
000 1 15
00015
Total General Cargo 49,992 7,335 632 579 252 686 6,203 95,773
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-22
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-14. Miscellaneous Ship Deep Sea Port Emissions
Installed
Power
Port Name (MW) NOx PMl°
Mobile, AL 709 99 9
Corpus Christi, TX 119 16 2
Pensacola, FL 65 11 1
Anchorage, AK 58 22 2
New York/New Jersey 26 7 1
Baltimore, MD 23 14 1
Portland, OR 21 7 1
Honolulu, HI 16 4 0
Houston, TX 1310
New Orleans, LA 12 7 1
Seattle, WA 950
Newport News, VA 620
Lake Charles, LA 300
Kahului, HI 100
Total Miscellaneous 1,081 195 18
Metric tonnes per year
PM2.5 HC CO SOx Fuel
9 3 30 74 1,464
1 1 5 12 239
10 1 8 156
2 1 2 15 312
10 1 5 102
1 0 1 10 212
00 0 4 85
00 0 2 47
000 1 15
101 4 88
00 0 3 56
00 0 2 32
00006
00003
17 7 41 140 2,816
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-23
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-15. Passenger Ship Deep
Installed
Power
Port Name (MW) NOx PMl°
Miami, FL 28,808 4,919 463
Everglades, FL 22,083 2,447 244
Canaveral, FL 15,756 2,758 256
New York/New Jersey 6,841 745 74
Long Beach, CA 5,756 629 52
New Orleans, LA 5,401 1,133 110
San Diego, CA 5,172 507 42
Los Angeles, CA 5,105 573 47
Hilo, HI 4,467 923 76
Honolulu, HI 4,359 637 58
Tampa, FL 3,599 352 34
Galveston, TX 3,248 644 76
Seattle, WA 3,017 739 72
Boston, MA 2,874 430 41
Kahului, HI 2,281 462 38
San Francisco, CA 2,241 237 19
Catalina, CA 919 87 7
Charleston.SC 758 113 10
Houston, TX 751 143 19
Manatee, FL 634 66 7
Nawiliwili, HI 583 120 10
Baltimore, MD 359 427 42
Mobile, AL 330 80 7
Anchorage, AK 200 66 5
Palm Beach, FL 146 15 1
Paulsboro, NJ 126 30 3
Corpus Christi, TX 113 21 2
Portland, OR 60 12 1
Eureka, CA 57 7 1
Philadelphia, PA 44 11 1
Valdez, AK 31 2 0
Hueneme, CA 29 2 0
New Bedford/Fairhaven, MA 16 2 0
Savannah, GA 1651
Fall River, MA 11 1 0
Total Passenger 126,193 19,345 1,816
Sea Port Emissions
Metric tonnes per year
PM2.5 HC CO SOx Fuel
430 142 373 3,712 72,350
227 73 187 1,897 36,860
238 80 209 2,044 39,854
68 25 59 551 10,802
48 19 48 371 8,313
102 37 91 835 16,195
38 15 39 298 6,677
43 17 44 338 7,555
70 27 72 622 13,862
53 19 48 427 8,968
25 12 28 271 5,079
64 23 42 559 9,042
66 23 54 540 11,206
38 13 33 326 6,346
34 14 36 304 6,771
18 7 18 140 3,140
7 3 7 51 1,134
9 3 9 83 1,601
15 5 9 131 2,054
5 2 5 52 963
9 4 9 82 1,825
39 13 33 320 6,230
6 2 11 52 1,112
5 2 5 43 784
1 0 1 11 215
3 1 2 23 438
2 1 3 14 300
111 8 175
1 0 1 4 93
101 8 160
000 2 34
004 1 32
000 2 30
000 4 76
000 1 20
1,665 583 1,482 14,127 280,295
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-24
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-16. Refrigerated Cargo Ship Deep Sea Port Emissions
Installed
Power
Port Name (MW) NC
Metric tonnes per year
x PMio PM2.5 HC CO SOx
Camden, NJ 2,088 531 44 41 19 45 341
New York/New Jersey 1,575 195 16 15 7 16 123
Manatee, FL 1,277 453 37 33 14 36 307
Bridgeport, CT 1,086 188 15 14 6 15 121
Philadelphia, PA 994 246 21 19 9 21 158
Hueneme, CA 963 161 11 10 5 81 99
Miami, FL 742 130 11 10 4 10 84
Wilmington, DE 733 171 14 13 6 14 110
Long Beach, CA 662 94 6 6 3 7 56
Los Angeles, CA 587 85 6 5 2 7 51
Galveston, TX 532 87 9836 70
Canaveral, FL 525
Gulfport, MS 374
Tampa, FL 245
Pascagoula, MS 232
Jacksonville, FL 173
96 8 7 3 7 63
56 5 4 2 4 37
38 3 3 1 3 25
54 5 4 2 4 38
34 3 3 1 3 22
New Orleans, LA 163 109 9 8 3 9 72
Brunswick, GA 158
Anchorage, AK 140
Everglades, FL 116
Corpus Christi, TX 97
Charleston, SC 82
Houston, TX 78
New Bedford/Fairhaven, MA 69
Seattle, WA 55
San Diego, CA 48
Baltimore, MD 47
Mobile, AL 22
Honolulu, HI 6
Morehead City, NC 6
Searsport, ME 5
Paulsboro, NJ 4
Port Angeles, WA 3
Pensacola, FL 2
32 3 2 1 2 20
62 5 4 2 5 36
71 6 5 2 5 47
21 2 2 1 3 13
16 1 1 0 1 10
13 1 1 0 1 11
15 1 1 0 1 10
30 2 2 1 2 17
911015
58 5 4 2 4 38
50001 3
3 0000 2
1 0000 1
1 0000 1
1 0000 1
1 0000 1
000000
Total Reefer 13,889 3,068 251 229 100 317 1,994
Fuel
7,137
2,561
6,235
2,575
3,330
2,280
1,780
2,299
1,281
1,152
1,170
1,323
729
502
750
466
1,542
431
709
1,013
282
215
177
214
378
126
830
66
36
18
14
17
18
4
41,657
Table 3-17. Roll-On/Roll-Off Ship Deep Sea Port Emissions
Installed
Power
Port Name (MW) NC
Metric tonnes per year
x PMio PM2.5 HC CO SOx
Everglades, FL 3,734 285 27 25 10 23 209
Miami, FL 3,646 268 33 31 12 30 261
New York/New Jersey 3,323 289 24 22 11 25 176
Baltimore, MD 3,284 836 65 60 28 65 494
Savannah, GA 2,578 277 22 20 9 22 169
Gulfport, MS 1,028 299 26 23 9 23 222
NewOrleans, LA 1,008 161 14 13 6 15 102
Oakland, CA 901 104 8 73 8 59
Fuel
4,206
5,375
3,546
9,700
3,374
4,326
2,065
1,267
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-25
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-17. Roll-On/Roll-Off Ship Deep Sea Port Emissions (continued)
Port Name
Installed
Power
(MW)
Metric tonnes per year
NOx
PM10
PM2.5
HC
CO
SOx
Fuel
Jacksonville, FL
Houston, TX
Mobile, AL
Long Beach, CA
Charleston, SC
Los Angeles, CA
Palm Beach, FL
Wilmington, NC
Philadelphia, PA
Portland, ME
Boston, MA
Brunswick, GA
Tampa, FL
Tacoma, WA
Portland, OR
Newport News, VA
Canaveral, FL
Beaumont, TX
Galveston, TX
Hueneme, CA
Chester, PA
Honolulu, HI
New Haven, CT
Everett, WA
Morehead City, NC
Bridgeport, CT
Camden, NJ
Pensacola, FL
Vancouver, WA
Seattle, WA
Wilmington, DE
Richmond, VA
Corpus Christi, TX
Lake Charles, LA
Albany, NY
Penn Manor, PA
Longview, WA
Anchorage, AK
South Louisiana, LA
Barbers Point, HI
Manatee, FL
892
810
576
483
455
428
423
342
333
305
235
219
166
148
110
77
75
62
59
52
47
39
32
27
27
23
22
18
11
11
10
8
6
6
6
6
5
5
4
4
3
105
72
82
67
45
61
49
33
58
28
29
17
48
45
8
12
9
13
7
5
8
3
3
2
2
3
4
5
2
4
1
2
1
1
1
1
1
2
1
0
1
9
10
7
5
4
4
4
3
5
3
3
2
4
4
1
1
1
1
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
16
5
3
5
4
3
5
2
2
2
4
4
1
1
1
1
0
8
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
73
75
53
39
29
36
35
22
42
20
21
14
36
25
5
8
6
9
5
3
5
2
2
1
2
2
3
4
1
2
1
1
1
1
1
1
1
1
1
0
1
1,499
1,285
1,088
852
588
773
723
437
855
414
426
277
701
520
102
167
131
164
91
56
95
41
51
20
33
35
56
73
25
47
15
27
10
10
16
17
12
24
11
1
11
Total RoRo
26,071 3,360
292
269
118
296 2,279 45,635
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-26
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-18. Tanker Ship Deep Sea Port Emissions
Installec
Power
Port Name (MW)
i
NOx PMio
Houston, TX 19,096 2,334 319
Beaumont, TX 10,807 1,791 228
New York/New Jersey 9,361 1,851 157
Corpus Christi, TX 7,498 1,188 99
Texas City, TX 6,856 889 121
Valdez, AK 6,632 338 36
South Louisiana, LA 5,886 2,187 177
Other Puget Sound 5,678 2,111 219
Freeport, TX 5,206 584 80
Lake Charles, LA 4,517 619 60
NewOrleans, LA 3,506 1,149 96
Long Beach, CA 3,380 419 31
Los Angeles, CA 2,998 383 28
Boston, MA 2,954 516 50
Paulsboro, NJ 2,952 589 48
Richmond, CA 2,871 323 24
Portland, ME 2,813 601 49
Baton Rouge, LA 2,603 1,133 93
Marcus Hook, PA 2,472 904 74
Philadelphia, PA 2,352 609 50
Tampa, FL 2,282 323 27
Pascagoula, MS 2,275 313 27
Savannah, GA 2,083 395 31
Everglades, FL 2,036 495 41
Carquinez, CA 1,977 270 20
Matagorda Ship 1,875 268 22
San Francisco, CA 1,839 184 14
Port Arthur, TX 1,751 230 31
ElSegundo, CA 1,685 192 14
Jacksonville, FL 1,633 263 28
Plaquemines, LA 1,322 349 28
Charleston.SC 1,213 237 20
Wilmington, NC 1,167 183 21
Mobile, AL 1,114 182 15
Baltimore, MD 979 424 33
New Haven, CT 951 184 15
Nikishka, AK 840 189 19
Honolulu, HI 687 221 18
Providence, Rl 554 116 10
Galveston, TX 552 68 12
Newcastle, DE 524 147 12
Searsport, ME 498 103 9
Anacortes, WA 455 370 29
Barbers Point, HI 387 101 8
Stockton, CA 370 79 6
Manatee, FL 355 39 4
Canaveral, FL 351 44 6
Camden, NJ 349 103 8
Metric tonnes per year
PM2.5
294
210
144
91
111
32
164
197
74
55
89
28
26
46
45
22
45
86
68
46
24
24
29
37
19
20
13
28
13
26
26
18
19
14
31
14
18
16
9
11
11
8
26
7
6
4
5
8
HC
80
76
65
40
31
11
68
71
20
26
37
13
12
17
20
10
19
36
28
17
11
10
13
15
9
9
6
9
6
11
11
7
8
6
14
6
6
8
4
2
5
3
13
3
3
2
2
3
CO
178
159
156
263
69
27
170
169
45
169
92
33
30
44
48
25
47
90
71
49
26
25
31
39
21
22
14
19
15
27
27
19
20
46
33
14
15
18
9
5
12
8
29
8
6
4
6
8
SOx
2,494
1,742
1,202
752
942
296
1,365
1,745
630
446
744
245
223
592
2,021
181
383
711
2,255
1,733
202
206
258
320
151
166
104
237
108
419
221
255
375
115
256
131
165
130
173
93
357
114
207
58
44
32
55
218
Fuel
29,499
22,410
25,112
15,315
11,348
6,466
27,913
37,269
7,399
9,082
15,312
5,189
4,726
8,414
7,402
3,809
8,023
14,412
11,976
7,970
4,176
4,201
5,198
6,784
3,164
3,444
2,188
2,901
2,272
4,572
4,586
3,216
3,378
2,335
5,011
2,465
3,437
2,879
1,542
890
1,941
1,408
4,465
1,287
923
660
952
1,337
ICF International
EPA Contract EP-C-06-094/WA 0-02
3-27
U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-18. Tanker Ship Deep Sea Port Emissions (continued)
Installed
Power
Port Name (MW) NOx PMl°
Brownsville, TX 320 45 4
Portland, OR 309 222 19
Morehead City, NC 286 51 4
Tacoma, WA 277 1,306 104
Bridgeport, CT 206 31 3
Miami, FL 161 33 3
Wilmington, DE 159 22 3
Vancouver, WA 133 64 5
Palm Beach, FL 124 22 2
Newport News, VA 118 21 2
New Bedford/Fairhaven, MA 96 22 2
Hueneme, CA 95 13 1
Anchorage, AK 78 45 4
Seattle, WA 74 152 12
Port Angeles, WA 72 34 3
Kalama, WA 67 112 9
San Diego, CA 60 6 0
Panama City, FL 38 5 0
Sacramento, CA 34 8 1
Longview, WA 30 86 7
Albany, NY 28 8 1
Brunswick, GA 21 5 0
Hopewell, VA 1440
Fall River, MA 13 3 0
Hilo, HI 13 2 0
Kahului, HI 920
Catalina, CA 910
Nawiliwili, HI 820
Everett, WA 6 23 2
Total Tanker 145,399 28,938 2,760
Metric tonnes per year
PM2.5 HC CO SOx Fuel
3 1 4 28 584
17 8 18 133 2,905
4 2 4 41 681
94 45 105 723 15,440
3 1 3 29 536
3 1 3 26 472
3 1 3 83 504
5 2 5 37 817
2 1 2 19 290
1 1 2 13 264
2 1 2 22 290
1 0 14 8 180
3 2 4 25 505
11 5 12 87 1,870
3 1 3 25 541
8 4 9 66 1,439
000 3 72
000 3 64
1 0 1 4 90
6 3 7 49 1,075
101 5 102
0 0 0 13 63
0 0 0 25 48
000 5 38
000 1 24
000 1 24
000 0 10
000 1 23
2 1 2 13 277
2,529 982 2,664 27,359 389,888
ICF International
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U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-19. Ocean Going Tug Deep Sea Port Emissions
Installed
Power
Port Name (MW) NOx PMl°
Corpus Christi, TX 47 5 0
Mobile, AL 46 6 1
Miami, FL 31 3 0
Everglades, FL 28 3 0
Canaveral, FL 28 3 0
Palm Beach, FL 28 3 0
New Orleans, LA 21 4 0
Galveston, TX 19 2 0
Portland, OR 1861
Jacksonville, FL 1720
Houston, TX 1610
Kahului, HI 1620
Lake Charles, LA 710
Manatee, FL 710
South Louisiana, LA 720
Pascagoula, MS 710
Plaquemines, LA 410
Seattle, WA 420
Boston, MA 400
New York/New Jersey 300
Brownsville, TX 300
Total Ocean Going Tug 360 48 5
Metric tonnes per year
PM2.5 HC CO SOx Fuel
001 4 71
002 4 85
000 2 48
000 2 40
000 2 42
000 2 42
000 3 62
000 1 25
000 3 73
000 2 30
000 1 22
000 1 24
000 1 12
000 1 15
000 1 24
000 1 13
000 1 14
000 1 25
0000 6
0000 6
0000 5
4 2 6 34 684
3.2. Great Lake Ports
Emissions inventories for 28 Great Lake ports were developed and are presented here. Great
Lake ships include self-unloading bulk carriers (Bulk Carrier, SU) which tend to operate within
the Great Lakes only. Other ships travel down the St. Lawrence River from the open ocean.
Ships which operate in the Great Lakes only are known as Lakers while ships that come into the
Lakes from the open ocean are called Salties. Integrated tug-barges (ITB) are also used on the
Great Lakes.
National Ship age distributions were determined using the USAGE entrances and clearances
data married with Lloyds data to determine ship engine characteristics and build date. All ships
with Category 1 or 2 propulsion engines were eliminated from the database. Age profiles were
determined separately for deep sea and Great Lake ports. Build dates were weighted by
installed power (calls times total propulsion power) as this is the best indicator of ship
emissions. Table 3-20 presents the age profiles by engine type for Great Lake ports. These
can be used to determine the effects of new emission regulations. Figure 3-3 shows installed
power by engine type. About 43 percent of installed power comes from ships with slow speed
engines and 48 percent have medium speed diesel engines, with about 8 percent having steam
turbines at Great Lake ports. Installed power is the ship's propulsion power times the number of
calls. The breakdown of ship registry is shown in Figure 3-4. Almost 7 percent of ships calling
on Great Lake ports are US flagged. Matched port file codes are provided in Table 3-21.
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Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-20. Great Lake Ship Age Fractions by Engine Type
Age
(yrs)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35+
Engine Type
MSD
0.00244
0.01047
0.00965
0.00171
0.00459
0.00152
0.00170
0.00118
0.00000
0.00000
0.00000
0.00072
0.00000
0.00048
0.00128
0.00000
0.00000
0.00946
0.00049
0.04711
0.05184
0.12474
0.02368
0.12340
0.06341
0.22059
0.06471
0.00190
0.04553
0.05975
0.04932
0.00506
0.00000
0.00000
0.00000
0.07328
SSD
0.01102
0.02450
0.00079
0.02116
0.00000
0.01875
0.01270
0.00092
0.00184
0.00000
0.00553
0.00000
0.00395
0.00451
0.00229
0.04216
0.02501
0.10376
0.09568
0.06781
0.02124
0.05185
0.09478
0.00398
0.00237
0.00222
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.02807
0.16168
0.19141
ST
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
1.00000
All
0.00596
0.01569
0.00500
0.01002
0.00221
0.00888
0.00634
0.00097
0.00080
0.00000
0.00240
0.00035
0.00172
0.00219
0.00161
0.01831
0.01086
0.04962
0.04179
0.05216
0.03421
0.08264
0.05257
0.06121
0.03159
0.10729
0.03119
0.00091
0.02194
0.02880
0.02377
0.00244
0.00000
0.01219
0.07022
0.20217
MSD = Medium speed diesel, SSD = Slow speed diesel
ST = Steam turbine
ICF International
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U.S. Environmental Protection Agency
September 2007
-------
Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Figure 3-3. Installed Power by Engine Type at Great Lake Ports
ST, 8.4%
SSD, 43.4%
MSD, 48.2%
Figure 3-4. Great Lake Ship Registry Breakdown
Norway, 1.18%
Panama, 1.79%-
^Other, 5.93%
Hong Kong, 2.02%
Liberia, 2.02%—-
Netherlands, 2.35%
Bahamas, 2.87%—
Cyprus, 3.20%—
Marshall Islands,
3.34%
USA, 6.91%
Canada, 68.39%
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U.S. Environmental Protection Agency
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Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-21. Match Port File Codes and File Names
Code
DS
CL
Typical Port
Duluth-Superior
Cleveland
Detailed Inventory Spreadsheet File
Duluth Ports New.xls
Cleveland Ports New.xls
Total emissions by port for Great Lakes Ports are shown in Table 3-22. Auxiliary engine
emissions for Great Lake ports are shown in Table 3-23. Emissions by mode for Great Lake
ports are shown in Table 3-24 for cruise, Table 3-25 for reduced speed zone, Table 3-26 for
maneuvering, and Table 3-27 for hotelling. Emissions by ship type are shown in Tables 4-28
through 4-32.
Table 3-22. Total Emissions by Port (Great Lake Ports)
Installed
Power
Port Name (MW) NOx PMl°
Alpena, Ml 88.6 1.5 0.3
Buffalo, NY 83.6 2.9 0.3
Burns Waterway, IN 818.8 45.5 3.9
Calcite, Ml 125.5 3.4 0.3
Cleveland, OH 559.9 32.6 2.8
Dolomite, Ml 66.9 1.9 0.2
Erie, PA 55.3 2.2 0.2
Escanaba, Ml 117.7 3.1 0.3
Fairport, OH 114.0 3.0 0.3
Gary, IN 84.1 3.2 0.3
Lorain, OH 64.1 1.5 0.2
Marblehead, OH 26.0 0.5 0.1
Milwaukee, Wl 494.8 26.1 2.3
Muskegon, Ml 36.9 0.9 0.1
Presque Isle, Ml 562.4 16.2 1.4
StClair, Ml 155.9 4.2 0.4
Stoneport, Ml 22.3 0.7 0.1
Two Harbors, MN 48.2 1.2 0.1
Ashtabula, OH 1,179.2 36.8 3.4
Chicago, IL 491.5 22.0 1.9
Conneaut, OH 1,863.1 52.6 5.0
Detroit, Ml 1,359.3 51.4 4.7
Duluth-Superior, MN&WI 3,440.6 131.8 12.0
Indiana, IN 139.7 5.9 0.5
Inland Harbor, Ml 55.6 1.5 0.1
Manistee, Ml 163.9 17.8 1.5
Sandusky, OH 741.8 21.0 2.0
Toledo, OH 1,516.6 57.5 5.0
Total Emissions 14,476.4 548.9 49.6
Metric tonnes per year
PM2.5 HC CO SOx Fuel
0.2 0.0 0.1 2.5 49.0
0.3 0.1 0.2 2.3 47.1
3.6 1.5 3.7 30.0 622.7
0.3 0.1 0.3 2.5 49.7
2.5 1.0 2.6 21.8 454.9
0.1 0.1 0.2 1.1 22.9
0.2 0.1 0.2 1.7 35.2
0.3 0.1 0.3 2.3 45.8
0.3 0.1 0.3 2.5 49.1
0.3 0.1 0.3 2.2 44.2
0.2 0.1 0.1 1.3 26.3
0.1 0.0 0.0 0.5 10.5
2.1 0.8 2.1 17.8 369.9
0.1 0.0 0.1 0.7 14.9
1.3 0.7 1.4 10.0 200.2
0.4 0.2 0.4 3.0 60.7
0.1 0.0 0.1 0.4 8.7
0.1 0.0 0.1 0.9 17.5
3.1 1.3 3.1 26.4 530.2
1.8 0.7 1.8 15.3 314.1
4.7 1.9 4.4 39.5 785.6
4.4 1.7 4.2 37.5 764.1
11.1 4.5 10.7 94.5 1,925.8
0.5 0.2 0.5 4.1 85.4
0.1 0.1 0.1 1.1 21.8
1.4 0.5 1.4 12.2 259.8
1.8 0.8 1.8 15.2 302.2
4.6 2.0 4.7 39.2 798.1
45.7 18.9 44.9 388.8 7,916.7
Match
Port
File
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
CL
DS
DS
DS
DS
DS
DS
DS
DS
DS
DS
ICF International
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September 2007
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Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-23. Auxiliary Engine Emissions by Port (Great Lake Ports)
Auxiliary
Power
Port Name (MW) NOx PMl°
Alpena, Ml 19.7 1.2 0.1
Buffalo, NY 18.6 1.5 0.1
Burns Waterway, IN 181.1 29.6 2.5
Calcite, Ml 27.9 1.4 0.1
Cleveland, OH 122.5 22.5 1.9
Dolomite, Ml 14.9 0.6 0.1
Erie, PA 11.9 1.5 0.1
Escanaba, Ml 26.1 1.2 0.1
Fairport, OH 25.3 1.3 0.1
Gary, IN 18.5 1.5 0.1
Lorain, OH 14.2 0.7 0.1
Marblehead, OH 5.8 0.3 0.0
Milwaukee, Wl 108.8 17.5 1.4
Muskegon, Ml 8.2 0.4 0.0
Presque Isle, Ml 124.9 5.6 0.5
StClair, Ml 34.6 1.6 0.1
Stoneport, Ml 4.9 0.2 0.0
Two Harbors, MN 10.7 0.5 0.0
Ashtabula, OH 261.6 16.3 1.3
Chicago, IL 107.9 13.6 1.1
Conneaut, OH 413.6 20.9 1.7
Detroit, Ml 302.6 29.6 2.5
Duluth-Superior, MN&WI 759.9 74.0 6.1
Indiana, IN 31.0 3.7 0.3
Inland Harbor, Ml 12.3 0.6 0.0
Manistee, Ml 34.7 15.1 1.3
Sandusky, OH 164.7 8.1 0.7
Toledo, OH 335.5 30.6 2.5
Total Auxiliary Emissions 3,202.4 301.6 25.0
Metric tonnes per year
PM2.5 HC CO SOx Fuel
0.1 0.0 0.1 0.8 17.8
0.1 0.0 0.1 1.0 22.4
2.2 0.8 2.2 19.7 429.0
0.1 0.0 0.1 0.9 19.7
1.7 0.6 1.7 15.0 326.6
0.0 0.0 0.0 0.4 9.4
0.1 0.0 0.1 1.0 21.4
0.1 0.0 0.1 0.8 17.5
0.1 0.0 0.1 0.9 18.9
0.1 0.0 0.1 1.0 21.9
0.1 0.0 0.1 0.5 10.0
0.0 0.0 0.0 0.2 3.8
1.3 0.5 1.3 11.7 253.4
0.0 0.0 0.0 0.3 5.7
0.4 0.2 0.4 3.7 80.7
0.1 0.0 0.1 1.1 23.8
0.0 0.0 0.0 0.2 3.4
0.0 0.0 0.0 0.3 6.6
1.2 0.4 1.2 10.9 236.1
1.0 0.4 1.0 9.1 197.9
1.6 0.6 1.6 13.9 302.8
2.2 0.8 2.2 19.8 429.4
5.6 2.0 5.6 49.4 1,073.8
0.3 0.1 0.3 2.5 53.5
0.0 0.0 0.0 0.4 8.5
1.2 0.4 1.2 10.1 219.6
0.6 0.2 0.6 5.4 118.2
2.3 0.8 2.3 20.4 444.4
22.9 8.3 22.9 201.3 4,376.2
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Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-24. Cruise Emissions by Port (Great Lake Ports)
Installed
Power .... _,„.
Port Name (MW) NOx PMl°
Alpena, Ml 88.6 0.3 0.1
Buffalo, NY 83.6 0.9 0.1
Burns Waterway, IN 818.8 11.7 1.0
Calcite, Ml 125.5 1.4 0.1
Cleveland, OH 559.9 7.7 0.7
Dolomite, Ml 66.9 0.8 0.1
Erie, PA 55.3 0.6 0.1
Escanaba, Ml 117.7 1.5 0.1
Fairport, OH 114.0 1.2 0.1
Gary, IN 84.1 1.1 0.1
Lorain, OH 64.1 0.6 0.1
Marblehead, OH 26.0 0.1 0.0
Milwaukee, Wl 494.8 6.4 0.6
Muskegon, Ml 36.9 0.4 0.0
Presque Isle, Ml 562.4 7.3 0.6
StClair, Ml 155.9 1.7 0.2
Stoneport, Ml 22.3 0.3 0.0
Two Harbors, MN 48.2 0.6 0.1
Ashtabula, OH 1,179.2 15.1 1.4
Chicago, IL 491.5 6.3 0.6
Conneaut, OH 1,863.1 23.1 2.3
Detroit, Ml 1,359.3 16.5 1.6
Duluth-Superior, MN&WI 3,440.6 43.3 4.2
Indiana, IN 139.7 1.7 0.2
Inland Harbor, Ml 55.6 0.7 0.1
Manistee, Ml 163.9 2.0 0.2
Sandusky, OH 741.8 9.4 0.9
Toledo, OH 1,516.6 20.1 1.8
Total Cruise Emissions 14,476.4 182.8 17.3
Metric tonnes per year
PM2.5 HC CO SOx Fuel
0.1 0.0 0.0 1.2 23.5
0.1 0.0 0.1 0.9 17.2
0.9 0.4 0.9 7.5 142.2
0.1 0.0 0.1 1.1 20.6
0.6 0.3 0.6 5.2 98.7
0.1 0.0 0.1 0.5 9.4
0.1 0.0 0.0 0.5 10.2
0.1 0.1 0.1 1.2 22.2
0.1 0.0 0.1 1.1 20.8
0.1 0.0 0.1 0.8 15.1
0.1 0.0 0.0 0.6 11.6
0.0 0.0 0.0 0.3 4.9
0.5 0.2 0.5 4.5 86.6
0.0 0.0 0.0 0.3 6.5
0.5 0.2 0.6 4.4 83.4
0.2 0.1 0.1 1.4 25.7
0.0 0.0 0.0 0.2 3.6
0.0 0.0 0.0 0.4 8.0
1.3 0.5 1.2 11.3 214.8
0.5 0.2 0.5 4.6 88.4
2.1 0.8 1.8 18.4 349.8
1.5 0.6 1.3 13.1 249.2
3.9 1.5 3.4 33.3 634.4
0.2 0.1 0.1 1.3 24.4
0.1 0.0 0.1 0.5 9.8
0.2 0.1 0.2 1.6 30.3
0.8 0.3 0.7 7.1 134.5
1.7 0.7 1.6 14.0 266.3
16.1 6.3 14.3 137.2 2,612.5
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Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-25. Reduced Speed Zone Emissions by Port (Great Lake Ports)
Installed
Power .... _,„.
Port Name (MW) NOx PMl°
Alpena, Ml 88.6 0.1 0.0
Buffalo, NY 83.6 0.2 0.0
Burns Waterway, IN 818.8 2.8 0.2
Calcite, Ml 125.5 0.3 0.0
Cleveland, OH 559.9 1.9 0.2
Dolomite, Ml 66.9 0.2 0.0
Erie, PA 55.3 0.1 0.0
Escanaba, Ml 117.7 0.4 0.0
Fairport, OH 114.0 0.3 0.0
Gary, IN 84.1 0.3 0.0
Lorain, OH 64.1 0.1 0.0
Marblehead, OH 26.0 0.0 0.0
Milwaukee, Wl 494.8 1.6 0.1
Muskegon, Ml 36.9 0.1 0.0
Presque Isle, Ml 562.4 1.7 0.1
StClair, Ml 155.9 0.4 0.0
Stoneport, Ml 22.3 0.1 0.0
Two Harbors, MN 48.2 0.1 0.0
Ashtabula, OH 1,179.2 3.7 0.3
Chicago, IL 491.5 1.5 0.1
Conneaut, OH 1,863.1 5.8 0.6
Detroit, Ml 1,359.3 4.1 0.4
Duluth-Superior, MN&WI 3,440.6 10.8 1.0
Indiana, IN 139.7 0.4 0.0
Inland Harbor, Ml 55.6 0.2 0.0
Manistee, Ml 163.9 0.5 0.0
Sandusky, OH 741.8 2.3 0.2
Toledo, OH 1,516.6 4.9 0.4
Total RSZ Emissions 14,476.4 45.0 4.2
Metric tonnes per year
PM2.5 HC CO SOx Fuel
0.0 0.0 0.0 0.3 5.9
0.0 0.0 0.0 0.2 4.3
0.2 0.1 0.2 1.8 35.2
0.0 0.0 0.0 0.3 4.9
0.1 0.1 0.1 1.3 24.4
0.0 0.0 0.0 0.1 2.3
0.0 0.0 0.0 0.1 2.5
0.0 0.0 0.0 0.3 5.6
0.0 0.0 0.0 0.3 5.1
0.0 0.0 0.0 0.2 3.8
0.0 0.0 0.0 0.1 2.8
0.0 0.0 0.0 0.1 1.2
0.1 0.1 0.1 1.1 21.2
0.0 0.0 0.0 0.1 1.6
0.1 0.1 0.1 1.0 20.3
0.0 0.0 0.0 0.3 6.3
0.0 0.0 0.0 0.0 0.9
0.0 0.0 0.0 0.1 1.9
0.3 0.1 0.3 2.8 53.3
0.1 0.1 0.1 1.1 21.6
0.5 0.2 0.5 4.5 87.0
0.4 0.1 0.3 3.1 60.7
0.9 0.4 0.8 8.1 157.6
0.0 0.0 0.0 0.3 5.9
0.0 0.0 0.0 0.1 2.3
0.0 0.0 0.0 0.4 7.6
0.2 0.1 0.2 1.7 33.3
0.4 0.2 0.4 3.4 65.4
3.9 1.5 3.5 33.3 645.0
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Commercial Marine Port Inventory Development
2002 Baseline Near Port Inventories
Table 3-26. Maneuvering Emissions by Port (Great Lake Ports)
Installed
Power .... _,„.
Port Name (MW) NOx PMl°
Alpena, Ml 88.6 0.2 0.0
Buffalo, NY 83.6 0.6 0.1
Burns Waterway, IN 818.8 4.4 0.4
Calcite, Ml 125.5 0.9 0.1
Cleveland, OH 559.9 2.0 0.2
Dolomite, Ml 66.9 0.5 0.0
Erie, PA 55.3 0.2 0.0
Escanaba, Ml 117.7 0.3 0.0
Fairport, OH 114.0 0.8 0.1
Gary, IN 84.1 0.7 0.1
Lorain, OH 64.1 0.4 0.0
Marblehead, OH 26.0 0.1 0.0
Milwaukee, Wl 494.8 2.3 0.2
Muskegon, Ml 36.9 0.2 0.0
Presque Isle, Ml 562.4 4.2 0.4
StClair, Ml 155.9 1.1 0.1
Stoneport, Ml 22.3 0.2 0.0
Two Harbors, MN 48.2 0.2 0.0
Ashtabula, OH 1,179.2 6.0 0.6
Chicago, IL 491.5 1.9 0.2
Conneaut, OH 1,863.1 9.7 1.0
Detroit, Ml 1,359.3 5.7 0.6
Duluth-Superior, MN&WI 3,440.6 15.2 1.6
Indiana, IN 139.7 0.5 0.1
Inland Harbor, Ml 55.6 0.3 0.0
Manistee, Ml 163.9 0.6 0.1
Sandusky, OH 741.8 3.8 0.4
Toledo, OH 1,516.6 6.6 0.7
Total Maneuver Emissions 14,476.4 69.6 7.2
Metric tonnes per year
PM2.5 HC CO SOx Fuel
0.0 0.0 0.0 0.3 5.3
0.1 0.0 0.1 0.4 8.8
0.4 0.3 0.5 3.0 59.8
0.1 0.1 0.1 0.6 12.9
0.2 0.1 0.2 1.4 27.9
0.0 0.0 0.1 0.3 6.1
0.0 0.0 0.0 0.2 3.6
0.0 0.0 0.0 0.2 4.9
0.1 0.0 0.1 0.6 11.9
0.1 0.0 0.1 0.5 9.9
0.0 0.0 0.0 0.3 5.8
0.0 0.0 0.0 0.1 1.8
0.2 0.1 0.3 1.6 32.9
0.0 0.0 0.0 0.2 3.3
0.4 0.3 0.5 2.6 52.4
0.1 0.1 0.1 0.7 14.8
0.0 0.0 0.0 0.1 2.3
0.0 0.0 0.0 0.2 3.3
0.6 0.3 0.7 4.4 87.2
0.2 0.1 0.2 1.4 27.5
0.9 0.6 1.1 7.3 146.3
0.6 0.3 0.6 4.5 89.2
1.5 0.9 1.7 11.3 225.5
0.0 0.0 0.1 0.4 7.4
0.0 0.0 0.0 0.2 4.1
0.1 0.0 0.1 0.4 8.5
0.4 0.2 0.4 2.7 54.7
0.6 0.4 0.8 4.6 91.4
6.7 4.2 8.0 50.4 1,009.6
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Table 3-27. Hotelling Emissions by Port (Great Lake Ports)
Installed
Power .... _,„.
Port Name (MW) NOx PMl°
Alpena, Ml 88.6 1.0 0.1
Buffalo, NY 83.6 1.2 0.1
Burns Waterway, IN 818.8 26.6 2.2
Calcite, Ml 125.5 0.8 0.1
Cleveland, OH 559.9 20.9 1.7
Dolomite, Ml 66.9 0.4 0.0
Erie, PA 55.3 1.3 0.1
Escanaba, Ml 117.7 0.9 0.1
Fairport, OH 114.0 0.8 0.1
Gary, IN 84.1 1.1 0.1
Lorain, OH 64.1 0.4 0.0
Marblehead, OH 26.0 0.2 0.0
Milwaukee, Wl 494.8 15.8 1.3
Muskegon, Ml 36.9 0.2 0.0
Presque Isle, Ml 562.4 3.0 0.3
StClair, Ml 155.9 1.0 0.1
Stoneport, Ml 22.3 0.1 0.0
Two Harbors, MN 48.2 0.3 0.0
Ashtabula, OH 1,179.2 12.1 1.0
Chicago, IL 491.5 12.2 1.0
Conneaut, OH 1,863.1 14.0 1.2
Detroit, Ml 1,359.3 25.1 2.1
Duluth-Superior, MN&WI 3,440.6 62.6 5.2
Indiana, IN 139.7 3.3 0.3
Inland Harbor, Ml 55.6 0.4 0.0
Manistee, Ml 163.9 14.7 1.2
Sandusky, OH 741.8 5.5 0.5
Toledo, OH 1,516.6 25.8 2.1
Total Hotel Emissions 14,476.4 251.5 20.9
Metric tonnes per year
PM2.5 HC CO SOx Fuel
0.1 0.0 0.1 0.7 14.3
0.1 0.0 0.1 0.8 16.8
2.0 0.7 2.0 17.7 385.5
0.1 0.0 0.1 0.5 11.2
1.6 0.6 1.6 14.0 303.9
0.0 0.0 0.0 0.2 5.2
0.1 0.0 0.1 0.9 18.9
0.1 0.0 0.1 0.6 13.2
0.1 0.0 0.1 0.5 11.2
0.1 0.0 0.1 0.7 15.5
0.0 0.0 0.0 0.3 6.2
0.0 0.0 0.0 0.1 2.6
1.2 0.4 1.2 10.5 229.3
0.0 0.0 0.0 0.2 3.5
0.2 0.1 0.2 2.0 44.1
0.1 0.0 0.1 0.6 13.8
0.0 0.0 0.0 0.1 1.9
0.0 0.0 0.0 0.2 4.2
0.9 0.3 0.9 8.0 175.0
0.9 0.3 0.9 8.1 176.6
1.1 0.4 1.1 9.3 202.5
1.9 0.7 1.9 16.8 364.9
4.8 1.7 4.8 41.8 908.2
0.2 0.1 0.2 2.2 47.7
0.0 0.0 0.0 0.3 5.6
1.1 0.4 1.1 9.8 213.3
0.4 0.2 0.4 3.7 79.7
2.0 0.7 2.0 17.2 374.9
19.1 7.0 19.1 167.9 3,649.6
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Table 3-28. Self-Unloading Bulk Carrier Emissions by Port
Installed
Power .... _,„.
Port Name (MW) NOx PMl°
Duluth-Superior, MN&WI 2,201.1 61.8 6.0
Conneaut, OH 1,842.7 51.3 4.9
Ashtabula, OH 1,047.0 29.2 2.8
Toledo, OH 987.0 28.1 2.5
Detroit, Ml 801.9 20.3 2.1
Sandusky, OH 734.5 20.6 1.9
Presque Isle, Ml 562.4 16.2 1.4
Burns Waterway, IN 236.1 7.6 0.7
Chicago, IL 207.5 5.5 0.5
Milwaukee, Wl 168.5 4.3 0.5
StClair, Ml 155.9 4.2 0.4
Calcite, Ml 125.5 3.4 0.3
Escanaba, Ml 117.7 3.1 0.3
Fairport, OH 114.0 3.0 0.3
Alpena, Ml 88.6 1.5 0.3
Cleveland, OH 74.7 1.3 0.2
Gary, IN 71.3 2.3 0.2
Buffalo, NY 70.7 2.0 0.2
Dolomite, Ml 66.9 1.9 0.2
Lorain, OH 64.1 1.5 0.2
Inland Harbor, Ml 55.6 1.5 0.1
Indiana, IN 52.1 1.3 0.1
Two Harbors, MN 48.2 1.2 0.1
Muskegon, Ml 36.9 0.9 0.1
Erie, PA 27.0 0.5 0.1
Marblehead, OH 26.0 0.5 0.1
Stoneport, Ml 22.3 0.7 0.1
Manistee, Ml 9.0 0.2 0.0
Total SU Bulk Carrier 10,015.2 275.9 26.7
Metric tonnes per year
PM2.5 HC CO SOx Fuel
5.6 2.3 5.2 47.9 953.2
4.6 1.9 4.3 38.7 769.3
2.6 1.1 2.5 21.6 428.3
2.3 1.0 2.4 19.3 385.2
2.0 0.7 1.7 17.5 347.5
1.8 0.8 1.7 14.8 293.8
1.3 0.7 1.4 10.0 200.2
0.7 0.3 0.7 5.7 113.8
0.5 0.2 0.5 4.4 88.4
0.4 0.2 0.4 3.8 74.7
0.4 0.2 0.4 3.0 60.7
0.3 0.1 0.3 2.5 49.7
0.3 0.1 0.3 2.3 45.8
0.3 0.1 0.3 2.5 49.1
0.2 0.0 0.1 2.5 49.0
0.2 0.0 0.1 1.7 34.3
0.2 0.1 0.2 1.6 32.6
0.2 0.1 0.2 1.8 35.2
0.1 0.1 0.2 1.1 22.9
0.2 0.1 0.1 1.3 26.3
0.1 0.1 0.1 1.1 21.8
0.1 0.0 0.1 1.1 21.4
0.1 0.0 0.1 0.9 17.5
0.1 0.0 0.1 0.7 14.9
0.1 0.0 0.0 0.6 11.8
0.1 0.0 0.0 0.5 10.5
0.1 0.0 0.1 0.4 8.7
0.0 0.0 0.0 0.2 3.4
24.7 10.4 23.3 209.7 4,170.1
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Table 3-29. Bulk Carrier Emissions by Port
Installed
Power .... _,„.
Port Name (MW) NOx PMl°
Duluth-Superior, MN&WI 1,031.6 61.1 5.2
Burns Waterway, IN 562.0 36.8 3.0
Detroit, Ml 457.9 27.2 2.2
Cleveland, OH 427.0 27.7 2.3
Toledo, OH 421.4 25.1 2.1
Milwaukee, Wl 292.3 19.9 1.6
Chicago, IL 219.0 12.8 1.1
Ashtabula, OH 126.0 7.4 0.6
Indiana, IN 87.6 4.6 0.4
Conneaut, OH 20.4 1.2 0.1
Erie, PA 17.2 1.1 0.1
Buffalo, NY 12.9 0.9 0.1
Sandusky, OH 7.3 0.4 0.0
Gary, IN 6.9 0.5 0.0
Total Bulk Carrier 3,689.4 226.7 18.8
Metric tonnes per year
PM2.5 HC CO SOx Fuel
4.7 1.9 4.8 40.5 845.9
2.8 1.1 2.9 23.5 492.4
2.1 0.9 2.2 17.3 361.1
2.1 0.9 2.2 17.7 370.4
2.0 0.8 2.0 16.8 350.5
1.5 0.6 1.6 12.7 267.8
1.0 0.4 1.0 8.3 172.9
0.6 0.2 0.6 4.7 98.3
0.4 0.1 0.4 3.1 63.9
0.1 0.0 0.1 0.8 16.3
0.1 0.0 0.1 0.7 14.4
0.1 0.0 0.1 0.6 11.9
0.0 0.0 0.0 0.4 8.4
0.0 0.0 0.0 0.3 6.8
17.4 7.1 17.9 147.3 3,081.1
Table 3-30. General Cargo Ship Emissions by Port
Port Name
Installed
Power
(MW)
Metric tonnes per year
NOx
PM10
PM2.5
HC
CO
SOx
Fuel
Duluth-Superior, MN&WI 166.6 6.7 0.6 0.5 0.2 0.5 4.7 95.9
Toledo, OH 76.8 3.1 0.3 0.3 0.1 0.2 2.2 44.9
Cleveland, OH 58.2 3.5 0.3 0.3 0.1 0.3 2.4 50.2
Detroit, Ml 44.5 2.0 0.2 0.2 0.1 0.2 1.3 27.6
Chicago, IL 43.6 1.8 0.2 0.1 0.1 0.1 1.2 25.4
Milwaukee, Wl 34.0 1.9 0.2 0.2 0.1 0.2 1.3 27.5
Burns Waterway, IN 20.8 1.2 0.1 0.1 0.0 0.1 0.8 16.5
Erie, PA 11.1 0.6 0.1 0.1 0.0 0.0 0.4 9.0
Ashtabula, OH 6.2 0.2 0.0 0.0 0.0 0.0 0.2 3.7
Total General Cargo
461.9 21.1
1.9
1.7
0.7 1.7 14.5
300.7
Table 3-31. Tanker Ship Emissions by Port
Port Name
Installed
Power
(MW)
Metric tonnes per year
NOx
Manistee, Ml 154.9 17.6
Chicago, IL 15.4 1.8
Duluth-Superior, MN&WI 11.9 1.4
Detroit, Ml 5.8 0.7
Toledo, OH 4.6 0.5
PM10
1.5
0.1
0.1
0.1
0.0
PM2.5
1.4
0.1
0.1
0.1
0.0
HC
0.5
0.1
0.0
0.0
0.0
CO
1.4
0.1
0.1
0.1
0.0
SOx
12.1
1.2
0.9
0.4
0.3
Fuel
256.4
25.1
19.7
9.3
7.4
Total Tanker
192.6 22.0
1.9
1.7
0.6 1.7 15.0 318.0
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Table 3-32. Integrated Tug-Barge Emissions by Port
Port Name
Installed
Power
(MW)
Metric tonnes per year
NOx
Detroit, Ml 49.2 1.2
Duluth-Superior, MN&WI 29.4 0.7
Toledo, OH 26.9 0.7
Gary, IN 5.9 0.3
Chicago, IL 5.9 0.1
PM10
0.1
0.1
0.1
0.0
0.0
PM2.5
0.1
0.1
0.1
0.0
0.0
HC
0.0
0.0
0.0
0.0
0.0
CO
0.1
0.1
0.1
0.0
0.0
SOx
0.9
0.6
0.5
0.2
0.1
Fuel
18.5
11.1
10.1
4.8
2.2
Total ITB
117.3
3.1
0.3
0.3
0.1 0.3
2.3
46.7
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4. Spatial Allocation of Near Port Inventory and Blending
of Near Port and STEEM Inventories
This chapter describes the methodology for geospatially combining the near port inventory and
the STEEM data, both of which are described in Chapter 1. The STEEM data are spatially
allocated in a gridded format, whereas the spatial allocation of the near port inventory was
performed by ENVIRON as part of this work. Specifically, ENVIRON spatially allocated the near
port inventory into a gridded format then blended the near port emissions with the STEEM data,
which together create the merged, gridded commercial marine vessel emissions inventory.
4.1. STEEM Modeling Domain
The emissions inventory produced by the Delaware STEEM model spans the Pacific and
Atlantic oceans in a gridded format with 4 km grid cell resolution. Figure 4-1 is an illustration of
the spatial extent of the STEEM data.
Figure 4-1. Extent of STEEM data
Legend
Annual CO2 emissions (kg)
• 0- 10,000
I 1 10,000 - 50,000
50,000- 100,000
100,000- 11,735,908
4.2. Near Port Emissions Types
The near port emissions for each port are categorized as either hotelling, manuveuring, reduced
speed zone (RSZ) or cruise mode (CM) emissions. The hotelling and manuveuring emissions
are in the vicinity of the port. The RSZ emissions are allocated to RSZ lanes whose length
varies from port to port. Typically the RSZ ends where pilots board or disembark, and where the
ships reach unconstrained shipping lanes. The CM emissions are allocated to CM lanes, which
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are defined to extend 25 nautical miles beyond the end of the RSZ lanes. ENVIRON created
geographic databases to define the locations of the ports, RSZ lanes and CM lanes to facilitate
allocation of emissions to those locations.
4.3. Spatial Allocation Methodology
All spatial allocation work was accomplished with the use of ArcGIS software. ENVIRON
created shapefiles to specify the geographic locations for each type of near port emissions:
hotelling, maneuvering, RSZ, and CM. ArcGIS shapefiles can be viewed as layers in ArcMAP,
and other geographic shapefiles, such as the United States coastline and rivers, can be
superimposed in a map document. ENVIRON created a point shapefile representing the 117
ports, where each port was modeled as a single point. Both the hotelling and maneuvering
emissions were assigned to the port point. The RSZ and CM lanes were both modeled as line
shapefiles, and the RSZ and CM emissions were allocated to those lines. ArcGIS includes a tool
to measure distances that was utilized to calculate RSZ and CM lengths. A more detailed
description describing how each of the different shapefiles was created and converted to a
gridded format for emission allocation, and how the STEEM data was replaced with near port
emissions close to shore follows.
4.3.1. Port L ocations
Each port, and thus the designated location for hotelling and maneuvering emissions, is
modeled as a single point using the port center as defined by the Army Corp of Engineers in the
Principal Ports of the United States dataset.40 One additional port, "Other Puget Sound", was
added to the database. Some port locations were inspected by consulting Google Earth satellite
images. This resulted in ENVIRON slightly modifying the locations of five ports. The ports
chosen for inspection were those with measured-RSZ-distances and ICF-estimated-RSZ-
distances in poor agreement (see next section). However, modifying the locations of the five
ports never reconciled the two distance estimates. In all five cases the modification is very
slight, and has no significant effect on the RSZ distances or port locations. The ports that
ENVIRON moved are: Gray's Harbor, WA; Freeport, TX; Houston, TX; Jacksonville, FL; and
Moreshead City, NC. See Appendix A, Table A-1 for the coordinates of all 117 ports used in this
work. To allocate hotelling and maneuvering emissions for each port to a gridded format,
ENVIRON assigned all those emissions to the gridcell that contained the port point. Note that
modeling a port as a point over specifies the location of that port if it is spread over a distance
wider than one grid cell.
4.3.2. Reduced Speed Zone Transit
ENVIRON defined a line type shapefile to spatially represent the RSZ lanes, and wrote a PERL
program to spatially allocate the RSZ mode emissions to the grid. The creation of the RSZ
shapefile was accomplished in 4 main stages:
1. Downloaded the National Waterway Network dataset
2. Created a circular buffer around each port
40 Army Corps of Engineers, Principal Ports of the United States, Available online at
http://www.iwr.usace.army.mil/ndc/data/datappor.htm, File pport02.dbf downloaded April 2006.
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3. Selected NWN links within buffer
4. Refined endpoints of NWN links to be physically appropriate
ENVIRON downloaded the Army Corp of Engineers National Waterway Network (NWN) which
is a geographic database of navigable waterways in and around the United States.41 The NWN
database contains more waterways than were needed for this work so it required modification.
In particular, ENVIRON created the RSZ shapefile for only the 117 ports in the study, reducing
the number of NWN ports. Also the RSZ shapefile represents ocean-bound commercial marine
vessels routes only, not intracoastal waterways, which are included in the NWN database.
To reduce the number of NWN links to those associated with the relevant ports, and as a first
approximation of the lengths of RSZ routes, ENVIRON defined circular buffers around each port
at an estimated RSZ distance provided to us by ICF. See Figure 4-2. ENVIRON created a
shapefile from the intersection of the NWN links and the circular buffers. This had the effect of
discarding the NWN links that fell outside of the circular buffers. On a port-by-port basis,
ENVIRON inspected the remaining links to verify whether or not they appeared to represent the
route a commercial marine vessel would take in transit to the open ocean. Links heading inland
from a port were discarded as were links not associated with the 117 ports in the study. The
endpoints of the links were each studied, and if they did not end in a location that in ENVIRON's
best judgement was physically appropriate, ENVIRON adjusted the endpoint accordingly. The
set of all adjusted links comprised the final RSZ shapefile that determined where the RSZ
emissions were to be allocated.
For each port ENVIRON measured the length of the RSZ path in ArcMAP and reported the
values to ICF for emissions calculations. As a quality assurance check, ENVIRON compared
the measured lengths to the original ICF estimated distances. In most cases, ENVIRON found
close agreement between the two values. See Appendix A, Table A-2 for the original RSZ
distance estimates, final RSZ distances, and the coordinates of the RSZ endpoints.
To spatially allocate RSZ emissions onto the grid, ENVIRON considered the emissions constant
along the length of the RSZ line. When the emissions were inserted into the grid cells, they were
proportional to the length of the RSZ line in the grid cells. See Figure 4-3
41 Army Corps of Engineers, National Waterway Network, Available online at
http://www.iwr.usace.armv.mil/ndc/data/datanwn.htm, Downloaded April 2006.
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Figure 4-2. Demonstration of creation of RSZ shapefile in the San Francisco Bay Area. Top left,
NWN links; top right, circular buffer around Stockton; middle left, intersection of circular buffers
with NWN; middle right, discard irrelevant NWN links; bottom left, refined RSZ links; bottom right,
more detail showing final RSZ shapefile.
Sacramento. CA
Sacramento. CA
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Figure 4-3 . Gridded RSZ emissions from near port inventory
4.3.3. Multiple RSZ paths
In a few instances, the NWN links and STEEM data indicated two RSZ paths and ENVIRON
defined two RSZ links. The ports with two RSZ paths are: Honolulu, HI; Los Angeles, Long
Beach and El Segundo, CA; Brunswick, GA; and the four ports along the Mississippi river,
Baton Rouge, New Orleans, Port of South Louisiana, and Plaquemines, LA. In each of these
cases the RSZ length was measured, and the lengths of the two paths were compared. In no
case is there a significant discrepancy in the two lengths. The slightly longer fork length is used
in the near port inventory for the Mississippi ports. In the Los Angeles area all distances used
are the southward-heading branches. See Appendix B for errata on Brunswick, GA. In all cases
the RSZ emissions are divided equally between both branches. See Figure 4-4 for an example
of branched RSZ links.
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Figure 4-4. Branched RSZ link (pink lines) and CM links (green).
Brunswick, GA
Jacksonville, FL
4.3.4. Cruise Mode Transit
For each port, ENVIRON assigned cruise mode links extending 25 nautical miles into the open
ocean from the end of the revised RSZ links. ENVIRON assigned either one, two or three
cruise mode links to each port following the direction and number of shipping lanes evident in
the STEEM data. In the cases of multiple links, ENVIRON inspected the underlying STEEM
data values to determine whether any particular route should be assigned larger emissions than
the others. However, the STEEM data did not provide adequate evidence to justify that degree
of specificity, so ENVIRON concluded that allocating equal emissions for all cruise mode links
associated with a single port would be sufficient. ENVIRON followed the same methodology for
allocating the emissions to the grid for the CM links as the RSZ links. Figure 4-4 provides an
example of CM links for Brunswick, GA and Jacksonville, FL, and Figure 4-5, shows three CM
links in Tampa, and Port Manatee, FL.
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Figure 4-5. Three Cruise Mode links (dark green) extending 25 nautical miles out from RSZ links
(magenta), and RSZ buffer region enclosing all near port emission types.
4.3.5. Regions of O verlapping Near Port and S TEEM Emissions
A final component of the spatial allocation procedure consisted of determining how the two
inventories would be merged (i.e. which STEEM emissions would be replaced with near port
emissions). To accomplish this task, ENVIRON created a buffer around the RSZ links at a
radius of 25 nautical miles. The buffer is defined by drawing a circle with radius 25 nautical
miles around each point of the RSZ, from the endpoint to the port, and then using the ArcGIS
tool "dissolve buffer" to create one buffer from the intersection of all the circular buffers. The
RSZ buffer encloses the port, RSZ links and cruise mode links. See Figure 4-5. The RSZ buffer
region represents the area where near port emissions are inserted. On a port-by-port basis
ENVIRON inspected the STEEM emissions underneath the RSZ buffers to determine the
validity of completely replacing them with near port emissions. In cases such as Figure 4-6,
where STEEM data shows ship traffic routed directly to an isolated port, ENVIRON determined
that all the STEEM emissions under the buffer are attributable to that port and were completely
replaced by near port emissions. In cases such as Figure 4-7, where the RSZ buffer covers
STEEM emissions that include ship passages not associated with the port, ENVIRON
determined the percentage of STEEM emissions within the RSZ buffer that were attributable to
the port, and reduced (without completely zeroing out) the STEEM emissions in these regions.
4.3.6. Partial Replacement of S TEEM Emissions
To determine the percentage of STEEM emissions attributable to a port, ENVIRON divided the
magnitude of STEEM emissions in the isolated portion of the route leading only to the port by
the STEEM emissions in the major shipping lane. For example, in Figure 4-7, the STEEM
emissions in the portion of the buffer including only the route going to the port were
approximately 347 kg/cell/year, while the STEEM emissions in the major shipping lane were
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Commercial Marine Port Inventory Development
Spatial Allocation of Near Port Inventory and Blending of Near Port and STEEM
Inventories
6996 kg/cell/year. Therefore, in the portion of the buffer overlaying the major shipping lane, we
reduced the emissions by 347/6996, or 5% before adding in the near port emissions.
Figure 4-6. Example of complete replacement of Figure 4-7. Example of partial replacement of
STEEM data with near port data. STEEM data with near port data. Only some of
the emissions within the circular region are
attributable to that port.
4.4. Scaling of STEEM PM and SOx emissions
PM emissions calculated by STEEM are total PM emissions using an emission factor of 1.5
g/kWh. To convert to PM10 emissions with an emission factor of 1.4 g/kWh, STEEM PM
emissions were multiplied by a factor of 0.933. In addition, SOx emissions were different
between the STEEM and near port analyses. STEEM used a composite emission factor of 10.6
g/kWh while ICF used a composite SOx emission factor of 10.33 g/kWh. Thus STEEM SOx
emissions were multiplied by a factor of 0.975 to be consistent with the near port emission
calculations.
4.5. Geographic Projection Specification
The final, merged gridded emissions inventory is in the projection of the Delaware STEEM data.
This projection differs from the typical projection that EPA and Regional Planning Organizations
(RPO) use for air quality modeling as shown in Table 4-1. Additional processing work will be
required to convert the data into RPO format.
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Inventories
Table 4-1. Map projection specifications
Corbett (2006) STEEM Projection
Projection: Equidistant Cylindrical
False Easting: 0.0
False Northing: 0.0
Central Meridian: 180.0
Standard Parallel 1: 0.0
Inter-Regional Planning Organization
Projection: Lambert
Spheroid: Sphere
Datum: None
Units: Meters
False Easting: 0.0
False Northing: 0.0
Central Meridian: -97.0
Latitude of Origin: 40.0
Standard Parallel 1: 33.0
Standard Parallel 2: 45.0
4.6. Results of Blending Emissions
Figure 4-8 provides side-by-side comparisons of the original STEEM emissions and the merged
near port and STEEM data. The results indicate that the two-step spatial allocation procedure
provides a more precise assessment of near port vessel travel than that of the STEEM
approach. As previously described, the near port ship emissions may be over specified, but this
approach provides a more reasonable assessment than STEEM's wide shipping lanes near
land, which in some cases also include emissions overland (see Southwest US, Southernmost
port).
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Spatial Allocation of Near Port Inventory and Blending of Near Port and STEEM
Inventories
Figure 4-8. Graphical comparison of original STEEM emissions (left) and final merged STEEM and
near port inventory emissions (right) for the Chesapeake Bay and Southeast US
N0x2002
[tonnes/(ceiryear)]
| 0-1 Q ^ 14 - 20 ^^Q 45 - 55 ^^| 65 - SO | | 120 - ' SC
| ] 1-4 | ] 2Q - 25 | j 55 - 55 BB SO - 150 ^| 130-160
| ] 4-S | | 28-35 ^^ 65-75^^ IOD-110 ^^ 160- !9D
I I 6- 14 I I 35-^5 •• 75-65 ^H HO- 120 ^H 190-35D
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5. Total Emissions and Fuel Consumption by Region
This chapter describes the methodology used to sum the appropriate grid cells in the 2002
merged, gridded baseline emissions inventory to obtain totals by pollutant and fuel consumption
for the following U.S. regions:
• South Pacific [SP]
• North Pacific [NP]
• East Coast [EC]
• Gulf Coast [GC]
• Alaska Southeast [AE]
• Alaska West [AW]
• Hawaii [HI]
• Great Lakes (U.S. only, based on international border) [GL]
• Great Lakes (entire region) [GE]
5.1. Definition of Regions
Following EPA guidance, ENVIRON defined the U.S. boundaries and all regions (except the
Great Lakes) using the U.S. Exclusive Economic Zone (EEZ). ENVIRON downloaded shapefiles
for the U.S. EEZ from the National Oceanic and Atmospheric Administration Office of Coast
Survey.42 (NOAA, 2007). ENVIRON then verified the accuracy of the downloaded NOAA
shapefiles against images provided by the U.S. Geological Survey (USGS, 2007). In order to
divide and further define several of the regions, ENVIRON used instructions provided by EPA as
follows:
• Divide the North Pacific and South Pacific regions along a horizontal line originating from
the Washington/Oregon border (Latitude 46° 15' North).
• Divide the East Coast and Gulf Coast regions along a vertical line roughly through Key
Largo (Longitude 80° 26' West).
• Divide Alaska into two regions defined by a straight line intersecting the cities Naknek and
Kodiak
• Define the Great Lakes U.S. region to encompass the total area of Great Lakes in U.S.
territory as defined by the international boundary and to end in the east at the point in New
York where the St. Lawrence River parts from U.S. soil.
To finalize the polygons representing each region, ENVIRON supplemented the EEZ boundaries
with a shapefile of the U.S. international border downloaded from the National Atlas43 (National
Atlas, 2007). Figure 5-1 shows the final nine regional polygons. These polygons extend an
arbitrary distance on shore to ensure full coverage of all ports and inland waterways in the near
port inventory.
42 NOAA, Exclusive Economic Zone, Available online at http://nauticalcharts.noaa.gov/csdl/eez.htm, Downloaded
March 2007
National Atla
http://www.nationalatlas.qov/mld/boundOm.html, Downloaded April 2007
43 National Atlas, North American Atlas - Political Boundaries, Available online at
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Commercial Marine Port Inventory Development
Total Emissions and Fuel Consumption by Region
Figure 5-1. Nine regions defined by the U.S. Exclusive Economic Zone and guidance from EPA
5.2. STEEM Fuel Consumption Estimate
STEEM fuel consumption was estimated based on STEEM CO2 emissions by using the
conversion 3.183 tonnes CO2 = 1 tonne fuel to be consistent with the conversion used in the
near port inventory.
5.3. Summing by Region Methodology
ENVIRON used ArclNFO GIS software to intersect the final shapefile of regional polygons with the
4 km gridded domain to reduce the number of grid cell from the entire STEEM extent to those that
span the nine regions. ENVIRON also created a file that identifies which region each of the
remaining grid cells are in. Grid cells split by the EEZ or by a regional divider were only
considered to be within a region if over 50 percent of their area was within that region. Once the
grid cells in each region were identified, ENVIRON created a PERL computer program to sum the
corresponding 2002 merged, gridded baseline emissions by pollutant and fuel consumption for
each region. Final gridded emissions by region are detailed below.
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Commercial Marine Port Inventory Development
Total Emissions and Fuel Consumption by Region
5.4. 2005 Growth Factors
To estimate 2005 emissions from 2002 emissions, a growth factor was applied to each of the
regions that is constant for all species.44'45 In addition, the NOx emissions were multiplied by a
factor to account for the IMO standard implementation for ships built since 2002. See Table 5-1.
Table 5-1. Growth and Emission Adjustment Factors
Region
AE
AW
EC
GC
GE
GL
HI
NP
SP
Annualized
Growth
3.30%
3.30%
4.50%
2.90%
1 .70%
1 .70%
5.00%
3.30%
5.00%
2002-2005
Growth Factor
1.102
1.102
1.141
1.090
1.052
1.052
1.158
1.102
1.158
NOx 2005 EAF
0.957
0.957
0.957
0.957
0.994
0.994
0.957
0.957
0.957
5.5. 2002 and 2005 Blended Emission Totals by Region
2002 blended emission totals by region are given in Table 5-2 and 2005 blended emissions
totals by region are given in Table 5-3.
Table 5-2. 2002 Regional U.S. emissions
[Metric tonnes per year]
Region
AE
AW
EC
GC
HI
NP
SP
GL
GE
Total (U.S)
Total (with GE)
NOx
18,231
60,625
220,844
174,454
54,935
26,278
105,380
15,171
20,275
675,918
681,022
PM
1,439
4,736
17,665
14,187
4,315
2,176
8,175
1,191
1,591
53,884
54,284
HC
603
2,009
7,345
5,817
1,820
946
3,503
503
672
22,546
22,715
CO
1,424
4,732
17,383
14,322
4,291
2,108
8,534
1,186
1,585
53,980
54,379
SOx
10,725
35,137
146,295
105,926
32,040
15.388
60,997
8,851
11,821
415,359
418,329
Fuel
208,700
680,296
2,578,467
2,012,970
625,084
314,310
1,211,337
171,786
229,264
7,802,950
7,860,428
RTI International, Global Trade and Fuels Assessment - Future Trends and Effects of Designation Requiring Clean
Fuels in the Marine Sector: Task Order No. 1, Draft Report, EPA Report Number EPA420-R-07-012, December
2006
' RTI International, RTI Estimates of Growth in Bunker Fuel Consumption, Memorandum with spreadsheet from
Michael Gallaher and Martin Ross, RTI, to Barry Garelick and Russ Smith, U.S. Environmental Protection Agency,
April 24, 2006
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Total Emissions and Fuel Consumption by Region
Table 5-3. 2005 Regional U.S. emissions
Metric tonnes
Region NOx PM
AE 19,232 1,586
AW 63,954 5,221
EC 241,183 20,159
GC 181,903 15,457
HI 60,860 4,995
NP 27,721 2,399
SP 116,745 9,464
GL 15,862 1,253
GE 21,199 1,674
; per year]
HC CO SOx Fuel
665 1,570 11,823 230,051
2,215 5,216 38,731 749,892
8,382 19,837 166,947 2,942,943
6,338 15,604 115,411 2,193,226
2,107 4,967 37,091 723,613
1,043 2,324 16,962 346,465
4,055 9,879 70,611 1,402,274
529 1,248 9,310 180,697
707 1,667 12,435 241,156
Total (U.S) 727,459 60,533 25,333 60,645 466,886 8,768,676
Total (with GE) 732,795 60,954 25,510 61,065 470,010 8,829,136
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Appendix A
Table A-1. Port Coordinates
USAGE
Port Name Code
Albany, NY C0505
Alpena, Ml L3617
Anacortes, WA C4730
Anchorage, AK C4820
Ashtabula, OH L3219
Baltimore, MD C0700
Barbers Point, Oahu, HI C4458
Baton Rouge, LA C2252
Beaumont, TX C2395
Boston, MA C0149
Bridgeport, CT C0311
Brownsville, TX C2420
Brunswick, GA C0780
Buffalo, NY L3230
Burns Waterway Harbor, IN L3739
Calcite, Ml L3620
Camden-Gloucester, NJ C0551
Carquinez, CA CCA01
Catalina, CA CCA02
Charleston, SC C0773
Chester, PA C0297
Chicago, IL L3749
Cleveland, OH L3217
Conneaut, OH L3220
Coos Bay, OR C4660
Corpus Christi, TX C2423
Detroit, Ml L3321
Duluth-Superior, MN and Wl L3924
El Segundo, CA CCA03
Erie, PA L3221
Escanaba, Ml L3795
Eureka, CA CCA04
Everett, WA C4725
Fairport Harbor, OH L3218
Fall River, MA C0189
Freeport, TX C2408
Galveston, TX C2417
Gary, IN L3736
Georgetown, SC C0772
Grays Harbor, WA C4702
Port Coordinates
Longitude Latitude
-73.74816 42.64271
-83.42227 45.05560
-122.59953 48.49617
-149.89500 61.23778
-80.79172 41.91873
-76.51712 39.20899
-158.10890 21.29723
-91.19934 30.42292
-94.08811 30.08716
-71.05229 42.35094
-73.17886 41.17200
-97.39814 25.95220
-81.49988 31.15856
-78.89526 42.87830
-87.15521 41.64325
-83.77560 45.39293
-75.10432 39.94305
-122.12333 38.03556
-118.49609 33.43943
-79.92160 32.78878
-75.32220 39.85423
-87.63799 41.88662
-81.67191 41.47852
-80.54860 41.96671
-124.20950 43.36351
-97.39789 27.81277
-83.10959 42.26909
-92.09641 46.77836
-118.42546 33.91354
-80.06792 42.15154
-87.02500 45.73351
-124.18639 40.79528
-122.22940 47.98476
-81.29407 41.76666
-71.15875 41.72166
-95.33040 28.93840
-94.81270 29.31049
-87.32513 41.61202
-79.28964 33.36682
-124.12230 46.91167
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Appendix A
Table A-1. Port Coordinates (continued)
US;
Port Name Co
^Q^ Port Coordinates
de Longitude
Gulfport, MS C2083 -89.08533
Hilo, HI C4400 -155.07580
Honolulu, HI C4420 -157.87170
Hopewell, VA C0738 -77.27634
Houston, TX C2012 -95.26774
Indiana Harbor, IN L3738 -87.44547
Jacksonville, FL C2017 -81.62013
Kahului, Maui, HI C4410 -156.47280
Kalama, WA C4626 -122.86280
Lake Charles, LA C2254 -93.22207
Long Beach, CA C4110 -118.20950
Longview, WA C4622 -122.91400
Lorain, OH L3216 -82.19514
Los Angeles, CA C4120 -118.24100
Manistee, Ml L3720 -86.34428
Marblehead, OH L3212 -82.70906
Marcus Hook, PA C5251 -75.40424
Matagorda Ship Channel, TX C2410 -96.56412
Miami, FL C2164 -80.18317
Milwaukee, Wl L3756 -87.89967
Mobile, AL C2005 -88.04113
Morehead City, NC C0764 -76.69473
Muskegon, Ml L3725 -86.35014
Nawiliwili, Kauai, HI C4430 -159.35310
New Bedford, MA C0187 -70.91624
Newcastle, DE C0299 -75.56158
New Haven, CT C1507 -72.90473
New Orleans, LA C2251 -90.08526
New York, NY and NJ C0398 -74.03836
Newport News, VA C0736 -76.45823
Nikishka, AK C4831 -151.31440
Oakland, CA C4345 -122.30810
Olympia, WA C4718 -122.90890
Other Puget Sound, WA C4754 -122.71960
Palm Beach, FL C2162 -80.05267
Panama City, FL C2016 -84.19926
Pascagoula, MS C2004 -88.55879
Paulsboro, NJ C5252 -75.22655
Penn Manor, PA C0298 -74.74081
Latitude
30.35216
19.72861
21.31111
37.32231
29.72538
41.67586
30.34804
20.89861
46.02048
30.22358
33.73957
46.14222
41.48248
33.77728
44.25082
41.52962
39.81544
28.59540
25.78354
42.98824
30.72527
34.71669
43.19492
21.96111
41.63641
39.65668
41.29883
29.91414
40.67395
36.98522
60.74793
37.82152
47.06827
48.84099
26.76904
30.19009
30.34802
39.82689
40.13598
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Appendix A
Table A-1. Port Coordinates (continued)
USAGE
Port Name Code
Pensacola, FL C2007
Philadelphia, PA C0552
Plaquemines, LA, Port of C2255
Port Angeles, WA C4708
Port Arthur, TX C2416
Port Canaveral, FL C2160
Port Dolomite, Ml L3627
Port Everglades, FL C2163
Port Hueneme, CA C4150
Port Inland, Ml L3803
Port Manatee, FL C2023
Portland, ME C0128
Portland, OR C4644
Presque Isle, Ml L3845
Providence, Rl C0191
Redwood City, CA CCA05
Richmond, CA C4350
Richmond, VA C0737
Sacramento, CA CCA06
San Diego, CA C4100
San Francisco, CA C4335
Sandusky, OH L3213
Savannah, GA C0776
Searsport, ME C0112
Seattle, WA C4722
South Louisiana, LA, Port of C2253
St. Clair, Ml L3509
Stockton, CA C4270
Stoneport, Ml L3619
Tacoma, WA C4720
Tampa, FL C2021
Texas City, TX C2404
Toledo, OH L3204
Two Harbors, MN L3926
Valdez, AK C4816
Vancouver, WA C4636
Wilmington, DE C0554
Wilmington, NC C0766
Port Coordinates
Longitude Latitude
-87.25793 30.40785
-75.20219 39.91882
-89.68745 29.48000
-123.45320 48.13050
-93.96069 29.83142
-80.60815 28.41409
-84.31281 45.99139
-80.11780 26.09339
-119.20848 34.14824
-85.86279 45.95508
-82.56130 27.63376
-70.25134 43.64951
-122.66530 45.47881
-87.38517 46.57737
-71.39844 41.81178
-122.20972 37.51306
-122.37423 37.92424
-77.41936 37.45701
-121.54389 38.56167
-117.17840 32.70821
-122.39904 37.80667
-82.71227 41.47022
-81.09538 32.08471
-68.92497 44.45285
-122.35922 47.58771
-90.61794 30.03345
-82.49413 42.82663
-121.31560 37.95270
-83.47031 45.28073
-122.45150 47.28966
-82.52235 27.78534
-94.91809 29.36307
-83.50751 41.66294
-91.66261 47.00428
-146.34640 61.12473
-122.68060 45.62244
-75.50697 39.71589
-77.95395 34.23928
Datum = NAD83
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Appendix A
Table A-2. RSZ Distances and End Point Coordinates
RSZ distant
Port Name Original
Albany, NY 140.0
Alpena, Ml 7.0
Anacortes, WA 99.0
Anchorage, AK 144.0
Ashtabula, OH 7.0
Baltimore, MD 110.8
Barbers Point, Oahu, HI 7.0
Baton Rouge, LA 277.0
Beaumont, TX 56.5
Boston, MA 15.0
Bridgeport, CT 2.0
Brownsville, TX 18.8
Brunswick, GA 45.5
Buffalo, NY 7.0
Burns Waterway Harbor, IN 7.0
Calcite, Ml 7.0
Camden-Gloucester, NJ 84.0
Carquinez, CA 40.4
Catalina, CA 22.2
Charleston, SC 17.0
Chester, PA 73.0
Chicago, IL 7.0
Cleveland, OH 7.0
Conneaut, OH 7.0
Coos Bay, OR 10.0
Corpus Christi, TX 30.0
Detroit, Ml 7.0
Duluth-Superior, MN and Wl 7.0
El Segundo, CA 25.4
Erie, PA 7.0
Escanaba, Ml 7.0
Eureka, CA 8.7
Everett, WA 113.0
Fairport Harbor, OH 7.0
Fall River, MA 22.0
Freeport, TX 2.5
;e (naut mi) Final RSZ End Point(s)
Revised Longitude
142.5 -73.89287
7.0 -83.20368
108.3 -124.77071
143.6 -152.30923
7.0 -80.80969
157.1 -75.80671
5.1 -158.13220
-89.42484
219.8
-89.13701
53.5 -93.75518
14.3 -70.78316
2.0 -73.18627
18.7 -97.09206
-80.93453
38.8
-81.13572
7.0 -79.09961
7.0 -87.10322
7.0 -83.53828
94.0 -75.00952
39.0 -122.63183
11.9 -118.46480
17.3 -79.64519
78.2 -75.00952
7.0 -87.41412
7.0 -81.76504
7.0 -80.56392
13.0 -124.35866
30.1 -96.87531
7.0 -83.13843
7.0 -91.85357
-118.92559
23.3
-118.46480
7.0 -80.11496
7.0 -86.92241
9.0 -124.34677
123.3 -124.77071
7.0 -81.39173
22.7 -71.33344
2.6 -95.29492
Latitude
40.47993
44.99298
48.49074
59.56080
42.08549
36.84680
21.21756
28.91161
28.98883
29.55417
42.37881
41.13906
26.06129
31.29955
30.68935
42.81683
41.80625
45.39496
38.79004
37.76094
33.63641
32.62557
38.79004
41.86971
41.63079
42.13361
43.35977
27.74433
42.10308
46.78916
33.91252
33.63641
42.31510
45.58297
40.75925
48.49074
41.91401
41.41708
28.93323
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Appendix A
Table A-2. RSZ Distances and End Point Coordinates (continued)
RSZ distance (n<
Port Name Original Re
Galveston, TX 9.0
Gary, IN 7.0
Georgetown, SC 17.0
Grays Harbor, WA 74.0
Gulfport, MS 17.0
Hilo, HI 7.0
Honolulu, HI 10.0
Hopewell, VA 82.0
Houston, TX 34.0
Indiana Harbor, IN 7.0
Jacksonville, FL 15.0
Kahului, Maui, HI 7.0
Kalama, WA 69.0
Lake Charles, LA 24.0
Long Beach, CA 22.2
Longview, WA 59.0
Lorain, OH 7.0
Los Angeles, CA 22.2
Manistee, Ml 7.0
Marblehead, OH 7.0
Marcus Hook, PA 70.0
Matagorda Ship Channel, TX 24.0
Miami, FL 3.0
Milwaukee, Wl 7.0
Mobile, AL 35.0
Morehead City, NC 2.0
Muskegon, Ml 7.0
Nawiliwili, Kauai, HI 7.0
New Bedford, MA 22.0
Newcastle, DE 58.0
New Haven, CT 2.0
New Orleans, LA 98.0
New York, NY and NJ 21.9
Newport News, VA 22.0
Nikishka, AK 84.0
aut mi) Final RSZ End Point(s)
vised Longitude Latitude
9.3 -94.66110 29.32470
7.0 -87.28239 41.77658
17.6 -79.07790 33.19240
4.9 -124.23996 46.89509
17.4 -88.92627 30.11401
7.1 -154.98524 19.76978
-157.95563 21.17658
10.0
-157.78534 21.23827
91.8 -75.80671 36.84680
49.6 -94.66110 29.32470
7.0 -87.40072 41.84010
18.6 -81.36490 30.39769
7.5 -156.44046 21.01066
68.2 -124.13727 46.22011
38.0 -93.33885 29.73094
-118.46480 33.63641
18.1
-118.13027 33.45211
67.3 -124.13727 46.22011
7.0 -82.27014 41.64023
-118.46480 33.63641
20.6
-118.13027 33.45211
7.0 -86.38189 44.41573
7.0 -82.72931 41.69638
94.7 -75.00952 38.79004
24.0 -96.22874 28.33472
3.8 -80.12014 25.75787
7.0 -87.67176 42.97343
36.1 -88.06442 30.14570
2.2 -76.66791 34.68999
7.0 -86.53765 43.29151
7.3 -159.26595 21.87705
22.4 -71.10131 41.38499
60.5 -75.00952 38.79004
2.1 -72.91207 41.26588
-89.42484 28.91161
104.2
-89.13701 28.98883
15.7 -73.89287 40.47993
24.3 -75.80671 36.84680
90.7 -152.30923 59.56080
ICF International
EPA Contract EP-C-06-094/WA 0-02
A-5
U.S. Environmental Protection Agency
September 2007
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Commercial Marine Port Inventory Development
Appendix A
Table A-2. RSZ Distances and End Point Coordinates (continued)
RSZ distant
Port Name Original
Oakland, CA 20.6
Olympia, WA 167.0
Other Puget Sound, WA
Palm Beach, FL 3.0
Panama City, FL 10.0
Pascagoula, MS 17.0
Paulsboro, NJ 73.0
Penn Manor, PA 102.0
Pensacola, FL 12.0
Philadelphia, PA 84.0
Plaquemines, LA, Port of 46.7
Port Angeles, WA 57.0
Port Arthur, TX 20.0
Port Canaveral, FL 4.0
Port Dolomite, Ml 7.0
Port Everglades, FL 2.0
Port Hueneme, CA 2.8
Port Inland, Ml 7.0
Port Manatee, FL 30.0
Portland, ME 11.0
Portland, OR 93.0
Presque Isle, Ml 7.0
Providence, Rl 22.0
Redwood City, CA 38.7
Richmond, CA 24.7
Richmond, VA 100.0
Sacramento, CA 92.7
San Diego, CA 12.4
San Francisco, CA 16.7
Sandusky, OH 7.0
Savannah, GA 45.5
Searsport, ME 22.0
Seattle, WA 122.0
South Louisiana, LA, Port of 158.5
St. Clair, Ml 7.0
Stockton, CA 87.7
Stoneport, Ml 7.0
;e (naut mi) Final RSZ End Point(s)
Revised Longitude
18.4 -122.63183
185.9 -124.77071
106.0 -124.77071
3.1 -79.99729
10.0 -84.17969
17.5 -88.48038
83.5 -75.00952
114.5 -75.00952
12.7 -87.29801
88.1 -75.00952
-89.42484
52.4
-89.13701
65.0 -124.77071
21.0 -93.75518
4.4 -80.53282
7.0 -84.24454
2.1 -80.08200
2.8 -119.23808
7.0 -85.65243
27.4 -83.03643
11.4 -70.10767
105.1 -124.13727
7.0 -87.08199
24.9 -71.33344
36.0 -122.63183
22.6 -122.63183
106.4 -75.80671
90.5 -122.63183
11.7 -117.31476
14.4 -122.63183
7.0 -82.52508
45.5 -78.04980
22.2 -68.76450
133.3 -124.77071
-89.42484
142.8
-89.13701
7.0 -82.58379
86.9 -122.63183
7.0 -83.23551
Latitude
37.76094
48.49074
48.49074
26.77129
30.08180
30.09597
38.79004
38.79004
30.27777
38.79004
28.91161
28.98883
48.49074
29.55417
28.41439
45.83181
26.08627
34.10859
45.87553
27.59078
43.54224
46.22011
46.58040
41.41708
37.76094
37.76094
36.84680
37.76094
32.62184
37.76094
41.56193
33.83598
44.11790
48.49074
28.91161
28.98883
42.55923
37.76094
45.25919
ICF International
EPA Contract EP-C-06-094/WA 0-02
A-6
U.S. Environmental Protection Agency
September 2007
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Commercial Marine Port Inventory Development
Appendix A
Table A-2. RSZ Distances and End Point Coordinates (continued)
RS
Port Name O
Tacoma, WA
Tampa, FL
Texas City, TX
Toledo, OH
Two Harbors, MN
Valdez, AK
Vancouver, WA
Wilmington, DE
Wilmington, NC
Z distance (naut mi)
riginal Revised
140.0 150.5
30.0 30.0
13.8 15.1
7.0 7.0
7.0 7.0
27.0 27.2
94.0 95.7
62.0 65.3
28.0 27.6
Final RSZ End Point(s)
Longitude
-124.77071
-83.03643
-94.66110
-83.30339
-91.44141
-146.88099
-124.13727
-75.00952
-80.32502
Latitude
48.49074
27.59078
29.32470
41.73230
46.93391
60.86513
46.22011
38.79004
31.84669
Datum = NAD83
ICF International
EPA Contract EP-C-06-094/WA 0-02
A-7
U.S. Environmental Protection Agency
September 2007
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Appendix B for EPA420-D-07-007
ESTIMATION, VALIDATION, AND FORECASTS OF
REGIONAL COMMERCIAL MARINE VESSEL
INVENTORIES
Final Report
Submitted by
James J. Corbett, RE., Ph.D.
Jeremy Firestone
University of Delaware
Coauthored by
Chengfeng Wang, Ph.D.
ARB Contract Number 04-346
CEC Contract Number 113.111
Prepared for
the California Air Resources Board
and the California Environmental Protection Agency
and for
the Commission for Environmental Cooperation of North America
5 April 2007
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Disclaimer
The statements and conclusions in this Report are those of the contractor and not necessarily
those of the California Air Resources Board. The mention of commercial products, their source,
or their use in connection with material reported herein is not to be construed as actual or implied
endorsement of such products.
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Acknowledgments
This work received partial funding from the Commission for Environmental Cooperation
(CEC) project number 113.111. This work also benefited from significant in-kind support from
members of the North American SOx Emission Control Area (SEC A) team and their contractors.
In particular, this work was shared with Research Triangle Institute (RTI), at the direction of the
United States Environmental Protection Agency (U.S. EPA) and ARE. RTI developed a trade-
based model forecasting energy demand from commercial ships, and the forecasts described in
this work are a product of coordination with this U.S. EPA-funded effort. Necessarily, we cite
these communications in this study, although future work may cite the RTI report to U.S. EPA
when that report can be referenced.
In particular, we thank John Callahan of the Research & Data Management Services of
the University of Delaware for his help applying the GIS tools. We acknowledge with
appreciation the significant review comments, and contributions by ARE staff (including
Dongmin Luo, Todd Sax, Andy Alexis, Michael Benjamin, and Kirk Rosenkranz); the work
greatly benefited from their guidance. We acknowledge collaborative discussions with the North
American SECA team, including representatives of Environment Canada (Joanna Bellamy,
Naomi Katsumi (who provided the Canada data), Patrick Cram, Andrew Green, Veronique
Bouchet, and Morris Mennell), the Commission for Environmental Cooperation (Paul Miller,
now at NESCAUM, who obtained the Mexico data on our behalf), and the U.S. Environmental
Protection Agency (Barry Garelick, Penny Carey, and others). In addition, we acknowledge the
work and review of fellow SECA contractors, including Brewster Boyd at Ross and Associates,
Louis Browning at ICF, and Chris Lindhjem at Environ. We acknowledge the good work
products and collaborative discussions regarding forecast results with Mike Gallagher and
Martin Ross at RTI and with Dave St. Amand at Navigistics Consulting.
This Report was submitted in fulfillment of contract number 04-346, Estimation,
Validation, and Forecasts of Regional Commercial Marine Vessel Inventories, by the University
of Delaware under the partial sponsorship of the California Air Resources Board (ARE). Tasks
1 and 2 received partial funding from the Commission for Environmental Cooperation (CEC)
project number 113.111, and significant in-kind support from member of the North American
SOx Emission Control Area (SECA) team and their contractors. Work on Tasks 1 and 2 of the
project was completed as of March 2006. Work on Tasks 3 and 4 of the project was completed
as of October 2006. Project work was completed as of January 2007.
11
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Table of Contents
Disclaimer i
Acknowledgments ii
Table of Contents iii
List of Figures iv
List of Tables iv
ABSTRACT v
EXECUTIVE SUMMARY vi
1.0 INTRODUCTION 1
1.1 Purpose and Scope 1
1.2 Project Background and Assumptions 1
1.3 Previous Work 2
1.3.1 Inventory Development 3
1.3.2 Trends and Forecasting 4
2.0 MATERIALS AND METHODS 7
2.1 Baseline Conditions: STEEM description 8
2.2 Rates of Change: Installed power as first-order trend indicator for CMV emissions 12
2.2.1 Evaluating coupled growth in cargo and energy 13
2.3 Patterns of Change: First-order consideration at North American scale 14
3.0 RESULTS 16
3.1 Baseline Emissions Estimates 16
3.2 Producing Spatially Resolved Emissions Inventories for Various Pollutants (Task 1) 18
3.3 Inventory Summary by Vessel Type 20
3.4 Comparison with Other Emissions Studies (Task 2) 20
3.5 Forecasting principles 23
3.6 Activity-based modeling of freight growth 24
3.5.1 Growth Rates 26
3.5.2 Growth Patterns 29
3.7 Future Emissions without SECA region (Task 3) 30
3.8 Future Emissions with Potential SECA (Task 4) 31
4.0 DISCUSSION 33
4.1 Comparison with global forecast trends 33
4.2 Uncertainty and Bounding 33
5.0 SUMMARY AND CONCLUSIONS 35
5.1 Baseline Inventory 35
5.2 Forecast Trends 36
6.0 RECOMMENDATIONS 39
6.1 Improve precision 39
6.2 Reduce Base-Year Uncertainty 39
6.3 Improve Trend Extrapolation 39
6.4 Incorporate additional detail among drivers affecting change 41
6.5 Incorporate planned or proposed signals to modify technological change trends 41
6.6 Model fleet behavior in response to potential action 41
6.7 Extend voyage data or analytical detail 41
7.0 REFERENCES 42
LIST OF ACRONYMS 48
Appendix: Summary ofNorth American Ports and Waterways 49
in
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List of Figures
Figure 1. Illustration of Waterway Network Ship Traffic, Energy and Environment Model
(STEEM) as applied to emission estimation 9
Figure 2. Illustration of spatial distribution of SC>2 from North American shipping; shaded areas
represent approximate delineation of coastal exclusive economic zones (EEZs) 19
Figure 3. Comparison of the inventories produced with Waterway Network-STEEM and top-
down approach using ICO ADS 22
Figure 4. Illustration of domains of regional/port emission inventories studies 22
Figure 5. Comparison of emissions inventories of different approaches; emissions for Houston &
Galveston are NOx, emissions for the other areas are SO2 23
Figure 6. Container statistics from U.S. Maritime Administration and American Association of
Port Authorities 25
Figure 7. South Coast (South Pacific) growth rates derived from historic data (1997-2003),
showing upper-bound (exponential), lower-bound (linear), and average trends 28
Figure 8. US container growth trends from data extrapolation (1997-2003) and from unpublished
draft RTI trade-energy model 28
Figure 9. Model domain showing hypothetical with-SECA region and baseline 2002 model
results 30
Figure 10. Illustration of 2020 ship SOx emissions without SECA reductions 31
Figure 11. Illustration of 2020 ship SOx emissions with hypothetical SECA region 32
Figure 12. Global indices for seaborne trade, ship energy/fuel demand, installed power 34
Figure 13. Forecast reduction in 2020 of annual SOx emissions due to hypothetical SECA 36
Figure 14. Forecast increases from base-year inventory in SOx emitted in 2020 with SECA 37
Figure 15. Trends with and without EVIO-compliant SECA, and with 0.5% SECA 38
Figure 16. Uncertainty in model output from input parameters scaled by contribution to output
variance 40
List of Tables
Table ES-1. Baseline 2002 inventory of emissions and fuel use in North American Domain
(metric tonnes) vii
Table 1. Summary of engine power and at-sea load profile 16
Table 2. Emission Factors 17
Table 3. Summary of auxiliary engine SO2 emissions factor 17
Table 4. Baseline 2002 inventory of emissions and fuel use in North American Domain1 19
Table 5. Estimated Domain Emissions by Vessel Type 20
Table 6. Estimated Percent of Total Emissions from Auxiliary Engines (AEs) 20
Table 7. Power-based growth rate summary for commercial ships 2002 -2020 (CAGR) 29
Table A-l. State-by-state Summary of Ports and Port Calls 50
Table A-2. U.S. Port and Waterway Summary from US ACE Foreign Commerce Data 51
Table A-3. Canadian Port and Waterway Summary from LMIU Movement Data 57
Table A-4. Mexican Port and Waterway Summary from LMIU Movement Data 61
IV
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ABSTRACT
This report presents results of a project to develop and deliver commercial marine
emissions inventories for cargo traffic in shipping lanes serving U.S. continental coastlines. A
regional scale methodology consistent with port-based inventory methods was applied for
estimating commercial marine vessel (CMV) emissions in coastal waters. Geographically
resolved inventories were produced for a 2002 baseline year (Task 1). Several port-based
inventories were evaluated to validate the regional inventory (Task 2). Using average growth
trends describing trade and energy requirements for North American cargo and passenger
vessels, an unconstrained forecast was developed to describe a business as usual (BAU) scenario
without sulfur controls (Task 3), and a with-SECA scenario assuming IMO-compliant reductions
in fuel sulfur to 1.5% by weight for all activity within the Exclusive Economic Zone (200
nautical miles) of North American nations (Task 4). This work contributes to better regional
inventories of commercial marine emissions for North America that supports the California Air
Resources Board (ARB), Commission for Environmental Cooperation of North America (CEC),
western regional states, United States federal, and multinational efforts to quantify and evaluate
potential air pollution impacts from shipping in U.S, Canadian, and Mexican coastal waters.
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EXECUTIVE SUMMARY
Background: Current best practices for marine vessel emissions inventories have not
been applied to spatially and temporally describe North American interport shipping activity
until now. (Interport shipping is ship activity voyaging between ports; it does not include
dockside hotelling.) We produced a baseline (2002) emissions inventory for ships engaged in
foreign commerce arriving at U.S. ports, and for ship activity in Canada and Mexico by
commercial cargo and passenger vessels (excluding ferries). We forecast inventories for
business-as-usual (BAU) and for a hypothetical SOx Emission Control Area (SECA) including
the Exclusive Economic Zone (EEZ) of North American nations (i.e., 200 nautical miles). The
base-year inventory and forecasts assist the California Air Resources Board (ARE) in evaluating
air quality and health impacts in California, and help evaluate national impacts, providing part of
the required information to request a North American SECA (or SEC As) on behalf of the United
States, Canada, and Mexico at the International Maritime Organization (IMO).
Methods: We use a network model, the Waterway Network Ship Traffic, Energy and
Environment Model (STEEM), to quantify and geographically represent inter-port vessel traffic
and emissions for North America, including the United States, Canada, and Mexico. The model
estimates main and auxiliary engine emissions from nearly complete historical North American
shipping activities and individual ship attributes, applying activity-based emissions estimates in a
GIS platform using an empirically derived network of shipping routes.
We evaluate various sources of growth projections for commercial marine activity and
energy use, ultimately choosing an adjusted extrapolation scenario from historic trends in
installed power on ships calling on North American ports. Use of installed power trends depends
on the following assumptions: 1) commercial marine vessels in cargo service design power
systems to satisfy trade route speed and cargo payload requirements; 2) commercial marine
vessels operate under duty cycles that are well understood, especially at sea speeds; 3) installed
power trends for ships calling on North American ports directly reveals the trend in speed and
size for these routes. Trend extrapolations for installed power reveal the correlated trend in
energy use by ships, although different extrapolations approaches yield different forecasts. An
unconstrained exponential fit may be overly optimistic given economic cycles in shipping and
technological change in the fleet; a linear fit may be unrealistic with regard to fundamental work-
energy principles and economic drivers for global trade. These define bounding limits for
expected change in ship activity. We average these to describe a BAU growth trend that
implicitly reflects a mix of positive and negative drivers for ship energy requirements.
Results for Baseline Inventory: North American shipping consumed about 47 million
tons of heavy fuel oil and emitted -2.4 million tons of SC>2 in 2002, with approximately 30
million tons fuel and 1.6 million tons SC>2 within the North American domain for this project.
Comparison of our results with port and regional studies shows good agreement, and improved
accuracy over existing top-down methods. Shipping activity within the domain, defined for this
project by consensus with the North American SECA team. Table ES-1 summarizes the
interport inventory estimates for the baseline year of 2002. The table presents results for coastal
regions (defined as the 200 nautical mile EEZ) by nation, and the total for all domain areas
outside coastal regions. Comparison of our results with five inventories from other regional and
port emissions inventories studies (including Great Lakes, Western Canada, the Port of Los
Angeles, Houston & Galveston area, and the Port of New York and New Jersey) showed no bias
and better accuracy using STEEM than top-down emissions inventories.
VI
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Results for Forecasts: We estimate a growth trend for North America (including United
States, Canada, and Mexico) of about 5.9%, compounded. We produce two classes of forecasts:
1) a business as usual (BAU) forecast applying a common growth trend without sulfur controls
(but with existing IMO NOx requirements); and 2) a with-SECA scenario assuming IMO-
compliant reductions in fuel sulfur to 1.5% by weight for all activity within the Exclusive
Economic Zone (200 nautical miles) of North American nations. Our BAU scenario compares
reasonably well with available energy and fuel usage trends and with trends describing growth in
trade volume; our growth trends are lower than have been reported since 2002 by major US
ports. We identify no systemic bias in our forecasts. Various trends agree under BAU scenarios
that energy used by ships bringing global trade to and from North America will double by or
before 2020. Forecasts show that implementing a North American SECA region reducing fuel-
sulfur content from 2.7% to 1.5% (whether through fuel changes or through control technology)
will reduce future SOx emissions (as 802) by more than 700 thousand metric tons (-44%) from
what they may otherwise grow to be in 2020. However, our 2020 inventory with an IMO-
compliant SECA represents an increase over emissions in the 2002 base-year of more than 2
million metric tons of SOx emissions throughout the North American domain. At a growth rate
of 5.9% from the baseline year 2002, trade growth offsets emissions under a 1.5% fuel-sulfur
SECA by 2012; using alternative growth rates of 3.6% (separate work presented to the West
Coast SECA team), emissions within a North American SECA return to 2002 levels by 2019.
Conclusions: Baseline (2002) inventory results are being used by ARB, the U.S.
Environmental Protection Agency (U.S. EPA), Environment Canada, and others to model
atmospheric fate and transport of pollution, evaluate air quality impacts, and assess potential
health effects attributed to ships. Health and environmental impacts evaluated using these
inventories may merit emissions control beyond current IMO standards to maintain emissions
targets despite trade growth. Future work could improve precision of near-port inventories
through improved network or vessel activity details.
Table ES-1. Baseline 2002 inventory of emissions and fuel use in North American Domain (metric tonnes)1
NOx as NO2 SO2 CO2 HC PM CO Fuel Use
United States EEZ2
West Coast 135,000 80,200 4,817,000 4,470 11,300 10,500 1,480,000
East Coast 255,000 151,000 9,095,000 8,440 21,300 19,900 2,800,000
Gulf Coast 174,000 103,000 6,201,000 5,750 14,500 13,600 1,910,000
Great Lakes 16,200 9,620 578,000 540 1,350 1,260 178,000
Alaska 63,300 37,600 2,260,000 2,100 5,300 4,940 697,000
Hawaii 20,500 12,200 732,400 680 1,720 1,600 226,000
Canada EEZ23
West Coast 21,900 13,000 781,000 720 1,830 1,700 241,000
East Coast 96,200 57,200 3,440,000 3,190 8,050 7,500 1,060,000
Great Lakes 10,100 5,980 359,000 330 840 800 111,000
Mexico EEZ2
West Coast 99,400 59,100 3,550,000 3,290 8,320 7,800 1,090,000
Gulf Coast 107,000 63,700 3,827,000 3,550 8,970 8,000 1,180,000
Total Coastal regions 998,000 593,000 35,640,000 33,100 83,500 77,900 10,980,000
Non-coastal regions
1,740,000 1,040,000 62,200,000 57,700 146,000 136,000 19,170,000
Total in Domain
2,740,000 1,630,000 97,800,000 90,800 229,000 214,000 30,160,000
1. Values are rounded to three significant figures for presentation; sums may vary as a result of rounding.
2. National estimates of EEZ boundaries use an ArcGIS buffer of 200 nautical miles and informal national divisions.
3. Western Canada summaries include emissions in the Northwestern part of the domain; Eastern Canada summaries
include emissions in the Northeastern part of the domain.
4. Non-coastal regions are areas in the Domain not within the EEZ of Canada, United States or Mexico.
Vll
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1.0 INTRODUCTION
This report is intended to assist the role of the California Air Resources Board (ARE) and
other agencies evaluating the feasibility and extent of a North American Sulfur Emissions
Control Area (SECA) as defined by the International Maritime Organization (IMO) in terms of
potential impact to air quality and human health by oceangoing commercial marine vessels in
transit.
1.1 Purpose and Scope
A primary objective of this project is to describe a regional scale methodology for
estimating commercial marine vessel (CMV) emissions in coastal waters (i.e., the Exclusive
Economic Zone or EEZ) that is consistent with port-based inventory methods. There are several
tasks that follow from this objective, including:
Task 1 Provide a baseline inventory of CMV emissions at a regional scale appropriate for modeling
impacts relevant to potential SECA designation. Using this methodology, this work produced a
spatially resolved inventory of CMV emissions for North America for a baseline year of 2002.
This represents a distance larger than the Exclusive Economic Zone for the continental United
States and Canada and Mexico, a legal area beyond and adjacent to the territorial sea that
provides certain federal authority to protect and preserve the marine environment (1).
Task 2 Evaluate several port-based inventories in terms of their potential agreement and validation of the
regional inventory. We conclude that different assumptions, inputs, or methods applied in port-
based inventories produce expected differences reflecting more detailed local information at the
port level that cannot be easily reflected at the regional scale. Based on our results, we offer
recommendations to improve regional inventory methods or otherwise reconcile differences with
port-based inventories.
Task 3 Forecast how baseline emissions may change in future years. Future emissions will be dependent
in part upon the changes in emission factors (due to MARPOL Annex VI, other policy, and other
changes in engine characteristics), changes in vessel size and number. Additionally, changes may
occur in vessel activity patterns and trade routes, and changes in fuel quality (especially sulfur
content) - from a mix of technology, economic, and/or policy drivers.
Task 4 Forecast future-year ship emissions under a potential SECA designation. Modification of future-
year baseline emissions are made using MARPOL Annex VI requirements that requires the sulfur
content of marine fuel used by marine engines within a SECA be equal to or less than 1.5% S by
weight.
This project supports ARB efforts to understand the significance of ship emissions, by
providing forecasts of CMV emissions under assumptions that describe trade-driven fleet
growth, technological changes, and potential designation of special areas under the EVIO's
MARPOL Annex VI convention, called SOx Emission Control Areas (SECAs).
1.2 Project Background and Assumptions
ARB is participating in a collaborative effort to understand and quantify potential impacts
of CMV activity on North American pollutant emissions, air quality, and public health. This
collaboration is led by the U.S. EPA, with agency support also from Environmental Canada, and
ARB, and with funded participation by various university researchers and consulting firms.
Similarly, the California Goods Movement Action Plan and related efforts to improve freight
transportation infrastructure and environmental performance are multi-scale and multi-
-------
dimensional interests that depend on a good understanding of international freight movement
through major U.S. ports, including but not limited to California ports.
While ARB may be most interested in how CMV emissions and their mitigation may
affect California, the international nature of shipping and multi-jurisdictional nature of policy
alternatives established a scale of interest that includes all North America. According to the
World Shipping Council's container cargo rankings of U.S. ports (2), the ports of Los Angeles
and Long Beach together accounted for more than 36% of all U.S. containerized imports and
exports in 2003; together with Oakland, CA ports handle nearly half of all U.S. waterborne
containerized cargoes.
This report presents inventory methodology, results, and validation for ships engaged in
foreign commerce arriving at U.S. ports, and for ship activity in Canada and Mexico by
commercial cargo and passenger vessels (excluding ferries). We produce a spatially-resolved,
activity-based inventory of North American shipping activity derived from 172,000 port calls in
2002 to Canada, Mexico, and the United States, employing activity-based methods in a GIS
network of empirical shipping routes. We derive emissions forecast trends directly from
aggregated installed power of ships calling on North American ports; this is because emissions
are directly proportional to engine power and load, which for at-sea conditions is highly
correlated with total installed power on commercial ships; this direct proportionality of stack
emissions to engine power is implicit in the use of power-based emissions factors in activity-
based inventory best practices. We then adjust base-year inventory to estimate emissions from
commercial marine vessels for 2010 and 2020. Using observed trends in installed power by
cargo and passenger vessels calling on North America, we produce two classes of forecasts: 1)
an unconstrained forecast applying a common growth trend to forecast a business as usual
(BAU) scenario without sulfur controls; and 2) a with-SECA scenario assuming IMO-compliant
reductions in fuel sulfur to 1.5% by weight for all activity within the North American nations.
1.3 Previous Work
Air pollutants from marine vessels account for a non-negligible portion of the emissions
inventory and contribute to air quality, human health and climate change issues at local, regional
and global levels (3-25). According to the U.S. EPA, heavy duty truck, rail, and water transport
together account for more than 25% of U.S. CC>2 emissions, about 50% of NOx emissions, and
nearly 40% of PM emissions from all mobile sources (26, 27). In Europe, freight modes together
generate more than 30% of the transportation sector's CC>2 emissions (28). In California, marine
vessel ship emissions are a significant concern with regard to state implementation of federal air
quality requirements (http://www.arb.ca.gov/msprog/offroad/marinevess/marinevess.htm),
particularly for air districts (21, 29)) and for major ports (http://www.portoflosangeles.org/ and
http://www.polb. com/).
Better estimation of current and future emissions inventories, including spatial
representation, is needed for atmospheric scientists, pollution modelers, and policy makers to
evaluate and mitigate the impacts of ship emissions on the environment and human health. In
fact, understanding the nature of commercial marine (e.g., cargo) vessel activity and energy use
serves both environmental and goods movement goals for the State of California and the nation.
This is particularly true for major ports which represent nodes connecting imported and exported
ship cargoes with road and rail freight transportation serving the U.S. and global economies.
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1.3.1 Inventory Development
Although emissions estimates and fuel use are related to the energy used by ships, recent
studies call into question the validity of relying on the statistics of marine fuel sales (4, 30-33).
Best practices of estimating emissions from transportation overall, and marine vessel emissions
inventories specifically, have focused on activity-based estimation of energy and power demands
from fundamental principles (4, 30, 32, 34). These approaches have shown that fuel allocated to
international fuel statistics is insufficient to describe total estimated energy demand of
international shipping. Even if marine fuel sales statistics were perfect, ships may consume fuel
far from where they purchase it. At best, regional statistics provide limited insight into the
spatial and temporal characteristics of ship energy consumption.
Principle existing approaches for producing spatially-resolved ship emissions inventories
generally can be categorized as either top-down or bottom-up. The fundamental difference
between these is that in bottom-up approaches emissions are directly estimated within a spatial
context, whereas in top-down approaches emissions are calculated without respect to location at
an aggregate level and may later be associated with spatial characteristics. In this work, a mixed
approach is developed. First, we associate port arrival-departure data with ship characteristics
data to identify more than 170,000 voyages for North America and to allow for activity-based
inventory methods of estimating emissions for each voyage. Second, we assign routes to voyage
origin-destination pairs using an empirically derived routing network in the Ship Traffic Energy
and Environmental Model (STEEM); this is a top-down analytical approach in the sense that we
are not directly observing actual voyage routes, but modeling them according to a least-distance
algorithm intended to approximate a least-cost voyage. Third, we apply activity-based
assumptions about vessel speed, power, energy, and emissions directly within the voyage routing
network to produce spatially resolved emissions estimates.
Using a top-down approach, Corbett, et al. produced the first global spatial representation
of ship emissions using a shipping traffic intensity proxy derived from the Comprehensive
Ocean-Atmosphere Data Set (COADS), a data set of voluntarily reported ocean and atmosphere
observations with ship locations (3, 11). They assumed that the reporting ship fleet is
representative of the world fleet, spatial distribution of ship reporting frequencies represents the
distribution of ship traffic intensity, and emissions are proportional to traffic intensity. Endresen,
et al. improved the global spatial representation of ship emissions by using ship size (gross
tonnage) weighted reporting frequencies from the Automated Mutual-assistance Vessel Rescue
system (AMVER) data set (5). They implicitly assumed that ship energy consumption and
emissions are proportional to ship size, which is not true for some types of ships, and they
observed that COADS and AMVER lead to highly different regional perturbations (5). Wang, et
al. addressed the potential statistical and geographical sampling bias of the International
Comprehensive Ocean-Atmosphere Data Set (ICOADS, current version of COADS) and
AMVER data sets, the two "best" global ship traffic intensity proxies, and made four
advancements to improve the accuracy of the top-down approach using ICOADS as spatial
proxy (35): i) trimming over-reporting vessels to mitigate geographic and statistical sampling
bias; ii) increasing sample size by using multiple-year ICOADS data; iii) weighting ship
observations with installed ship power to reflect emissions variability among different sizes and
types of vessels; and iv) smoothing the inventory with GIS tools.
The quality of top-down approaches is limited by the accuracy of global emissions
estimates, and inventory precision is limited by the representativeness of spatial proxies.
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Significant differences exist among the various global ship emission inventories (4, 5, 30, 31).
Activity-based energy consumption and emissions in the updated inventory by Corbett and
Koehler roughly doubled the results of earlier studies (4). Uncertainly exists in the updated
inventory such that the upper bound is about 60% higher than the lower bound (4). Discrepancies
among different studies and the range between lower and upper bound of the same study can be
explained by the uncertainties of marine engine load factor, time in operation, and fuel
consumption rates, which vary by ship type, size, age, fuel type, and market situation (30, 31).
Variation in these inputs represents first-order barriers to improving the accuracy of the global
ship inventory. Second, since both ICOADS and AMVER data sets rely on voluntary reporting
and neither of them is randomly sampled, both of them are statistically and spatially biased (35).
Bottom-up approaches were applied by Lloyd's register and Entec UK Limited to
produce regional ship emissions inventories for the European Monitoring and Evaluation
Programme (EMEP) area, the Baltic Sea, and the Mediterranean Sea (17, 24, 25). In this type of
approach, ship and route specific emissions are estimated based on historical ship movements,
ship attributes, and ship emissions factors. The locations of emissions are determined by the
locations of the most probable navigation routes, which are great-circle (i.e., radius) routes
between transoceanic origins and destinations, adjusted where prohibited by land, ice, or depth;
the Lloyds and Entec work was more regional (not transoceanic) and generally followed straight-
line routes. Streets, et al. estimated emissions from international shipping in Asian waters based
on commodity flow associated with major sea routes (7, 8).The accuracy of this method, which
can be categorized as a bottom-up approach using trade as a proxy for emissions, is limited by
the assumed relationships between the volume of trade flow and emissions, which are more
closely related to ship installed power, load profile, etc., and by the aggregation of individual
voyage routes into major shipping lanes.
Although bottom-up approaches appear more precise than top-down methods, large-scale
bottom-up inventories also are uncertain because they must estimate engine workload, ship
speed, and most importantly, the speculative locations of the routes which determine the spatial
distribution of emissions. Given the large number of ship movements and potentially dynamic
shipping routes, the accuracy of regional annual inventories in bottom-up approaches is limited
when selected periods within a calendar year studied are extrapolated to represent annual totals
(17, 24).
1.3.2 Trends and Forecasting
Trend analyses are useful in describing changes that may have occurred in the past or
how changes may occur in the future. While past trends can often be observed without an
understanding of underlying causes, they are useful when exploring relationships among
correlating histories to evaluate causal drivers or correlated indicators of change. Developing
future trends (forecasts) represents an uncertain extrapolation of past observations considering
explicit or implicit assumptions about how the trend may be affected by sustained or modified
drivers or indicators of change.
Forecasts differ depending on their purposes and scales. Some forecasts look to reveal
where timely investment and action at a local scale or by a single firm can produce the most
benefit (e.g., profit). Validity of insights is determined by whether recommended actions produce
expected outcomes for a given decision, not whether the forecast trend or future value is realized.
Other forests are intended to be conservative or aggressive; that is, they intend to be biased to
serve the decision makers' value and tolerance for risk and surprise. This may describe large
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scale forecasts such as emissions or trade trends. One challenging class of forecasts may be
considered "difference'" forecasts, where alternative scenarios illustrate how "a path taken" may
differ from "a path not taken" rather than to determine which is most probable. These kinds of
forecasts are common in policy domains, such as energy, environment, and economics (e.g.,
IPPC scenarios). Certainly, freight forecasting presents one challenging example, especially at
the international or multinational scales, and especially when considering policy actions like a
SOx Emissions Control Area (SECA) under IMO MARPOL Annex VI (36).
Previous studies described global growth rates for maritime shipping energy and
emissions based on fleet size, trade growth, and/or cargo ton-km, mostly calibrated to linear or
conservative extrapolations of historic data. The IMO Study on Greenhouse Gas Emissions from
Ships (37) used fleet growth rates based on two market forecast principles, validated by historical
seaborne trade patterns: 1) World economic growth will continue; and 2) Demand for shipping
services will follow the general economic growth. The IMO study correctly described that
growth in demand for shipping services was driven by both increased cargo (tonnage) and
increased cargo movements (ton-miles), and considered that these combined factors make
extrapolation from historic data difficult. Nonetheless, their forecast for future seaborne trade
(combined cargoes in terms of tonnage) was between 1.5% and 3% annually. The IMO study
applied these rates of growth in trade to represent growth in energy requirements. The ENTEC
study (38) adopted growth rates from the IMO study.
Eyring et al. (39) estimated "future world seaborne trade in terms of volume in million
tons for a specific ship traffic scenario in a future year" using a linear fit to historical gross
domestic product (GDP) data. Interestingly, this represents one of the only studies to forecast
growth in seaborne trade for energy and emissions purposes at rates faster than GDP. The
TREMOVE maritime model (40, 41) estimates fuel consumption and emissions trends derived
from forecast changes in ship voyage distances (maritime movements in km) and the number of
port calls. According to the TREMOVE report, maritime "fleet and vehicle kilometres grow
annually by 2.5% for freight and 3.9% for passengers," while "port callings grew by 8%
compared to the previously used input figures."
For national CMV emissions, U.S. EPA's 2003 forecast methodology improved the
similarity between economic and emissions forecasts from earlier analyses (23, 42-44), although
emissions forecasts represent a compound annual growth rate (CAGR) of about 3.4% (range of
2.8% to 3.8%, depending on pollutant). While shipping growth rates accounted for the effect of
increased tonnage in a newer fleet, they do not consider the effect of faster speeds - specifically
the additional installed power to meet combined size and speed requirements. Correcting for
these factors brings the forecasts for international marine activity into closer agreement with
trucking growth rates (especially when rail cargo volume increases are considered), and better
describes the role of imports growth on the intermodal freight system.
Freight energy use is correlated to increased goods movement, unless substantial energy
efficiency improvements are being made within a freight mode (e.g., U.S. rail) or across the
logistics supply network. Even assuming that efficiency improvements from economies of scale
reduce energy intensity and emissions rather than being directed to larger and faster ships (e.g.,
containerships), compounding increases in trade volumes outstrip energy conservation efforts
unless technological or operational breakthroughs in goods movement emerge. However, except
for the Eyring et al. work, these linear extrapolations appear to present growth rates slower than
the economy; these linear extrapolations are likely biased underestimates, because shipping and
trade activity has grown (and is forecast to grow) faster than the economy. Freight
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transportation, particularly international cargo movement, is an important and increasing
contributor to global and national economic growth, as well as state and regional economic
growth in and around major cargo ports. If growth in GDP and trade volumes is compounded as
forecast by economic and transportation demand studies, then growth in energy requirements
should be non-linear also. The U.S. Bureau of Transportation Statistics (BTS) recently released
a report that describes North American freight activity and trends (45). This document reports
growth rates for North America above 7.4% for international trade and above 7.2% across all
measures of value, and states that:
"Since 1994, the value of freight moved among the three countries has averaged
almost 8 percent annual growth in both current and inflation-adjusted terms,
compared with about 7-percent growth for U.S. goods trade with all countries
(table 1). In 2005, both goods trade and gross domestic product (GDP) grew in
inflation-adjusted terms. Except in 2001 and 2002, during the past decade, U.S.
trade with Canada and Mexico has increased at a faster rate than U.S. GDP."
Growth in goods movement by dollar value may be expected to differ from growth in the
volume of goods moved, and in the change in activity by the multimodal fleets (ships, trucks,
trains, and aircraft) moving cargo. We confirmed that the contribution of international trade is
increasing as a proportion of U.S. gross domestic product (GDP) - i.e., freight transportation is
growing faster than U.S. GDP (45, 46). Economic activity related to imports and exports
together contribute about 22% of recent U.S. GDP in recent years; whereas, goods movement
contributed only about 10% of GDP in the 1970s. Moreover, the dominance of containerized
cargoes in seaborne trade suggests that truck and containerized shipments may double by 2025 or
sooner (47). GDP in the U.S. is growing at -3.7% CAGR since 1980, and the freight sector is
growing at -6.4% CAGR over the same period (46). This freight-sector growth rate in terms of
dollar value is reflected in the observed -6.3% to 7.2% annual growth rates of "high-value"
containerized trade volumes, particularly from Asia (48).
California studies also describe significant growth expected in commercial marine
emissions. The recent Clean Air Action Plan for Southern California ports estimates that
emissions of NOx and PM from oceangoing vessels will increase at baseline rates between 5.5%
and 6% CAGR, respectively, unless measures are taken to reduce emissions (49).l These growth
rates are consistent with trade growth rates, perhaps modified for IMO-compliant NOx
reductions in new vessels expected to call on California ports and descriptive of modest
improvements in fuel efficiency through fleet modernization and economies of scale. Studies for
Southern California (San Pedro Bay) ports agree that growth in cargo volumes equivalent to 6-
7% compounding annual growth rates is expected (50-53). However, increased cargo may not
produce a corresponding increase in port calls, as some studies interpret (51). Historic data on
port calls to San Pedro Bay have shown the number of ship calls remained between 5,000 and
7,000 calls per year since the 1950s (54). Furthermore, proportional relationships between
environmental impacts and goods movement trends are reflected in recent port and regional
studies of goods transport and economic activity, particularly for California ports (50, 55-57).
lrThe Clean Air Action Plan shows emissions control measures may offset near-term growth (at least through 2011)
if fully implemented.
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2.0 MATERIALS AND METHODS
This section describes principles, methods, and data used to produce baseline inventories
and future emissions inventory scenarios for North America. This project represents one of the
first applications of a network model developed to evaluate ship activity characteristics on large
regional and global scales using best-practice assumptions and methods comparable to the latest
port-based inventories of ship activity. The Ship Traffic Energy and Environmental Model
(STEEM) enables emissions inventory analyses that are not scaled from studies of a subset of
ports or smaller regions or patched together from separate inventory efforts (58, 59). Starting
with a global empirical network of observed shipping lanes, commercial cargo and passenger
ship arrivals and departures from all ports in North America are routed along coastal and
transoceanic shipping lanes. Vessel engine, speed, and size data for these vessels are applied to
estimate emissions from these vessels in both spatial and temporal domains.
In general, materials for this work include the global network developed at the University
of Delaware primarily by Dr. Chengfeng Wang (60), vessel activity data for the United States
from the U.S. Army Corps of Engineers (61), vessel movement data for Canada and Mexico
from Lloyds Maritime Intelligence Unit (LMIU) provided by Environment Canada and the
Commission for Environmental Cooperation, respectively (62, 63). Ship characteristics were
also obtained from Lloyd's ship registry data (64). Inventory assumptions and other model
inputs were primarily derived from earlier ARB reports and published work by Dr. Corbett (4,
30, 65), modified through discussion with U.S. EPA contractors and review of port-based best
practices (34).
Emissions trends are derived from a pluralistic evaluation of historic time series of the
above data and forecast studies that together describe: a) growth expected in international goods
movement in economic terms (e.g., seaborne trade); and b) correlated trends in energy required
to move more goods in service of global trade in terms of ship fleet characteristics (e.g., vessel
type and installed power). For cargo activity, we reviewed studies at port, regional, national, and
global scales, all of which document strong growth trends and/or forecast similar rates of
continued growth (50-53, 66-71). For vessel activity specific to North American ports, we were
able to construct detailed trend characteristics information including vessel type, power, size, and
speed characteristics for the period between 1997 and 2003; at the global scale, we developed
longer time-series trends in ship characteristics by year of build and from related global studies
(39, 64).
Three critical questions for understanding freight activity and environmental impacts
defined two phases of the project:
1. Baseline Conditions: What are freight energy and activity patterns?
2. Rates of Change: What is forecast trend in energy needed?
3. Patterns of Change: Where is future freight activity located?
While interrelated, these questions may be evaluated with some independence, and were
separated into phases combining Tasks 1 and 2 and combining Tasks 3 and 4, described above.
The first phase evaluated baseline conditions by applying STEEM, a model that integrates a GIS
routing algorithm allocating North American voyage data to empirically derived global ocean
routes with activity-based methodology to estimate emissions. The second phase analyses
considered rates of change in energy and emissions, demonstrating that installed power was not
only a direct input to estimating baseline emissions, but that installed-power trends described
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rates of change in fleet energy requirement. These phases are described in detail in earlier
technical memoranda, and summarized below.
2.1 Baseline Conditions: STEEM description
By applying advanced GIS tools and using better data sets, STEEM adopts the strengths
of both top-down and bottom-up approaches and attempts to overcome the weaknesses in each
approach and improves ship emissions inventory both mathematically and theoretically. First, the
model builds an empirical waterway network based on shipping routes revealed from observed
historical ship locations. The spatial allocation approaches the accuracy of a bottom-up approach
by assigning routes from a historically accurate network of actual routes, and is more accurate
than a top-down approach, which uses biased spatial proxies. Second, as in a bottom-up
approach, this model estimates energy use and emissions using complete historical ship
movements, ship attributes, and the distances of routes. Best-practices applied to baseline
inventories include identification and use of installed power characteristics, current power-based
emissions factors, engine load service corrections, and engine operating time (34, 72, 73).
STEEM improves baseline emissions inventories for North American shipping in the following
ways:
1. STEEM employs an emprical global waterway network derived from 20-year
International Comprehensive Ocean-Atmosphere Data Set (ICOADS) data;
2. The model estimates emissions from nearly complete historical North American
shipping activities (some 172,000 trips in U.S. Foreign Commerce Entrances and
Clearances data set and Lloyds' Movement data set) and individual ship attributes
while a top-down approach estimates emissions based on statistical analysis;
3. The model is constructed using advanced GIS network analyst technology to solve the
most probable route for each individual trip on a global scale;2
4. STEEM establishes explicit mathematical relationships among trips, ships, routes, pairs
of ports, and segments of the waterway network using a matrix approach;
5. STEEM uses actual lengths of routes, together with service speed of each individual
ship, to calculate hours of operation while top-down approaches estimate annual hours
of operation based on fleetwide statistics;
6. STEEM follows best practice to estimate emissions based on ship installed power,
service speed, and traveling distance for each trip;
7. STEEM assigns emissions based on the locations of solved routes while earlier bottom-
up approaches drew straight lines between origins and destinations manually and top-
down approaches allocate global emissions based on biased proxies;
8. STEEM captures transit traffic which contributes to local air quality problems in some
areas like Santa Barbara, CA, while port-wide inventories have often ignored or been
unable to quantify these effects.
Figure 1 illustrates the ship traffic module of STEEM, which can geographically and
temporally characterize ship traffic based on an empirical waterway network, historical ship
movement data, and ship attributes data set. The lower boxes in Figure 1 illustrate how we
applied ship attributes data to produce activity-based, spatially-resolved emissions inventories.
2 A summary of-400 North American ports and waterways is provided in the Appendix; these ports connect about
with -1,300 foreign ports in the 2002 U.S. Entrances and Clearances data set; about 950 ports are in the 2002
Lloyd's movement data set, with some overlapping ports among Canada, Mexico, and the United States.
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The empirical waterway network built in this model not only aligns the shipping lanes
with actual shipping activity, but also defines the relationships among routes, segments and
nodes with ArcGIS Network Analyst tools. In the empirical waterway network, intersections of
shipping lanes and ports are defined as nodes, and shipping lanes between two immediate nodes
are defined as segments. Traffic can only flow in and out of segments through nodes. A route is
defined as an actual non-stop path ships take between one origin and one destination port. We
next describe the model when applied to ship energy, fuel use, or emissions. With minor
modifications to account for different attributes, the model is generalizable to the other
catergories specified in the lower part of Figure 1.
Ship Traffic Spatial Proxy
T
Ship Movement Data Set
Empirical Waterway Network
Shipping Routes
Network Analysis
Ship Attributes Data Set
Individual Trips
Routes & Trips Relationship
Routes & Network Segments Relationship
Spatial Distribution of Shipping Activities
Emissions from Shipping Activities
Spatial Distribution of Emissions
Figure 1. Illustration of Waterway Network Ship Traffic, Energy and Environment Model
(STEEM) as applied to emission estimation.
The distance of each route can be determined by multiplying the transposition of matrix
A with matrix E and is denoted as matrix F where, A' is the transposition of matrix A, and dn is
the distance of route «. Energy, fuel use, or emissions per unit of length for route « can be
determined by dividing the emissions en for each route by its length dn and can be denoted as un.
Enery and emissions per unit of length for all routes are denoted as matrix G
Total energy, fuel use, or emissions from each segment within one period can be obtained
by summing up the calculations from all trips on that segment during that period. Energy, fuel
use, or emissions per unit of length for all segments are denoted as matrix H, where hm indicates
the distribution of energy, fuel use, or emissions per unit of length for segment m. Total energy,
fuel use, or emissions for segment m can be calculated by multiplying each segment length lm by
its per-unit fuel use or emissions hm and can be denoted as km. Total energy, fuel use, or
emissions for each segment can be further allocated to each grid to produce spatially-resolved
inventories per gridded area if the segment was established as a polygon.
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Matrix A describes the many-to-many relationships across m segments and « routes in
the empirical waterway network, where, bm>n is a binary variable that shows whether segment m
is part of route n (value of "0" if no, "1" if yes).
"i i t/i ~> ts~\ 7 " t/i M
(1)
Relationships between routes and trips can be denoted as matrix B where, tn is the
number of trips on route n within one period. The actual number of trips on each route in any
temporal period, where trips are defined as a one-way movement on one route, can be derived
from ship movement data set, where, tn is the number of trips on route n within one period.
'2
(2)
Depending on need and data availability, we can either assume ships are identical (as one
group or in subsets by vessel type, fuel properties, etc.) or incorporate individual ship
characteristics into the model. The number of trips or the indicator of traffic volume weighted by
ship attributes on each segment can be denoted as matrix C,where, vm is the number of trips or
the indicator of traffic volume of segment m in one period.
vi
v2
C = AxB = v3 (3)
To estimate fuel use and air emissions out of port areas, we assume ships travel at a
typical cruising speed, which appears true in most cases. Fuel use and air emissions from
individual trips can be estimated with current best-practice models based on route distance, ship
characteristics, and ship operating profile. Total emissions en on route n in one period in which
there were tn trips is estimated by equation (4), and fuel use fn can be estimated by equation (5).
fn = X f^n > Si > mi > ai >lm>la, Sf°C f ' ' 0 (5)
i=l
Where, dn is the length of route «, s is vessel speed, m is main engine power, a is
auxiliary engine power, lm and /« are load factors for main and auxiliary engines, and ep
represents emission factor for pollutant p; sfoc in equation (5) represents specific fuel oil
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consumption (energy rate factor) for fuel type/ Equations (4) and (5) denote that total emissions
en or fuel use/, on route « in one period is a function of the length of route, the characteristics of
the ships on that route, the operating profile of the ships, and other variables concerned like the
quality of fuel, etc. Where vessel-specific estimates are not required, average vessel values can
be assigned by vessel type (e.g., tankers, containerized vessels, bulk carriers) to estimate energy,
fuel use, or emissions by route.
Energy, fuel use, or emissions from each route can be denoted as matrix D.
(6)
Fuel use and emissions per unit of length are determined by dividing the total emissions
on one route by the length of that route, which is the sum of the lengths of all segments of the
route. The length of each segment can be obtained by GIS tools and can be detonated as matrix
E, where, lm is the length of segment m.
E=13 (7)
L
The distance of each route can be determined by multiplying the transposition of matrix
A with matrix E and is denoted as matrix F, where, A ' is the transposition of matrix A, and dn is
the distance of route «.
F = A'xE = d3 (8)
dn
Energy, fuel use, or emissions per unit of length for route « can be determined by
equation (8) and can be denoted as un.
«,=%- (9)
d»
Enery and emissions per unit of length for all routes are denoted as matrix G.
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(10)
Total energy, fuel use, or emissions from each segment within one period can be obtained
by summing up the calculations from all trips on that segment during that period. Energy, fuel
use, or emissions per unit of length for all segments are denoted as matrix H, where, hm is
energy, fuel use, or emissions per unit of length for segment m hm indicates the distribution of
emissions over the waterway network.
(11)
Total energy, fuel use, or emissions for segment m can be calculated by equation (12) and
can be denoted as km.
km=lm*hm (12)
Total energy, fuel use, or emissions for each segment can be further allocated to each grid
to produce spatially-resolved inventories per gridded area if the segment was established as a
polygon.
2.2 Rates of Change: Installed power as first-order trend indicator for CMV emissions
Given that energy used and emissions produced during goods movement increases at a
rate correlated to growth in activity, a number of proxies may be used to estimate inventory
growth rates. These include: economic activity (GDP and imports/exports value), trade activity
(tons and ton-miles), and fuel usage (sales and estimates). All of these are indirect proxies
(second or higher order) of the activity that produces emissions. Except for complete and
accurate fuel usage statistics, none directly describe power requirements for shipboard power
plants (propulsion and auxiliary engine systems). Best practices for ship emissions inventories
typically use power-based (or fuel-based) emissions factors, because of the implicit
proportionality between engine load and pollutant emissions - especially for uncontrolled
sources (34, 72). Therefore, we derive emissions trends directly from installed power data for
cargo ships in the world fleet.
Assumptions we must make to use trends in installed power are rather simple: 1)
international vessels in cargo service generally design power systems to satisfy trade route speed
and cargo payload requirements; in other words, there is no economic reason to design
propulsion systems for containerships, tankers, etc., with more power than their cargo transport
operation requires; 2) international vessels operate under duty cycles that are well understood,
especially at sea speeds, which for most vessel types utilize the majority of installed power as
reflected in best practice methodologies for activity based inventories of energy and emissions
from ships; and 3) ships in commercial cargo service on major trade routes reflect the best fit of
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ship design to service requirements; in other words, the trends revealed in installed power of
ships reveals fleet trends in speed and size. With these assumptions, trends in installed power
reveal the correlated trend in energy use by ships.
We evaluated installed power data associated with port calls from USAGE and Lloyds
Registry (for U.S. activity) and from LMIU data (for Canada and Mexico). Where data were
missing in the installed power field for some vessels, we used linear regression statistics within
each vessel type associating gross registered tonnage (GRT) and rated power to fill data gaps.
Over a period from 1997 to 2003, we observed the trend in total ship calls, their collective cargo
capacity (tonnage), and aggregate installed power. Observations provided further confirmation
that ship calls change over time differently than cargo capacity; we also observed the expected
relationship between growth in cargo capacity and installed power. Based on this analysis
(performed for major ports in the U.S. and Canada using 1997-2003 data), related evaluation of
trends in world fleet propulsion back to 1970, and discussions with the North American SECA
team and with ARE, we used installed power trends to develop emissions forecast growth rates.
2.2.1 Evaluating coupled growth in cargo and energy
A variety of curves could be fit to the multi-year installed-power data. We believe that
the underlying driver for growth in energy and emissions for CMVs is economic trade, which has
and is expected by all accounts to grow at compounding rates. In theoretical terms, if the
underlying functional form driving growth is non-linear, we see no justification for fitting a
linear growth curve to the available data points. In practical terms, work and energy to move
goods by ship are coupled fundamentally unless operational or technological change occurs.
Compounding growth in goods movement could not be associated with a linear trend in energy
or emissions unless that decoupling is dramatic. Air emissions control in onroad mobile sources
provides examples where this has occurred; emissions trends of CO2 and NOx from heavy-duty
trucks were decoupled, because regulatory action required new technologies that reduced NOx
emissions substantially despite increased energy use over the same period (26, 27).
An important question is whether forecasts that directly apply seaborne trade growth rates
to energy and emissions trends should assume any change in the fleet-average energy intensity
over time. In international shipping, economies of scale and a shift to thermally efficient slow-
speed diesels over the past three-to-five decades have served as the major drivers for
technological change; ship air emissions remain the least regulated mobile source, and IMO
regulations do not compare with the stringency of onroad standards. A common belief is
technological change improves energy efficiency in ocean freight transportation (i.e., reduces
energy intensity) over time; rationale for this belief may extend from two historical facts about
shipping and energy use: 1) shipping has traditionally been less energy intensive than other
freight modes (especially trucking), and 2) marine propulsion engineering developments over the
past century produced what are arguably the most fuel-efficient internal combustion (diesel)
engines in the world (74).
Our hypothesis was that these conditions may, at best, result in a less aggressive
compounding growth in installed power, not a decoupling of work and energy significant enough
to justify a linear fit to installed-power data. Depending on change in energy intensity and/or
emissions through investments in economies of scale, fuel conservation measures, or emissions
control measures, the rate of change in energy and emissions could be a modified growth curve
from the growth in cargo activity. If so, one indication would be different rates of change for
installed power on ships providing goods movement compared to changes in cargo volume. In
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other words, if a fleet of ships can carry more cargo without a proportional increase in installed
power, then it must be adopting improved technologies (e.g., hull forms, engine combustion
systems, plant efficiency) or innovating its cargo operations (e.g., payload utilization).
In fact, the opposite trend is observed in the world fleet over the past 20 to 30 years,
where fleet installed power has grown at rates faster than global trade growth. Fleetwide
improvements in fuel economy (indicated for marine engines by in-service specific fuel oil
consumption averages and/or thermal efficiency) have been much smaller than growth in
seaborne trade and CMV installed power. The compound annual growth rate (CAGR) for
installed power since 1985 is -10.7% per year, more than twice the rate of world seaborne trade
growth, driven by increases in containership power which grew at more than 16% CAGR over
these two decades. While the slope before 1980 appears similar to the slope after 1985, one can
observe the significant fleet restructuring (particularly for tankers) during the economic recession
in the early 1980s. Choosing a period since 1970 (inclusive of the 1980s shipping recession), the
rate of installed power growth for the world fleet -5.1% CAGR; even so, power growth rates for
the liner fleet over this period were still greater than 9% CAGR.
Rephrasing, ocean shipping may have become more energy intensive, not more energy
conserving. This seemingly counter-intuitive observation is explainable in terms of globalization
and containerization of international trade. Globalization has resulted in longer shipping routes,
and containerization serves just-in-time (or at least on-time) liner schedules; both of these drivers
motivated economic justification for larger and faster ships which require greater power to
perform their service. Increasingly over the past two decades, ships serving all routes became
faster and larger through intentional expansion and aging fleet transition from prime routes to
secondary markets.
Of course, trends in installed power serving North America may differ from this global
installed-power trend. Introduction of the fastest, largest ships first occurs on the most valuable
trade routes (e.g., serving North America and Europe) where economics most justify the higher
performing freight services. Given this, recent power growth trends for North America could be
lower than the global average rate because recapitalization of ships on these mature
containerized routes is not so heterogeneous, while larger and faster ships sold on the current
second-hand market may have significantly more power than the ships they replace. We
observed this to be true. A simple exponential curve fit to installed power produced an initial
growth rate estimate of-7% per year for North America, compared to -11% globally.
2.3 Patterns of Change: First-order consideration at North American scale
This project identified heterogeneity in growth rates among several other dimensions.
Containership growth rates are significantly larger than growth in dry bulk and tanker ships, for
both seaborne trade volume and installed power. Energy use and emissions on routes to major
containerized ports, therefore grows faster than routes primarily serving bulk trades. Regionally,
growth in West Coast ports is generally stronger than North American average growth rates.
While results reveal heterogeneity in CMV growth rates, timing and budget limitations
prevented us from forecasting growth rates spatially by vessel-route combination. Maps
forecasting emissions applied North American average growth rates to our base-year inventory
patterns. By increasing emissions proportionally for all routes on all North American coastlines,
our spatially resolved forecasts necessarily underestimate growth on the West Coast where
emissions from containerized trade are growing faster than the national average and
overestimates emissions growth in regions where overall trade growth is slower, such as the Gulf
14
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of Mexico served mostly by bulk ships. As such, this represents a first-order forecast appropriate
to consider the value of a SECA for North America but not explicit enough without additional
work to apply to other large-scale issues such as port development or regional shifts in traffic.
15
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3.0 RESULTS
This section describes specific input parameters chosen for STEEM and presents 2002
baseline inventory results required under Task 1; we also summarize Task 2 comparisons and
validation using port-based and regional inventories. This section then presents results of BAU
forecast trends required in Task 3 using the adjusted power-based extrapolations discussed
previously, and a with-SECA scenario under Task 4 that assumes IMO-compliant reductions in
fuel sulfur to 1.5% by weight for all activity within the Exclusive Economic Zone (200 nautical
miles) of North American nations.
3.1 Baseline Emissions Estimates
Main engine power of individual ships was used to estimate ship energy, fuel use, or
emissions for each trip. We adopted the at-sea main engine load factors used by Corbett and
Koehler for the updated emissions inventory for international shipping (4). Based on engine
manufacturer data used in other global analyses, we assumed that 55% of passenger vessel total
main engine power is devoted to propulsion, and 25% of remaining power serves Auxiliary
Engine (AE) power (4, 30). We used maneuvering load profile (lower engine load factor and
slower ship speed) for the first and last 20 kilometers of each trip when a ship is entering or
leaving a port. If the trip was shorter than 20 kilometers, we assumed that ships were
maneuvering for the whole trip; although this assumption may underestimate emissions from
some short-sea routes. We assumed that main engines operate at 20% of the installed power
during maneuvering, the same number used by Entec UK Limited (17).
Since most of auxiliary engine data for ships are missing in the ship attributes data set,
average auxiliary power of each ship type was used to estimate the energy, fuel use, or emissions
from auxiliary engines. California Air Resources Board (ARB) survey results indicate that "29
percent of the auxiliary engines used marine distillate and 71 percent used HFO, except for
passenger vessels that use approximately 8 percent marine distillate and 92 percent HFO" (75).
This number was adopted to adjust the SC>2 emissions factor for auxiliary engines. Table 1
summarizes the engine power and at-sea load profile used in this work. The average total
installed auxiliary engine power was adopted from ARB survey (75); as documented by ARB
and others, most vessels have multiple auxiliary engines.
Table 1. Summary of engine power and at-sea load profile
Vessel Type Average ME At-sea ME Average Total AE At-Sea AE
Power (kW) load(%MCR) Power (kW) Load
Bulk Carrier
Containership
General Cargo
Passenger/Cruise
Refrigerated Cargo
Roll On-Roll Off
Tanker
Miscellaneous
7,954
30,885
9,331
39,563
9,567
10,696
9,409
6,252
75%
80%
80%
55%
80%
80%
75%
70%
1,169
5,746
1,777
39,563
1,300
2,156
1,985
1,680
17%
13%
17%
25%
20%
15%
13%
17%
We use emissions factors shown in Table 2. Consistent with previous studies and with
both the ICF report and ARB survey results, we assume all main engines use residual fuel - this
is standard practice especially in transit at sea. The emissions factors reported in the recent ARB
16
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report "Emissions Estimation Methodology for Ocean-Going Vessels" are nearly identical to
those in the ICF best practices paper, and indeed nearly identical to emission factors used in all
recent analyses in the U.S., Canada, and Europe (4, 17, 34, 75, 76). We use the composite EF for
our work because our data do not explicitly identify by voyage whether the main engine is slow
or medium speed or whether the auxiliary engine uses distillate or heavy fuel. This composite
may be recalculated for the Great Lakes if data for that region enables more specific analysis of
the vessel, engine, and fuel characteristics.
Table 2. Emission Factors
Main Engine Emission Factors - In-Transit Operations (g/kWh)
Engine Type
Slow Speed
Medium Speed
Composite EF
Fuel Type
Heavy Fuel Oil
Heavy Fuel Oil
Heavy Fuel Oil ****
NOx
18.1
14
17.9
SOx
10.5
11.5
10.6
CO2
620
677
622.9
HC
0.6
0.5
0.6
PM*
1.5
1.5
1.5
CO**
1.4
1.1
1.4
Auxiliary Engine Emission Factors (g/kWh)
Engine Type
Medium Speed
Fuel Type
Marine Distillate
Heavy Fuel Oil
Composite EF ****
NOx
13.9
14.7
14.5
SOx
4.3 MDO
1.1 MGO
12.3
9.1
CO2
690
722
713
HC
0.4
0.4
0.4
PM
0.3**
1.5*
1.2
CO***
1.1
1.1
1.1
* Emission Factors from ARB Staff
** Emission Factors from Environ Report
*** Port of Los Angeles
**** Composite used population weighting from ARB OGV Survey, 2005
Considering emissions factors used in previous studies, we used a composite SO2
emissions factor of 10.6 g/kWh to estimate main engine SC>2 emissions (4, 17). The SC>2
emissions factors for auxiliary engines using marine distillate oil (MDO) and heavy fuel oil are
4.3 g/kWh and 12.3 g/kWh respectively; for this study we do not assume oceangoing ships use
marine gas oil (MGO). A composite SO2 emission factor was adopted for each type of ship,
weighted by the percent of marine distillate used by that type of vessel (75). Table 3 summarizes
the auxiliary engine SO2 emissions factors used for each type of ship in this work. The percent
in-use marine distillate of auxiliary engines was adopted from the ARB survey (75). For
estimating fuel consumption, 206 g/kWh was used as Specific Fuel Oil Consumption (SFOC) for
transport ships and 221 g/kWh for miscellaneous (non-transport) ships, including fishing and
factory vessels, research and supply ships, and tugboats, as adopted in other studies (4).
Table 3. Summary of auxiliary engine SOi emissions factor
Vessel Type
Bulk Carrier
Containership
General Cargo
Passenger/Cruise
Reefer
RORO
Tanker
Miscellaneous
Percent In-Use Marine
Distillate
29%
29%
29%
8%
29%
29%
29%
100%
Composite Aux. EF
(g/kWh)
9.98
9.98
9.98
11.66
9.98
9.98
9.98
4.3
17
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We estimated that inter-port transport of North American commerce (including global
voyage transits on route segments outside the project domain) consumed more than 44.7 million
tons of heavy fuel oil and emitted about 2.3 million tons of SC>2 in 2002, about 16.5% of SC>2
emissions from all sources in the U.S. in the same year (77). Given that in-port emissions are
about 2 to 6% of total emissions, as reported by Streets et al. and Entec UK Limited (8, 17), total
heavy fuel use and SC>2 emissions from North American shipping are approximately 47 million
tons and 2.4 million tons, respectively. The North American shipping fuel use and SCh emissions
are between 18-20% of the world commercial fleet estimated by Corbett and Koehler and
between 28-34% of the world cargo and passenger fleet estimated by Endresen et al. (4, 5).
We estimated that ships carrying U.S. foreign commerce consumed about 38 million tons
of fuel in 2002 (again including global voyage transits on route segments outside the project
domain). This number agrees well with Energy Information Administration statistics that
estimate that ships consumed about 44 million tons of fuel in 2002. U.S. domestic waterborne
commerce, which we did not include in this work, may be partially responsible for the
difference. Moreover, it is likely that the actual distance ships travel often is longer than the
distance estimated by the STEEM because data for this work include North American voyages
only between prior and next ports and do not model multi-port logistics activity common to
commercial shipping (especially containerships).
Containerships, bulk carriers, and tankers account for about 35%, 22%, and 17% of 862
emissions from North American shipping, respectively. Other types of ships jointly account for
the remaining 26%. The top ten maritime countries collectively account for about 71% of the 2.3
million tons of SO2 emissions. Panama, the largest flag of convenience country, accounts for
23% of the SC>2 emissions. Liberia, Bahamas, and the U.S. account for 13%, 8%, and 5% of the
emissions, respectively. The Norwegian International Register, Singapore, Greece, Cyprus,
Malta, and Hong Kong each account for between 3-4% of the emissions. The other 111 countries
account for the remaining 29% of the emissions. The energy use profile is similar to the SC>2
emissions profile.
3.2 Producing Spatially Resolved Emissions Inventories for Various Pollutants (Task 1)
Based on relationships among trips, routes and segments of the network, we allocated
total emissions onto the waterway network. We buffered the network with the width of each
segment and calculated the area of the segments in ArcMap. We calculated average emissions
per square kilometer by dividing total emissions for each pollutant in each segment with its area.
We converted the buffered network to a raster file with a resolution of 4 kilometers by 4
kilometers, where each grid value is emissions from this 16 square kilometer area. We adjusted
emissions within a 20-kilometer radius circle of ports to match maneuvering load profiles. Table
4 summarizes interport inventory estimates for 2002 by coastal regions (defined as the 200
nautical mile exclusive economic zone), by nation, and totals for areas outside coastal regions.
Figure 2 illustrates the spatial distribution of annual SC>2 from North American shipping. Coastal
zones resemble the 200 nautical miles Exclusive Economic Zone (EEZ) but national divisions
serve illustration purpose only. Monthly and annual pollutant inventories are posted at
http://www.ocean.udel.edu/cms/jcorbett/sea/NorthAmericanSTEEM (SOx as sulfur dioxide,
NOx as nitrogen dioxide, CO as carbon monoxide, CO2 as carbon dioxide, PM as PM2.5, and HC
as total hydrocarbons).
18
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Table 4. Baseline 2002 inventory of emissions and fuel use in North American Domain1
Units: metric tonnes NOx as NO2 SO2 CO2 HC PM CO Fuel Use
United States EEZ2
West Coast 135,000 80,200 4,817,000 4,470 11,300 10,500 1,480,000
East Coast 255,000 151,000 9,095,000 8,440 21,300 19,900 2,800,000
Gulf Coast 174,000 103,000 6,201,000 5,750 14,500 13,600 1,910,000
Great Lakes 16,200 9,620 578,000 540 1,350 1,260 178,000
Alaska 63,300 37,600 2,260,000 2,100 5,300 4,940 697,000
Hawaii 20,500 12,200 732,400 680 1,720 1,600 226,000
Canada EEZ2'3
West Coast 21,900 13,000 781,000 720 1,830 1,700 241,000
East Coast 96,200 57,200 3,440,000 3,190 8,050 7,500 1,060,000
Great Lakes 10,100 5,980 359,000 330 840 800 111,000
Mexico EEZ2
West Coast 99,400 59,100 3,550,000 3,290 8,320 7,800 1,090,000
Gulf Coast 107,000 63,700 3,827,000 3,550 8,970 8,000 1,180,000
Total Coastal regions 998,000 593,000 35,640,000 33,100 83,500 77,900 10,980,000
Non-coastal regions
1,740,000 1,040,000 62,200,000 57,700 146,000 136,000
19,170,000
Total in Domain
2,740,000 1,630,000 97,800,000 90,800 229,000 214,000 30,160,000
1. Values are rounded to three significant figures for presentation; sums may vary as a result of rounding.
2. National estimates of EEZ boundaries are approximate, using an ArcGIS buffer of 200 nautical miles
and informal divisions between nations.
3. Western Canada summaries include emissions in the Northwestern part of the domain; Eastern Canada
summaries include emissions in the Northeastern part of the domain.
4. Non-coastal regions are areas in the Domain not within the EEZ of Canada, United States or Mexico.
CA West Coast
CA Great Lakes
US Great Lakes / ~
- US Gulf Coast
2,800 4,200 5,600
Kilometers
Figure 2 Illustration of spatial distribution of SOi from North American shipping; shaded
areas represent approximate delineation of coastal exclusive economic zones (EEZs).
19
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3.3 Inventory Summary by Vessel Type
While the scope did not require a spatial or temporal representation of emissions by vessel type,
the STEEM input data did include vessel-type data. This enabled a post-hoc analysis to estimate
emissions contribution by vessel type, as shown in Table 5. This summary represents a
proportionally accurate distribution of global routes solved by STEEM, applied to the domain
inventory in Table 4. Further work with STEEM to produce maps by ship type would perhaps
refine these estimates within the study domain.
Table 5. Estimated Domain Emissions by Vessel Type
Ship Type
Bulk Carrier
Container
Fishing
General Cargo
Miscellaneous
Passenger
Reefer
RO-RO
Tanker
Total in Domain
NOx as NO2
610,000
964,000
1,000
228,000
45,000
157,000
60,000
213,000
461,000
2,740,000
S02
363,000
574,000
1,000
136,000
27,000
94,000
36,000
127,000
274,000
1,630,000
C02
21,756,000
34,413,000
51,000
8,152,000
1,605,000
5,614,000
2,150,000
7,607,000
16,453,000
97,800,000
HC
18,000
32,300
20
7,800
1,600
5,900
2,300
7,100
15,800
91,000
PM
45,300
81,400
60
19,800
4,100
14,800
5,700
17,900
39,900
229,000
CO
42,300
76,100
50
18,500
3,800
13,800
5,400
16,800
37,300
214,000
Fuel Use
5,968,000
10,723,000
7,000
2,601,000
536,000
1,948,000
756,000
2,362,000
5,258,000
30,160,000
These emissions represent both main and auxiliary engines, as discussed above. However, we
recognize that the distribution of auxiliary engines differs among vessel types in installed power,
fuel type, and emissions.
Table 6. Estimated Percent of Total Emissions from Auxiliary Engines (AEs)
Ship Type
Bulk Carrier
Container
Fishing
General
Cargo
Miscellaneous
Passenger
Reefer
Ro-Ro
Tanker
AENOx
2.60%
2.40%
5.00%
3.20%
5.00%
26.90%
7.60%
3.00%
2.90%
AESOx
3.04%
2.77%
2.58%
3.67%
2.58%
33.33%
8.76%
3.44%
3.33%
AECO2
3.70%
3.30%
7.00%
4.40%
7.00%
34.20%
10.40%
4.10%
4.00%
AEHC
2.20%
2.00%
4.20%
2.60%
4.20%
23.30%
6.40%
2.50%
2.40%
AEPM
2.60%
2.40%
5.00%
3.10%
5.00%
26.70%
7.50%
2.90%
2.80%
AECO
2.50%
2.30%
4.90%
3.10%
4.90%
26.30%
7.40%
2.90%
2.80%
AE Fuel Use
3.22%
2.93%
6.13%
3.89%
6.13%
31.25%
9.25%
3.64%
3.53%
3.4 Comparison with Other Emissions Studies (Task 2)
We compared emissions inventories that we produced using a top-down approach with
ICOADS as the spatial proxy with the inventories produced in this work using STEEM (35). In
Figure 2, U.S. Coasts are the areas within the 200 nautical mile Exclusive Economic Zone (EEZ)
as defined by NOAA in its Office of Coast Survey (78); the Great Lakes include Lake Superior,
Lake Michigan, Lake Huron, Lake Erie, Lake Ontario, and connecting waters on both the U.S.
and Canadian sides. Figure 3 shows that the emissions calculated with these two approaches
agree very well for the US East Coast EEZ but differ to varying degrees on the other two coasts
and the Great Lakes (both U.S. and Canadian side).
20
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The amount of SC>2 emissions within the Gulf Coast EEZ estimated by the network
approach is 109% higher than the amount estimated with ICOADS; the amounts by the network
approach for the West Coast and the Great Lakes are 32% and 89% lower than the ICOADS
approach. The discrepancies between the two inventories can be explained by geographic
sampling bias of ICOADS which significantly oversamples the Great Lakes and undersamples
the Gulf of Mexico (35).
We also compared our results with the inventories from other regional and port emissions
inventories studies (76, 79-82). Figure 4 illustrates the domains of the ports and regions we
compared. The Great Lakes include the lakes and connecting waters within the Canadian
boundary (76). "Western Canada" represents the coastal areas in British Columbia (B.C.) outside
of the Greater Vancouver Regional District (GVRD) and Fraser Valley Regional District
(FVRD), and a portion of Washington State, as defined in the Levelton report (82). The Port of
Los Angeles, Houston & Galveston area, and the Port of New York and New Jersey (NYNJ) are
the areas defined by the Starcrest Consulting Group, LLC in its port-wide air emissions
inventory reports (79-81).
Figure 5 shows that the regional/port air emissions inventories produced with different
approaches look very different. The emissions inventory produced with the top-down approach
using ICOADS as a spatial proxy is significantly higher for the Great Lakes on the Canadian
side, but significantly lower for the "Western Canada", the Port of Los Angeles, and the Port of
New York and New Jersey. The conclusion can be drawn that ICOADS is spatially biased as
observed in other studies and small-scale emissions inventories produced with ICOADS as
spatial proxies may be greatly distorted (5, 35).
Figure 5 also shows that the amount of emissions estimated by STEEM are higher than
that of the regional/port studies for the Port of Los Angeles and "Western Canada", but lower for
the Great Lakes on the Canadian side, the port of New York and New Jersey, and the Houston
and Galveston area.
We understand that: (1) the STEEM captures transit traffic, which might be ignored in
the port-wide studies (Port of Los Angeles and Western Canada) that used arrivals and
departures of the specific ports (e.g., the Port of Los Angeles study does not include shipping
activity to other San Pedro Bay ports); (2) port-wide studies used more complete arrivals and
departure data for the Great Lakes, the Port of New York and New Jersey, and Houston and
Galveston; (3) emissions from dockside hotelling are included in the port-wide studies for the
Port of New York and New Jersey, and the Houston and Galveston area but are not included in
the STEEM results (the portion of hotelling emissions increases and might dominate the
emissions inventory when the domain becomes smaller around the terminals and when ships
spend less time in transiting); (4) the motivation behind the creation of the STEEM was to
improve the emissions inventories from inter-port movements; emissions around ports have to be
adjusted by either plugging in the inventories produced by port-wide studies or modifying the
model itself to include the dockside emissions; (5) comparisons showing both higher and lower
port and reigional estimates suggest there is no systemic error in the STEEM; and (6) our
assumption that ships generally maneuver within 20 km (-12.4 miles) of ports may be
conservative for many ports, since ARB reports recent Automatic Identification System (AIS)
data suggests that ships may operate at sea-speeds until closer to port.
21
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i on nnn
en 120,000
0
8 100,000 -
" 80,000 -
-------
t/3
c
.0
t/3
t/3
o
t/3
c
o
-<—>
o
20,000
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
0
D STEEM D ICOADS Top-down S Port/Regional Inventory
!H
O
s
(D
T-i
^2
ej
fl
03
u
H-l
o
O
ffi
02
(D
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Figure 5. Comparison of emissions inventories of different approaches; emissions for
Houston & Galveston are NOx, emissions for the other areas are
We also observe that the emissions from ships carrying foreign cargo within the 200
nautical miles coastal areas of the United States estimated by the STEEM are about five times of
the results estimated by Corbett and Fischbeck using cargo as a proxy (10). We understand that
the STEEM is superior to the method used by Corbett and Fischbeck and is more accurate,
consistent with the uncertainty discussion in the earlier paper and with the upward correction of
more accurate work for the Northwest, United States also published previously (9).
3.5 Forecasting principles
Forecasts can differ depending on their purposes and scales. Some forecasts look to
reveal where timely investment and action at a local scale or by a single firm can produce the
most benefit (e.g., profit). Validity of insights is determined by whether recommended actions
produce expected outcomes for a given decision, not whether the forecast trend or future value is
realized. Other forests are intended to be conservative or aggressive; that is, they intend to be
biased to serve the decision makers' value and tolerance for risk and surprise. This may describe
large scale forecasts such as emissions or trade trends. One challenging class of forecasts may
be considered "difference" forecasts, where alternative scenarios illustrate how "a path taken"
may differ from "a path not taken" rather than to determine which is most probable. These kinds
of forecasts are common in policy domains, such as energy, environment, and economics (e.g.,
IPPC scenarios). Certainly, freight forecasting presents one challenging example, especially at
the international or multinational scales, and especially when considering policy actions like a
SOx Emissions Control Area (SEC A) under IMO MARPOL Annex VI (36).
Admittedly, the quality of forecasts of maritime shipping and trade is limited (83), and
thus forecasting of environmental impact from shipping is constrained by the quality of shipping
and trade forecasts. Therefore, we employed a comparison of historic trends and forecast
23
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indicators related to maritime trade and energy to provide reasonable insight into a range of
feasible forecasts. Individually, none of these forecasts can be considered more correct than
another, as they represent different assumptions about the relationship between transportation
energy, trade, and North American port activity. However, taken together, they reveal a bounded
range of trends with common insights useful in comparing sulfur controls with no action. We
look for converging growth trends that are representative at several scales (port, region, coastal,
and national) and informed by historic data. These lead to a set of principles for describing how
freight transport emissions may change:
1. Define the forecast domain broadly through multiple perspectives on freight and economy.
2. Compare global, large regional forecasts with local efforts for converging insights, perhaps
allowing for probabilistic assessment.
3. Include the rear-view mirror in forecasting (i.e., compare with persistence).
4. Consider first principles involving energy and environment: Some work-energy relationship
must hold if fuel price matters to freight.
5. Make extrapolation adjustments as simple as possible, but no simpler: Assumptions inter-
relating energy, economy, and technology should be checked for potential inconsistencies.
6. Look for surprise, avoid overconfidence: Recognize heterogeneity at all scales; use detailed
scenarios to help broaden or delineate the forecast range, but do not rely on them as likely.
3.6 Activity-based modeling of freight growth
Seaborne cargo activity has increased at significant rates over time. World seaborne
trade growth has increased monotonically except for a short period in the early 1980s (66-69).
Containerized trade is growing faster than global rates. Figure 6 illustrates recent containerized
cargo trends and TEU throughput since 1980. U.S. Maritime Administration (MARAD)
statistics include cargo on both government and non-government shipments by vessels into and
out of U.S. foreign trade zones, the 50 states, District of Columbia, and Puerto Rico, excluding
postal and military shipments; AAPA statistics describe total container throughput, including
empty container movements. Containerized cargo throughput (including empty container
movements) grew at -6.5% CAGR since 1985, with imported cargo grow since 1997 at more
than 10% CAGR and total cargo TEUs (excluding empty container movements) growing at -7%
CAGR since 1997. Given the high-value nature of containerized cargoes, it is not surprising that
these growth trends are most similar to growth in the value of cargo moved, reported by BTS.
Conceptually, growth in seaborne cargo movement should influence (if not determine)
activity growth in the freight modes (truck and rail) carrying imports and exports to or from U.S.
metropolitan regions and inland regions. For example, if growth in rail and truck modes is
primarily a result of increasing imports, observed in the U.S. to range between 4.6% and 4.8%
CAGR for all cargoes and between 6% and 9% for containerized (intermodal) cargoes (-6.5%
CAGR for total container throughput including empty containers), then combining these modes
should reflect seaborne trade growth rates (71, 84). The multimodal transportation of empty
containers presents a unique challenge in understanding how international goods movement
affects landside freight modes (85). Moreover, trucking and rail movements include exported
and domestic freight movements, which are growing at much lower rates than containerized
imports, effectively dampening national growth rates in intermodal freight transportation
compared to port throughput. Considering these activities together helps provide an intuitively
consistent explanation reconciling steeper seaborne trade trends reported in major ports, and
obtained or derived from economic and trade analyses, with less-steep truck and rail freight
24
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trends. In other words, we should expect growth rates in goods movement to be shared among
modes because freight transportation is an intermodal network of imports, exports, empty
repositioning, and domestic freight flows.3
1980
1985
1990
1995
2000
2005
"TEU throughput
•Total Cargo TEUs
•Export TEUs
•Import TEUs
Figure 6. Container statistics from U.S. Maritime Administration and American
Association of Port Authorities (70, 71).
The U.S. Department of Transportation launched two of the first federal efforts to
consider together multimodal and intermodal freight effects of imported cargoes, generally
through its "Assessment of the U.S. Marine Transportation System and spatially through the
Freight Analysis Framework (FAF) (86, 87). This work produced a forecast of freight
transportation activity based on trade increases, primarily to identify infrastructure needs rather
than estimate energy and environmental impacts. According to the Freight Analysis Framework
(87),4 domestic freight volumes will grow by more than 65 percent from 1998 levels by the year
2020, increasing from 13.5 billion tons (in 1998) to 22.5 billion tons (in 2020). This represents a
-2.3% compound annual growth rate (CAGR), similar to that obtained from VMT growth rates
(not adjusted for sales growth) in MOVES (88). In other reports, truck freight has doubled since
1980 (an average annual increase of 3.7%), while domestic waterborne freight has declined by
nearly 30% (an average annual decline of 1.8%) (89) 5 These rates represent the lowest growth
trends we could find in the literature for goods movement.
This background discussion does not necessarily imply a direct relationship between energy and emission growth
rates and seaborne trade growth rates; depending on efficiency gains and economies of scale (e.g., shown for the rail
sector), the rate of change in energy and emissions for ships could be different. This background reinforces the
purpose of and need for the forecasts analysis presented in this report.
4 See Freight Analysis Framework documents at http://ops.fhwa.dot.gov/freight/freight analysis/faf/.
5 BTS Pocket Guide to Transportation 2003, http://www.bts.gov/publications/pocket guide to transportation/2003/.
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Currently, growth factors embedded in U.S. mobile source energy and emissions models
appear to capture better this economic-driven growth in freight transportation. Growth factors
for trucking (single-unit and combination trucks) in the U.S. EPA's mobile source models
include a combination of a population (sales) and VMT growth factors, with adjustments for fuel
economy and other operational factors (88). U.S. EPA compared rail freight ton-miles with
railroad distillate fuel consumption data to indicate substantial improvements in rail freight
energy intensity, adjusting emissions based on regulatory requirements (90). And, in its 2003
rulemaking, U.S. EPA assumed that freight growth was linked to increased tonnage volume (23).
Historic and future growth rates for particular modes are consistent with coupled growth
in economic-energy-emissions trends. For example, U.S. EPA projects that truck population and
VMT will increase by 4.2% to 4.8% CAGR between 2002 and 2025 (88). For rail, U.S. EPA
showed that growth rates in cargo ton-miles transported nearly doubled in recent periods, from
-2.4% CAGR between 1980 and 1995 to -4.8% CAGR between 1990 and 1995 (illustrated in
Figure 1-1 in U.S. EPA's regulatory support document). In fact, updating observed growth rates
in cargo ton-miles moved by rail to include more recent years reveal a rail-cargo growth rate of
-3.6% CAGR from 1985 to 2004 (91, 92).
3.5.1 Growth Rates
Most forecasts essentially take historic trends for some recent period and extrapolate with
adjustment for expected change in trends (e.g., response to economic and population drivers
affecting global trade or consumption). In coming decades, a number of events could modify an
unconstrained growth trend in energy use. In terms of technology, there could be further
improvements in thermal efficiency, fuel type and quality, and propulsion design. In terms of the
economy, we expect shipping cycles to continue to provide periods of slower or negative growth
in oceangoing goods movement (83). In terms of logistics operations, trends in containerization
economies of scale and vessel speed over the past three decades could change over the next three
decades if global inventory, energy, and labor costs change.
A simple exponential curve fit to installed power produced an initial growth rate estimate
of 7.1% per year for North America, before averaging with a linear extrapolation. While we
recognized the need for similar adjustment in our forecasts, we hesitated to arbitrarily insert
"inflection points" in out-year forecasts corresponding to optimistic or pessimistic assumptions.
We acknowledge that an unconstrained exponential curve fit would likely overestimate future
emissions, particularly given expected shipping cycles; we also observed that a linear growth rate
did not match known or expected technology changes relative to cargo growth. A linear trend in
energy use would imply less power required to achieve the cargo throughput- where cargo
volumes are projected to see compounded growth. We don't believe that average technology in
the fleet will change that much from its current path over the next 35 years without strong policy
incentive or substantial changes in fleet energy pricing and supply. Overall, fleet propulsion
technologies will remain more similar than different to the current profile at least through 2040.
Moving more cargo will require more power, in a similar manner to the current fleet (either
through larger ships, faster ships, more ships, or some combination). Moreover, we did not
identify physical capacity limits to ports or snipping routes (that are not being addressed through
infrastructure investment) which would constrain trade growth.
Through discussions with ARE, we agreed that the unconstrained exponential trend and
the linear trend define bounding limits for expected change in ship activity. Averaging these
curves defines an arbitrary middle-growth trend, which implicitly describes a mix of positive and
26
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negative drivers for ship energy requirements without articulating a detailed scenario of
conditional events. After adjustment, we estimate a growth trend for North America (including
United States, Canada, and Mexico) of about 5.9%, compounded.
Studies for Southern California (San Pedro Bay) ports supported this adjustment. These
studies agree that growth in cargo volumes equivalent to 6-7% compounding annual growth rates
is expected (50-53).6 Some studies articulate different pathways of growth than simple
extrapolation; for example, the no-net-increase (NNI) forecast produces nearly the same result
for 2020, but describes substantial increases in the near-term as a result of planned investment in
the ports (50). In Figure 7, we show bounding curves (exponential and linear) and the average
growth curve for Southern California ports. We converted growth trends from the no-net-
increase study and from an unpublished trade-energy model (by RTI under U.S. EPA direction)
to describe change in installed power and plotted them in Figure 7 with our extrapolation (50,
93)1
These comparisons demonstrate that increasing cargo throughput is related to technology
innovation (e.g., larger ship sizes, higher speeds, and containerization) that promotes economies
of scale with more powerful ships, more so than increased cargoes determine the number of
voyages. Independent derivations of growth trends all describe at least a doubling of commercial
marine energy use and emissions in California by 2020, corresponding to similar change in the
expected port cargo throughput.
Agreement between the draft trade-energy model by RTI and extrapolation of observed
data is even stronger for containerships. As shown in Figure 8, preliminary results from the draft
RTI trade-energy forecast are more aggressive than our power-based extrapolation. RTFs trade-
energy model exception to calibrate on inbound containerized cargoes ("heavy-leg" activity)
may explain this (93). Note excellent agreement in RTI draft model results with observed
power-trend history for containership calls to U.S. ports.
These sources of growth trends and forecasts are consistent with and validate our observed trends
in installed power and support our extrapolation of power-based trends to forecast emissions
under business-as-usual (BAU) conditions. Using our adjusted extrapolation to forecast growth
at -5.9%, we observe that power-based growth rates derived here are comparable to growth rates
for land-based freight modes, by about 1% to 2% (45-48, 71, 84, 88, 91, 92, 94)* This
comparison is expected due to the fact that trucking and rail are also engaged in domestic and
intra-continental trade with Canada and Mexico that would not require commercial shipping.
Moreover, our forecast rates are generally lower than dollar-value growth in North American
seaborne trade, and a bit lower than growth in containerized cargo volume. Again, such
comparisons are expected given the importance of bulk cargoes (liquid and dry) to North
American international trade. In addition, the lower growth in power-based rates compared to
cargo activity provide confirming evidence that economies of scale are improving the energy
intensity and emissions intensity of international shipping - but perhaps by not more than 1% to
2% overall yet. Additional analysis by vessel type could quantify these improvements in more
detail, perhaps discerning relative roles of speed, size, and operational factors (e.g., average
6 Other studies interpret strong growth in cargo volume to produce a corresponding increase in port calls (51, 54).
7 While RTI work is in draft form, U.S. EPA and ARE coordinated discussions and comparisons between this
project and the RTI project. NNI shows only the Southern California ports of Los Angeles and Long Beach, while
the RTI work describes the "South Pacific" ports, which are considered to be mainly LA and LB but could include
Oakland.
8 Multimodal comparisons are discussed in more detail in Technical Memorandum for Tasks 3-4.
27
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payload utilization rate). Lastly, we observe emissions and energy use by the fastest, most
powerful ships (containerships) are increasing at the fastest rates, along with demand for
containerized trade.
1200
1990
1995
2000
2005
2010
2015
2020
2025
2030
—•— Unconstrained 7-point Trend
Baseline NNI
—•—This Work
—*— Linear Trend
6 RTI US South Pacific
Figure 7. South Coast (South Pacific) growth rates derived from historic data (1997-2003),
showing upper-bound (exponential), lower-bound (linear), and average trends. Also shown
are trends from NNI Task Force and from unpublished draft RTI trade-energy model.
1400
1200
800
400
- Average
-Observed Container Power
111 RTI Container
1990 1995 2000 2005 2010 2015 2020 2025 2030
Figure 8. US container growth trends from data extrapolation (1997-2003) and from
unpublished draft RTI trade-energy model.
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Table 7 presents an overview of power-based growth rates for selected ports and North
American regions. Growth rates for North America, US, Mexico, and Canada use regression
statistics within each vessel type associating gross registered tonnage (GRT) and rated power to
fill data gaps. General similarity is observed across all regions, with Canada installed-power data
presenting the highest rate of growth and with Mexico presenting the lowest rate of growth.
These growth rates represent an average of unconstrained exponential curve-fits with linear
extrapolation of the data, which implicitly describes an implicit mix of positive and negative
growth drivers. Given that such adjustments may not equally influence growth at different ports
or regions, it is possible that actual growth in emissions will be higher for some places (and
perhaps lower for others), depending on events that modify unconstrained growth trends over the
next decades.
Table 7. Power-based growth rate summary for commercial ships 2002 -2020 (CAGR)
Ports, or Region Emissions Growth Rate
Los Angeles/Long Beach 5.24%
Oakland/San Francisco 5.68%
New York/New Jersey 6.03%
California (all ports) 5.53%
U.S. West Coast 5.93%
U.S. National 5.86%
Canada 6.57%
Mexico 5.06%
North America (U.S., Canada and Mexico) 5.86%
1. Growth rates represent an average of exponential and linear fit extrapolations,
presented in terms of compound annual growth rate (CAGR).
2. US data are from USAGE and Lloyds Registry data, per this and other work by
Wang and Corbett.
3. Canada and Mexico data are from Lloyds Movement data (LMIU)
3.5.2 Growth Patterns
We produced a set of baseline (Tasks 1 and 2) emissions estimates and forecast estimates
(this work, Tasks 3 and 4) conforming to a consensus domain and resolution appropriate for most
of the atmospheric modeling that will use our North American ship emissions inventory. This
consensus resulted from several meetings with the SECA team. Annual emissions are resolved
into twelve monthly components, following time-resolved patterns in ship activity in North
America, as discussed in the report for Tasks 1 and 2. The North American inventory estimates
for each pollutant uses the following projection parameters from ESRI's ArcGIS software:
Projection: Equidistant_Cylindrical
Parameters:
False Easting: 0.0 - default ESR1parameter
False Northing: 0.0 - default ESRIparameter
Central Meridian: 180.0 degrees - UD defined
Standard Parallel_l: 0.0 - default ESRI parameter
Linear Unit: User Defined Unit (1000 m) - UD defined
Cell Units: kilograms per 16 square kilometers
We delivered inventory files using the following domain:
left -1000 km, right 18000 km, top 8000 km, bottom 0 km.
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A hypothetical SECA region conforming to the Exclusive Economic Zone (EEZ) for
North America was defined for the with-SECA scenarios. Figure 9 shows the model domain and
also reproduces the SOx inventory illustration for the base-year 2002. The scale shown for
emissions is delineated using units common to forecast inventory illustrations discussed below.
Figure 9. Model domain showing hypothetical with-SECA region and baseline 2002 model results.
3.7 Future Emissions without SECA region (Task 3)
Based on trend comparisons discussed above, we use the following ratios for SOx
forecasts: For 2010, we multiply the 2002 base year inventory by 1.61 times; for 2020, we
multiply the 2002 base year inventory by 2.79 times, corresponding to a growth rate of 5.9%
compounded annually.
For NOx emissions we make adjustment for the introduction of IMO-compliant engines
into the international cargo fleet. We use industry data to estimate -11% percent average
reductions in NOx for new engines complying with MARPOL Annex VI (95). This estimated
reduction is similar but slightly lower than assigned in other analyses of IMO-compliant NOx
reductions (42, 44)', further study into the NOx reduction from uncontrolled to IMO-compliant
engines is ongoing and may produce better per-engine reduction estimates. Introduction rates for
new engines into the fleet are based on fleet scrapping and new ship orders used in previous
work (3, 96). Following standard assumptions for the introduction of new engines in the fleet
used of 2% per year, we estimate that about 46% of the fleet in 2010 and about 78% of the fleet
in 2020 will be IMO-compliant. This accounts for fleet-weighted NOx reductions of 5% and
8.4% in 2010 and 2020, respectively, resulting in NOx multiplier ratios of 1.53 for 2010 and 2.55
for 2020.
Per project scope, we considered whether fuel-sulfur content may change in coming
years, e.g., would refining practices result in generally higher fuel-sulfur averages over time as
distillate fuels (particularly diesel) removed more sulfur. We chose not to make any adjustments
to the average fuel-sulfur content in this work for two reasons. First, we observe very little
change in world-average fuel-sulfur content for residual fuels over the past decade; in fact, most
of the differences may be attributed to better statistical tracking on behalf of MARPOL Annex
VI, more so than real changes in the global average. Second, we recognize that variation in fuel-
suflur content regionally may be greater than the average change over time; we understand that
U.S. EPA is sponsoring study of this issue, and that results of that work are not yet available. If
30
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such trends are proven, they can be implemented at the regional level using STEEM in future
work.
An illustration of 2020 emissions without applying any SECA reductions is presented in
Figure 10. Annual and monthly data files for 2010 and 2020 for all forecasted pollutants (SOx as
SO2, NOx as NO2, CO2, PM, CO, and HC) are provided in both raster and ASCII formats at the
project website (http://coast.cms.udel.edu/NorthAmericanSTEEM/).
North American SOx in 2020
Kg SCJ'le s
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some set of assumptions to estimate total PM (primary plus secondary). Atmospheric modeling
will convert the SC>2 gas emissions to sulfate particles needed to estimate total PM health effects.
Figure 11. Illustration of 2020 ship SOx emissions with hypothetical SECA region.
In these forecasts, auxiliary use of distillate fuel was not taken into account when
adjusting for fuel sulfur content. Auxiliary engines only consume a small percentage of marine
fuels and only 29% of auxiliary engine fuels are marine distillate (8% of passenger vessel AE
fuels are marine distillate), which on average has 0.57% of sulfur by weight (varying from 0.05
to 1.5) according to ARB Ocean Going Vessel Survey. We expect this will affect North
American estimates of fuel use and sulfur emissions by 1-2% (see baseline inventory estimates
for AEs, Table 6).
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4.0 DISCUSSION
4.1 Comparison with global forecast trends
For validation, we considered whether analyses at a global scale might yield similar
results. We compared world fleet trends in installed power (derived from average power by year
of build) with energy trends (Eyring work and fuel sales), with trade-based historical data (tons
and ton-miles). Activity-based energy results for similar base-years (2001 or 2002) are within
close agreement (72, 97, 98)9 This allows us to index trends to nearly the same value and year,
to index trade-based trends similarly, and to compare these with trends in installed power, as
summarized in Figure 12. Three insights emerge from this global comparison.
1) Extrapolating past data (with adjustments) produces a range of BAU trends that is
bounded and reveals convergence around a set of similar trends; in other words, while
the range of growth may vary within bounds of a factor of two, one cannot get "any
forecast they want" out of the data. If we consider that global trade and technology
drivers mutually influence future trends, then we may interpret convergence within the
bounds as describing a likely forecast of global shipping activity.
2) World shipping activity and energy use are on track to double from 2002 by about
2030 (-2015 if one considers seaborne trade since 1985, -2050 if one considers
Eyring's BAU trend).10 Growth rates are not likely to be reduced without significant
changes in freight transportation behavior and/or changes in shipboard technology.
3) Confirming earlier discussion, trends in installed power are clearly coupled with trends
in trade and energy. This reinforces the analysis of installed power as a proxy for
forecasting growth, not only for use in baseline inventory estimates.
Coincidentally, averaging bounding extrapolations yields between 3.8% and 4.5% CAGR
growth in installed power, nearly the same 4.1% CAGR as observed for past world seaborne
trade (66, 67, 69). In other words, this explains and confirms the use of seaborne trade growth to
project ship fuel use and emissions, as other studies have done. Therefore, we consider this
BAU forecast to be informed by observed past trends and consistent with adjustments intended
to avoid overly aggressive growth estimates. Consistent with the market-forecast principles
reflected in the IMO study, and given the strong relationship observed between cargo moved
(work done) and maritime emissions (fuel energy used), we estimate that global emissions from
CMVs are increasing at average growth rate of at least 4.1%. This suggests that the rate of
growth in emissions for North America is greater than the global average growth rate.
4.2 Uncertainty and Bounding
There are six types of uncertainty that affect these results. Three primary sources of
uncertainty involving parameters directly used in this study include a) uncertainty in the base-
year estimates; b) uncertainty in the trend used to produce the forecasted inventories; and c)
uncertainty in the patterns of future ship traffic. Additionally, uncertainty arises from factors not
addressed in this work to date - but that could improve future efforts using these methods.
Additional detail could be incorporated to describe better underlying drivers of change in freight
9 An exception is work by Endresen et al, that tends to adjust parameters to agree with international marine fuel sales
statistics; their results are within uncertainty ranges described in other work (5, 94, 99).
10 A review of forecast trends for global marine fuel use in unpublished draft results from RTI work suggests that the
trade-energy model developed in parallel with this project falls within these ranges.
33
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activity and consumption, to include planned or proposed signals (e.g., policy action) modifying
vessel activity and propulsion technology, to make alternate assumptions about fleet response in
terms of under- or over-compliance with standards or in terms of price-effects, and to better
depict spatially the asymmetric growth among vessel types and trade routes expected within the
shipping network.
Extrapolating trends since ~1980-85
depending on data source
1950
1960
1970
1980
1990
2000
2010
2020
2030
Seaborne Trade (tons)
-OECDHFOInt'l Sales
•World Marine Fuel (Eyring, 2005)
•Seaborne Trade (ton-miles)
•Seaborne Trade (trend since 1985)
•Installed Power-This work
Figure 12. Global indices for seaborne trade, ship energy/fuel demand, installed power.
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5.0 SUMMARY AND CONCLUSIONS
5.1 Baseline Inventory
The 2002 inventory of emissions from North American shipping successfully applies
bottom-up estimation methods, extending best-practices for commercial marine inventories to
the largest spatially resolved scale so far, and the STEEM model is capable of conducting similar
analyses for other regions and even globally. STEEM achieves many of the goals of nonroad
marine modeling efforts, such as the U.S. EPA Mobile Vehicle Emissions Simulator
(MOVES).11 STEEM exceeds MOVES current design in two important ways: 1) our approach
produces spatial and temporal assignment of emissions in GIS; and 2) our model considers
individual vessel movements, rather than binning vessels of similar type. (Similar to binning by
MOVES, our model applies emissions factor and engine activity assumptions by vessel type, but
considers installed power, routing, and speed individually.)
Our results for U.S. EEZ regions in the North American interport shipping inventory can
be compared to US domestic freight overall, and compared to US domestic marine statistics (26,
27). For carbon dioxide, our results in U.S. EEZ regions are 32% of CO2 estimate by U.S. EPA
for all shipping (coastal and inland ships and boats plus bunkers) and 85% of bunkers only; our
estimates represent 6% of CO2 for U.S. surface freight transportation. Our estimates of CO2
emissions and fuel use conform generally to the expected ratio implicit in the fuel-based CO2
emissions factors, around 3200 tons of CO2 per 1000 tons fuel, suggesting the bunker fuel
comparison with U.S. EPA is most appropriate for cargo ship activity addressed in this study.
The comparison of our work with international bunkers is very good agreement, given the
independent analysis and considering that we do not account for bunkers used in port or for fuel
used on voyages in addition to transits from prior port or to next port. For NOx, our estimates
are 70% of 2002 U.S. EPA NOx estimates from shipping, and represent approximately 12% of
NOx from all U.S. surface freight modes (heavy-duty diesel truck, locomotive, and marine
including bunkers). For SO2, our estimates in U.S. EEZ regions are 2.5 times greater than
estimated by U.S. EPA for shipping, and 1.2 times greater than SO2 from U.S. surface freight
transportation. For PM2.s, our inventory estimates in U.S. EEZ regions are 1.4 times greater than
current estimates for US shipping, and 34% of U.S. surface freight transportation.
It is important to recognize that at least parts of our inventory may represent shipping not
included in these national inventories, and that our inventory does not include some marine
activity included in these comparison statistics. For example, we do not include inland river
navigation12, and our data does include Canadian and Mexican vessel activity that may transit
within U.S. coastal regions. In this regard, the emissions estimated in this work both augment
and complement current national inventories. Therefore, further work would be required to
evaluate the degree that our inventory may increase existing estimates; therefore, the percentages
resulting from this comparison represent a first-order comparison.
11 See http://www.epa.gov/otaq/ngm.htm for MOVES information.
12 Inland river navigation refers to voyages entirely within inland river regions, typically not navigable by deep-draft
or oceangoing vessels. River transits by deepwater vessels in bays and deepwater river channels are included in this
study (e.g., in San Francisco Bay to Benicia or Redwood City, or in the Columbia River to the Port of Portland).
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5.2 Forecast Trends
Important conclusions from this comparison and validation of independent forecast
approaches include the following two points. First, these forecasts are not fundamentally more
or less "correct" than comparison forecasts, as they all extrapolate observed trends with
adjustments for factors expected to influence future ocean freight activity and ship technologies.
In this regard, insights that result from our analysis of independent forecast models reveal a
range of future scenarios within which our emissions forecasts fall. Second, all models agree
that ship emissions are increasing along with growth in trade, and that these growth trends are
non-linear. Using 2002 as a base year, these models agree under BAU scenarios that energy
used by ships in global trade will double by or before 2020; some scenarios predict doubling
before 2015. Insights support the significant attention that international, federal, state and other
agencies are devoting to understanding the impacts and mitigation options for ocean freight in
North America.
Implementing a North American SECA region reducing fuel-sulfur content from 2.7% to
1.5% (whether through fuel changes or through control technology) will reduce future SOx
emissions by more than 700 thousand metric tons (-44%) from what they may otherwise grow to
be in 2020 (see Figure 13).
2020 SOx Difference with SECA
kg SO2 reduced per 16 sq km
| -239 774 to -100.001
Q-100000to-10D01
Q-1000010-1 001
Q-1.000 to-101
-100 to -1
Figure 13. Forecast reduction in 2020 of annual SOx emissions due to hypothetical SECA.
Figure 14 illustrates the change in SOx forecast for 2020 as a ratio of 2002 base-year
emissions and in metric tons difference. Note that Figure 14 depicts only increased ship SOx
emissions. Forecasted increases in trade will overcome IMO-compliant reductions in ship SOx
emissions in less than two decades (before 2020 at 5.9% CAGR). Specifically, our results
36
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forecast more than 2 million metric tons of SOx additional emissions throughout the North
American domain, even with an IMO-compliant SECA in 2020. Similar results occur under RTI
draft forecasts (at 3.7% CAGR), which under 1.5% sulfur limits will equal base-year emissions
in about 2030.
Figure 14. Forecast increases from base-year inventory in SOx emitted in 2020 with SECA.
Figure 15 illustrates this further by representing the change in emissions within the EEZ
(hypothetical SECA) over time. This helps reveal three insights:
1. There are emissions reductions from an IMO-compliant (1.5% fuel-sulfur SECA) over BAU
trends;
2. Shipping emissions and resultant health effects and/or other impacts that may be offset in a
base year by implementing a SECA will return to base-year levels within one or two decades;
3. An estimation of benefits from reducing ship emissions can be made using the North
American data we report here, or incorporating more refined regional and local data.
These insights appear robust, regardless of the range in possible forecasts. Using the forecast
trend derived in this work, trade growth offsets emissions under a 1.5% fuel-sulfur SECA by
2012; using lower growth rates from preliminary RTI results, emissions within a North American
SECA return to 2002 levels by 2019.
However, Figure 15 also shows that a 0.5% fuel-sulfur limit - such as has been discussed
for Europe - provides substantial benefits longer into the future under reasonable growth
assumptions. A North American SECA requiring 0.5% fuel-sulfur or control technologies
achieving these reductions would offset trade growth continuing to the early 2030s under a 5.9%
CAGR or to about 2050 under a 3.6% CAGR, respectively. This conclusion from either growth
curve means that long-term emissions reductions are possible from ships operating in North
American waters, and that the IMO-compliant SECA requirements (1.5% fuel-sulfur) represents
an important first step.
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2,500,000
< 2,000,000
O
LJJ
CO
~ 1,500,000
O
CO
(A
O 1,000,000
o
0)
500,000
2000
2005
2010
2015
2020
2025
2030
—Q—5.9% Growth (This work)
—0—5.9% Trend With SECA
2002 Baseline
• Alternate Growth (~3.6%)
—* 3.6% Growth with SECA
0.5% SECA at 5.9% Growth
Figure 15. Trends with and without IMO-compliant SECA, and with 0.5% SECA
38
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6.0 RECOMMENDATIONS
6.1 Improve precision
The ability of this work to assign interport ship traffic to empirically derived shipping
patterns is an inherent strength of STEEM. However, the precision of this waterway network
can be improved - particularly for regions near port. Some of this work is being attempted by
U.S. EPA contractors to insert adjustments to the location and intensity of near-port traffic. This
limitation is a function of the global context in which the STEEM network was developed, rather
than a limitation in model capability. Using global ship location data (ICOADS), a network of
shipping lanes was developed; this network could benefit from near-shore comparisons with
Automatic Identification System (AIS) data, Vessel Traffic Management Systems (VTMS), or
other local information describing near-port routing.
6.2 Reduce Base-Year Uncertainty
The baseline inventory effort followed general best practices for calculating emissions
inventories, which enables general analysis of uncertainty due to estimating input parameters, as
discussed in the report for Tasks 1 and 2, and elsewhere (100, 101). Results show good
agreement with other inventories, including the draft trade-energy model estimate for 2001 by
RTI (93). National level uncertainty includes four major elements: A) uncertainty in input
parameter assumptions (e.g., emissions factors, engine activity profile, etc.); B) uncertainty in
U.S. domestic shipping not included in foreign commerce vessel movement data; C) uncertainty
in U.S. Army Corp of Engineers data, and in Canadian and Mexican LMIU data; and D) spatial
uncertainty in routing choices, particularly within confined bay and port regions and seasonally
for open ocean routes where weather routing may occur. An uncertainty analysis was performed
on fundamental input parameters in the model, and potential undercounting of voyages or their
misassignment in the routing model was discussed, including opportunities to improve the
baseline inventory produced by STEEM for this work.
Figure 16 illustrates the influence of primary inputs on uncertainty for different pollutant
estimates. This shows that the uncertainty of output is nearly symmetric, but that the emission
factor (i.e., fuel-sulfur content for SO2 and possibly for PM) is the most uncertain input for SO2,
PM, HC and CO. For NOx and CO2, similar internal engine combustion conditions (e.g., similar
cylinder peak temperatures, pressures, etc.) result in similar emissions factors; this results in
greater certainty for emissions factors and relatively greater contribution to variance from
uncertainties in engine load, power, and hours of operation. Localized and in-port inventory
uncertainties are expected to be larger than national-level bounds estimated here.
6.3 Improve Trend Extrapolation
These forecasts must be considered to represent what other forecast scenarios often refer
to as "business as usual" (BAU). The primary uncertainty in the forecast trend applied to the
2002 baseline inventory can be best understood in terms of backcast validation efforts described
above. Improving confidence in extrapolated trends for North American ship activity requires
longer historic trends, regionally resolved. Improving the nature of extrapolations would require
better articulated relationships among drivers and industry trends. However, as shown above, the
extrapolated trends developed in this work are within bounded agreement with other forecasts
more dependent on trade economics.
39
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0% 5% 10% 15% 20% 25% 30% 35% 40%
20% 40%
50% 80% 100%
Emissions
Factor
Power Rating
Operating Hours
Fuel Sulfur/
Emissions
Factor
Power Rating
Operating
Hours
10% 20% 30% 40% 50% 60% 70'
Engine Load
Emissions
Factor
Power Rating
Operating Hours
Figure 16. Uncertainty in model output from input parameters scaled by contribution to
output variance.
A secondary element in trend uncertainty could reside in missing data fields associating
installed power with ships calling on North America. For this work, we used linear regressions
within each vessel type associating gross registered tonnage (GRT) and installed power to fill
data gaps. During later review, we compared overall power-based growth trends using net
registered tonnage (NRT) regressions. There was less than 0.6% difference between the
regressed power and reported power in registry data for 2002, indicating that both GRT and NRT
regressions yield similar results. However, as we move back in time, we note empty fields in the
GRT data increase faster than empty fields in the NRT data; this could result in different trend
estimates for the same historic ship calls. Upon review, we confirmed that using NRT
correlations with installed power could increase the 1997 estimates by less than 9%; none of the
other years' installed power totals changed much. This could decrease the overall growth trends
used in this work by less than 1%.
We think this uncertainty in trend extrapolation could be worth further research, but
acknowledge that revised trends would still compare well in our validation analysis. No major
insights or conclusions would change. Ship emissions activity would still be on track to double
before 2020 in North America, and an IMO-compliant SECA would still return to 2002 levels
within two decades. A lower growth rate in installed power could indicate slightly greater
reductions in energy intensity (e.g., faster decoupling of trade and emissions) over time, but this
would still be within the 1% to 2% range reported in this work.
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6.4 Incorporate additional detail among drivers affecting change
Underlying drivers of freight activity and the energy systems that produce emissions will
continue to merit analysis. For example, growing GDP may remain highly correlated with
growth in imports as it has over past decades. This correlation could become stronger in the
future, or one might consider how and whether change in population age and demographics
could reduce the rate of consumption and trade in North America without a downturn in GDP.
These sorts of effects on global and regional shipping are not considered in this work, either
directly or through any of the BAU forecast trends considered; a potential exception could be
include work by Eyring et al., which modifies growth on major trade routes greater than recent
trends and North American analyses would suggest (39). Better consideration of drivers for
change in freight transportation represents a rich area for future research, particularly in terms of
goods movement.
6.5 Incorporate planned or proposed signals to modify technological change trends
This work explicitly accounts for the expected impacts of NOx emissions limits imposed
by MARPOL Annex VI - already in force, as discussed above. In addition to the Annex VI NOx
limits, one could consider including fuel switching measures proposed by the State of California
for auxiliary engines and/or in a recent proposal by INTERTANKO (102). We forecast
emissions without considering such interventions, to compare BAU results with a SECA regime.
This enables atmospheric modeling analyses by members of the North American SECA team to
consider what reductions may achieve air quality goals in North America. Future work could
consider actions (e.g., emissions trading regimes) that could accelerate or out-perform a SECA
for North America; recent work has begun to consider these issues (103).
6.6 Model fleet behavior in response to potential action
Few assumptions about influences of EU regulatory activity, EVIO decisions, or changes
in marine fuel supply and demand are imposed in forecasts presented here. Moreover, this work
assumes full compliance with SECA requirements and no change in fleet logistics associated
with these scenarios. Additional modeling of fleet responses to policy or economic signals may
reveal motivations for unintended behavior and assess their likelihood. This could help clarify
whether increased regulation could deter trade, or whether observations confirming such
behavior are mostly anecdotal.
6.7 Extend voyage data or analytical detail
Overall the inventories produced for this project using STEEM are shown to be valid
geospatial depictions of emissions from commercial ship activity in North America. Some
limitations reveal potential for future analyses to become more accurate and descriptive.
Consideration of heterogeneous forecast trends separately for different vessel types and trade
routes would produce spatial results revealing asymmetry among future trends for liner trades
and bulk trades.
STEEM is a global model that can provide significant insights beyond the North
American domain defined for this project. Additionally, STEEM can be run with updated
information at multiple scales to produce time series, vessel-type comparisons, or to reveal other
characteristics important to understand industry-level effects of alternative mitigation strategies.
41
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47
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AAPA
AE
AIS
AMVER
ARE
BAU
BTS
CAGR
CEC
CMV
CO
CO2
COADS
EEZ
EF
GDP
CIS
CRT
HC
HFO
IMO
ICOADS
INTERTANKO
kW
LMIU
MARAD
MARPOL
MDO
MOVES
NESCAUM
NNI
NRT
NOx
PM
RTI
SECA
SFOC
SOx
STEEM
TREMOVE
USAGE
American Association of Port Authorities
Auxiliary Engine
Automatic Identification System
Automated Mutual Vessel Emergency Response
California Air Resources Board
Business as usual
U.S. Bureau of Transportation Statistics
Compound annual growth rate
Commission for Environmental Cooperation of North America
Commercial Marine Vessel
Carbon monoxide
Carbon dioxide
Comprehensive Ocean-Atmosphere Data Set (now ICOADS)
Exclusive Economic Zones
Emissions factor
Gross Domestic Product
Geographic Information System
Gross Registered Tonnage
Hydrocarbon
Heavy fuel oil
International Maritime Organization
International Comprehensive Ocean-Atmosphere Data Set
Independent Tanker Owners And Operators
Kilowatts
Lloyds Maritime Intelligence Unit
U.S. Maritime Administration
Maritime Pollution Convention
Marine distillate oil
U.S. EPA Mobile Vehicle Emissions Simulator
Northeast States for Coordinated Air Use Management
No net increase
Net Register Tonnage
Oxides of nitrogen
Particulate matter
Research Triangle Institute, Inc.
SOx Emission Control Area
Specific fuel oil consumption
Oxides of sulfur
Waterway Network Ship Traffic, Energy and Environment Model
Transportation and environment policy assessment model (European Commission)
U.S. Army Corps Engineers
U.S. EPA
United States Environmental Protection Agency
48
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Appendix: Summary of North American Ports and Waterways
There are over 400 North American ports and waterways; these ports connect about with
-1,300 foreign ports in the 2002 U.S. Entrances and Clearances data set; about 950 ports are in
the 2002 Lloyd's movement data set, with some overlapping ports among Canada, Mexico, and
the United States. These ports are located based on longitude and latitude and connected to the
STEEM network in ArcMap. The following tables summarize ports and waterways from input
data for the United States, Canada, and Mexico, respectively. These are listed according to
descending order of port calls, by percentage. Because of some duplicate voyages where arrivals
in some ports represent departures in other ports, we do not report absolute counts of port calls
from these input data (estimated from STEEM to be -172,000 for North America in 2002) and
we do not merge these into one North American ranking.
Table Al and Table A2 represent U.S. ports and waterways as reported in USAGE
foreign commerce data. Thirty-seven states and some 250 ports and waterways are represented
in U.S. Foreign Commerce data. The top 12 states and the top 50 ports and waterway locations
account for more than 80% of U.S. foreign commerce ship calls, respectively.
Table A3 represents Canadian ports as provided in LMIU data. The top 21 of 150 ports
represent more than 80% of Canadian ship calls.
Table A4 represents Mexican ports as provided in LMIU data. The top 7 of 42 ports
represent more than 80% of Mexican ship calls.
49
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Table A-l. State-by-state Summary of Ports and Port Calls
Rank by
Foreign
Commerce
Port Calls
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
US State or Region
Florida
Texas
Louisiana
California
Washington
New York/New Jersey
Virgin Islands
Puerto Rico
Alaska
Georgia
Virginia
South Carolina
Maryland
Michigan
Ohio
Alabama
Oregon
Pennsylvania
Mississippi
Hawaii
Maine
Massachusetts
New Jersey
North Carolina
Delaware
Minnesota and Wisconsin
Connecticut
Louisiana and Texas
New York
Rhode Island
Gulf
Maine and New Hampshire
Great Lakes
Indiana
Illinois
Wisconsin
New York
Maryland and Virginia
Michigan and Wisconsin
Minnesota
Washington, DC
Minnesota
Grand Total
Ports and
Waterways in
Input Data
14
12
9
17
13
1
6
9
35
2
9
3
2
29
10
3
4
5
3
7
7
7
5
4
3
1
3
1
4
2
1
1
2
3
2
1
6
1
1
1
1
1
251
Percent of U.S. Calls
16.18%
12.90%
9.94%
9.59%
6.62%
5.20%
4.94%
3.92%
3.41%
2.99%
2.77%
2.64%
2.08%
1.54%
1.37%
1.32%
1.29%
1.27%
1.20%
1.19%
1.16%
1.06%
1.00%
0.73%
0.59%
0.59%
0.42%
0.27%
0.27%
0.23%
0.21%
0.20%
0.19%
0.18%
0.17%
0.13%
0.11%
0.07%
0.06%
0.01%
0.00%
0.00%
100.00%
Cumulative Percent
16.18%
29.08%
39.02%
48.60%
55.22%
60.43%
65.37%
69.29%
72.69%
75.68%
78.45%
81.09%
83.17%
84.71%
86.08%
87.40%
88.68%
89.95%
91.15%
92.34%
93.50%
94.57%
95.57%
96.30%
96.89%
97.48%
97.89%
98.17%
98.44%
98.67%
98.88%
99.08%
99.27%
99.45%
99.62%
99.75%
99.86%
99.93%
99.99%
100.00%
100.00%
100.00%
50
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Table A-2. U.S. Port and Waterway Summary from USACE Foreign Commerce Data
Rank by
Foreign
Commerce
Port Calls
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
US Port/Waterway Name
Houston
Port Of New York
Miami Harbor
Port Everglades Harbor
San Juan Harbor
Port Of New Orleans
Los Angeles Harbor
Long Beach Harbor
Charleston Harbor (Including Ashley River,
Cooper River, Shem Creek)
St. Thomas Harbor
Savannah Harbor
Seattle Harbor
Port Of South Louisiana
Baltimore Harbor And Channels
Elizabeth River
Bayou Lafourche And Lafourche-Jump
Waterway
Tacoma Harbor
Oakland Harbor
Jacksonville Harbor
Galveston Channel
Beaumont
Corpus Christi
Tampa Harbor
Palm Beach Harbor
Mobile Harbor
Port Of Portland
Port Of Boston
San Diego Harbor
Canaveral Harbor
Texas City Channel
Port Hess St. Croix Island
Freeport Harbor
Calcasieu River And Pass (Lake Charles)
Ketchikan Harbor
Atchafalaya River (Morgan City To Gulf Of
Mexico)
Port Of Baton Rouge
Philadelphia Harbor
Honolulu Harbor, Oahu
Key West Harbor
Delaware River At Camden
Duluth-Superior Harbor
Port Of Plaquemines
Pascagoula Harbor
Percent of U.S. Calls
5.64%
5.20%
4.95%
4.16%
2.99%
2.98%
2.74%
2.59%
2.52%
2.51%
2.42%
2.35%
2.16%
2.07%
1.99%
.93%
.79%
.71%
.67%
.59%
.40%
.39%
.39%
.38%
.23%
.12%
0.94%
0.92%
0.92%
0.91%
0.90%
0.88%
0.84%
0.84%
0.73%
0.69%
0.68%
0.62%
0.62%
0.59%
0.59%
0.59%
0.57%
Cumulative Percent
5.64%
10.84%
15.79%
19.95%
22.94%
25.91%
28.65%
31.24%
33.76%
36.27%
38.70%
41.04%
43.20%
45.28%
47.27%
49.20%
50.99%
52.70%
54.36%
55.95%
57.35%
58.74%
60.12%
61.50%
62.73%
63.86%
64.80%
65.72%
66.64%
67.54%
68.45%
69.33%
70.18%
71.02%
71.75%
72.43%
73.12%
73.74%
74.35%
74.95%
75.54%
76.12%
76.69%
51
-------
Rank by
Foreign
Commerce
Port Calls
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
US Port/Waterway Name
East River And Oglethorpe Bay
Port Of Wilmington
Juneau Harbor
Port Harvey St. Croix Island
Port Arthur
St. John Island
Christiansted Harbor, St. Croix
Anacortes Harbor
Port Of Newport News
Wilmington Harbor
Other Puget Sound Area Ports
Gulfport Harbor
Portland Harbor
Ponce Harbor
Toledo Harbor
Skagway Harbor
Everett Harbor And Snohomish River
Port Manatee
Port Hueneme
Conneaut Harbor
Matagorda Ship Channel
Rouge River
Paulsboro
Port Of Vancouver
Fernandina Harbor
Unalaska Bay And Island
Cleveland Harbor
Richmond Harbor
Kivilina
Eastport Harbor
Gulf Intracoastal Waterway, Mississippi River,
LA, To Sabine River, TX
Port Of Longview
New Haven Harbor
Fore River
Marcus Hook
Mayaguez Harbor
Carquinez Strait
East Pearl River
Sitka Harbor
Port Angeles Harbor
Gulf Via Tiger Pass
Port Huron
Chester Area
Piscataqua River And New Hampshire
Brownsville
San Francisco Harbor
Percent of U.S. Calls
0.57%
0.52%
0.52%
0.52%
0.51%
0.48%
0.47%
0.45%
0.42%
0.42%
0.41%
0.40%
0.40%
0.39%
0.39%
0.38%
0.37%
0.37%
0.36%
0.35%
0.34%
0.34%
0.32%
0.32%
0.31%
0.31%
0.30%
0.29%
0.29%
0.28%
0.27%
0.27%
0.27%
0.27%
0.26%
0.25%
0.24%
0.23%
0.23%
0.22%
0.21%
0.21%
0.21%
0.20%
0.20%
0.18%
Cumulative Percent
77.26%
77.78%
78.31%
78.83%
79.34%
79.82%
80.30%
80.74%
81.16%
81.58%
81.99%
82.39%
82.79%
83.19%
83.58%
83.96%
84.33%
84.70%
85.05%
85.40%
85.75%
86.09%
86.42%
86.74%
87.05%
87.36%
87.66%
87.95%
88.24%
88.52%
88.79%
89.06%
89.33%
89.59%
89.85%
90.10%
90.34%
90.57%
90.80%
91.02%
91.23%
91.44%
91.65%
91.85%
92.04%
92.22%
52
-------
Rank by
Foreign
Commerce
Port Calls
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
US Port/Waterway Name
Sandusky Harbor
Other Hawaiian Islands Area Ports
Grays Harbor And Chehalis River
Elizabeth River (Southern Branch)
PortOfKalama
Providence River And Harbor
Fort Pierce Harbor
Port Of Chicago
Hilo Harbor, Hawaii Is.
Stockton
Morehead City Harbor
Milwaukee Harbor
Bridgeport Harbor
Ecorse
Burns Waterway Harbor
Detroit Harbor
El Segundo
Lower Delaware Bay
Oswego Harbor
Seward Harbor
Guayanilla Harbor
Nikishka
Searsport Harbor
Panama City Harbor
Georgetown Harbor
Coos Bay
Wrangell Harbor
Nawiliwili Harbor, Kauai
Perm Manor Area
Anchorage
Manistee Harbor
Presque Isle Harbor
Bar Harbor
Lake Huron
Lake Michigan
San Joaquin River
Calcite
Pensacola Harbor
Whittier Harbor
Kahului Harbor, Maui
Dauphin Island Bay
Barbers Point
Port Of Richmond
Fajardo Harbor
Chesapeake Bay
Sacramento
Haines
Percent of U.S. Calls
0.18%
0.18%
0.17%
0.17%
0.16%
0.16%
0.15%
0.15%
0.14%
0.14%
0.14%
0.13%
0.13%
0.13%
0.12%
0.12%
0.12%
0.12%
0.12%
0.12%
0.12%
0.12%
0.11%
0.11%
0.11%
0.11%
0.11%
0.10%
0.10%
0.10%
0.10%
0.10%
0.10%
0.09%
0.09%
0.08%
0.08%
0.08%
0.08%
0.07%
0.07%
0.07%
0.07%
0.07%
0.07%
0.07%
0.06%
Cumulative Percent
92.40%
92.58%
92.75%
92.91%
93.08%
93.24%
93.40%
93.55%
93.69%
93.83%
93.96%
94.10%
94.23%
94.36%
94.48%
94.60%
94.73%
94.85%
94.97%
95.08%
95.20%
95.31%
95.43%
95.54%
95.65%
95.76%
95.86%
95.97%
96.07%
96.17%
96.27%
96.36%
96.46%
96.55%
96.65%
96.73%
96.81%
96.89%
96.98%
97.05%
97.12%
97.19%
97.26%
97.32%
97.39%
97.46%
97.52%
53
-------
Rank by
Foreign
Commerce
Port Calls
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
US Port/Waterway Name
Narragansett Bay
Old Tampa Bay
Salem River
Ashtabula Harbor
Homer
Port Of Albany
Humboldt Harbor And Bay
Bellingham Bay And Harbor
Northeast (Cape Fear) River
Menominee Harbor And River
New Castle Area
Port Of Astoria
Port Of Buffalo
Jobos Harbor
PortOfHopewell
Fall River Harbor
Algonac
Yabucoa Harbor
Frederiksted St. Croix Island
Rochester (Charlotte) Harbor
Sabine Pass Harbor
Sault Ste. Marie
Marysville
Suisun Bay Channel
Little River (Creek)
New Bedford And Fairhaven Harbor
Alexandria Bay
Olympia Harbor
St. Clai
Fairport Harbor
Redwood City Harbor, Ca
Monroe Harbor
Port Dolomite
Port Inland
York River
Adak Island
Indiana Harbor
Akutan Island
Lorain Harbor
Trenton
Alpena Harbor
Charlevoix Harbor
Ogdensburg Harbor
Gary Harbor
Hudson River
New London Harbor
Northville, L.I.
Percent of U.S. Calls
0.06%
0.06%
0.06%
0.06%
0.06%
0.06%
0.06%
0.06%
0.06%
0.06%
0.05%
0.05%
0.05%
0.05%
0.05%
0.05%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.04%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.03%
0.02%
0.02%
0.02%
0.02%
Cumulative Percent
97.58%
97.65%
97.71%
97.77%
97.83%
97.89%
97.95%
98.01%
98.06%
98.12%
98.17%
98.23%
98.28%
98.33%
98.38%
98.42%
98.47%
98.51%
98.55%
98.60%
98.64%
98.68%
98.72%
98.76%
98.79%
98.83%
98.86%
98.90%
98.93%
98.97%
99.00%
99.03%
99.06%
99.09%
99.12%
99.15%
99.18%
99.21%
99.24%
99.27%
99.29%
99.32%
99.35%
99.37%
99.40%
99.42%
99.44%
54
-------
Rank by
Foreign
Commerce
Port Calls
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
US Port/Waterway Name
Marblehead
Michoud Canal
San Pablo Bay And Mare Island Strait
Stoneport
Waukegan Harbor
Detroit District Small Ports - Lake Michigan
Erie Harbor
Hoonah Harbor
Kingston Harbor (North Plymouth)
Muskegon Harbor
Bayou La Batre
Drummond Island
Kodiak Island
Ludington Harbor
Salem Harbor
Escanaba
Port Royal Harbor
Afognak Bay
Burlington-Florence-Roebling
Chignik Bay
Southport
Intracoastal Waterway, Jacksonville To Miami
St. Paul Island, Pribilof Island
Icy Bay
Inland Wtwy From Franklin To The Mermentau
River
Togiak Bay
Two Harbors (Agate Bay)
Detour And Vicinity
Penobscot River
Catalina Island Ports
St. Lawrence Island
Nome
Pearl Harbor, Oahu
Absecon Inlet
Elizabeth River (Eastern Branch)
Guanica Harbor
Hydaburg
Marquette Harbor
Valdez Harbor
Mitrofania Bay
Asharoken, Li.
Cape Cod Canal
Clayton
Columbia River At Bakers Bay, Wa
King Cove Lagoon
Marine City
Percent of U.S. Calls
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
Cumulative Percent
99.46%
99.48%
99.50%
99.52%
99.54%
99.56%
99.58%
99.60%
99.61%
99.63%
99.65%
99.67%
99.68%
99.70%
99.72%
99.73%
99.74%
99.76%
99.77%
99.78%
99.79%
99.80%
99.81%
99.83%
99.84%
99.85%
99.86%
99.86%
99.87%
99.88%
99.89%
99.90%
99.90%
99.91%
99.91%
99.92%
99.92%
99.93%
99.94%
99.94%
99.94%
99.95%
99.95%
99.95%
99.96%
99.96%
55
-------
Rank by
Foreign
Commerce
Port Calls
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
US Port/Waterway Name
Potomac River
Rockland Harbor
Wyandotte
Amchitka Island
False Pass
Gulf Intracoastal Waterway, Galveston To
Corpus Christi
Humboldt Harbor
Huron Harbor
Naknek River
Port Isabel
Ports Other Than Portland, Astoria, St. Helens,
Longview, Vancouver
Annapolis Harbor
Arecibo Harbor
Cold Bay
Gladstone Harbor
Gloucester Harbor
Harbor Beach
Kodiak Harbor
Port Clinton Harbor
Port Moller
Potomac River At Alexandria
Taconite Harbor
Total
Percent of U.S. Calls
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
100.00%
Cumulative Percent
99.96%
99.97%
99.97%
99.97%
99.97%
99.98%
99.98%
99.98%
99.98%
99.99%
99.99%
99.99%
99.99%
99.99%
99.99%
99.99%
99.99%
100.00%
100.00%
100.00%
100.00%
100.00%
56
-------
Table A-3. Canadian Port and Waterway Summary from LMIU Movement Data
Rank in LMIU data
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
Canada Ports
Vancouver (Canada)
Halifax
Montreal
Quebec
Fraser River Port
Saint John (Canada)
Hamilton (Canada)
Welland Canal
Seven Islands
Point Tupper
Prince Rupert
Port Carder
Mulgrave
Thunder Bay
Pointe aux Trembles
St. John's (Canada)
Comeau Bay
Sorel
Port Hawkesbury
Three Rivers
Windsor (Canada)
Come by Chance
Toronto
Canso Strait
Port Alfred
Corner Brook
Goderich
Sarnia
Crofton
Nanticoke
Belledune
Victoria (British Columbia)
Port Colborne
Harmac
Clarkson
Contrecoeur
Bayside
Sault Ste. Marie
Charlottetown (Canada)
Dalhousie
Duncan Bay
Becancour
WhiffenHead
Sydney (Nova Scotia)
Cote Ste. Catherine
Meldrum Bay
Corunna (Canada)
Percent of Canadian Port Calls
17.54%
12.49%
11.05%
5.23%
3.72%
3.26%
3.22%
3.12%
2.66%
2.45%
2.22%
.88%
.75%
.75%
.60%
.23%
.12%
.05%
0.97%
0.93%
0.86%
0.84%
0.80%
0.74%
0.70%
0.69%
0.66%
0.58%
0.52%
0.52%
0.48%
0.48%
0.44%
0.43%
0.43%
0.40%
0.38%
0.37%
0.36%
0.35%
0.34%
0.33%
0.33%
0.32%
0.30%
0.30%
0.29%
Cumulative Percent
17.54%
30.03%
41.08%
46.31%
50.04%
53.30%
56.52%
59.63%
62.29%
64.74%
66.96%
68.84%
70.60%
72.35%
73.95%
75.18%
76.31%
77.36%
78.32%
79.25%
80.11%
80.94%
81.74%
82.48%
83.19%
83.88%
84.54%
85.12%
85.63%
86.15%
86.63%
87.11%
87.55%
87.98%
88.40%
88.80%
89.18%
89.55%
89.91%
90.26%
90.60%
90.93%
91.26%
91.58%
91.88%
92.18%
92.46%
57
-------
Rank in LMIU data
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
Canada Ports
Bowmanville
Hibernia Field
Valleyfield
Nanaimo
Argentia
Cap aux Meules
Magdalen Is.
Sable Is.
Picton (Canada)
Chemainus
Holyrood
Prescott
Stephenville
Botwood
Pointe au Pic
Oshawa
Chicoutimi
Kitimat
Port Weller
Gros Cacouna
Liverpool (Nova Scotia)
Canada
Bruce Mines
Tofino
Havre St. Pierre
Pugwash
Pictou
Bronte
Courtright
East coast Canada
Shelburne
Sheet Hbr.
Summerside
Campbell River
Cowichan Bay
Goose Bay
Port Alberni
Grande Anse
Matane
Thessalon
Churchill
Lower Island Cove
Marathon
Tracy
Amherstburg
Gaspe
Lower Cove
Rimouski
Hantsport
Percent of Canadian Port Calls
0.28%
0.26%
0.25%
0.24%
0.24%
0.24%
0.24%
0.21%
0.21%
0.20%
0.18%
0.18%
0.17%
0.16%
0.16%
0.15%
0.15%
0.15%
0.15%
0.14%
0.14%
0.13%
0.13%
0.13%
0.12%
0.12%
0.11%
0.10%
0.10%
0.10%
0.10%
0.10%
0.10%
0.09%
0.09%
0.09%
0.09%
0.08%
0.08%
0.08%
0.07%
0.07%
0.07%
0.07%
0.06%
0.05%
0.05%
0.05%
0.05%
Cumulative Percent
92.74%
93.00%
93.25%
93.49%
93.73%
93.96%
94.20%
94.41%
94.62%
94.82%
95.00%
95.18%
95.35%
95.51%
95.68%
95.83%
95.97%
96.12%
96.27%
96.41%
96.54%
96.68%
96.81%
96.93%
97.05%
97.16%
97.27%
97.38%
97.48%
97.58%
97.69%
97.78%
97.88%
97.97%
98.06%
98.15%
98.24%
98.32%
98.40%
98.48%
98.55%
98.62%
98.69%
98.76%
98.82%
98.88%
98.93%
98.99%
99.03%
58
-------
Rank in LMIU data
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
Canada Ports
Little Narrows
Owen Sound
Cartwright
Duke Point
Little Cornwallis Is.
Squamish
Les Mechins
Bath (Canada)
Nanisivik
Port Mellon
Yarmouth (Canada)
Kingsville
Midland
Newfoundland
Oakville
Alert Bay
Georgetown (Canada)
Kingston (Canada)
Lanoraie
Levis
Long Pond
Morrisburg
Parry Sound
Sombra
Stewart (Canada)
Thorold
Aulds Cove
Bridgewater (Canada)
Burin
Clarenville
Dartmouth (Nova Scotia)
Grindstone
Louisburg
Port Credit
River St Lawrence
Thebaud Field
Tuktoyaktuk
Weymouth (Canada)
Bay Roberts
Burlington (Ontario)
Chedabucto Bay
Cohasset-Panuke Term.
Cole Hbr.
Country Hbr.
Gold River (Canada)
Harbour Grace
Lewisporte
Lunenburg
Marystown
Percent of Canadian Port Calls
0.05%
0.05%
0.04%
0.04%
0.04%
0.04%
0.04%
0.03%
0.03%
0.03%
0.03%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
0.01%
Cumulative Percent
99.08%
99.13%
99.17%
99.22%
99.26%
99.30%
99.34%
99.37%
99.40%
99.43%
99.46%
99.48%
99.51%
99.53%
99.56%
99.57%
99.59%
99.61%
99.63%
99.65%
99.67%
99.68%
99.70%
99.72%
99.74%
99.76%
99.77%
99.78%
99.79%
99.81%
99.82%
99.83%
99.84%
99.85%
99.87%
99.88%
99.89%
99.90%
99.91%
99.91%
99.92%
99.93%
99.93%
99.94%
99.95%
99.95%
99.96%
99.96%
99.97%
59
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Rank in LMIU data
146
147
148
149
150
Canada Ports
Port Alice
Souris
St. Andrews (Canada)
St. Anthony
Tadoussac
Total
Percent of Canadian Port Calls
0.01%
0.01%
0.01%
0.01%
0.01%
100.00%
Cumulative Percent
99.98%
99.98%
99.99%
99.99%
100.00%
60
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Table A-4. Mexican Port and Waterway Summary from LMIU Movement Data
Rank in LMIU Data
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Mexico Ports
Coatzacoalcos
Tampico
Veracruz
Altamira
Manzanillo (Mexico)
Guaymas
Tuxpan
Progreso
Lazaro Cardenas
Campeche
Cayo Areas Term.
Dos Bocas
Cozumel
Puerto Chiapas
Morro Redondo
Puerto Vallarta
Mazatlan
Ensenada (Mexico)
Acapulco
Mexico
Topolobampo
Ciudad del Carmen
Cabo San Lucas
La Paz (Mexico)
Playa del Carmen
Lerma
San Bias
Salina Cruz
San Marcos Is.
Puerto Juarez
Rosarito Term.
Puerto Morelos
Escondido
Guadalupe Is.
Isla Mujeres
Chetumal
Las Coloradas
Loreto
Pichilingue
Puerto Angel
Tecolutla
Zihuatanejo
Total
Percent of Mexican Port Calls
14.38%
13.59%
13.33%
12.43%
10.22%
6.00%
5.82%
4.88%
2.48%
.74%
.64%
.50%
.35%
.31%
.23%
0.98%
0.90%
0.84%
0.73%
0.73%
0.64%
0.48%
0.44%
0.40%
0.39%
0.37%
0.34%
0.29%
0.11%
0.10%
0.08%
0.06%
0.03%
0.03%
0.03%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
0.02%
100.00%
Cumulative Percent
14.38%
27.97%
41.30%
53.73%
63.95%
69.95%
75.77%
80.65%
83.14%
84.88%
86.52%
88.02%
89.38%
90.68%
91.91%
92.89%
93.79%
94.63%
95.36%
96.08%
96.73%
97.21%
97.65%
98.05%
98.44%
98.81%
99.15%
99.44%
99.55%
99.65%
99.73%
99.79%
99.82%
99.85%
99.89%
99.90%
99.92%
99.94%
99.95%
99.97%
99.98%
100.00%
61
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Appendix C for EPA420- D-07-007
Global Trade and Fuels Assessment -
Future Trends and Effects of Requiring
Clean Fuels in the Marine Sector
Draft Report
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
Prepared for EPA by
RTI International
Research Triangle Park, NC
EnSys Energy & Systems, Inc.
Lexington, Ma
Navigistics Counsulting
Boxborough, Ma
EPA Contract No. EP-C-05-040
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments.
SER&
United States
Environmental Protection
Agency
EPA420-D-07-006
October 2007
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CONTENTS
Section Page
1 Introduction 1-1
1.1 Regulations and Options for Compliance 1-2
1.2 Summary of the Analysis 1-5
1.3 Organization of the Report 1-8
2 Overview of the Marine Fuels Industry 2-1
2.1 Marine Fuels Types 2-2
2.2 Refining of Petroleum Products (Including Marine Fuels) 2-4
2.3 Bunker Fuel Suppliers 2-17
3 Demand for Bunker Fuels in the Marine Industry 3-1
3.1 Summary of the Modeling Approach 3-1
3.2 Methods of Forecasting Bunker Fuel Consumption 3-4
3.3 Results ofBunkerFuel Forecasts 3-21
3.4 Implications of Bunker Fuel Forecasts for the WORLD Model Analysis 3-31
4 Estimating Business- as-Usual Proj ections Using the WORLD Model 4-1
4.1 Overview of Enhancements to the WORLD Model 4-1
4.2 Bunker Fuel Forecasts Used in the WORLD Model Analysis 4-3
4.3 WORLD Model Assumptions and Structural Changes 4-7
4.4 Input Prices for the WORLD Model 4-25
4.5 Reporting 4-26
5 The WORLD Model's Projections for 2012 and 2020 5-1
5.1 Supply-Demand Balance 5-1
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5.2 Refining Capacity Additions 5-4
5.3 Refining Economics and Prices 5-7
5.4 Crude and Product Trade 5-11
5.5 Bunker Fuels Quality and Blending 5-12
6 Summary and Implications for Future SECA Analyses 6-1
References R-l
Appendix A: Status of Technology and Trading Options for Compliance with
Advanced Bunkers Regulations A-l
Appendix B: Review of Refinery Process Costs B-l
in
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SECTION 1
INTRODUCTION
The U.S. Environmental Protection Agency (EPA), along with other regulatory bodies in
the U.S. and Canada, are considering whether to designate one or more SOX Emission Control
Areas (SEC As) along the North American coastline, as provided for by MARPOL Annex VI.
This addition to the international MARPOL treaty went into effect on May 19, 2005 and places
limits on both NOX and SOX emissions. According to the terms of the treaty, ships calling on
ports in signatory countries must use bunker fuel with a sulfur content at or below 4.5 percent.
Countries participating in the treaty are also permitted to request designation of SEC As, in which
ships must treat their exhaust to a level not exceeding 6.0 grams of SOX per kilowatt-hour or
further reduce the sulfur content of their fuel to 1.5 percent. The Baltic and North Sea areas have
already been designated as SECAs, and the effective dates of compliance in these bodies of
water are 2006 and 2007, respectively.
To evaluate possible recommendations regarding North American SECAs, EPA requires
a thorough examination of potential responses by the petroleum-refining and ocean-transport
industries to such a designation, along with any resulting economic impacts. As Task Order #1
under this contract between RTI International and EPA, this report provides a foundation for
these recommendations through developing the knowledge, data, and modeling capabilities
needed for such an analysis. Thus, the analytic team comprised of RTI, EnSys Energy &
Systems, and Navigistics Consulting has assessed current and future conditions in global-fuels
market to provide this foundation. Accomplishing the goals of this report involved several
component tasks:
• Examining the current petroleum-refining industry and bunker-fuel markets,
• Developing a model of shipping activities with Navigistics Consulting to estimate future
demands formaline bunker fuels, and
• Enhancing the EnSys model of petroleum refining (World Oil Refining Logistics and
Demand, or the WORLD model) to include the new information on bunker-fuel markets
and then using the model to establish baseline projections of future refining activities.
This section provides a background for the analysis by discussing existing regulations on
marine bunker fuels. It then summarizes how the components of the analysis are implemented
and examines the resulting implications of "Business-as-Usual" (BaU), or baseline, conditions
for the international marine fuel markets in the years 2012 and 2020.
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1.1 Regulations and Options for Compliance
Existing regulations regarding marine bunker fuels provide an important backdrop for the
modeling conducted in this analysis and, as such, are summarized in this section - along with an
initial discussion of how bunker-fuel markets may comply with regulations. The International
Maritime Organization's (IMO) "MARPOL Annex VT' sets out a series of regulations impacting
international marine bunker fuels. These new regulations center on limits for emissions of nitrous
oxides (NOX), sulfur oxides (SOX), and volatile organic compounds (VOCs). Fuel quality
regulations in Annex VI have been implemented in the form of the ISO-8217 2005 specification
(see Figure 4-2 for details and discussion). This specification updates selected bunker qualities,
provides protections to prevent the blending of used lubricating oil (ULO) into marine fuels, and
limits the presence of refinery streams which contain high levels of "catalyst fines".
The MARPOL Annex VI sets limits on NOX emissions as a function of ships' engine
speed, which range from a high of 17 grams per kilowatt-hour (gm/kWh) for engines running at
less than 130 rpm to a low of 9.8 gm/kWh for engines running at or above 2000 rpm. Since
residual bunker fuels contain nitrogen that is typically at a level equal to around 20% of the
fuel's sulfur content, NOX emissions will be impacted in part by fuel quality (as well as by
specific combustion conditions). For example, a bunker fuel containing 3% sulfur will contain
around 0.6% nitrogen, which translates into around 3 gm of NOX per kWh (Hanashima, 2006).
This level is well below the standard set forNOx emissions, however, residuum desulfurization
in a refinery also reduces nitrogen levels and can therefore play into the comparative economics
of bunker-fuel sulfur reduction versus other options (e.g., on-board abatement of SOx).1
Through the ISO-8217 specifications, MARPOL Annex VI sets a limit on SOX emissions,
expressed as a maximum 4.5% fuel sulfur content. This compares to a prior maximum limit of
5%. The new level was set based on a survey of residual bunkers qualities (the intermediate fuel
oil, or "IFO," grades), which showed that essentially all bunkers currently supplied have sulfur
contents below 4.5% (see Figure 1-1). Since the same survey showed global average residual
bunker fuel content is currently around 2.7%, this change has limited practical impact on bunkers
quality. More significant for any potential future SOX regulations is the fact that MARPOL
Annex VI explicitly allows for on-board abatement as an alternative means for meeting SOX
requirements (thus recognizing that the ultimate goal is a reduction in SOX emissions, rather than
1 To cover the eventuality that NOX may need to be considered in any future investigations of SECAs, EnSys added
the nitrogen contents of residual streams to the WORLD model, along with impacts on nitrogen content of
desulfurization.
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a reduction of fuel sulfur content per se). The IMO, however, has yet to set up necessary
guidelines for this provision.
26.0
21.0
9.0
4.0
4.0
0.5
23.0
12.0
1.0
0.0
Below 0.5 -1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0 3.0 - 3.5 3.5 - 4.0 4.0 - 4.5 Below
0.5 4.5
Residual fuel oil sulphur content, % m/m
Figure 1-1. Sulfur Content in Bunker Fuels
Figure 1-2 below illustrates the current timeline of the MARPOL Annex VI and other
SECA-related regulations. In addition establishing emissions limits and considering reductions
achieved through on-board abatement, MARPOL Annex VI and ISO-8217 2005 explicitly allow
for the existence of regional SEC As. In the European Union (EU), these agreements have been
established with a marine fuel sulfur maxima of 1.5%, potentially advancing to 0.2% and 0.1%
on marine distillates. Again, these regulations recognize on-board abatement as an alternative,
with a stated standard of 6 gm SOx / kWh (to correspond to the initial 1.5% sulfur limit).
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Legislative overview - IMO and European Union
„ « y
£004
acae
11 Jkugu* MH
EU Member SH« low
tnuad
J 1 5%. KM »-| (antnttr :hips
Hilng bct**-ro EU ports
i*chnot jv * r. 40 Atlpnqbn
to 1 E* bd
EU C Ornrfiiiimri i*>ldwQn
•H^tntf rcrtMrtmni on
ijur?.- riffiirvrf- fij*(i.
«UHional SEC AS
Figure 1-2. Timeline of MARPOL Annex VI and SECA Implementation
Beyond currently announced initiatives, it appears likely that the MARPOL Annex VI
regulations and newly effective EU SEC As are only the first steps of progressively tightening
regulation of marine fuels qualities. This is being driven by the fact that, as major steps are
being taken to reduce sulfur in other products, especially in gasoline and non-marine distillates,
bunkers are becoming an increasingly significant - and unacceptable - source of SOX and other
emissions. Already, there is a review of MARPOL Annex VI underway with international
consultative meetings. Current intentions are for a second round of EVIO/ISO marine fuels
regulations to be established by 2008 and be enforceable by 2011/2012, with potential further
steps beyond. In addition, the EU is expected to tighten the initial SECA regulations beyond
2008. Required residual bunker-fuel sulfur levels could move to as low as 0.5% regionally, or
even globally. One current element of uncertainty is the size of the geographic areas of future
SECAs, i.e., how many miles offshore they will apply. This in turn affects the proportion of total
bunkers consumption that will need to comply with SECA regulations. Anticipated policy
decisions on this issue will have significant implications for any analysis conducted in the future
regarding the potential effects of North American SECAs.
The above proposals focus on improving the quality of the current mix of distillate and
residual bunkers fuels in the future. More radical alternative have been put forward as part of the
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on-going review by the IMO of MARPOL Annex VI. One group (INTERTANKO) is proposing
that all marine bunker fuels be converted to MDO (i.e. no more residual bunker fuels) with a
maximum sulfur content of 1% initially, dropping to 0.5% after 2015. Benefits claimed include
greater reductions in SOx, NOx and PM, elimination of need for on-board scrubbing and
simplification of on-board fuel handling and storage, creation of a single global standard for
marine bunkers and an associated level competitive playing field among shippers. Improved
vessel safety is also cited since the regulation would avoid the need for vessels to change fuel
types when entering or leaving SECA areas, thereby eliminating associated risk of engine
outage, vessel loss of control and potential environmental disaster.
Other groups, including BEVICO (an owners' organization covering a claimed 65% of the
world merchant fleet), have proposed that all vessels use MDO (no IFO) within SECA areas.
This would lead to a partial shift in bunker demand from IFO to MDO.
The vigorous debate that has developed among the parties concerned with global
shipping and fuels is on-going at the time of writing of this report. As a result, the realm of
potential policy decisions on marine bunkers and hence analytical requirements goes beyond the
immediate Annex VI and SECA regulations and has potentially far reaching implications for US
and global refining and oil markets.
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Marine Environment Protection Committee (MEPC) - 53rd session 18-22 July 2005
Review of Annex VI
The Committee agreed on the need to undertake a review of Annex VI and the NOx
Technical Code with a view to revising the regulations to take account of current
technology and the need to further reduce emissions from ships. MEPC instructed the
Sub-Committee on Bulk Liquids and Gases (BLG) to carry out the review by 2007, and
specifically to:
examine available and developing techniques for the reduction of emissions of air
pollutants; review the relevant technologies and the potential for a reduction of
NOx emissions and recommend future limits for NOx emissions;
_ review technology and the need for a reduction of SOx emissions and justify and
recommend future limits for SOx emissions;
_ consider the need, justification and possibility of controlling volatile organic
compounds emissions from cargoes;
with a view to controlling emissions of particulate matter (PM), study current
emission levels of PM from marine engines, including their size distribution and
quantity, and recommend actions to be taken for the reduction of PM from ships.
Since reduction of NOx and SOx emission is expected to also reduce PM emission,
estimate the level of PM emission reduction through this route;
- consider reducing NOx and PM emission limits for existing engines;
_ consider whether Annex VI emission reductions or limitations should be extended to
include diesel engines that use alternative fuels and engine systems/power plants
other than diesel engines; and
review the texts of Annex VI, NOx Technical Code and related guidelines and
recommend necessary amendments.
The language in the Annex VI regulations, and the economics of the refining and
shipping industries, lead to a situation where several, non-exclusive, options can potentially be
used to achieve compliance with SEC As. While some of these options are not fully explored in
this report (they will be evaluated in the next steps of the analysis), it is still important to note the
range of responses. Among these options are:
1) Desulfurize refinery fuels and use lower sulfur content fuel.
2) Switch entirely or partially to middle distillates for bunker fuel.
3) Reduce SOX emissions via on-board scrubbers (also helps reduce particulate matter, PM).
4) Reduce NOX emissions by lowering nitrogen content of the fuel.
5) NOX and PM reductions via on-board emission controls and engine design.
6) Undertake custom blending of fuels on board and/or use segregated bunkers tanks.
7) Establish emissions trading, which could allow trading of marine and shore-based credits.
8) Switch to alternative fuel sources (e.g., LNG).
9) To the extent feasible, some ship owners might also elect non-compliance through re-
registration of ships to a country that has not ratified the EVIO standards.
There is general industry agreement in principle on the need for SOX emissions reduction.
There are, however, major industry concerns over operational issues, such as custom blending of
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fuels on board due to safety and other concerns (Gregory, 2006). Similarly, there is industry
agreement about a reduction in NOx limits for new engines, but also concerns about the
application of NOX limitations to existing engines due to practicality and cost factors (Metcalf,
2006) and concerns about a regional approach to NOX controls due to technical considerations
(Gregory, 2006).
With regard to emissions trading and sulfur reduction, the European Commission has
been asked to give particular consideration to proposals for alternative or complementary
measures and to consider submitting proposals on economic instruments in their 2008 review.
For NOx reductions, the Commission studies suggest that, given the range of technologies, there
is a sound basis for a trading environment (Madden, 2006). In addition, SOX emissions trading
and compliance monitoring schemes are being actively promoted.
Initial studies indicate on-board scrubbing is cheaper in terms of cost per ton of SOX
removed than refinery residual desulfurization. However, the technology is only just reaching
the commercial demonstration stage (with initial positive results). Issues have also been raised
over how to ensure compliance and how to dispose onshore of the resulting sludge waste.
Scrubbing requires an extended lead time to achieve widespread utilization and is least costly
when built in to new ships, rather than retrofitted onto existing ones (where retrofit costs are
estimated on the order of $14 million). Current estimates also indicate ships will have to spend
appreciable time in SECA areas for scrubbing to be economic. Conversely, building a refinery
residual desulfurization unit with ancillaries could cost of the order of $500 million and, if done,
would create a feedstock that could be more attractive for upgrading to light clean fuels than for
sale as low-sulfur residual fuel for bunkers or inland use. Within any one SECA, it is not certain
what proportion of compliance will be achieved by scrubbing versus fuel supply and what the
impact on that balance is of complementary regulations on NOX and PM in addition to SOX.
1.2 Summary of the Analysis
The purpose of this report is to develop the information and modeling techniques that
would be required if EPA decides to proceed with an analysis of the potential effects of
designating North American SECAs as part of the MARPOL Annex VI. In support of these
goals, this report details the development of techniques to estimate bunker demands in the
shipping industry and also enhancements that have been made to the EnSys WORLD model of
the petroleum-refining industry. The resulting information from these processes is used to
establish baseline projections of international petroleum markets in the years 2012 and 2020,
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against which the effects of SEC As - and other potential regulatory scenarios - on shipping and
bunker fuel demands could be evaluated.
RTI and Navigistics Consulting developed a multi-step approach for estimating future
bunker demands involving: (1) identifying major trade routes, (2) estimating volumes of cargo of
various types on each route, (3) identifying types of ship serving those routes and carrying those
cargoes, (4) characterizing types of engines used by those ships, and (5) identifying the types and
estimated quantities of fuels used by those engines. In general, this approach can be described as
an "activity-based" approach with a focus on the international cargo vessels that represent the
majority of fuel consumption. Similar techniques for combining data on specific vessels with
data on engine characteristics have been used in other analyses (e.g., Corbett and Koehler [2003,
2004]; Koehler [2003]; Corbett and Wang [2005]; and Gregory [2006]). The approach in this
analysis extends these previous works by linking ship data to projections of world-wide trade
flows from Global Insights (2005) in order to determine the total number of trips undertaken in
each year and hence fuel use.
The methodology gives the following results for historical and forecasted bunker-fuel
consumption:
• World-wide bunker use in 2001 is estimated at 278 million tons, of which around 212
million tons are residual fuels.
• Between 2001 and 2020, total consumption grows at an average annual rate of 3.1%
(from 2006 to 2020, the growth rate is 2.6%).
• Around 47 million tons of bunker fuel was used in 2001 to transport international cargo
flows into and out of the United States (not all of which is purchased in the U.S.).
• This fuel consumption related to U.S. trade is forecasted to grow at around 3.7% between
2001 and 2020 (or 3.4% from 2006 to 2020), which is somewhat higher than the world
average because of high growth in container traffic arriving at U.S. ports.
The estimates of world-wide bunkers are quite similar to those in the published works
cited above, in spite of differences in techniques. Koehler (2003) uses calculations of average
engine loads, run times, and specific fuel consumption for the existing vessel fleet to come up
with bunker fuel demands of around 281 million tons. Similarly, Corbett and Koehler (2003,
2004) estimate bunker demands at 289 million tons in 2001. These findings on fuel consumption
tend to be significantly higher than data published by the International Energy Agency (IEA),
which places international marine bunkers at around 140 million tons per year, of which around
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120 million tons are residual fuels (see the discussion of these points in Section 4.2). Given the
far-reaching implications of these demand estimates for petroleum markets and related potential
effects of future SEC As, this analysis has chosen to evaluate baseline conditions in the refining
industry for both lEA's bunker-fuel estimates and the estimates developed in this report (termed
the "RTF' estimates for clarity).
For this report, these two bunker-fuel estimates are incorporated in the EnSys WORLD
model, which is a comprehensive, bottom up model of the global oil downstream. It
encompasses crudes and non-crudes supply, refining operations and investment, crude, products
and intermediates trading/transport, product blending/quality and demand. It yields as outputs
detailed simulations of how this global system can be expected to operate under a wide range of
different circumstances, with outputs including price effects as well projections of sector
operations and investments. WORLD is not a forecasting tool per se, but rather uses as a starting
point a global supply-demand world-oil price outlook - in this study, the outlook is based on the
Energy Information Administration's (EIA) Annual Energy Outlook 2006 Reference Case.
To accomplish the goals of this study, WORLD has been expanded to incorporate seven
grades of bunker fuels, covering the major distillate and residual grades used in the marine
shipping industry. The latest international specifications applying to low-sulfur grades of these
fuels were also included because of their applicability for future SEC As. In addition, flexibility
was built in to allow the model user to vary the proportion of SECA compliance that is achieved
through fuel sulfur reduction versus other means such as on-board abatement or emissions
trading. This was necessary since it is feasible that widespread adoption of on-board abatement
could enable shippers to continue using high sulfur bunker fuels - and might even enable refiners
to raise the sulfur level towards the upper limit of 4.5% from today's average global level of
2.7% and still meet required SOx emission standards. In addition, the model was given the
capability of covering the "extreme" scenario of switching residual bunkers entirely to marine
diesel. In addition, since any eventual estimates of bunker-fuel production costs in SECA cases
will derive directly from refinery processing costs, a technology review of the WORLD model
assumptions was undertaken. This involved checking on capital costs for the processes with the
most influence on costs of reducing sulfur in bunkers; also on examining and adjusting
processing and blending options to guard against production of unstable bunker fuels. Finally, to
ensure that the model was correctly specified for any future policy scenarios that might be run on
implementation of SEC As, the related regulations were thoroughly reviewed.
Once these processes were complete, business-as-usual cases (consistent with the
regional oil supply and demand projections from AEO) were set up in WORLD. The resulting
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Ball cases for the years 2012 and 2020 were then executed on both the TEA and RTI bunkers
estimates - key results from all four cases are included in the body of the report. The full results
are rich in detail, however, the important drivers that will impact on future SEC A analyses
revolve around the outlook for product demand. Since the rigorous analysis of shipping activity
and fuel consumption conducted in this report estimates high bunkers demands, the impacts of
SEC As or other marine fuels regulations will be similarly greater than for those estimated using
lower demand forecasts. A second major driver evident in these and other WORLD analyses is
that the on-going shift toward distillates, especially in Europe and non-OECD regions, will
materially alter gasoline and distillate trade patterns, their product pricing and refining
investments and economics. These developments will in turn impact the market and supply
effects of SECAs and other global marine fuels regulations.
The overall objective of the refinery modeling conducted under Task #1 of this contract
was to develop and implement any modifications to the WORLD model that are needed to
accommodate details of bunkers grades and other issues such as updated technology costs, etc.
These features have been successfully implemented and applied (the 2012 and 2020 Ball cases
were developed and represent a sound starting basis to examine the impacts of broader SECA
regulations and/or tighter global marine fuels limits). Section 5 provides details of the WORLD
model estimates for the Ball cases.
This modeling foundation is particularly important because the nature of the MARPOL
Annex VI regulations and goals, and the characteristics of the international marine fuels industry,
mean that there is a much greater potential for variability in future scenarios than is true for most
types of fuels regulations. The WORLD model can be used to case study such alternative
scenarios and address key uncertainties through case studies. Among these, which will be
important in the follow-up SECA analyses, are the following:
• The regional make up of bunkers demand.
• Associated with this, the extent to which consumption of low sulfur bunkers for
SECA compliance will be met by supplies within the SECA or elsewhere.
• The extent of switching, either regionally or globally, to marine distillate fuels.
• The degree to which compliance with the MARPOL regulations will be achieved
through improved fuel quality versus via on-board scrubbing and/or emissions
trading. Using the WORLD model, plausible "high" and "low" scenarios can be
applied and analyzed (the model has already been set up to deal with these).
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• Whether bunkers blend compositions will need to be still further restricted to capture
ship operational limits such as relate to fuel instability.
1.3 Organization of this Report
The remainder of this report is organized as follows to accomplish the goals of Task #1:
Section 2 presents a profile of the marine bunker fuels, their refining processes, and
the overall supply chain used to deliver the fuels to marine vessels.
• Section 3 develops a model of shipping activity and estimates bunker fuel demands.
• Section 4 describes how the analysis of baseline conditions in petroleum markets is
implemented in the WORLD model.
• Section 5 then presents estimated results from the WORLD model regarding Ball
conditions in 2012 and 2020.
Section 6 summarizes and discusses implications for future SECA analyses.
• Appendix A provides additional information on options for reducing SOx emissions.
• Appendix B reviews cost assumptions regarding refinery technologies used in the
analysis of the WORLD model.
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SECTION 2
OVERVIEW OF THE MARINE FUELS INDUSTRY
This section provides an overview of the marine fuels industry, which is a very complex
network of organizational and trade relationships and is also quite geographically dispersed. The
supply chain for this industry begins with integrated petroleum refineries, where "bottoms" from
atmospheric and vacuum distillation unit operations are combined to form the bulk of residual
fuel stocks (see Section 2.2). Marine distillates historically come from poorer quality distillate
recycle streams that are unsuitable for upgrading to diesel fuel or other low-sulfur products. The
dominant producers of marine fuels are divisions of the major petroleum firms such as Shell
Trading (STUSCO) and BP Marine. Around the world, these large producers are joined by
hundreds of smaller firms that contract to transport, blend, and sell low-quality stocks to the
shipping industry.
Although some of the major petroleum refiners also contract for and deliver marine fuels,
much of the worldwide volume is sold to firms that operate bunkering facilities around the
world. These large firms, including the Chemoil Group, O.W. Bunker, and the Chinese
government-owned Chimbusco, purchase blended stocks from the producers and also blend,
transport, and store some products themselves. As much as 25 percent of the world's marine
fuels are purchased and resold by brokers or other intermediaries that never actually take
physical control of the bunker fuel. Arbitrage activities of these firms help keep the worldwide
market efficient, as excess price differentials are quickly exploited and eliminated.
The final stage of the marine fuel supply chain is the bunkering itself, which can either be
done while the ship is docked at a port or directly from bunker barges while the ship is anchored.
There are hundreds of bunkering ports around the world and thousands of firms that provide the
actual bunkering service. Logistics and transport cost factors influence the location of these
bunker ports. In addition to being located close to supply sources (petroleum refineries) and
consumers of transported goods (major population centers), bunkering ports are often
strategically located along high-density shipping lanes. The largest port of this type is in
Singapore and handles more than twice as much bunker fuel volume as the next biggest provider.
Panama and Gibraltar are other examples of strategically located facilities. In North America, the
largest facilities follow the general pattern suggested by location theory. Los Angeles, San
Francisco, New York, Philadelphia, Houston, and New Orleans are close to both refinery supply
and transport destinations.
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The following subsections briefly review characteristics of marine fuels, the petroleum
refining process (focusing on distillation and additional downstream treatment processes that
further refine crude oil into higher-value petroleum products), and the supply chains that deliver
the refined marine fuels.
2.1 Marine Fuel Types
Marine fuels used in vessel bunkering are primarily comprised of heavy distillate and
residual fuels. For this reason, the remainder of this subsection focuses on these two refinery
production outputs (the complete refining process is discussed in more detail in Section 2.2).
There are three major types of marine fuel: diesel, residual, and a combination of the two to
create a fuel type known as "intermediate" fuel oil (IFO). A large number of marine fuel grades
within these three types represent the broad spectrum of fuels available to the shipping industry
for vessel bunkering. In this section, the various grades of marine fuel are introduced using the
colloquial industry names to group the different fuels types. See Section 4 for a more specific
breakdown of the product specifications of marine fuels.
Distillate and residual fuels are blended into various combinations to derive the different
grades of marine fuel oil. Table 2.1 lists examples of the major marine fuel grades and their
vernacular industry nomenclature. In terms of cost, distillates are more expensive than
intermediates, and residual fuels are the cheapest marine fuel-oil option.
Table 2-1. Marine Fuel Types
Fuel Type Fuel Grade
Colloquial Industry Name
Distillate DMX, DMA, DMB, DMC
Intermediate RME/F-25, RMG/H-35
Gas Oil or Marine Gas Oil (MGO)
Marine Diesel Fuel or Intermediate Fuel Oil
(IFO180andIFO380)
Residual RMA- RMH, RMK, and RML Fuel Oil or Residual Fuel Oil
Source: Adapted from EPA, 1999.
Marine fuel characteristics depending on the refinery systems complexity (Spreutels and
Vermeire, 2001). Hydroskimming create marine fuels by blending straight run product streams,
while more advanced cracking refineries use produce similar products by blending outputs from
catcracker and visbreaker units. See section 2.2 for highlights these manufacturing
specifications.
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Distillates and/or residual fuel oil stocks are blended with blending components or cutter
stocks to achieve internationally-accepted product specifications provided by the 1987 (revised
in 1996) international standard, ISO 8217, that defines the requirements for fuel grades for use in
marine diesel engines. Marine fuel grades carry three letters, the first "D" or "R" specifies
"distillate fuel" vs. "residual fuel". The second "M" signifies "marine fuel" use. The third letter
designates the individual grade. Distillate marine (DM) fuels have three grades from A to C.
Residual marine (RM) fuels have fifteen grades depicted by letters A through H, K and L. For
example, RME -35 stands for "Residual Marine fuel E at a maximum viscosity (at 100 degrees
C) of 35 centistokes (EPA, 1999).
Marine Fuel Blending Stocks
As described in Section 2.2, "hydroskimming" type refineries produce straight run stocks
used in marine fuel blending, including light diesel, heavy diesel, and straight run residue. More
complex refineries derive similar blending stock components as the output from fluidized bed
catalytic cracking (FCC) units which includes light and heavy diesel, as well as light cycle gas
oil (LCO) and heavy cycle gas oil (HCO). HCO also comes from the residual output from
visbreaker units. These blending stocks are mixed with existing product streams from a refinery
to manufacture a variety of marine fuel grades.
Marine Gas Oil (MGO)
Marine gas oil is the result of blending LCO with distillate oil to produce one of the
highest marine fuel grades. MGO is more expensive because it is a lighter fraction and better
quality fuel that diesel fuel. This type of fuel is produced at cracking refineries after vacuum
distillate feedstock is put through a FCC catcracker. The catcracker produces FCC gasoline and
LCO. MGO is a fuel best suited for faster moving engines (Spreutels and Vermeire, 2001).
Marine Distillate Oil (MDO)
Straight run marine gas oil and distillate type marine distillate oil (MDO) are
manufactured by combining kerosene, light, and heavy gasoil fractions. DMA and DMB are
typically used in small to medium sized marine vessels. Distillate fuels or heavy (high and low
sulfur) distillates, and light fuel oil represent the more expensive range of marine fuels as they
are most closely related to diesel fuel used in other transportation sectors. DMC is heavier fuel
oil and may sometimes be referred to as an intermediate fuel oil because it can be blended with
residual fuel. MDO is manufactured by blending DMC with 10 to 15 percent residual fuel
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(Spreutels and Vermeire, 2001). MDO is a more expensive than the more common intermediate
fuel types.
Intermediate Fuel Oil (IFO)
Residual marine fuel grade G (RMG-35) is one of the more common residual fuels used
in transoceanic sea-going vessels. Also know as IFO380, this residual marine fuel is
manufactured at the refinery and contains visbroken residue, HCO, and LCO (Spreutels and
Vermeire, 2001). IFO380 typically has a high sulfur content of 5 percent. IFOs less than 380
such as IFO 180 represent a blend starting with IFO380 and blending it with a cutterstock of
marine diesel, gasoil, LCO, or some combination of the three. IFO 180 has a lower viscosity and
metals content, but maintains the same sulfur content as IFO380.
2.2 Refining of Petroleum Products (Including Marine Fuels)
The refining processes used to produce petroleum products, including marine fuels,
involve the physical, thermal and chemical separation of crude oil into its major distillation
fractions, followed by further processing (through a series of separation and conversion steps)
into finished petroleum products. EPA's sector notebook of the petroleum industry (EPA, 1995)
details the primary products of refineries grouped into three major categories: fuels (motor
gasoline, diesel and distillate fuel oil, liquefied petroleum gas, jet fuel, residual fuel oil, kerosene,
and coke); finished nonfuel products (solvents, lubricating oils, greases, petroleum wax,
petroleum jelly, asphalt, and coke); and chemical industry feedstocks (naphtha, ethane,
propane, butane, ethylene, propylene, butylenes, butadiene, benzene, toluene, and xylene). This
discussion focuses on the "fuels" product category, and specifically the distillate and residual
fuels that are blended to form marine fuels.
Refineries are complex operations and often have unique configurations based on the
properties of the crude oil to be refined (which varies significantly depending on the source) and
the desired distribution of refined products. The major unit operations outlined below represent
a generic set of operations found in refineries around the world. Figure 2-1 illustrates general
unit operations and product flows for a typical refinery. These refinery operations can be broken
down into four major stages: distillation, desulfurization, refining, and blending.
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Basic Refining Concepts
Atmospheric
Distillation
Tower
(Crude Unit)
Gases
Straight Run
Gasoline
Naphtha
Kerosene
Light Gas Oil
Gas Processing
Processed Gasoline
Further processed to gasoline
Heavy naphtha for jet fuel
Further processed to Jet Fuel,
Diesel and Fuel Oils
Further processed to Gasoline,
Diesel and Fuel Oil
Further processed to Gasoline,
Diesel and Fuel Oil
Further processed to Gasoline,
Diesel, Fuel Oil, and Lube Stocks
Figure 2-1. Basic Refining Process and Product Streams
Source: Adapted from Marcogliese, 2005.
Following an initial desalting process to remove corrosive salts and excess water, crude
oil is fed into an atmospheric distillation column that separates the feed into the subsequent
distillation fractions. The lightest of the fractions, which include light gasoline, ethane, propane,
and butane (also know as the top gases), are further processed through reforming and
isomerization to produce gasoline or may be diverted to lower-value uses such as liquefied
petroleum (LP) gas and petrochemical feedstocks. The middle-boiling fractions, which include
kerosene, gasoil, and spindle oil, make up most of the aviation fuel, diesel, and heating oil
produced from the crude feed. The remaining undistilled liquids (called "bottoms") represent the
heavier fractions that require vacuum distillation at very low pressures (0.2 to 0.7 psia) to
facilitate volatilization and separation. Vacuum distillates and residues can be further processed
through catalytic cracking and visbreaking into low-value products such as residual fuel oil,
asphalt, or petroleum coke.
The lower middle distillates may also require additional processing through additional
downstream processing. These fractions are treated using one of several techniques including:
"cracking/visbreaking," which breaks apart large hydrocarbon molecules into smaller ones; and
"combining" (e.g., alkylation, and isomerization), which joins smaller hydrocarbons to create
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larger more useful molecules, or reshaping them into higher value molecules. Additionally
catalytic "hydrocracking" is a downstream processing method used to crack fractions that can
not be cracked in typical cracking units. These fractions include middle distillates, cycle oils,
residual fuel oils, and reduced crudes. Typically, the feedstock to a hydrocracking unit is first
hydrotreated to eliminate any impurities (e.g., sulfur, nitrogen, oxygen, halides, and trace metals)
that could deactivate the catalyst.
Following the completion of downstream processing stages, several product streams are
blended by the refinery to produce finished products. Generally, these blending operations
include gasoline, middle distillate, and fuel oil blending.
2.2.1 Primary Refinery Inputs
Crude oil is the dominant input in the manufacture of refined petroleum products,
accounting for approximately 79 percent of total material costs of U.S. refineries, or $132 billion
in 2002, according to the latest Economic Census (U.S. Bureau of the Census, 2004). Table 2-2
provides a summary of these inputs. Similarly, crude accounts for over 92 percent of the volume
of refinery inputs in the United States. Crude oil is likely to have greater representative share of
both material costs and inputs in developing countries due fewer environmental regulatory
product specifications.
Table 2-2. Total U.S. Refinery Input of Crude Oil and Petroleum Products in 2004
Product
Crude Oil
Natural Gas Liquids
Other Liquids
Other Hydrocarbons/Oxygenates
Other Hydrocarbons-Hydrogen
Oxygenates
Fuel Ethanol
MTBE
All Other Oxygenates
Unfinished Oils (net)
Motor Gasoline Blending Components (net)
Aviation Gasoline Blending Components (net)
Total Input to US Refineries
Year 2004
(1,000s barrels)
5,663,861
154,356
316,838
150,674
28,039
122,635
74,095
47,600
940
186,826
-18,558
-2,104
6,135,055
% of Total
92.3%
2.5%
5.2%
2.5%
0.5%
2.0%
1.2%
0.8%
0.0%
3.0%
-0.3%
0.0%
100.0%
Source: EIA, 2005a.
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Crude Oil
Characteristics of crude oil - including relative density, sulfur, and acid content - have a
significant influence on the distribution of petroleum products a refinery is able to produce. The
cost of production also varies significantly depending on the type of crude oil used in the refining
process. Such characteristics tend to vary significantly based on the crude's regional origins.
Crude-oil density can be measured using the API gravity number, which provides a
measure of relative density. Crude oils are typically classified as light, medium, and heavy oils.
Light crude has the highest API number, equating to low density, which makes this crude type
the easiest to refine into gasoline products. Heavy crudes, with the lowest API number and
higher relative density, require additional processing to obtain the same distribution of refinery
products.
Sulfur content determines whether a specific type of crude is "sweet" (low sulfur) or
"sour" (high sulfur). Sweet crude is defined as crude oil with a sulfur content of less than 0.5
percent, and sour crude has sulfur content higher than 0.5 percent. Sweet crude is less corrosive
due to low levels of sulfur compounds such as hydrogen sulfide (H2S). Sour crude requires
additional equipment and processing to extract the higher amounts for sulfur.
Crude oils' relative density and sulfur content vary, depending on the region of the world
that it was extracted from. Light, sweet crude types typically have the highest prices due to
limited availability and high demand. Heavy, sour crude typically sells at a discount relative to
the light sweet crude due to its relative abundance, compared to light sweet, and its high sulfur
content. Light sweet crude includes WTI (West Texas Intermediate) found in the western
hemisphere, and Brent (North Sea Crude) found in Europe. Heavy sour crude includes Arabian
Heavy (Middle East) and Maya (Mexico). Figure 2-2 illustrates the spectrum of crude qualities.
Density is plotted along the horizontal and sulfur content along the vertical axis.
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3.5 -
3 -
_. Z5 "
II 2
|;
|g 1.5
w w.
"" 1 -
0.5 -
0 -
Maya
i Arabian Heavy
• Arabian Medium
_ Mars Blend
AFateh
A OPEC Basket
Urals
Arabian Light
Iran Light
Alaska North Slope
(ANS)
Cabinda •
Brent BlendA
AWTI
• Bonny Light
Tapis Blend
20
25
30 35
API Gravity
(Heavy => Light)
40
45
50
Figure 2-2. Quality by Crude Type
Source: Adapted from Marcogliese, 2005.
Note: A = Benchmark Crude Types
In Figure 2-2, crude types near the lower right-hand corner of the figure represent the
crude types that require the least amount of processing. As you move towards the top left-hand
corner of the figure, the crude is more difficult to process. The majority of the world's supply of
crude oil is light to medium sour, which is trending towards heavier and more sour crude as
reserves of light sweet crude are depleted (Marcogliese, 2005).
WTI, Brent, and Dubai Fateh are the most commonly used benchmarks. These
benchmark crude types are used in international trading, and varying qualities of crude are sold
at a discount or premium relative to the benchmark price. OPEC has its own reference known as
the OPEC Basket, which consists of 11 crude types and represents the weighted average of
density and sulfur content for all the member countries' crude types, according to production
levels and export volumes. Table 2-3 lists these 11 crudes:
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Table 2-3. Crude Oil Types Included in the OPEC Basket
Type of Crude Country of Origin
Saharan Blend Algeria
Minas Indonesia
Iran Heavy Islamic Republic of Iran
Basra Light Iraq
Kuwait Export Kuwait
Es Sider Libya
Bonny Light Nigeria
Qatar Marine Qatar
Arab Light Saudi Arabia
Murban UAE
BCF 17 Venezuela
Source: EIA, 2005b.
Blending Stocks and Additives
Following initial atmospheric distillation of crude oil, a variety of specialized inputs may
be added to output product streams (see Figure 2-1) in downstream units to enhance the
refinery's ability to recover a desired mix of products. Among these products might be
unfinished oil, residual fuel oil used as input to a vacuum distillation unit (see Table 2-2 for a list
of additives). Motor gasoline and aviation fuels require blending components that include
oxygenates as well as other hydrocarbons. While they are counted as "refinery inputs," they are
brought to saleable specifications in terminals and blending facilities, not in conventional
refineries.
2.2.2 Refinery Production Models
Across the globe, refineries are typically concentrated near major consumption areas,
based on the principal that transporting crude oil is cheaper than transporting refined products.
In addition, proximity to consumption areas allows refineries to more quickly respond to
seasonal or weather-related demand shifts (Trench, 2005). Their goal is to meet the regional
demand for petroleum products, hence maximizing the value of product mix produced. For
example, in the United States, as well as other developed countries, refineries strive to maximize
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gasoline and low-sulfur diesel fuels, while simultaneously minimizing output of lower value
heavy oils such as residual fuel and petroleum coke.
Building on the basic refinery concepts presented in Figure 2-1, refineries can be grouped
into four basic configurations: topping, hydroskimming, cracking {medium conversion), and
coking (high conversion). Each configuration builds on the previous production model by
adding on additional downstream processing equipment that allows the refinery to further expand
its yield of the desired mix of petroleum products.
Topping Refineries
Topping refineries are the simplest example of a refinery production model. Their
primary function is to produce feedstocks for petrochemical manufacturing. Topping refineries
typically consist of storage tanks, an atmospheric distillation unit, and recovery facilities for top
gases and light hydrocarbons such as ethane/propane/butane. These facilities produce naphtha,
but do not produce gasoline (Reliance, 2005).
Hydroskimming Refineries
Building on the basic topping configuration, hydroskimming refineries incorporate
hydrotreating (distillate desulfurizer) and catalytic-reforming units to improve the output of high
value fuels such as distillates and straight-run gasoline. Table 2-4 lists the typical mix of product
yields from hydroskimming refineries.
Table 2-4. Typical Production Yield from a Hydroskimming Refinery
Product
Propane/butane
Gasoline
Distillate
Heavy fuel oil & other
Total Yield
% Yield
4%
30%
34%
32%
100%
Source: Marcogliese, 2005.
Note: Gasoline includes reformulated gasoline (RFG), conventional, CARB, and Premium. Distillate includes jet fuel, diesel,
and heating oil.
These facilities typically rely primarily on light sweet crude as their primary input in
order to minimize the resulting heavy fuel and residual fuel products because they have limited
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upgrading capabilities of distilled fractions. Hydrotreating removes impurities such as sulfur,
nitrogen, oxygen, halides and trace metals. Hydrotreating also upgrades the quality of these
fractions by converting olefms and diolefms to paraffins to reduce gum formation in fuels (EPA,
1995). Catalytic reforming units process straight-run low-octane gasoline and naphthas into
high-octane aromatics through four reactions that to create aromatics by removing hydrogen
from the feedstock (see EPA [1995] for details of these reactions).
Cracking Refineries
Cracking refineries build in complexity from the hydroskimming configuration by adding
vacuum distillation, catalytic cracking, and alkylation units. The vacuum distillation unit further
fractionates heavy "bottoms" from the atmospheric distillation process into gas oil and residual
fuel. Table 2-5 lists the typical mix of product yields from cracking refineries. The total yield of
104% represents a volumetric gain due to the cat cracker's ability to convert large hydrocarbon
molecules into multiple smaller molecules. These facilities typically rely on light sour crude as
the primary input. Moderate upgrading capabilities allow cracking refineries to increase the
yield of higher value products as well as gain volumetric output per volume of crude oil input
(Marcogliese, 2005).
Table 2-5. Typical Production Yield from a Cracking Refinery
Product
Propane/butane
Gasoline
Distillate
Heavy fuel oil & other
Total Yield
% Yield
8%
45%
27%
26%
104%
Source: Marcogliese, 2005.
Note: Gasoline includes reformulated gasoline (RFG), conventional, CARB, and Premium. Distillate includes jet fuel, diesel,
and heating oil.
The catalytic cracking unit (i.e., fluidized and moving-bed) uses heat, pressure, and
catalysts to breakdown heavy complex hydrocarbon molecules (i.e., gas oil) into smaller/lighter
molecules such as Light Cycle Oil (LCO). LCO is then processed with other distillates in the
hydrotreating process to remove to produce diesel and heating oils. Once the LCO and FCC
Gasoline are removed, an alkylation unit converts the remaining iosbutane feedstock into
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alkylates (i.e., propane/butane liquids), which are widely-used blending additives in high octane
gasoline production.
Coking Refineries
Coking refineries extend the cracking refinery by adding hydrogen processing,
hydrocracker, and delayed coking units to increase their capabilities to convert fuel oil into
distillates (Reliance, 2005). Coking refineries are able to use medium to heavy sour crude as the
primary input to the refining process. These refineries also have the highest light product yields
and volume gains, compared to other refinery configurations (Marcogliese, 2005).
Table 2-6. Typical Production Yield from Coking Refineries
Product
Propane/butane
Gasoline
Distillate
Heavy fuel oil & other
Total Yield
% Yield
7%
58%
28%
15%
108%
Source: Marcogliese, 2005.
Note: Gasoline includes reformulated gasoline (RFG), conventional, CARB, and Premium. Distillate includes jet fuel, diesel,
and heating oil.
The hydrogen facility produces hydrogen that is used as a feedstock in the hydrocracker
as well as the hydrotreater units. The hydrocracker units apply hydrogen and significant pressure
in a fixed-bed catalytic cracking reactor. Feedstocks for this unit include low distillate fractions,
as well as LCO, residual fuel oils. The hydrogen mitigates the formation of residual fuels and
increases the yield in middle distillate fuels such as diesel and jet fuels (EPA, 1995). Delayed
coking is a thermal cracking process that upgrades and converts petroleum residuum (heavy fuel
oil) into liquid and gas product streams. The delayed coker unit eliminates residual fuel oil
leaving behind a solid concentrated carbon material know as petroleum coke (Ellis and Paul,
1998).
2.2.3 Refineries Arou ndthe World
There are major concentrations of refineries around the world, representing 674
individual installations and 82.4 million barrels per day of crude oil refining capacity at the end
of 2004 (OGJ, 2004). The number of operable refineries had fallen by 43 from 717 in 2003,
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which represented a decline of 6.4 percent. Over the last five years, the number of refineries
worldwide has declined, while the total crude capacity has continued to rise (Nakamura, 2004).
Table 2-7 summarizes the number, estimated crude capacity, and fuel "processing"
capacity for refineries in seven world regions at the end of 2004. Historically, the mature
markets of the United States and Europe have contained the largest number of refineries.
However, recent dramatic growth in Asian markets has resulted in increased number of refineries
in South Korea, along with other South Pacific countries.
Table 2-7. Refinery Presence by World Region in 2004
Refinery Crude Capacity F«els Processing Processing
Region <-„,,„/ Capacity Capacity as
Africa
Asia & Oceania
Central & South America
Eastern Europe & Former U. S. S.R.
Middle East
North America
Western Europe
World Total
46
161
66
86
45
159
111
674
(barrels\calendar day) '*•
3,230,362
20,695,031
6,572,359
9,764,712
6,471,615
20,476,228
15,198,594
82,408,901
506,470
2,052,728
529,190
1,467,693
691,730
5,598,388
2,480,458
13,326,657
• of Crude
2.4%
10.0%
3.5%
15.0%
10.5%
86.5%
76.8%
16.2%
Source: OGJ, 2004.
a. Processing capabilities are defined as conversion capacity (catalytic cracking, and hydrocracking) and fuels producing processes (catalytic
reforming and alkylation) divided by crude distillation capacity (% on crude) this measure represents the presence of downstream processing
technology that improves the refinery's ability to produce high value refined products such as high octane gasoline.
The concentrations of refineries in Asia, North America, and Western Europe represent
approximately 68 percent of total refinery capacity. North American and Western European
refineries have invested heavily in processing units that will maximize their output of gasoline
and other high value outputs. This is illustrated by their processing capabilities as a percent of
crude capacity. In other regions of the world, refineries rely on atmospheric distillation to obtain
straight-run product streams. As a result, residual fuel oil tends to be a greater share of total
refinery output in these regions.
Refineries typically address regional fuel demands, while maintaining only a minimal
stock of additional output for international trade and unexpected supply shocks due to weather.
They are constrained by local demand, as well as the crude types that are proximal to the facility.
Table 2-8 lists the 25 largest refinery companies of the world by total crude capacity. These
firms represent 60 percent of the world's crude refining capacity. The refinery companies on this
list have focused on expanding capacity and reducing the total number of operable refineries
over the last ten years (Nakamura, 2004).
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Table 2-8. World Largest Refinery Companies by Capacity in 2004
_ , „ Crude Capacity
Rank Company (1,000s b/cd)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Source:
ExxonMobil Corp.
Royal Dutch/Shell
BPPLC
Sinopec
Petroles de Venezuela SA
Total SA
ConocoPhillips
ChevronTexaco Corp.
Saudi Aramco
Petroleo Brasileiro
Valero Energy Corp.
Petroleos Mexicanos
China National Petroleum Corp.
National Iranian Oil Corp.
Nippon Oil Co. Ltd.
OAO Lukoil
Respsol YPF SA
Kuwait National Petroleum Co.
OAO Yukos
Pertamina
Marathon Ashland Petroleum LLC
Agip Petroli SpA
Sunoco Inc.
SK Corp.
Indian Oil Corp. Ltd.
Nakamura, 2004.
5,693
4,934
3,867
2,793
2,641
2,622
2,615
2,063
2,061
1,965
1,930
1,851
1,782
1,474
1,157
1,150
1,106
1,085
1,048
993
935
906
880
817
777
Many of the largest refinery companies have been investing heavily to supply Asian
markets due to anticipated long-term growth in the region, which growing at approximately four
percent, compared to the more mature markets of Europe and Japan that are expected to grow at
less than one half of one percent (Mergent, 2005). This high growth in Asia can largely be
attributed to expected growth in the transportation sector, including both freight shipping and
personal vehicles.
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As discussed, refinery products are diverse in character and functionality, and the specific
mix of products will vary dramatically depending on the refinery's configuration and type of
crude used. Table 2-9 summaries how these effects alter production of different refinery
products varies across regions of world in 2003.
Table 2-9. World Refinery Product Outputs of World Refineries per Day for 2003
Region
Africa
Asia & Oceania
Central & South America
Eastern Europe & FSU
Middle East
North America
Western Europe
World Total
Motor
Gasoline
0.5
3.8
1.3
1.0
0.9
9.7
3.7
20.8
Distillate
Fuel Oil
0.7
6.0
1.7
1.5
1.8
4.6
5.7
22.1
Residual
Fuel Oil
(Million Barrels
0.7
2.9
1.1
1.5
1.7
1.2
2.2
11.3
Other
per Day)
0.8
7.1
1.9
1.5
2.1
5.8
4.7
23.9
Total Refinery
Products
2.7
19.8
5.9
5.6
6.4
21.4
16.3
78.1
Source: EIA, 2005d.
Motor gasoline is the highest-value product in the refinery output mix, hence facilities
typically engineer their unit operations to maximize its production. In North America, motor
gasoline is typically the largest share of refined products - representing 45 percent of refinery
output per day - while distillate and residual fuel accounted for 22 and 6 percent, respectively, in
North America's refineries output. However, in all other major regions of the world, motor
gasoline represented around 20 percent of total refinery output on average. Figure 2-3 illustrates
these regional differences in the distribution of motor gasoline, diesel, and residual fuel
production for seven world regions.
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12
10
n Motor Gasoline 0 Distillate Fuel Oil H Residual Fuel Oil
9
a.
1
1
1
North America Asia & Oceania Western Europe Middle East Central & South Eastern Europe & Africa
America FSU
World Region
Figure 2-3. Product Outputs of World Refineries per Day in 2003
Source: EIA, 2005d.
Distillate fuel represents the largest share of refinery outputs for all regions outside of
North America, on average accounting for 31 percent of total refinery products in 2002.
Residual fuel oil accounted for an additional 18 percent, on average. Other products such as
petroleum feedstocks, jet fuels, and LPG gas accounted for 18, 5, and 5 percent respectively.
The demands for gasoline in mature markets (e.g., United States, Europe, and Japan), and
resulting refinery configurations, have resulted in dramatic reductions in production of residual
and distillates. North American refinery executives agree that relative market prices for refined
motor gasoline make it a more attractive refinery output than low-sulfur residual fuels
(BunkerWorld, 2005). Despite the potential of hydroprocessing to treat high sulfur residual
fuels, the technology is not yet cost effective for refiners.
For these reasons, bunker fuels may witness shortages as refineries continue to keep pace
with demands for motor gasoline and other high value refined products in the North America and
Western Europe, where motor gasoline prices are equally high relative to other refined products
(these trends are included in the WORLD model and discussed in Sections 4 and 5). Industry
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experts have estimated that the North America could witness a shortage of low-sulfur residual
fuel of 20 million metric tons per year by 2015 and a surplus of high sulfur residual oil of 40
million metric tons per year (BunkerWorld, 2005). To address these shortages, the industry
expects an increase in low-sulfur residual fuel oil imported from South America or other areas of
the world with low conversion capacity (and thus high residual fuel output).
In developing regions such as the African, the Middle Eastern, and Asian markets,
availability of sweet crude supplies, coupled with limited conversion capacity in existing
regional refineries, will result in continued production of residual fuels. Over time, as sweet
crude becomes increasingly scarce and the sulfur content of crude feedstocks increases,
refineries in these regions will be forced to upgrade their conversion capacity by adding
additional downstream processing to existing facilities or the share of heavy distillates and
residual fuel oils of their total refinery outputs will increase.
Finally, as China's market for fuel demand increases, Chinese oil companies are
beginning to compete with U.S. and European companies for depleting supplies of the world's
crude oil. The Energy Information Administration (EIA) predicts that China will begin to invest
in petroleum products in countries around the world, including Canada and South America,
which have traditionally represented over 25 percent of the United States' energy imports. China
signed its first oil deal with Venezuela in 2004, marking the beginning of a battle for resources
with more mature markets such as the United States. If China continues to increase its presence
in the West through acquiring petroleum resources that traditionally supplied residual fuel-oil
demands in North America, any shortages in residual fuel-oil could increase exponentially
(Mergent, 2005).
2.3 Bunker Fuel Suppliers
The supply chain providing marine fuels to the shipping industry is a complex network of
organizational and trade relationships and is quite geographically dispersed. Aside from
integrated petroleum refiners such as the operations discussed in Section 2.2, the industry's
supply chain includes traders, suppliers, brokers, bunkering-service providers or facility
operators, and bunkering ports. The information available on different segments of the bunker-
fuel supply chain varies dramatically, and hence this section not comprehensive, but rather
intended to provide an overview of the industry focusing on four of the largest bunkering ports
(Singapore, Rotterdam, Fujairah and Houston).
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Around the world, there are approximately 400 major bunkering ports. Logistics and
transport cost factors influence the location of these bunker ports as well as local environmental
regulations. In addition to being located close to supply sources (petroleum refineries) and
consumers of transported goods (major population centers), bunkering ports are often
strategically located along high-density shipping lanes. For example, Singapore handles more
than twice the bunker-fuel volume of Rotterdam, the next largest port. Panama and Gibraltar are
examples of strategically located facilities. In North America, the largest facilities follow the
general pattern suggested by location theory - with Los Angeles, San Francisco, New York,
Philadelphia, Houston, and New Orleans close to both refinery supply and transport destinations.
2.3.1 Singapore
Singapore's strategic location, in regards to the Straight of Malacca, makes it the largest
port in the world in terms of cargo throughput and bunker-fuel sales. The total cargo throughput
in 2005 equaled 423 million tons. The port of Singapore handles large volumes of oil1 and dry
bulk cargo. In 2005, Singapore surpassed Hong Kong by almost 1 million twenty-foot equivalent
units (TEUs) and claimed the lead in handling containerized cargo (Sina, 2006). Its tonnage of
containerized, oil, and dry-bulk cargo has been steadily increasing over the past five years.
Although the number of vessel calls has been slowly declining, Singapore still handles more
vessel calls than any other port in the world - almost 173,000 vessel calls in 2005. (MPAS,
2005a).
The port of Singapore is also the largest bunker fuel market in the world. Bunker
turnover was reported at 25.48 mmt (million metric tons) in 2005 (MPAS, 2006b). Turnover at
the port grew at the average rate of 5.6 percent over the past six years, equaling 20.8 mmt in
2003 and 23.6 mmt in 2004. Heavy fuel-oil sales accounted for 71 percent of total bunker sales
by volume in 2004, with lighter fuel and distillate oils accounting for 19 percent and others
(including lube oils) for remaining 2 percent. (MPAS, 2005c). The majority of bunker deliveries
to vessels in the port of Singapore are made by bunker tankers, however, other types of deliveries
are available as well.
Refineries
Singapore is the one of the top three refining centers in the world, accompanied by
Houston and Rotterdam. Petroleum refining accounted for approximately 16.5 percent of
Singapore's Gross Domestic Product (GDP) in 2004. Singapore's refineries have major
influence on Asian markets: their petroleum product exports were valued at $17.5 billion in
1 Including chemical and gas
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2004.2 Singapore also exported $4.7 billion worth of bunker fuels, which equalled 2.6 percent of
national GDP (SMTI, 2005).
Operating at 92 percent capacity, the top three refineries in Singapore have a combined
production of around 1.3 million bpd (EIA, 2005f). Out of that quantity, bunker fuels consumed
in the Singapore shipping market comprise approximately 400,000 bpd. Another 400,000 bpd
are consumed locally for various purposes, and the remainder (mostly gasoline and diesel fuels)
are exported to Vietnam, China, and Indonesia (Reuters, 2006).
Refineries producing bunker fuel that is sold in the local market are:
• Jurong Island Refinery, owned by ExxonMobil
o Capacity of 605,000 bpd
• Pulau Bukom Island Refinery, owned by Royal Dutch/Shell
o Capacity of 458,000 bpd
• SRC Jurong Island Refinery, partially owned by Singapore Refining
Corporation (SRC), partially owned by ChevronTexaco through its subsidiary
Caltex
o Primary plant - a joint venture between SPC and Caltex (ChevronTexaco)
with 285,000 bpd capacity
o Owns a bunker storage terminal on the Pulau Sebarok Island, with storage
capacity of 1.4 million barrels
These three refineries have a combined storage capacity of 88 million barrels, and the
demands for storage have been increasing. Singapore's three largest independent storage
operators, Vopak, Oiltanking, and Tankstore, have been utilizing 90 percent of their combined
total capacity of 22.3 million barrels in the past five years. Production plans are underway that,
when complete, will almost triple the storage capacity of local operators (EIA, 2005f).
Even though refining has a strong presence in Singapore, imports of refined petroleum
products equalled $12.6 billion (11.4 percent of national GDP) (SMTI, 2005). Consumption of
imported oil products reached 750,000 bpd in 2004 (EIA, 2005f). The Singapore bunker fuel
market is very diverse - fuel from all major refineries around the world gets delivered to the port.
" Numbers are reported in US dollars
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Even though no numerical data are readily available, based on qualitative assessments, majority
of these world imports come from Venezuela, Chile, and Russia (Bunkerworld, 2005d).
Bunker Traders
There are 233 companies that serve as traders in the Singapore shipping market. Among
them are smaller local companies such as Bunker House Petroleum, as well as larger
international oil companies such as Lukoil and OW Bunker. Among the leaders are OW Bunker
and Hin Leong, the latter of which recently scheduled construction of the largest petroleum
terminal in the area with total storage capacity of 14.5 million barrels.
Bunker Suppliers
Thirty-four companies serve as bunker suppliers, with an additional 18 that perform
functions of suppliers and traders. Three refinery operators are also among top four suppliers
(British Petroleum, Shell, and ExxonMobil). They are joined by Global Energy Trading, a
smaller company that owns and operates 14 vessels at the port. Other major suppliers include
Consort Bunkers, Singapore Petroleum Company, Chevron Singapore, OW Bunker, and
Chemoil (SMP, 2006).
Barge Operators
The number of independent barge operators is also large: there are 32 companies
performing this function in the port of Singapore. The bunker barge fleet contained
approximately 120 vessels of various sizes in 2005 (Bunkerworld, 2005e). The largest among the
barge operators is Ocean Tankers, a sister company of Hin Leong, which owns and operates 70
bunker barges.
2.3.2 Rotterdam
Rotterdam is the second largest port in the world with throughput of more than 369
million tonnes of cargo in 2005 (Port Authority of Rotterdam, 2005). Some 30,000 seagoing
vessels call at the port every year and 110-120 thousand inland vessels. Activities related to the
port contribute around 12 percent of the Gross National Product of the Netherlands
(Bunkerworld, 2000). Overall, the port of Rotterdam has experienced a 5 percent increase in
3 Consort Bunkers Pte Ltd, Searights Maritime Services Pte ltd, Bunker House Petroleum Pte Ltd, Northwest
Resources Pte Ltd, Golden Island Diesel Oil Trading Pte Ltd, Lukoil Asia Pacific Pte Ltd, Alliance Oil Trading
Pte Ltd, Costank (S) Pte Ltd, Sentek Marine & Trading Pte Ltd, Lian Hoe Leong & Brothers Pte Ltd, Standard
Oil & Marine Services Pte Ltd, Panoil Petroleum Pte Ltd, Ocean Bunkering Services Pte Ltd (owned by Hin
Leong Marine International Pte Ltd), O. W. Bunker Far East Pte Ltd, The Barrel Oil Pte Ltd, Fratelli Cosulich
Bunkers (S) Pte Ltd, Prestige Marine Services Pte Ltd, Gas Trade (S) Pte Ltd, Wired Bunkering Pte Ltd, Cockett
Marine Oil (Asia) Pte Ltd, Ignition Point Pte Ltd, Prospeibiz Petroleum (S) Pte Ltd
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cargo handling with the majority of growth coming from container cargo, which had a 12 percent
increase to 9.3 million TEUs between 2004 and 2005. General cargo was up 7 percent, or 7
million tonnes, to a total of 110 million tonnes in 2005.
Rotterdam is also the largest bunker port in Europe. Bunker turnover in 2004 for the port
was 12.5 million cubic meters (m ). In 2002 and 2003, bunker turnover was 10.6 and 11.4
million m , respectively (Port Authority of Rotterdam, 2004a). These volumes include heavy
fuel oil, light gas oil, distillate oil, and lube oils (heavy fuel oil represents the majority of overall
bunker turnover). Russian oil imports represent a significant share of total refined oil product
supply. Between 2002 and 2003, Russian imports of crude and refined oil products grew by 17
percent (Port Authority of Rotterdam, 2004b).
Refineries
The Port of Rotterdam has a significant petroleum refinery presence (in 2004, oil
refineries represented 6.5 percent of the 58,000 workers directly employed by the Port).
However, due to environmental regulations and European fuel market conditions, refineries in
the region around Rotterdam are producing much less heavy fuel oil (3-3.5% sulphur), which
typically dominates bunker markets. Consequently, the local refinery output can no longer cover
the Rotterdam bunker demand.
This shortage has led to increased reliance on fuel oil from import sources. Fuel oil
imports are estimated to be 300 to 400 thousand metric tonnes per day. As mentioned earlier,
Russian fuel oil products typically dominate the market. Venezuelan fuel oils are also a common
import in the Rotterdam bunker market.
The local refineries that still produce bunkers sold in the Rotterdam market include:
• The Pernis Refinery , owned by Royal Dutch/Shell;
o Capacity approximately 416,000 b/d.
• NEREFCO (Netherlands Refining Co.), owned by BP (69%) and Texaco (31%).
o Capacity in excess of 3 80,000 b/d.
• Q-8 refinery, owned by Kuwait Petroleum Corporation.
o Capacity about 75,500 b/d.
• The Esso Refinery (ExxonMobil) does not produce fuel oil, but the company sources
from a plant in Antwerp, Belgium with capacity of 225,000 b/d.
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Bunker Traders
Bunker traders secure bunker volumes for their shipping clients in local supply markets
or in their own refined-products distribution channels. Traders include both major oil companies
as well as independents. Both types perform the functional service of in the timely procurement
of bunker fuel orders. Traders act as midway between local customers and refinery suppliers,
where the majority of transactions occur under long term contracts.
Traders in the Rotterdam market include oil majors, such as Shell Marine Products, and
Lukoil. Shell Marine Products utilizes the majority of its' Pernis refinery's marine fuel output
for its own clients (Bunkerworld, 2000), while the majority of NEREFCO's output is purchased
by independent traders in the local fuel-barge market.
Independents typically purchase their bunker fuel on the local barge market. In addition,
it is common for traders to import cargos of bunker fuel and store the fuel in rented storage tanks
in the petroleum zones of the port. Vitol, Allround Fuel Trading/Chemoil, and the oil majors,
especially Texaco, BP and Elf (TotalFinaElf), are the largest bunker traders of import oil product
cargos (Bunkerworld, 2000).
Bunker Suppliers
Physical supplying of bunker fuel to ships is conducted by barge in the bunkering
designated zones. Europort and Botlek areas are two primary bunkering areas within the port of
Rotterdam. In 2000, over 90 percent of the bunkers in Rotterdam were delivered by barge
(Bunkerworld, 2000).
Barges are loaded at various fuel-terminal facilities owned by Vopak and the oil majors.
Most suppliers, including the oil majors, do not own or operate their own barges. Most majors
and some independents have specially dedicated barges or barges on exclusive time charter.
Among many independents, it is common practice to pool barge transportation services
(Bunkerworld, 2000).
Due to the nature of physically supplying bunkers, large storage capacity is needed to
enable flexibility in the suppliers' ability to respond to sudden fluctuations in bunker demand.
The most recent example of traders enlarging storage capacity is the partnership of Lukoil and
Fuel Transport Services (FTS)/Hofftrans (a local barge operator) partnering to build a bulk
terminal named the Service Terminal Rotterdam (STR). STR is designed for better bunkering
and ship-ship transhipment. This expansion is estimated to increase total storage capacity to
120,000 m3. Another expansion is currently under way by the Vitol Group, which is
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3
constructing a 278,000 m storage tank terminal in the Europort area. The Vitol facility is
expected to begin operations in 2006 and will provide jetties capable of accommodating vessels
ranging between bunker barges and very large crude oil carriers (VLCCs).
Barge Operators
The biggest barge operator is VT/Unilloyd, which works exclusively in transportation
and owns more than 20 barges. FTS/Hoftrans has around 10 barges of up to 2000 mt capacity. A
group of companies, which includes the suppliers Atlantic/Postoils, operate their own fleet of 21
barges ranging from 300-3,900 mt capacity. These barges also deliver on behalf of other
suppliers (Bunkerworld, 2000).
Additionally, some suppliers own their own fleet of barges. One example is Argos
Bunkers BV, which has its own fleet of six barges ranging from 200 to 1,400 mt capacity, plus
the company charters three more barges ranging from 700-2,000 mt. Ceetrans/Ceebunker
Services BV is owned by Argos and has access to the same barges. Frisol Bunkering BV has
three time-chartered barges totalling 4,270 mtin capacity. NIOC (Netherlands Independent Oil
Co.) has access to the 23 strong barge fleet of its Belgian parent company, Wiljo Bunkering NV
(Bunkerworld, 2000).
2.3.3 Fujairah
Fujairah is the third largest bunkering port in the world, supplying over 12 million mt of
bunker fuel annually (Gulf News, 2006). The Fujairah bunker market is comprised of three port
areas, which include the United Arab Emirates (UAE) ports of Khor Fakkan, Fujairah and Kalba.
Fujairah is situated in the middle of these three ports, with Khor Fakkan to the north. The three
ports and their offshore counterpart in the Gulf of Oman, constitute "the Fujairah bunker market"
- although there are some local differences, unless otherwise stated, "Fujairah" is seen as
incorporating the entire area (Bunkerworld, 2002). Fujairah is located in the outer Gulf, just
outside the Straits of Hormuz, which are the gateway to the Arabian Gulf (the inner Gulf).
Because of their proximity to Middle Eastern oil production, Fujairah's bunker customers are
predominately VLCCs, which are often anchored in the Gulf of Oman waiting for cargo in the
inner Gulf.
While official data regarding the turnover of bunker fuel in the Fujairah market are not
available, industry experts have estimated the annual volume to be over 12 million metric tons
(mt) in 2002, with an average monthly supply volume of bunkers of around 1 million mt.
Because tankers are the major customers in the Fujairah market, large bunkers rather than
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numerous small deliveries are the norm. The average supply volume varies between 2,000 mt to
15,000 mt (Bunkerworld, 2002). Assuming an average volume per vessel, this implies that
approximately 120,000 bunkering transactions take place in the Fujairah market each year.
Several estimates exist regarding the market share of each bunker fuel grade. IFO 380 is
estimated to account for between 80-95 percent of total bunkers supplied. The remaining 5-20
percent are split between IFO 180 and MGO, but exact shares are not available. Typically,
Fujairah is host to the most competitive pricing of bunker fuel in the Arabian Gulf. However,
the price differences between IFO 380 and 180 cst grades in Fujairah are typically higher than
those found in Singapore or Rotterdam (Bunkerworld, 2002). The significant price difference
between IFO 380 and 180 is due to a lack of cheap cutter stock typically used in blending to
create lighter fuel grades in the Arabian Gulf. As a result, Fujairah's bunker suppliers are forced
to use MGO in blending activities. This, more expensive, alternative makes purchasing lighter
grades of residual fuel such as IFO 180 less attractive in the Fujairah market (Bunkerworld,
2002).
Refineries
Fujairah itself has only one refinery facility- the Fujairah Refinery Company (FRC)
(Nakamura, 2005). The FRC plays a vital role in supplying straight-run fuel oil to the Fujairah
bunker market and has been attributed as what enabled the port to emerge as a leader in the
region. Metro Oil Corporation ran the facility until the late 1990s when it was shutdown. The
F AL Energy Company took over the facility in 2004 to utilize its 460,000 m3 of storage capacity
(Nakamura, 2005). The Fujairah government in 2005 announced a desire to revitalize the facility
and update processing technologies. Currently, the FRC refinery does not contribute a huge
amount of bunkers to the local market.
The Abu Dhabi National Oil Company (ADNOC) operates two refineries in the UAE,
including the Umm Al Nar and Ruwais refineries. The two refineries produce over 23 million
mt of products annually, which are sold to both international and local markets (Bunkerworld,
2002). The Umm Al Nar refinery processes 150,000 bpd of crude oil, and the Ruwais refinery
has two units with a total design capacity of 350,000 bpd. The Emirates National Oil Company
Limited (ENOC) operates the 120,000 bpd Jebel Ali plant (Nakamura, 2005).
Other refineries located near Fujairah cover 14 major refineries and include: Bahrain
National Oil Company's refinery, Aramco's five Saudi refineries, the National Iranian Oil
Company's (NIOC) six refineries in Iran, and from Kuwait Petroleum Corporation's (KPC) three
Kuwaiti plants (Nakarmura, 2005; Bunkerworld, 2002).
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Bunker Traders
Through contracts with local suppliers, bunker traders arrange supply deliveries in the
Fujairah bunker market. These firms provide services that ensure that bunker supplies are
available and delivered in timely fashion. The Fujairah bunker market is presently serviced by
approximately 11 trading companies that include FAL Energy Company, GAC Bunkers Co., and
FAMM Middle East Ltd.
Bunker Suppliers
The offshore terminals in Fujairah make it an ideal bunkering stop-off for both inbound
and outbound tankers leaving the Gulf (Bunkerworld, 2002). Typical bunkering entails bunker
barges loading from storage tankers and supplying bunkers to passing vessel traffic that is
moving through the Hormuz strait between the Arabian Gulf and the Gulf of Oman.
Most suppliers import their products and then store bunkers in large tankers that reside in
the Gulf or in shore-based fuel terminals. The majority of companies purchase product from
refineries in the UAE or other regional refineries. The port of Fujairah is serviced by 20
suppliers, representing a mix of local business as well as international bunker suppliers such as
German based Bominflot, BP Marine Middle East located in Dubai, UAE.
EPPCO International, a joint venture between ENOC and Caltex, owns and operates
some of the largest refined-petroleum terminalling facilities in the UAE. The terminals are
spread between Jebel Ali and Fujairah, and represent 6.44 million barrels in storage capacity. In
2002, Vopak ENOC Fujairah Terminal Company had 30 tanks (10 tanks designed to handle fuel
oil) with a total capacity of Imillion m3 storing fuel oil, gasoil, gasoline, naphtha, and jet
kerosene. The Vopak terminal offers products to the local market via three berths capable of
accommodating vessels up to 175,000dwt (Bluewater, 2002). Additional capacities are designed
to serve the active fuel-oil market offshore, whether for cargo trading or for bunkering purposes.
Other examples of suppliers in the Fujairah market include FAL and EPPOC. The
longest established bunker company in the UAE is FAL Energy Company, which leases storage
capacity at the Fujairah Refinery (FRC) and has 24 tanks with a combined capacity of 422,000
cubic meters storing fuel oil, gasoil, naphtha, and jet kerosene. Finally, the Emirates Petroleum
Products Co. (Eppco), a subsidiary of ENOC, expanded its existing storage capacity from
100,000 m3 to over 150,000 m3 in 2003. These investments in supplier infrastructure indicate the
growing importance of this bunker market.
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Barge Operators
The Fujairah market is largely served through off-shore deliveries by barge. For this
reason, many suppliers operate their own barge fleet in the Gulf of Oman. In addition, there are
eight independent barge operators offering service. The FAL Energy Company has a number of
bunkering vessels operating in both the Arabian Gulf and the Gulf of Oman. Larger international
suppliers such as ExxonMobile's Marine Fuels (EMMF) Company often contract with
independent barge operators in the Fujairah market, following detailed certification by EMMF
(EMMF, 2006).
2.3.4 Houston
The Port of Houston ranks second in U.S. foreign waterborne commerce and total
tonnage. In 2004, 6,539 ships called at Houston (traffic is dominated by container ships, tankers
and bulk carriers). Houston is a mix of private and public terminals. The areas controlled by the
Port of Houston Authority can be divided into four main areas:
• The City Dock, also called the Turning Basin
• Barbours Cut Terminal, the main terminal for containers ( 940,000 TEU's in 1996)
• Jacintoport Terminal, a general cargo handling port
• Woodhouse Terminal, for ro-ro cargo vessels
Development of a new container terminal is now at the design stage at the Port. It is
intended to alleviate pressure at the Barbours Cut Terminal, which was forecast to pass one
million TEUs by 1998.
Refineries
Surrounding the port of Houston, local refineries include (among others) ExxonMobile's
Baytown Refinery, BP's Texas City Refinery, Marathon Ashland's Texas City and the Valero
Refinery. While these refineries represent a significant share of the U.S. capacity in refined
products, they do not produce marine fuels. Typically, marine fuel is imported from countries in
the western hemisphere where refinery production of heavy fuel oil is greater than in the United
States. These imports most often come from Venezuela, Aruba, and Mexico.
Bunker Traders
Iso Industry Fuels and Chemoil Corporation are the two bunker traders associated with
the Port of Houston bunker market. In addition, there are several international trading groups
conducting transactions in the Houston bunker market.
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Bunker Suppliers
There are between six and 15 major suppliers operating in the Houston Port area. Major
suppliers to the area include Shell Marine Products, Valero Marketing and Supply Co., Chemoil
Corp., BP Marine Fuels, and Bominflot Atlantic LLC.
In addition, there are several smaller suppliers that have storage terminals in or near the
port area and operate barge delivery services. Houston Marine Services and Midstream Fuel
Services operate storage terminals, bunker supply vessels, and fleets of barges along the Gulf
coast. Matrix Marine Fuels, Enjet, and Difco Fuel Systems are examples of smaller suppliers in
the Houston bunkering market. Suncoast Resources delivers primarily by truck at local berths,
supplied by a network of fuel terminals in the Houston area (Bunkerworld, 2000).
Barge Operators
Currently, only very limited information is available on the barge market in Houston.
Most existing barge operations appear to be conducted by local suppliers.
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SECTION 3
DEMAND FOR BUNKER FUELS IN THE MARINE INDUSTRY
This section discusses the demand side of the marine fuels market. The analysis of
current and expected future shipping activity in this section is used to estimate regional and
world-wide projections of future marine bunkers demand through the year 2020. These
consumption forecasts then provide a baseline for the WORLD model, against which the
shipping industry's possible response to the adoption of a U. S. or North American SEC A
regulation could be evaluated.
3.1 Summary of the Modeling Approach
In general, the approach used to estimate marine bunker-fuel use can be described as an
"activity-based" approach with a focus on the international cargo vessels that represent the
majority of fuel consumption. Components of the estimation include:
• identifying maj or trade routes,
• estimating volumes of cargo of various types on each route,
• identifying types of ship serving those routes and carrying those cargoes,
• characterizing types of engines used by those ships, and
• identifying the types and estimated quantities of fuels used by those engines.
Implementing this approach involves combining information from a variety of sources:
data on the existing fleet of shipping vessels from Clarksons (2005), information from Corbett
and Wang (2005) and various industry sources on engine characteristics, and projections of
future global trade flows from Global Insights (2005). The data on vessels and engines provide a
characterization of fuel use associated with delivering a particular load of cargo, and the data on
trade flows control how many times, and over what distances, these loads have to be delivered.
Estimating fuel consumption though an activity-based methodology that combines data
on specific vessels with data on engine characteristics is similar to the approaches used in
Corbett and Koehler (2003, 2004), Koehler (2003), Corbett and Wang (2005), and Gregory
(2006). The approach in this report extends previous analyses by linking these ship data to
projections of world-wide trade flows in order to determine the total number of trips undertaken
in each year, and hence fuel use, rather than using estimates of the number of hours a ship/engine
typically runs in a year.
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Accordingly, the model developed in this section estimates fuel consumption based on an
underlying economic model's projections of international trade by commodity category (Global
Insights, 2005). Demand for marine fuels is derived from the demand for transportation of
various types of cargoes by ship, which in turn is derived from the demand for commodities that
are produced in one region of the world and consumed in another. The flow of commodities is
matched with typical vessels for that trade (characterized according to size, engine horsepower,
age, specific fuel oil consumption and engine load factors). Next, typical voyage parameters are
assigned, including average ship speed, round trip mileage, tons of cargo shipped, and days in
port. Fuel consumption for each trade route and commodity type thus depends on commodity
projections, ship characteristics, and voyage characteristics.
Figure 3-1 illustrates the broad steps involved in developing baseline projections of
marine fuel consumption. It is a multi-step process that relies on data and forecasts from
numerous sources, some of which are listed above, to inform the projections. The flow chart in
the figure illustrates the relationships to be profiled in characterizing baseline marine fuel
consumption by cargo vessels.
Also, while the focus of this analysis of bunker-fuel forecasts is on projecting use by
vessels carrying cargo among international ports, it includes other vessel types when estimating
total demand for bunker fuels, as discussed below. These vessel types, discussed below, include
passenger vessels such as ferries and cruise ships, service vessels such as tugs and offshore
supply vessels (OSV), and military vessels.
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Ship Analysis: by Vessel Type and Size Category
Inputs
Outputs
Deadweight for all Vessels of
Given Type & Size3
Average Cargo
Carried (Tons)
Horsepower, Year of Build
for all Vessels of Given
Type & Size3
Specific Fuel Consumption
(g/SHP-HR) by Year of Build"
Average Daily Fuel
Consumption
(Tons/Day)
Engine Load Factors0
Average Daily Fuel
Consumption (Tons/Day)
- Main, Aux. Engine at Sea
- Aux. Engine in Port
-W B
Trade Analysis: by Commodity and Trade Route
Inputs
Average Ship Speed0
Round Trip Mileaged
Tons of Cargo Shipped6
Average Cargo Carried/'
per Ship Voyage I
Outputs
Days at Sea and in
Port, per Voyage
Number of Voyages
Total Estimated Bunker Fuel Demand
Average Daily Fuel Consumption
(Tons/Day)
- Main, Aux. Engine at Sea f~
-Aux. Engine in Port V
Total Days at Sea
and in Port /"
Bunker Fuel
Demand
Driven by changes in engine efficiency.
Driven by growth in
commodity flows.
a — Clarksons Ship Register Database
b — Engine Manufacturers' Data, Technical Papers
c - Corbett and Wang (2005) "Emission Inventory Review: SECA Inventory Progress Discussion"
d - Combined trade routes and heavy leg analysis
e - Global Insight Inc. (Gil) Trade Flow Projections
Figure 3-1. Method for Estimating Bunker Fuel Demand
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3.2 Methods of Forecasting Bunker Fuel Consumption
Underlying the projections of bunker-fuel consumption by cargo vessels worldwide are
projected flows of commodities between regions of the world. These are commodities produced
in one region of the world and demanded in another.
3.2.1 Composite Commodities and Regions
The first step in analyzing trade flows was examining the relevant omposite commodities
and obtaining forecasts for them, which are based on the following categories:
• 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 (including neobulk, lumber/forest products)
• containerizable cargo
Next, countries of the world were grouped into approximately 20 larger regions. Table 3-
1 shows the mapping of countries to regions. From Global Insight, Inc. (Gil) World Trade
Service, a specialized forecast was obtained that reports flows of each commodity among regions
for the period 1995-2024. GIFs forecast of shipments of these commodities among these
regions drives the overall forecast of demand for shipping services and thus for marine fuels.
Gil is a widely recognized macroeconomic forecasting firm. The Gil World Trade
Service provides annual macroeconometric analysis and forecasts of economic activity and trade
for over 200 individual countries and for the global economy. Gil provides integrated analyses
and forecasts for individual countries and regions of the world and for the world economy as a
whole, including an analysis of the relationship of each region's economy to the world economy.
To facilitate integration of the fuel demand analysis with the fuel supply analysis, Gil grouped its
countries and regions into aggregate regions comparable to those used in EnSys Energy's
WORLD model. The aggregate regions and associated source countries/regions are shown in
Table 3-1.
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Table 3-1. Aggregate Regions and Associated Countries
Aggregate Regions Containing Gil Base Countries / Regions
U.S. Atlantic Coast
U.S. Great Lakes
U.S. Gulf Coast
E. Canada3
W. Canada3
U.S. Pacific North
U.S. Pacific South
Greater Caribbean
South America
Africa - West
Africa-North/East-
Mediterranean
Africa-East/South
Europe-North
Europe-South
Europe-East
Caspian Region
Russia/FSU
Middle East Gulf
Australia/NZ
Japan
Pacific-High Growth
China
Rest of Asia
U.S. Atlantic Coast
U.S. Great Lakes
U.S. Gulf Coast
Canada3
Canada3
U.S. Pacific North
U.S. Pacific South
Colombia, Mexico, Venezuela, Caribbean Basin, Central America
Argentina, Brazil, Chile, Peru, Other East and West Coast of S. America
Western Africa
Mediterranean Northern Africa, Egypt, Israel
Kenya, Other Eastern Africa, South Africa, Other Southern Africa
Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Netherlands, Norway,
Sweden, Switzerland, United Kingdom
Greece, Italy, Portugal, Spain, Turkey, Other Europe
Bulgaria, Czech Republic, Hungary, Poland, Romania, Slovak Republic
Southeast CIS
The Baltic States, Russia Federation, Other Western CIS
Jordan, Saudi Arabia, UAE, Other Persian Gulf
Australia, New Zealand
Japan
Hong Kong S.A.R., Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan,
Thailand
China
Viet Nam, India, Pakistan, Other Indian Subcontinent
'Canada is treated as a single destination in the Gil base 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. (Transport Canada, 2004).
The Gil World Trade Forecasting Model is a non-linear multi-stage econometric switch
model.(Gil, 2005) It uses several data sources, economic theory, and multi-stage modeling
linked by top-down control adjustment to capture and project commodity flows in the world.
There is no single data source that provides a complete baseline picture of international trade.
Gil bases their model on UN historical international trade data (published by Statistics Canada).
These data are supplemented with OECD International Trade by Commodity Statistics to reflect
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more realistic data for developing countries, and the U.S. Customs and IMF Direction of Trade
data to calibrate and enhance historical commodity trade flows. Additional macroeconomic data
(such as population, GDP, GDP Deflators, industrial output, foreign exchange rates, and export
prices by country, and geographical distances are used as exogenous variables.
The general structure of the 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 country's
export price and importing country's import cost for the commodity. Gil then estimates demand
forces, country-specific exporting capacities, export prices, and import costs. To arrive at each
country's trade with each of its trading partners, non-linear multi-stage switch modeling is
required.
Switch models are not continuous functions. Thus, they can not be estimated using
conventional derivative methods; a direct search method is used instead. Although uncommon
for economics, this method is widely used in other scientific fields. A direct search method
estimates switch functions, while allowing one to define error minimization functions and set
boundaries for model parameters. Gil's approach to forecasting is unorthodox as well. Gil
contends that the three commonly used approaches—bottom-up, top-down, and manual (hybrid)
approach—fail because of their limitations1. Gil uses a system that could be referred to as
controlled top-down approach.
Gil defines four levels, with the bottom level being the most detailed: commodity flows
between each pair of countries/regions. The third level is how much of each commodity each
country exports/imports from the world. The second level is the total commodity flows that each
country exports/imports from the world, and the first level is world trade of total commodities.
The second, third, and fourth levels have their own behavioral equations, but individual forecasts
at the lower levels are forecast under the constraint of their aggregate forecast at the higher level.
Thus, if there is a discrepancy between the sum of individual forecasts and aggregate forecasts,
the program identifies the items that could be adjusted and adjusts them step by step to eliminate
the discrepancy.
1 The bottom-up approach forbids forecasted items to be a subject to total resource constraints or equilibrium. For
example, this approach would disallow the possibility of country's import limitations due to income constraint.
The top-down approach requires forecasted items to have identical dynamic patterns. However, the historical
data reveals it is rare to find a country's imports of a commodity from two different countries to exhibit identical
dynamic patterns. The hybrid method solves the problems of the latter two, but is very time consuming.
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GIFs output for this project included detailed annual region-to-region trade flows for
eight composite commodities, for the period 1995 to 2024. The projections for 2012 and 2020
are shown, along with baseline data for 2005, in Table 3-2. In 2005, dry bulk accounts for 41
percent of the total trade volume. Crude oil accounts for 28 percent, and containers account for
12 percent. Dry bulk and crude oil shipments grow more slowly over the forecast period than do
container shipments; by 2020, dry bulk is 39 percent of the total, crude oil is 26 percent, and
containers have risen to 17 percent.
Table 3-2. World Trade Estimates for Composite Commodities, 2005, 2012, and 2020
Commodity Type
Dry Bulk
Grade Oil
Container
Refined Petroleum
General Cargo
Residual Petroleum and Other Liquids
Chemicals
Natural Gas
Total International Cargo Demand
2005
(in million tons)
2,473
1,703
714
416
281
190
122
79
5,979
2012
(in million tons)
3,051
2,011
1,048
471
363
213
175
91
7,426
2020
(in million tons)
3,453
2,243
1,517
510
452
223
228
105
8,737
3.2.2 Ship Analysis by Vessel Type and Size
Different types of vessels are required to transport these different commodities to the
various regions of the world. Profiles of these vessels were developed to provide a
characterization of ships assigned to transport commodities of each type along each route. These
profiles analyze data provided by the Clarksons Ship Register (Clarksons, 2005) on size,
horsepower, age, and engine fuel efficiency to identify typical vessels of each overall vessel type
and each size category. The main purpose of the analysis is to determine the average amount of
cargo carried by and average daily fuel consumption of each vessel type.
First, the eight Gil commodity categories were mapped to the type of vessel that would
be used to transport them. These assignments appear in Table 3-3.
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Table 3-3. Assignment of Commodities to Vessel Types
Gil Commodity
Ship Category
"Type" Defined in Clarksons Register"
Liquid bulk - crude oil
Liquid bulk - refined
petroleum products
Crude Oil Tankers
Product Tankers
Tanker
Product Carrier
Liquid bulk - residual
petroleum products
Liquid bulk - chemicals
(organic and inorganic)
Liquid bulk - natural gas
(including LNG and LPG)
Dry bulk (e.g. grain, coal,
steel, ores and scrap)
General cargo (including
neobulk, lumber/forest
products)
Containerizable cargo
Product Tankers
Chemical Tankers
Gas Carriers
Dry Bulk Carriers
General Cargo
Container Ships
Product Carrier
Chemical & Oil Carrier
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
Bulk Carrier
General Cargo Liner, Reefer, General Cargo Tramp, Reefer
Fish Carrier, Ro-Ro, Reefer/Container, Ro-Ro
Freight/Passenger, Reefer/Fleet Replen., Ro-Ro/Container,
Reefer/General Cargo, Ro-Ro/Lo-Lo, Reefer/Pallets
Carrier, Reefer/Pass./Ro-Ro, Reefer/Ro-Ro Cargo
Fully Cellular Container
a Vessel operators self-report these types to Clarksons Research Services for inclusion in their shipping databases.
Each of these vessel types were further classified by size in deadweight tons (DWT).
Appropriate size categories were identified based on both industry definitions and natural size
breaks within the data. Table 3-4 summarizes these subcategories, and provides other
information on the general characteristics of vessels represented in the Clarksons' data. The size
descriptions imply the size limitations as defined by canals or straits through which ships of that
size can pass. Crude oil tankers (VLCC) are the largest by DWT; the largest container ships
(Suezmax) are also very large. For each ship type and size category, data on typical ships'
capacity in DWT, speed, and horsepower are used to estimate average daily fuel consumption.
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Table 3-4. Fleet Characteristics in Clarksons Data
Ship Type
Container
General Cargo
Dry Bulk
Crude Oil Tanker
Chemical Tanker
Petroleum Product
Tanker
Natural Gas
Carrier
Other
Total
Size by DWT
Suezmax
PostPanamax
Panamax
Intermediate
Feeder
All
Capesize
Panamax
Handymax
Handy
VLCC
Suezmax
AFRAmax
Panamax
Handymax
Coastal
All
AFRAmax
Panamax
Handy
Coastal
VLGC
LGC
Midsize
All
Minimum
Size (DWT)
83,000
56,500
42,100
14,000
0
Maximum
Size
(DWT)
140,000
83,000
56,500
42,100
14,000
All
79,000
54,000
40,000
0
180,000
120,000
75,000
43,000
27,000
0
0
79,000
54,000
40,000
0
180,000
120,000
75,000
43,000
27,000
All
68,000
40,000
27,000
0
60,000
35,000
0
0
68,000
40,000
27,000
0
60,000
35,000
All
Number
of Ships
101
465
375
1,507
1,100
3,214
715
1,287
991
2,155
470
268
511
164
100
377
2,391
226
352
236
349
157
140
863
7,675
26,189
Total
DWT
(millions)
9.83
30.96
18.04
39.80
8.84
26.65
114.22
90.17
46.50
58.09
136.75
40.63
51.83
10.32
3.45
3.85
38.80
19.94
16.92
7.90
3.15
11.57
6.88
4.79
88.51
888.40
Total
Horse
Power
(millions)
8.56
29.30
15.04
32.38
7.91
27.07
13.81
16.71
10.69
19.58
15.29
5.82
8.58
2.17
1.13
1.98
15.54
3.60
4.19
2.56
1.54
5.63
2.55
3.74
53.60
308.96
Source: Authors' calculations based on data from Clarksons Ship Register (2005).
Fleet Average Daily Fuel Consumption
Average fuel consumption for each vessel type and size category was estimated in a
multi-step process using individual vessel data on engine characteristics. Clarksons' Ship
Register provides each ship's horsepower (HP), type of propulsion (diesel or steam), and year of
build. These characteristics are then matched to information on typical Specific Fuel Oil
Consumption (SFOC) from engine manufacturers and the technical literature. SFOC is
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measured in grams of fuel burned per horsepower-hour, so to determine the average daily fuel
consumption of the fleet, the following equation is used:
Fleet AFC = -
iev,s
SFOC, x HP, x
24
1,000,000
(3.1)
where /' denotes an individual ship of vessel type v and size category s. This calculation results
in a fleet average value for daily fuel consumption, measured in metric tons per day.
Key Assumptions Affecting the Forecast
The specific SFOC numbers used for this analysis are based on historical data provided
by Wartsila Sulzer, a popular manufacturer of diesel engines for marine vessels. An additional
10% has been added to their "test bed" or "catalogue" numbers to account for the guaranteed
tolerance level and an in-service SFOC differential.2 Figure 3-2 shows data used in the model
regarding the evolution of specific fuel oil consumption rates for diesel engines over time. (For
steam engines, a fixed SFOC of 220 g/HP-hr is used)
Engine efficiency in terms of SFOC has improved over time, most noticeably in the early
1980s in response to rising fuel prices. However, there is a tradeoff between improving fuel
efficiency and reducing emissions. Conversations with engine manufacturers indicate that it is
reasonable to assume SFOC will remain constant for the 15 year time horizon of this study,
particularly as they focus on meeting more stringent NOx emissions requirements, such as those
imposed by MARPOL Annex VI.
' Overall this 10 percent estimate is consistent with other analyses which show variation between the "test bed"
SFOC values reported in manufacturers' product catalogues and the actual SFOCs observed in service. The
difference is explained by the fact that old, used engines consume more than brand new engines and that fuels
used in-service may be different than the test bed ISO fuels. See Koehler (2003).
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200
180
160
140
120
100
- 80
I
SH 6°
vi 40
20
i,
o
.a
0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
Figure 3-2. Specific Fuel Oil Consumption Over Time
Source: Authors' calculations based on communications with Wartsila Sulzer and other diesel engine manufacturers.
The values for fleet average daily fuel consumption calculated in Equation 3.1 are based
on installed horsepower, and therefore they must be scaled down to reflect true engine loads.
Engine load factors reported by Corbett and Wang (2005) are used to estimate average daily fuel
consumption (tons/day) for the propulsion engine and auxiliary engines, both at sea and in port.
These assumptions are summarized in Table 3-5.
Table 3-5. Assumptions Regarding Engine Loads
Vessel Type
Container Vessels
General Cargo Carriers
Dry Bulk Carriers
Crude Oil Tankers
Chemical Tankers
Petroleum Product Tankers
Natural Gas Carrier
Other
Main Engine Auxiliary Engine as
Load Factor Percent of Main Engine
80%
80%
75%
75%
75%
75%
75%
70%
22.0 %
19.1%
22.2 %
21.1%
21.1%
21.1%
21.1%
20.0 %
Auxiliary Engine as Percent of
Main Engine at Sea
11.0%
9.5 %
11.1%
10.6 %
10.6 %
10.6 %
10.6 %
10.0 %
Source: Corbett, James and Chengfeng Wang. October 26, 2005. "Emission Inventory Review SECA Inventory
Progress Discussion." page 11.
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Changing Fleet Characteristics
The population of vessels operating is assumed to change over time as older vessels are
scrapped and new ones are built. In our analysis, vessels built over 25 years ago are retired and
are assumed to be replaced by new ships of the most up-to-date configuration. Specifically,
these ships are assumed to have a new engine (rated at the current SFOC) and are assumed to
weigh as much as the average ship built in 2005. So even though improvements in SFOC over
the next 15 years are not assumed, the fuel efficiency of the fleet as a whole is expected to
improve over time through retirement and replacement. In the same way, even though specific
increases in the size of ships being built are not projected, the total deadweight of the fleet will
increase over time as smaller ships retire and are replaced. The analysis also reflects trends on
the trade routes between Asia and North America or Europe for container ships to increase in
size over time.
3.2.3 Trade Analysis by Commodity Type and Trade Route
Based on information from Navigisties Consulting, the distribution of ship size categories
deployed on each of the trade routes were identified. For example, to serve the large crude oil
trade from the Middle East Gulf region to the U.S. Gulf region, 98% of the deadweight tonnage
is carried on Very Large Crude Carriers (VLCCs) while the remaining 2% is carried on the
smaller Suezmax vessels. In addition to the volume of trade being moved, the limitations of the
canals through which the vessels must pass determine the size categories deployed on each trade
route. These size category distributions are assumed to remain constant throughout the forecast
horizon, with the exception of two of the largest container trade routes. We introduce
Malacamax containerships (>11,000 TEU) to Trans-Pacific trade per a recent container vessel
forecast for the ports of San Pedro Bay and at a similar rate to Europe-Asia trade (Mercator
Transport Group, 2005).
Once a vessel type and size distribution have been assigned to each region pair and
commodity trade type, a set of voyage parameters are estimated. Days at sea and in port are
based primarily on ports called, sea distance, and ship speed. The number of voyages is based on
the cargo volume projected by Gil to move along a given route and the cargo capacity of the
vessels on that route.
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Days at Sea and Days in Port
Most trades are characterized by voyages that are essentially round trips, moving from a
single region of origin to a single destination region, and back.3 For these trades, Navigistics
Consulting identified ports that were either in the middle of the trade region or ports through
which the particular commodity was most likely to travel. For example, the Port of Singapore
was selected as the port of origin for the Pacific High-Growth region for most commodities, but
for dry bulk, Inchon was selected. Then, for each route, information was gathered on the
distances between ports (NGA, 2001 and MaritimeChain, 2005).4 Since carriers of crude oil,
chemicals, petroleum products, natural gas, and dry bulk tend to travel full for a delivery and
then return empty, round-trip distances were used to determine the length of the voyage. The
days at sea are calculated by dividing the sea distance by the average vessel speed:
(3.2)
round trip di stance route
DaysatSeaPerVoyagevsroute =
speedvsx 24x1.1508
Table 3-6 presents the values used for speed by vessel type (based on Corbett and Wang, 2005).
These values are the same for all size categories and are assumed to remain constant over the
forecast period.
Table 3-6. Vessel Speed by Type
Vessel Type Speed (knots)
Grade 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 19.9a
Other 12.7
a Length of voyages by container ships estimated from additional sources. See below.
Vessels may stop at multiple ports within each region, but we assume that, for the most part, they do not string
together trips to multiple regions. Two important exceptions to this are the general cargo and container trades,
which are described in further detail below.
4 http://maritimechain.com/. This calculator provides nautical distances, which account for the particular routes
vessels must take when traveling from port to port, e.g. movement through straights or canals.
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Source: Corbett, James and Chengfeng Wang. October 26, 2005. "Emission Inventory Review SECA Inventory
Progress Discussion." page 11.
In addition to calculating the average days at sea per voyage, the average days in port per
voyage are also estimated. It is assumed that most types of cargo vessels spend 4 days in port
per voyage; however, this can vary somewhat by commodity and by port.5 Tables 3-7 and 3-8
shows the results of these estimates of voyages lengths - focusing on U.S. trade routes. Table 3-
7 presents average lengths across types of non-container vessels (these times are cargo specific
and vary slightly based on the speed of the vessels - speeds are taken from Dr. Corbett's work).
Two sources are used for non-container trades and voyage times in Table 3-7 - Worldscale
(2002), and Maritime Chain (2005).
The Worldscale tables, based on underlying BP Shipping Marine Distance Tables, are the
industry standard for measuring port-to-port distances, particularly for tanker traffic. The reported
distances account for common routes through channels, canals, or straits. This distance information
was supplemented by data from Maritime Chain, a web service that provides port-to-port distances
along with some information about which channels, canals, or straits must be passed on the voyage.
This distance information is then combined with Dr. Corbett's speed parameters to determine the
length of a voyage in days.
As discussed above, voyage times for container trade in Table 3-8 are based on information
from Containerization International (Degerlund, 2005), and calculations by Navigistics Consulting.
This resource provides voyage information for all major container services. Based on the frequency
of the service, number of vessels assigned to that service, and the number of days in operation per
year, the average length of voyages for the particular bilateral trade routes in the Global Insights
trade forecasts are estimated.
' Some ports do not run as efficiently because of a lack of good shoreside facilities, labor problems, or other
inadequacies. The maximum number of days in port for a non-container trade is 8 days.
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Table 3-7. Length of Voyages for Non-Container Cargo Ships (approx. average)
Days per Voyage
US South US North
Global Insights Trade Regions Pacific Pacific
Africa East-South 68 75
Africa North-Mediterranean 49 56
Africa West 56 63
Australia-New Zealand 48 47
Canada East 37 46
Canada West 11 5
Caspian Region 95 89
China 41 36
Europe Eastern 61 68
Europe Western-North 53 60
Europe Western-South 54 61
Greater Caribbean 26 33
Japan 35 31
Middle East Gulf 77 72
Pacific High Growth 52 48
Rest of Asia 68 64
Russia-FSU 64 71
Rest of South America 51 30
Table 3-8. Length of Voyages for Container-Ship
Origin ~ Destination Regions
Asia ~ North America (Pacific)
Europe ~ North America (Atlantic)
Mediterranean ~ North America
Australia/New Zealand ~ North America
South America ~ North America
Africa South ~ North America (Atlantic)
Africa West ~ North America (Atlantic)
Asia ~ North America (Atlantic)
Europe ~ North America (Pacific)
Africa South ~ North America (Pacific)
Africa West ~ North America (Pacific)
Caspian Region ~ North America (Atlantic)
Caspian Region ~ North America (Pacific)
Middle East/Gulf Region ~ North America (Atlantic)
Middle East/Gulf Region ~ North America (Pacific)
US East
Coast
57
37
36
65
7
40
41
73
38
24
30
16
65
56
67
66
38
41
US Great
Lakes
62
43
46
81
18
58
46
87
45
32
37
29
81
65
76
64
46
46
US Gulf
54
47
43
63
19
39
48
69
46
34
37
17
62
83
88
73
48
44
Trade Routes
Days per Voyage
37
37
41
61
48
54
43
68
64
68
38
42
38
63
80
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Number of Voyages
The number of voyages along each route for each trade is computed by dividing, for each
vessel type v and size category s serving a given route, the tons of cargo moved by the estimated
amount of cargo per voyage:
(3.3)
,T . .,,, tons cargo to move
Number or Voyagesv s trade =
Fleet Avg. DWTv s x (utilization rate)
The cargo per voyage is based on the fleet average ship size (in deadweight tons) calculated in
the vessel profile analysis. For most cargo trades, a utilization factor of 0.9 is assumed to
account for the fact that ships do not always run at full capacity. This factor is assumed to be
constant throughout the forecast period. Lowering this utilization factor would increase the
estimated number of voyages required to move the forecasted cargo volumes, which would in
turn increase our estimated fuel demand.
Exceptions: General Cargo and Container Trades
The exceptions to the above approach for calculating voyage parameters are the general
cargo and container trades. These routes tend to have multiple stops, with cargo loaded and
discharged at each stop. Unlike the other types of vessels, these carriers rarely travel empty.
Thus, for each trade route, the focus only on the "heavy" leg of the journey, the direction with
the highest trade volume.
For general cargo, port-to-port round-trip distances and the average vessel speeds are
used to calculate days at sea. Days in port are estimated at 4 days per voyage. The difference is
that the number of voyages is based only on the tons of cargo projected to be moved on the
heavy leg of the journey. The assumption is that the projected trade volume associated with the
"light" leg will be carried on the return trip of these round-trip voyages.
For the container trades, the voyage parameters are determined based on actual ship
routings. Navigistics Consulting first identified major container trade lanes, to which the
individual region pairs were assigned. For example, trade volumes from the Pacific High
Growth region to the U.S. South Pacific and from China to the U.S. North Pacific are both
included on a Transpacific trade route. Major shipping lines active on these trade routes are
identified and their individual container services are analyzed, as recorded in the
Containerization International (CI) Yearbook 2005 and other sources. The CI Yearbook
provides detailed information about each container service, including the ports visited, the
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frequency and length of the voyage, and the vessels deployed. It is assumed there is one day in
port for each port visited, and then the days at sea are calculated by subtracting total days in port
from the total length of the voyage.
The number of voyages for the container trade is again calculated by dividing the
projected volume on the heavy leg by the estimated average cargo per voyage (i.e. average ship
size times a utilization factor). We use the information from the CI Yearbook_about the vessels
deployed to determine the average ship size on each major trade route. These sizes are reported
in terms of Twenty-Foot Equivalent Units (TEU), a volume measure which we convert using a
baseline capacity factor of 14 deadweight tons per TEU. The utilization factor is calibrated so
that the number of voyages implied by 2005 historical Gil trade volume data matches the actual
number of voyages recorded in the CI Yearbook. Table 3-9 reports these estimated factors for
some of the major trade routes in Table 3-9. These rates, which average 0.51 across all trade
routes, are generally lower than the utilization factor of 0.9 used on all other commodity trades.
However, these estimates are consistent with what industry experts predict for capacity
utilization.6 The main reason for the lower utilization rate is that container ships usually reach a
maximum volume capacity well before they reach a maximum weight capacity. A vessel may be
only 50% "full" in terms of deadweight, but still be unable to fit more containers on board.
Table 3-8. Estimated Utilization Rates for Top 10 Container-Ship Trade Routes
Top 10 Container-Ship Trade Routes by Volume" Utilization Rate
Asia ~ North America (Transpacific) 47%
Northern Europe - Asia 52%
Mediterranean-Asia 40%
North America - Northern Europe (Transatlantic) 66%
South America - North America 85%
South America - Europe 50%
Mediterranean - North America 27%
Australia - Asia 33%
South America - Asia 46%
West Africa--Europe 28%
Average for All Trades 51%
6 The utilization factors estimated correspond to approximately 7-9 deadweight tons per Twenty-Foot Equivalent
Unit (TEU), which is the volume measure most often used to describe a container ship's size. This is consistent
with industry reports. Discussions with experts in the container trade stated that containers coming out of Asia to
the U.S. and Europe weigh around 6.75 - 7 tons per TEU. Cargoes out of the U.S. weigh on the order of 9 - 9.5
tons per TEU. The combination of weight utilization (based on 14 tons per TEU) and a maximum workable slot
utilization of 90 - 95 percent gives credence to our 51 percent overall utilization value.
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"Based on Gil trade data for 2005.
3.2.4 Calculating Total Estimated Fuel Demand for Cargo Vessels
As described in Figure 3-1, estimates from the vessel analysis and trade analysis are used
to obtain an estimate of total fuel demand related to international cargo trade flows.
Total Fuel Demand in Yeary,fory = 2005, 2012, 2020
For each year, total marine fuel consumed is computed as the sum of fuel consumed on
each route of each trade (commodity). Fuel consumed in each route of each trade is in turn
computed by summing the fuel consumed for each route and trade for that year by propulsion
engines and auxiliary engines, both at sea and in port.
T7/"1 X"1 X"1 T7/"1
y trade, route, year
trade route
= Z Z ~AFC , t t xDaysatSeat , t +AFC , t t rt x Days at Port t , t
_ trade, route, y at sea J trade,route, y trade, route, y at port J trade,route, >
where
trade route
de.route.yatsea =^ (Percent of trade along route)v_, [Fleet AFC¥>, x (MELF + AE at sea LF)]
de route yatport = ^ (Percent of trade along route)v, [Fleet AFCV, x AE import LF]
v,s,t,r ' L ' J
Days at Seatad t = I (Percent of trade along route) Days at sea per voyage x Number of voyages 1
v,s,t,r ' - ' ' J
Days at Port^^ route y = I (Percent of trade along route)v s [Days at port per voyage x Number of voyages ]
MELF: Main Engine Load Factor
AE at sea LF: Auxiliary Engine at-sea Load Factor
AE in port LF: Auxiliary Engine in-port Load Factor
The parameters used in these last four equations are all derived from the vessel and trade
analyses discussed above. The (Percent of trade along route)v,s indicates the fraction of trade
volume carried by each vessel size category, as discussed in Section 3.2. Fleet AFCv,sis the fleet
average daily fuel consumption calculated using Equation 3.1. The main propulsion and
auxiliary engine load factors are discussed in Section 3.2.2, and the specific values used are
reported in Table 3-5. Days at sea per voyage and number of voyages are calculated using
Equations 3.2 and 3.3, respectively.
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3.2.5 V. S. Domestic Navigation
The Gil forecasts are primarily designed to analyze international trade flows, so they do
not include projected trade volumes for shipments within the U.S. In addition, these domestic
shipments are primarily transported by carriers that are governed by the restrictions of the Jones
Act. For these reasons, the methodology for estimating fuel demand by vessels transporting
cargo domestically differs slightly from the methodology for international cargo vessels
presented in Sections 3.2.2 through 3.2.4.
Ship Analysis by Vessel Type and Size
This analysis begins with a vessel profile. Navigistics Consulting helped compile a
database listing vessels in the "Jones Act fleet." Four types of trade constitute a vast majority of
the domestic cargo trade flows that are transported by ships through waterways: dry bulk trade on
Great Lakes, crude oil trade (primarily from Alaska), petroleum product trade, and container trade.
Accordingly, the four types of vessels that are utilized in these trades are considered: crude oil
tankers, dry bulk carriers, container ships, and product tankers (which also carry chemicals).
As with international vessel fleet, vessel types of the domestic fleet were further
classified by size in deadweight tons (DWT). Table 3-9 illustrates these breaks, along with
summaries of deadweight and horsepower for each vessel type and size. As seen below, the
Jones Act fleet composes only a small fraction of the international fleet. The Great Lakes bulk
category makes up the largest share by the number of vessels, while the container category is the
largest in terms of horsepower, and the crude oil tanker category is the largest in terms of
deadweight. These four categories have a total of 151 vessels, with a combined deadweight of
7.9 million tons and a combined horsepower of 2.6 million.
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Table 3-9. Jones Act Fleet
Vessel Type
Container*
Great Lakes
Bulk**
Crude Oil
Tanker***
Petroleum Product
Tanker***
Total
Size by DWT
Panamax
Intermediate
Feeder
Panamax
Handymax
Handy
VLCC
Suezmax
AFRAmax
Panamax
Panamax
Handy
Coastal
Minimum
Size
(DWT)
42,100
14,000
0
54,000
40,000
0
180,000
120,000
75,000
43,000
40,000
27,000
0
Maximum
Size
(DWT)
56,500
42,100
14,000
79,000
54,000
40,000
0
180,000
120,000
75,000
68,000
40,000
27,000
Number
of Ships
2
35
1
12
3
33
8
10
4
1
24
17
1
151
Total DWT
(thousands)
92.0
924.0
13.9
729.2
367.9
800.1
1,508.0
1,289.4
367.9
57.7
1,112.4
609.8
19.2
7,891.5
Total Horse
Power
(thousands)
47.0
890.4
22.9
187.8
40.2
218.8
219.3
299.1
98.0
17.0
300.4
204.9
7.2
2,553.0
Source: Authors' calculations based on data from Colton and Company (*), Greenwood's Directory (**), U.S.
Maritime Administration (***)
Fleet Average Daily Fuel Consumption
Average fuel consumption for each vessel type and size category was estimated using the
same basic approach that was used to estimate fuel consumption for international vessel fleet.
The main difference lies in how fleet characteristics change over time through retirement and
replacements.
U.S. Jones Act vessels are more costly to build, and therefore are kept in service longer than
international fleet vessels, making their replacement age above the international fleet average.
Replacement ages for Jones Act vessel categories are listed below:
• Containers- 35 years
• Great Lakes Bulk - 60 years (these ships are not a subject to salt water and thus last longer)
• Crude Oil Tanker- 35 years or OPA-907 requirement
• Petroleum Product Tanker - 35 years or OPA-90 requirement
The replacement ships are assumed to have a new engine (rated at the current SFOC) and are
assumed to weigh as much as the average ship of a similar category and deadweight class (for
example, a Panamax Size Container Vessel) built in 2005, based on the statistics from the
international fleet database.
1 Oil Pollution Act of 1990 (OPA-90) was introduced after the Exxon Valdez incident. OPA-90 requires all single-
hull ships to be replaced by double-hull ships by certain date, based on deadweight and horsepower.
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Voyage Parameters
Calculation of the voyage parameters was also slightly different. The average number of
days required for a trip, as well as average number of days spent in port were estimated based on
actual ship routings and calculated distances between Alaska, Hawaii, Puerto Rico and the
continental U.S.
The number of days the ships will be engaged in trade (activity level) are then estimated
for each ship category. For container, crude oil tanker, and petroleum product tanker categories
activity levels are estimated at 350 days. The estimate of Great Lakes bulk vessels activity level
was set at 290 days to account for winter weather conditions, when the lakes are frozen over.
Given the activity level and the average number of days required for a trip at sea and in port, the
total number of days at sea and in per port per ship per year are calculated as:
Voyages per Year Per Ship = Activity Level
Average Number of Trip Days
Total Number of Days at Sea per Ship = Average Number of Days at Sea x Voyages Per Year Per Ship
Average Number of Trip Days
Total Number of Days in Port per Ship = Average Number of Days in Port x Voyages Per Year Per Ship
Average Number of Trip Days
Total number of days in port and at sea per year per ship is then multiplied by the number
of vessels in each category, to get the total number of days ships spend at sea and the total
number of days ships spend in port each year. Given the average fuel consumption, the days at
sea per voyage, and days in port per voyage for an average ship within each vessel category, the
total estimated fuel demand is then calculated in the same way as for international vessel fleet.
3.2.6 Ship Analysis for Non-Cargo Vessels
As with domestic U.S. navigation, because the Gil forecasts focus on international trade
flows, they do not cover activities of several remaining types of vessels: passenger ships, fishing
vessels, military vessels, and other support ships such as tugboats or supply ships. Data on fuel
consumption by the ship categories have been based on available literature and information in
the Clarksons database.
Historical fuel consumption by passenger ships, fishing vessels, and military vessels have
been based on data from Corbett and Koehler (2003). Trends in passenger ships are based on a
study by Ocean Shipping Consultants that projects increases in cruise-ship demands through
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2020. Trends in fishing are based on data from the United Nation's Food and Agriculture
Organization (FAO) on world-wide fish capture trends between 1997 and 2002. Trends in
military vessel energy use are based on forecasts from the U.S. Energy Information
Administration's Annual Energy Outlook 2006, which provides estimates of trends in future U.S.
military distillate and residual consumption. Historical fuel consumption by other types of ships
are based on data in the Clarksons database (the "Other" category shown in Table 3-4). These
data on vessel characteristics are combined with engine load assumptions from Corbett and
Wang (2005) and activity levels from Corbett and Koehler (2004) to determine fuel use. Trends
in this fuel use are then assumed to follow patterns of economic activity as reflected in Gross
Domestic Product (GDP) forecasts from EIA.
3.2.7 Bunker Fuel Grades
Fuel consumption by specific grades is evaluated as follows: information from Koehler
(2003) on consumption of heavy fuel oil and marine distillate oil (MDO) and marine gas oil
(MGO) by vessel type is used to assign overall fuel grades, this information is then combined
with the main and auxiliary engine factors discussed in Section 3.2.4 - where main engines are
assumed to use mostly Intermediate Fuel Oil (IFO) 380 and auxiliary engines use IFO180.
3.3 Results of Bunker Fuel Forecasts
This section presents estimates of bunker fuel consumption based on the methodology
outlined above. The focus of the discussion and associated graphs is on: first, world-wide bunker
fuel consumption estimates that can be compared to those by IEA and in other published works;
second, U.S. regional fuel consumption estimates related to the cargo fleet engaged in
international trade; and, finally, on growth rates in bunker fuel demand and the underlying factors.
Figure 3-3 shows estimated world-wide bunker fuel consumption by vessel type. Fuel
consumption in year 2001 is equal to 278 million tons, which can be compared to the estimate in
Corbett and Koehler (2004) of 289 million tons. By 2020, bunker fuel demand reaches 500
million tons per year. Note: the "historical" bunker fuel data shown going back to 1995 are also
model estimates based on historical Global Insights trade flows. (Comparisons of these
estimates to others in the literature are discussed in more detail in Section 4.2, given their
importance to modeling of the petroleum-refining industry in the WORLD model.)
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Figure 3-3. World-Wide Bunker Fuel Use
600
500
H Container BH General Cargo ElDry Bulk S Crude Oil
D Chemicals D Petroleum • Natural Gas • Other
• Fishing Vessels D Passenger Ships D Military Vessels
Figure 3-4 shows the annual growth rates by vessel-type/cargo that underlie the
projections in Figure 3-3. Total annual growth is generally between 2.5 percent and 3.5 percent
over the time period between 2006 and 2020 and generally declines over time, resulting in an
average annual growth of around 2.6 percent. As shown in the "container" categories in Figures
3-3 and Figure 3-4, fuel consumption by container ships is the fastest growing component of
world-wide bunker fuel demand - in 2004, consumption by container ships is around 75 million
tons, growing to 87 million tons by 2006 and close to 180 million tons by 2020 (the historical
estimates can be compared to Gregory (2006), which places container ship consumption in 2004
at 85 million tons, based on installed power). While overall growth is less than three percent a
year, growth in container-ship demand remains above five percent a year on an average annual
basis for the next 15 years. Across all vessel types, growth in bunker fuel consumption is
somewhat lower than world-wide Gross Domestic Product (GDP) growth forecasts from EIA
(InternationalEnergy Outlook 2005) of around 3.9 percent a year, but higher than IEA estimates
of overall fuel consumption growth (around 1.6 percent in the World Energy Outlook 2005). The
estimate of growth in marine bunkers over the next 15 years, however, is consistent with
historical growth of 2.7 percent per year shown in LEA data from 1983 to 2003.
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Figure 3-4. Annual Growth Rate in World-Wide Bunker Fuel Use
10%
-2% J
o
8
X
o
a
a
8
a
a
00
i-H
a
o
n
a
-O—Total -•— Container —•—General Cargo -•-Dry Bulk
-*-Crude Oil *~Chemicals ~*~Petroleum ~*~Natural Gas
•"Other Fishing Vessels Passenger Ships Military Vessels
Growth in fuel use by container ships and the overall contribution by these vessels to
world-wide demand is driven by several factors. The first is overall growth in world-wide GDP
mentioned above. This growth leads to increases in international trade flows over time (shown in
Figures 3-5 and 3-6 below). These figures illustrate that, although container trade is smaller in
total volume than other categories, it is the fastest growing component of the trade flows.
Measuring trade flows in tons of goods, as shown in Figure 3-5, also does not provide a good
proxy for the fuel consumption needed to transport the goods. Liquids and dry bulk are much
denser than container goods, for example. As mentioned in Section 3.2.3, it is estimated that
utilization rates for container ships (comparing dead weight tons of capacity to actual cargo
transported) are around 50 percent. Thus, it takes approximately twice as many ships to transport
the same amount of container tons compared to liquid/dry bulk tons. This relationship tends to
influence total bunker fuel use and weight it towards container trade. In addition, growth rates in
particular trade flows such as Asia to the U.S. will also influence overall fuel consumption,
especially as related to container ships as discussed in relation to U.S. regional trade flows below.
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Figure 3-5. World-Wide Trade Flows (Global Insights)
9,000
B Container ffll General Cargo D Dry Bulk B Crude Oil D Chemicals D Petroleum DNatural Gas
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Figure 3-6. Annual Growth Rate in World-Wide Trade Flows
10% T
-O-Total
-A-Crude Oil
• Container
X Chemicals
• General Cargo
~*~ Petroleum
•Dry Bulk
•Natural Gas
Figures 3-7 to 3-9 show estimated consumption of specific grades of bunker fuels from
Figure 3-3.
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Figure 3-7. World-Wide IFO380 Use
400
350
H Container
D Chemicals
• Fishing Vessels
H General Cargo D Dry Bulk H Crude Oil
D Petroleum • Natural Gas D Other
n Passenger Ships D Military Vessels
Figure 3-8. World-Wide IFO180 Use
H Container 01 General Cargo D Dry Bulk H Crude Oil
d Chemicals d Petroleum • Natural Gas d Other
• Fishing Vessels • Passenger Ships D Military Vessels
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Figure 3-9. World-Wide MDO-MGO Use
120
100
13
fa
B
o
H
§
=
H Container ffl General Cargo D Dry Bulk H Crude OU
D Chemicals D Petroleum • Natural Gas D Other
B Fishing Vessels U Passenger Ships D Military Vessels
Figures 3-10 to 3-13 present estimates of fuel use by the international cargo fleet engaged
in delivering trade goods to and exporting trade goods from the United States. These estimates
comprise part of the total world-wide bunker fuel use shown in Figure 3-3 and do not include fuel
used for domestic navigation. The results in Figure 3-10 show estimated historical bunker fuel use
in year 2001 of around 47 million tons (note: while this fuel is used to carry trade goods to and
from the U.S., it is not necessarily all purchased in the U.S. and is not all burned in U.S. waters).
This amount grows to over 90 million tons by 2020 with the most growth occurring on trade routes
from the East Coast and the "South Pacific" region of the West Coast.
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Figure 3-10. Bunker Fuel Used by the International Cargo Fleet Importing To and
Exporting From the United States (by Region)
100
North Pacific 01 US Great Lakes DUS Gulf SUS East Coast ^US South Pacific
Figure 3-11 shows the annual growth rate projections for the fuel consumption estimates
in Figure 3-10. The South Pacific and East Coast regions of the United States are growing the
fastest, largely as the result of container ship trade (see Figures 3-12 and 3-13). Overall, the
average annual growth rate in marine bunkers associated with future U.S. trade flows is 3.4
percent between 2005 and 2020. This growth rate is somewhat higher than world-wide totals,
but is similar to estimated GDP growth in the U.S. of3.1 percent between 2005 and 2020 (EIA,
2006) and is influenced by particular components of U.S. trade flows.
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Figure 3-11. Annual Growth Rate in Bunker Fuel Used by the International Cargo Fleet
Importing To and Exporting From the United States (by Region)
10%
-2%
X
o
-o-United States
-•-US Great Lakes
•US South Pacific
•US Gulf
•US North Pacific
•US East Coast
The growth rate in bunker fuel consumption related to U.S. imports and exports is driven
by container ship trade (see Figure 3-15), which grows by more than four percent a year. U.S.
trade volumes are also influenced by high world-wide growth in GDP and resulting demands for
U.S. goods. Along with the fact that container ships use a disproportionately large amount of
fuel to move a given number of tons of cargo (as discussed in Section 3.2.3), fuel use by
container ships is also influenced by shifts in trading routes over time. In the future, trade is
expected to shift to the Pacific region (an increase in Asia - U.S. routes), which causes the
average distance per voyage to increase. Thus, while ship efficiency is increasing over time as
older ships retire, this effect to dominated by the increase in voyage distance, leading to higher
bunker fuel growth.
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Figure 3-12. Bunker Fuel Used by the International Cargo Fleet Importing To and
Exporting From the United States (by Vessel/Cargo Type)
100
B Container H General Cargo D Dry Bulk S Crude Oil D Chemicals D Petroleum • Natural Gas
Figure 3-13. Annual Growth Rate in Bunker Fuel Used by the International Cargo Fleet
Importing To and Exporting From the United States (by Vessel/Cargo Type)
10%
-O-Total
-*-Crude Oil
•Container
Chemicals
~*~ General Cargo
—*— Petroleum
•Dry Bulk
'Natural Gas
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Figure 3-14. U.S. Trade Flows - Imports plus Exports (Global Insights)
2,000
1,800
BContainer M General Cargo D Dry Bulk GCrude Oil D Chemicals D Petroleum •Natural Gas
Figure 3-15. Annual Growth in U.S. Trade Flows - Imports plus Exports (Global Insights)
10%
-O-Total
-*- Grade Oil
-•—Container
Chemicals
—*—General Cargo
-*— Petroleum
-Dry Bulk
"Natural Gas
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SECTION 4
ESTIMATING BUSINESS-AS-USUAL PROJECTIONS USING THE WORLD MODEL
A key component of Task #1 was to develop business-as-usual projections for bunker fuels.
This required enhancing an analytical tool focused on the petroleum-refining industry, i.e., the
EnSys WORLD model, to the point where it would provide a sound basis and starting point for
future analyses of the effects of potential SOX Emissions Control Areas in North America and
elsewhere, along with other possible global tightening of marine fuels qualities. These abilities
were required for a time horizon covering the years 2012 and 2020.
4.1 Overview of Enhancements to the WORLD Model
WORLD is a comprehensive, bottom-up model of the global oil downstream that includes
crude and non-crude supplies, refining operations and investments, crude, products and
intermediates trading and transport, product blending/quality and demands. Its detailed simulations
are capable of estimating how this global system can be expected to operate under a wide range of
different circumstances, and then generating model outputs including price effects and proj ections
of refinery operations and investments. As part of the overall model enhancements, the refinery
data, capacity additions, technology assumptions, and costs were reviewed (see Section 4.3).
Beyond these enhancements, the relevant regulations were thoroughly reviewed to ensure
that the WORLD model was correctly positioned to undertake future analyses of marine-fuels
SECAs. Issues brought to light in this review (discussed below) raise uncertainty over how
compliance with SECAs and other potential regulations may be achieved within the petroleum-
refining and shipping industries. The issues also tend to create an analytical situation that is less
clear and more complex than, for example, a mandate to move all U.S. gasoline to 30 ppm sulfur.
Among the issues and uncertainties considered are:
• the prospective timetable for reducing SEC A marine-fuel requirements from 1.5 to 1.0 to
0.5 wt% sulfur,
• the possible scenario of part or all bunker fuel demand shifting to marine diesel,
• costs and effects of vessel-based emission reduction strategies,
• how fast and how effectively abatement technology may mature,
• costs of refining including the capital expenditures required to reduce bunker-fuel sulfur
content and the potential for process technology improvements,
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• likely market reactions to increased bunker fuel costs - i.e., availability, impacts on the
overall transportation fuels balance and competition with land-based diesel and residual
fuels plus as feedstock for upgrading
• the effects of emissions trading, and
• the potential for bunkers sources and hence consumption - of both low and high sulfur
grades - to partially shift location depending on volume supply potential and economics.
The analytical system thus had to be set up to allow for alternative compliance scenarios,
particularly with regard to (a) adequately differentiating bunker fuel grades, (b) allowing for differing
degrees to which the SECA or other standards in a region were presumed to be met by bunker-fuel
sulfur reductions, rather than by other means such as scrubbing or emissions trading and (c) allowing
for all residual fuel bunkers demand to be re-allocated to marine diesel. Beyond any international
specifications, the analytical system needed to be able to accommodate future consideration of
regional, national, and local specifications (e.g., those being promulgated in California).
The primary approach taken to manage these issues was to (a) expand the number of
bunkers grades in the model to three distillates and four residual grades1, to (b) allow for variation
where necessary in (regional) sulfur standards on specific bunkers grades, and to (c) enable residual
bunker demand to be switched to marine diesel. The approach, nonetheless, necessitates estimating
- external to the main WORLD model - the details of compliance in any particular region. For
example, in the existing EU SEC As, what percentage of the bunkers consumption applicable to the
region will be met by low sulfur fuels versus using high sulfur fuels plus alternative methods such
as scrubbing or emissions trading (Appendix A provides a more detailed background on the options
for SECA compliance and how they are currently viewed in the model).
A main focus to date of debates about SECA regulations has been on the degree to which
the regulations will require refinery production of lower sulfur residual fuels. However, the SOX
scrubbing option raises the possibility that higher sulfur bunkers fuels could be supplied. The
MARPOL SECA standard states a scrubber SOx emission level of 6 gm/kWh, which is equivalent
to 1.5% sulfur content bunker fuel. Thus, a scrubber operating at 67% efficiency could enable a
ship to burn 4.5% sulfur fuel and still meet the 6 mg/kWh standard. Given that pre-commercial
scrubber tests on European ferries have been reporting efficiencies in the range of 65 to 95+%, the
technology could enable a supply option whereby refiners continue to supply high-sulfur IFO
1 Specifically, the following seven grades were implemented: Marine Gasoil (MGO), plus distinct high and low sulfur
blends for Marine Diesel Oil (MDO) and the main residual bunkers grades IFO 180 and IFO 380. The latest
international specifications applying to these fuels were used, as were tighter sulfur standards for the low sulfur
grades applicable in SECA's.
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bunker fuels at up to 4.5% sulfur- in other words, maintain or increase sulfur levels versus the
current world-wide average of 2.7%. With a scrubber operating at 95% SOX efficiency, a shipper
can easily surpass the possible EU 2008 standard of 2 gm/kWh using 4.5% sulfur fuel (versus
otherwise using 0.5% sulfur fuel). Even the standard of 0.4 gm/kWh (which corresponds to 0.1%
sulfur fuel for in port use) can be met using scrubbing and 2% sulfur fuel. This method of
compliance enables refiners to avoid the costs of desulfurization and shippers to buy lower priced
fuels. The route also potentially plays into emissions-trading schemes since, provided emissions
levels can be verified, a ship with a scrubber can reduce its emissions below the 6 or 2 gm/kWh
standards and realize credits and any associated economic value. On-board scrubbing also helps
reduce emissions of parti culates but has limited impact on NOx, hence- in part - the interest that
has been generated in using marine diesel in place of residual grades.
The analytical process therefore needed to be able to capture potential economic tradeoffs of
scrubber use in terms of how its impacts might feed back on refinery bunkers quality, supplies, and
economics. A scrubber "unit" could be built into the WORLD model in the future, but additional
information will need to be developed to allow accurate estimates of their costs and utilization
potential. More operational experience is required to fully gauge scrubber costs, including such
elements as onshore sludge disposal. Estimates to date, however, put the cost per ton of SOX
removal via scrubbing at around one third or less of the cost via residual fuel desulfurization
(Meech, 2006). Therefore, given this simple degree of cost difference, the WORLD model would
always opt for the scrubber route to the extent it was allowed. The net effect is that a key scenario
variable, developed external to the model (or in conjunction with cost functions developed for the
model), is the proportion of SECA-compliant regional bunker fuel that needs to be supplied in the
form of low sulfur product versus high sulfur product being scrubbed. The WORLD model is
readily capable of studying parametric effects associated with varying this proportion. The
development of specific premises for the base case SEC As is set out in Sections 4-2 and 4-3.
4.2 Bunker Fuel Forecasts Used in the WORLD Model Ball Analysis
The WORLD model has also modified to accommodate the bunkers demand forecasts
estimated in Section 3. These projections required appreciable re-thinking and re-working of the
model since the estimates of recent historical bunkers demand are twice the levels used by TEA and
EIA. This has far-reaching implications, leading to reduced current and future demands for inland
residual fuels and increased future total residual demands as bunkers demand growth is projected to
be significant, while that of inland residual is declining. The net implication of the findings in
Section 3 is that other forecasters, including IEA, EIA, OPEC, etc. are currently underestimating
future global residual and total oil demands. In order to accommodate these differing demand
4-3
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projections, and to enable their implications to be understood, the WORLD model was modified so
that it could be run for each time horizon on either an IEA bunkers or an "RTF bunkers basis.
Although the bunker fuel estimates in Section 3 (equal to 278 million tons in 2001) are
higher than LEA estimates of around 140 million tons, these findings are comparable to estimates
from other works - e.g., Koehler (2003) at 281 million tons or Corbett and Koehler (2003, 2004) at
289 million tons. Industry sources contacted by Navigistics Consulting indicated that there is no
agreement on world-wide bunker demands. Meech (2006) estimates world bunkers at 255 million
tons in 2004, and Madden (2006) places marine residual fuel use at roughly 185 million tons in
2004, based on data from Meech (2006).
Given the differences between the bunker fuel projections estimated in Section 3 and LEA
statistics or data from EIA:'s International Energy Annual 20032, it was necessary to incorporate
the "RTF' bunker estimates carefully into the WORLD model since, as discussed below, the
implications of the difference between these estimates and the IEA/EIA bases are far reaching.
During this process, when establishing an historical base within WORLD (for the 2000 base year),
the view was taken that total reported global oil demand - and with that total distillates and residual
fuels demands- are correct; therefore, that there is no issue of under-reporting of total historical
demand, ratherthe issues across bunker estimates represent a misallocation of residual fuels, i.e.,
fuel which is reported as (inland) residual fuel is in fact used as marine bunker fuel. The potential
for such misreporting is evident. For instance, statistical sources tend to show total bunkers demand
for the Middle East that is less than that for the port of Fujairah alone and show essentially nil
bunkers demand in the FSU. In the industry press, references can be found to the lack of
transparent reporting of bunkers sales/demands - see the illustrative text below:
' Table 3.1 in the International Energy Annual 2003 states that global bunkers demand was 3,443 mmbpd in 2002,
equating to 191 mmtpa.
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Excerpt from the BunkerWorld Library on Bunkers Ports
So how big is the Fujairah bunker market? There are no official data available regarding the size
of the Fujairah market, but according to Harbour .Master, Captain Tamer Masoud, from the Port
of Fujairah. the annual volume of bunkers in the area is approximately 12 million metric tonnes.
The average monthly supply volume of bunkers is around 1 million metric tonnes.
It is unclear whether this volume includes export figures. Some players appear to survive mainly
by exporting fuel cargoes, for example to nearby countries such as Pakistan for power stations.
In Fujairah, approximately 60-80 percent of the supplied bunkers is IFO380, and the rest is
divided between IFO180 and MGO, though it is difficult to estimate exact figures.
In the Arab Gulf, if we include sales from ports in Saudi Arabia, Iran, Kuwait, as well as other
UAE ports, the total volume of bunkers is well over 1 million mt per month. The Fujairah
market is definitely the largest single bunker market in this area.
Exactly how much the Fujairah bunker market accounts for is. it transpires, a subject of much
dispute - with established players worried that newcomers and relative 'outsiders' have an
unrealistic view of the market size and its potential profit margins.
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In terms of simulating the global oil downstream today, a potential misallocation between
bunkers and inland fuel is not significant since the ultimate fuel volumes and qualities are not
affected. However, this changes when future years are considered. This is because the growth rate
for inland residual fuel is essentially 0% globally, whereas for marine bunkers it is around 3% p.a.
in the RTI and other projections. (The RTI bunkers growth rate is consistent with an historical
growth rate of 2.7% p.a. in EA data from 1983 to 2003).
Petroleum product demand projections are built up sector by sector. What appears to be
happening in current forecasts, on the basis of the bunker estimates from Section 3 and the related
works, is that total inland residual fuel demand is being overestimated - but its demand growth is
flat- and total bunkers demand - with its attendant appreciable growth rate - is being
underestimated. The net effect/implication is that today's oil demand projections by EIA, IEA, and
others underestimate total future bunkers demand, residual demand and global oil demand.
Table 4-1 and Figure 4-1 show the impacts on 2003, 2012 and 2020 oil demand projections,
based on AEO 2006 Reference case, of applying LEA and alternatively the "RTI" estimates of
bunkers. Both bases have the same growth rates for each product type as listed in Table 4-2.
Table 4-1. Global Oil Demand by Product Category - IEA and RTI Bases for Bunkers
Bunkers Basis
DEMAND BY PRODUCT TYPE
ETHANE
LPG
NAPHTHA
GASOLINE
KERO/JET
GASOIL/DIESEL/N02
GASOIL/DIESEL - BKRS - MGO
GASOIL/DIESEL - BKRS - MDO
RESIDUAL - INLAND INCL RFO
RESIDUAL - BKRS - IFO180
RESIDUAL - BKRS - IFO380
OTHER
TRANSPORT LOSSES
TOTAL OIL DEMAND
TOTAL DISTILLATES DEMAND
TOTAL RESIDUAL DEMAND
2003
IEA
1.11
6.71
4.63
21.03
6.33
21.19
0.02
0.43
8.20
0.31
2.01
7.49
0.18
79.64
21.63
10.52
2003
RTI
1.11
6.71
4.63
21.03
6.33
20.25
0.18
1.16
6.67
0.55
3.48
7.49
0.18
79.78
21.60
10.70
2003
impact
of switch
to RTI basis
0.00
0.00
0.00
0.00
0.00
(0.94)
0.16
0.74
(1.53)
0.24
1.47
0.00
0.00
0.15
(0.03)
0.18
2012
IEA
2012
RTI
2012
impact
of switch
2020
IEA
to RTI basis
1.60
7.82
5.83
23.40
7.43
26.59
0.02
0.53
8.28
0.40
2.67
8.57
0.21
93.35
27.14
11.35
1.60
7.82
5.83
23.40
7.43
25.36
0.19
1.47
6.83
0.76
4.77
8.57
0.21
94.23
27.01
12.36
0.00
0.00
0.00
0.00
0.00
[1.23)
D.17
D.94
11.46)
D.36
2.10
0.00
0.00
D.88
10.13)
1.01
1.82
8.56
6.88
25.20
8.07
30.59
0.02
0.61
8.17
0.47
3.23
9.83
0.24
103.70
31.22
11.87
2020
RTI
2020
impact
of switch
to RTI basis
1.82
8.56
6.88
25.20
8.07
29.15
0.19
1.73
6.84
0.95
5.92
9.83
0.24
105.38
31.07
13.71
0.00
0.00
0.00
0.00
0.00
[1.44)
0.17
1.12
11.33)
0.48
2.69
0.00
0.00
1.68
10.15)
1.84
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Impact of RTI Bunkers Projections on Global Oil Demand 2020
DGASOIL/DSL
•BKRS-MGO
• BKRS-MDO
• RESIDUAL-INLAND
• BKRS-IFO180
• BKRS-IFO380
D
DTOTAL OIL
D TOTAL DISTILLATES
• TOTAL RESIDUAL
Figure 4-1. Impact of RTI Bunkers Projections on Global Oil Demand in 2020
Table 4-2. Product Growth Rates
PRODUCT GROWTH RATES
Basis RTI Bkrs Projections
ETHANE
LPG
NAPHTHA
GASOLINE
KERO/JET
GASOIL/DIESEL/N02
GASOIL/DIESEL - BKRS - MGO
GASOIL/DIESEL - BKRS - MDO
RESIDUAL - INLAND INCL RFO
RESI DUAL- BKRS -IF01 80
RESIDUAL - BKRS - IFO380
OTHER
TRANSPORT LOSSES
TOTAL OIL DEMAND
1 . WORLD base demand year is 2000
2000(1) to
2012
2.06%
1 .99%
2.53%
1 .46%
1 .25%
2.51%
0.13%
2.73%
0.09%
3.61%
3.59%
1 .21 %
1 .50%
1 .82%
2020
1 .89%
1 .65%
2.36%
1 .25%
1.17%
2.21%
0.20%
2.46%
0.06%
3.30%
3.25%
1 .42%
1 .50%
1 .66%
It can be seen that total demands for other products such as gasoline and naphtha are not
affected. Total distillate demand is slightly impacted, but there is a significant shift under the "RTF'
basis to distillate bunkers grades with less land based diesel. The main impacts are on product
quality since on-road and off-road diesel specifications are advancing more rapidly towards low and
ultra-low sulfur levels than are marine distillate fuels. Demand for residual fuel is also significantly
modified. Under the "RTF' basis, it is 1.0 mmbpd higher in 2012 (bunkers and inland grades
combined) and for 2020, the figure is 1.84 mmbpd. The implication is that the FEA basis for
bunkers understates future global oil demand; by 0.9 mmbpd in 2012 and 1.7 mmbpd by 2020
versus the AEO Reference demand figures.
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The increase in residual demand will materially impact total refining investments and
economics as well as increase oil supply requirements, most likely the call on OPEC to produce
additional crude. Of further significance is that, with higher volumes of bunker fuels, the impacts
of marine fuels regulations and SEC As will be correspondingly greater, in terms of volumes of
marine fuels that may have to be produced to low sulfur standards and the associated impacts on
refining investments and supply economics.
To deal with these bunkers demand projections, and also to accommodate potential SEC A
scenarios including differing assumptions about the degree to which SOX targets are met by fuel
sulfur reduction versus abatement and trading, the WORLD model was modified so that it could (a)
work with oil demand projections on both IEA and RTI bases for bunker fuels and (b) could
accommodate user-specified proportions of low sulfur distillate and residual marine fuels for any
horizon and region. In addition, the model user has the ability to set the sulfur level for each
horizon and region for each high and low sulfur fuel, e.g. to capture potential progression under the
EU SEC As from 1.5% to 0.5% sulfur.
Another facet of marine bunkers demand is that shippers have flexibility in terms of where
they bunker, i.e., unlike other fuels demands, that for bunkers is not necessarily static and can shift
to some degree from region to region. This phenomenon is part and parcel of the daily bunkers
business and buyers shift their buying based on a few dollars a ton price differences. For Task 1,
this situation was recognized but bunkers demands were kept static, i.e. no feature was introduced
to partially shift demand toward regions where supply is cheapest.
4.3 Bunker Fuel Stability
During the early stages of the study, concerns were raised about the potential impact of
quality and compositional changes on the stability of the residual bunker fuel grades. To quote the
refining technology author, Robert Maples, "ENTER QUOTE HERE". Literature research was
undertaken and knowledgeable individuals contacted in industry to ensure a sound understanding of
fuel stability issues as a basis for ensuring the WORLD model processing and blending options
were consistent with stable IFO blends.
Fuel instability is a serious and not uncommon issue in bunkering. It centers on the
asphaltenes contained in the blend precipitating out. This renders the fuel unusable and - if already
on-board - the only remedy is to de-bunker the ship. The presence in the blend of different classes
of blendstocks act to either prevent or cause precipitation of asphaltenes:
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• The primary "bad actor" with regard to causing instability is the presence and
concentration of visbroken vacuum residuum fractions. These have high asphaltenes
content and are generally limited in IFO blends.
• Similarly, asphaltic heavy residuum fractions are potentially problematic
• The tendency for asphaltenes to precipitate out is exacerbated by the presence in the
blend of paraffin!c streams, such as paraffin!c distillate or vacuum gasoil cutter
stocks. These act to strip the ???? coating from the asphaltenic compounds, causing
them to precipitate.
• Conversely, the presence of aromatic stocks, notably FCC cycle and decant/slurry
oils, has the reverse effect. These are beneficial and regarded as important
components on bunkers blends as they create a "reserve of stability" which acts to
reduce the risk of asphaltene precipitation.
Contacts with knowledgeable industry experts on bunkers confirmed that there is a degree of "black
art" in bunkers blending in that refiners and blenders learn what blends work and stick to these.
Further, the blending "art" is highly refinery-specific. While it was not possible within the
WORLD model to capture differences between individual refineries, steps were taken to prevent the
model from producing IFO blends that could tend to be unstable. The main factors reviewed and
steps taken were as follows:
• The visbreaker yields in the model were reviewed and adjusted. Data from Maples states
that the propensity for visbreaker vacuum residuum product streams to be unstable is highly
dependent on the feed asphaltene content, hence that - to maintain stability - the heavier,
more asphaltic feeds need to be processed at reduced severity relative to less asphaltic
feeds. This view was reinforced by bunkers experts. Again, according to Maples, who
undertook a specific study of visbreaking and fuel stability, the typical range of conversion
is 8-12% where the objective is to maximize distillate production and 6-10% where it is to
reduce resid viscosity, with an overall observed conversion range of 4-16%. To reflect
these ranges and to establish a conservative set of visbreaker yields across vacuum resids
from low to high sulfur/asphaltene contents, a graduated set of yields was applied.
Conversion was inversely related to residuum quality such that it was limited to 6% for the
poorest quality resid, rising to 10% for the highest quality feed. In addition, visbreaker
utilities consumptions and capital cost were checked.
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• The vacuum and atmospheric residuum hydro-desulfurizer yields, utilities and costs
were reviewed. With the prospect of lower IFO sulfur limits, the VRDS and ARDS units
gain additional importance. Feedback from industry contacts and literature research was
that - for purposes of maintaining stability in residual fuel blends- VRDS/ARDS operating
severities should not be so severe as to cause significant hydro-cracking. Information from
Meyers and others sources indicated a typical percent desulfurization range from the high
80s to 95-97%. Yields and desulfurization levels in the model were adjusted to close to
90% in order to stay in the conservative range.
• The physical properties of the potential main IFO blend components were reviewed with
particular attention paid to gravity, sulfur, carbon residue and viscosity. Adjustments were
made to the viscosities of several vacuum and atmospheric residuum streams. These had
been previously over-stated, leading to excessive levels of distillates and cracked-stocks in
early case run blends.
• Carbon residue specification was added as a control against unstable blends
• The blendstocks allowed into the IFO blends were also reviewed. All kerosene type
blendstocks were checked as blocked from residual fuel blends (inland as well as bunkers).
Similarly all paraffmic middle distillate and vacuum gasoil stocks were blocked from
residual blending. Cracked stocks, notably FCC cycle and clarified oils were allowed into
all residual blends but concentrations limited to a maximum of 25% based on literature
research and industry feedback. Visbroken vacuum residuum streams were limited to
maximum 10% regional average3, again based on feedback. The overall intent here was to
prevent the model from producing blends that could be readily unstable.
• Fuel stability additives were considered but were not included in the modeling analysis.
Reputable suppliers do make available additive packages designed to improve fuel stability.
However, they are not universally used for marine bunker fuels. Major oil company
suppliers are known to not use additives. Also, feedback from industry experts was
skeptical in terms of the degree of reliance that could be placed on such additives to prevent
fuel stability issues. Thus they were excluded from the analysis. At worst, this may mean
the analysis slightly understates the costs of future bunker fuels by omitting the cost of the
additive package.
3 The limits on visbroken resids and also on cracked stocks are regional averages. Therefore, they allow that, in the real
world individual blends/suppliers would have levels either higher or lower.
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4.4 Model Reporting
Given the importance attached to fuel stability and the focus of the study on bunker fuels,
the WORLD model reports were extended to directly summarize the regional blend compositions of
each residual grade (inland and bunkers). Thus, any anomalous blends could be more easily
identified.
In addition, since GHG emissions are becoming part of the debate on bunker fuels, a
recently added feature to post-optimally report the CO2 emissions from each world refining region
was activated. This enabled quantitative comparison of the effects of moving to more intense
processing of bunker (or other) fuels to achieve lower sulfur and/or shift to distillate grades.
4.5 WORLD Model Assumptions and Structural Changes
The following table summarizes these, and other, changes made to WORLD model structure
and features for this analysis, followed by additional discussions of the specific premises used as
the basis for the 2012 and 2020 BaU cases.
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Table 4-3. Summary of Structural Changes to the WORLD Model
Product Grades
The distillate and residual fuel specifications in the model were expanded to
fully differentiate international marine bunkers from inland fuels and to
enable clear distinctions between "traditional" and low sulfur bunkers
grades. The resulting model bunkers grades were:
• MGO - marine gas oil
• MDO - marine diesel, high sulfur
• MDO - marine diesel, low sulfur
• IFO 180-high sulfur
• IFO 180-low sulfur
• IFO 380-high sulfur
• IFO 380-low sulfur
Notes:
1. Only one grade of MGO was represented per region on the basis that
demands for MGO are small and mainly restricted to local ship
movements, hence any change in specification would apply to the whole
MGO volume for the region
2. separate low and high sulfur grades were implemented for the main
bunkers grades precisely to correctly capture the processing, blending and
economic effects of regions moving partly or fully to low sulfur
specifications
3. In reality, there is a trend in the market for "IFO 380" grade to be
displaced by IFO 500 and even 700. The approach was taken to simply
tighten the "IFO 380" viscosity specification where appropriate to
represent this. This approach is adequate since the reduction in distillate
cutter stock needed in the blend when gong from 380 to 500 or 500 to 700
centistokes is small as is the associated cost impact
4. the above grades were used to represent international or "blue water"
consumption of bunker fuels. Domestic uses of marine bunkers (primarily
distillates) were accounted for under the corresponding inland diesel or
residual fuel categories. See also below for discussion of bunker demand.
Product Specifications
The following specifications were already active in the model:
• MDO:
• IFO:
To these the following were added:
• Carbon residue - in order to prevent any inappropriate blends for
MDO or IFO grades
• Nitrogen - to cover the possible need to study nitrogen as a
component of NOx regulation. Not activated in BAU cases.
The following were considered but not added:
• Vanadium was not added as (a) it appears to be a rarely limiting
specification and (b) because to add it in would have entailed
significant model modifications
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Product Transportation
Product transportation matrices covering tanker, inter-regional pipeline and
minor modes were expanded to embody the additional distillate and residual
bunkers grades
Bunker Fuel Demand
A new model sub-system was built to import the RTI bunker fuel demand
projections. Given the differences between the RTI and TEA levels of
demand, the model was set up so that it could be run on both bases. Under
the RTI basis, global residual fuel demand is the same as that based on IEA
for the 2000 base year, but for forward years leads to an increase in total
global demand oil demand, i.e. upward adjustments versus the AEO 2006
Reference Case projections for 2012 and 2020
Fuel Stability
As detailed above, yield patterns on the residuum desulfurization and
visbreaker units were adjusted and paraffmic streams were locked out of
residual fuel blends.
Model Reports
Reports were added for blend composition of residual fuels and also for
reporting of refinery CO2 emissions.
4.5.1 AEO 2006 Outlook - Supply/Demand/Price Basis
Overall, oil supply, demand, and price parameters were set in the model based on the AEO
2006 Reference Case as summarized in Tables 4-4 and 4-5. Detailed supply premises, including
production by crude type by country/region, were based on internal WORLD model data and
projections. Non-crudes supply in the model is detailed by major fuel type and region. Projections
were set based on in-house data and also with reference to detailed EIA data.
Product demands for 2012 and 2020 were set using a year 2000 basis of historical data by
product type with growth rates by region and product. These growth projections are believed to be
broadly in line with those of other current forecasts, e.g., strongest growth for distillates among the
major fuel categories including continuing dieselization in Europe, emphasis on distillates in
Asia/China, no major shifts in USA transport fuels patterns (i.e., to diesel from gasoline),
essentially flat growth for inland residual fuel consumption, and significant growth for naphtha and
LPG.
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Table 4-4. AEO 2006 Petroleum Supply Forecast
Table 20. International Petroleum Supply and Disposition Summary
(million barrels per day, unless otherwise noted)
Crude Oil Prices (2004 dollars per barrel)
Imported Low Sulfur Light Crude Oil Price 1
Imported Crude Oil Price 1/
Production (Conventional) 21
Mature Market Economies
United States (50 states)
Canada
Mexico
Western Europe 3/
Japan
Australia and New Zealand
Total Mature Market Economies
Transitional Economies
Former Soviet Union
Russia
Caspian Area 4/
Eastern Europe 5/
Total Transitional Economies
Emerging Economies
OPEC Ql
Asia
Middle East
North Africa
West Africa
South America
Non-OPEC
China
Other Asia
Middle East 71
Africa
South and Central America
Total Emerging Economies
Total Production (Conventional)
Production (Nonconventional) 8/
United States (50 states)
Other North America
Western Europe
Asia
Middle East
Africa
South and Central America
Total Production (Nonconventional)
Total Production
2005
55.93
49.70
8.33
2.45
4.13
6.68
0.14
0.64
22.37
9.61
2.36
0.26
12.23
1.44
22.25
3.07
2.01
2.88
3.17
2.59
1.71
3.67
4.36
47.15
81.74
0.25
0.96
0.04
0.31
0.02
0.13
0.73
2.44
84.18
2012
47.65
43.59
9.51
1.56
4.06
5.64
0.08
0.86
21.71
9.65
3.47
0.32
13.44
1.45
25.09
3.50
2.44
3.48
3.30
2.50
2.15
3.97
4.62
52.49
87.65
0.63
1.98
0.10
0.83
0.57
0.28
1.32
5.71
93.36
2020
50.70
44.99
9.51
1.45
4.48
5.22
0.07
0.84
21.58
10.66
5.16
0.39
16.21
1.26
26.99
3.70
2.61
3.70
3.33
2.61
2.45
5.41
5.83
57.89
95.68
0.94
2.67
0.12
1.25
0.73
0.53
1.78
8.02
103.70
2004-
2030
1.3%
1.3%
0.2%
-2.0%
0.8%
-1.7%
-2.8%
0.7%
-0.3%
0.7%
4.6%
2.5%
1.9%
-0.9%
1.5%
0.6%
1.7%
1.5%
0.0%
-0.5%
1.9%
3.2%
2.0%
1.4%
1.1%
7.6%
5.4%
6.4%
9.4%
18.3%
9.4%
6.1%
7.1%
1.4%
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Table 4-5. AEO 2006 Petroleum Supply Forecast - continued
Table 20. International Petroleum Supply and Disposition Summary
(million barrels per day, unless otherwise noted)
Consumption 97
Mature Market Economies
United States (50 states)
United States Territories
Canada
Mexico
Western Europe 37
Japan
Australia and New Zealand
Total Mature Market Economies
Transitional Economies
Former Soviet Union
Eastern Europe 57
Total Transitional Economies
Emerging Economies
China
India
South Korea
Other Asia
Middle East 11
Africa
South and Central America
Total Emerging Economies
Total Consumption
OPEC Production 107
Non-OPEC Production 107
Net Eurasia Exports
OPEC Market Share
20.82
0.34
2.17
2.01
13.55
5.17
1.10
45.17
4.16
1.42
5.59
7.35
2.53
2.26
6.37
6.32
3.12
5.49
33.43
84.18
32.15
52.03
6.64
0.38
22.82
0.34
2.14
2.15
13.38
4.72
1.18
46.74
4.58
1.64
6.22
9.09
3.08
2.44
8.06
7.39
3.78
6.56
40.40
93.36
37.34
56.02
7.22
0.40
24.81
0.38
2.25
2.24
13.52
4.40
1.28
48.89
4.93
1.87
6.81
11.38
3.81
2.57
9.85
8.34
4.31
7.75
48.01
103.70
40.27
63.43
9.40
0.39
1.1%
1 .2%
0.3%
0.5%
0.2%
-0.9%
1 .2%
0.6%
1.0%
1 .6%
1 .2%
3.2%
2.7%
0.7%
2.6%
1.7%
1.9%
2.1%
2.3%
1 .4%
1.5%
1.3%
2.4%
0.2%
4.5.2 Product Quality
The 2012 and 2020 Ball cases were on the basis of a "best estimate" of fuels quality, given
implementation of already active regulations and continuation of current product-quality trends.
Specific premises built in to the cases were as follows:
Industrialized World
USA / Canada / Europe / Japan / Australasia
• Gasoline, on-road and off-road diesel ultra-low sulfur regulations are fully in place by the
2010/2011 timeframe, i.e., before 2012 with an essentially total phase-out of non ultra low
sulfur gasolines and diesel fuels.
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• Gasoline clear pool octanes remain flat.
• MTBE phase-out is completed in the US in 2006, and the RFS is in place.
• MTBE assumed not phased out in other world regions.
• Regulations that impact other fuels qualities, such as EPA toxics "anti-backsliding", Euro V,
CARBIII are in place.
• Consumption of high sulfur inland residual fuel entirely replaced by low sulfur (1% or less).
Non-OECD Regions
• Completion of lead phase-out in gasoline.
• An overall gradual upward trend in regional pool octanes such that, by 2020, all non-OECD
regions are within 1 octane or less of US average pool octane. Globally, the octane rise is
moderated by the fact that the large gasoline volumes in OECD regions are projected to
remain at constant, even slightly declining, octane levels.
• Progressive adoption of advanced (generally Euro II/III/IV) fuels standards for transport
fuels such that a moderate proportion of transport fuel demand has reached advanced
standards by 2012 and the majority by 2020.
• A gradual/partial trend toward mandates for low sulfur residual fuel for inland use.
4.5.3 Residual Fuel for Industrial/Inland Use
As the result of trends across both OECD and non-OECD regions, the global percentage of
low-sulfur industrial/inland residual fuel (less than 1% sulfur content) rises from an estimated 41%
in 2000, to 52% in 2012, and 63% in 2020. Thus, the basis is that these progressive shifts toward
low residual sulfur will be occurring in addition to parallel shifts toward lower sulfur residual
bunkers fuels (to the extent SECA regulations are met by sulfur reduction). The same is true for
distillates, where the continuing global trend toward low and ultra-low sulfur standards for on- and
off-road diesels will be occurring over the same time frame as the shift to tighter sulfur standards
for marine distillate bunkers.
4.5.4 Biofuels
The AEO 2006 Reference Case contains large increases in U.S. and global biofuels
production. Initial WORLD case projections were set at total global supply/demand of 1.5 mmbpd
of biofuels by 2012 and 1.8 mmbpd by 2020. These were later refined based on more detailed
analysis and projections contained in the TEA World Energy Outlook, 2006, released in November
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2006 as summarized in Table 4.6. At 1.4 mmbpd for 2012 and 1.94 mmbpd for 2020, these
projections are similar to the original AEO numbers.
Table 4.6 Projected Biofuels Consumption
Projected Biofuels Consumption
Source: IEA World Energy
Outlook 2006, Chapter 14 &
ethanol consumption
OECD
North America
United States
Canada
Europe
Pacific
Transition economies
Russia
Developing countries
Developing Asia
China
India
Indonesia
Rest of Dev Asia
Middle East
Africa
North Africa
Rest of Africa
Latin America
Brazil
World
2005
274
258
254
4
16
17
5
277
579
2012
785
482
465
17
298
5
2
2
0
0
9
2
3
11
1
8
0
7
0
275
1094
kbpd
2020
1060
608
585
23
444
8
2
2
0
0
26
5
6
22
2
16
2
14
0
382
1523
Tables 14.1, 14.2, 14.4
biodiesel consumption
2005 2012
61 231
5 68
5 66
0 2
56 160
3
1
1
0
0
14
3
5
17
2
12
0
11
0
1 22
62 306
kbpd
2020
253
83
78
5
164
6
1
1
0
0
40
8
9
34
3
25
3
22
0
39
413
Recent oil price rises and energy security concerns have spurred numerous biofuels projects
and legislative incentives in the USA, Europe and elsewhere. The IEA projection used was taken
from their Reference Scenario, not the Alternative Scenario which had more aggressive biofuels
growth projections. According to the IEA Reference Scenario, the United States, Brazil and Europe
will continue to dominate biofuels supply and consumption. In both the USA and Brazil, the IEA
projects that the proportion of biodiesel will slowly rise. Conversely, the LEA estimates that, in
Europe, where biodiesel currents comprises 84% of total biofuels supply, the proportion will drop
steadily as the main growth is expected to lie in ethanol production. Based on LEA and other data,
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current biofuels supply and consumption is assessed at approximately 75% Northern Europe
(dominated by Germany and secondarily France), 20% Southern Europe (mainly Italy and Spain),
5% Eastern Europe. These proportions were assumed to remain constant throughout the period to
2020. According to the TEA, Europe's growth in biofuels supply will result in these fuels
constituting around 4.9% of total transport fuel demand by 2010, versus a declared EU target of
5.75%. The 2020 biofuel volumes correspond to around 7.5% of European transport fuel demand
as projected in the WORLD BAU case. Relatively small volumes of biofuels are projected by TEA
to be forthcoming in Asia (led by China) and Africa. In the WORLD cases, the majority of these
biofuels were projected to be biodiesel.
Total U.S. plus Canada biofuels production was projected to reach 0.69 mmbpd by 2020,
dominated by ethanol. Ethanol was allowed to be used in RFG by adding to RBOB's at either 0%,
5.7% or 10% ethanol by volume (maximum 5.7% for CARB RFG). Additional ethanol was allowed
to be absorbed in CG at concentrations up to 3.7 wt% maximum oxygen content. In reality, a small
but increasing volume of ethanol looks likely to be sold as "E85" type gasoline. Consideration was
initially given to modeling E85 as a distinct grade but the decision was made to not model it
explicitly.
4.5.5 Regional Bunker Demands
As discussed above, the WORLD model was set up so that it could be run under both IEA
and "RTF (see Section 3) premises for bunker fuel base demand and growth. A two step procedure
was adopted. Firstly, the bunkers basis was set to "LEA" and overall and regional oil supply and
demand projections were matched to the AEO 2006 Reference Case for either 2012 or 2020,
respectively 93.4 and 103.7 mmbpd. Then, the bunkers basis was reset to "RTF'. This led to an
increase in total residual and total oil demand, which was met by rebalancing supply through raising
OPEC production.
The bunkers demand projections were taken directly from findings discussed in Section 3.
A primary issue here entailed the regional allocation of bunkers consumptions, given that the base
2003 IEA bunkers demand totaled 145 mmtpa and the "RTF' demand is estimated at 305 mmtpa.
Table 4-8 summarizes 2003 bunkers demands per LEA and the findings in Section 3 ("RTF') and
then projections for 2012 and 2020. As can be seen, judgment was applied to allocate the 157
mmtpa delta in demand. All regions were increased versus LEA forecasts, but with the major
increases in non-OECD areas. The regional allocations were driven in large part by the trade flows
built in to the shipping model developed in Section 3. The allocation was also considered logical on
the basis that bunkers fuel demands are less likely to be accurately separated out and reported in the
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national statistics of non-OECD regions. As discussed above, there is open acknowledgement that
bunkers consumption data are incomplete. For instance, IEA data report bunkers demand for Africa
at a total of only 9.5 mmtpa or 6.4% of global bunkers demand. However, BunkerWorld data on
ports and companies active in bunkering list some 93 bunkering ports spread across 38 countries in
Africa and with often several suppliers active in each port. This does not seem consistent with data
indicating only minimal bunkers consumption. Note that the situation regarding these statistics and
estimation reinforces that the regional allocations of bunkers demands used in the BaU cases are
approximate and that further work could be pursued to arrive at more rigorous values.
Table 4-8. World Regional Bunker Sales
World Regional Bunkers Sales mmtpa
Bunkers Sales
WORLD 2003 2003 Comparison RTI vs IEA
region
basis
USEC
USGICE
USWCCW
GrtCAR
SthAm
AfWest
AfN-EM
Af-E-S
EUR-No
EUR-So
EUR-Ea
CaspRg
RusFSU
MEGulf
Paclnd
PacHi
China
RoAsia
World
IEA
6.0
8.9
5.5
4.5
5.4
1.2
4.6
3.7
32.4
14.9
0.5
0.0
0.4
10.3
6.1
37.6
5.4
0.3
147.8
RTI
7.5
11.6
8.4
11.7
16.8
2.3
12.3
7.1
42.3
27.1
1.4
0.0
7.8
25.0
25.9
57.0
31.5
9.2
304.9
Delta
1.5
2.6
2.9
7.2
11.4
1.1
7.6
3.5
9.9
12.2
0.9
0.0
7.3
14.7
19.8
19.5
26.1
8.9
157.2
Percent
124%
130%
152%
260%
312%
186%
265%
194%
131%
182%
293%
0%
1865%
242%
421%
152%
587%
2853%
206%
Bunkers Sales
2012 2020
Growth Rates to
2012 2020
from 2003
RTI
9.5
14.7
10.7
15.9
21.0
2.7
14.5
8.7
52.8
34.8
2.0
0.0
10.3
31.8
29.0
69.4
66.5
12.0
406.2
RTI
11.2
17.2
12.5
21.5
24.0
2.9
16.1
10.0
60.0
42.4
2.6
0.0
12.3
36.8
31.6
78.4
101.5
14.1
495.3
RTI
2.7%
2.7%
2.7%
3.4%
2.5%
1.9%
1.8%
2.2%
2.5%
2.8%
4.0%
3.1%
3.2%
2.7%
1.3%
2.2%
8.7%
2.9%
3.2%
RTI
2.4%
2.4%
2.3%
3.7%
2.1%
1.5%
1.6%
2.0%
2.1%
2.7%
3.7%
2.5%
2.8%
2.3%
1.2%
1.9%
7.1%
2.5%
2.9%
where:
USEC is U.S. East Coast
USGICE is U.S. Gulf Coast and Interior, plus Eastern Canada
USWCCW is U.S. West Coast, plus Western Canada
GrtCAR is the Greater Caribbean
SthAm is South America
AfWest is Africa West
AfN-EM is Africa North and the Mediterranean
Af-E-S is Africa East and South
EUR-No is Europe North
EUR-So is Europe South
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• EUR-Ea is Europe East
• CaspRg is the Caspian Region
• RusFSU is Russia/Former Soviet Union
• MEGulf is the Middle East Gulf
• Paclnd is Pacific Industrialized
• PacHi is Pacific High Growth
• China is China
• RoAsia is Rest of Asia
4.5.6 Regulatory Outlook for Bunker Fuels
Primary Bunker Quality Regulations
For the Ball cases, the bunkers demand and quality basis was that existing regulations
would apply, but that there would be no additional regulations, thus setting the modeling framework
for later subject cases to quantify the impacts of U.S. SEC As, etc. Specifically:
• MARPOL Annex VI (ISO 8217 2005) specifications were applied to all international
distillate and residual bunkers as set out in Figures 4-2 and 4-3. MGO specifications were
taken from those for DMA and the MDO specifications from DMC. Based on industry
advice, buyers almost exclusively opt for the higher grade versions of IFO180 and 380.
These are the ISO8217 2005 grades RME and RMG respectively (rather than RMF and
RMH). RME and RMG have tighter specifications for carbon residue and vanadium. The
carbon residue specifications, at 15 and 18 respectively, were activated in the model to
provide a limit on possible future degradation of IFO quality. Carbon residue was also
activated on the DMC MDO blend, even though this is likely to play less of a role as sulfur
limits on MDO are tightened.
• The EU Baltic and North Sea SECAs take effect in 2006 and therefore were applied. They
were, however, "locked" at the 1.5% sulfur level, even though current EU initiatives make it
clear that the intent is to achieve the equivalent of 0.5% sulfur fuel across a broad swath of
EU waters by 2012. Note, the ISO8217 2005 specification explicitly allows for the 1.5%
sulfur grades in SECAs.
• Regulations currently being finalized were applied to California bunkers consumption.
There are two regulatory tracks under way in the state which will be examined as part of the
future subject cases. Firstly, CARB is considering additional bunker fuel regulation.
Specifically, the CARB rule under which both MGO and MDO in California Regulated
Waters used in auxiliary engines must comply with a 0.5% sulfur maximum was included in
the 2012 and 2020 BAU cases. CARB is evaluating further tightening of PM, NOX, and SOX
limits on auxiliary engine emissions, including a possible 0.1% limit for MGO by January
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2010, with analysis due by July 2008. In addition, the port authorities for Long Beach and
Los Angeles have finalized their own plans which go beyond the CARB regulations. The
San Pedro Bay Ports Clean Air Action Plan contains measures to require ships to use marine
gasoil (MGO) with a sulphur content of less than 0.2% in their main and auxiliary engines
within a 40 nautical mile zone. The regulations will either be implemented fully in
2007/2008 or will be applied more gradually through 2011 as shipping companies' lease
agreements are renegotiated. A report on the legality of the ports' plans by the California
Office of Administrative Law is due by December 5 2006. Note, these regulations replace
use of IFO fuels with the highest quality marine fuel MGO, not MDO.
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Table 4-9. Summary of Bunkers Sulfur Specifications Used for 2012 & 2020 BAU Cases
Annex VI Percent of N WE Percent of SECA Fuel
/ISO8217 EU Bunkers Under Requirement Met by
2005 SECAs SECA LSFO
MGO
MDO
IFO 180/3 80
California
MGO/MDO
1.5%
2.0%
4.5%
0.2% (1)
1.5%
1.5%
CA
Jan
2007
Reg
0.5%(2)
2012 2020
50% 50%
50% 50%
50% 50%
Percent of Model's
USWCCW Region
MGO/MDO Under
CA Jan 2007 Reg
75% 75%
2012
95%
95%
95%
Percent
MGO/MDO
2020
80%
80%
80%
ofCA
Under Jan
2007 Reg Met by LSFO
95%
80%
Notes:
1. EU has proposed tightening MGO to 0.1% from 2008. BaU case is on basis of 0.2%.
2. CARS has proposed tightening the MGO regulation to 0.1% by January 2010. 0.5%
was used in the BaU cases.
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Characteristic
Density at 15 !C
Viscosity at 40 "C
Fiash point
Pour point (upper) c
— winter quality
— summer qually
Ooud oosnt
Sulfur
Cetane index
Carbon residue
on !0%{V/V) distillation
bottoms
Carbon residue
Ash
Appearance1
Tola! sed'Ttent. existent
Water
Vanadium
AluTuniurn plus Silicon
Used luoncatina oil (ULO)
- Zinc
- Phosphorus
- Calcium
Unit
kg.'rr
rnm~/s b
T,
°C
°c
% M'O
—
% f'DI^ii)
% (!n/!i;i
% frn^i!)
—
% (m/mi
% ilW}
mg/kg
mg.'kg
nng/kg
Tig/kg
mg/kg
Limit
max
min
max
mm
nin
nax.
max.
max.
max
riin
nax
max.
max.
-
max.
max.
max.
max.
max
max
max.
Category ISO-F-
DMX
-
1,40
5,50
43
-
-I6d
1.00
45
0.30
0.01
DMA
8900
1.50
6,00
60
- 6
0
—
1,60
vs f jei is suitable tor use -.vtho.it heating at ambier: temperatures dovn to ~ It :C.
* A su '"ur ! mrt of 1 .5 % 0^'w; ^silS apply -r) SO, emission con^o; areas designated ay the ntemstioial Mantle Organization, whei
rts ^eevart protocol enters nto 'orce T^iere may as 'oca variations, for example ^*e EU 'equ-'ss that su?p^ur content of certain distillate
grades be mited to C 2 % ff*,V! in certa-'i app fcations See C 5 ana reference "7\
]' tne sample s c ear a'xJ -.v "ji -x> * sible sed nsent or -.vate', tie totai sedr^enl existent and y*atei tests snail not be required -See
7 4 and 1 5
g A ^ue; sha? be considered to se sr@e of used lLDricatin9 oils (LLOs) f' one or more 0* the elements liix:. phosphorus and calcium
are beic* 01 at (he specrf «*d imits All th'ee etemsrts sholl exceed the same limrts tsefon? a f ,» shall be deemed to ccntai'i ULOs
Figure 4-2. Requirements for marine distillate fuels
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I 0
S o
is
2=»
is
Ho
£ »
Figure 4-3. Requirements for marine residual fuels.
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S
1
Figure 4-3 (continued). Requirements for marine residual fuels.
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EUSECA Compliance
A decision process was followed to set up the 2012 and 2020 premises related to the EU
SEC As (essentially the same process will need to be followed for all other SEC As studied in the
future). The WORLD model contains projections of total bunkers demand broken down into MGO,
MDO, IFO180 and IFO380; thus demand in the North Europe region. The first step in the process
was to assess the proportion/volume of each type of bunker fuel that would fall under the SEC A
standard (Baltic plus North Sea in this instance) within the region. For the two North Europe
SECAs, this was estimated at 50%, equivalent to 26mmtpa total in 2012.4 Secondly, an assessment
was made regarding how much of the affected fuel would be low sulfur, i.e., what part of the SEC A
fuel requirement would be met by this means, rather than through abatement (or emissions trading).
For 2012, the base premise was that 90% of the bunker fuel would be low sulfur; for 2020, 60%.
The underlying rationale was that abatement technology needs time to be proven commercially and
to be taken up by the shipping fleets. This will constrain the proportion of SECA requirements that
can be met by abatement (or emissions trading) in 2012, but by 2020 its potential expands. These
premises can readily be altered and need to be in the future subject cases to examine the
refining/supply impacts of growing SECA areas and tightening emissions standards with alternative
compliance scenarios.
For California, the proportion of the MGO/MDO in the WORLD model region called
USWCCW needing to comply with the California regulation was estimated at 75%, i.e., that
California's economy, trade, and shipping dominates this West Coast region. It was further
estimated that, of this, 90% of compliance would be achieved by LSFO in 2012 and 60% in 2020 in
the BAU cases. Again, these premises can be revised and also sensitivities studied.
4.5.7 IFO Viscosity / Grade Mix
Many marine engines today can handle IFO with a viscosity higher than 380 centistokes.
Raising viscosity to 500 or 700 centistokes slightly reduces the cutter stock content of the bunker
fuel. In today's market, this has led to IFO 380 to IFO500 price differentials of the order of $2-
4/ton. This in turn has created a growing interest in supplies of IFO500 and even IFO700. The
trend has been especially marked in Singapore where IFO500 sales have grown rapidly in the last
two years. To reflect this trend, the maximum viscosity of the "IFO380" bunkers grade in the
model was raised moderately.
4 Robin Meech at the DC MARPOL Consultative Meeting February 2006 estimated 2012 North Europe SECA bunkers
at approximately 21 mmtpa but against a base projection understood to be based primarily on IEA statistics. This
figure was adjusted to arrive at the 2012 base volume to be used.
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The global bunkers market is trending toward higher viscosity fuels containing less
distillate. Since raising the distillate content of an IFO fuel is one way to lower its sulfur content,
the SEC A regulations could have the effect of reversing this trend in the affected regions. The
model was not set up to allow switching from IFO 180/3 80 to MDO as a means to meet sulfur
standards. Such a feature was not considered necessary as the model was set up to allow
IFO 180/380 viscosities to be lowered, thereby allowing more distillate streams into the IFO blend if
found to be economic as the means to reduce sulfur.
4.5.8 Refinery Capacity & Projects
The WORLD model contains a detailed bottom-up database by process unit and refinery
worldwide. This is brought up to date as new refinery capacity survey data are published. EnSys
have found, however, that extensive cross-checking of and corrections to data presented in sources
such as Oil & Gas Journal (OGJ) are necessary. The Ball cases were run with a capacity database
that was based on January 2005 OGJ data plus extensive review and revision.
For forward cases, WORLD has four ways of modifying the base capacity:
1. known projects are added in to the base.
2. revamping of selected existing units is allowed to take place (principally conventional to
ultra-low distillate desulfurization).
3. debottlenecking of selected major units is allowed, subject to annual limits.
4. investments in major new unit capacity are allowed.
The projects database used for the Ball cases was based on detailed review of project
announcements through the end of 2005. In WORLD, projects are classified at four levels: under
construction, under engineering, planned, and announcement. These correspond to descending
levels of follow through to completion and also an increasing tendency for project delays versus the
initial start-up target date. The model user sets parameters by region that govern both the
proportion of each class of project to be completed and the associated delay profile.
Since mid-2005 especially, there have been numerous announcements of new projects,
many for major refinery expansions or new grassroots refineries. Nearly 11 mmbpd of refinery
crude unit capacity expansion projects are currently listed, with somewhat higher figures according
to more recent project reviews. However, based on experience, factors were applied to curtail and
delay particularly the "planned" and "announcement" projects in order to arrive at a realistic level
of projects likely to go ahead. The net effect was that the 2012 (and also 2020) Ball case contained
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a total of 6.1 mmbpd of new project capacity as summarized in Table 4-10. (This estimate compares
to a figure of around 8 mmbpd by 2015 according to a Wood Mackenzie review.5) The main
regions expected to see expansions are the U.S. and then the Middle East, China, and the rest of
Asia (India). The growing list of project announcements in India was particularly discounted.
Capacity expansion in Europe is projected to be minimal. While Table 4-10 lists crude unit major
capacity additions, the complete project database covers the full suite of refinery processes,
including upgrading and desulfurization. In the Ball cases, the model added capacity, using first
the low-cost revamp and debottlenecking potential allowed and then balanced on major new unit
additions.
Table 4-10. Major Capacity Additions
Base Major Capacity Additions
included in 2012 & 2020 cases
mmbpcd
USEC 0.0
USGICE 0.8
USWCCW 0.1
GrtCAR 0.4
SthAM 0.2
AfWest 0.1
AfN-EM 0.1
Af-E-S 0.1
EUR-No 0.0
EUR-So 0.1
EUR-Ea 0.0
CaspRg 0.1
RusFSU 0.0
MEGulf 1.4
Paclnd 0.0
PacHi 0.0
China 1.6
RoAsia 1.0
Total 6.1
4.5.9 Refinery Technology & Costs
Based on a review of refinery process technologies centered on desulfurization, adjustments
were made to process unit capital costs in the model. Details of the base data researched as part of
the technology review are set out in Appendix B. Technologies in the WORLD model represent
those which are proven or recently commercialized. In any long term study, this approach is
conservative as it does not allow for the possible effects of more far reaching technology advances.
An example in this study which could prove to be significant in the future is the development of
' "Refiners See Strong Returns Near-Term Despite Looming Capacity Build-up", Oil & Gas Journal, Mar 13 2006,
Aileen Jamieson, Wood Mackenzie.
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ultrasound-based desulfurization processes, as in that of Sulphco. That particular technology is
nearing commercial scale with the installation of seven 30,000 bpd units in Fujairah. Should the
supplier's claims be proven out by sustained operation, the outlook for future desulfurization and
partial upgrading of residual fuels, crudes and other streams could be markedly altered relative to
the projections made in this study. Other similar developments may also occur. Excluding such
processes does have the effect of ensuring that the quantitative modeling results are based on
known, feasible and economic process paths.
The WORLD technology database has been the subject of on-going review. A further
review was made to check the capital and operating costs and yields of units most likely to impact
bunker fuels economics, notably residual hydro-desulfurization and visbreaking, as described in
Section 4.3.
The process unit capital costs in WORLD originally were based on year 2000 (U.S. Gulf
Coast). The impacts of changes that have occurred since to raise costs of construction were
examined. The Nelson Farrar Refinery Construction Inflation Index was found to have risen by a
factor of 1.32 between 2000 and February 2006, driven by well publicized increases in costs for
steel, cement, specialty equipment items and labor. However, applying this multiplier directly to the
2000 basis capital costs in WORLD would have had the effect of stating that the costs of new
construction would remain at this elevated level for all new investments through 2020. The large
increase in the costs of refining and other oil sector facilities is reflected in the IEA World Energy
Outlook 2006. IEA estimates that capital costs will "fall back somewhat after 2010" based on
conditions in the A&E sector gradually easing.
In WORLD, the decision was taken to use a multiplier of 1.30 versus 2000 for capacity
additions in the 2012 case and 1.20 for additions in the period from 2012 to 2020, i.e. in the 2020
case. Similarly, Nelson indices indicate that refinery chemicals "OVC" type costs have risen by
some 60% since 2000. Multipliers of 1.50 and 1.30 were used for 2012 and 2020 cases respectively.
WORLD results are sensitive to the interplay between crude (and fuel) costs, refinery capital
costs and freight rates. Broadly:
• raising crude oil price results in more refinery capacity investment, especially in
upgrading processes, with the logical effect of reducing the volume of - now high
cost - raw material used to make a given product slate
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• raising refinery process unit costs has an opposite effect; total dollar investments
may rise but the new capacity bought for the money is less and the industry responds
by using somewhat more crude oil
• raising tanker freight rates has the effect of in turn justifying additional refinery
process investment in order to minimize high cost inter-regional movements of crude
and products.
Part of the "dilemma" of the EPA analysis was that we have entered into a high cost
world where the traditional levels of and relationships between capital cost, crude and fuel
costs, and transport costs are being re-written. In the BAU cases, higher crude oil price
(versus history) was a given, hence also higher refinery fuel and natural gas prices. As
discussed above and below, both refinery capital costs and tanker freight rates were moved
upward relative to history. This resulted in scenarios where all costs - crude, fuel, OVC's,
freight- were elevated versus historical levels.
Nelson Refinery Cost Indices
•Refinery Construction
Inflation Index
•Refinery Fuel Cost Index
Refinery Chemicals cost
Index
1997
2000
2003
2006
4.5.10 Transportation
WORLD contains details of inter-regional crude, non-crude, finished, and intermediate
product movements by tanker, pipeline, and minor modes. Each tanker movement is assigned to
one of five tanker size classes, and freight costs are built up based on Worldscale flat rate times
percent of Worldscale plus ancillary costs such as canal dues and lightering where applicable; also
duties. Reflecting the factors reviewed above, Worldscale percentage rates were applied (see Table
4-11), that were higher than recent freight rate history. Again these reflect increases in
steel/construction and fuel costs plus the fact that (a) there is current tightness in capacity in
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Draft Report - Do Not Cite or Quote
shipbuilding yards, (b) there is an on-going requirement to turn over world fleets to new vessels, in
part because of double hull regulations and (c) there is a need to expand the world's tanker fleets to
meet growing trade requirements.
In general, high steel prices directly impact the cost of a tanker and, as such, may place a
damper on orders for new ships. High steel prices also indicate a potential "tight" supply of steel
that can also place a constraint on shipyard contracting practices (i.e., higher prices or flexible
pricing requirements). High steel prices also increase the price paid for scrap tankers potentially
inducing tanker owners to hasten scrapping. In general, the supply of tankers looks to be
constrained in the next few years by shipyard construction capacity. Tankers are competing for new
construction space (berths) with LNG, container and dry bulk ships. Usually only one sector is
doing well financially, which increases pressure for newbuilding in the strong sector. At this time
all sectors (LNG, container and dry bulk ships) are doing very well. This has led to difficulty for
tanker owners to secure newbuilding contracts. This all leads to higher prices for newbuildings.
In WORLD, freight rates are arrived at by multiplying percent of WorldScale by
Worldscale 100 flat rate. (Other cost items such as canal tariffs or lightering are also added in
where relevant.) One issue is that the WorldScale Association issues updated flat rates each
January. These reflect cost changes, including for fuel, i.e. the underlying flat rates are not constant
over time. To best assess how to represent future freight rate levels in the model, recent freight rate
history was examined. The three figures below show that - although bunker fuel costs have risen
substantially since 2002/2003 and the other factors described above have been at play, most freight
rates (stated as $/bbl) have increased only slightly. The factors which explain this apparent
discrepancy are Thus, the implication for future freight levels is (WE ARE STILL
WORKING ON THIS TO GET EXPLANATION..)
The resulting multipliers used versus a reference basis of January 2005 (??) for the
WorldScale 100 flat rates were ?? for 2012 and ?? for 2020.
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400
350
300
200
150
100
50
0
Index of Bunker Fuel Price 1989 = 100
^
2.00
1.00
0.00
o
Spot Crude Freight Costs $/bbl
-Gulf/EAST
-Carib./USEC
o
o°
o
0.00
<§>
Spot Clean Product Freight Costs $/bbl
o
o
o°
Gulf/EAST
Carib./USG
Med./NWE
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Table 4-11. Tanker Class - to be replaced
tanker class
MR2
Pana Max
AFRA Max
Suez Max
VLCC
size DWT
40,000
55,000
70,000
135,000
270,000
Percent WS
2012/2020
260
220
180
130
90
In addition, note was taken that - in the planned Phase II SEC A etc. cases - tightening of
bunker fuel regulations and/or shifts from IFO to marine diesel will inevitably increase bunker fuel
costs and consequently freight rates, i.e. in those cases, freight rates will need to be adjusted
upward, potentially regionally. EnSys intends to employ an in-house tanker cost model to assess
the appropriate increases for those cases.
As a component of recent assignments, care has been taken in WORLD to build in accurate
representations of major new and expanded as well as existing pipelines. Particular emphasis has
been put on ensuring an accurate profile of pipelines and expansions for export routes for crudes
(including syncrudes) ex Canada and export routes both east and west ex Russia and the Caspian.
For Canada, the Ball premise was that one, but not both, of the export lines to the West Coast /
PADDV / Pacific would go ahead. This impacts the amount of syncrude and conventional crudes
routed into the US PADDs II, IV and potentially III versus west to PADDV and Asian regions. For
Russia, based on recent developments, the Ball case assumed the pipeline to the Pacific would go
ahead and would have a spur into China. In reality, this latter will most likely partially displace
growing rail movements of crude into China from Russia that were already in the model.
4.6 Input Prices for the WORLD Model
4.6.1 Marker Crude Price
WORLD operates with a single marker crude price and all other crudes and nearly all non-
crudes supplies and product demands fixed. Crude and product prices are thus generally produced
as model outputs. For the Ball cases, the model was run with Saudi Light as the marker crude.
This crude price was taken from the AEO 2006, but since EIA uses a U.S. average acquisition price
as its "world oil price", the EIA price was adjusted to obtain a corresponding Saudi Light price
using recent historical crude price data.
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4.6.2 Natural Gas Price
Certain other prices are also inputs to the model. The most important among these is natural
gas price since natural gas is the balancing refinery fuel supply in most regions, as well as a primary
feedstock for hydrogen production. Regional natural gas prices (major industrial user) were set in
the range of $4 to $6 per MMBtu - in line with AEO 2006 and third-party long-term projections.
4.6.3 Miscellaneous Prices
Input prices for the byproducts, coke low sulfur, coke high sulfur and elemental sulfur were
set respectively at $25, $5 and $10 per ton. Purchased electricity prices were taken - for the U.S.
regions - from AEO 2006 and were generally in the range of 6 cents per kWh.
4.7 Reporting
The WORLD model's standard reports were modified to accommodate the revised distillate
and residual fuels products structure. Standard reports provide global and regional information on:
• refinery throughputs, capacity additions, investments
• inter-regional crude, intermediate and product movements
• supply/demand balance
• crude FOB and GIF prices
• regional product prices.
As discussed in Section 4.4, blend reports were added for the residual grades, in part as a
check to ensure avoidance of potentially unstable blends.
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SECTION 5
THE WORLD MODEL'S PROJECTIONS FOR 2012 AND 2020
SECTION TO BE UPDATED ONCE WE HAVE AGREED THE BASIS FOR THE
FINAL CASES.
This section sets out the specific results obtained for the 2012 and 2020 WORLD Model
cases, based on the projections and premises reviewed in Section 4. Business-as-usual
projections were estimated for these two years using both the IEA and "RTF' bunker demand
assumptions discussed in previous sections. Full results are presented in this section in detail.
In summary, the findings indicate that the important drivers affect any SECA analyses
center on product-demand outlooks. Adopting the bunker forecasts developed in Section 3 leads
to a 2020 global demand for residual bunkers by 2020 of 6.87 mmbpd - versus 1.92 mmbpd
based on LEA premises (this is partially offset by a reduction in inland residual fuel from 6.5 to
5.2 mmbpd). The 2020 levels for MGO plus MDO in Section 3 are equivalent to 1.9 mmbpd
versus 0.6 mmbpd based on IEA premises. Consequently, these forecasts imply that estimated
impacts of SEC As or other marine fuels regulations will be similarly greater. The second major
driver in the WORLD analyses discussed in this section is the on-going shift toward distillates,
especially in Europe and non-OECD regions, which is expected to materially alter gasoline and
distillate trade patterns, their product pricing and refining investments and economics. These
developments will also affect impacts of SEC As/global marine fuels regulations.
5.1 Supply-Demand Balance
Tables 5-1 and 5-2 summarize the supply-demand inputs and results from the 2012 and
2020 WORLD BAU cases. Results for both LEA basis and RTI basis projections of bunkers
demand are shown. As discussed in Section 4, the IEA basis case was matched to the AEO 2006
(since it is on an IEA/EIA basis in terms of bunkers demand) and the case run. Than a second
case was run with the bunkers basis adjusted to RTI which increases bunkers and total residual
demand globally. The needed incremental supply was taken to be OPEC crude. WORLD results
generally do not exactly match the underlying forecast (AEO) numbers for total oil supply and
demand. This is because several demand factors, including internal refinery fuel, coke and sulfur
by-products, are dynamic within WORLD and not fixed.
The 2012 and 2020 cases reflect the overall global trend for (a) demand increase to be
predominantly light, clean products and (b) for the main growth globally to be in distillates. This
5-1
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Draft Report - Do Not Cite or Quote
latter is combined with an assumed continuing process of dieselization in Europe which reduces
gasoline demand growth there.
The main effect of applying the RTI bunkers projections, versus an IEA basis, is to raise
total residual demand by 1 mmbpd by 2012 and over 1.8 mmbpd by 2020. This also entails a
switching between inland and bunkers residual fuel grades. In the IEA Basis BAU cases, global
inland residual fuel quality was projected to progress partially toward a 1% standard by 2020.
Therefore, the BAU cases with RTI bunkers basis, increase total residual fuel demand but,
because the only active SECA's are in Northern Europe in the cases, they shift global residual
fuel toward a higher average sulfur.
The change in overall global demand between the LEA and RTI basis cases is 0.6 mmbpd
for 2012 and 1.3 mmbpd for 2020. The increase in residual demand is met by an increase in
(OPEC) crude runs. The incremental crude supply contains both light and heavy cuts. As
discussed further below, the net effect of higher residual demand under RTI projections is thus
an easing in light-heavy supply-demand tightness.
5-2
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Draft Report - Do Not Cite or Quote
Table 5-1. WORLD Model Case Results - Supplies
WORLD MODEL CASE RESULTS
Bunkers Basis
2012
IEA
2012
RTI
2020
IEA
2020
RTI
SUPPLY
SUPPLY - CRUDES (INCLUDES SYNCRUDES & CONDENSATES)
Crude gross production
of which
Crude Direct Use
Crude Direct Loss Total
Crude net to refs before TRLOS
Crudes net to refineries
GSY- SYN CRUDE (fully upgraded)
GCO-CONDENSATE
GSW - SWEET <0.5S
GLR - LT SR >36 API >0.5%S
GMR - MD SR 36-29 API >.5S
GHR-HVYSR 20-29 API >.5S
GXR - XHVY SR <20 API >.5 S
CRUDE SUPPLY TO REFINERIES
Crude Direct Loss in Refineries
Crude TRLOS
Crude net to refs before TRLOS
SUPPLY - NON CRUDES
NGL ETHANE
NGLs C3+
PETCHEM RETURNS
BIOMASS
METHANOL (EX NGS)
GTL LIQUIDS (EX NGS)
CTL LIQUIDS (EX COAL)
HYDROGEN (EX NGS)
TOTAL
PROCESS GAIN
MMBPD
79.637
0.832
0.638
78.167
after
TRLOS
1.164
1.922
26.257
11.022
25.813
9.067
2.149
77.395
0.638
0.135
78.167
1.597
5.587
0.709
1.527
0.130
0.796
0.488
0.981
11.815
2.223
MMBPD
80.352
0.832
0.638
78.882
after
TRLOS
1.164
1.922
26.473
11.214
26.055
9.131
2.149
78.108
0.638
0.136
78.882
1.597
5.587
0.709
1.527
0.128
0.796
0.488
0.940
11.771
2.151
MMBPD
86.667
0.832
0.638
85.197
after
TRLOS
1.555
2.062
29.432
10.806
28.140
9.529
2.882
84.405
0.638
0.154
85.197
1.797
6.387
0.789
1.866
0.146
1.248
0.891
1.307
14.431
2.602
MMBPD
88.160
0.832
0.638
86.690
after
TRLOS
1.555
2.062
29.771
11.122
28.871
9.633
2.882
85.896
0.638
0.156
86.690
1.797
6.387
0.789
1.866
0.146
1.248
0.891
1.205
14.328
2.509
5-3
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Draft Report - Do Not Cite or Quote
Table 5-2. WORLD Model Case Results - Demands
WORLD MODEL CASE RESULTS
Bunkers Basis
2012
IEA
2012
RTI
2020
IEA
2020
RTI
DEMAND
EXTERNAL DEMANDS - FINISHED PRODUCTS NON SOLID
ETHANE
LPG
NAPHTHA
GASOLINE
JET/KERO
DISTILLATE
RESIDUAL FUEL
OTHER PRODUCTS (excl coke.sulphur)
CRUDE DIRECT USE
PETR COKE LOW SULPHUR MMBPD
PETR COKE HIGH SULPHUR MMBPD
PETR COKE LS AS % OF TOTAL
PETR COKE TOTAL MMBPD
ELEMENTAL SULPHUR MMBPD
TOTAL
INTERNAL DEMANDS/CONSUMPTION
REFINERY FUEL - CRUDE BASED STREAMS
PROCESS GAS
FCC CATALYST COKE
MINOR STREAMS
RESIDUAL FUEL
NATURAL GAS TO RFO
TOTAL INCL NATURAL GAS
RFO INCL NGS AS PCT OF CRUDE TO REFS
RFO EXCL NGS AS PCT OF CRUDE TO REFS
MERCH FO - INTERNAL STREAMS
TOTAL INTERNAL CONSUMPTION & LOSS EXCL NAT GAS
TRANSPORT/DISTRIBUTION LOSSES
TRANSPORT LOSS TOTAL
- ALLOCATION TO CRUDE
- ALLOCATION TO PRODUCTS & INTERMEDIATES
1.597
7.856
5.850
23.535
7.459
27.255
10.082
3.532
0.832
0.416
0.527
44%
0.943
0.215
1.158
2.458
0.377
0.000
1.291
1.641
5.766
7.5%
5.3%
0.005
4.130
0.189
0.135
0.054
1.597
7.856
5.850
23.535
7.459
27.128
11.088
3.532
0.832
0.442
0.240
65%
0.681
0.193
D.874
2.415
0.388
0.000
1.291
1.614
5.708
7.3%
5.2%
0.005
4.099
D.190
0.136
0.054
1.797
8.632
6.930
25.426
8.139
31.459
10.235
3.808
0.832
0.352
0.906
28%
1.259
0.261
1.520
2.574
0.379
0.000
1.682
1.813
6.448
7.6%
5.5%
0.007
4.641
0.215
0.154
0.061
1.797
8.632
6.930
25.426
8.139
31 .298
12.060
3.808
0.832
0.405
0.510
44%
0.914
0.229
1.143
2.477
0.383
0.000
1.682
1.849
6.391
7.4%
5.3%
0.007
4.548
D.219
0.156
0.063
SUPPLY DEMAND SUMMARY
SUPPLY - TOTAL
Crude - gross production incl condensates & syn crudes
Non Crudes incl H2 ex NGS
Process Gain
TOTAL SUPPLY
Crude as percent of total supply
DEMAND - TOTAL
External - gases & liquid products (incl crude direct use but not loss)
External - solid products
Internal - fuel excl natural gas incl FCC cat coke
Internal - process & crude losses
Internal -transport/distribution losses
TOTAL DEMAND
TOTAL DEMAND -TOTAL SUPPLY
TOTAL DEMAND -TOTAL SUPPLY
WORLD
79.637
11.815
2.223
93.675
85%
87.998
1.158
4.130
0.000
0.189
93.475
-0.21%
(0.200)
WORLD
80.352
11.771
2.151
94.275
85%
88.877
0.874
4.099
0.000
0.190
94.040
-0.25%
(0.234)
WORLD
86.667
14.431
2.602
103.699
84%
97.258
1.520
4.641
0.000
0.215
103.634
-0.06%
(0.065)
WORLD
88.160
14.328
2.509
104.998
84%
98.922
1.143
4.548
0.000
0.219
104.832
-0.16%
(0.166)
5-4
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Draft Report - Do Not Cite or Quote
Table 5-3. Refining Capacity Additions
Table 5-3 and Figure 5-1 summarize the refinery capacity additions, investments and
utilizations in each case. Again, a major effect of the RTI bunkers basis is to ease the
requirement (versus the TEA basis) for resid upgrading and desulfurization. As a consequence
less refining investment is needed, by 2020 $107.7 bn under RTI basis versus $117.6bn under
TEA basis1. Particularly by 2020, the effect is to reduce the required capacity additions for
coking/visbreaking, cat cracking and especially hydro-cracking. Vacuum gas oil / resid
desulfurization requirements drop in shifting to the RTI basis because demand for low sulfur
inland residual fuel is lowered. Similarly, the increase in proportion of the total distillate pool
occupied by bunkers products moderately lowers the proportions of ultra low sulfur diesel in the
distillate pool and, hence, slightly reduces the total requirement for distillate desulfurization.
Refinery utilizations are projected to continue to rise globally by 2020. This stems in part
from an assumption that levels in current low-utilization regions (notably Russia/FSU, Caspian,
Africa) will gradually improve. Appreciable capacity growth is projected for North and South
America (although not enough in the USA to keep up with demand growth), for Africa and for
Russia as driven by AEO projections of regional demand growth. The major refinery capacity
growth areas are projected to be the Middle East and Asia, led by China which is projected to
double its capacity by 2020. Conversely, essentially no crude capacity growth is projected for
Western Europe and only a modest increase for Eastern Europe.
1 The capital investments detailed in current WORLD reports are generally lower than those projected by say the
IEA for the same time frame. There are three reasons for this. Firstly, the WORLD costs are currently reported
in 2001 dollars. This will be changed in the future. Secondly, the stated WORLD investments generally need to
be increased to allow for extra capacity to cover seasonal variations (e.g. Summer gasoline peak). Thirdly, the
WORLD reports do not include an allowance for on-going capital replacement. This is typical estimated at 1.5-
3% per annum of the total installed capital base (which of course grows over time). It is EnSys' intent to expand
the WORLD reports in the future to make the basis consistent with IEA and others.
5-5
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Draft Report - Do Not Cite or Quote
Table 5-3. Capacity Additions and Investment
WORLD MODEL CASE RESULTS
Bunkers Basis
2012
IEA
2012
RTI
2020
IEA
2020
RTI
CAPACITY ADDITIONS & INVESTMENTS - OVER & ABOVE 2005 BASE + KNOWN CONSTRUCTION
REFINERY
REVAMP
DEBOTTLENECKING
MAJOR NEW UN ITS
TOTAL REFINING
MERCHANT
MAJOR NEW UN ITS
TOTAL REFINING + MERCHANT
$bn($2001)
$ 5.4 $
$ 0.5 $
$ 58.2 5
$ 64.1 5
$ 0.3 $
$ 64.4 $
5.3
0.5
54.9
60.7
0.3
61.1
B 6.4
B 1.4
i 109.8
i 117.6
B 0.9
B 118.5
$ 6.1
$ 1.2
& 100.4
& 107.7
$ 0.9
$ 108.6
CRUDE DISTILLATION BASE CAPACITY & ADDITIONS mmbpcd
BASE CAPACITY
FIRM CONSTRUCTION
DEBOTTLENECKING ADDITIONS
MAJOR NEW UNIT ADDITIONS
TOTAL ADDITIONS OVER BASE
TOTAL CRUDE UNIT CAPACITY USED
SECONDARY PROCESSING CAPACITY ADDITIONS
COKING + VISBREAKING
CATALYTIC CRACKING
HYDRO-CRACKING
CATALYTIC REFORMING - INCL REVAMP
CATALYTIC REFORMING
DESULPHURIZATION (TOTAL)
- GASOLINE -ULS
- DISTILLATE ULS - INCL REVAMP
- DISTILLATE ULS - REVAMP ONLY
- DISTILLATE CONV/LS
-VGO/RESID
HYDROGEN (MMBFOED)
SULPHUR PLANT (TPD)
MTBE TO ISO-OCTANE (REVAMPING USA)
83.74
5.82
0.92
2.07
8.80
83.6%
77.39
83.74
5.82
1.01
2.66
9.49
83.8%
78.11
83.74
6.08
1.80
7.87
15.74
84.9%
84.41
83.74
6.08
1.90
9.04
17.01
85.3%
85.90
- DEBOTTLENECKING + MAJOR UNITS
0.10
0.10
0.70
1.16
0.54
7.16
1.81
4.93
4.25
0.16
0.26
0.52
6350
0.08
0.13
0.16
0.48
1.10
0.53
6.91
1.72
4.79
4.22
0.17
0.23
0.48
5400
0.08
0.25
0.39
3.48
2.02
0.92
11.18
2.70
7.02
6.25
0.44
1.01
0.87
14400
0.08
0.15
0.25
2.85
2.03
1.01
10.12
2.62
6.68
6.02
0.41
0.41
0.75
9230
0.08
5-6
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Draft Report - Do Not Cite or Quote
i88
Figure 5-1. Refinery Capacity Additions
5-7
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Draft Report - Do Not Cite or Quote
5.3 Refining Economics and Prices
Tables 5-4 to 5-6 summarize key price results from the 2012 and 2020 cases. In
reviewing these results, it must be borne in mind that WORLD was run for 2012 and 2020 in
"long run" mode, i.e. with investment open and therefore that the price results equate to long run
equilibrium prices - not short run. Long run equilibrium prices are more stable than short run
prices as they incorporate an assumed long run return on capital. Shot run prices can be
relatively higher or lower depending on whether refining capacity is tight (as today) or slack.
A central feature of these and other recent EnSys WORLD cases is that the global higher
growth rates for distillates relative to gasoline, driven by Europe's dieselization policy and
distillate oriented demand growth in many non-OECD regions, leads to a situation where future
distillate prices are projected to exceed those for gasoline. Projected ULS diesel to ULS gasoline
premiums lie in the range of $3/bbl USGC by 2012 and 2020, and up to $7 - 9/bbl in Asia and
especially Europe.
Table 5-6 summarizes (long run) price differentials as output from the WORLD cases.
For ULSD versus high sulfur IFO 380 (as the lowest quality fuel) these average of the order of
$14/bbl. For ULS gasoline they are lower again especially in Europe.
The results show the effect of the switch from an IEA to the RTI bunkers basis as
discussed under Supply Demand. Light-heavy product differentials (gasoline and diesel to
IFO380) narrow by around $l/bbl USGC and $2/bbl Europe and Asia for 2020. The effect is
less marked in 2012 as the impact on residual fuel demand volumes is smaller.
In the BAU cases, only the Northern European SECA's were included. Therefore it is
the Northwest Europe prices that provide the best insight into and cross-check on pricing of high
versus low sulfur marine fuels. For IFO 180 and 380, (nominal sulfur limits of 4.5% for high
sulfur and 1.5% for low sulfur), the indicated price differential is around $l/bbl. For low versus
high sulfur MDO, it is lower. These differentials appear reasonable as a starting point for
examining the effects of wider SEC A introduction and/or further tightening of marine fuels
standards regionally or globally. Such developments, which would be the subject of follow-up
WORLD cases, will raise price differentials versus those seen here with the degree of change
dependent of specific scenarios for sulfur specifications and for the compliance methods used by
shippers.
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Draft Report - Do Not Cite or Quote
Table 5-4. Product Prices
WORLD MODEL CASE RESULTS
Bunkers Basis
2012
IEA
2012
RTI
2020
IEA
2020
RTI
CRUDE PRICES SELECTED MAJOR CRUDES (FOB)
SAUDI ARABIAN LIGHT (33.4 , 1 .8)
input - marker crude price
WORLD output prices
TEXAS WEST INTERMEDIATE (40.1 , 0.4)
TEXAS WEST SOUR (34 ,1.9)
COM DEEP SOUR (35 , 1.3)
UK NORTH SEA BRENT (36.9 , 0.3)
NIGERIAN BONNY/LIGHT (38.3,0.14)
NIGERIAN MEDIUM (25 , 0.28)
RUSSIA URALS (32.5 , 1.56)
UAE DUBAI (32.6 , 1 .96)
IRAQ BASRAH (33.9 , 2.08)
SAUDI ARABIAN HEAVY (28.2 , 2.84)
ALASKAN NORTH SLOPE (30 , 1 .05)
CALIFORNIA SJV HEAVY (14.1 , 1.06)
MEXICAN ISTHMUS (32.8 , 1 .51)
MEXICAN MAYA (22 , 3.3)
VENEZ HEAVY(BACH LIGHT) (17.4,2.8)
CANADIAN LIGHT (42.5 , 0.3)
CANADIAN HEAVY (25 ,2.8)
CANADIAN SYNCRUDE (33.5 , 0.05)
PRODUCT PRICES
WORLD output prices
USGC
LPG
PETCHEM NAPHTHA
CG - ULS PREMIUM
CG - ULS REGULAR
RFC - PREMIUM (0/5.7/10% ETOH)
RFC - REGULAR (0/5.7/10% ETOH)
KERO/JET JTA/A1
DSL NO2 ULSD (50-10 PPM)
MGO NO2 HSD (5000-1 5000PPM)
MDO N04 HSD (5000-20000PPM)
RESID < .3%
RESID. 3-1.0%
IF0180HS
IF0380 HS
PETCHEM GAS OIL
AROMATICS
LUBES & WAXES
ASPHALT
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
44
47
46
46
45
46
46
44
43
42
41
43
42
45
40
41
46
38
47
45
40
54
51
52
48
52
55
10
68
67
90
54
32
16
49
50
04
51
72
54
94
71
42
13
65
44
20
31
81
10
33
36
78
08
#N/A
$
$
$
$
$
$
$
$
$
47
49
44
42
41
51
55
66
34
28
61
60
49
56
00
73
97
99
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
44.10
47.61
46.71
46.92
45.46
46.17
45.90
44.63
43.58
42.35
41.94
43.73
42.75
45.94
40.79
41.45
46.03
38.74
47.36
45.08
40.45
54.96
51.18
52.40
48.33
52.59
54.73
50.33
48.08
49.34
44.51
42.38
41.49
51.12
55.84
67.15
35.13
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
45.50
49.30
47.92
48.34
47.07
48.06
47.40
45.95
44.74
43.25
42.53
45.29
44.05
47.22
41.52
42.42
46.88
39.33
49.25
46.46
41.51
56.16
52.77
53.38
49.71
54.75
56.96
#N/A
48.65
50.18
44.48
43.37
42.31
52.69
57.39
71.22
35.00
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
45.50
49.07
48.07
48.31
46.84
47.66
47.27
45.97
44.85
43.66
43.12
45.60
44.99
47.34
41.99
42.78
46.64
39.85
48.94
46.63
41.31
55.90
52.45
53.10
49.35
54.49
56.67
51.91
49.78
50.63
45.52
44.01
43.01
52.74
56.77
71.09
36.13
5-9
-------
Draft Report - Do Not Cite or Quote
Table 5-4. Product Prices - continued
WORLD MODEL CASE RESULTS
Bunkers Basis
NORTH WEST EUROPE
LPG
PETCHEM NAPHTHA
RFC - PREMIUM (EURO III/IV/V)
RFC - REGULAR (EURO III/IV/V)
KERO/JET JTA/A1
DSL N02 RFD
MGO N02
MDO N04 HSD (5000-20000PPM)
MDO N04 LSD (10-1500PPM)
RESID < .3%
RESID. 3-1.0%
IF0180LS
IF0180HS
IF0380 LS
IF0380 HS
AROMATICS
LUBES & WAXES
ASPHALT
PACIFIC (SINGAPORE)
LPG
PETCHEM NAPHTHA
RFC - PREMIUM (EURO III/IV/V)
RFC - REGULAR (EURO III/IV/V)
KERO/JET JTA/A1
DSL NO2 RFD
DSL NO2 LSD (500 PPM)
DSL NO2 MSD (1000-5000 PPM)
DSL NO2 HSD (5000-10000 PPM)
MGO NO2 HSD (5000-1 5000PPM)
MDO NO4 HSD (5000-20000PPM)
RESID < .3%
RESID .3-1.0%
RESID 1.0-3.0%
IFO180HS
IFO380 HS
AROMATICS
LUBES & WAXES
ASPHALT
2012
IEA
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
46
40
51
48
54
57
50
46
46
48
43
43
43
42
42
54
70
37
48
41
52
49
54
56
55
54
53
53
45
48
45
43
42
41
51
65
35
52
53
74
31
09
32
50
00
50
34
61
73
43
85
27
16
55
41
80
19
37
53
56
00
10
15
67
13
66
08
35
88
66
34
68
77
56
2012
RTI
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
46.40
40.51
51.82
48.33
53.94
57.02
50.43
46.81
47.44
47.60
43.33
44.52
44.36
43.50
43.30
54.32
70.53
37.88
48.69
41.18
52.44
49.57
53.94
55.27
54.47
53.47
53.05
52.61
46.89
48.37
45.80
44.46
43.67
42.55
51.85
66.28
37.73
2020
IEA
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
47.81
41.96
53.11
49.37
56.24
58.96
52.75
48.05
48.87
49.29
45.24
45.50
43.97
44.55
42.63
55.97
73.33
36.92
50.10
43.13
54.97
51.89
57.19
58.56
57.77
56.78
56.16
55.46
47.19
50.13
46.80
44.50
43.96
42.50
53.48
70.12
34.99
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
2020
RTI
47.98
41.62
52.80
49.03
56.02
58.73
52.55
48.95
49.22
49.10
44.98
46.19
44.80
45.15
44.65
55.07
73.21
38.52
50.27
42.69
54.59
51.46
56.49
57.92
57.10
56.01
55.34
54.59
47.91
50.08
47.13
45.28
45.19
43.95
52.59
69.99
38.12
5-10
-------
Draft Report - Do Not Cite or Quote
Table 5-6. Product Price Differentials
WORLD MODEL CASE RESULTS
Bunkers Basis
PRODUCT PRICE DIFFERENTIALS
WORLD output prices
USGC
CG ULS REG - IFO380 HS
DSL ULSD - IFO380 HS
MDOHS - IFO380HS
RESID1%S - IFO380HS
IFO180HS - IFO380HS
CG ULS REG - DSL ULSD
DSL ULSD - MDOHS
NORTH WEST EUROPE
RFC REG (EURO) - IFO380 HS
DSL ULSD (EURO) - IFO380 HS
MDOHS - IFO380HS
RESID1%S - IF0380HS
RESID1%S - IF0180LS
IF0180LS - IF0380LS
IF0180HS - IF0380HS
RFC REG (EURO) - DSL ULSD (EURO)
DSL ULSD (EURO) - MGO
DSL ULSD (EURO) - MDOHS
MDOLS - MDOHS
PACIFIC (SINGAPORE)
RFC REG (EURO) - IFO380 HS
DSL ULSD (EURO) - IFO380 HS
MDOHS - IF0380HS
RESID1%S - IFO380HS
IFO180HS - IFO380HS
CG ULS REG - DSL ULSD
DSL ULSD - MDOHS
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
2012
IEA
9.54
13.53
5.72
3.04
0.93
(3.98)
7.80
6.04
15.05
3.74
1.34
(0.13)
0.89
1.16
(9.01)
6.82
11.32
0.50
8.19
14.66
4.32
4.01
1.32
(6.47)
10.34
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
2012
RTI
9.69
13.24
6.59
3.02
0.89
(3.55)
6.65
5.03
13.72
3.51
0.04
(1.18)
1.01
1.06
(8.69)
6.59
10.21
0.63
7.02
12.72
4.34
3.25
1.12
(5.70)
8.38
2020
IEA
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
10.46
14.65
6.34
2.17
1.06
(4.19)
8.30
6.74
16.33
5.42
2.61
(0.26)
0.95
1.34
(9.59)
6.21
10.91
0.82
9.40
16.06
4.69
4.30
1.47
(6.66)
11.37
2020
RTI
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
9.44
13.66
6.77
2.52
1.01
(4.22)
6.89
4.38
14.08
4.30
0.33
(1.21)
1.04
0.15
(9.70)
6.18
9.78
0.26
7.51
13.97
3.96
3.18
1.23
(6.46)
10.01
5-11
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Draft Report - Do Not Cite or Quote
5.4 Crude and Product Trade
Figures 5-2 through 5-7 below summarize inter-regional trade movements from WORLD
for the 2012 and 2020 RTI basis cases.
Major trends and highlights on crude trade include the following:
• growing production from West and North Africa (totaling nearly 12 mmbpd by 2020)
helps offset the decline in North Sea production. Significant volumes move into the US
PADDs 1, 2 and 3 and Eastern Canada.
• West African crudes are widely distributed, including to Caribbean/South America,
Europe, Asia/Pacific and even the US West Coast
• Considerable uncertainty continues to exist over future Russian crude production
volumes and export routes. The 2012 and 2020 cases were run with export options open
with the result that Russian crudes continue to move in substantial volumes into
Western and Eastern Europe but otherwise move predominantly into Asia/Pacific. No
Russian crude is projected as coming in to the US, although this could change if
northerly routes via Murmansk and the Baltic are expanded. Russian crude production
was projected at below 11 mmbpd for 2020 with domestic demand growing to 6.5
mmbpd. This in turn trims the volumes of crude available for export
• Middle Eastern crudes are projected to be refined increasingly within the region, in part
as that region's export refining capacity grows, and to move increasingly to Asia/Pacific
where the majority of demand growth will occur. Continuance of movements into
Europe and the USA depends on the level of competition with other suppliers and on
discounting policy by Saudi ARAMCO and other Middle East Gulf producers
• The 2012 and 2020 cases are exhibiting a new phenomenon which bears further
investigation, relating ultimately to level of Canadian crude production. The AEO 2006
has a high level of Canadian production, 4.5 mmbpd in 2020. Even with western outlets
to the Pacific and the US/Canada West Coast expanded to a projected 0.8 mmbpd, the
high production volume moves predominantly into the US interior (PADDs 2, 4 and
potentially some to PADDS). This has the effect of backing out foreign, especially
Caribbean crude which in turn gets reallocated - in the cases - to Europe where it in
turn backs out Middle Eastern crude which moves to Asia/Pacific, the highest demand
growth area. This phenomenon is plausible but whether it is indeed realistic bears
further assessment, with potential adjustments to be made to finalize the BAU cases.
5-12
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Draft Report - Do Not Cite or Quote
The period through 2020 will witness continuing growth in trade of finished and
intermediate products, as illustrated by the WORLD case results. The case projections point to
the following main trends:
• Increases in product volumes being shipped into and between Asia/Pacific regions
• Continued products and intermediates exports from Russia, mainly into Europe but also
into the USA and Far East
• Potentially major exports from Europe of gasoline, on the basis of continuing
dieselization. WORLD cases indicate these growing to over 1.75 mmbpd by 2020.
However, the cases also show the premium for diesel in Europe at $9/bbl above gasoline
which raises questions over whether European authorities and consumers will continue
to opt predominantly for diesel vehicles. This is a premise that can be revisited but the
cases are on the basis if continuing dieselization.
• Should dieselization continue, its impacts on product trade patterns will be far reaching.
2020 exports of European gasoline to the US are projected at close to 1 mmbpd with
other destinations likely to include Africa, Asia and the Caribbean. Offsetting the
gasoline exports are a projected 1.65 mmbpd (2020) of distillates imports from Russia,
Caspian, Caribbean and Africa.
• With US refining capacity projected to not keep up with demand, gasoline imports
continue to rise into the US East Coast (nearly 1.4 mmbpd into PADD1 in 2020 from
Europe, Caribbean, South America, Africa and Russia) but also are indicated into the
US Gulf Coast and Interior (over 0.4 mmbpd net) and the US West Coast (0.3 mmbpd
net).
• Inter-regional movements of residual fuels are projected as limited, except for small
volumes of low sulfur resid moving into the US East Coast and of high sulfur resid and
vacuum gasoil streams ex Russia, mainly into Europe.
• This situation is projected as applying to residual bunker fuels (Figure 5-7), although
shifts is assumed locations of bunkers demand could well lead to changes in trade
patterns.
5.5 Bunker Fuels Quality and Blending
The current WORLD version does not possess standard reports for the details of fuels
blends. For the Task 1 (BAU cases), spot blends were inspected. MGO blends included light
and middle distillate streams characteristic of a lower quality, higher sulfur No2 type fuel.
5-13
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Draft Report - Do Not Cite or Quote
MDO No4 fuel blends included heavier streams, consistent with a minimum API gravity allowed
of 22.3, and tended to limit on sulfur, and carbon residue (maximum 2.5%). Blend components
included vacuum gasoils and small proportions of atmospheric and vacuum residua, subject to
the limits placed by the carbon residue, sulfur, viscosity (14 cks max at 40 degC) and gravity.
The residual IFO blends for 2012 and 2020 comprised predominantly vacuum and
visbroken residua cut back with kerosene cutter stock plus small constrained (max 5%) volumes
of FCC clarified oils. In a departure from historical patterns, the blends contained small
proportions at most of atmospheric residua and no vacuum gasoils. (A traditional IFO blend
would contain either atmospheric resid and cutter stock or a mix of vacuum resid and vacuum
gasoil and cutter stock.) This development in the blend compositions would appear to be logical
given that global demand growth is predominantly for light clean products which can be readily
produced inter alia from vacuum gasoils via catalytic and hydro cracking. In other words, in the
future, vacuum gasoil will be too valuable as potential gasoline and distillate to blend into bunker
fuels. It will be more economical to blend in vacuum and visbroken residua plus a higher than
traditional quantity of kerosene which is the most effective cutter stock by virtue of its low
viscosity. The IFO blends universally limited on maximum viscosity. Sulfur was a limiting
constraint on the low sulfur (1.5% nominal) blends but otherwise rarely constrained (at 4.5%).
The indicated shift in residual bunkers blend compositions does raise questions. Firstly,
in the model cases, expansion of visbreakers was partially constrained since, generally, the recent
trend has been to invest in cokers. Shifting to the RTI bunkers basis from IEA led to a
significant cut back in coker throughputs, because of the rise in residual fuel demand. For 2020,
the global coker throughput was 4.7 mmbpd in the LEA basis case and 3.7 under the RTI basis.
However, the case allowed little additional visbreaker throughput/capacity addition. Yet, an
increase in demand for residual bunker fuels argues for an increase in attractiveness of visbroken
vacuum residua. In short, the BAU cases should arguably be tested with additional visbreaking
allowed. Unlike resid desulfurization, visbreaking is a low cost process and one refiners could
readily engage in.
The second question these blends bring forward is an operational one, namely, are there
any operational issues with residual bunkers blends that comprise "dumbbell" blends of kerosene
with visbroken and vacuum residua? This should be checked as part of further analysis.
5-14
-------
Draft Report - Do Not Cite or Quote
B
«
-o
_1
3
aS
SNOioaa ONionaoad
Figure 5-2. Total Crude Deliveries
5-15
-------
Draft Report - Do Not Cite or Quote
s s
SNOioay ONionaoyd
Figure 5-3. Total Crude Exports
5-16
-------
Draft Report - Do Not Cite or Quote
61
SNOI03M ONIOnaOMd
SNOI03M ONIOnaOMd
Figure 5-4. Production in 2012
5-17
-------
Draft Report - Do Not Cite or Quote
r- S &
S £
Figure 5-5. Production in 2020
5-18
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Draft Report - Do Not Cite or Quote
[T
P
S U X
53 "
- e c
ONionaoyd
Eg "G X
K 3 «
SNOioay ONionaoyd
Figure 5-6. Product Movements
5-19
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Draft Report - Do Not Cite or Quote
SNOioaa ONionaoad
SNOioaa ONionaoad
Figure 5-7. Residual Bunkers
5-20
-------
Draft Report - Do Not Cite or Quote
SECTION 6
SUMMARY AND IMPLICATIONS FOR FUTURE SECA ANALYSES
The overall objective of the analyses conducted under Task 1 was to develop a detailed
methodology to estimate bunker fuel demands and to modify the WORLD model to
accommodate details of bunkers grades, technology costs, etc. These changes have been
successfully implemented and applied. The 2012 and 2020 Ball cases were developed and
represent sound starting bases for examining the impacts of broader SECA introduction and/or
tighter global marine fuels limits.
As mentioned earlier in the report, the nature of the MARPOL Annex VI regulations and
goals, and the characteristics of the international marine fuels industry mean that there is a much
wider range of potential variability in future scenarios than is the case with most fuels
regulations. Key uncertainties that can be addressed through case study and which will be
important in the follow-up SECA analyses, include the following:
• Further assessment of the regional make-up of bunkers demand. Arguably further
investigation is warranted to gain a clearer understanding of regional bunker
sales/demand patterns and, in doing so, to further corroborate the analysis of total global
bunkers demand.
• Associated with this, further assessment could be conducted on the extent to which
consumption of low-sulfur bunkers for SECA compliance will be met by supplies within
the SECA or elsewhere. Again, the WORLD model contains transport options to route
bunkers (and other products) from region to region when the economics warrant.
• Assessment of how compliance with the MARPOL regulations will be achieved, in
particular what proportion will be met through improved fuel quality versus via on-board
scrubbing and/or emissions trading. Using the WORLD model, plausible "high" and
"low" scenarios can be applied and analyzed (it has already been set up to deal with this).
6-1
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Draft Report - Do Not Cite or Quote
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BF99F40976289B32&method=display_body&er=l&bitmask=002005001000000000.
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http://www.val ero.com/Investor+Relations/Management+Presentations/
Maritime and Port Authority of Singapore (MPAS). 2006a. Vessel Calls (>75 GT) by Purpose.
. As obtained on February
9, 2006.
Maritime and Port Authority of Singapore (MPAS). 2006b. January 12 2006. 2005 Annual
Report, .
Maritime and Port Authority of Singapore (MPAS). 2006c. Bunker Sales.
. As obtained on February 9,
2006.
Maritime Chain. 2005. http://www.maritimechain.com/port/port_distance.asp.
Meech, R. 2006. "The Impact of Marine Emission Legislation on the Bunker Industry."
Presented by the International Petroleum Industry Environmental Conservation
Association at the Bunker Fuel: MARPOL Annex VI Consultation Meeting, Arlington
VA, Feb 2006. http://api-ep.api.org/training/index.cfm?objectid=D5FC25AO-3462-4914-
BF99F40976289B32&method=display_body&er=l&bitmask=002005001000000000.
Metcalf, K. 2006. "Chamber of Commerce of Shipping of American Shipowners Perspective -
Present and Future Direction of Regulation of Air Emissions." Presented by the
International Petroleum Industry Environmental Conservation Association at the Bunker
Fuel: MARPOL Annex VI Consultation Meeting, Arlington VA, Feb 2006. http://api-
ep.api.org/training/index.cfm?objectid=D5FC25AO-3462-4914-
BF99F40976289B32&method=display_body&er=l&bitmask=002005001000000000.
Mergent Inc. 2005. The Global Oil & Gas Industry: A Company and Industry Analysis.
Charlotte, NC: Mergent Inc. Obtained on October 20, 2005. Available at:
http ://webreports.mergent. com
Nakamura, David N. 2004. "Worldwide refinery capacity creeps ahead in 2004". Tulsa: Oil &
Gas Journal 102(47): 46-53.
Nakamura, David N. 2005. "Refineries add 2.7 million b/d of crude refining capacity in 2005."
Tulsa: Oil and Gas Journal. 103(47): 60-64.
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National Geospatial-Intelligence Agency. 2001.
http://pollux.nss.nima.mil/pubs/pubsj _show_sections.html?dpath=DBP&ptid=5&rid=l 8
9.
Ocean Shipping Consultants. 2005. "Healthy Outlook for Cruise Ships."
Oil and Gas Journal (OGJ). 2004. "2004 Worldwide Refining Survey." Tulsa: Oil and Gas
Journal 102(47): 1-2.
Oil and Gas Journal (OGJ). 2005. "Worldwide Construction Update." Tulsa: Oil and Gas
Journal. 103(16): 1-2
Organization of Economic Cooperation and Development (OECD) International Energy Agency
(IEA). 2005. Oil Market Statistics, .
Platt's Bunkerwire [electronic resource]. 2005. Houston, TX: McGraw-Hill.
Port Authority of Rotterdam, The. 2005. "Port of Rotterdam continues to do extremely well".
Rotterdam: The Netherlands. Press release December 30, 2005. Obtained on January 25,
2005. Available at:
http://www.portofrotterdam.com/news/UK/Pressreleases/2005/HBR 30122005 5.asp?ln
g=UK
Port Authority of Rotterdam, The. 2004a. "10% Rise in bunker sales in Rotterdam".
Rotterdam: The Netherlands. Press release April 16,2004. Obtained on January 25,
2005. Available at:
http://www.portofrotterdam.com/news/UK/Pressreleases/Pressreleases/HBR 16042004
03.asp?lng=UK?lng=UK
Port Authority of Rotterdam, The. 2004b. "Rotterdam handles 17% more Russian cargo".
Rotterdam: The Netherlands. Press release June 23, 2004. Obtained on January 25,
2005. Available at:
http://www.portofrotterdam.com/news/UK/Pressreleases/Pressreleases/HBR 23062004
Ql.asp?ComponentID=57208&SourcePageID=0?lng=UK
Port of Houston Authority. 2006. Trade Statistics. Houston, TX: The Port of Houston Authority.
Reliance Industries LTD. 2005. Refinery and Marketing Learning Center (Company Website).
Obtained on November 29, 2005. Available at:
http://www.ril.com/business/petroleum/refmingmktg/lc/business_petroleum refmingmkt
g_l c_refmetyp e. html
Reuters. January 13 2006. "UPDATE 1-ExxonMobil to shut Singapore refinery in March".
.
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Singapore Maritime Portal (SMP). Bunkering Services.
. As obtained
February 10 2006.
Singapore Ministry on Trade and Industry (SMTI). 2005. Economic Survey of Singapore: 2004.
Spreutels, Paula J. and Monique B. Vermeire. 2001. Everything you need to know about marine
fuels. U.K.: Fuel and Marine Marketing, LLC.
Transport Canada. 2004. Transportation in Canada Annual Report 2004. Especially Tables 3-26
and 8-27. http://www.tc.gc.ca/pol/en/report/anre2004/8F_e.htm.
Trench, Cheryl J. 2005. Oil Market Basics. Washington, D.C.: U.S. Department of Energy,
Energy Information Administration. Obtained on November 29, 2005.Available at:
http://www.eia.doe.gov/basics/petrol eum_basics.html
United Nations Food and Agriculture Organization (FAO). 2005. 2002 Capture Production
with Respect to the Previous Year. http://www.fao.org/fi/Prodn.asp.
U.S. Census Bureau. 2005. "Annual Survey of Manufactures—2003 Statistics for Industry
Groups and Industries."M03(AS)-l (RV). Washington, DC: U.S. Bureau of the Census.
U.S. Census Bureau. 2004. "Petroleum Refineries: 2002 —2002 Economic Census
Manufacturing Industry Series." EC02-311-324110(RV). Washington, DC: U.S. Bureau
of the Census.
U.S. Coast Guard. National Vessel Movement Center (electronic resource)
http://www.nvmc.uscg.gov/.
U.S. Energy Information Administration (EIA) at Department of Energy (DOE). 2006. Annual
Energy Outlook2006. DOE/EIA-0383. .
U.S. Energy Information Administration (EIA) at Department of Energy (DOE). 2005.
International Energy Outlook 2005. DOE/EIA-0484.
.
U.S. Department of Energy, Energy Information Administration (EIA). 2005a. Petroleum Supply
Annual 2004, Volume 1. Washington, DC: U.S. Department of Energy, Energy
Information Administration.
U.S. Department of Energy, Energy Information Administration (EIA). 2005b. "OPEC Brief
Washington DC: DOE/EIA. Obtained on November 29, 2005.Available at:
http://www.eia.doe.gov/emeu/cabs/opec.html
U.S. Department of Energy, Energy Information Administration (EIA). 2005c. "International
Energy Outlook 2005." Washington DC: DOE/EIA. nr DOE/EIA-0484, pp. 25-35.
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U.S. Department of Energy, Energy Information Administration (EIA). 2005d. "International
Energy Annual 2003: Table 3.2" Washington DC: DOE/EIA. Obtained on November 20,
2005. Available at http://www.eia.doe.gov/iea/
U. S. Department of Energy, Energy Information Administration (EIA). June 2005f. Country
Analysis Briefs: Singapore .
U.S. Department of Energy, Energy Information Administration (EIA). 2004. Fuel Oil and
Kerosene Sales 2003. nr DOE/EIA-0535. Washington, DC: U.S. Department of Energy,
Energy Information Administration, .
U.S. Department of Energy, Energy Information Administration (EIA). 2003. Petroleum
Marketing Annual 2004. Washington, DC: U.S. Department of Energy, Energy
Information Administration, .
U.S. Department of Transportation, Maritime Administration. 2004. "Vessel Calls at U.S.
Ports—2003." Washington DC: MARAD.
http://www.marad.dot.gov/MARAD_stati sties/index.html>.
US Embassy at Singapore. October 2000. Singapore's Chemical Industry Report.
.
U.S. Environmental Protection Agency (EPA). September 1995. Profile of the Petroleum
Refining Industry. EPA Industry Sector Notebook Series. Washington, DC: U.S.
Environmental Protection Agency.
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027. Washington, DC: U.S. Environmental Protection Agency.
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London: Worldscale Association.
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APPENDIX A
STATUS OF TECHNOLOGY AND TRADING OPTIONS FOR COMPLIANCE WITH
ADVANCED BUNKERS REGULATIONS
This section provides an overview of the technology and other options (such as emissions
trading) that currently or potentially exist to achieve compliance with regulations for emissions
control of bunker fuels. Since potential regulations go beyond sulfur/SOx to NOX and PM, all
three are discussed here.
Existing choices for switching to low sulfur marine fuels include: (8, 10)
• Blend MD and LS MFC with HS MFC (limited)
• Switch to lower sulfur Crude Feedstock (limited)
• Segregate LS HFO to the extent possible (limited)
• Upgrade HS HFO to Light Oil
• Desulfurization of Residual Fuel Oil
• Conversion of Residual Fuel Oil distillate and gasoline
Desulfurizing Refinery Fuels and Switching to Low Sulfur Fuels (6,10)
The European Commission and Entec have published a number of reports rich in
information and addressing Marine fuel supply and demand, emission abatement technology and
related subjects. One of these reports relates the cost of production of low sulfur bunker fuel on
a regional basis to the level of the bunker fuel demand. As bunker fuel demand increases,
production costs increase as available refinery flexibility is fully utilized and refinery
investments are required to desulfurize the bunker fuel blending components to meet the higher
demand. The increase in the price differential between low and high sulfur fuel oil is significant
as the system moves from reblending within the current refining system to residue
desulfurization. This in turn affects the comparative economics of refinery processing versus on
board abatement measures. For this reason, an accurate estimate of bunker fuel demand is a key
requirement of the study.
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Using Distillate Fuel (7, 9)
An Entec report discusses the effect of EU sulfur regulations on marine distillate fuel
(MGO and MDO). Under directive 1999/32/EC, shipping vessels using marine distillate must
use a distillate with a sulfur content of no more than 0.2 wt% within the EU. This limit was
applicable from July 1, 2000 and tightens to 0.1% max by January 1, 2008. This is in contrast to
the ISO/8217/2005 MGO/DMA specification of 1.5% max sulfur and MDO/DMC specification
of 2% max sulfur. Reducing the MDO /DMC) maximum sulfur specification to 0.2 %
effectively backs out all currently allowed heavy fuel oil from MDO and increases its value to
the level of heating oil (No. 2). There is also the potential for ULS blend stocks to be included in
the MGO/MDO marine fuels if economically attractive. This 2002 report estimates a price
premium of $10-15/metric ton on low sulfur distillate fuel oil.
This report also considers the feasibility of ships installing separate fuel tanks for high
and low sulfur grades if ships are operating between regions with differing environmental
regulations.
Distillate fuel is used in smaller vessels as well as a secondary fuel on larger vessels for
Canada inland, maximum sulfur dropping from up to 20,000 ppm(current) to 500 ppm (LSD) in
2007 and 15 ppm (ULSD) in 2012. Canada's current typical is 3000 ppm sulfur content.
Reducing SOx Emissions On Board (1, 2)
The technology of water washing has been in use for several decades in oil tankers for
cleaning the exhaust gas of boilers to produce effectively SO2 free inert gas for the cargo tanks.
The effluent from the seawater scrubber is highly acidic, however, on discharge into the sea it
rapidly disperses so as to give no adverse environmental or ecological effects.
Seawater has a natural alkalinity and the hot exhaust gases mix with seawater to remove
SO2 and particulate matter (PM). The SO2 is absorbed into the seawater which is discharged back
to the ocean. The PM including ash is trapped in a settling or sludge tank where it is collected for
disposal. At normal load conditions, the SO2 sulfur removal rates are reported as 70% -90%, and
potentially higher with scrubber optimization. The lowest removal rate is reported as 65% and
the highest - -from an early 2006 trial on a European Ferry is 99%. The PM removal rates have
been estimated at 25% or higher.
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The quality of the discharge water is limited to 25 ppm hydrocarbon by the IMO
OILPOL and trials have indicated that this can be achieved. (The IMO has not yet imposed limits
on Ph, suspended solids and heavy metals). The U.S. EPA however does have regulations
governing these.
The alternative to exhaust gas scrubbing is to limit the sulfur content of the fuel. At
present the typical sulfur level in residual fuel is approximately 3 % m/m, and follows a skewed
distribution.
Estimated sea water costs per tonne of 862 reduced are estimated to average 350 euros
for new ships and 550 for retrofit shops, with some dependence on ship size. By contrast, the
cost of fuel switching from 2.7 % to 1.5% sulfur fuel is estimated to be greater by a factor of 6,
depending on a number of premises.
A rigorous comparison requires considering all the cost elements including disposal,
storage and additional manning requirements to operate the seawater scrubbers properly (the
ship's Chief Engineer requests the required crew manning from the owner/operator to perform
the additional duties associated with seawater scrubbing).
A realistic comparison also requires constraints on the rate of introduction of seawater
scrubbers. It is likely that there will be some BAU uptake, but since seawater scrubbing is at an
early stage of development for commercial application, current estimates will be subject to
significant uncertainty.
NOx Reduction by lowering the nitrogen content of the marine fuel (1, 3)
Nitrogen in the bunker fuel is a significant source of NOx emissions which represent a
potentially controlled parameter. A CEVIAC report estimated the conversion of nitrogen in the
fuel to NOx in the emissions as between zero and 100 percent as the engine efficiency increases
from 30 to 40 percent.
To put this into perspective, for a fuel nitrogen content of 0.5 weight percent, this
translates into approximately 3 g NOx per kwh, as opposed to current IMO legislation which
calls for a maximum of 17g NOx emissions per kwh for marine diesel engines having a revolving
speed of less than 130 rpm. (4)
An EPA Proposed Rulemaking Document (5) states that residual fuels normally vary
from 0.2 to 0.6 wt% nitrogen and proposes a broad spec between zero and 0.6 wt percent (page
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77). Also, that the test fuel for category 3 engines must meet ASTM D 2069-91 specification for
RMH-55 and have a nitrogen content of 0.6 weight percent or less and that NOx emissions are to
be adjusted based on the nitrogen content of the fuel (page 127). An ISO specification remains a
possibility as well.
A NOx removal constraint calls a complex model optimization into play since there is a
relationship between the nitrogen content of the bunker fuel and NOx emissions. Marine vessel
engine design and operation alternatives which reduce NOx emissions are discussed below.
Seawater scrubbers do not effectively reduce NOx emissions as they do SOx and PM.
To accommodate the potential for nitrogen control in bunkers, a nitrogen specification
was added in to the fuel oil blending streams and specification in WORLD. A NOx constraint
will influence the comparative economics regarding the choice of refinery processing versus on-
board vessel engine design and operation measures, a fundamental objective of the study. A
layer of optimization complexity is added when considering the SOx and NOx constraints and
the related fuel quality issues together. A nitrogen limitation may be imposed on bunker fuel in
the future and hydro processing will reduce both nitrogen and sulfur, but to different extents,
linking SOx and NOx emissions. In addition, some (but not all) vessel design and operation
measures for reducing NOx emissions also reduce SOx and PM.
On Board NOx Reduction Measures (3)
The on-board measures to abate NOx emissions are covered in considerable detail in an
Entec Report. The principal measures and their NOx reduction efficiency and CAPEX+OPEX
costs are:
• Basic IEM (20% NOX reduction/9euro/tonne NOx)
• Advanced IEM (30% NOXreduction/19euro/tonne NOx)
• Direct water injection (50% NOX reduction/345euro/tonne NOx)
• Humid air Motors (70% NOX reduction/263 euro/tonne NOx)
• Exhaust air recirculation (3 5% NOX reduction/na euro/tonne NOx)
• Selective Catalyst reduction (90% NOX reduction/3 5Seuro/tonne NOx)
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Exhaust air recirculation and Selective catalyst reduction also reduce SOx emissions by
approximately 90% and PM by approximately 60%, which must be accounted for in any
optimization analysis.
Again NOX on-board abatement technologies, with their potential to also reduce SOX and
PM, play into the assessment of how the industry will comply with future regulations.
Appendix A References
(1) European Commission Directorate General Environment Service Contract on Ship
Emissions: Assignment, Abatement and Market-based Instruments Task 2 - General
Report Final Report August 2005 Entec UK Limited
(2) European Commission Directorate General Environment Service Contract on Ship
Emissions: Assignment, Abatement and Market-based Instruments Task2c - SOX
Abatement Final Report August 2005 Entec UK Limited
(3) European Commission Directorate General Environment Service Contract on Ship
Emissions: Assignment, Abatement and Market-based Instruments Task2b -NOX
Abatement Final Report August 2005 Entec UK Limited
(4) Internal Memo, Osamu Hanashima. Technical Consultant. Shell Marine Products
(5) EPA Notice of Proposed Rulemaking, 40CFR Part 94, Control of Air Pollution from
New Marine Compression Ignition Engines at or Above 30 liters /Cylinder
(6) European Commission Directorate General Environment. Advice on the Costs to Fuel
Producers and Price Premia Likely to Result From a Reduction in the Level of Sulphur
in Marine Fuels Marketed in the UK Study C. 1/012002 Contract
ENV.C1/SER/2001/0063 Final Report April 2002
(7) Market Survey of Marine Distillates with 0.2 wt% Sulphur Content, Final Report, Entec
2002
(8) Bunker Fuel : Marpol Annex VI, Consultation Meeting Proceedings, February 23, 2006,
Hyatt Regency, Crystal City, Arlington, Va. Among the papers presented were the
following:
(9) Rob Cox, IPIECA, Overview and Background for the Workshop
(10) Gerry Ertel, Canadian Petroleum Products Institute, Framing the Issues- Refiners
(11) Andy Madden, Exxon Mobil, Refining to meet Low Sulfur Bunker Fuel
(12) Don Gregory, SEeat, Emissions Abatement and Trading, Emissions Trading, a Potential
Tool for the Shipping Industry
(13) Kathy Metcalf, Chamber of Commerce of Shipping of American Shipowners
Perspective - Present and Future Direction of Regulation of Air Emissions.
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APPENDIX B
REVIEW OF REFINERY PROCESS COSTS1
Task 1 called for an analysis of the potential technical and economic impacts of
designating one or more SOx Emission Control Areas (SECA's) along the North American
Coastline, as provided by the MARPOL treaty, Annex VI, which places limits on both NOx and
SOx emissions. Countries participating in the treaty must use a bunker fuel with a sulfur content
at or below 4.5 percent. Countries participating in the treaty are also permitted to request
designation of SEC As in which ships must treat their exhaust to 6.0 g of SO2 per kwh, or further
reduce the sulfur level of their fuel to 1.5 percent.
The results obtained from this study will be primarily cost-of-production driven with
respect to the different components of bunker fuel and the resulting fuel oil blend. These tie back
directly to the investment and operating costs applied to the various refinery processes involved
in their production as one of the key factors in determining economic impacts.
Not all refinery processes affect the results in equal measure. Obviously, those processes
directed to producing residual fuel blend components are key, along with processes which
produce blend stocks in the diesel fuel boiling range. The following Table illustrates a typical
composition of Bunker Fuel Oil, in this example blended to 380 centistokes for Bunker Grade
RMG35.
1 The mention of certain Licensors and Companies in the text of this appendix and supporting references does not
imply any preference for or endorsement of these processes or endorsement of operating practices as opposed to
alternatives made available or employed by others. This is particularly so since there are several process
alternatives available and several companies involved in any given area of refinery technology and any one may
be more appropriate based on a specific refinery situation. Those processes cited are therefore cited for
illustrative purposes only. The views and opinions of authors expressed herein do not necessarily reflect those of
the United States Government or any agency thereof.
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Table B-l. Bunker Fuel Composition
Stream Quantity Weight Viscosity Density® Sulfur Vanadium AL+SI Water
MT Percent cks@50 deg C 15 deg C Wt Pet Mg/kg Mg/kg Vol Pet
Residual 15000 43 1500 1.006 3 600 12 0.3
VGO 15000 43 100 0.979 1.5 10 5 0
MidDistillate 5000 14 3 0.85 0.2 000
Target 35000 380 0.991 4.5 300 250 0.5
max
Blend 35000 100 380 0.972 1.96 261 7.3 0.13
Source: based on "Bunkers", Fisher and Lux, page 33
Using current and recognized sources, the following section provides base data on
investment costs and operating requirements for a variety of refinery processes, with the stress
on the "bottom of the barrel". These are estimates based on current known refinery technology
and do not include revolutionary technology breakthroughs, although these could occur in an
extended 2010-2030 timeframe. They were used to review and guide any modifications required
to cost and operating data in the WORLD model.
Recent progress in refinery technology development has been reported for several of the
refinery process areas considered below. This progress reflects process unit potential for
investment and operating cost reduction and capacity increase through technology advances and
revamp experience, as well as by process product quality and yield improvement. These are
described, again based on current and recognized sources and extend the timeframe. In general,
these refer to incremental improvements as opposed to revolutionary breakthroughs, with the
exception of using ultrasound to reduce residual fuel sulfur, which is briefly described.
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Atmospheric Resid Desulphurization
Investment and Operating Costs
Basis 2nd Quarter 1995 U.S. Gulf Coast
Similar erected Chevron Units
Feed Rate 70,000 bpd AR 650+
Feed 11.8 API, 4.37% sulfur, 0.4 % 650+ product for RFCC feed
Investment Cost Summary, millions U.S. dollars:
Total On-Plot Cost 234.2
Total Off-plot Cost 70.3 (30% of on-plot)
Catalyst Charge 8.8 per charge
Hydrogen and Utility Requirements:
Hydrogen 71.7 million SCFD
Fuel 272 BPD EFO
Power 27,000 kwh
Net Steam 94 klb/h
Cooling water 8200 gal/min
Net process & BOW -25 kgal/min
Catalyst 8.8 million dollars/year
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 8.22-8.33
Using the latest technology catalysts and improved operational procedures, a large
Middle East refinery has reported a 30% increase in the amount of feed processed in the first
cycle. (NPRA Annual Meeting, March 13-15, 2005. NPRA Paper AM-05-54).
Vacuum Resid Hydro cracking
Investment cost depending on feedstock properties and product requirements, typical
investment costs range from $2000 to $ 5000 ISBL per BPSD. Basis 2002. This corresponds to
60-95% desulphurization.
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 8.81-8.83 - LC-Fining
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Ultra Sound Process to Reduce Heavy Sour Crude Sulfur
Patents awarded in 2005 and earlier describe the application of ultrasound to upgrade
sour heavy crude oil into sweeter lighter crude.(U.S. Patent No. 6,897,628, May 24,2005.) A
5,000 bpd commercial demonstration unit is planned with potential scale-up to 25,000 bpd and
joint venture agreements have been entered into. It is anticipated that the technology could have
upstream and down stream applications. A preliminary capital investment estimate of one
million dollars for a 2,000 bpd unit or $500 per bpd signals the potential for a dramatic reduction
in the cost of desulphurization of residual fuel oil blend fractions (Chemical Engineering., March
and June 2005). This process development is cited here because of its potential impact, but it
must be realized that it is very much in the research and development stage ( see
www.Sulphco.com for additional information).Tracking of future progress is warranted.
Delayed Coking Process
Investment and Operating Requirements:
Investment costs may range from $45,000 to $95,000 per short ton of coke produced.
This excludes the VRU unit and support facilities, but includes the coke handling costs. The
basis is 4th quarter 2002 and the Foster Wheeler process.
Operating requirements based on 1000 BPSD of fresh feed are as follows:
Fuel Liberated 5.1 mmbtu/h
Power consumed 150 kw
Steam exported 1700 Ib/h
Boiler feed water consumed 2400 Ib/h
Cooling water 5-25 gal/min
Raw water consumed 20-35 gal/day per short ton/day coke
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 12.86-12.88
Visbreaker Process
Investment and Operating Requirements:
Battery limits investment costs are 17 million dollars for a 10,000 bpsd unit and 33
million dollars for a 40,000 bpsd unit. This excludes the vacuum flasher and the gas plant. The
basis is 4th quarter 2002 and the Foster Wheeler/UOP process.
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Typical operating requirements per bpsd of fresh feed are as follows:
Fuel consumed 0.1195 million btu
Power consumed .0358 kw
Steam consumed 6.4 Ib
Boiler feed water consumed 2400 Ib/h
Cooling water 71 gal/min
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 12.104-12.105
Solvent Deasphalting Process (ROSE Process)
Investment and Operating Requirements:
The estimated installed cost for a 30,000 bpsd unit is $1250 per bpsd. The basis is 2nd
quarter 2002, U.S. Gulf Coast, Typical operating requirements per bbl of feed with propane
deasphalting are as follows:
Process heat consumed 12 million btu
Power consumed 1.5-2.1 kwh
Steam consumed 12 Ib
Solvent loss, wt% of feed 0.05-0.10
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition., 2003,
pg. 10.27-10.28
Gas Oil Hydro cracker
Investment and Operating Requirements:
Basis Jan 1, 2002 U.S. Gulf Coast
Similar projects executed for UOP Unicracking Process
VGOfeed 22.2 API, 2.5% sulfur
Product 94% distillate vs.98% naphtha
Investment Cost Summary, millions U.S. dollars
Total Erected Cost $/BPSD
Distillate Mode 2500-3500
Naphtha Mode 2000-3000
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Typical Utility Requirements, per 1000 BPSD fresh feed
Fuel 2-6 million BTU/h
Power 20(MOO kw
Net Steam 0.11 - 0.22 klb/h
Cooling water 40-120 gal/min
Net process & BFW 0.08 klb/h
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 7.33
Fluid Catalytic Cracking (FCC)
Investment and Operating Requirements:
Basis 1st Quarter 2002 U.S. Gulf Coast
Similar projects executed for KB RFCC Process
50,000 bpd VGO feed
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 3.32
Total Installed Cost $/BPSD $2250 to $2500 - Includes gas system (without power recovery),
main fractionator,VRU and amine treater.
Typical Utility Requirements, per BPSD fresh feed
Steam 40-200 Ib HP steam
Power 0.7tol.0kwh
Resid cat cracking is significantly different than gas oil cracking with respect to feed
properties and gasoline and distillate yields (conversion). As old FCC units are being replaced
and new capacity is being added, up to 50% of the worldwide FCC capacity will become resid
crackers
Recent advances in RDS catalyst technology and integration with RFCC catalyst design
have resulted in a 40% reduction on light cycle oil sulfur and a 50% reduction in RFCC sulfur
along with allowing the FCC to process heavier feedstocks. Also a new RDS catalyst system
developed allows substantially more 1000 degF + material to be processed. (NPRA Annual
Meeting, March 21-23 ,2004. NPRA Paper AM-04-29).
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Conversions approach 65% with recently tested FCC catalysts.
The heaviest resids contain high levels of contaminant metals such as nickel, vanadium
and iron. New FCC catalysts have been developed which improve the passivation of contaminant
metals over previous resid matrix technologies. A typical feedstock is a mix of reduced crude,
vacuum bottoms, deasphalted oil and bulk distillate, with feed properties typically 20 API (18-
29), 7 wt% Conradson Carbon (0-9), 42 ppm nickel +vanadium(10-50), 2.0 wt% sulfur (0.2-
2.4), and 0.3 wt% nitrogen(0.05-0.35). The values in parent theses are current commercial
ranges. (NPRA Annual Meeting, March 21-23, 2004. NPRA Papers AM-04-16 and AM-04-31).
Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003, p 3.81.
FCC Stack Emission Reduction
Total 2002 dollar total annualized (operating plus capital) costs range from $300 to 600
per ton of SCh removed depending on the specific type of SO2 wet scrubbing system used.
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition., 2003,
pg. 11.28
With the advent of consent decrees, SOX and NOX additives are being used increasingly to
achieve ultra-low FCC stack emissions and reduce acid rain formation. With extensive research
on how these additives work in the FCC regenerator, refiners have been able to reduce SOX
emissions to less than 25 ppm. without the high capital cost of installing hardware. NOX emission
reduction poses a more difficult problem and results vary from unit to unit. Commercial
examples demonstrate that NOX reduction can be achieved in excess of 75%. In many units
additives can reduce NOX emissions to 35 ppm and at times below 25ppm of NOx. (NPRA
Annual Meeting, March 13-15, 2005 NPRA Papers AM-05-21).
Low Sulfur and Ultra Low Sulfur Diesel Production
Units are per barrel feed
Stream
Diesel
Hvy. Gas Oil
Electric
(Kwh)
3.
6.
Fuel
(Mmbtu)
0.15
0.2
Steam
(Lb)
8.
10.
Hydrogen
(Scf)
300.
600.
B-7
-------
Draft Report - Do Not Cite or Quote
Investment Requirements for Hydro treating Diesel and Gas Oil Streams:
Basis: 1999 U.S. Gulf Coast, ISBL million of dollars, 30,000 bpsd
Diesel Feed 35.0
Heavy Gas Oil Feed 50.0
• Source: Gary and Handwerk, Petroleum Refining Process Economics, Fourth Edition, 2001,
pg.182-183
Ultra Low Sulfur Diesel Processes
It is highly unlikely that ultraslow diesel production would be blended with residual fuel
oil because of the high cost of production and the fact that its substitution for conventional diesel
fuel does not exert sufficient leverage on the residual fuel blend sulfur content. It is more likely
that it would be blended with the higher sulfur middle distillate components to produce the
marine diesel fuel grades. Representative ultraslow diesel processes are described below:
Operating and Investment Requirements for the Phillips S Zorb Process
Feed rate, BPD 20,000 40,000
Feed sulfur wt ppm 2600 500
Product Sulfur wt ppm 6 6
Power kwh 2511 3698
Steam nil nil
Nitrogen, million scfd 807 332
Cooling water gpm 1835 1870
Fuel gas , million btu/h 46.5 109.6
Total hydrogen, million scfd 1.24 1.44
Sorbent makeup, Ib per month 9970 19085
Erected Equipment, million dollars 20.85 30.60
Basis 2nd Quarter 2002 U.S. Gulf Coast
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 11.56
Operating and Investment Requirements for the UOP/Eni Oxadative Desulphurization Process
30% LCO, 70% straight run diesel
30,000 bpsd feed @400ppm sulfur and 10 ppm diesel product sulfur
U.S. Gulf Coast, 2nd quarter 2003
Capital cost, MM$ 16.0
B-8
-------
Draft Report - Do Not Cite or Quote
Hydrogen cost $MM/year 13.4
Utilities cost $, MM$/year 1.0
Catalyst cost $MM/year 1.3
Total cost $, MM$/year 15.7
• Source: NPRA Annual Meeting, March 21-23, 2004, Paper AM-04-48.
Syntroleum Gas to Liquids (diesel)
Capital Cost of Plant 25,000 dollars per bpd capacity
Operating Cost $5.00 per barrel excluding cost of natural gas
Product nil sulfur and aromatics, 74 Cetane number
Basis 2001 U.S. Gulf Coast
• Source: Robert A. Meyers Handbook of Petroleum Refining Processes, Third Edition, 2003,
pg. 15.23
Process Unit Revamping For Ultra Low Sulfur Diesel Production
Claims have been made that revamping for ultraslow sulfur diesel production with
countercurrent reactors can save up to 50 percent in Capex and 20 percent in OPEX based on
recent pilot plant tests (NPRA Annual Meeting, March 21-23, 2005 NPRA Papers AM-04-22).
Also, that integration of Isotherming into an existing conventional unit is 60 percent of the total
cost of a conventional revamp. (NPRA Annual Meeting, March 21-23, 2005 NPRA Papers AM-
04^0).
The estimated ISBL Investment Cost for (U.S. Gulf Coast, 1st Quarter 2005) for
upgrading a 20,000 bpsd Unit with Light Cycle Oil (LCO) feed to produce lOppm ULSD at 45
cetane number is estimated at 36.4 million dollars. (NPRA Annual Meeting, March 13-15, 2005
NPRA Paper AM-05-53.)
B-9
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TO: Barry Garelick
Russ Smith
FROM: Michael Gallaher
Martin Ross
DATE: April 24, 2006
SUBJECT: RTI Estimates of Growth in Bunker Fuel Consumption
This memorandum discusses specific features and assumptions in the RTI analysis of
bunker fuels that control estimated growth rates in demand. As discussed in the previously-
distributed memo, the focus of RTFs forecasts are on fuel demands going forward over the
next fifteen years. In the analysis, three features of the model used to estimate these
forecasts can potentially impact growth rates in fuel demand:
1) any improvements in engine efficiency,
2) changes in vessel size or speed that affect fuel efficiency per ton of cargo, and
3) the Global Insights projections of international trade that form the basis of the
analysis.
While engine efficiency has improved over time, most noticeably in the early
1980s, there are tradeoffs between improving fuel efficiency and reducing emissions.
During this study, engine manufacturers indicated to RTI that fuel efficiencies are likely
to remain constant for the next fifteen years, particularly as shippers focus on meeting
new NOX emissions requirements (the features of the model related to engine efficiency
are shown in the "Shipping Analysis" portion of Figure 1-1 in the previous memo - also
reproduced below). Similarly, although the RTI analysis considers how vessel sizes,
especially those of container ships on the Asia to North America trade routes, may
increase over time, these changes are not the driving force in the fuel-demand growth
(this model feature partially determines the average cargo per voyage in the "Trade
Analysis" portion of the figure).
The overriding feature in the RTI analysis that controls estimated growth in
bunker fuel demand is the Global Insights trade forecast (which largely determines the
number of voyages necessary in a year to ship the required amount of cargo - shown in
the "Trade Analysis" portion of the figure). It is RTFs understanding that in ongoing,
unpublished work for the California Air Resources Board (CARB), Dr. James Corbett has
calculated an installed-power growth rate for the historical period of 1996 through 2003
D-2
-------
of around 6.6% per year for ships entering and leaving the United States. This finding is
somewhat similar to Global Insights' data on growth in imports to the United States over
this time period, which shown 5.5% growth per year across all types of imports (Cell C14
in the "Gil Trade Forecasts" tab) - exports declined in total over the same period. At any
particular point over this time horizon, the year-to-year growth in commodity trade, and
associated fuel use by vessels estimated by RTI, can be quite variable, depending on
economic conditions and Global Insights' analysis of trade flows (e.g., changes in U.S.
exports of bulk goods - shown in Row 20 in the "Gil Trade Forecasts" tab).
Although for completeness RTI has presented fuel consumption estimates for the
years prior to 2005, based on the historical trade data, the focus of the analysis is on fuel
demands going forward over the next 15 years. As such, the modeling is based on today's
industry conditions (e.g., utilization rates) in conjunction with Global Insights' forecasts of
future trade flows. Any past events in the shipping industry that altered growth in installed
power or the ratio of installed power to overall shipping activity (possibly as the result of
temporary business fluctuations) are not evaluated/removed from these historical fuel-
consumption "estimates". Moving forward into the future, it is assumed that current
conditions in the shipping industry, in conjunction with the Global Insights' forecasts,
provide the most accurate method of estimating future bunker-fuel demands.
The historical growth in import trade discussed above is significantly higher than
Global Insights' projections of future growth in these trade flows. Over the time period
from 2005 to 2020, average annual growth in imports is expected to be around 1.7% per
year (Cell E14 in the "Gil Trade Forecasts" tab), while growth in exports is 1.6% per
year (Cell E25). Growth in imports ranges from a low of 0.7% per year for crude oill to
a high of 5.1% per year for containers. Export growth, especially for containers, is
generally lower than imports. It should be noted that import growth in container ships is
a significant driver in fuel consumption, however, since these vessels carry fewer tons per
ship (the units of the Global Insights trade data are tons) and the number of ships needed
will depend on the greater of either imports or exports.
The fuel-demand forecasts that are the focus of the RTI analysis are shown in the
"RTI Fuel Forecasts" tab of the Excel file. The main growth rates of interest are those
1 Although past forecasts by the Energy Information Administration, which are based in part on Global
Insights trade projections, have shown significant growth in crude oil imports (see, for example, EIA
[2004], which has import growth of more 2.6% per year from 2005 to 2020), Table 1 in the most recent
forecast from EIA (EIA, 2006) shows crude oil imports growing at only around 0.6% per year.
D-3
-------
shaded in yellow - these show an overall annual growth in fuel consumption of 3.4% per
year for the 2005-2020 time horizon (compared to growth in trade of around 1.7% per year,
where the higher growth in fuel consumption is driven by growth in container trade). Total
growth in container trade of 4.5% per year in the Global Insights forecasts (Cell E30 in the
"Gil Trade Forecasts" tab) gives an average growth in fuel use by container ships of around
5.5% per year (Cell E7 in the "RTI Fuel Forecasts" tab). Here, growth in fuel consumption
by container ships is higher than the average growth in container trade, which partially
reflects slow growth in container exports - since ships will leave the U.S. with an
incomplete load if no export goods are available.2 Growth in fuel consumption by all other
types of vessels is significantly lower, which reduces total growth across types of cargo.
Tables 1 and 2 below shows estimates of the lengths of international voyages used in
the analysis. Table 1 presents average lengths across types of non-container vessels (these
times are cargo specific and vary slightly based on the speed of the vessels - speeds are
taken from Dr. Corbett's work). Two sources were used for non-container trades and
voyage times in this table:
1) Worldscale Association. New Worldwide Tanker Nominal Freight Scale,
"Worldscale". 2002. London: Worldscale Association.
2) Maritime Chain. 2005. http://www.maritimechain.com/port/port_distance.asp.
The Worldscale tables, based on underlying BP Shipping Marine Distance Tables, are the
industry standard for measuring port-to-port distances, particularly for tanker traffic. The
reported distances account for common routes through channels, canals, or straits. This
distance information was supplemented by data from Maritime Chain, a web service that
provides port-to-port distances along with some information about which channels, canals,
or straits must be passed on the voyage. This distance information is then combined with
Dr. Corbett's speed parameters to determine the length of a voyage in days.
Voyage times for container trade are based on information from Containerization
International and calculations by David St. Amand. This resource provides voyage
information for all major container services. Based on the frequency of the service, number
2 Based on a discussion with Dr. Corbett, RTI reexamined the fuel-demand model to ensure that it
calculates fuel consumption by container ships based on growth in import shipments, rather than relying on
lower growth in net trade flows (imports and exports). The model is estimating fuel use properly. If the
model were run based on growth in net trade in containers, the growth rate in total fuel use by these vessels
would drop from 5.5% to 4.9%, which is roughly equivalent to the difference between import growth and
total trade growth (Cells E7 and E30 in the "Gil Trade Forecasts" tab, respectively).
D-4
-------
of vessels assigned to that service, and the number of days in operation per year, we
estimated the average length of voyages for the particular bilateral trade routes in the Global
Insights trade forecasts.
• Degerlund, J. (ed). 2005. Containerization International Yearbook 2005. London:
TF Informa UK Ltd.
D-5
-------
Table 1. Length of Voyages for Non-Container Cargo Ships (approx. average)
Days per Voyage
US South US North
Global Insights Trade Regions Pacific Pacific
Africa East-South 68 75
Africa North-Mediterranean 49 56
Africa West 56 63
Australia-New Zealand 48 47
Canada East 37 46
Canada West 1 1 5
Caspian Region 95 89
China 41 36
Europe Eastern 61 68
Europe Western-North 53 60
Europe Western-South 54 61
Greater Caribbean 26 33
Japan 35 31
Middle East Gulf 77 72
Pacific High Growth 52 48
Rest of Asia 68 64
Russia-FSU 64 71
Rest of South America 51 30
US East
Coast
57
37
36
65
7
40
41
73
38
24
30
16
65
56
67
66
38
41
US Great
Lakes
62
43
46
81
18
58
46
87
45
32
37
29
81
65
76
64
46
46
US Gulf
54
47
43
63
19
39
48
69
46
34
37
17
62
83
88
73
48
44
Table 2. Length of Voyages for Container-Ship Trade Routes
Origin ~ Destination Regions
Asia ~ North America (Pacific)
Europe ~ North America (Atlantic)
Mediterranean ~ North America
Australia/New Zealand ~ North America
South America ~ North America
Africa South ~ North America (Atlantic)
Africa West ~ North America (Atlantic)
Asia ~ North America (Atlantic)
Europe ~ North America (Pacific)
Africa South ~ North America (Pacific)
Africa West ~ North America (Pacific)
Caspian Region ~ North America (Atlantic)
Caspian Region ~ North America (Pacific)
Middle East/Gulf Region ~ North America (Atlantic)
Middle East/Gulf Region ~ North America (Pacific)
Days per Voyage
37
37
41
61
48
54
43
68
64
68
38
42
38
63
80
D-6
-------
References
U.S. Energy Information Administration (EIA) at Department of Energy (DOE). 2004. Annual Energy
Outlook 2004. DOE/EIA-0383(2004). .
U.S. Energy Information Administration (EIA) at Department of Energy (DOE). 2006. Annual Energy
Outlook 2006. DOE/EIA-0383(2006). .
D-7
-------
Figure 1-1. Method for Estimating Bunker Fuel Demand
Ship Analysis: by Vessel Type and Size Category
Inputs
Outputs
Deadweight for all Vessels of
Given Type & Size3
Average Cargo
Carried (Tons)
Horsepower, Year of Build
for all Vessels of Given
Type & Size3
Specific Fuel Consumption
(g/SHP-HR) by Year of Build"
Average Daily Fuel
Consumption
(Tons/Day)
Engine Load Factors0
Average Daily Fuel
Consumption (Tons/Day)
- Main, Aux. Engine at Sea
- Aux. Engine in Port
Trade Analysis: by Commodity and Trade Route
Inputs
Average Ship Speed0
Round Trip Mileaged
Tons of Cargo Shipped6
Average Cargo
per Ship Voyage
Outputs
Days at Sea and in
Port, per Voyage
Total Days at
Sea and in Port
Number of Voyages
Total Estimated Bunker Fuel Demand
Average Daily Fuel Consumption
(Tons/Day)
- Main, Aux. Engine at Sea
- Aux. Engine in Port
Total Days at Sea
and in Port
Bunker Fuel
Demand
Driven by changes in engine efficiency.
Driven by growth in
commodity flows.
a - Clarksons Ship Register Database
b - Engine Manufacturers' Data, Technical Papers
c- Corbett and Wang (2005) "Emission Inventory Review: SECA Inventory Progress Discussion"
d - Combined trade routes and heavy leg analysis
e - Global Insight Inc. (Oil) Trade Flow Projections
D-8
-------
From Spreadsheet Accompanying RTI April 24, 2006 Memorandum
RTI Forecasts of Fuel Consumption by Vessel Type and Region
Average Annualized Growth Rates
U.S. TOTAL - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
1995-2005
8.6'
-3.1
-1.9
3.1?
12.2'
3.6?
1996-2003
2000-2020
2010 growth factor relative to 2002
2020 growth factor relative to 2002
5.5°/
0.9°/
l.l0/
l.O0/
U.S. South Pacific - Fuel to Transport Cargo (Import + Export)
1995-2005
1996-2003
2000-2020
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
6.1%
0.8%
2010 growth factor relative to 2002:
2020 growth factor relative to 2002:
U.S. North Pacific - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
1995-2005
4.1%
-12.6%
24.4%
1996-2003
2000-2020
2010 growth factor relative to 2002
2020 growth factor relative to 2002
4.3%
-l.l0/
1.7%
0.7%
0.5%
-0.1°,
0.7%
1.30
1.81
U.S. East Coast - Fuel to Transport Cargo (Import + Export)
1995-2005
1996-2003
2000-2020
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
2010 growth factor relative to 2002:
2020 growth factor relative to 2002:
U.S. Great Lakes - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
1995-2005
-0.50/
-1.90/
1996-2003
2000-2020
-3.0% 0.8%
2010 growth factor relative to 2002
2020 growth factor relative to 2002
4.5%
1.42
2.21
1.14
1.35
U.S. Gulf - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
1995-2005
1996-2003
2010 growth factor relative to 2002:
2020 growth factor relative to 2002:
D-9
-------
From Spreadsheet Accompanying RTI April 24, 2006 Memorandum
(million tons of fuel)
U.S. TOTAL - Fuel to Transport Cargo (Import + Export)
1995
Container 11.6
General 7.9
Bulk 10.3
Crude 4.4
Chem 1 .6
Oil 4.1
Gas 0.4
Total 40.4
1996
11.9
8.0
9.9
4.3
1.7
4.3
0.5
40.5
U.S. South Pacific - Fuel to Transport Car;
1995
Container 2.6
General 1 . 1
Bulk 0.5
Crude 0.3
Chem 0.2
Oil 0.7
Gas 0.0
Total 5.3
1996
2.7
1.0
0.5
0.3
0.3
0.6
0.0
5.5
1997 1998
13.0 14.5
7.8 7.7
9.4 9.6
4.9 5.3
1.7 1.7
4.4 4.7
0.5 0.6
41.6 44.1
go (Import + Export)
1997 1998
3.0 3.4
1.1 1.1
0.5 0.5
0.3 0.5
0.3 0.3
0.7 0.7
0.0 0.0
5.9 6.6
1999
15.7
7.8
9.2
5.7
1.8
4.7
0.6
45.3
1999
3.9
1.3
0.5
0.6
0.3
0.8
0.0
7.4
2000
19.6
5.8
8.9
5.9
1.9
5.1
0.9
48.3
2000
4.8
1.0
0.6
0.7
0.4
0.8
0.0
8.3
2001
19.2
5.4
8.2
6.3
1.9
5.4
0.9
47.3
2001
4.8
1.0
0.6
0.8
0.4
0.9
0.0
8.4
2002
20.8
5.5
7.6
6.0
1.8
4.9
0.7
47.3
2002
5.2
1.1
0.6
0.8
0.3
0.8
0.0
8.7
2003
22.0
5.7
7.6
6.7
2.0
5.0
1.1
50.1
2003
5.5
1.1
0.5
0.9
0.2
0.7
0.0
9.0
2004
24.5
6.8
8.4
7.3
2.0
5.5
1.2
55.7
2004
6.3
1.3
0.6
1.0
0.1
0.7
0.0
10.1
2005
26.6
5.8
8.6
7.5
2.1
5.5
1.3
57.3
2005
6.9
1.2
0.6
1.0
0.1
0.7
0.0
10.5
2006
28.1
5.8
8.7
7.6
2.1
5.6
1.3
59.2
2006
7.4
1.2
0.6
1.1
0.1
0.7
0.0
11.1
2007
29.6
5.8
8.7
7.7
2.2
5.6
1.3
60.8
2007
7.9
1.2
0.6
1.1
0.1
0.7
0.0
11.6
U.S. North Pacific - Fuel to Transport Cargo (Import + Export)
1995
Container 1 .7
General 2.3
Bulk 1.2
Crude 0.0
Chem 0.1
Oil 0.1
Gas 0.1
Total 5.6
1996
1.7
2.2
1.1
0.0
0.1
0.1
0.1
5.4
1997 1998
1.8 1.6
1.9 1.4
1.0 0.9
0.0 0.0
0.1 0.1
0.1 0.1
0.1 0.1
5.1 4.2
1999
1.7
1.4
0.9
0.1
0.1
0.2
0.1
4.4
2000
2.3
0.9
0.9
0.1
0.1
0.1
0.1
4.5
2001
2.2
0.8
0.8
0.0
0.1
0.1
0.1
4.1
2002
2.2
0.7
0.7
0.0
0.1
0.1
0.1
4.0
2003
2.3
0.7
0.8
0.1
0.1
0.2
0.1
4.4
2004
2.4
0.8
1.0
0.1
0.1
0.1
0.1
4.6
2005
2.6
0.6
1.0
0.1
0.1
0.1
0.1
4.7
2006
2.8
0.6
1.0
0.1
0.1
0.1
0.1
4.8
2007
2.9
0.6
1.0
0.1
0.1
0.1
0.1
5.0
U.S. East Coast - Fuel to Transport Cargo (Import + Export)
1995
Container 5.1
General 2.2
Bulk 3.1
Crude 1 .0
Chem 0.3
Oil 0.9
Gas 0.1
Total 12.6
1996
5.3
2.2
3.1
0.8
0.3
1.1
0.1
12.9
1997 1998
5.9 6.7
2.3 2.5
3.0 2.9
0.8 0.9
0.3 0.3
1.1 1.5
0.2 0.2
13.6 14.8
1999
7.3
2.6
2.2
0.8
0.3
1.4
0.2
14.8
2000
8.7
2.0
2.0
0.9
0.3
1.5
0.2
15.6
2001
8.8
2.0
1.8
1.0
0.3
1.9
0.2
15.9
2002
9.8
2.2
1.7
0.9
0.3
1.6
0.1
16.7
2003
10.5
2.4
1.9
1.0
0.4
1.8
0.2
18.2
2004
11.6
2.7
2.1
1.1
0.4
1.8
0.4
20.1
2005
12.5
2.3
2.2
1.2
0.4
1.8
0.4
20.7
2006
13.1
2.3
2.2
1.2
0.4
1.8
0.4
21.4
2007
13.7
2.3
2.2
1.2
0.4
1.8
0.4
22.0
U.S. Great Lakes - Fuel to Transport Cargo (Import + Export)
1995
Container 0.0
General 0.4
Bulk 1.0
Crude 0.0
Chem 0.0
Oil 0.0
Gas 0.0
Total 1.4
1996
0.0
0.4
0.9
0.0
0.0
0.0
0.0
1.3
1997 1998
0.0 0.0
0.4 0.5
0.9 1.0
0.0 0.0
0.0 0.0
0.0 0.0
0.0 0.0
1.3 1.5
1999
0.0
0.5
1.0
0.0
0.0
0.0
0.0
1.5
2000
0.0
0.3
0.9
0.0
0.0
0.0
0.0
1.2
2001
0.0
0.3
0.8
0.0
0.0
0.0
0.0
1.1
2002
0.0
0.3
0.7
0.0
0.0
0.0
0.0
1.0
2003
0.0
0.3
0.7
0.0
0.0
0.0
0.0
1.1
2004
0.0
0.4
0.8
0.0
0.0
0.0
0.0
1.2
2005
0.0
0.4
0.8
0.0
0.0
0.0
0.0
1.2
2006
0.0
0.4
0.8
0.0
0.0
0.0
0.0
1.2
2007
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.2
U.S. Gulf - Fuel to Transport Cargo (Import + Export)
1995
Container 2.2
General 2.0
Bulk 4.6
Crude 3.1
Chem 1.0
Oil 2.4
Gas 0.2
Total 15.5
1996
2.2
2.1
4.3
3.2
0.9
2.4
0.2
15.3
1997 1998
2.4 2.9
2.1 2.2
4.0 4.3
3.7 4.0
0.9 0.9
2.5 2.4
0.2 0.3
15.8 17.0
1999
2.9
2.0
4.5
4.2
1.0
2.3
0.3
17.3
2000
3.8
1.5
4.5
4.3
1.1
2 7
0.6
18.7
2001
3.5
1.3
4.2
4.6
1.0
2.5
0.7
17.8
2002
3.6
1.2
4.0
4.3
1.0
2.4
0.5
16.9
2003
3.6
1.1
3.7
4.6
1.2
2.3
0.8
17.5
2004
4.2
1.6
4.0
5.0
1.3
2 9
0.7
19.7
2005
4.6
1.4
4.0
5.1
1.4
2.9
0.8
20.2
2006
4.8
1.4
4.1
5.2
1.4
2.9
0.8
20.6
2007
5.1
1.4
4.1
5.3
1.5
2.9
0.8
21.0
D-10
-------
From Spreadsheet Accompanying RTI April 24, 2006 Memorandum
(million tons of fuel)
2 (HIS
31.2
5.9
8.8
7.7
2.3
5.5
1.3
62.7
2008
8.5
1.2
0.6
1.1
0.1
0.7
0.0
12.2
2008
3.1
0.6
1.0
0.1
0.1
0.1
0.1
5.2
2008
14.4
2.4
2 2
1.2
0.4
1.8
0.4
22.8
2008
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.2
2008
5.3
1.4
4.2
5.3
1.5
2.9
0.8
21.4
2009
33.0
5.9
8.9
7.8
2.3
5.6
1.3
64.7
2009
9.1
1.2
0.6
1.1
0.1
0.7
0.0
12.8
2009
3.2
0.6
1.1
0.1
0.1
0.1
0.1
5.3
2009
15.1
2.4
2.2
1.2
0.4
1.8
0.4
23.5
2009
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.2
2009
5.6
1.4
4.2
5.4
1.6
2.9
0.8
21.8
2010
35.0
5.8
9.0
7.9
2.4
5.6
1.3
67.0
2010
9.7
1.2
0.6
1.1
0.1
0.7
0.0
13.5
2010
3.4
0.6
1.1
0.1
0.1
0.1
0.1
5.5
2010
16.0
2.3
2.2
1.2
0.4
1.8
0.4
24.5
2010
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.2
2010
5.9
1.4
4.3
5.4
1.6
2.9
0.8
22.3
2011
36.8
5.8
9.2
7.9
2.5
5.6
1.3
69.0
2011
10.2
1.2
0.6
1.1
0.1
0.7
0.0
14.0
2011
3.5
0.5
1.1
0.1
0.1
0.1
0.1
5.7
2011
16.9
2.4
2.3
1.2
0.4
1.8
0.4
25.4
2011
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.2
2011
6.2
1.3
4.4
5.4
1.7
2.9
0.8
22.7
2012
38.8
5.9
9.2
7.9
2.5
5.5
1.3
71.3
2012
10.8
1.2
0.6
1.1
0.1
0.7
0.0
14.6
2012
3.7
0.5
1.1
0.1
0.1
0.1
0.1
5.8
2012
17.8
2.4
2.3
1.3
0.5
1.8
0.4
26.4
2012
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.2
2012
6.5
1.4
4.4
5.5
1.7
2.9
0.8
23.2
2013
40.9
6.0
9.4
8.0
2.6
5.6
1.3
73.7
2013
11.4
1.2
0.7
1.1
0.1
0.7
0.0
15.3
2013
3.8
0.5
1.1
0.1
0.2
0.1
0.1
6.0
2013
18.8
2.4
2.3
1.3
0.5
1.8
0.4
27.5
2013
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.3
2013
6.9
1.4
4.5
5.5
1.8
2.9
0.8
23.8
2014
43.3
6.1
9.5
8.2
2.7
5.6
1.3
76.7
2014
12.1
1.3
0.7
1.1
0.1
0.7
0.0
16.1
2014
4.0
0.5
1.2
0.1
0.2
0.1
0.1
6.2
2014
19.9
2.5
2.3
1.3
0.5
1.8
0.4
28.8
2014
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.3
2014
7.3
1.4
4.5
5.6
1.8
2.9
0.8
24.4
2015
45.7
6.3
9.6
8.3
2.7
5.7
1.4
79.5
2015
12.8
1.3
0.7
1.1
0.2
0.7
0.0
16.9
2015
4.1
0.5
1.2
0.1
0.2
0.1
0.1
6.3
2015
21.0
2.6
2.4
1.3
0.5
1.8
0.4
30.0
2015
0.0
0.4
0.8
0.0
0.1
0.0
0.0
1.3
2015
7.7
1.4
4.6
5.7
1.9
2.9
0.8
25.1
2016
48.1
6.3
9.7
8.4
2.8
5.7
1.4
82.4
2016
13.5
1.3
0.7
1.1
0.2
0.7
0.0
17.6
2016
4.3
0.5
1.2
0.1
0.2
0.1
0.1
6.5
2016
22.2
2.6
2.4
1.3
0.5
1.9
0.4
31.3
2016
0.0
0.5
0.8
0.0
0.1
0.0
0.0
1.3
2016
8.1
1.4
4.7
5.8
1.9
3.0
0.8
25.7
2017
50.7
6.4
9.8
8.4
2.8
5.8
1.4
85.3
2017
14.3
1.3
0.7
1.2
0.2
0.7
0.0
18.4
2017
4.4
0.5
1.2
0.1
0.2
0.1
0.1
6.7
2017
23.4
2.6
2.4
1.3
0.5
1.9
0.4
32.6
2017
0.0
0.5
0.8
0.0
0.1
0.0
0.0
1.3
2017
8.5
1.4
4.7
5.8
1.9
3.0
0.9
26.3
2018
53.3
6.5
10.0
8.5
2.9
5.8
1.4
88.4
2018
15.0
1.4
0.7
1.2
0.2
0.8
0.0
19.2
2018
4.6
0.5
1.2
0.1
0.2
0.1
0.1
6.8
2018
24.7
2.7
2.4
1.3
0.5
1.9
0.4
34.0
2018
0.0
0.5
0.8
0.0
0.1
0.0
0.0
1.3
2018
9.0
1.5
4.8
5.9
2.0
3.0
0.9
27.0
2019
56.1
6.6
10.1
8.6
2.9
5.9
1.4
91.6
2019
15.9
1.4
0.7
1.2
0.2
0.8
0.0
20.1
2019
4.7
0.5
1.2
0.1
0.2
0.1
0.1
7.0
2019
26.1
2.7
2.5
1.3
0.5
1.9
0.4
35.5
2019
0.0
0.5
0.8
0.0
0.1
0.0
0.0
1.4
2019
9.5
1.5
4.8
5.9
2.0
3.0
0.9
27.6
D-11
-------
From Spreadsheet Accompanying RTI April 24, 2006 Memorandum
(Annual Growth Rate)
U.S. TOTAL - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
U.S. South Pacific - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
U.S. North Pacific - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
1996
-0.1%
-4.1%
-1 3.8%
57.5%
15.7%
11.2%
-1.0%
-4.1%
1997
1.9%
-14.3%
-4.0%
41.3%
17.6%
-22.0%
-23.7%
-6.6%
1998
-12.0%
-25.4%
-13.2%
35.5%
-19.9%
10.1%
-4.3%
-16.6%
1999
7. 8%
1.7%
0.8%
48.2%
-2.3%
43.1%
-16.8%
4.8%
2000
40.0%
-37.2%
-0.5%
-4.7%
-3.9%
-38.4%
7.6%
1.5%
U.S. East Coast - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
2004
10.6%
12.6%
1 1 . 0%
12.0%
-0.1%
-2.1%
65.9%
10.2%
2005
7.5%
-14.6%
5.8%
2.3%
-1.0%
0.0%
2.5%
3.1%
2006
5.0%
0.4%
0.7%
1.3%
3.9%
0.8%
-0.9%
3.3%
2007
4.40/
0.40/
-0.60/
1.20/
2.40/
O.O0/
-0.9%
2.8%
U.S. Great Lakes - Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
1996
1997
1.6%
-7.7%
1998
1999
-0.8% -44.1%
-0.9% -10.0%
-12.5%
11.3%
-12.6%
7.4%
6.4%
25.1%
5.9%
-11.3%
-0.8%
-1.9%
U.S. Gulf-Fuel to Transport Cargo (Import + Export)
Container
General
Bulk
Crude
Chem
Oil
Gas
Total
1996
-1.6%
9.6%
-6.9%
2.4%
-2.0%
-0.7%
1997
10.2%
-2.5%
-6.3%
15.3%
-4.9%
-14.3%
2.9%
1998
19.4%
4.8%
8.0%
8.2%
-0.5%
84.0%
7.5%
1999
-0.9%
-7. 1 %
4.4%
6.7%
13.0%
0.2%
1.6%
200
32. 7°/
-24.70/
-0.2
2.9
14.0
17.9
95.2
8.1
0 2001
4 -9.1%
4 -15.4%
-6.6%
5.1%
-8.8%
i -5.9%
-4.7%
D-12
-------
From Spreadsheet Accompanying RTI April 24, 2006 Memorandum
(Annual Growth Rate)
2014
5.9%
2.1%
1.3%
1.8%
2.7%
0.8%
2.4%
4.0%
2015
5.5%
1.9%
1 .4%
1 .4%
2.3%
0.8%
1.8%
3.7%
2016 2
5.3%
1.5%
1.3%
0.9%
1.9%
0.7% 0
1.7% 1
3.6% 3
2008
6.9%
-0.4%
9.5%
4.9%
2014
6.4%
2.6%
2008
-0.8%
2013
3.8%
-0.6%
1.7%
0.8%
1.1%
0.2%
1.0%
2.7%
2014
4.2%
-0.5%
1.6%
1.8%
0.8%
0.0%
0.9%
3.0%
2015
3.8%
-0.6%
1.5%
1.1%
0.5%
0.2%
0.2%
2.7%
2016
3.6%
-0.7%
1 .4%
1.0%
0. 1 %
0.1%
-0.3%
2.6%
2008
5.0%
0.5%
0.8%
2009
0.0%
0.5%
2.6%
0.4%
0.6%
2012
5.5%
1 .4%
0.7%
0.6%
2.5%
-0.4%
0.4%
3.9%
2013
5.4%
1.9%
0.9%
0.7%
2.8%
1.1%
0.9%
4. 1 %
2014
6.1%
2.4%
1.1%
1.7%
2.7%
1.1%
2.1%
4.7%
2015
5.6%
2.1%
1.2%
1.3%
2.4%
1.0%
1.6%
4.4%
2016
5.5%
1.7%
1.3%
0.9%
2.0%
0.9%
1.4% (
4.3% '
Z017
.6%
.4%
.0%
.1%
.5%
.1%
).9%
t.3%
2018
5.5%
1 .4%
1.0%
0.9%
1.5%
1.2%
1.1%
4.3%
2019
5.4%
1 .4%
1.1%
0.5%
1.5%
0.9%
0.8%
4.2%
2020
5.4%
1.7%
0.8
0.4
1.9
1.2
0.9
4.2
1.9%
0.7%
0.0%
0.7%
2.2°/
0.6°/
2.5°/
0.6°/
2008
5.0%
1.0%
0.9%
-0.1%
-0.1%
D-13
-------
Appendix E for EPA420-D-07-007
ESTIMATION, VALIDATION, AND FORECASTS OF
REGIONAL COMMERCIAL MARINE VESSEL
INVENTORIES
Tasks 3 and 4: Forecast Inventories for 2010 and 2020
Final Report
Submitted by
James J. Corbett, RE., Ph.D.
University of Delaware
Coauthored by
Chengfeng Wang, Ph.D.
California ARB
ARB Contract Number 04-346
Prepared for
the California Air Resources Board
and the California Environmental Protection Agency
8 December 2006
-------
Disclaimer
The statements and conclusions in this Report are those of the contractor and not necessarily
those of the California Air Resources Board. The mention of commercial products, their source,
or their use in connection with material reported herein is not to be construed as actual or implied
endorsement of such products.
-------
Acknowledgments
This Report was submitted in partial fulfillment of contract number 04-346, Estimation,
Validation, and Forecasts of Regional Commercial Marine Vessel Inventories, by the University
of Delaware under the partial sponsorship of the California Air Resources Board (ARE). Work
on Tasks 3 and 4 of the project was completed as of October 2006. This work benefited from
significant in-kind support from member of the North American SOx Emission Control Area
(SECA) team and their contractors. In particular, this work was shared with Research Triangle
Institute (RTI), at the direction of the United States Environmental Protection Agency (U.S.
EPA) and ARE. RTI developed a trade-based model forecasting energy demand from
commercial ships, and the forecasts described in this work are a product of coordination with this
EPA-funded effort. Necessarily, we cite these communications in this study, although we may
cite the RTI report to EPA when that report can be referenced.
In particular, we thank John Callahan of the Research & Data Management Services of
the University of Delaware for his help applying the GIS tools. We acknowledge with
appreciation the significant review comments, and contributions by ARE staff (including
Dongmin Luo, Todd Sax, Andy Alexis, Michael Benjamin, and Kirk Rosenkranz); the work
greatly benefited from their guidance. We acknowledge collaborative discussions with the North
American SECA team, including representatives of Environment Canada (Joanna Bellamy,
Naomi Katsumi (who provided the Canada data), Patrick Cram, Andrew Green, Veronique
Bouchet, and Morris Mennell), the Council on Environmental Cooperation (Paul Miller, now at
NESCAUM, who obtained the Mexico data on our behalf), and the U.S. Environmental
Protection Agency (Barry Garelick, Penny Carey, and others). In addition, we acknowledge the
work and review of fellow SECA contractors, including Brewster Boyd at Ross and Associates,
Louis Browning at ICF, and Chris Lindhjem at Environ. We acknowledge the good work
products and collaborative discussions regarding forecast results with Mike Gallagher and
Martin Ross at RTI and with Dave St. Amand at Navigistics Consulting. We thank the U.S. EPA
and RTI for providing their forecast data prior to its publication.
11
-------
Table of Contents
Disclaimer i
Acknowledgments ii
Table of Contents iii
List of Figures iv
List of Tables iv
ABSTRACT v
ABSTRACT v
EXECUTIVE SUMMARY vi
INTRODUCTION 1
Tasks 3 and 4 Questions & Research Objectives 1
Background 2
Summary of Significance 2
MATERIALS AND METHODS 3
Baseline: Ship Traffic Energy and Environmental Model (STEEM) Description 3
Forecasting principles 4
Installed power as first-order trend indicator for commercial marine emissions 5
Other freight energy and emissions forecasts 7
Economic forecasting of goods movement 8
Activity-based modeling of freight transportation 8
Emissions and energy forecasting of goods movement 10
Validation of power-based trends 11
RESULTS 14
Review of hypothetical SEC A region and baseline domain 15
Future Emissions without SEC A region (Task 3) 16
Future Emissions with Potential SECA (Task 4) 17
SUMMARY AND CONCLUSIONS 19
Uncertainty and Bounding 20
Base-Year Uncertainty 21
Uncertainty in Trend Extrapolation 22
Incorporation of additional detail among drivers affecting change 22
Incorporation of planned or proposed signals to modify technological change trends 22
Inclusion of fleet action in response to potential action 23
Spatial Limitations and Opportunities for Improvement 23
REFERENCES 24
LIST OF ACRONYMS 29
APPENDIX: Comparison with other SECA-team forecasts 30
in
-------
List of Figures
Figure ES-1. In-year reductions of 2020 SOx emissions with hypothetical SEC A, and cross-year
increases in SOx emissions comparing 2020 with SECA to base-year 2002
inventory viii
Figure 1. West Coast Pacific US ACE Foreign Cargo Ship Traffic (includes AK, CA, HI, OR,
WA) 6
Figure 2. Container statistics from U.S. Maritime Administration and American Association of
Port Authorities (47, 48) 9
Figure 3. South Coast (South Pacific) growth rates derived from historic data (1997-2003),
showing upper-bound (exponential), lower-bound (linear), and average trends 12
Figure 4. US container growth trends from data extrapolation (1997-2003) and from draft RTI
trade-energy model 13
Figure 5. Global trend indices for seaborne trade, ship energy/fuel demand, and installed power.
14
Figure 6. Model domain showing hypothetical with-SECA region and baseline 2002 model
results 16
Figure 7. Illustration of 2020 ship SOx emissions without SECA reductions 17
Figure 8. Illustration of 2020 ship SOx emissions with hypothetical SECA region 18
Figure 9. Forecast reduction in 2020 of annual SOx emissions due to hypothetical SECA 19
Figure 10. Forecast increases from base-year 2002 inventory in SOx emitted in 2020 with SECA.
20
Figure 11. Comparison of trends with and without EVIO-compliant SECA, and with 0.5% SECA
21
Figure 12. North American growth in installed power and in percent (from 2002) using installed
power from ships in USAGE foreign commerce data, Lloyds movement data for Canada
and Mexico 30
Figure 13. Comparison of North America trends using extrapolation from installed power data
(this work) with fuel use trends from trade-energy model for U.S. (RTI draft work) 31
List of Tables
Table 1. Power-based growth rate summary for commercial ships 2002 -2020 (CAGR) 15
IV
-------
ABSTRACT
This report presents results of Tasks 3 and 4 of a project to develop and deliver
commercial marine emissions inventories for cargo traffic in shipping lanes serving U.S.
continental coastlines. A primary objective of this project is to describe a regional scale
methodology for estimating commercial marine vessel (CMV) emissions in coastal waters (i.e.,
the Exclusive Economic Zone or EEZ) that is consistent with port-based inventory methods.
Using average growth trends describing trade and energy requirements for North American cargo
and passenger vessels, we produce an unconstrained forecast applying a common growth trend to
forecast a business as usual (BAU) scenario without sulfur controls (Task 3), and a with-SECA
scenario assuming IMO-compliant reductions in fuel sulfur to 1.5% by weight for all activity
within the Exclusive Economic Zone (200 nautical miles) of North American nations (Task 4).
Methodologies and validation developed in this work will provide better regional inventories of
commercial marine emissions for North America that supports the California Air Resources
Board (ARB), Commission for Environmental Cooperation in North America (CEC), western
regional states, United States federal, and multinational efforts to quantify and evaluate potential
air pollution impacts from shipping in U.S, Canadian, and Mexican coastal waters.
-------
EXECUTIVE SUMMARY
This report is intended to assist the role of the California Air Resources Board (ARB) and
other agencies evaluating the feasibility and extent of a North American Sulfur Emissions
Control Area (SECA) as defined by the International Maritime Organization (IMO) in terms of
potential impact to air quality and human health by oceangoing commercial marine vessels in
transit. A primary objective of this project is to describe a regional scale methodology for
estimating commercial marine vessel (CMV) emissions in coastal waters (i.e., the Exclusive
Economic Zone or EEZ) that is consistent with port-based inventory methods. Fundamental
methodology for current (base-year) inventories was addressed in large part through Tasks 1 and
2, for a base year of 2002. Tasks 3 and 4 contribute to this objective by adjusting the 2002 North
American inventory for future years to allow a comparison of scenarios with and without sulfur
emissions control, specifically:
Task 3 Forecast how baseline emissions may change in future years. Future emissions will be
dependent in part upon the changes in emission factors (due to MARPOL Annex VI,
other policy, and other changes in engine characteristics), changes in vessel size and
number. Additionally, changes may occur in vessel activity patterns and trade routes, and
changes in fuel quality (especially sulfur content) - from a mix of technology, economic,
and/or policy drivers.
Task 4 Forecast future-year ship emissions under a potential SECA designation. Modification of
future-year baseline emissions are made using MARPOL Annex VI requirements that
requires the sulfur content of marine fuel used by marine engines within a SECA be equal
to or less than 1.5% S by weight.
Using average growth trends describing trade and energy requirements for North
American cargo and passenger vessels, we produce two classes of forecasts: 1) an unconstrained
forecast applying a common growth trend to forecast a business as usual (BAU) scenario without
sulfur controls; and 2) a with-SECA scenario assuming IMO-compliant reductions in fuel sulfur
to 1.5% by weight for all activity within the Exclusive Economic Zone (200 nautical miles) of
North American nations. This report summarizes the baseline model, presents an empirically
representative growth rate based on the observed trend in installed power by ships calling on
North America. We employ a comparative analysis of several forecasting approaches to validate
power-based trends, and discuss the implications of the inventories with and without SECA
reductions.
For this project, we evaluate various sources of growth projections for commercial
marine activity and energy use, ultimately choosing an adjusted extrapolation scenario from
historic trends in installed power on ships calling on North American ports. This scenario
compares reasonably well all available energy and fuel usage trends and with trends describing
growth in trade volume. We grow the baseline inventory to geospatially represent energy and
emissions under this forecast scenario. We geographically characterize future ship emissions for
North America, including the United States, Canada, and Mexico, both with and without a
hypothetical Sulfur Emission Control Area (SECA) chosen to conform to the Exclusive
Economic Zones of these nations.
Our growth trends are also lower than have been reported since 2002 by major US ports.
We identify no systemic bias in our forecasts, especially given that other forecast results vary
through alternate input assumptions within expected bounds to bound our estimates. These
bounding comparisons are of similar magnitude to regional variability within the power-based
VI
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trends themselves. We interpret this to mean that our trend is generally representative as a BAU
forecast of ocean shipping emissions for North America. While available trade-based
extrapolations of energy use by ships may describe more explicitly the pattern of change
informed by trade economics, our extrapolation conforms more closely to recent past
observations in installed power trends.
Results show that implementing a North American SECA region reducing fuel-sulfur
content from 2.7% to 1.5% (whether through fuel changes or through control technology) will
reduce future SOx emissions (as SCh) by more than 700 thousand metric tons (-44%) from what
they may otherwise grow to be in 2020. However, future inventories with an IMO-compliant
SECA represent an increase over emissions in the 2002 base-year of more than 2 million metric
tons of SOx emissions throughout the North American domain.
2020 SOx Difference with SECA
kg SO2 reduced per 16 sq km
2020 w/ SECA Difference from Base Year
Figure ES-1. In-year reductions of 2020 SOx emissions with hypothetical SECA, and cross-year increases in
SOx emissions comparing 2020 with SECA to base-year 2002 inventory.
Important conclusions from this comparison and validation of independent forecast
approaches include the following two points. First, these forecasts are not fundamentally more
or less "correct" than comparison forecasts, as they all extrapolate observed trends with
adjustments for factors expected to influence future ocean freight activity and ship technologies.
In this regard, insights that result from our analysis of independent forecast models reveal a
range of future scenarios within which our emissions forecasts fall. Second, all models agree
that ship emissions are increasing along with growth in trade, and that these growth trends are
non-linear. Using 2002 as a base year, these models agree under BAU scenarios that energy
used by ships bringing global trade to and from North America will double by or before 2020;
some scenarios predict doubling before 2015. Insights support the significant attention that
international, federal, state and other agencies are devoting to understanding the impacts and
mitigation options for ocean freight in North America.
Annual and monthly files for 2010 and 2020 for SECA-compliant SOx emissions can be
found in both raster and ASCII formats at (http://coast.cms.udel.edu/NorthAmericanSTEEM/).
Together with the base-year inventory, these inventory forecasts assist ARB in evaluating air quality
and health impacts in California, and contributing to other efforts to evaluate national impacts. In
particular, the work provides part of the required information to request a North American SECA (or
SECAs) on behalf of the United States, Canada, and Mexico at the International Maritime
Organization (EVIO).
vn
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INTRODUCTION
This report is intended to assist the role of the California Air Resources Board (ARB) and
other agencies evaluating the feasibility and extent of a North American Sulfur Emissions
Control Area (SECA) as defined by the International Maritime Organization (IMO) in terms of
potential impact to air quality and human health by oceangoing commercial marine vessels in
transit. Using a spatially-resolved, activity-based inventory of North American shipping activity
derived from 172,000 port calls in 2002 to Canada, Mexico, and the United States, we adjust the
base-year inventory to estimate emissions from commercial marine vessels for 2010 and 2020.
Using observed trends in installed power by cargo and passenger vessels calling on North
America, we produce two classes of forecasts: 1) an unconstrained forecast applying a common
growth trend to forecast a business as usual (BAU) scenario without sulfur controls; and 2) a
with-SECA scenario assuming IMO-compliant reductions in fuel sulfur to 1.5% by weight for all
activity within the Exclusive Economic Zone (200 nautical miles) of North American nations.
This report summarizes the baseline model, presents an empirically representative growth rate
based on a comparative analysis of several forecasting approaches, and discusses the
implications of the inventories with and without SECA reductions.
Tasks 3 and 4 Questions & Research Objectives
A primary objective of this project is to describe a regional scale methodology for
estimating commercial marine vessel (CMV) emissions in coastal waters (i.e., the Exclusive
Economic Zone or EEZ) that is consistent with port-based inventory methods. Methodology for
current (base-year) inventories was addressed in large part through Tasks 1 and 2, for a base year
of 2002. Tasks 3 and 4 contribute to this objective by adjusting the 2002 North American
inventory for future years to allow a comparison of scenarios with and without sulfur emissions
control, specifically:
Task 3 Forecast how baseline emissions may change in future years. Future emissions will be dependent
in part upon the changes in emission factors (due to MARPOL Annex VI, other policy, and other
changes in engine characteristics), changes in vessel size and number. Additionally, changes may
occur in vessel activity patterns and trade routes, and changes in fuel quality (especially sulfur
content) - from a mix of technology, economic, and/or policy drivers.
Task 4 Forecast future-year ship emissions under a potential SECA designation. Modification of future-
year baseline emissions are made using MARPOL Annex VI requirements that requires the sulfur
content of marine fuel used by marine engines within a SECA be equal to or less than 1.5% S by
weight.
This project will support ARB efforts to understand the significance of ship emissions, by
providing forecasts of CMV emissions under assumptions that describe trade-driven fleet
growth, technological changes, and potential designation of special areas under the EVIO's
MARPOL Annex VI convention, called SOx Emission Control Areas (SECAs). We derive
emissions forecast trends directly from aggregate installed power of ships calling on North
American ports; this is because emissions are directly proportional to engine power and load,
which for at-sea conditions is highly correlated with total installed power on commercial ships.1
To validate our power-based extrapolation assumptions, we employ a comparison of historic
trends and forecast indicators related to maritime trade and energy to provide reasonable insight
1 This direct proportionality of stack emissions to engine power is implicit in the use of power-based emissions
factors in activity-based inventory best practices.
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into a range of feasible forecasts. Individually, none of these comparison forecasts can be
considered more correct than another, as they represent different assumptions about the
relationship between transportation energy, trade, and North American port activity. However,
taken together, they reveal a bounded range of trends with common insights useful in comparing
sulfur controls with no action. Our analysis confirms that power-based trends are representative
at several scales (port, region, coastal, and national) and informed by historic data, producing a
forecast of North American ship emissions with and without IMO-compliant sulfur reductions.
Background
Air pollutants from marine vessels account for a non-negligible portion of the emissions
inventory and contribute to air quality, human health and climate change issues at local, regional
and global levels (1-23). According to the U.S. EPA, heavy duty truck, rail, and water transport
together account for more than 25% of U.S. CCh emissions, about 50% of NOx emissions, and
nearly 40% of PM emissions from all mobile sources (24, 25). In Europe, freight modes together
generate more than 30% of the transportation sector's CCh emissions (26). In California, marine
vessel ship emissions are a significant concern with regard to state implementation of federal air
quality requirements (http://www.arb.ca.gov/msprog/offroad/marinevess/marinevess.htm),
particularly for air districts (19, 27)) and for major ports (http://www.portoflosangeles.org/ and
http://www.polb.com/).
Better estimation of current and future emissions inventories, including spatial
representation, is needed for atmospheric scientists, pollution modelers, and policy makers to
evaluate and mitigate the impacts of ship emissions on the environment and human health. In
fact, understanding the nature of commercial marine (e.g., cargo) vessel activity and energy use
serves both environmental and goods movement goals for the State of California and the nation.
This is particularly true for major ports which represent the node connecting imported and
exported ship cargoes with road and rail freight transportation serving the U.S. and global
economies.
Summary of Significance
This work forecasts emissions from commercial marine vessels (CMVs) in California and
across North American regions (including U.S., Canada and Mexico). Power-based growth
trends were validated through comparison with other marine vessel and oceangoing forecasts at
global, national, and regional scales, including major ports in California. We also compared
forecasts for other freight modes, compared draft results of a trade-energy model developed for
the U.S. EPA by RTI International as part of the North American SEC A team activities. Our
BAU results conclude that ship energy use and emissions will grow significantly through 2020,
with doubling from the 2002 base year inventory before 2020. We adjust for a slightly lower
growth rate for NOx due to IMO-compliant engines diffusing into the fleet through new vessel
orders or major conversions of existing vessels. Spatially resolved inventories represent national
average growth scenarios; power-based growth rates for selected regions are summarized (see
Table 1). Data support extended work producing maps using regionally-specific growth rates;
however, additional modeling is needed beyond the scope of this project (discussed in the
Uncertainty and Bounding part of the Summary section).
Results of this research will support ARE efforts to develop effective measures to reduce
ship emissions, and provides information needed to request a North American SEC A (or several
SEC As) at EVIO.
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MATERIALS AND METHODS
This section describes principles, methods, and data used to produce future emissions
inventory scenarios for North America. Three critical questions for forecasting freight activity
and environmental impacts include:
1. Baseline Conditions: What are freight energy and activity patterns?
2. Rates of Change: What is forecast trend in energy needed?
3. Patterns of Change: Where is future freight activity located?
While interrelated, these questions may be validated with some independence. In this
work, additional complexity involves understanding the spatial nature of the forecasted seaborne
trade, energy use, and emissions. In particular, a spatially allocated baseline estimate must
include identification of major trade routes, and make adjustments for routes on which the most
energy-demanding vessels operate. The spatially allocated forecast would ideally consider how
asymmetric growth among commodities and vessel types may affect the spatial dimensions of
the forecast, and would include adjustments for emissions control measures as existing or
forecasted policies begin to take effect.
We use the baseline inventory from ARB project Tasks 1 and 2 (28), partly funded by the
Council on Environmental Cooperation. We derive emissions forecast trends directly from
aggregate installed power of ships calling on North American ports; this is because emissions are
directly proportional to engine power and load, which for at-sea conditions is highly correlated
with total installed power on commercial ships.2 To validate our power-based extrapolation
assumptions, we explore a range of forecasts and trends that derive from trade flows, marine
energy consumption estimates, available sales statistics, and detailed scenarios about possible
future fleet activity. Our analysis is pluralistic in its inclusion of forecast trends, looking for
robust forecast trends rather than attempting to conform to a single likely scenario. We evaluate
how power-based trends differ across North American, U.S., and West Coast regions to help
illustrate expected asymmetry of faster-growing major trade routes with global average trends.
This ensures that our power-based extrapolation provides a representative forecast path within
the bounded range of potential trends from which to produce spatially resolved forecasts for
2010 and 2020. This helps us begin to consider how spatial representation of future ship energy
and emissions in North America may differ from other sources and regions. Our emissions
trends are consistent with available backcast trends in installed power and with independent
forecasts for major ports. Lastly, this comparison leads us to identify remaining limitations in
these spatial forecasts and future improvements to provide additional insights.
Baseline: Ship Traffic Energy and Environmental Model (STEEM) Description
By applying advanced GIS tools and using better data sets, STEEM adopts the strengths
of both top-down and bottom-up approaches and attempts to overcome the weaknesses in each
approach and improves ship emissions inventory both mathematically and theoretically. First, the
model builds an empirical waterway network based on shipping routes revealed from observed
historical ship locations. The spatial allocation is more accurate than a bottom-up approach,
which uses speculative routes, and than a top-down approach, which uses biased spatial proxies.
This direct proportionality of stack emissions to engine power is implicit in the use of power-based emissions
factors in activity-based inventory best practices.
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Second, as in a bottom-up approach, this model estimates energy use and emissions using
complete historical ship movements, ship attributes, and the distances of routes. The calculations
are expected to be more accurate than a top-down approach, which relies on the statistics of the
world fleet and its operating profiles. Third, the automation of repetitive processes makes this
method capable of producing global energy and emissions inventories, which is a daunting task
with existing bottom-up approaches. Fourth, since the network can be updated, modified, re-
used, and shared among users, STEEM is perhaps more cost-effective than both the top-down
and the bottom-up approaches. STEEM improves the baseline emissions inventory for North
American shipping in the following ways:
1. STEEM employs an emprical global waterway network derived from 20-year
International Comprehensive Ocean-Atmosphere Data Set (ICOADS) data;
2. The model estimates emissions from nearly complete historical North American
shipping activities (some 172,000 trips in U.S. Foreign Commerce Entrances and
Clearances data set and Lloyds' Movement data set) and individual ship attributes
while a top-down approach estimates emissions based on statistical analysis;
3. The model is constructed using advanced GIS network analyst technology to solve the
most probable route for each individual trip on a global scale;
4. STEEM establishes explicit mathematical relationships among trips, ships, routes, pairs
of ports, and segments of the waterway network using a matrix approach;
5. STEEM uses actual lengths of routes, together with service speed of each individual
ship, to calculate hours of operation while top-down approaches estimate annual hours
of operation based on fleetwide statistics;
6. STEEM follows best practice to estimate emissions based on ship installed power,
service speed, and traveling distance for each trip;
7. STEEM assigns emissions based on the locations of solved routes while earlier bottom-
up approaches drew straight lines between origins and destinations manually and top-
down approaches allocate global emissions based on biased proxies;
8. STEEM captures transit traffic which contributes to local air quality problems in some
areas like Santa Barbara, CA, while port-wide inventories have often ignored or been
unable to quantify these effects.
Forecasting principles
Forecasts can differ depending on their purposes and scales. Some forecasts look to
reveal where timely investment and action at a local scale or by a single firm can produce the
most benefit (e.g., profit). Validity of insights is determined by whether recommended actions
produce expected outcomes for a given decision, not whether the forecast trend or future value is
realized. Other forests are intended to be conservative or aggressive; that is, they intend to be
biased to serve the decision makers' value and tolerance for risk and surprise. This may describe
large scale forecasts such as emissions or trade trends. One challenging class of forecasts may
be considered "difference" forecasts, where alternative scenarios illustrate how "apath taken"
may differ from "apath not taken" rather than to determine which is most probable. These kinds
of forecasts are common in policy domains, such as energy, environment, and economics (e.g.,
IPPC scenarios). Certainly, freight forecasting presents one challenging example, especially at
the international or multinational scales, and especially when considering policy actions like a
SOx Emissions Control Area (SECA) under EVIO MARPOL Annex VI (29).
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Admittedly, the quality of forecasts of maritime shipping and trade is limited (30), and
thus forecasting of environmental impact from shipping is constrained by the quality of shipping
and trade forecasts. Rather than attempt to define one forecast path among many conditional
events determining future ship emissions, we employ a comparison of historic trends and
forecast indicators related to maritime trade and energy to provide reasonable insight into a range
of feasible forecasts. Individually, none of these forecasts can be considered more correct than
another, as they represent different assumptions about the relationship between transportation
energy, trade, and North American port activity. However, taken together, they reveal a bounded
range of trends with common insights useful in comparing sulfur controls with no action. We
look for converging growth trends that are representative at several scales (port, region, coastal,
and national) and informed by historic data. These lead to a set of principles for describing how
freight transport emissions may change:
1. Define the forecast domain broadly through multiple perspectives on freight and economy.
2. Compare global, large regional forecasts with local efforts for converging insights, perhaps
allowing for probabilistic assessment.
3. Include the rear-view mirror in forecasting (i.e., compare with persistence).
4. Consider first principles involving energy and environment: Some work-energy relationship
must hold if fuel price matters to freight.
5. Make extrapolation adjustments as simple as possible, but no simpler: Assumptions inter-
relating energy, economy, and technology should be checked for potential inconsistencies.
6. Look for surprise, avoid overconfidence: Recognize heterogeneity at all scales; use detailed
scenarios to help broaden or delineate the forecast range, but do not rely on them as likely.
Installed power as first-order trend indicator for commercial marine emissions
Given that energy used and emissions produced during goods movement increases at a
rate correlated to growth in activity, a number of proxies may be used to estimate inventory
growth rates. These include: economic activity (GDP and imports/exports value), trade activity
(tons and ton-miles), fuel usage (sales and estimates). All of these are indirect proxies (second or
higher order) of the activity that produces emissions. Except for fuel usage statistics, none
directly describe power requirements for shipboard power plants (propulsion and auxiliary
engine systems). Best practices for CMV emissions inventories typically use power-based (or
fuel-based) emissions factors, because of the implicit proportionality between engine load and
pollutant emissions - especially for uncontrolled sources (31, 32). Therefore, we derive
emissions trends directly from installed power data for ships calling on North American ports.
Assumptions we must make to use trends in installed power are rather simple: 1)
commercial marine vessels in cargo service generally design power systems to satisfy trade route
speed and cargo payload requirements; in other words, there is no economic reason to design
propulsion systems for containerships, tankers, etc., with more power than their cargo transport
operation requires; 2) commercial marine vessels operate under duty cycles that are well
understood, especially at sea speeds; these speeds utilize the majority of installed power as
reflected in best practice methodologies for activity based inventories of energy and emissions
from ships; 3) ships in commercial cargo service on major trade routes (to a major and growing
market like North America) reflect the best fit of ship design to service requirements; in other
words, the trends revealed in installed power of ships calling on North American ports directly
reveals the trend in speed and size for these routes. With these assumptions, trends in installed
power reveal the correlated trend in energy use by ships.
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We used installed power data associated with port calls from USAGE and Lloyds
Registry (for U.S. activity) and from LMIU data (for Canada and Mexico). We used historic
data as far back as 1997, where installed power data characteristics were provided. Where data
were missing in the installed power field for some vessels, we used linear regression statistics
within each vessel type associating gross registered tonnage (GRT) and rated power to fill data
gaps.
Depending on change in energy intensity and/or emissions through investments in
economies of scale, fuel conservation measures, or emissions control measures, the rate of
change in energy and emissions could be a modified growth curve from the growth in cargo
activity. If so, this should be observable directly in different rates of change for installed power
on ships providing goods movement compared to changes in cargo volume. In other words, if a
fleet of ships can carry more cargo without a proportional increase in installed power, then it
must be adopting improved technologies (e.g., hull forms, engine combustion systems, plant
efficiency designs) or innovating its cargo operations (e.g., payload utilization).
We evaluated available data for North American ports to determine historic trends in
installed power. Over a period from 1997 to 2003, we observed the trend in total ship calls, their
collective cargo capacity (tonnage), and aggregate installed power. Observations provided
further confirmation that ship calls change over time differently than cargo capacity; we also
observed the expected relationship between growth in cargo capacity and installed power. An
example is shown in Figure 1 for West Coast ship traffic in foreign cargo service. Based on this
analysis (performed for major ports in the U.S. and Canada) and discussions with the North
American SECA team and with ARB, we used installed power trends to develop emissions
forecast growth rates.
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1999
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2000
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2001
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2002
615.5
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2003
666.4
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33
32
31
30
29
28
27
26
25
Figure 1. West Coast Pacific USAGE Foreign Cargo Ship Traffic (includes AK, CA, HI, OR, WA).
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A variety of curves could be fit to the multi-year data, and fitting compound growth
curves to historic data points requires some judgment. An unconstrained exponential curve fit
would likely overestimate future emissions, particularly given expected shipping cycles (30). As
discussed above, a linear growth rate did not match known or expected technology changes
relative to cargo growth; a linear trend in energy use would imply less power required to achieve
the cargo throughput - where cargo volumes are projected to see compounded growth. We don't
believe that average technology in the fleet will change that much from its current path over the
next 35 years without strong policy incentive or substantial changes in fleet energy pricing and
supply. Overall, fleet propulsion technologies will remain more similar than different to the
current profile at least through 2040. Moving more cargo will require more power, in a similar
manner to the current fleet (either through larger ships, faster ships, more ships, or some
combination). Moreover, we did not identify physical capacity limits to ports or shipping routes
(that are not being addressed through infrastructure investment) which would constrain trade
growth.
Most forecasts essentially take historic trends for some recent period and extrapolate with
adjustment for expected change in trends (e.g., response to economic and population drivers
affecting global trade or consumption). Shipping cycles, recessions, and other surprises are
likely to produce growth trends less aggressive than simple exponential curve fits. We
recognized the need for similar adjustment in our forecasts; however, we could not determine a
location in time or the adjustment magnitude for these events. In other words, we expect that
future trade growth may not conform to a simple growth rate assumption, but we hesitated to
arbitrarily insert an "inflection point" in out-year forecasts corresponding to optimistic or
pessimistic assumptions.
A simple exponential curve fit to installed power produced an initial growth rate estimate
of 7.1% per year for North America, before averaging with a linear extrapolation. Through
discussions with ARB, we agreed that the unconstrained exponential trend and the linear trend
define bounding limits for expected change in ship activity. Averaging these curves defines an
arbitrary middle-growth trend, which implicitly describes a mix of positive and negative drivers
for ship energy requirements without articulating a detailed scenario of conditional events. After
adjustment, we estimate a growth trend for North America (including United States, Canada, and
Mexico) of about 5.9%, compounded ,
Other freight energy and emissions forecasts
Freight transportation, particularly international cargo movement, is an important and
increasing contributor to global and national economic growth, as well as state and regional
economic growth in and around major cargo ports. The multimodal and multicargo freight
context must be considered when forecasting oceangoing environmental trends. This is because
all freight modes respond to common drivers of change (e.g., economic growth, population
demographics, energy prices), and cross-mode influences need to be included (e.g., metropolitan
road congestion around one port diverting some cargoes to other ports). This applies whether
one is considering air emissions or other environmental impacts. Convergence is emerging on
global estimates - at least in terms of major insights, through academic dialogue about
uncertainty ranges in oceangoing energy and emissions (33).
The U.S. Bureau of Transportation Statistics (BTS) recently released a report that
describes North American freight activity and trends (34). This document reports growth rates
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for North America above 7.4% for international trade and above 7.2% across all measures of
value, and states that:
"Since 1994, the value of freight moved among the three countries has averaged
almost 8 percent annual growth in both current and inflation-adjusted terms,
compared with about 7-percent growth for U.S. goods trade with all countries
(table 1). In 2005, both goods trade and gross domestic product (GDP) grew in
inflation-adjusted terms. Except in 2001 and 2002, during the past decade, U.S.
trade with Canada and Mexico has increased at a faster rate than U.S. GDP."
Growth in goods movement by dollar value may be expected to differ from growth in the
volume of goods moved, and in the change in activity by the multimodal fleets (ships, trucks,
trains, and aircraft) moving cargo. This section describes growth trends for freight transportation
reported by or derived from available sources that are used to consider the validity of growth
trends derived for CMV inventoies.
Economic forecasting of goods movement
We confirmed that the contribution of international trade is increasing as a proportion of
U.S. gross domestic product (GDP) - i.e., freight transportation is growing faster than U.S. GDP
(34, 35). Economic activity related to imports and exports together contribute about 22% of
recent U.S. GDP in recent years; goods movement contributed about 10% of GDP in the 1970s.
Moreover, the dominance of containerized cargoes in seaborne trade suggests that truck and
containerized shipments may double by 2025 or sooner (36). GDP in the U.S. is growing at
-3.7% CAGR since 1980, and the freight sector is growing at -6.4% CAGR over the same
period (35). This freight-sector growth rate in terms of dollar value is reflected in the observed
-6.3% to 7.2% annual growth rates of "high-value" containerized trade volumes, particularly
from Asia (37).
Studies for Southern California (San Pedro Bay) ports agree that growth in cargo
volumes equivalent to 6-7% compounding annual growth rates is expected (38-41). However,
increased cargo may not produce a corresponding increase in port calls, as some studies interpret
(39). Historic data on port calls to San Pedro Bay have shown the number of ship calls remained
between 5,000 and 7,000 calls per year since the 1950s (42). This demonstrates that increasing
cargo throughput is related to technology innovation (e.g., larger ship sizes, higher speeds, and
containerization) that promotes economies of scale, more so than increased cargoes determine
the number of voyages. In fact, the trend in cargo growth is more closely related to work and
energy, i.e., installed power, than to ship calls.
Activity-based modeling of freight transportation
Seaborne cargo activity has increased at significant rates over time. World seaborne
trade growth has increased monotonically except for a short period in the early 1980s (43-46).
Containerized trade is growing faster than global rates. Figure 2 illustrates containerized cargo
trends 1997-2005. U.S. Maritime Administration (MARAD) statistics include cargo on both
government and non-government shipments by vessels into and out of U.S. foreign trade zones,
the 50 states, District of Columbia, and Puerto Rico, excluding postal and military shipments;
AAPA statistics describe total container throughput, including empty container movements.
Containerized cargo throughput (including empty container movements) grew at -6.5% CAGR
since 1985, with imported cargo grow since 1997 at more than 10% CAGR and total cargo TEUs
(excluding empty container movements) growing at -7% CAGR since 1997. Given the high-
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value nature of containerized cargoes, it is not surprising that these growth trends are most
similar to growth in the value of cargo moved, reported by BTS.
1980
1985
1990
1995
2000
2005
•TEU throughpu
•Total Cargo TEUs
•Export TEUs
•Import TEUs
Figure 2. Container statistics from U.S. Maritime Administration and American Association of Port
Authorities (47, 48).
Conceptually, growth in seaborne cargo movement should influence (if not determine)
activity growth in the freight modes (truck and rail) carrying imports and exports to or from U.S.
metropolitan regions and inland regions. For example, if growth in rail and truck modes is
primarily a result of increasing imports, observed in the U.S. to range between 4.6% and 4.8%
CAGR for all cargoes and between 6% and 9% for containerized (intermodal) cargoes (-6.5%
CAGR for total container throughput including empty containers), then combining these modes
should reflect seaborne trade growth rates (48, 49). The multimodal transportation of empty
containers presents a unique challenge in understanding how international goods movement
affects landside freight modes (50). Moreover, trucking and rail movements include exported
and domestic freight movements, which are growing at much lower rates than containerized
imports, effectively dampening national growth rates in intermodal freight transportation
compared to port throughput. Considering these activities together helps provide an intuitively
consistent explanation reconciling steeper seaborne trade trends reported in major ports, and
obtained or derived from economic and trade analyses, with less-steep truck and rail freight
trends. In other words, we should expect growth rates in goods movement to be shared among
modes because freight transportation is an intermodal network of imports, exports, empty
repositioning, and domestic freight flows3
3 This background discussion does not necessarily imply a direct relationship between energy and emission growth
rates and seaborne trade growth rates; depending on efficiency gains and economies of scale (e.g., shown for the rail
sector), the rate of change in energy and emissions for ships could be different. This background reinforces the
purpose of and need for the forecasts analysis presented in this report.
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The U.S. Department of Transportation launched two of the first federal efforts to
consider together multimodal and intermodal freight effects of imported cargoes, generally
through its "Assessment of the U.S. Marine Transportation System and spatially through the
Freight Analysis Framework (FAF) (51, 52). This work produced a forecast of freight
transportation activity based on trade increases, primarily to identify infrastructure needs rather
than estimate energy and environmental impacts. According to the Freight Analysis Framework
(52),4 domestic freight volumes will grow by more than 65 percent from 1998 levels by the year
2020, increasing from 13.5 billion tons (in 1998) to 22.5 billion tons (in 2020). This represents a
-2.3% compound annual growth rate (CAGR), similar to that obtained from VMT growth rates
(not adjusted for sales growth) in MOVES (53). In other reports, truck freight has doubled since
1980 (an average annual increase of 3.7%), while domestic waterborne freight has declined by
nearly 30% (an average annual decline of 1.8%) (54).5 These rates represent the lowest growth
trends we could find in the literature for goods movement.
Emissions and energy forecasting of goods movement
If growth in GDP and trade volumes is compounded as forecast by economic and
transportation demand studies, then growth in energy requirements should be non-linear also.
Freight energy use is correlated to increases goods movement, unless substantial energy
efficiency improvements are being made within a freight mode (e.g., U.S. rail) or across the
logistics supply network. Even assuming that efficiency improvements from economies of scale
reduce energy intensity and emissions rather than being directed to larger and faster ships (e.g.,
containerships), compounding increases in trade volumes outstrip energy conservation efforts
unless technological or operational breakthroughs in goods movement emerge.
Proportional relationships between environmental impacts and goods movement trends
are reflected in recent port and regional studies of economic activity and goods transportation,
particularly those focused on Southern California ports (38, 55-57). Federal energy forecasts also
link freight activity (and associated energy consumption) to economic growth projections. For
example, the EIA Energy Outlook "uses projections of dollars of industrial output to estimate
growth in freight truck travel; industrial output is converted to an equivalent measure of volume
output using freight adjustment coefficients" that assume constant average ton-miles per truck-
year (58).
Correlations between energy, emissions, and economic activity are observed also in
modal emissions forecasts for freight transportation. Until recently, most state and federal
studies have considered trucking forecasts to be part of an onroad domain, and other freight
modes (e.g., rail and waterborne) to be nonroad, even though containerized freight flows are
more typically inter-modal complements rather than multimodal substitutes. EPA's Emissions
Growth Analysis System (EGAS) contains growth factors for on-road mobile source categories,
generally computing growth factors based on VMT projections (59, 60). Acknowledging that
vehicle miles traveled (VMT) growth factors in EGAS are not differentiated by road
classification or vehicle type, EPA suggests that other methods, like travel demand forecasting or
regional growth rates may be more accurate. Since then, the EPA has been working to develop
improved models specific to mobile sources (61).
4 See Freight Analysis Framework documents at http://ops.fhwa.dot.gov/freight/freight analysis/faf/.
5 BTS Pocket Guide to Transportation 2003, http://www.bts.gov/publications/pocket guide to transportation/2003/.
10
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Currently, growth factors embedded in U.S. mobile source energy and emissions models
appear to capture better this economic-driven growth in freight transportation. Growth factors
for trucking (single-unit and combination trucks) in the U.S. EPA's mobile source models
include a combination of a population (sales) and VMT growth factors, with adjustments for fuel
economy and other operational factors (53). EPA compared rail freight ton-miles with railroad
distillate fuel consumption data to indicate substantial improvements in rail freight energy
intensity, adjusting emissions based on regulatory requirements (62). And, in its 2003
rulemaking, EPA assumed that freight growth was linked to increased tonnage volume (21).
Historic and future growth rates for particular modes are consistent with coupled growth
in economic-energy-emissions trends. For example, EPA projects that truck population and
VMT will increase by 4.2% to 4.8% CAGR between 2002 and 2025 (53). For rail, EPA showed
that growth rates in cargo ton-miles transported nearly doubled in recent periods, from -2.4%
CAGR between 1980 and 1995 to -4.8% CAGR between 1990 and 1995 (illustrated in Figure 1-
1 in U.S. EPA's regulatory support document). In fact, updating observed growth rates in cargo
ton-miles moved by rail to include more recent years reveal a rail-cargo growth rate of-3.6%
CAGR from 1985 to 2004 (63, 64).
For the marine sector, EPA's 2003 forecast methodology improved the similarity
between economic and emissions forecasts, although emissions forecasts represent a CAGR of
about 3.4% (range of 2.8% to 3.8%, depending on pollutant). While shipping growth rates
accounted for the effect of increased tonnage in a newer fleet, they do not consider the effect of
faster speeds - specifically the additional installed power to meet combined size and speed
requirements. Correcting for these factors brings the forecasts for international marine activity
into closer agreement with trucking growth rates (especially when rail cargo volume increases
are considered), and better describes the role of imports growth on the intermodal freight system.
California studies also describe significant growth expected in commercial marine
emissions. The recent Clean Air Action Plan for Southern California ports estimates that
emissions of NOx and PM from oceangoing vessels will increase at baseline rates between 5.5%
and 6% CAGR, respectively, unless measures are taken to reduce emissions (65) 6 These growth
rates are consistent with trade growth rates, perhaps modified for EVIO-compliant NOx
reductions in new vessels expected to call on California ports and descriptive of modest
improvements in fuel efficiency through fleet modernization and economies of scale.
Validation of power-based trends
We also compared our power-based trend to early results of a trade-energy model
developed along with our work for the SECA team (by RTI under EPA direction). While that
work is in draft form, our power-based and their trade-energy-based approaches compare well.
In Figure 3, we show bounding curves (exponential and linear) and the average growth curve for
Southern California ports. We converted growth trends in comparison studies from the no-net-
increase study and from the RTI trade-energy model (38, 66) to describe change in installed
power and plotted them in Figure 3 with our extrapolation7 We observe good agreement at this
scale between the draft RTI model and our trends. Moreover, while the no-net-increase (NNI)
forecast produces nearly the same result for 2020, neither of these approaches describes the
6The Clean Air Action Plan shows emissions control measures may offset near-term growth (at least through 2011)
if fully implemented (see Table 6.4).
7
NNI shows only the Southern California ports of Los Angeles and Long Beach, while the RTI work describes the
"South Pacific" ports, which are considered to be mainly LA and LB but could include Oakland.
11
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substantial increases estimated by the NNI report in the near-term as a result of planned
investment in the port(s) (38). These independent derivations of growth trends describe at least a
doubling of commercial marine energy use in California by 2020, corresponding to similar
change in the expected port cargo throughput.
2010
2015
Unconstrained 7-point Trend
-Baseline NNI
-Average Unconstrained-Lineal
Linear Trend
•RTI US South Pacific
Figure 3. South Coast (South Pacific) growth rates derived from historic data (1997-2003), showing upper-
bound (exponential), lower-bound (linear), and average trends. Also shown are trends derived from the NNI
Task Force and from the draft RTI trade-energy model.
Agreement between the draft trade-energy model by RTI and extrapolation of observed
data is even stronger for containerships. As shown in Figure 4, preliminary results from the draft
RTI trade-energy forecast are more aggressive than our power-based extrapolation. RTFs trade-
energy model exception to calibrate on inbound containerized cargoes ("heavy-leg" activity)
may explain this (66). Note excellent agreement in RTI draft model results with observed
power-trend history for containership calls to U.S. ports.
These sources of growth trends and forecasts are consistent with and validate our
observed trends in installed power and support our extrapolation of power-based trends to
forecast emissions under business-as-usual (BAU) conditions. Using our adjusted extrapolation
to forecast growth at -5.9%, we observe that power-based growth rates derived here are higher
than growth rates for land-based freight modes, by about 1% to 2%. This comparison is
expected due to the fact that trucking and rail are also engaged in domestic and intra-continental
trade with Canada and Mexico that would not require commercial shipping. Moreover, our
forecast rates are generally lower than dollar-value growth in North American seaborne trade,
and a bit lower than growth in containerized cargo volume. Again, such comparisons are
expected given the importance of bulk cargoes (liquid and dry) to North American international
trade. In addition, the lower growth in power-based rates compared to cargo activity provide
confirming evidence that economies of scale are improving the energy intensity and emissions
intensity of international shipping - but perhaps by not more than 1% to 2% overall yet.
Additional analysis by vessel type could quantify these improvements in more detail, perhaps
discerning relative roles of speed, size, and operational factors (e.g., average payload utilization
rate). Lastly, we observe emissions and energy use by the fastest, most powerful ships
(containerships) are increasing at the fastest rates, along with demand for containerized trade.
12
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Average
n Observed Container Power
RTI Container
1990 1995 2000 2005 2010 2015 2020 2025 2030
Figure 4. US container growth trends from data extrapolation (1997-2003) and from draft RTI trade-energy
model.
13
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RESULTS
This section compares results of alternative measures of growth at multiple scales,
demonstrating general similarity among power-based and other ocean shipping trends. At the
global scale, we evaluate available trends in energy use and/or emissions from published
literature with the seaborne cargo and trade data discussed earlier. Eyring et al estimate fuel
usage and emissions over a historical period from 1950 to 2000 and forecasts for 2020 and 2050
using an activity-based approach describing a B AU scenario and a number of alternate scenarios
combining different ship traffic and technology assumptions (67, 68). For these purposes, we
use their B AU scenario for a diesel-only fleet.
We compare world fleet trends in installed power (derived from average power by year of
build) with energy trends (Eyring work and fuel sales), with trade-based historical data (tons and
ton-miles), and with (preliminary) global results from RTFs draft trade-energy model. Activity-
based energy results for similar base-years (2001 or 2002) are within close agreement (31, 67,
69, 70)s This allows us to index trends to nearly the same value and year, to index trade-based
trends similarly, and to compare these with trends in installed power, as summarized in Figure 5
Extrapolating trends since -1980-85
depending on data source
1950
1960
1970
1980
1990
2000
2010
2020
2030
•Seaborne Trade (tons)
•Seaborne Trade (ton-miles)
"Seaborne Trade (trend since 1985)
-Installed Power-This work
•RTI Trade-energy Model (world)
-OECD HFO Int'l Sales
-World Marine Fuel (Eyring, 2005)
1)
2)
Figure 5. Global trend indices for seaborne trade, ship energy/fuel demand, and installed power.
Three insights emerge from this global comparison.
Extrapolating past data (with adjustments) produces a range of business-as-usual
trends that is bounded and reveals convergence around a set of similar trends; in other
words, one cannot get "any forecast they want" out of the data. If we consider that
global trade and technology drivers mutually influence future trends, then we may
interpret this convergence as describing a likely forecast of global shipping activity.
World shipping activity and energy use are on track to double by about 2030 (-2015 if
one considers seaborne trade since 1985, -2050 if one considers Eyring's BAU trend).
An exception is work by Endresen et al, that tends to adjust parameters to agree with international marine fuel sales
statistics; while not considered here, their results are within uncertainty ranges described in other work (3, 33, 71).
14
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3)
Growth rates are not likely to be reduced without significant changes in freight
transportation behavior and/or changes in shipboard technology.
Confirming earlier discussion, trends in installed power are clearly coupled with trends
in trade and energy. This reinforces the analysis of installed power as a proxy for
forecasting growth, not only for use in baseline inventory estimates.
Table 1 presents an overview of power-based growth rates for selected ports and North
American regions. Growth rates for North America, US, Mexico, and Canada use regression
statistics within each vessel type associating gross registered tonnage (GRT) and rated power to
fill data gaps. For port growth rates, and for California, and West Coast growth rates, this table
presents regression statistics within each vessel type associating net registered tonnage (NRT)
and rated power, because later analysis by Wang and ARB identified that more complete NRT
data was available for the regressions in past years. This adjustment modifies growth rates
generally lower by less than 0.5%.
General similarity is observed across all regions, with Canada installed-power data
presenting the highest rate of growth and with Mexico presenting the lowest rate of growth.
These growth rates represent an average of unconstrained exponential curve-fits with linear
extrapolation of the data, which is the methodology discussed above. As such, they represent an
arbitrary middle-growth trend, which implicitly describes an implicit mix of positive and
negative growth drivers. Given that such adjustments may not equally influence growth at
different ports or regions, it is quite possible that actual growth in emissions will be higher for
some places (and perhaps lower for others), depending on events that modify unconstrained
growth trends over the next decades.
Table 1. Power-based growth rate summary for commercial ships 2002 -2020 (CAGR)
Ports, or Region
Los Angeles/Long Beach
Oakland/San Francisco
New York/New Jersey
California (all ports)
U.S. West Coast
U.S. National
Canada
Mexico
North America (U.S., Canada and Mexico)
Emissions Growth Rate
5.24%
5.68%
6.03%
5.53%
5.93%
5.86%
6.57%
5.06%
5.86%
1. Growth rates represent an average of exponential and linear fit extrapolations,
presented in terms of compound annual growth rate (CAGR).
2. US data are from USAGE and Lloyds Registry data, per this and other work by
Wang and Corbett.
3. Canada and Mexico data are from Lloyds Movement data (LMIU)
Review of hypothetical SECA region and baseline domain
We produced a set of baseline (Tasks 1 and 2) emissions estimates and forecast estimates
(this work, Tasks 3 and 4) conforming to a consensus domain and resolution appropriate for most
of the atmospheric modeling that will use our North American ship emissions inventory. This
consensus resulted from several meetings with the SECA team. Annual emissions are resolved
into twelve monthly components, following time-resolved patterns in ship activity in North
15
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America, as discussed in the report for Tasks 1 and 2. The North American inventory estimates
for each pollutant uses the following projection parameters from ESRI's ArcGIS software.
Projection: Equidistant_Cylindrical
Parameters:
False_Easting: 0.0 - default ESRIparameter
False_Northing: 0.0 - default ESRI parameter
Central_Meridian: 180.0 degrees - UD defined
Standard_Parallel_l: 0.0 - default ESRI parameter
Linear Unit: User_Defmed_Unit (1000 m) - UD defined
Cell Units: kilograms per 16 square kilometers
We delivered inventory files using the following domain:
left -1000 km, right 18000 km, top 8000 km, bottom 0 km.
A hypothetical SECA region conforming to the Exclusive Economic Zone (EEZ) for
North America was defined for the with-SECA scenarios. Figure 6 shows the model domain and
also reproduces the SOx inventory illustration for the base-year 2002. The scale shown for
emissions is delineated using units common to forecast inventory illustrations discussed below.
Legend
Figure 6. Model domain showing hypothetical with-SECA region and baseline 2002 model results.
Future Emissions without SECA region (Task 3)
Based on trend comparisons discussed above, we use the following ratios for SOx
forecasts: For 2010, we multiply the 2002 base year inventory by 1.61 times; for 2020, we
multiply the 2002 base year inventory by 2.79 times. This corresponds to a growth rate of 5.9%
compounded annually.
For NOx emissions we make adjustment for the introduction of IMO-compliant engines
into the international cargo fleet. We use industry data to estimate -11% percent average
reductions in NOx for new engines complying with MARPOL Annex VI (72). Following
standard assumptions for the introduction of new engines in the fleet used by ARB (and others),
we estimate that about 46% of the fleet in 2010 and about 78% of the fleet in 2020 will be IMO-
compliant. This accounts for fleet-weighted NOx reductions of 5% and 8.4% in 2010 and 2020,
respectively, resulting in NOx multiplier ratios of 1.53 for 2010 and 2.55 for 2020.
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Per project scope, we considered whether fuel-sulfur content may change in coming
years, e.g., would refining practices result in generally higher fuel-sulfur averages over time as
distillate fuels (particularly diesel) removed more sulfur. We chose not to make any adjustments
to the average fuel-sulfur content in this work for two reasons. First, we observe very little
change in world-average fuel-sulfur content for residual fuels over the past decade; in fact, most
of the differences may be attributed to better statistical tracking on behalf of MARPOL Annex
VI, more so than real changes in the global average. Second, we recognize that variation is fuel-
suflur content regionally may be greater than the average change over time; we understand that
EPA is sponsoring study of this issue, and that results of that work are not yet available. If such
trends are proven, they can be implemented at the regional level using STEEM in future work.
An illustration of 2020 emissions without applying any SECA reductions is presented in
Figure 7. Annual and monthly data files for 2010 and 2020 for all forecasted pollutants (SOx as
SO2, NOx as NO2, CO2, PM, CO, and HC) are provided in both raster and ASCII formats at the
project website (http://coast.cms.udel.edu/NorthAmericanSTEEM/).
North American SOx in 2020
Kg SO2/16sqkm
n°
_|0-00
QlQ1 -500
|5Q1 -1 000
• ' 001 - 544.939
Figure 7. Illustration of 2020 ship SOx emissions without SECA reductions.
Future Emissions with Potential SECA (Task 4)
To produce with-SECA forecast scenarios, uncontrolled inventories for 2010 and 2020
are modified to depict a reduction in average fuel-sulfur content from 2.7% to 1.5%, a SOx
emissions reduction of about 44%. Only SOx emissions are assumed to change under this SECA
scenario; no additional reductions in primary PM, NOx or other pollutants are calculated. Within
GIS, we select the emissions within the hypothetical SECA region and multiply them by 66% (1
minus 44%). Similar to the forecast without SECA, this makes no assumptions for changes in
fuel quality or supply between now and 2020. Such changes could occur through regulatory
action in addition to an EVIO-compliant SECA, or through a combination of fuel supply and price
effects not considered in this work. Such considerations could be included in updated forecasts,
17
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based on insights from further (ongoing) studies. Figure 8 illustrates annual SOx emissions in
2020 depicting compliance with the hypothetical SECA domain.
An illustration of 2020 emissions with SECA reductions is presented in Figure 8. Annual
and monthly files for 2010 and 2020 for SECA-compliant SOx emissions can be found in both
raster and ASCII formats at (http://coast.cms.udel.edu/NorthAmericanSTEEM/).
North American w/ SECA 2020
kg SO2/16 sq km
Figure 8. Illustration of 2020 ship SOx emissions with hypothetical SECA region.
It is worth noting that sulfur inventories represent stack emissions of gaseous sulfur
dioxide (802), not aerosol sulfate. Ours is a stack emissions inventory, before total fate and
transport impacts. It is inappropriate to pre-process gaseous emissions from the stack within an
inventory using some set of assumptions to estimate total PM (primary plus secondary).
Atmospheric modeling will convert the SO2 gas emissions to sulfate particles needed to estimate
total PM health effects.
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SUMMARY AND CONCLUSIONS
Implementing a North American SECA region reducing fuel-sulfur content from 2.7% to
1.5% (whether through fuel changes or through control technology) will reduce future SOx emissions
by more than 700 thousand metric tons (-44%) from what they may otherwise grow to be in 2020
(see Figure 9).
Important conclusions from this comparison and validation of independent forecast
approaches include the following two points. First, these forecasts are not fundamentally more
or less "correcf than comparison forecasts, as they all extrapolate observed trends with
adjustments for factors expected to influence future ocean freight activity and ship technologies.
In this regard, insights that result from our analysis of independent forecast models reveal a
range of future scenarios within which our emissions forecasts fall. Second, all models agree
that ship emissions are increasing along with growth in trade, and that these growth trends are
non-linear. Using 2002 as a base year, these models agree under BAU scenarios that energy
used by ships in global trade will double by or before 2020; some scenarios predict doubling
before 2015. Insights support the significant attention that international, federal, state and other
agencies are devoting to understanding the impacts and mitigation options for ocean freight in
North America.
2020 SOx Difference with SECA
kg SO2 reduced per 16 sq km
-239 774 to-100 001
-TOO ooo to -10.001
[-10000 to-1.001
[J-1 000 to-101
-100 to-1
Figure 9. Forecast reduction in 2020 of annual SOx emissions due to hypothetical SECA.
Figure 10 illustrates the change in SOx forecast for 2020 as a ratio of 2002 base-year
emissions and in metric tons difference. Note that Figure 10 depicts only increased ship SOx
emissions. Forecasted increases in trade will overcome IMO-compliant reductions in ship SOx
19
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emissions in less than two decades (before 2020 at 5.9% CAGR). Specifically, our results
forecast more than 2 million metric tons of SOx additional emissions throughout the North
American domain, even with an IMO-compliant SECA in 2020. Similar results occur under RTI
draft forecasts (at 3.7% CAGR), which under 1.5% sulfur limits will equal base-year emissions
in about 2030.
Figure 11 illustrates this further by representing the change in emissions within the EEZ
(hypothetical SECA) over time. This helps reveal two insights:
1. There are benefits from an IMO-compliant (1.5% fuel-sulfur SECA) over BAU trends; and
2. Health effects and/or other impacts that may be offset from the 2002 base year by
implementing a SECA will return to 2002 levels within one or two decades. (Of course,
mitigation benefits will be determined in other work by ARB and the SECA team.)
These insights appear robust, regardless of the range in possible forecasts. Using the forecast
trend derived in this work, trade growth offsets emissions under a 1.5% fuel-sulfur SECA by
2012; using lower growth rates from preliminary RTI results, emissions within a North American
SECA return to 2002 levels by 2019.
However, Figure 11 also shows that a 0.5% fuel-sulfur limit - such as has been discussed
for Europe - provides substantial benefits longer into the future under reasonable growth
assumptions. A North American SECA requiring 0.5% fuel-sulfur or control technologies
achieving these reductions would offset trade growth continuing to the early 2030s under a 5.9%
CAGR or to about 2050 under a 3.7% CAGR, respectively. This conclusion from either growth
curve means that long-term emissions reductions are possible from ships operating in North
American waters, and that the IMO-compliant SECA requirements (1.5% fuel-sulfur) represents
an important first step.
. \ i III
Figure 10. Forecast increases from base-year 2002 inventory in SOx emitted in 2020 with SECA.
Uncertainty and Bounding
There are six types of uncertainty that affect these results. Two primary sources of
uncertainty involving parameters directly used in this study include a) uncertainty in the base-
year estimates, and b) uncertainty in the trend used to produce the forecasted inventories.
Additionally, uncertainty arises from factors not addressed in this work to date - but that could
improve future efforts using these methods. Additional detail could be incorporated to describe
20
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better underlying drivers of change in freight activity and consumption, to include planned or
proposed signals (e.g., policy action) modifying vessel activity and propulsion technology, to
make alternate assumptions about fleet response in terms of under- or over-compliance with
standards or in terms of price-effects, and to better depict spatially the asymmetric growth among
vessel types and trade routes expected within the shipping network.
2,500,000
2,000,000
o
LU
•- 1,500,000
c>j
O
V)
(A
C
o
'l_
•5
1,000,000
500,000
2000
2005
2010
2015
2020
2025
2030
—6—5.9% Growth (This work)
—o—Our Trend With SECA
2002 Baseline
•Preliminary RTI Growth (-3.6%)
RTI with SECA
- 0.5% SECA at 5.9% Growth
Figure 11. Comparison of trends with and without IMO-compliant SECA, and with 0.5% SECA
Base-Year Uncertainty
The baseline inventory effort followed general best practices for calculating emissions
inventories, which enables general analysis of uncertainty due to estimating input parameters, as
discussed in the report for Tasks 1 and 2, and elsewhere (28, 73). Results show good agreement
with other inventories, including the draft trade-energy model estimate for 2001 by RTI (66).
National level uncertainty includes four major elements: A) Uncertainty in input parameter
assumptions (e.g., emissions factors, engine activity profile, etc.); B) Uncertainty in U.S.
domestic shipping not included in foreign commerce vessel movement data; C) Uncertainty in
U.S. Army Corp of Engineers data, and in Canadian and Mexican LMIU data; and D) Spatial
uncertainty in routing choices, particularly within confined bay and port regions and seasonally
for open ocean routes where weather routing may occur. An uncertainty analysis was performed
on fundamental input parameters in the model, and potential undercounting of voyages or their
misassignment in the routing model was discussed, including opportunities to improve the
baseline inventory produced by STEEM for this work.
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Uncertainty in Trend Extrapolation
These forecasts must be considered to represent what other forecast scenarios often refer
to as "business as usual" (BAU). The primary uncertainty in the forecast trend applied to the
2002 baseline inventory can be best understood in terms of backcast validation efforts described
above. Improving confidence in extrapolated trends requires longer historic trends, regionally
resolved. Improving the nature of extrapolations would require better articulated relationships
among drivers and industry trends. However, as shown above, the extrapolated trends developed
in this work are within bounded agreement with other forecasts more dependent on trade
economics.
A secondary element in trend uncertainty could reside in missing data fields associating
installed power with ships calling on North America. For this work, we used linear regressions
within each vessel type associating gross registered tonnage (GRT) and installed power to fill
data gaps. During later review, we compared overall power-based growth trends using net
registered tonnage (NRT) regressions. There was less than 0.6% difference between the
regressed power and reported power in registry data for 2002, indicating that both GRT and NRT
regressions yield similar results. However, as we move back in time, we note empty fields in the
GRT data increase faster than empty fields in the NRT data; this could result in different trend
estimates for the same historic ship calls. Upon review, we confirmed that using NRT
correlations with installed power could increase the 1997 estimates by less than 9%; none of the
other years' installed power totals changed much. This could decrease the overall growth trends
used in this work by less than 1%.
We think this uncertainty in trend extrapolation could be worth further research, but
acknowledge that revised trends would still compare well in our validation analysis. No major
insights or conclusions would change. Ship emissions activity would still be on track to double
before 2020 in North America, and an EVIO-compliant SECA would still return to 2002 levels
within two decades. A lower growth rate in installed power could indicate slightly greater
reductions in energy intensity (e.g., faster decoupling of trade and emissions) over time, but this
would still be within the 1% to 2% range reported in this work.
Incorporation of additional detail among drivers affecting change
Underlying drivers of freight activity and the energy systems that produce emissions will
continue to merit analysis. For example, growing GDP may remain highly correlated with
growth in imports as it has over past decades. This correlation could become stronger in the
future, or one might consider how and whether change in population age and demographics
could reduce the rate of consumption and trade in North America without a downturn in GDP.
These sorts of effects on global and regional shipping are not considered in this work, either
directly or through any of the BAU forecast trends considered; a potential exception could be
include work by Eyring et al., which modifies growth on major trade routes greater than recent
trends and North American analyses would suggest (68). Better consideration of drivers for
change in freight transportation represents a rich area for future research, particularly in terms of
goods movement.
Incorporation of planned or proposed signals to modify technological change trends
This work explicitly accounts for the expected impacts of NOx emissions limits imposed
by MARPOL Annex VI - already in force, as discussed above. In addition to the Annex VI NOx
limits, one could consider including fuel switching measures proposed by the State of California
22
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for auxiliary engines and/or in a recent proposal by INTERTANKO (74). We forecast emissions
without considering such interventions, to compare BAU results with a SECA regime. This
enables atmospheric modeling analyses by members of the North American SECA team to
consider what reductions may achieve air quality goals in North America. Future work could
consider actions (e.g., emissions trading regimes) that could accelerate or out-perform a SECA
for North America; recent work has begun to consider these issues (75).
Inclusion of fleet action in response to potential action
Few assumptions about influences of EU regulatory activity, IMO decisions, or changes
in marine fuel supply and demand are imposed in forecasts presented here. Moreover, this work
assumes full compliance with SECA requirements and no change in fleet logistics associated
with these scenarios. Additional modeling of fleet responses to policy or economic signals may
reveal motivations for unintended behavior and assess their likelihood. This could help clarify
whether increased regulation could deter trade, or whether observations confirming such
behavior are mostly anecdotal.
Spatial Limitations and Opportunities for Improvement
Overall the inventories produced for this project using STEEM are shown to be valid
geospatial depictions of emissions from commercial ship activity in North America. Some
limitations reveal potential for future analyses to become more accurate and descriptive. In
particular, we emphasize that this work increased emissions proportionally for all routes on all
North American coastlines. This necessarily underestimates growth on the West Coast where
emissions from containerized trade are growing faster than the national average and
overestimates emissions growth in regions where overall trade growth is slower, such as the Gulf
of Mexico served mostly be bulk ships. Consideration of heterogeneous forecast trends
separately for different vessel types and trade routes would produce spatial results revealing
asymmetry among future trends for liner trades and bulk trades. As such, this work represents a
first-order set of spatial forecasts appropriate to consider the value of a SECA for North America
but not explicit enough without additional work to apply to other large-scale issues such as port
development or regional shifts in traffic.
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LIST OF ACRONYMS
AAPA
ARE
BAU
BTS
CAGR
CEC
CMV
CO2
EEZ
EGAS
EIA
FAF
GDP
GRT
IMO
ICOADS
INTERTANKO
kW
MARAD
MARPOL
MOVES
NNI
NRT
NOx
POLA
PM
RTI
SECA
SOx
STEEM
USAGE
U.S. EPA
VMT
American Association of Port Authorities
California Air Resources Board
Business as usual
U.S. Bureau of Transportation Statistics
Compound annual growth rate
Commission for Environmental Cooperation in North America
Commercial Marine Vessel
Carbon dioxide
Exclusive Economic Zones
Emissions Growth Analysis System
Energy Information Administration
Freight Analysis Framework
Gross Domestic Product
Gross Registered Tonnage
International Maritime Organization
International Comprehensive Ocean-Atmosphere Data Set
Independent Tanker Owners And Operators
Kilowatts
U.S. Maritime Administration
Maritime Pollution Convention
U.S. EPA Mobile Vehicle Emissions Simulator
No net increase
Net Register Tonnage
Oxides of nitrogen
Port of Los Angeles
Particulate matter
Research Triangle Institute, Inc.
SOx Emission Control Area
Oxides of sulfur
Waterway Network Ship Traffic, Energy and Environment Model
U.S. Army Corps Engineers
United States Environmental Protection Agency
Vehicle miles traveled
29
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