Draft Regulatory Impact Analysis: 

Control of Emissions of Air Pollution 

from Category 3 Marine Diesel Engines 
Draft Regulatory Impact Analysis:


Control of Emissions of Air Pollution 


from Category 3 Marine Diesel Engines


Assessment and Standards Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

EPA-420-D-09-002 

June 2009 
Regulatory Impact Analysis 

Table of Contents 

Executive Summary 

Chapter 1: Industry Characterization 

Chapter 2: Air Quality and Resulting Health and Welfare Effects 

Chapter 3: Emission Inventory 

Chapter 4: Technological Feasibility 

Chapter 5: Engineering Cost Estimates 

Chapter 6: Cost-Benefit Analysis 

Chapter 7: Economic Impact Analysis 

Chapter 8: Small Entity Impact Analysis 

i 
List of Acronyms 

μm Micrometers 

bext Light-Extinction Coefficient 

μg Microgram 

μg/m3 Microgram per Cubic Meter 

ABT Average Banking and Trading 

ACS American Cancer Society 

AE Alaska Southeast Region 

AE Auxiliary Engine 

AEO Annual Energy Outlook (an EIA publication) 

AESS Automatic Engine Stop/Start System 

AFC Average Daily Fuel Consumption 

AIM Aerosol Inorganics Model 

AIRS Aerometric Information Retrieval System 

AMVER Automated Mutual-Assistance Vessel Rescue 

APHEA Air Pollution and Health: A European Approach 

APU Auxiliary Power Unit 

AQ Air Quality 

AQCD Air Quality Criteria Document 

AQMTSD Air Quality Modeling Technical Support Document 

ARB Air Resources Board (California) 

ASPEN Assessment System for Population Exposure Nationwide 

ATAC Average Total Cost 

avg Average 

AW Alaska West Region 

BAF Bunker Adjustment Factor; a surcharge reflecting the fluctuation in fuel cost 

BenMAP Benefits Mapping and Analysis Program 

bhp Brake Horsepower 

BNSF Burlington Northern Santa Fe 

BSFC Brake Specific Fuel Consumption 

BTS Bureau of Transportation 

C Celsius 

C1 Category 1; marine diesel engines up to 7 liters per cylinder displacement 

C2 Category 2; marine diesel engines 7 to 30 liters per cylinder 

C3 Category 3; marine diesel engines at or above 30 liters per cylinder 

CA California 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule (CAIR) (70 FR 25162, May 12, 2005) 

CAMR Clean Air Mercury Rule 

CAND Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004) 

CARB California Air Resources Board 

CASAC Clean Air Scientific Advisory Committee 

CAVR Clean Air Visibility Rule 

CB Chronic Bronchitis 

CCV Closed Crankcase Ventilation 

CDC Centers for Disease Control 

CDPF Catalyzed Diesel Particulate Filter 

CEA Cost Effective Analysis 

CES Constant Elasticity of Substitution 

CFR Code of Federal Regulations 

CI Compression Ignition (i.e., diesel engines) 

CI Confidence Interval 

CIMT Carotid Intima-Media Thickness 

CITT Chemical Industry Institute of Toxicology 

CMAQ Community Multiscale Air Quality 

ii 
CMB Chemical Mass Balance 

CMV Commercial Marine Vessel 

CO Carbon Monoxide 

CO2 Carbon Dioxide 

COI Cost of Illness 

COPD Chronic Obstructive Pulmonary Disease 

CPI-U Consumer Price Index - All Urban Consumers 

C-R Concentration Response 

CSS Coastal Sage Scrub 

CUA Cost Utility Analysis 

cyl Cylinder 

D Demand 

DE Diesel Exhaust 

DEM Domestic Engine Manufacturer 

DDHS Diesel Driven Heating System 

diff Difference 

disp Displacement 

DM Distillate Marine Grade 

DOC Diesel Oxidation Catalyst 

DOE Department of Energy 

DOT Department of Transportation 

DPF Diesel Particulate Filter 

DPM Diesel Particulate Matter 

DR Discount Rate 

DRIA Draft Regulatory Impact Analysis 

DSP Deep Sea Port 

DV Design Values 

DWT Dead Weight Tonnage 

EAC Early Action Component 

EC East Coast Region 

EC Elemental Carbon 

ECA Emission Control Area 

EDHS Electric Driven Heating System 

EEZ Exclusive Economic Zone 

EF Emission Factor 

EGR Exhaust Gas Recirculation 

EIA Energy Information Administration (part of the U.S. Department of Energy) 

EIA Economic Impact Analysis 

EIM Economic Impact Model 

EMD Electromotive Diesel 

EMS-HAP Emissions Modeling System for Hazardous Air Pollution 

EO Executive Order 

EPA Environmental Protection Agency 

EPAct Energy Policy Act of 2005 

ESPN EPA speciation network 

F Fahrenheit 

FEM Foreign Engine Manufacturer 

FEV Functional Expiratory Volume 

FR Federal Register 

FRM Final Rulemaking 

FRP Fiberglass-Reinforced Plastic 

g Gram 

g/bhp-hr Grams per Brake Horsepower Hour 

g/kW-hr Grams per Kilowatt Hour 

gal Gallon 

GAO Government Accountability Office 

iii 
GC Gulf Coast Region 

GDP Gross Domestic Product 

GEOS Goddard Earth Observing System 

GETS General Electric Transportation Systems 

GI Global Insight 

GIS Geographic Information System 

GL Great Lakes Region 

GRT Gross Registered Tonnage 

GT Gas Turbine 

H2 Hydrogen Gas 

HAD Diesel Health Assessment Document 

HAP Hazardous Air Pollutant 

HC Hydrocarbon 

HD Heavy-Duty 

HE Hawaii East Region 

HEI Health Effects Institute 

HEP Head End Power 

HES Health Effects Subcommittee 

HFO Heavy Fuel Oil 

hp Horsepower 

hp-hrs Horsepower Hours 

hrs Hours 

HW Hawaii West Region 

IACS International Association of Classification Societies 

IARC International Agency for Research on Cancer 

ICD International Classification of Diseases 

ICOADS International Comprehensive Ocean-Atmospheric Data Set 

IFO Intermediate Fuel Oil 

IMO International Maritime Organization 

IMPROVE Interagency Monitoring of Protected Visual Environments 

IRIS Integrated Risk Information System 

ISCST3 Industrial Source Complex Short Term Model 

ISO International Standardization Organization 

ISORROPIA Inorganic Aerosol Thermodynamic Model 

ITB Integrated Tug Barge 

JAMA Journal of the American Medical Association 

K Kelvin 

k Thousand 

km Kilometer 

kts Knots 

kW Kilowatt 

kWH Kilowatt Hour 

L Liter 

L/cyl Liters Per Cylinder 

lb Pound 

LCO Light Cycle Oil 

LF Load Factor 

LGC Large Gas Carrier 

LNG Liquefied Natural Gas 

LPG Liquefied Petroleum Gas 

LRS Lower Respiratory Symptoms 

LSD Low Sulfur Diesel fuel 

m3 Cubic Meters 

MARAD U.S. Maritime Administration 

MARPOL The International Convention for the Prevention of Pollution of Ships 

MC Marginal Cost 

iv 
MCIP Meteorology-Chemistry Interface Processor 

MDO Marine Diesel Oil 

ME Main Engine 

MECA Manufacturers of Emission Controls Association 

mg Milligram 

MGO Marine Gas Oil 

MDO Marine Diesel Oil 

MI Myocardial Infarction 

MILY Morbidity Inclusive Life Years 

min Minute 

MM Million 

MM-1 Inverse Megameter 

MOBILE6 Vehicle Emission Modeling Software 

MRAD Minor Restricted Activity Days 

MSAT Mobile Source Air Toxic 

MSAT1 2001 Mobile Source Air Toxics Rule 

MSB Major Shipbuilding Base 

MSD Medium Speed Diesel 

MSDS Material Safety Data Sheet 

MVUS Merchant Vessels of the U. S. 

MW Megawatt 

MW-hrs Megawatt Hours 

N Nitrogen 

N2 Nitrogen Molecule 

NA Not Applicable 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industry Classification System 

NAS National Academy of Sciences 

NASA National Aeronautics and Space Administration 

NASSCO National Steel and Shipbuilding Company 

NATA National Air Toxic Assessment 

NBER National Bureau of Economic Research 

NCAR National Center for Atmospheric Research 

NCDC National Clean Diesel Campaign 

NCI National Cancer Institute 

NCLAN National Crop Loss Assessment Network 

NEI National Emissions Inventory 

NESCAUM Northeast States for Coordinated Air Use Management 

NESHAP National Emissions Standards for Hazardous Air Pollutants 

NH3 Ammonia 

NIOSH National Institute of Occupational Safety and Health 

NLEV National Low Emission Vehicle 

NM Nautical Mile 

NMHC Nonmethane Hydrocarbons 

NMIM National Mobile Inventory Model (EPA software tool) 

NMIM2005 National Mobile Inventory Model Released in 2005 

NMMA National Marine Manufacturers Association 

NMMAPS National Morbidity, Mortality, and Air Pollution Study 

NO Nitrogen Oxide 

NO2 Nitrogen Dioxide 

NOAA National Oceanic and Atmospheric Administration 

NONROAD EPA’s Non-road Engine Emission Model 

NONROAD2005 EPA’s Non-road Engine Emission Model Released in 2005 

NOx Oxides of Nitrogen 

NP North Pacific Region 

NPRM Notice of Proposed Rulemaking 

v 
NPV Net Present Value 

NRC National Research Council 

NRLM Nonroad, Locomotive and Marine diesel fuel 

NRT Net Registered Tonnage 

NRT4 Nonroad Tier 4 Rule 

NSTC National Science and Technology Council 

NTE Not To Exceed 

NWN National Waterway Network 

O&M Operating and maintenance 

O3 Ozone 

OAQPS Office of Air Quality Planning and Standards 

OC Organic Carbon 

oCA Degree Crank Angle 

OEHHA Office of Environmental Health Hazard Assessment 

OEM Original Equipment Manufacturer 

OGV Ocean-Going Vessel 

OMB Office of Management and Budget 

OTAQ Office of Transportation and Air Quality 

P Price 

PAH Polycyclic Aromatic Hydrocarbons 

PCB Polychlorinated Biphenyls 

PGM Platinum Metals Group 

PM Particulate Matter 

PM AQCD EPA Particulate Matter Air Quality Criteria Document 

PM/NMHC Particulate Matter to Non-Methane Hydrocarbon Ratio 

PM10 Coarse Particulate Matter (diameter of 10 μm or less) 

PM2.5 Fine Particulate Matter (diameter of 2.5 μm or less) 

PMM Post-Manufacturer Marinizer 

PMNAAQS Particulate Matter National Ambient Air Quality Standards 

POM Polycyclic Organic Matter 

POLA/LB Ports of Los Angeles, Long Beach 

ppb Parts per Billion 

PPI Producer Price Index 

ppm Parts per Million 

psi Pounds per Square Inch 

PSR Power Systems Research 

Q Quantity 

QALY Quality Adjusted Life Years 

R&D Research and Development 

RfC Reference Concentration 

RFA Regulatory Flexibility Analysis 

RFS Renewable Fuels Standard 

RIA Regulatory Impact Analysis 

RM Residual Marine 

rpm Revolutions per Minute 

RPO Regional Planning Organization 

RRF Relative Reduction Factors 

RSZ Reduced Speed Zone 

RV Revision 

RVP Reid Vapor Pressure 

S Sulfur 

S Supply 

SAB Science Advisory Board 

SAB-HES Science Advisory Board - Health Effects Subcommittee 

SAE Society of Automotive Engineers 

SAPS Sulfated-Ash, Phosphorus, and Sulfur Content 

vi 
SBA Small Business Administration 

SBREFA Small Business Regulatory Enforcement Fairness Act 

SCC Source Classification Code 

SCR Selective Catalyst Reduction 

SFC Specific Fuel Consumption 

SI Spark Ignition 

SIC Standard Industrial Classification 

SiC Silicon Carbide 

SMAT Speciated Modeled Attainment Test 

SO2 Sulfur Dioxide 

SOx Oxides of Sulfur 

SOA Secondary Organic Carbon Aerosols 

SOF Soluble Organic Fraction 

SP South Pacific Region 

SSD Slow Speed Diesel 

ST Steam Turbine 

STB Surface Transportation Board 

STEEM Waterway Network Ship Traffic, Energy and Environment Model 

SVOC Semi-Volatile Organic Compound 

SwRI Southwest Research Institute 

TBN Total Base Number 

TCC Total Compliance Cost 

TCM Total Carbon Mass 

TDC Top Dead Center 

TEU Twenty-foot Equivalent Unit; basic container measurement used in the shipping industry 

THC Total Hydrocarbon 

TSD Technical Support Document 

TVCC Total Variable Compliance Cost 

ULCC Ultra Large Crude Carrier 

ULSD Ultra Low Sulfur Diesel fuel 

URS Upper Respiratory Symptoms 

USACE United States Army Corps of Engineers 

USDA United States Department of Agriculture 

UV Ultraviolet 

UV-b Ultraviolet-b 

VLCC Very Large Crude Carrier 

VLGC Very Large Gas Carrier 

VOC Volatile Organic Compound 

VOF Volatile Organic Fraction 

VOS Voluntary Observing Ships 

VSL Value of Statistical Life 

WLD Work Loss Days 

WTP Willingness-to-Pay 

$2,005 U.S. Dollars in calendar year 2005 

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viii
Executive Summary 

Executive Summary 

EPA is proposing emission standards for new Category 3 marine diesel engines 

(engines with per cylinder displacement at or above 30 liters) installed on U.S. vessels. The 

proposed standards are part of a coordinated strategy to address emissions from ocean-going 

vessels (OGV) and are an important step in EPA’s ongoing National Clean Diesel Campaign 

(NCDC). 

Emissions from OGV remain at high levels. The Category 3 engines on these vessels 

use emission control technology that is comparable to that used by nonroad engines in the 

early 1990s, and use fuel that can have a sulfur content of 30,000 ppm or more. As a result, 

these engines emit high levels of pollutants that contribute to unhealthy air in many areas of 

the U.S. As we look into the future, however, emissions from ocean-going vessels are 

expected to become a dominant inventory source. This will be due to both emission 

reductions from other mobile sources as new emission controls go into effect and to the 

anticipated activity growth for ocean transportation. 

Our coordinated strategy to control emissions from ocean-going vessels consists of 

actions at both the national and international levels. It includes: (1) the engine and fuel 

controls we are proposing in this action under our Clean Air Act authority; (2) the proposalA 

submitted by the United States Government to the International Maritime Organization to 

amend MARPOL Annex VI to designate U.S. coasts as an Emission Control Area (ECA)B in 

which all vessels, regardless of flag, would be required to meet the most stringent engine and 

marine fuel sulfur requirements in Annex VI; and (3) the new engine emission and fuel sulfur 

limits contained in the amendments to Annex VI that are applicable to all vessels regardless of 

flag and that are implemented in the U.S. through the Act to Prevent Pollution from Ships 

(APPS).. 

We project that by 2030 the coordinated strategy would reduce annual emissions of 

NOX and PM by 1.2 million and 143,000 tons, respectively, and the magnitude of these 

reductions would continue to grow well beyond 2030.C The estimated annual monetized 

health benefits of this coordinated strategy in 2030 would be between $110 and $280 billion, 

A Proposal to Designate an Emission Control Area for Nitrogen Oxides, Sulphur Oxides and Particulate Matter, 

Submitted by the United States and Canada. IMO Document MEPC59/6/5, 27 March, 2009. A copy of this 

document can be found at http://www.epa.gov/otaq/regs/nonroad/marine/ci/mepc-59-eca-proposal.pdf B

 For the purpose of this proposal, the term “ECA” refers to both the ECA and internal U.S. waters. Refer to 

Section VI.B. of the preamble for a discussion of the application of the fuel sulfur and engine emission limits to 

U.S. internal waters through APPS. C

 These emission inventory reductions include reductions from ships operating within the 24 nautical mile 

regulatory zone off the California Coastline, beginning with the effective date of the coordinated strategy 

program elements. The California regulation contains a provision that would sunset the requirements of the rule 

if the federal program achieves equivalent emission reductions. See 

http://www.arb.ca.gov/regact/2008/fuelogv08/fro13.pdf at 13 CCR 2299.2(j)(1). 

ES-1 
Regulatory Impact Analysis 

assuming a 3 percent discount rate (or between $100 and $260 billion assuming a 7 percent 

discount rate). The annual cost of the overall program in 2030 would be significantly less, at 

approximately $3.1 billion. 

This Regulatory Impact Analysis provides technical, economic, and environmental 

analyses of the coordinated strategy. Chapter 1 provides industry characterization for the 

marine industry. Chapter 2 presents air quality modeling results and describes the health and 

welfare effects associated with NOX, SOX, PM, and ozone. Chapter 3 provides our estimates 

of the current emission inventories and the reductions that can be expected from the 

coordinated strategy. Chapter 4 contains our technical feasibility justification for the emission 

limits, and Chapter 5 contains the estimated costs of complying with those standards. Chapter 

6 presents the estimated societal benefits of the coordinated strategy. Chapter 7 contains our 

estimates of the market impacts of the coordinated strategy and the distribution of costs 

among stakeholders. Finally, Chapter 8 provides the results of our small entity screening 

analysis for the proposed rule. 

ES-2 
Executive Summary 

1. Coordinated Strategy to Reduce Emissions from Ocean-Going Vessels 

The components of the coordinated strategy are summarized below. Readers should 

refer to the preamble for additional information about these provisions. 

Clean Air Act Engine and Fuel Standards 

We are proposing new engine standards for Category 3 marine diesel engines under 

our Clean Air Act authority. The proposed Tier 2 and Tier 3 NOX limits are set out in Table 

ES-1 and would apply to engines with per cylinder displacement at or above 30 liters installed 

on U.S. vessels. In addition to the NOX emission limits, we are proposing standards for 

emissions of hydrocarbons (HC) and carbon monoxides (CO) from new Category 3 engines. 

We are not proposing to set a standard for PM emissions for Category 3 engines. However, 

significant PM emissions benefits will be achieved through the ECA fuel sulfur requirements, 

described below, that will apply to ships that operate in areas that affect U.S. air quality. We 

are also proposing to require engine manufacturers to measure and report PM emissions 

pursuant to our authority in section 208 of the Act. 

Table ES-1 Existing and Proposed NOX Emission Standards for Category 3 Engines (g/kW-hr) 

LESS THAN 

130 RPM 

130-2000 

RPMa 

OVER 2000 

RPM 

Tier 1 2004b 17.0 45.0•n(-0 .20) 9.8 

Tier 2 2011 14.4 44.0•n(-0.23) 7.7 

Tier 3 2016 3.4 9.0•n(-0.20) 2.0 

Notes: 

a

 Applicable standards are calculated from n (maximum in-use engine speed in RPM), rounded to one decimal

place.

b Tier 1 NOX standards currently apply for engines originally manufactured after 2004. 

With regard to fuels, we are proposing fuel sulfur limits under section 211(c) of the 

Clean Air Act that match the limits that apply under Annex VI for ECAs (see below). The 

adoption of such standards would: (1) forbid the production and sale of fuel oil above 1,000 

ppm sulfur for use in the waters within the proposed U.S. ECA and internal U.S. waters; and 

(2) allow for the production and sale of 1,000 ppm sulfur fuel for use in Category 3 marine 

vessels. 

ECA Designation of U.S. Coasts 

To realize the benefits from the MARPOL Annex VI Tier III NOx and fuel sulfur 

controls, areas must be designated as Emission Control Areas. On March 27, 2009, the U.S. 

and Canadian governments submitted a proposal to amend MARPOL Annex VI to designate 

North American coastal waters as an ECA (referred to as the “U.S./Canada ECA” or the 

“North American ECA”). ECA designation would ensure that U.S. and foreign ships that 

affect U.S. air quality meet stringent NOX and fuel sulfur requirements while operating 

within 200 nautical miles of U.S. coasts. The area of the U.S./Canada ECA described in the 

proposed amendment is presented in Figure ES-1. The fuel sulfur limits that apply in ECAs 

pursuant to Annex VI are contained in Table ES-2. The engine emission limits that apply in 

ES-3 
Regulatory Impact Analysis 

ECAs are the MARPOL Annex VI Tier III limits, which are equivalent to the Tier 3 NOx 

limits contained in Table ES-1.Table ES-2 Annex VI Fuel Sulfur Limits 

 GLOBAL ECA 

Fuel Sulfur 2004 45,000 ppma 2005 15,000 ppma 

2012 35,000 ppma 2010a 10,000 ppma 

2020 5,000 ppma,b 2015 1,000 ppma 

Notes: 

a

 Annex VI standards are in terms of percent sulfur. Global sulfur limits are 4.5%; 3.5%; 0.5%. ECA 

sulfur limits are 1.5%; 1.0%; 0.1%. b

 Subject to a feasibility review in 2018; may be delayed to 2025.

Figure ES-1 Proposed U.S./Canada Emission Control Area 

The ECA stringent international engine NOx standards and fuel sulfur limits will 

apply to U.S. and foreign vessels while they operate in the designated area upon adoption of 

the proposed amendment to Annex VI. If this proposal is not timely adopted by IMO, we 

intend to take supplemental action to control emissions from vessels, including foreign 

vessels, that affect U.S. air quality. 

MARPOL Annex VI and the Act to Prevention Pollution from Ships 

The United States became a party to MARPOL Annex VI by depositing its instrument 

of ratification with IMO on October 8, 2008. This was preceded by the President signing into 

law the Maritime Pollution Prevention Act of 2008 (Public Law 110-280) on July 21, 2008, 

that contains amendments to the Act to Prevent Pollution from Ships (33 USC 1901 et seq.). 

These APPS amendments require compliance with Annex VI by all persons subject to the 

engine and vessel requirements of Annex VI. The amendments also authorize the United 

States Coast Guard and EPA to enforce the provisions of Annex VI against domestic and 

foreign vessels and to develop implementing regulations, as necessary. In addition, APPS 

ES-4 
Executive Summary 

gives EPA sole authority to certify engines installed on U.S. vessels to the Annex VI 

requirements. The preamble contains proposed regulations to implement several aspects of 

the Annex VI engine and fuel regulations, which we are proposing under that APPS authority. 

Our cost and benefit analyses for the coordinated strategy includes the costs for U.S. vessels 

of implementing those provisions of the MARPOL Annex VI program that are in addition to 

the ECA requirements. 

2. Projected Inventory and Cost Impacts of the Coordinated Strategy 

This RIA presents estimated inventory and cost impacts for the entire coordinated 

strategy, including the Annex VI Tier II NOX requirements and the ECA controls that will be 

mandatory for U.S. and foreign vessels through the Act to Prevent Pollution from Ships (See 

Chapter 5 for more details). Specifically, the analysis estimates the costs of the proposed 

Clean Air Act (CAA) Tier 2 and Tier 3 emission standards for U.S.-flagged vessels, 

operational costs associated with the global Tier II and Tier III standards for foreign-flagged 

vessels operating in the ECA, and the ECA fuel sulfur requirements. We also include Clean 

Air Act compliance costs that will apply only to new U.S. vessels for verification testing after 

engine installation (PLT). The fuel program changes are implementation provisions and do 

not impose compliance costs, but instead may reduce the costs for fuel distributors of 

complying with EPA’s distillate diesel standards. Similarly, the programmatic changes under 

consideration for Category 1 and 2 engines (see Section VI.C of the preamble) would not 

impose compliance costs but instead are intended to facilitate compliance with both Annex VI 

and our Clean Air Act requirements for those engines. 

Inventory Reductions 

A discussion of the current and projected inventories for several key air pollutants are 

contained in Chapter 3. Nationally, in 2009, Category 3 vessels will contribute 10 percent of 

mobile source NOX emissions, 24 percent of mobile source diesel PM2.5 emissions, and 80 

percent of mobile source SO2 emissions. In 2030, absent the coordinated strategy, these 

vessels would become a larger portion of the total mobile source emissions inventory 

constituting 40 percent of mobile source NOX emissions, 75 percent of mobile source diesel 

PM2.5 emissions, and 95 percent of mobile source SO2 emissions. 

We estimate that the coordinated strategy would reduce annual NOX emissions by 1.2 

million tons, PM2.5 emissions by 143,000 tons, and SO2 emissions by 1.3 million tons in 2030. 

Engineering Costs 

The total engineering costs associated with the coordinated strategy are the summation 

of the engine and vessel costs and include both hardware and operating costs. This analysis 

can be found in Chapter 5. The cost of the coordinated strategy is estimated to be $1.9 billion 

in 2020 and $3.1 billion in 2030; over 98 percent of these costs are attributable to expected 

increases in operating costs for U.S. and foreign flag vessels traveling within the ECA. These 

increased operating costs include changes in fuel consumption rates, increases in fuel costs, 

and the use of urea for engines equipped with selective catalytic reduction (SCR). The total 

ES-5 
Regulatory Impact Analysis 

cost of the coordinated strategy based on a 3 percent discount rate from 2010 through 2040 is 

estimated to be $43 billion and $22 billion at a 7 percent discount rate. 

Cost per Ton of Reduced Emissions 

Using the inventory and engineering cost information, we can estimate the cost per ton 

of pollutant reduced as a result of the more stringent standards. Table ES-3 contains the 

estimated cost per ton of pollutant reduced based on the net present value of the engineering 

costs and inventory reductions from 2010 through 2040. This estimate captures all of the 

engineering costs and emissions reductions associated with the coordinated strategy. When 

attributed by pollutant, at a net present value of 3 percent from 2010 through 2040, the NOX 

controls are expected to cost about $510 per ton of NOX reduced, SOX controls are expected 

to cost about $930 per ton of SOX reduced, and the PM controls are expected to cost about 

$7,950 per ton of PM reduced ($500, $920, and $7,850 per ton of NOX, SOX, and PM 

respectively, at a net present value of 7 percent over the same period.) 

Table ES-3 Program Cost per Ton Estimates 

POLLUTANT 2010 THRU 2040 DISCOUNTED 

LIFETIME COST PER TON AT 3% 

2010 THRU 2040 DISCOUNTED 

LIFETIME COST PER TON AT 7% 

NOX $510 $500 

SOX $930 $920 

PM $7,950 $7,850 

3. Estimated Benefits and Economic Impacts of the Coordinated Strategy 

We estimated benefits for the entire coordinated strategy, including the Annex VI Tier 

II NOX requirements and the ECA controls that will be mandatory for U.S. and foreign 

vessels through the Act to Prevent Pollution from Ships. Note that the Clean Air Act-specific 

portions of the coordinated strategy are compliance measures (PLT, distillate fuel program 

changes) and do not impact the estimated benefits. The benefits analysis is presented in 

Chapter 6. It uses sophisticated air quality and benefit modeling tools and is based on peerreviewed studies of air quality and health and welfare effects associated with improvements in 

air quality and peer-reviewed studies of the dollar values of those public health and welfare 

effects. 

Estimated Benefits 

The range of benefits associated with this program are estimated based on the risk of 

several sources of PM- and ozone-related mortality effect estimates, along with other PM and 

ozone non-mortality related benefits information. These benefits are presented in Table ES-4. 

These estimates reflect EPA’s most current interpretation of the scientific literature on PM2.5 

and mortality, including our updated benefits methodology (i.e., a no-threshold model that 

calculates incremental benefits down to the lowest modeled PM2.5 air quality levels) 

compared to estimates in previous RIAs that did not include these changes. Please see 

Section 6.4.1.3 of the RIA for more discussion of the treatment of thresholds in this analysis. 

ES-6 
Executive Summary 

We present total benefits based on the PM- and ozone-related premature mortality 

function used. The benefits ranges therefore reflect the addition of each estimate of ozonerelated premature mortality (each with its own row in Table ES-4) to estimates of PM-related 

premature mortality derived from the epidemiological literature. 

Table ES-4 Estimated Monetized PM- and Ozone-Related Health Benefits of Coordinated U.S. Strategy to 

Control Ship Emissions 

2030 TOTAL OZONE AND PM BENEFITS – PM MORTALITY DERIVED FROM 

EPIDEMIOLOGY STUDIESa 

Premature Ozone Mortality 

Function or Assumption 

Reference Mean Total Benefits 

(Billions, 2006$)

c,d 

Multi-city Bell et al., 2004 $110 - $280 

Huang et al., 2005 $120 - $280 

Schwartz, 2005 $120 - $280 

Meta-analysis Bell et al., 2005 $120 - $280 

Ito et al., 2005 $120 - $280 

Levy et al., 2005 $120 - $280 

Notes: 

a

 Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was 

developed by adding the estimate from the ozone premature mortality function to the estimate of PM2.5 -related 

premature mortality derived from either the American Cancer Society (ACS) cohort study (Pope et al., 2002) or 

the Harvard Six-Cities study (Laden et al., 2006). 

b Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed 

listing of unquantified health and welfare effects is provided in Table 6-2. c

 Results reflect the use of a 3 percent discount rate. Using a 7% discount rate, the benefits are approximately 

10% less. Monetary results presented in Chapter 6 use both a 3 and 7 percent discount rate, as recommended by 

EPA’s Guidelines for Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two 

significant digits for ease of presentation and computation. 

We estimate that by 2030, the annual emission reductions associated with the 

coordinated strategy will annually prevent between 13,000 and 32,000 PM-related premature 

deaths (based on the American Cancer Society cohort study and the Harvard Six-Cities 

study), between 220 and 980 ozone-related premature deaths, 1,500,000 work days lost, and 

approximately 10,000,000 minor restricted-activity days. 

Benefit-Cost Analysis 

We estimate that the monetized benefits of the coordinated strategy in 2030 will range 

between approximately $110 and $280 billion, assuming a 3 percent discount rate. The 

annual cost of the coordinated strategy in 2030 is estimated to be significantly less, at 

approximately $3.1 billion. The 2030 benefits outweigh the costs by at least a factor of 32 

and could be as much as a factor of 90. Thus, even taking the most conservative benefits 

assumptions, benefits of the coordinated strategy clearly outweigh the costs. 

Economic Impact Analysis 

We performed an economic impact analysis to estimate the market-level changes in 

prices and outputs for affected markets, the social costs of the coordinated strategy, and the 

expected distribution of those costs across stakeholders. This analysis can be found in 

ES-7 
Regulatory Impact Analysis 

Chapter 7. We estimate the social costs of the new program to be approximately $3.1 billion 

in 2030.D These costs are expected to be borne by purchasers of marine transportation 

services. Because there are no close transportation alternatives for the vast majority of goods 

currently moved by ship, these costs are expected to be passed to consumers of marine 

transportation in the form of higher freight rates. Ultimately, these costs will be incurred by 

the purchasers of goods transported by ocean-going vessels in the form of higher prices for 

those goods. 

With regard to market-level impacts, the equipment costs of the coordinated strategy 

are expected to increase the price of a new vessel by 2 percent or less. The impact of the 

coordinated strategy, including the increase in operating costs due to the ECA fuel and 

emission requirements, on the price of ocean marine transportation services would vary, 

depending on the route and the amount of time spent in the proposed U.S. ECA. For example, 

we estimate that the cost of operating a ship in liner service between Singapore, Seattle, and 

Los Angeles/Long Beach, which includes about 1,700 nm of operation in the proposed ECA, 

would increase by about 3 percent. For a container ship, this represents a price increase of 

about $18 per container, assuming the total increase in operating costs is passed on to the 

purchaser of marine transportation services. This would be about a 3 percent price increase. 

The per passenger price of a seven-day Alaska cruise operating entirely within the ECA is 

expected to increase about $7 per day. For ships that spend less time in the ECA, the 

expected increase in total operating costs and therefore the impact on freight prices would be 

smaller. 

4. Alternatives 

In the course of designing our rulemaking, we investigated several alternative 

approaches to both the engine and fuel programs. The analysis for those alternatives is 

contained in the preamble, and is not duplicated in this RIA. The preamble also contains a 

description of a voluntary program to encourage emission reductions from existing Category 

3 marine diesel engines. 

D All estimates presented in this section are in 2006$. 

ES-8 
Chapter 1: Industry Characterization 

CHAPTER 1: Industry Characterization 

1.1 Introduction ...................................................................................................................... 1-2

1.2 Marine Transportation Sector ........................................................................................ 1-2

1.2.1 Engine Types .......................................................................................................... 1-3

1.2.2 Other Engine Types ............................................................................................... 1-6

1.3 Marine Vessels ..................................................................................................................1-7

1.3.1 Vessel Design and Construction ........................................................................... 1-9

1.3.2 Vessel Building Classification Societies ............................................................... 1-9

1.4 The Marine Transportation Sector .............................................................................. 1-10

1.5 Marine Fuels................................................................................................................... 1-11

1.5.1 Marine Gas Oil (MGO) .......................................................................................1-12

1.5.2 Marine Diesel Oil (MDO).................................................................................... 1-12

1.5.3 Intermediate Fuel Oil (IFO)................................................................................ 1-12

1.5.4 Marine Fuel Supply & Procurement.................................................................. 1-12

1.5.5 Fuel Monitoring and Testing .............................................................................. 1-13

1-1 
Regulatory Impact Analysis 

CHAPTER 1:Industry Characterization 

1.1 Introduction 

Marine transportation is a key component of the U.S. national economy, for both our 

internal and external trade. According to the U.S. Maritime Administration, the United States 

saw about 2.3 billion metric tons of goods shipped via waterborne transportation in 2006, of 

which about 1.4 billion, or nearly 65 percent, was foreign trade (imports and exports to and from 

the United States).1 This foreign trade, carried primarily by ocean-going vessels powered by 

Category 3 marine diesel engines, had a value of about $1.4 trillion. 

This chapter provides some basic information about the segment of the marine 

transportation sector, ocean-going marine that is affected by today’s proposal. The material 

presented below is a brief synopsis of the unique attributes of the maritime industry, derived 

from two detailed reports prepared for this rulemaking.2,3 These reports explore in greater detail 

the various aspects of the marine transportation sector and the marine fuel markets. We 

encourage readers to review the full reports for further information. 

1.2 Marine Transportation Sector 

In this report, the marine transportation sector refers to (1) Category 3 marine diesel 

engines, (2) the vessels that use those engines, and (3) the transportation services that use those 

vessels. EPA defines Category 3 marine engines as compression-ignition engines with a 

displacement greater than or equal to 30 liters per cylinder.A Category 3 engines can be 

incredibly large and can have anywhere from four to 20 cylinders with displacements ranging 

from 30 to 3,000 liters per cylinder. These engines can provide power output from 2,000 kW to 

over 100,000 kW. The two most common types of Category 3 engines are slow-speed diesel 

engines (SSD) with engine speeds of 150 rpm or less, and medium-speed diesel engines (MSD) 

with engine speeds of approximately 300 to 600 rpm, less common are steam or gas turbine 

engines. EPA adopted an initial level of emission standards for Category 3 (C3) engines on 

February 28, 2003 (68 FR 9746). This includes all marine diesel engines with per-cylinder 

displacement above 30 liters. These initial standards are identical to the standards specified in 

MARPOL Annex VI. 

The marine transportation industry relies on a variety of large ocean-going commercial 

vessel types powered by C3 engines to carry goods and passengers around the world. The EPA 

typically defines large commercial vessels as vessels engaged in waterborne trade and/or 

passenger transport that exceed 400 feet in length and/or weigh more than 2,000 GT.4 Marine 

A Marine diesel engines with per-cylinder displacement below 30 liters, called Category 1 and Category 2 engines 

(C1 and C2, respectively), became regulated under an initial U. S. Environmental Protection Agency (EPA) 

rulemaking in 1999 (64 FR 73300, December 29, 1999). EPA adopted more stringent standards for these engines as 

part of the Clean Diesel Locomotive and Marine Rule, which is a three-phased program and will ensure that all 

locomotives and C1 and C2 marine diesel engines will produce less pollution (73 FR 37096, June 30, 2008). 

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Chapter 1: Industry Characterization 

vessel owners and operators include U.S. and foreign entities that provide ocean marine 

transportation services to many industries including: consumer goods, chemical, agricultural, 

petroleum, personal transportation, etc. The statistics presented in this report were compiled in 

2008 using Lloyd’s Register of Ships Sea-Web service.5

 Sea-Web provides detailed information 

on the vessels that make up the global fleet including details on the installed engines, the vessels 

themselves, and the owners and operators of these vessels. Engine details available include: 

engine designer, builder, model, type, and propulsion power rating. Vessel details include: ship 

type, year built, gross tonnage (GT), flag state, and actual build details (e.g., hull type). The 

analyses presented here are based only on vessels built in or after 1990, with at least 5,000 kW, 

are at least 2,000 GT, and are in-service; only vessels with complete records were included; for 

the purposes of this report these vessels will be referred to as the “global fleet.” 

Table 1-1 Characteristics of the "Global Fleet" 

 AVERAGE 

YEAR 

BUILT 

AVERAGE 

GT 

NUMBER 

OF 2-

STROKES 

NUMBER 

OF 4-

STROKES 

NUMBER 

OF GAS 

TURBINES 

NUMBER 

OF 

STEAM 

TURBINES 

AVERAGE 

ENGINE 

POWER 

(KW) 

Auto Carrier 2002 49,000 386 18 0 0 13000 

Bulk Cargo 2000 37,000 4127 281 0 0 9400 

Container 2001 34,000 2977 492 0 0 27000 

Misc 2000 18,000 19 157 0 2 7500 

Passenger 1999 42,000 7 402 16 1 10000 

Reefer 1995 9,300 224 21 0 0 9700 

RoRo 2000 20,000 47 137 8 0 11000 

Tanker 2002 57,000 3464 191 4 182 13000 

The coordinated strategy for emission controls of C3 marine engines is slightly different 

than previous EPA rules in that, in addition to the Clean Air Act (CAA) authority, the U.S. 

Government has petitioned the International Maritime Organization (IMO) to create an Emission 

Control Area (ECA) around most of the U.S. coastline. The regulations for C3 marine diesel 

engine emissions could directly impact several industries: (1) manufacturers of marine diesel 

engines, (2) diesel engine marinizers, (3) marine diesel engine remanufacturers, (4) boat or 

vessel builders which install marine diesel engines installed on their vessels, (5) vessel operators 

who own existing marine diesel engines with engine displacement at or greater than 30 liters per 

cylinder (L/cyl), (6) marine fuel manufacturers, (7) marine fuel distributors/brokers, and (8) U.S. 

ports. 

1.2.1 Engine Types 

1.2.1.1 Two-Stroke Engines 

Two-stroke engines are usually SSD connected to a direct drive propulsion system. 

These engines have large displacements of up to 3,000 L/cylinder. SSD are used for propulsion 

on bulk carriers, container ships, larger tankers, general cargo and roll-on/roll-of (RoRo) ships. 

They are typically turbo-charged with aftercooling and have four exhaust valves per cylinder. 

Scavenge air enters the cylinder through a series of intake ports arranged around the bottom of 

the cylinder. Intake is controlled by the piston as it uncovers or covers the intake ports. Fuel 

injection is typically mechanical with three injectors per cylinder. 

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Regulatory Impact Analysis 

The top three two-stroke engine designers of the global fleet on a per-vessel basis are 

MAN which represents over 71 percent of that total, Wärtsila which produced nearly 18 percent, 

and Mitsubishi which captured just over 10 percent. MAN is headquartered in Munich, 

Germany and is a supplier of diesel engines, turbo machinery, special gear systems, trucks and 

buses. In 2008, MAN employed over 51,000 people and generated revenue of approximately 

$23 billion.6 Wartsila is headquartered in Helsinki, Finland and is a provider of ship design, 

engines, generator sets, gears and other propulsion equipment. They employ nearly 19,000 

people and have locations in close to 70 countries.7 Mitsubishi Power Systems, Inc. (MPS) 

headquartered in Lake Mary, FL is a subsidiary company of Mitsubishi Heavy Industries, Ltd. 

(MHI) which employs more than 40,000 people worldwide generating more than $25 billion in 

annual revenues.8 MPS produces gas and steam turbines in addition to medium speed engines up 

to nearly 15,000 kW, and low speed engines over 67,000 kW. MHI also builds and repairs ships, 

marine engines and equipment. 

Table 1-2 Number of Engines Built per Year by Manufacturer 

YEAR 

BUILTA 

MAN WARTSILAB MITSUBISHI OTHER TOTAL 

1990 170 86 41 0 297 

1991 178 91 34 0 303 

1992 171 107 46 0 324 

1993 193 92 55 0 340 

1994 260 108 46 0 414 

1995 302 96 59 0 457 

1996 352 125 67 0 544 

1997 369 147 92 0 608 

1998 377 136 82 0 595 

1999 368 106 69 0 543 

2000 331 155 62 0 548 

2001 442 122 44 1 609 

2002 474 80 53 0 607 

2003 497 92 60 0 649 

2004 579 87 80 0 746 

2005 703 116 81 0 900 

2006 764 115 68 0 947 

2007 833 100 92 0 1025 

2008 673 60 59 3 795 

Total 8036 2021 1190 4 11251 

Percent 71% 18% 10% 0.04% 

Notes: 

a Assumes that the engine was built the same year the vessel was reported as being built. b Wartsila count includes Sulzer engines. 

Wartsila manufactures the world’s most powerful diesel engine, the 14-cylinder Wärtsilä 

RT-flex96C marine engine has a maximum continuous power output of 84,000 kW (113,000 

bhp) at 102 rpm. This engine is nearly 90 feet long, and over 44 feet tall and weighs over five 

million pounds, see Figure 1-1.9,10 

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Chapter 1: Industry Characterization 

Source:http://www.aucklandshipbrokers.com/index.php?option=com_cont 

ent&task=view&id=100&Itemid=68 

Figure 1-1 Wartsila RT-flex96C 84,000 kW SSD Engine 

1.2.1.2 Four-Stroke Engines 

Four-stroke engines are usually MSD engines with significantly smaller cylinder 

displacements (30 to 200 L/cylinder) than SSD, and typically have six to 18 cylinders. These 

engines are commonly connected to an electric drive propulsion system which is actually a large 

generator that can be used to generate auxiliary power as well as drive the propulsion systems. 

They are typically used as propulsion engines on smaller tankers, general cargo, RoRo, ferries, 

cruise ships, and as auxiliary engines on large ships for power generation or refrigeration. They 

are generally turbo-charged and aftercooled, have two intake and two exhaust valves per cylinder 

and are mechanically fuel injected with one injector per cylinder. 

The top three four-stroke engine designers of the global fleet on a per-vessel basis are 

Wartsila which represents over 36 percent of that total, MAN which produced nearly 32 percent, 

and MAK which captured approximately 29 percent. MAK is owned by Caterpillar which 

1-5 
Regulatory Impact Analysis 

produces medium and high speed engines of up to 16,000 kW for main propulsion, and nearly 

7,700 kW for marine generator sets and is headquartered in Hamburg, Germany.

11 

Table 1-3 Number of Engines Built per Year by Manufacturer 

YEAR 

BUILTA 

WARTSILAB MANC MAK OTHER TOTAL 

1990 10 17 9 3 39 

1991 14 19 4 1 38 

1992 12 21 11 1 45 

1993 22 22 11 1 56 

1994 22 13 10 1 46 

1995 30 19 12 1 62 

1996 20 33 13 2 68 

1997 34 33 9 2 78 

1998 51 35 5 3 94 

1999 59 34 6 5 104 

2000 55 24 21 3 103 

2001 49 12 21 2 84 

2002 41 23 28 2 94 

2003 31 34 43 2 110 

2004 36 12 46 1 95 

2005 22 28 44 2 96 

2006 24 55 57 3 139 

2007 51 60 89 7 207 

2008 38 48 51 4 141 

Total 621 542 490 46 1699 

Notes: 

a Assumes that the engine was built the same year the vessel was reported as being built. b Wartsila count includes Sulzer engines. c MAN count includes Pielstick engines. 

1.2.2 Other Engine Types 

Turbine powered vessels accounted for less than two percent of the global fleet, and of 

those 13 percent are gas turbines, while the remaining 87 percent are steam turbines. The top 

three turbine engine designers include General Electric (GE), Kawasaki, and Mitsubishi and 

together account for over 91 percent of installed turbine engines. GE sold gas turbine engines 

exclusively to the global fleet representing 11 percent of the turbine powered fleet, while both 

Kawasaki and Mitsubishi only have steam turbine engines in the global fleet, accounting for 40 

and 39 percent of the turbine powered fleet respectively. Steam turbines have traditionally been 

the choice of Liquid Natural Gas carriers primarily because any boil-off gas could be sent 

through the turbine and burned. 

1-6 
Chapter 1: Industry Characterization 

T urbine Engin es in the Global Fle et 

0 

5 

10 

15 

20 

25 

30 

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 

Ye a r Built 

Nu

m ber of Engines 

GE M arine (Gas ) Kawas aki M itsubishi Other Steam 

Figure 1-2 Steam and Gas Turbines in the Current Global Fleet 

1.2.2.1 Auxiliary Engines 

Category 3 engines can also be used for auxiliary engines as well as Category 2. They 

are used to generate electrical power for navigation equipment, maneuvering equipment, and 

crew services. The engines used to generate electrical power are typically, however, Category 2 

diesel engines. Some vessels, such as refrigerated cargo vessels, may require Category 3 engines 

to meet electric power requirements. Cruise ships often employ diesel-electric engines that 

provide both propulsion and power generation. In addition to propulsion and electric power 

engines, an auxiliary engine is typically installed for emergency use. In 2007, over 10,000 

auxiliary engines were ordered, totally 11,600 megawatts.12,13 

1.2.2.2 Main Engines in the Global Fleet 

Category 3 engines are not typically mass-produced. They are built in different 

configurations with varying numbers of cylinders, engine displacement, power output, and 

engine speed. Because of the variety of configurations and applications, the selection of the main 

engine is a major consideration in the overall design of a vessel. As a result, the engine selected 

for a specific vessel is often a unique design or configuration that is built specifically for that 

vessel. In many cases, C3 engines designed by these manufacturers are built under license by 

other companies in Europe and Asia. It can take up to two or three years to receive delivery of 

components such as crankshafts and engine blocks, Wartsila notes that it is not engine assembly 

that slows production, but delivery of these larger components from sub-suppliers.14 

1.3 Marine Vessels 

The marine transport industry relies on a variety of vessel types to carry goods and 

passengers around the world. These vessels are typically categorized by the type of cargo the 

vessel is designed to transport and by the vessel size, in terms of carrying capacity and hull 

1-7 
Regulatory Impact Analysis 

dimensions. Table 1-3 outlines the vessel categories that constitute the majority of the current 

OGV world fleet. 

Table 1-4 Vessel type, category, and size range for the majority of the OGV world fleet. 

Vessel Type 

Vessel Size 

Category 

Average Size Range 

(DWT) 

Coastal 1,253 – 9,994 (5,576) 

Handy 10,095 – 39,990 (27,593) 

Bulk Carrier Handymax 40,009 – 54,881 (47,616) 

Panamax 55,000 – 78,932 (69,691) 

Capesize 80,000 – 364,767 (157,804) 

Feeder 1,000-13,966 (9,053) 

Intermediate 14,003-36,937 (24,775) 

Container Panamax 37,042-54,700 (45,104) 

Post Panamax 55,238-84,900 (67,216) 

Suezmax 85,250-120,892 (101,099) 

Liquid Gas Carrier 

(Liquid Petroleum Gas 

(LPG) / Liquid Natural 

Gas (LNG)) 

Midsize 1,001-34,800 (7,048) 

Large Gas Carrier 

(LGC) 35,760-59,421 (50,796) 

Very Large Gas 

Carrier (VLGC) 62,510-122,079 (77,898) 

Coastal Small 1,000-9,999 (3,789) 

General Cargo 

Coastal Large 10,000-24,912 (15,673) 

Handy 25,082-37,865 (29,869) 

Panamax 41,600-49,370 (44,511) 

Cruise / Passenger All 1,000–19,189 (6,010) 

Refrigerated (Reefer) All 1,000–19,126 (6,561) 

Roll-on / Roll-off 

(Ro-Ro) All 1,000–19,126 (7,819) 

Coastal 1,000-23,853 (7,118) 

Handymax 25,000-39,999 (34,422) 

Panamax 40,000-75,992 (52,300) 

Tanker 

AFRAmax 76,000-117,153 (103,112) 

Suezmax 121,109-167,294 (153,445) 

Very Large Crude 

Carrier (VLCC) 180,377-319,994 (294,475) 

Ultra Large Crude 

Carrier (ULCC) 320,051-441,893 (364,896) 

1-8 
Chapter 1: Industry Characterization 

1.3.1 Vessel Design and Construction 

Ship builders typically design their vessels based on the type of freight they intend to 

haul as the type of cargo transported necessitates specific design characteristics, for example, 

container vessels require a different structure than a vessel that hauls bulk freight. Six ship 

builders are responsible for the majority of commercial vessels constructed in the United States, 

including Bath Iron Works, Electric Boat Company, the National Steel and Shipbuilding 

Company (NASSCO), Avondale Operations, Ingalls Operations, and Newport News 

Shipbuilding. There is a much larger number of ship builders outside the United States. Since 

2000, U.S. ship builders have produced 20 to 40 vessels per year, while foreign ship builders 

have produced 60 to 120 vessels per year.15 

Vessel design is an iterative process that typically includes three stages: concept design, 

preliminary design, and contract design. The concept design stage considers the vessel’s general 

objectives, adjusting key vessel parameters and specifications based on the owner’s stated 

technical and economic criteria. The preliminary design stage further refines the concept design 

by analyzing expected performance and profitability of various alternatives for key design 

elements (e.g., proportions, lines, hydrostatics, layout, power). Upon completion, the preliminary 

design yields the final vessel attributes, including dimensions, displacement, stability, propulsive 

performance, and structural details.12 

1.3.2 Vessel Building Classification Societies 

Ships must be built in accordance with shipbuilding standards in the country where they 

are flagged or in accordance with standards imposed by the International Association of 

Classification Societies (IACS). The classification societies implement many of the national or 

international requirements that apply to marine vessels, including the various requirements under 

MARPOL Annex VI. Classification societies include, among others, American Bureau of 

Shipping, Det Norske Veritas, and Germanischer Lloyd. In the United States, the U.S. Coast 

Guard works closely with the American Bureau of Shipping to implement and enforce applicable 

requirements. It is important to note that EPA implements and enforces requirements related to 

exhaust emission standards cooperatively with the U.S. Coast Guard, but without the 

involvement of classification societies. 

The global shipping industry comprises a large number of diverse firms. Vessel owners 

and operators provide marine transportation services in support of international trade and 

commodity flows over water. Every ship in the world’s shipping fleet is designated by the flag 

of registry. The flag of registry is a useful way of characterizing the shipping industry. However, 

in many cases, the flag of registry has no correlation with the location of the parent company that 

owns/operates a vessel. This confusion results partly because “open registries” allow 

owners/operators to register ships in countries outside of their country of domicile 

(owner/operator country). The five countries with the most flagged ships in the “Global Fleet” in 

order are Singapore, the Marshall Islands, China, Liberia, and Panama. Table 1-5 presents these 

values, and shows the ships under the U.S. flag as well. 

The U.S. fleet of privately owned ocean-going vessels primarily includes bulk carriers, 

containerships, gas carriers, general cargo vessels, passenger vessels, refrigerated container 

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Regulatory Impact Analysis 

vessels, roll-on/roll-off vessels, and tankers. Containerships comprise the largest number of 

vessels in the U.S. commercial fleet with a total of approximately 75 ships (~45% of the total), 

while there are around 50 tankers (~33%). The average age of U.S.-flagged commercial oceangoing vessel is approximately 20 years. 

Table 1-5 Ship Type by Country of Flag 

SHIP TYPE SINGAPORE MARSHALL 

ISLANDS CHINAa LIBERIA PANAMA 

UNITED 

STATES OF 

AMERICA 

Auto Carrier 22 1 2 6 173 16 

Bulk Cargo 161 253 608 284 1337 11 

Container 236 164 234 676 577 37 

Misc 4 4 12 3 6 7 

Passenger 0 0 9 0 32 2 

Reefer 1 3 0 67 64 0 

RoRo 0 1 2 0 11 14 

Tanker 279 313 213 500 528 32 

Grand Total 703 739 1080 1536 2728 119 

Note: a

 This includes the People's Republic of China, Republic of (Taiwan), and Hong Kong. 

Section 27 of the Merchant Marine Act of 1920, more commonly known as the Jones 

Act, was enacted with the goal of maintaining a domestic merchant fleet of U.S.-owned and 

U.S.-crewed vessels that is sufficient to carry the majority of U.S. waterborne commerce and 

also to assist the military in times of war. The Jones Act fleet is a subset of the total U.S. fleet 

and accounts for 52% of U.S.-flagged ships. The Maritime Administration (MARAD) is the 

U.S. Department of Transportation agency responsible for monitoring and maintaining the 

domestic merchant fleet, including the Jones Act fleet. 

1.4 The Marine Transportation Sector 

Over 95 percent of foreign trade was moved by ship in 2006.16 Fifty ports in the U.S. 

handle approximately 84 percent of all waterborne domestic and international cargo; ten ports 

handle 85 percent of all containerized cargo and have seen a 54 percent increase in container 

movements between 2001 and 2006.10 The U.S. ranks second in container traffic after China; 

one in nine containers is either bound for or originated from the U.S.17 It is expected that this 

trade will continue to grow. 

In 2007, the number of vessels calling on U.S. ports increased nearly 13 percent when 

looking over the past five years; of these calls 34 percent were tankers, 31 percent 

containerships, 17 percent dry-bulk vessels, 10 percent roll-on roll-off, and 6 percent by general 

cargo ships.18 The size of vessels visiting U.S. ports has also increased, and in 2007, 54 percent 

of the calls to U.S. ports were by vessels less than 10 years old, up 47 percent over the previous 

five years.12 Figure 1-3 shows the vessel calls by flag to U.S. Ports in 2007.15 

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Chapter 1: Industry Characterization 

Source: http://marad.dot.gov/documents/Vessel_Calls_at_US_Ports_Snapshot.pdf 

Figure 1-3 2007 Vessel Calls by Flag to U.S. Ports 

1.5 Marine Fuels 

All marine fuel used today is created from the same basic distillation process that creates 

other liquid hydrocarbons such as motor gasoline, heating oil and kerosene. Distillate marine 

fuels are comparable to other forms of distillate hydrocarbon liquids, such as nonroad diesel fuel 

or No. 2 fuel oil, in that they have similar chemical properties and specification limits. Residual 

marine fuels, also called Intermediate Fuel Oils (IFO) or Heavy Fuel Oils (HFO), are composed 

of heavy, residuum hydrocarbons which are created as a by-product during petroleum refining, 

and can contain various contaminants such as heavy metals, water, and high sulfur levels. These 

contaminants can harm engines and fuel distribution lines and equipment, therefore residual fuel 

is typically treated and ‘cleaned’ of a large amount of these contaminants prior to combustion in 

the marine engine. 

Both residual and distillate marine fuels are required to meet international fuel 

specifications established in the International Organization for Standardization (ISO) 

specification 8217 Petroleum products—Fuels (class F)—Specifications of Marine Fuels.19 

Each category of fuel is discussed below. 

Marine distillate fuel is divided into four distinct fuel types: DMX, DMA, DMB, and 

DMC; however, only two of these fuels are commonly used in the marine transportation 

industry. DMX is a very low sulfur middle distillate hydrocarbon, and is therefore rather 

expensive when compared to other distillate fuels. This distillate type is mainly used onboard 

marine vessels for emergencies. The next two types of distillate fuel, DMA & DMB, are also 

called Marine Gas Oil (MGO) and Marine Diesel Oil (MDO) respectively. These two distillate 

fuels comprise the majority of marine distillate fuels sold. Lastly, DMC, is a higher sulfur fuel 

and is normally created by contaminating DMB fuel. 

Marine residual fuel is created through traditional petroleum refining as a ‘waste’ product 

of the refining process. Typically, this fuel is rather dense and viscous, and it tends to contain 

heavy metals and other contaminants normally contained within crude oil. Residual fuel oil is 

categorized by the viscosity of the fuel at a set reference temperature and there are several 

1-11 
Regulatory Impact Analysis 

categories of this fuel type; however, the most commonly used fuel in the marine transportation 

industry is Intermediate Fuel Oil (IFO) 180 and 380. 

1.5.1 Marine Gas Oil (MGO) 

MGO is a light distillate product that is clear and bright, typically amber in color, and can 

be manufactured by blending light cycle oil (LCO) with other light distillate oils. MGO is a 

relatively light and clean gas oil, compared to other marine fuels. MGO also has a relatively high 

cetane value and density, making it a fuel that is best suited for higher rpm engines. Typically, 

MGO is used for propulsion in small- to medium-sized marine vessels and for emergency, 

maintenance, and auxiliary engines in larger vessels.20 

1.5.2 Marine Diesel Oil (MDO) 

MDO is a distillate fuel that is a slightly heavier (i.e., higher density) gas oil and has a 

lower cetane value than MGO. MDO is designated as distillate marine fuel grade B (DMB) 

under ISO standards. Typically, MDO is created when MGO is blended with small amounts of 

residual fuel oil, which raises the sulfur content of the fuel beyond the maximum allowable level 

for MGO. 

1.5.3 Intermediate Fuel Oil (IFO) 

Typically, residual fuel oil is not usable as a stand-alone fuel because of purchasers’ need 

for specific performance characteristics, primarily viscosity. Thus, residual fuel oil normally 

requires blending with lighter components to meet specifications for use in marine engines. 

Blending with lighter components typically lowers the viscosity of the residual fuel oil to 

produce IFO. IFO is the industry colloquial name for the most common fuel blends. These fuels 

are categorized by their kinematic viscosity at a set reference temperature. IFO-180 and IFO-380 

are the most common fuel grades used in OGV, and these fuels are designated as residual marine 

fuel grades RME/F-180 and RMG/H-380 by ISO standard 8217. Additionally, since these fuels 

have such a high viscosity, they are normally in a ‘solid’ state at ambient temperatures and 

require constant heating in order to effectively pump and combust it in diesel engines.5 

1.5.4 Marine Fuel Supply & Procurement 

The actual volume of marine fuels supplied worldwide is the subject of great debate 

inside the maritime community. This is because the majority of marine fuel consumed is 

composed from residual waste, and other industries (such as power plants, asphalting, and 

roofing) use this waste as well. The current estimation is that the world consumes approximately 

350 million metric tonnes of marine fuel per year (350 mmt/yr), with about eighty percent 

(80%), or 280 mmt/yr, being residual grade fuel.21 

Marine fuels are purchased and delivered slightly differently than other fuels (like motor 

gasoline or highway diesel). Marine fuels have “brokers” to purchase fuel and arrange delivery. 

These broker companies typically never have custody of or title to the bunker fuel, but they 

represent ship operators in the solicitation and negotiation of marine fuel purchases, and they 

may help coordinate fuel delivery for the operators they represent. Fuel delivery can be achieved 

1-12 
Chapter 1: Industry Characterization 

through several ways; the most widely used method both in the United States and internationally 

is delivery by barge. Delivery by barge allows for bunkering of vessels at port berths or at 

anchorage within the port boundaries 

1.5.5 Fuel Monitoring and Testing 

In order to ensure that the fuel delivered is actually the fuel purchased, at least four 

marine fuel samples are taken at the time of delivery. One sample is for the vessel (Chief 

Engineer), one is for the bunker supplier, one is sent to an independent laboratory for testing 

(e.g., DNV Petroleum Services), and one is for the International Maritime Organization (as 

required by MARPOL Annex VI). Additionally, there are two other documents that provide 

information on the quality of the fuel delivered to the vessel: the material safety data sheet 

(MSDS) and the bill of sale or invoice. 

1-13 
2

Regulatory Impact Analysis 

References 

1

 U.S Department of Transportation, Maritime Administration (MARAD). (2008). U.S. Water 

Transportation Statistical Snapshot. Washington, D.C.: Office of Congressional and Public 

Affairs. Retrieved on March 27, 2009 from 

http://www.marad.dot.gov/documents/US_Water_Transportation_Statistical_snapshot.pdf 

Irvine, S. (2009). Marine Fuel Industry Overview. Ann Arbor, MI: U.S. Environmental 

Protection Agency. Docket EPA-HQ-OAR-2007-0121 

3

 Kopin, Amy (2009). Marine Vessel Industry Overiew. Ann Arbor, MI: U.S. Environmental 

Protection Agency. 

4

 U.S. Environmental Protection Agency, “Final Regulatory Support Document: Control of 

Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder,” 

January 2003. Docket ID EPA420-R-03-004, can be found at: 

http://www.epa.gov/otaq/regs/nonroad/marine/ci/r03004.pdf 

5

 Lloyd’s Sea-Web Register of Ships, can be found at: http://www.sea-web.com 

6

 MAN, 2008 Annual Report, can be found at: 

http://www.mandiesel.com/files/news/filesof587/MAN%20Diesel%20Annual%20Report_08.pdf 

7

 Wartsila, 2008 Annual Report, can be found at 

http://www.wartsila.com/Wartsila/global/docs/en/press/media_publications/annual_reports/Wart 

sila_Annual_Report_2008_EN.pdf 

8

 http://www.mhi.co.jp/en/index.html 

9

 Wartsila, “World’s Most Powerful Engine Enters Service.” September, 2006. Can be found at: 

http://www.datahotelli.com/servlet/Piccolo/2006/2006_09_12.html 

10 Source: http://www.wartsila.com/,en,press,0,,823457F6-5CFF-4D16-BE26­

A3664B2C1AFD,,,.htm 

11 http://www.mak-global.com 

12 Diesel & Gas Turbine Worldwide Journal, June 2007 through May 2008. 

13 Mercer, Mike. December 2007, “Onward and Upward.” Diesel & Gas Turbine Worldwide. 

14 Bo Svensson, “Wartsila Expands Thruster Facilities” September, 2008, Diesel & Gas Turbine 

Worldwide. 

15 Eyres, D.J. 2007. Ship Construction 6th Edition. 

16 MARAD, “A Vision for the 21st Century” November 2008, U.S. Department of Maritime 

Administration and the U.S. Department of Transportation. 

17 Department of Transportation, Bureau of Transportation Statistics, April 2007. 

18 MARAD, Vessel Calls at U.S. Ports Snapshot, 2007. Can be found at 

http://marad.dot.gov/documents/Vessel_Calls_at_US_Ports_Snapshot.pdf 

19 International Standard Organization (ISO). (2008) 8217:2005 Petroleum products -- Fuels 

(class F) -- Specifications of Marine Fuels. http://www.iso.org 

1-14 
Chapter 1: Industry Characterization 

20 Vermerie, M.B. (2008). Everything You Need to Know About Marine Fuels. Ghent, Belgium: 

Chevron Global Marine Products. Retrieved on March 27, 2009 from 

http://www.fammllc.com/famm/publications/fuels/EverythingAboutFuels_v0108_LO.pdf. 

21 Jameson, N. (2008). Complete Guide to the Bunker Market 2008. Singapore: Petromedial Pte 

Ltd. 

1-15 
Chapter 2: Air Quality, Health and Welfare Effects 

CHAPTER 2: AIR QUALITY, HEALTH AND WELFARE EFFECTS 

2.1 Background on Pollutants Reduced by this Proposal...................................................2-2

2.1.1 Particulate Matter..................................................................................................2-2

2.1.2 Ozone.......................................................................................................................2-2

2.1.3 Nitrogen Oxides and Sulfur Oxides......................................................................2-3

2.1.4 Air Toxics – Diesel Exhaust PM ...........................................................................2-4

2.2 Health Effects Associated with Exposure to Pollutants................................................2-4

2.2.1 Particulate Matter..................................................................................................2-5

2.2.2 Ozone.......................................................................................................................2-7

2.2.3 Sulfur Oxides..........................................................................................................2-8

2.2.4 Nitrogen Oxides......................................................................................................2-9

2.2.5 Air Toxics..............................................................................................................2-11

2.3 Environmental Impacts Associated with Pollutants...................................................2-23

2.3.1 Deposition of Nitrogen and Sulfur......................................................................2-23

2.3.2 Deposition of Particulate Matter ........................................................................2-53

2.3.3 Visibility Degradation..........................................................................................2-55

2.3.4 Ozone Impacts on Plants and Ecosystems.........................................................2-58

2.4 Impacts of the Coordinated Strategy on Air Quality..................................................2-65

2.4.1 Particulate Matter................................................................................................2-67

2.4.2 Ozone.....................................................................................................................2-72

2.4.3 Deposition of Nitrogen and Sulfur......................................................................2-78

2.4.4 Visibility Degradation..........................................................................................2-83

2.4.5 Air Quality Modeling Methodology ...................................................................2-89

2-1 
Regulatory Impact Analysis 

CHAPTER 2: Air Quality, Health and Welfare Effects 

2.1 Background on Pollutants Reduced by this Proposal 

The coordinated strategy that we are referencing in this proposal will be reducing 

emissions of PM, SOX and NOX. These emissions are associated with ambient PM, ozone, NOX

and SOX, and related health and environmental effects caused from exposure to these pollutants 

are presented in this section. 

2.1.1 Particulate Matter 

Particulate matter (PM) is a generic term for a broad class of chemically and physically 

diverse substances. It can be principally characterized as discrete particles that exist in the 

condensed (liquid or solid) phase spanning several orders of magnitude in size. Since 1987, EPA 

has delineated that subset of inhalable particles small enough to penetrate to the thoracic region 

(including the tracheobronchial and alveolar regions) of the respiratory tract (referred to as 

thoracic particles). Current National Ambient Air Quality Standards (NAAQS) use PM2.5 as the 

indicator for fine particles (with PM2.5 referring to particles with a nominal mean aerodynamic 

diameter less than or equal to 2.5 μm), and use PM10 as the indicator for purposes of regulating 

the coarse fraction of PM10 (referred to as thoracic coarse particles or coarse-fraction particles; 

generally including particles with a nominal mean aerodynamic diameter greater than 2.5 μm

and less than or equal to 10 μm, or PM10-2.5). Ultrafine particles are a subset of fine particles, 

generally less than 100 nanometers (0.1 μm) in aerodynamic diameter. 

Particles span many sizes and shapes and consist of hundreds of different chemicals. 

Particles originate from sources and are also formed through atmospheric chemical reactions; the 

former are often referred to as “primary” particles, and the latter as “secondary” particles. In 

addition, there are also physical, non-chemical reaction mechanisms that contribute to secondary 

particles. Particle pollution also varies by time of year and location and is affected by several 

weather-related factors, such as temperature, clouds, humidity, and wind. A further layer of 

complexity comes from a particle’s ability to shift between solid/liquid and gaseous phases, 

which is influenced by concentration, meteorology, and temperature. 

Fine particles are produced primarily by combustion processes and by transformations of 

gaseous emissions (e.g., SOX, NOX and VOCs) in the atmosphere. The chemical and physical 

properties of PM2.5 may vary greatly with time, region, meteorology and source category. Thus, 

PM2.5 may include a complex mixture of different pollutants including sulfates, nitrates, organic 

compounds, elemental carbon and metal compounds. These particles can remain in the 

atmosphere for days to weeks and travel through the atmosphere hundreds to thousands of 

kilometers.1

2.1.2 Ozone 

Ground-level ozone pollution is formed by the reaction of VOCs and NOX in the 

atmosphere in the presence of heat and sunlight. These pollutants, often referred to as ozone 

precursors, are emitted by many types of pollution sources such as highway vehicles and 

2-2 
Chapter 2: Air Quality, Health and Welfare Effects 

nonroad engines (including those subject to this rule), power plants, chemical plants, refineries, 

makers of consumer and commercial products, industrial facilities, and smaller area sources. 

The science of ozone formation, transport, and accumulation is complex. Ground-level 

ozone is produced and destroyed in a cyclical set of chemical reactions, many of which are 

sensitive to temperature and sunlight. When ambient temperatures and sunlight levels remain 

high for several days and the air is relatively stagnant, ozone and its precursors can build up and 

result in more ozone than typically would occur on a single high-temperature day. Ozone can be 

transported hundreds of miles downwind of precursor emissions, resulting in elevated ozone 

levels even in areas with low VOC or NOX emissions. 

The highest levels of ozone are produced when both VOC and NOX emissions are present 

in significant quantities on clear summer days. Relatively small amounts of NOX enable ozone 

to form rapidly when VOC levels are relatively high, but ozone production is quickly limited by 

removal of the NOX. Under these conditions, NOX reductions are highly effective in reducing 

ozone while VOC reductions have little effect. Such conditions are called “NOX-limited.” 

Because the contribution of VOC emissions from biogenic (natural) sources to local ambient 

ozone concentrations can be significant, even some areas where man-made VOC emissions are 

relatively low can be NOX-limited. 

Ozone concentrations in an area also can be lowered by the reaction of nitric oxide (NO) 

with ozone, forming nitrogen dioxide (NO2); as the air moves downwind and the cycle continues, 

the NO2 forms additional ozone. The importance of this reaction depends, in part, on the relative 

concentrations of NOX, VOC, and ozone, all of which change with time and location. When 

NOX levels are relatively high and VOC levels relatively low, NOX forms inorganic nitrates (i.e., 

particles) but relatively little ozone. Such conditions are called “VOC-limited”. Under these 

conditions, VOC reductions are effective in reducing ozone, but NOX reductions can actually 

increase local ozone under certain circumstances. Even in VOC-limited urban areas, NOX

reductions are not expected to increase ozone levels if the NOX reductions are sufficiently large. 

Rural areas are usually NOX-limited, due to the relatively large amounts of biogenic VOC 

emissions in such areas. Urban areas can be either VOC- or NOX-limited, or a mixture of both, 

in which ozone levels exhibit moderate sensitivity to changes in either pollutant. 

2.1.3 Nitrogen Oxides and Sulfur Oxides 

Sulfur dioxide (SO2), a member of the sulfur oxide (SOX) family of gases, is formed from

burning fuels containing sulfur (e.g., coal or oil), extracting gasoline from oil, or extracting 

metals from ore. Nitrogen dioxide (NO2) is a member of the nitrogen oxide (NOX) family of 

gases. Most NO2 is formed in the air through the oxidation of nitric oxide (NO) emitted when 

fuel is burned at a high temperature. 

 SO2 andNO2 can dissolve in water vapor and further oxidize to form sulfuric and nitric 

acid which react with ammonia to form sulfates and nitrates, both of which are important 

components of ambient PM. The health effects of ambient PM are discussed in Section 2.2.1. 

NOX along with non-methane hydrocarbons (NMHC) are the two major precursors of ozone. 

The health effects of ozone are covered in Section 2.2.2. 

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Regulatory Impact Analysis 

2.1.4 Air Toxics – Diesel Exhaust PM 

Marine diesel engines emit diesel exhaust (DE), a complex mixture composed of carbon 

dioxide, oxygen, nitrogen, water vapor, carbon monoxide, nitrogen compounds, sulfur 

compounds and numerous low-molecular-weight hydrocarbons. A number of these gaseous 

hydrocarbon components are individually known to be toxic including aldehydes, benzene and 

1,3-butadiene. The diesel particulate matter (DPM) present in diesel exhaust consists of fine 

particles (< 2.5μm), including a subgroup with a large number of ultrafine particles (< 0.1 μm). 

These particles have a large surface area which makes them an excellent medium for adsorbing 

organics, and their small size makes them highly respirable. Many of the organic compounds 

present in the gases and on the particles, such as polycyclic organic matter (POM), are 

individually known to have mutagenic and carcinogenic properties. In addition, while toxic trace 

metals emitted by marine diesel engines represent a very small portion of the national emissions 

of metals (less than one percent) and are a small portion of DPM (generally much less than one 

percent of DPM), we note that several trace metals of potential toxicological significance and 

persistence in the environment are emitted by diesel engines. These trace metals include 

chromium, manganese, mercury and nickel. In addition, small amounts of dioxins have been 

measured in highway engine diesel exhaust, some of which may partition into the particulate 

phase. Dioxins are a major health concern but diesel engines are a minor contributor to overall 

dioxin emissions. 

Diesel exhaust varies significantly in chemical composition and particle sizes between 

different engine types (heavy-duty, light-duty), engine operating conditions (idle, accelerate, 

decelerate), and fuel formulations (high/low sulfur fuel). Also, there are emissions differences 

between on-road and nonroad engines because the nonroad engines are generally of older 

technology. This is especially true for marine diesel engines.2 After being emitted in the engine 

exhaust, diesel exhaust undergoes dilution as well as chemical and physical changes in the 

atmosphere. The lifetime for some of the compounds present in diesel exhaust ranges from

hours to days.3

A number of health studies have been conducted regarding diesel exhaust. These include 

epidemiologic studies of lung cancer in groups of workers and animal studies focusing on noncancer effects specific to diesel exhaust exposure. Diesel exhaust PM (including the associated 

organic compounds which are generally high molecular weight hydrocarbon types but not the 

more volatile gaseous hydrocarbon compounds) is generally used as a surrogate measure for 

diesel exhaust. 

2.2 Health Effects Associated with Exposure to Pollutants 

The coordinated strategy that we are referencing in this proposal will be reducing 

emissions of PM, SOX and NOX. These emissions are associated with ambient PM, ozone, NOX

and SOX, and related health and environmental effects caused from exposure to these pollutants 

are presented in this section. 

2-4 
Chapter 2: Air Quality, Health and Welfare Effects 

2.2.1 Particulate Matter 

This section provides a summary of the health effects associated with exposure to 

ambient concentrations of PM.A The information in this section is based on the data and 

conclusions in the PM Air Quality Criteria Document (PM AQCD) and PM Staff Paper prepared 

by the U.S. Environmental Protection Agency (EPA).B,4,5 We also present additional recent 

studies published after the cut-off date for the PM AQCD.6,C Taken together this information 

supports the conclusion that exposure to ambient concentrations of PM are associated with 

adverse health effects. Information specifically related to health effects associated with exposure 

to diesel exhaust PM is included in Section 2.2.5.1 of this document. 

Short-term Exposure Mortality and Morbidity Studies 

As discussed in the PM AQCD, short-term exposure to PM2.5 is associated with 

premature mortality from cardiopulmonary diseases,7

 hospitalization and emergency department 

visits for cardiopulmonary diseases,8 increased respiratory symptoms,9 decreased lung 

function10 and physiological changes or biomarkers for cardiac changes.11,12 In addition, the P

AQCD described a limited body of new evidence from epidemiologic studies for potential 

relationships between short term exposure to PM and health endpoints such as low birth weight, 

preterm birth, and neonatal and infan 13

Among the studies of effects associated with short-term exposure to PM2.5, several

specifically address the contribution of mobile sources to short-term PM2.5-related effects on 

premature mortality. The results from these studies generally indicated that several combustionrelated fine particle source-types are likely associated with mortality, including motor vehicle 

A Personal exposure includes contributions from many different types of particles, from many sources, and in many

different environments. Total personal exposure to PM includes both ambient and nonambient components; and 

both components may contribute to adverse health effects. B

 The PM NAAQS is currently under review and the EPA is considering all available science on PM health effects,

including information which has been published since 2004, in the development of the upcoming PM Integrated 

Science Assessment Document (ISA). A first draft of the PM ISA was completed in December 2008 and was 

submitted for review by the Clean Air Scientific Advisory Committee (CASAC) of EPA’s Science Advisory Board.

Comments from the general public have also been requested. For more information, see 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201805. C

 These additional studies are included in the 2006 Provisional Assessment of Recent Studies on Health Effects of 

Particulate Matter Exposure. The provisional assessment did not and could not (given a very short timeframe) 

undergo the extensive critical review by CASAC and the public, as did the PM AQCD. The provisional assessment

found that the “new” studies expand the scientific information and provide important insights on the relationship 

between PM exposure and health effects of PM. The provisional assessment also found that “new” studies generally 

strengthen the evidence that acute and chronic exposure to fine particles and acute exposure to thoracic coarse

particles are associated with health effects. Further, the provisional science assessment found that the results 

reported in the studies did not dramatically diverge from previous findings, and taken in context with the findings of

the AQCD, the new information and findings did not materially change any of the broad scientific conclusions 

regarding the health effects of PM exposure made in the AQCD. However, it is important to note that this 

assessment was limited to screening, surveying, and preparing a provisional assessment of these studies. For 

reasons outlined in Section I.C of the preamble for the final PM NAAQS rulemaking in 2006 (see 71 FR 61148-49, 

October 17, 2006), EPA based its NAAQS decision on the science presented in the 2004 AQCD. 

2.2.1.1

M 

t mortality.

2-5 
Regulatory Impact Analysis 

emissions as well as other sources.14 The analyses incorporate source apportionment tools into 

short-term exposure studies and are briefly mentioned here. Analyses incorporating source 

apportionment by factor analysis with daily time-series studies of daily death rates indicated a 

relationship between mobile source PM2.5 and mortality.15,16,17,18 Another recent study in 14 

U.S. cities examined the effect of PM10 exposures on daily hospital admissions for 

cardiovascular disease. This study found that the effect of PM10 was significantly greater in 

areas with a larger proportion of PM10 coming from motor vehicles, indicating that PM10 from

these sources may have a greater effect on the toxicity of ambient PM10 when compared with

other sources.19 These studies provide evidence that PM-related emissions, specifically from

mobile sources, are associated with adv

Long-term Exposure Mortality and Morbidity Studies 

Long-term exposure to ambient PM2.5 is associated with premature mortality from

cardiopulmonary diseases and lung cancer,20 and effects on the respiratory system such as 

decreased lung function or the development of chronic respiratory disease.

21 Of specific 

importance, the PM AQCD also noted that the PM components of gasoline and diesel engine 

exhaust represent one class of hypothesized likely important contributors to the observed 

ambient PM-related increases in lung cancer incidence and mortality.22

The PM AQCD and PM Staff Paper emphasized the results of two long-term

epidemiologic studies, the Six Cities and American Cancer Society (ACS) prospective cohort 

studies, based on several factors: the large air quality data set for PM in the Six Cities Study, the 

fact that the study populations were similar to the general population, and the fact that these 

studies have undergone extensive reanalysis.23,24,25,26 27,28 These studies indicate that there are 

positive associations for all-cause, cardiopulmonary, and lung cancer mortality with long-term

exposure to PM2.5. One analysis of a subset of the ACS cohort data, which was published after 

the PM AQCD was finalized but in time for the 2006 Provisional Assessment, found a larger 

association than had previously been reported between long-term PM2.5 exposure and mortality 

in the Los Angeles area using a new exposure estimation method that accounted for variations in 

concentration within the city.29

As discussed in the PM AQCD, the morbidity studies that combine the features of crosssectional and cohort studies provide the best evidence for chronic exposure effects. Long-term

studies evaluating the effect of ambient PM on children’s development have shown some

evidence indicating effects of PM2.5 and/or PM10 on reduced lung function growth.30 In another 

recent publication included in the 2006 Provisional Assessment, investigators in southern 

California reported the results of a cross-sectional study of outdoor PM2.5 and a measure of 

atherosclerosis development in the Los Angeles basin.31 The study found significant 

associations between ambient residential PM2.5 and carotid intima-media thickness (CIMT), an 

indicator of subclinical atherosclerosis, an underlying factor in cardiovascular disease. 

erse health effects. 

2.2.1.2

2-6 
Chapter 2: Air Quality, Health and Welfare Effects 

2.2.2 Ozone 

Exposure to ambient ozone contributes to a wide range of adverse health effects.D These 

health effects are well documented and are critically assessed in the EPA ozone air quality 

criteria document (ozone AQCD) and EPA staff paper.32,33 We are relying on the data and 

conclusions in the ozone AQCD and staff paper, regarding the health effects associated with 

ozone exposure. 

Ozone-related health effects include lung function decrements, respiratory symptoms, 

aggravation of asthma, increased hospital and emergency room visits, increased asthma 

medication usage, and a variety of other respiratory effects. Cellular-level effects, such as 

inflammation of lungs, have been documented as well. In addition, there is suggestive evidence 

of a contribution of ozone to cardiovascular-related morbidity and highly suggestive evidence 

that short-term ozone exposure directly or indirectly contributes to non-accidental and 

cardiopulmonary-related mortality, but additional research is needed to clarify the underlying 

mechanisms causing these effects. In a recent report on the estimation of ozone-related 

premature mortality published by the National Research Council (NRC), a panel of experts and 

reviewers concluded that short-term exposure to ambient ozone is likely to contribute to 

premature deaths and that ozone-related mortality should be included in estimates of the health 

benefits of reducing ozone exposure.34 People who appear to be more susceptible to effects 

associated with exposure to ozone include children, asthmatics and the elderly. Those with 

greater exposures to ozone, for instance due to time spent outdoors (e.g., children and outdoor 

workers), are also of concern. 

Based on a large number of scientific studies, EPA has identified several key health 

effects associated with exposure to levels of ozone found today in many areas of the country. 

Short-term (1 to 3 hours) and prolonged exposures (6 to 8 hours) to ambient ozone 

concentrations have been linked to lung function decrements, respiratory symptoms, increased 

hospital admissions and emergency room visits for respiratory problems.35, 36, 37, 38, 39, 40

Repeated exposure to ozone can increase susceptibility to respiratory infection and lung 

inflammation and can aggravate preexisting respiratory diseases, such as asthma.

41, 42, 43, 44, 45

Repeated exposure to sufficient concentrations of ozone can also cause inflammation of the lung, 

impairment of lung defense mechanisms, and possibly irreversible changes in lung structure, 

which over time could affect premature aging of the lungs and/or the development of chronic 

respiratory illnesses, such as emphysema and chronic bronchitis.46, 47, 48, 49 

Children and adults who are outdoors and active during the summer months, such as 

construction workers, are among those most at risk of elevated ozone exposures.50 Children and 

outdoor workers tend to have higher ozone exposure because they typically are active outside, 

working, playing and exercising, during times of day and seasons (e.g., the summer) when ozone 

levels are highest.51 For example, summer camp studies in the Eastern United States and 

Southeastern Canada have reported statistically significant reductions in lung function in 

D Human exposure to ozone varies over time due to changes in ambient ozone concentration and because people 

move between locations which have notable different ozone concentrations. Also, the amount of ozone delivered to

the lung is not only influenced by the ambient concentrations but also by the individuals breathing route and rate. 

2-7 
Regulatory Impact Analysis 

children who are active outdoors.52, 53, 54, 55, 56, 57, 58, 59 Further, children are more at risk o

experiencing health effects from ozone exposure than adults because their respiratory systems 

are still developing. These individuals (as well as people with respiratory illnesses, such as 

asthma, especially asthmatic children) can experience reduced lung function and increased 

respiratory symptoms, such as chest pain and cough, when exposed to relatively low ozone levels 

during prolonged periods of moderate exertion.60, 61, 62, 63

2.2.3 Sulfur Oxides 

This section provides an overview of the health effects associated with SO2. Additional 

information on the health effects of SO2 can be found in the U.S. Environmental Protection 

Agency Integrated Science Assessment for Sulfur Oxides.64 Following an extensive evaluation 

of health evidence from epidemiologic and laboratory studies, the U.S. EPA has concluded that 

there is a causal relationship between respiratory health effects and short-term exposure to SO2. 

The immediate effect of SO2 on the respiratory system in humans is bronchoconstriction. This 

response is mediated by chemosensitive receptors in the tracheobronchial tree. These receptors 

trigger reflexes at the central nervous system level resulting in bronchoconstriction, mucus 

secretion, mucosal vasodilation, cough, and apnea followed by rapid shallow breathing. In some

cases, local nervous system reflexes also may be involved. Asthmatics are more sensitive to the 

effects of SO2 likely resulting from preexisting inflammation associated with this disease. This 

inflammation may lead to enhanced release of mediators, alterations in the autonomic nervous 

system and/or sensitization of the chemosensitive receptors. These biological processes are 

likely to underlie the bronchoconstriction and decreased lung function observed in response to 

SO2 exposure. In laboratory studies involving controlled human exposures to SO2, respiratory 

effects have consistently been observed following 5-10 min exposures at SO2 concentrations ≥

0.2 ppm in asthmatics engaged in moderate to heavy levels of exercise. In these studies, 5-30% 

of relatively healthy exercising asthmatics are shown to experience moderate or greater 

decrements in lung function (≥ 100% increase in sRaw (specific airway resistance) or ≥ 15% 

decrease in FEV1 (forced expiratory volume in 1 second)) with peak exposures to SO2

concentrations of 0.2-0.3 ppm. At concentrations ≥ 0.4 ppm, a greater percentage of asthmatics 

(20-60%) experience SO2-induced decrements in lung function, which are frequently 

accompanied by respiratory symptoms. A clear concentration-response relationship has been 

demonstrated in laboratory studies following exposures to SO2 at concentrations between 0.2 and 

1.0 ppm, both in terms of increasing severity of effect and percentage of asthmatics adversely 

affected. 

In epidemiologic studies, respiratory effects have been observed in areas where the mean 

24-hour SO2 levels range from 1 to 30 ppb, with maximum 1 to 24-hour average SO2 values 

ranging from 12 to 75 ppb. Important new multicity studies and several other studies have found 

an association between 24-hour average ambient SO2 concentrations and respiratory symptoms

in children, particularly those with asthma. Furthermore, limited epidemiologic evidence 

indicates that atopic children and adults may be at increased risk for SO2-induced respiratory 

symptoms. Generally consistent associations also have been observed between ambient SO2

concentrations and emergency department visits and hospitalizations for all respiratory causes, 

particularly among children and older adults (≥ 65 years), and for asthma. Intervention studies 

provide additional evidence that supports a causal relationship between SO2 exposure and 

respiratory health effects. Two notable studies conducted in several cities in Germany and in 

f 

2-8 
Chapter 2: Air Quality, Health and Welfare Effects 

Hong Kong reported that decreases in SO2 concentrations were associated with improvements in 

respiratory symptoms, though the possibility remained that these health improvements may be 

partially attributable to declining concentrations of air pollutants other than SO2, most notably 

PM or constituents of PM. A limited subset of epidemiologic studies have examined potential 

confounding by copollutants using multipollutant regression models. These analyses indicate 

that although copollutant adjustment has varying degrees of influence on the SO2 effect 

estimates, the effect of SO2 on respiratory health outcomes appears to be generally robust and 

independent of the effects of gaseous and particulate copollutants, suggesting that the observed 

effects of SO2 on respiratory endpoints occur independent of the effects of other ambient air 

pollutants. 

Consistent associations between short-term exposure to SO2 and mortality have been 

observed in epidemiologic studies, with larger effect estimates reported for respiratory mortality 

than cardiovascular mortality. While this finding is consistent with the demonstrated effects of 

SO2 on respiratory morbidity, uncertainty remains with respect to the interpretation of these 

associations due to potential confounding by various copollutants. The U.S. EPA has therefore 

concluded that the overall evidence is suggestive of a causal relationship between short-term

exposure to SO2 and mortality. Significant associations between short-term exposure to SO2

and emergency department visits and hospital admissions for cardiovascular diseases have also 

been reported. However, these findings have been inconsistent across studies and do not provide 

adequate evidence to infer a causal relationship between SO2 exposure and cardiovascular 

morbidity. 

2.2.4 Nitrogen Oxides 

This section provides an overview of the health effects associated with NO2. Additional 

information on the health effects of NO2 can be found in the U.S. Environmental Protection 

Agency Integrated Science Assessment (ISA) for Nitrogen Oxides.65 The U.S. EPA has 

concluded that the findings of epidemiologic, controlled human exposure, and animal 

toxicological studies provide evidence that is sufficient to infer a likely causal relationship 

between respiratory effects and short-term NO2 exposure.66 The ISA concludes that the 

strongest evidence for such a relationship comes from epidemiologic studies of respirator

effects including symptoms, emergency department visits, and hospital admissions.67 The

estimates from U.S. and Canadian studies generally indicate that ambient NO2 is associated with 

a 2-20% increase in risks for emergency department visits and hospital admissions. Risks 

associated with respiratory symptoms are generally higher.68 These epidemiologic studies are 

supported by evidence from experimental studies, in particular by controlled human exposure 

studies that evaluate airway hyperresponsiveness in asthmatic individuals.69 The ISA draws two 

broad conclusions regarding airway responsiveness following NO2 exposure.70 First, the ISA 

concludes that NO2 exposure may enhance the sensitivity to allergen-induced decrements in lung 

function and increase the allergen-induced airway inflammatory response at exposures as low as 

0.26 ppm NO2 for 30 minutes.71 Second, exposure to NO2 has been found to enhance the 

inherent responsiveness of the airway to subsequent nonspecific challenges in controlled human 

exposure studies.72 In general, small but significant increases in nonspecific airway 

responsiveness were observed in the range of 0.2 to 0.3 ppm NO2 for 30-minute exposures and at 

0.1 ppm NO2 for 60-minute exposures in asthmatics. These conclusions are consistent with 

results from animal toxicological studies which have detected (1) increased immune-mediated 

y 

 effect

2-9 
Regulatory Impact Analysis 

 Together, the epidemiologic and experimental data sets form a plausible, 

consistent, and coherent description of a relationship between NO2 exposures and an array of 

adverse health effects that range from the onset of respiratory symptoms to hospital admission. 

pulmonary inflammation in rats exposed to house dust mite allergen following exposure to 5 

ppm NO2 for 3-hour and (2) increased responsiveness to non-specific challenges following subchronic (6-12 weeks) exposure to 1 to 4 ppm NO2.

73 Enhanced airway responsiveness could 

have important clinical implications for asthmatics since transient increases in airway 

responsiveness following NO2 exposure have the potential to increase symptoms and worsen 

asthma control.74

Although the weight of evidence supporting a causal relationship is somewhat less certain 

than that associated with respiratory morbidity, NO2 has also been linked to other health 

endpoints. For example, results from several large U.S. and European multi-city studies and a 

meta-analysis study indicate positive associations between ambient NO2 concentrations and the 

risk of all-cause (nonaccidental) mortality, with effect estimates ranging from 0.5 to 3.6% excess 

risk in mortality per standardized increment (20 ppb for 24-hour averaging time, 30 ppb for 1-

hour averaging time).75 In general, the NO2 effect estimates were robust to adjustment for copollutants. In addition, generally positive associations between short-term ambient NO2

concentrations and hospital admissions or emergency department visits for cardiovascular 

disease have been reported.76 A number of epidemiologic studies have also examined the effects 

of long-term exposure to NO2 and reported positive associations with decrements in lung 

function and partially irreversible decrements in lung function growth.77 Specifically, results 

from the California-based Children’s Health Study, which evaluated NO2 exposures in children 

over an 8-year period, demonstrated deficits in lung function growth.78 This effect has also been 

observed in Mexico City, Mexico79 and in Oslo, Norway,80 with decrements ranging from 1 to 

17.5 ml per 20- ppb increase in annual NO2 concentration. Animal toxicological studies may 

provide biological plausibility for the chronic effects of NO2 that have been observed in these 

epidemiologic studies.81 The main biochemical targets of NO2 exposure appear to be 

antioxidants, membrane polyunsaturated fatty acids, and thiol groups. NO2 effects include 

changes in oxidant/antioxidant homeostasis and chemical alterations of lipids and proteins. 

Lipid peroxidation has been observed at NO2 exposures as low as 0.04 ppm for 9 months and at 

exposures of 1.2 ppm for 1 week, suggesting lower effect thresholds with longer durations of 

exposure. Other studies showed decreases in formation of key arachidonic acid metabolites in 

mornings following NO2 exposures of 0.5 ppm. NO2 has been shown to increase collagen 

synthesis rates at concentrations as low as 0.5 ppm. This could indicate increased total lung 

collagen, which is associated with pulmonary fibrosis, or increased collagen turnover, which is 

associated with remodeling of lung connective tissue. Morphological effects following chronic 

NO2 exposures have been identified in animal studies that link to these increases in collagen 

synthesis and may provide plausibility for the deficits in lung function growth described in 

epidemiologic studies.82

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Chapter 2: Air Quality, Health and Welfare Effects 

2.2.5 Air Toxics 

C3 vessel emissions contribute to ambient levels of air toxics known or suspected as human or 

animal carcinogens, or that have noncancer health effects. The population experiences an elevated 

risk of cancer and other noncancer health effects from exposure to air toxics.83 A number of these 

compounds would be impacted by the standards proposed in this rule, including polycyclic organic 

matter (POM) and DPM. These compounds were identified as national or regional risk drivers in the 

1999 National-Scale Air Toxics Assessment (NATA). 

According to NATA for 1999, mobile sources were responsible for 44 percent of outdoor 

toxic emissions and almost 50 percent of the cancer risk. Noncancer health effects can result 

from chronic,E subchronic,F or acuteG inhalation exposures to air toxics, and include 

neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the 

immune and reproductive systems. According to the 1999 NATA, nearly the entire U.S. 

population was exposed to an average concentration of air toxics that has the potential for 

adverse noncancer respiratory health effects. This will continue to be the case in 2030, even 

though toxics concentrations will be lower. 

The NATA modeling framework has a number of limitations which prevent its use as the 

sole basis for setting regulatory standards. These limitations and uncertainties are discussed on 

the 1999 NATA website.84 Even so, this modeling framework is very useful in identifying air 

toxic pollutants and sources of greatest concern, setting regulatory priorities, and informing the 

decision making process. 

Potential Cancer Effects of Exposure to Diesel Exhaust 

Exposure to diesel exhaust is of specific concern because it has been judged by EPA to 

pose a lung cancer hazard for humans at environmental levels of exposure. 

EPA’s 2002 final “Health Assessment Document for Diesel Engine Exhaust” (the EPA 

Diesel HAD) classified exposure to diesel exhaust as likely to be carcinogenic to humans by 

inhalation at environmental exposures, in accordance with the revised draft 1996/1999 EPA 

cancer guidelines.85,86 In accordance with earlier EPA guidelines, exposure to diesel exhaust 

would similarly be classified as probably carcinogenic to humans (Group B1).87,88 A number of 

other agencies (National Institute for Occupational Safety and Health, the International Agency 

for Research on Cancer, the World Health Organization, California EPA, and the U.S. 

Department of Health and Human Services) have made similar classifications.89, 90,91,92,93 The

E

 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database 

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately 

10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal

species). 

F

 Defined in the IRIS database as repeated exposure by the oral, dermal, or inhalation route for more than 30 days, 

up to approximately 10% of the life span in humans (more than 30 days up to approximately 90 days in typically

used laboratory animal species).. G Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 

2.2.5.1

2-11 
Regulatory Impact Analysis 

Health Effects Institute has prepared numerous studies and reports on the potential 

carcinogenicity of exposure to diesel exhaust.94,95,96

More specifically, the EPA Diesel HAD states that the conclusions of the document apply 

to diesel exhaust in use today including both onroad and nonroad engines. The EPA Diesel 

HAD acknowledges that the studies were done on engines with generally older technologies and 

that “there have been changes in the physical and chemical composition of some DE [diesel 

exhaust] emissions (onroad vehicle emissions) over time, though there is no definitive 

information to show that the emission changes portend significant toxicological changes.” In 

any case, the diesel technology used for marine diesel engines typically lags that used for onroad 

engines which have been subject to PM standards since 1998. Thus it is reasonable to assume

that the hazards identified from older technologies may be largely applicable to marine engines. 

For the Diesel HAD, EPA reviewed 22 epidemiologic studies on the subject of the 

carcinogenicity of exposure to diesel exhaust in various occupations, finding increased lung 

cancer risk, although not always statistically significant, in 8 out of 10 cohort studies and 10 out 

of 12 case-control studies which covered several industries. Relative risk for lung cancer, 

associated with exposure, ranged from 1.2 to 1.5, although a few studies show relative risks as 

high as 2.6. Additionally, the Diesel HAD also relied on two independent meta-analyses, which 

examined 23 and 30 occupational studies respectively, and found statistically significant 

increases of 1.33 to 1.47 in smoking-adjusted relative lung cancer risk associated with diesel 

exhaust. These meta-analyses demonstrate the effect of pooling many studies and in this case 

show the positive relationship between diesel exhaust exposure and lung cancer across a variety 

of diesel exhaust-exposed occupations.97,98,99

EPA generally derives cancer unit risk estimates to calculate population risk more 

precisely from exposure to carcinogens. In the simplest terms, the cancer unit risk is the 

increased risk associated with average lifetime exposure of 1 μg/m3. EPA concluded in the 

Diesel HAD that it is not currently possible to calculate a cancer unit risk for diesel exhaust due 

to a variety of factors that limit the current studies, such as a lack of standard exposure metric for 

diesel exhaust and the absence of quantitative exposure characterization in retrospective studies. 

In the absence of a cancer unit risk, the Diesel HAD sought to provide additional insight 

into the significance of the diesel exhaust-cancer hazard by estimating possible ranges of risk 

that might be present in the population. An exploratory analysis was used to characterize a 

possible risk range by comparing a typical environmental exposure level for highway diesel 

sources to a selected range of occupational exposure levels. The occupationally observed risks 

were then proportionally scaled according to the exposure ratios to obtain an estimate of the 

possible environmental risk. If the occupational and environmental exposures are similar, the 

environmental risk would approach the risk seen in the occupational studies whereas a much 

higher occupational exposure indicates that the environmental risk is lower than the occupational 

risk. A comparison of environmental and occupational exposures showed that for certain 

occupations the exposures are similar to environmental exposures while, for others, they differ 

by a factor of about 200 or more. 

A number of calculations are involved in the exploratory analysis of a possible risk range, 

and these can be seen in the EPA Diesel HAD. The outcome was that environmental risks from

2-12 
Chapter 2: Air Quality, Health and Welfare Effects 

diesel exhaust exposure could range from a low of 10-4 to 10-5 to as high as 10-3, reflecting the 

range of occupational exposures that could be associated with the relative and absolute risk 

levels observed in the occupational studies. Because of uncertainties, the analysis acknowledged 

that the risks could be lower than 10-4 or 10-5, and a zero risk from diesel exhaust exposure was 

not ruled out. 

EPA recently assessed air toxic emissions and their associated risk (the National-Scale 

Air Toxics Assessment or NATA for 1996 and 1999), and we concluded that diesel exhaust 

ranks with other emissions that the national-scale assessment suggests pose the greatest relative 

risk.100,101 This national assessment estimates average population inhalation exposures to DPM 

for nonroad as well as on-highway sources. These are the sum of ambient levels in various 

locations weighted by the amount of time people spend in each of the locations. 

In summary, even though EPA does not have a specific carcinogenic potency with which 

to accurately estimate the carcinogenic impact of exposure to diesel exhaust, the likely hazard to 

humans together with the potential for significant environmental risks leads us to conclude that 

diesel exhaust emissions from marine engines present public health issues of concern to this rule. 

Other Health Effects of Exposure to Diesel Exhaust 

Noncancer health effects of acute and chronic exposure to diesel exhaust emissions are 

also of concern to the EPA. The Diesel HAD established an inhalation Reference Concentration 

(RfC) specifically based on animal studies of diesel exhaust exposure. An RfC is defined by 

EPA as “an estimate of a continuous inhalation exposure to the human population, including 

sensitive subgroups, with uncertainty spanning perhaps an order of magnitude, which is likely to 

be without appreciable risks of deleterious noncancer effects during a lifetime.” EPA derived the 

RfC from consideration of four well-conducted chronic rat inhalation studies showing adverse 

pulmonary effects.102,103,104,105 The diesel RfC is based on a “no observable adverse effect” 

level of 144 μg/m3 that is further reduced by applying uncertainty factors of 3 for interspe

extrapolation and 10 for human variations in sensitivity. The resulting RfC derived in the Diesel

HAD is 5 μg/m3 for diesel exhaust as measured by DPM. This RfC does not consider allergenic

effects such as those associated with asthma or immunologic effects. There is growing evidence 

that exposure to diesel exhaust can exacerbate these effects, but the exposure-response data is 

presently lacking to derive an RfC. The EPA Diesel HAD states, “With DPM [diesel particulate 

matter] being a ubiquitous component of ambient PM, there is an uncertainty about the adequacy 

of the existing DE [diesel exhaust] noncancer database to identify all of the pertinent DE-caused 

noncancer health hazards.” 

While there have been relatively few human studies associated specifically with the 

noncancer impact of exposure to DPM alone, DPM is a component of the ambient particles 

studied in numerous epidemiologic studies. The conclusion that health effects associated with 

ambient PM in general are relevant to DPM is supported by studies that specifically associate 

observable human noncancer health effects with exposure to DPM. As described in the Diesel 

HAD, these studies identified some of the same health effects reported for ambient PM, such as 

respiratory symptoms (cough, labored breathing, chest tightness, wheezing), and chronic 

respiratory disease (cough, phlegm, chronic bronchitis and suggestive evidence for decreases in 

pulmonary function). Symptoms of immunological effects such as wheezing and increased 

2.2.5.2

cies 

2-13 
Regulatory Impact Analysis 

allergenicity are also seen. Studies in rodents, especially rats, show the potential for human 

inflammatory effects in the lung and consequential lung tissue damage from chronic diesel 

exhaust inhalation exposure. The Diesel HAD concludes “that acute exposure to DE [diesel 

exhaust] has been associated with irritation of the eye, nose, and throat, respiratory symptoms

(cough and phlegm), and neurophysiological symptoms such as headache, lightheadedness, 

nausea, vomiting, and numbness or tingling of the extremities.”106 There is also evidence for an 

immunologic effect such as the exacerbation of allergenic responses to known allergens and 

asthma-like symptoms.

107,108,109

The Diesel HAD briefly summarizes health effects associated with ambient PM and 

discusses the PM2.5 NAAQS. There is a much more extensive body of human data, which is also 

mentioned earlier in the health effects discussion for PM2.5 (Section 2.2.1 of this DRIA), 

showing a wide spectrum of adverse health effects associated with exposure to ambient PM, of 

which diesel exhaust is an important component. The PM2.5 NAAQS is designed to provide 

protection from the non-cancer and premature mortality effects of PM2.5 as a whole. 

Ambient Levels of Diesel Exhaust PM {tc "2.2.1.3 Diesel Exhaust PM Ambient 

Levels " \l 4} 

Because DPM is part of overall ambient PM and cannot be easily distinguished from

overall PM, we do not have direct measurements of DPM in the ambient air. DPM 

concentrations are estimated here using ambient air quality modeling based on DPM emission 

inventories. 

2.2.5.3.1 Toxics Modeling and Methods {tc "2.2.1.3.1 Toxics Modeling and Methods " \l 5} 

In addition to the general ambient PM modeling conducted for this rulemaking, DPM 

concentrations were recently estimated as part of the 1999 National-Scale Air Toxics 

Assessment.110 Ambient impacts of mobile source emissions were predicted using the 

Assessment System for Population Exposure Nationwide (ASPEN) dispersion model. 

Concentrations of DPM were calculated at the census tract level in the 1999 NATA. The 

median DPM concentration calculated nationwide is 0.91 μg/m3

 with levels of 1.06 μg/m3 in 

urban counties and 0.43 μg/m3

 in rural counties. Table 2-1 below summarizes the distribution of 

ambient DPM concentrations at the national scale. Over half of the DPM and diesel exhaust 

organic gases can be attributed to nonroad diesels. A map of median ambient concentrations is 

provided in Figure 2-1. Areas with high median concentrations are clustered in the Northeast, 

Great Lake States, California, and the Gulf Coast States, and are also distributed throughout the 

rest of the U.S. 

2.2.5.3

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Chapter 2: Air Quality, Health and Welfare Effects 

Table 2-1 Distribution of Census Tract Ambient Concentrations of DPM at the National Scale in 1999 NATAa

NATIONWIDE 

(ΜG/M3

) 

URBAN 

(ΜG/M3) 

5th Percentile 0.22 0.33

25th Percentile 0.54 0.70

Median 0.91 1.06 

75th Percentile 1.41 1.56

95th Percentile 2.91 3.21

Onroad Contribution to Mean 0.43 0.49 

Nonroad Contribution to Mean 0.78 0.90 

RURAL 

(ΜG/M3) 

0.08

0.28

0.43 

0.62

0.96

0.20 

0.28

Note: a

 This table is generated from data contained in the diesel particulate matter Microsoft Access database file found 

in the County-Level Ambient Concentration Summaries section of the 1999 NATA webpage 

(http://www.epa.gov/ttn/atw/nata1999/tables.html). 

2-15 
Regulatory Impact Analysis 

Figure 2-1 Estimated County Median Ambient Concentration of Diesel Particulate Matter 

2-16 
Chapter 2: Air Quality, Health and Welfare Effects 

2.2.5.4 Exposure to Diesel Exhaust PM 

Exposure of people to diesel exhaust depends on their various activities, the time spent in 

those activities, the locations where these activities occur, and the levels of diesel exhaust 

pollutants in those locations. The major difference between ambient levels of diesel particulate 

and exposure levels for diesel particulate is that exposure levels account for a person moving 

from location to location, the proximity to the emission source, and whether the exposure occurs 

in an enclosed environment. 

2.2.5.4.1 Occupational Exposures 

Occupational exposures to diesel exhaust from mobile sources, including marine diesel 

engines, can be several orders of magnitude greater than typical exposures in the nonoccupationally exposed population. 

Over the years, diesel particulate exposures have been measured for a number of 

occupational groups resulting in a wide range of exposures from 2 to 1280 μg/m3 for a variety of 

occupations. As discussed in the Diesel HAD, the National Institute of Occupational Safety and 

Health (NIOSH) has estimated a total of 1,400,000 workers are occupationally exposed to diesel 

exhaust from on-road and nonroad vehicles including marine diesel engines. 

2.2.5.4.2 Elevated Concentrations and Ambient Exposures in Mobile Source-Impacted Areas 

While occupational studies indicate that those working in closest proximity to diesel 

exhaust experience the greatest health effects, recent studies are showing that human populations 

living near large diesel emission sources such as major roadways, 111 rail yards, 112 and marine 

ports113 are also likely to experience greater exposure to PM and other components of diesel 

exhaust than the overall population, putting them at a greater health risk. 

Regions immediately downwind of marine ports may experience elevated ambient 

concentrations of directly-emitted PM2.5 from diesel engines. Due to the nature of marine ports, 

emissions from a large number of diesel engines are concentrated in a small area. 

A recent study from the California Air Resources Board (CARB) evaluated air quality 

impacts of diesel engine emissions within the Port of Long Beach and Los Angeles in California, 

one of the largest ports in the U.S.114 The port study employed the ISCST3 dispersion model. 

With local meteorological data used in the modeling, annual average concentrations of DPM 

were substantially elevated over an area exceeding 200,000 acres. Because the Ports are located 

near heavily-populated areas, the modeling indicated that over 700,000 people lived in areas with 

at least 0.3 μg/m3

 of port-related DPM in ambient air, about 360,000 people lived in areas with at 

least 0.6 μg/m3 of DPM, and about 50,000 people lived in areas with at least 1.5 μg/m3 of

ambient DPM emitted directly from the port. Figure 2-2 provides an aerial shot of the Port of

Long Beach and Los Angeles in California. 

2-17 
Regulatory Impact Analysis 

H This type of screening-level analysis is an inexact tool and not appropriate for regulatory decision-making; it is 

useful in beginning to understand potential impacts and for illustrative purposes. I

 The Agency selected a representative sample from the top 150 U.S. ports including coastal, inland, and Great Lake

ports. 

Figure 2-2 Aerial Shot – Port of LA and Long Beach, California 

This port study highlights the substantial contribution these facilities make to ambient 

concentrations of DPM in large, densely populated areas. 

EPA recently updated its initial screening-level analysis115,116 of selected marine port areas 

to better understand the populations, including minority, low-income, and children, that are 

exposed to diesel particulate matter (DPM) emissions from these facilities.H The results of this 

study are discussed here and are also available in the public docket.117,118

This screening-level analysis focused on a representative selection of national marine 

ports.I Of the 45 marine ports studied, the results indicate that at least 18 million people, 

including a disproportionate number of low-income households, African-Americans, and 

Hispanics, live in the vicinity of these facilities and are being exposed to ambient DPM levels that 

are 2.0 μg/m3

 and 0.2 μg/m3 above levels found in areas further from these facilities. Considering 

only ocean-going marine engine DPM emissions, the results indicate that 6.5 million people are 

exposed to ambient DPM levels that are 2.0 μg/m3 and 0.2 μg/m3 above levels found in areas 

further from these facilities. Because those populations exposed to DPM emissions from marine 

ports are more likely to be low-income and minority residents, these populations would benefit 

from the standards being proposed by the coordinated strategy. The detailed findings of this study 

are available in the public docket for this rulemaking. 

2-18 
Chapter 2: Air Quality, Health and Welfare Effects 

With regard to children, this analysis shows that at least four million children live in the 

vicinity of the marine ports studied and are also exposed to ambient DPM levels that are 2.0 

μg/m3 and 0.2 μg/m3 above levels found in areas further from these facilities. Of the 6.5 million 

people exposed to DPM emissions from ocean-going vessel emissions, 1.7 million are children. 

The age composition of the total affected population in the screening analysis matches closely 

with the age composition of the overall U.S. population. However, for some individual facilities, 

the young (0-4 years) appear to be over-represented in the affected population compared to the 

overall U.S. population. Detailed results for individual harbors are presented in the Appendices 

of the memorandum in the docket. 

As part of this study, a computer geographic information system was used to identify the 

locations and boundaries of the harbor areas, and determine the size and demographic 

characteristics of the populations living near these facilities. These facilities are listed in Table 

2-2. Figures 2-3 and 2-4 provide examples of digitized footprints of the marine harbor areas 

included in this study. 

Table 2-2 Marine Harbor Areas 

Baltimore, MD Los Angeles, CA

Boston, MA Louisville, KY 

Charleston, SC Miami, FL 

Chicago, IL Mobile, AL 

Cincinnati, OH Mount Vernon, IN

Cleveland, OH Nashville, TN 

Corpus Christi, TX New Orleans, LA 

Detroit, MI New York, NY 

Duluth-Superior, MN Oakland, CA 

Freeport, TX Panama City, FL 

Gary, IN Paulsboro, NJ

Helena, AR Philadelphia, PA 

Houston, TX Pittsburgh, PA 

Lake Charles, LA Port Arthur, TX

Long Beach, CA Port Everglades, FL

Port of Baton Rouge, LA

Port of Plaquemines, LA 

Portland, ME 

Portland, OR 

Richmond, CA

Savannah, GA 

Seattle, WA

South Louisiana, LA

St. Louis, MO

Tacoma, WA

Tampa, FL 

Texas City, TX

Tulsa - Port of Catoosa, OK 

Two Harbors, MN 

Wilmington, NC 

2-19 
Regulatory Impact Analysis 

Figure 2-3 Digitized Footprint of New York, NY harbor area. 

Figure 2-4 Digitized Footprint of Portland, OR harbor area. 

2-20 
Chapter 2: Air Quality, Health and Welfare Effects 

In order to better understand the populations that live in the vicinity of marine harbor 

areas and their potential exposures to ambient DPM, concentration isopleths surrounding the 45 

marine port areas were created and digitized for all emission sources at the marine port and for 

ocean-going vessel Category 3 engine emissions only. The concentration isopleths of interest 

were selected to correspond to two DPM concentrations above urban background, 2.0 μg/m3 and 

0.2 μg/m3. The isopleths were estimated using the AERMOD air dispersion model. Figures 2-5 

and 2-6 provide examples of concentration isopleths surrounding the New York, NY harbor area 

for all emission sources and for ocean-going vessel Category 3 only engine emissions, 

respectively.

Figure 2-5 Concentration Isopleths of New York, NY Harbor Area Resulting from All Emission Sources. 

2-21 
Regulatory Impact Analysis 

Figure 2-6 Concentration Isopleths of New York, NY Harbor Area Resulting from All Emission Sources. 

The size and characteristics of populations and households that reside within the area 

encompassed by the two DPM concentration isopleths were determined for each isopleth, and the 

demographic compositions were assessed, including age, income level, and race/ethnicity. 

In summary, the screening-level analysis found that for the 45 U.S. marine ports studied, 

al least 18 million people live in the vicinity of these facilities and are exposed to ambient DPM 

levels from all port emission sources that are 2.0 μg/m3 and 0.2 μg/m3 above those found in areas 

further from these facilities. If only Category 3 engine DPM emissions are considered, then the 

number of people exposed is 6.5 million. 

2-22 
Chapter 2: Air Quality, Health and Welfare Effects 

2.3 Environmental Impacts Associated with Pollutants 

2.3.1 Deposition of Nitrogen and Sulfur 

Large ships release emissions over a wide area, and depending on prevailing winds and 

other meteorological conditions, these emissions may be transported hundreds and even 

thousands of kilometers across North America.119 Section 2.4 discusses the results of U.S. air 

quality modeling which documents this phenomenon. Overall, these engines emit a large 

amount of NOX, SOX and direct PM, which impact not only ambient air concentrations but also 

contribute to deposition of nitrogen and sulfur in many sensitive ecological areas throughout the 

U.S. 

Sulfur in marine fuel is primarily emitted as SO2, with a small fraction (about 2 percent) 

being converted to SO3.

120 SO3 almost immediately forms sulfate and is emitted as primary PM 

by the engine and consists of carbonaceous material, sulfuric acid, and ash (trace metals). The 

vast majority of the primary PM is less than or equal to 2.5 μm in diameter, and accounts for the 

majority of the number of particles in exhaust, but only a small fraction of the mass of DPM. 

These particles also react in the atmosphere to form secondary PM, which exist there as a carbon 

core with a coating of organic carbon compounds, nitrate particles, or as sulfuric acid and ash, 

sulfuric acid aerosols, or sulfate particles associated with organic carbon. 

At the same time, ships emit large amounts of NO and NO2 (NOX) emissions which are 

carried into the atmosphere where they may be chemically altered and transformed into new 

compounds. For example, NO2 can also be further oxidized to nitric acid (HNO3) and can 

contribute in that form to the acidity of clouds, fog, and rain water and can also form ambient 

particulate nitrate (pNO3) which may be deposited either directly onto terrestrial and aquatic 

ecosystems (“direct deposition”) or deposited onto land surfaces where it subsequently runs off

and is transferred into downstream waters (“indirect deposition”). 

Deposition of nitrogen and sulfur resulting from ship operations can occur either in a wet 

or dry form. Wet deposition includes rain, snow, sleet, hail, clouds, or fog. Dry deposition 

includes gases, dust, and minute particulate matters. Wet and dry atmospheric deposition of 

PM2.5 delivers a complex mixture of metals (such as mercury, zinc, lead, nickel, arsenic, 

aluminum, and cadmium), organic compounds (such as polycyclic organic matter, dioxins, and 

furans) and inorganic compounds (such as nitrate and sulfate). Together these emissions from

ships are deposited onto terrestrial and aquatic ecosystems across the U.S., contributing to the 

problems of acidification and nutrient enrichment. 

Deposition of nitrogen and sulfur causes acidification, which alters biogeochemistry and 

affects animal and plant life in terrestrial and aquatic ecosystems across the U.S. Major effects 

include a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple 

(Acer saccharum); and a loss of biodiversity of fishes, zooplankton, and macro invertebrates. 

The sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur 

deposition is predominantly governed by geological characteristics. 

Biological effects of acidification in terrestrial ecosystems are generally linked to 

aluminum toxicity and decreased ability of plant roots to take up base cations. Decreases in the 

2-23 
Regulatory Impact Analysis 

acid neutralizing capacity and increases in inorganic aluminum concentration contribute to 

declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems. 

Across the U.S., ecosystems will continue to be acidified by current NOX and SOX emissions 

from stationary sources, area sources, and mobile sources. For example, in the Adirondacks 

Mountains of New York State, the current rates of nitrogen and sulfur deposition exceed the 

amount that would allow recovery of the most acid sensitive lakes to a sustainable acid 

neutralizing capacity (ANC) level.121

Excess nitrogen deposition also leads to nutrient enrichment which can result in 

eutrophication of aquatic ecosystems. In terrestrial ecosystems, nitrogen nutrient enrichment can 

lead to the loss of sensitive lichen species as they are outcompeted by invasive grasses. Nitrogen 

nutrient enrichment can also alter the biodiversity of terrestrial ecosystems, such as forests and 

grasslands. Excess nitrogen deposition contributes to eutrophication of estuaries and coastal 

waters which result in toxic algal blooms and fish kills. For example, the Chesapeake Bay 

Estuary is highly eutrophic and 21 - 30% of total nitrogen load comes from atmospheric 

deposition.122 Freshwater ecosystems may also be impacted by nitrogen deposition. For 

example, high elevation freshwater lakes in the western U.S. experience negative ecological 

effects at nitrogen deposition rates as low as 2 kg N/ha/yr.123

 There are a number of important quantified relationships between nitrogen deposition 

levels and ecological effects. Certain lichen species are the most sensitive terrestrial taxa to 

nitrogen with species losses occurring at just 3 kg N/ha/yr in the Pacific Northwest and the 

southern portion of the State of California (See Figure 2-9 for the geographic distribution of 

these lichens in the continental U.S.). The onset of declining biodiversity was found to occur at 

levels of 5 kg N/ha/yr and above within grasslands in Minnesota and in Europe. Altered species 

composition of Alpine ecosystems and forest encroachment into temperate grasslands was found 

at 10 kg N/ha/yr and above in the U.S.124

The biogeochemical cycle of mercury, a well-known neurotoxin, is closely tied to the 

sulfur cycle. Mercury is taken up by living organisms in the methylated form, which is easily 

bioaccumulated in the food web. Sulfate-reducing bacteria in wetland and lake sediments play a 

key role in mercury methylation. Changes in sulfate deposition have resulted in changes in both 

the rate of mercury methylation and the corresponding mercury concentrations in fish. In 2006, 

3,080 fish advisories were issued in the U.S. due to the presence of methyl mercury in fish. 

Although sulfur deposition is important to mercury methylation, several other interrelated 

factors seem to also be related to mercury uptake, including low lake water pH, dissolved 

organic carbon, suspended particulate matter concentrations in the water column, temperature, 

and dissolved oxygen. In addition, the proportion of upland to wetland land area within a 

watershed, as well as wetland type and annual water yield, appear to be important. 

2-24 
Chapter 2: Air Quality, Health and Welfare Effects 

2.3.1.1

J

 The first draft risk and exposure assessment and other documents associated with this review are available at: 

http://www.epa.gov/ttn/naaqs/standards/no2so2sec/cr_rea.html 

Areas Potentially Sensitive to Nitrogen and Sulfur Deposition in the U.S. 

Currently the secondary NAAQS for NOX and SOX are being reviewed, specifically 

addressing the welfare effects of acidification and nitrogen nutrient enrichment.J As part of this 

review, ecosystem maps (Figures 2-7 through 2-10) 125 for the continental U.S. have been created 

that depict areas that are potentially sensitive to aquatic and terrestrial acidification, and aquatic 

and terrestrial nutrient enrichment. Taken together, these sensitive ecological areas are of

greatest concern with regard to the deposition of nitrogen and sulfur compounds resulting from

ship emissions. NOX and SOX emissions from ships today and in 2020 will significantly 

contribute to higher annual total nitrogen and sulfur deposition in all of these potentially 

sensitive ecosystems. See Section 2.4.3.2 for a discussion and accompanying maps which 

document both the level and geographic impact of ship emissions in 2020 on nitrogen and sulfur 

deposition in the U.S. 

2.3.1.1.1 Terrestrial Acidification-U.S. Geography 

Deposition of total nitrogen (including both oxidized and reduced forms) and sulfur species 

contributing to acidification were routinely measured in the U.S. between 2004 and 2006 and those 

results are shown in Figures 2-7 and 2-8. Figure 2-7 depicts areas across the U.S. which are 

potentially sensitive to terrestrial acidification including forest ecosystems in the Adirondack 

Mountains located in the State of New York, the Green Mountains in the State of Vermont, the 

White Mountains in the State of New Hampshire, the Allegheny Plateau in the State of 

Pennsylvania, in the southeastern part of the U.S., and high-elevation ecosystems in the southern 

Appalachians. In addition, areas of the Upper Midwest and parts of the State of Florida are also at 

significant risk with regard to terrestrial acidification. 

2-25 
Regulatory Impact Analysis 

Figure 2-7 Areas Potentially Sensitive to Terrestrial Acidification 

2.3.1.1.2 Aquatic Acidification-U.S. Geography 

A number of national and regional assessments have been conducted to estimate the 

distribution and extent of surface water acidity in the U.S.126,127,128,129,130,131,132,133 ,134 As a 

result, several regions of the U.S. have been identified as containing a large number of lake

streams which are seriously impacted by acidification. 

Figure 2-8 illustrates those areas of the U.S. where aquatic ecosystems are at risk from

acidification. These sensitive ecological regions include: portions of the Northeast U.S., 

especially all the New England States, the Adirondacks, and the Catskill Mountains in the State 

of New York; the Southeast U.S., including the Appalachian Mountains and the northern section 

of the State of Florida; all upper Midwest States; and parts of the western U.S.,135 especially the 

Los Angeles Basin and surrounding area and the Sierra Nevada Mountains in the State of 

California. Two western mountain ranges with the greatest number of acid sensitive lakes136 are 

the Cascade Mountains, stretching from northern California, through the entire States of Oregon 

and Washington, and the Sierra Nevada’s, found within the State of California. The hydrologic 

cycles in these two mountain ranges are dominated by the annual accumulation and melting of a 

dilute, mildly acidic snow pack. Finally, also in the western U.S., many Rocky Mountain lakes 

in the State of Colorado are also sensitive to acidifying deposition effects.137 However, it does 

not appear that chronic acidification has occurred to any significant degree in these lakes, 

although episodic acidification has been reported for some.

138

s and 

2-26 
Chapter 2: Air Quality, Health and Welfare Effects 

Figure 2-8 Areas Potentially Sensitive to Aquatic Acidification 

2.3.1.1.3 Terrestrial Nutrient Enrichment-U.S. Geography 

Nitrogen deposition affects terrestrial ecosystems throughout large areas of the U.S.139

Atmospheric nitrogen deposition is the main source of new nitrogen in many terrestrial 

ecosystems throughout the U.S and impacts large numbers of forests, wetlands, freshwater bogs 

and salt marshes.140 Figure 2-9 depicts those ecosystems potentially sensitive to terrestrial 

nutrient enrichment resulting from nitrogen deposition, including nitrogen deposition from

ships.

Severe symptoms of nutrient enrichment or nitrogen saturation, have been observed in 

forest ecosystems of the State of West Virginia’s northern hardwood watersheds;141 in highelevation spruce-fir ecosystems in the Appalachian Mountains;142 in spruce-fir ecosystems 

throughout the northeastern U.S.;143,144 and in lower-elevation eastern U.S. forests.145,146,147,148

In addition, mixed conifer forests in the Los Angeles Air Basin within the State of California are 

also heavily impacted and exhibit the highest stream water nitrate concentrations documented 

within wild lands in North America.149,150 In general, it is believed that deciduous forest stands 

in the eastern U.S. have not progressed toward nitrogen saturation as rapidly or as far as 

coniferous stands in the eastern U.S.151

In addition to these forest ecosystems, nitrogen deposition adversely impacts U.S. 

grasslands or prairies which are located throughout the U.S.152 The vast majority of these 

grasslands are found in the Central Plains regions of the U.S. between the Mississippi River and 

the foothills of the Rocky Mountains. However, some native grasslands are scattered throughout 

2-27 
Regulatory Impact Analysis 

the Midwestern and Southeastern U.S.153 Also considered sensitive to nitrogen nutrient 

enrichment effects, and receiving high levels of atmospheric deposition, are some arid and semiarid ecosystems and desert ecosystems in the southwestern U.S.154 However, water is generally 

more limiting than nitrogen in these areas. The alpine ecosystems in the State of Colorado, 

chaparral watersheds of the Sierra Nevada Mountains in the State of California, lichen and 

vascular plant communities in the San Bernardino Mountains in California and the entire U.S. 

Pacific Northwest, and the Southern California coastal sage scrub community are among the 

most sensitive terrestrial ecosystems to nitrogen deposition in the U.S. 155,156

 Figure 2-9 Areas Potentially Sensitive to Terrestrial Nutrient Enrichment 

2.3.1.1.4 Aquatic Nutrient Enrichment –U.S. Geography 

Aquatic nutrient enrichment impacts a wide range of waters within the U.S. from

wetlands, to streams, rivers, lakes, estuaries and coastal waters. All are vital ecosystems to the 

U.S. and all are impacted by ship emissions that contribute to the annual total nitrogen deposition 

in the U.S. 

Wetlands are found throughout the U.S. and support over 4200 native plant species, of 

which 121 have been designated by the U.S. Government as threatened or endangered.157

Freshwater wetlands are particularly sensitive to nutrient enrichment resulting from nitrogen 

deposition since they contain a disproportionately high number of rare plant species that have 

evolved under nitrogen-limited conditions.158 Freshwater wetlands receive nitrogen mainly from

precipitation, land runoff or ground water. Intertidal wetlands develop on sheltered coasts or in 

estuaries where they are periodically inundated by marine water that often carries high nitrogen 

loads, in addition to receiving water and nutrient inputs from precipitation and ground/surface 

2-28 
Chapter 2: Air Quality, Health and Welfare Effects 

water. Wetlands can be divided into three general categories based on hydrology: (1) Peatlands 

and bogs, (2) fens, freshwater marshes, freshwater swamps and (3) intertidal wetlands. 

Fens and bogs are the most vulnerable type of wetland ecosystems with regard to nutrient 

enrichment effects of nitrogen deposition.159 In the U.S., they are mostly found in the glaciated 

northeast and Great Lakes regions and in the State of Alaska, but also in the southeast U.S. along 

the Atlantic Coastal Plain stretching from the States of Virginia through North Carolina to 

northern Florida.160 Like bogs, fens are mostly a northern hemisphere phenomenon, occurring in 

the northeastern United States, the Great Lakes region, western Rocky Mountains, and much of 

Canada, and are generally associated with low temperatures and short growing seasons where 

ample precipitation and high humidity cause excessive moisture to accumulate.161

The third type of wetlands sensitive to nitrogen deposition are marshes, characterized by 

emergent soft-stemmed vegetation adapted to saturated soil conditions. There are many different 

kinds of marshes in the U.S., ranging from the prairie potholes in the interior of the U.S. to the 

Everglades found in the extreme southern portion of the State of Florida. U.S. fresh water 

marshes are important for recharging groundwater supplies, and moderating stream flow by 

providing water to streams and as habitats for many wildlife species.162

Nitrogen deposition is the main source of nitrogen for many surface waters in the U.S. 

including headwater streams, lower order streams, and high elevation lakes.163,164 Elevated 

surface water nitrate concentrations due to nitrogen deposition occur in both the eastern and 

western U.S., although high concentrations of nitrate in surface waters in the western U.S. are 

not as widespread as in the eastern U.S. 

High concentrations of lake or stream water nitrate, indicative of ecosystem nitrogensaturation, have been found at a variety of locations throughout the U.S. including the San 

Bernardino and San Gabriel Mountains within the Los Angeles Air Basin in the State of 

California, 165 the Front Range Mountains in the State of Colorado,166,167 the Allegheny 

Mountains in the State of West Virginia,168 the Catskill and Adirondack Mountains in the State 

of New York,169, 170,171,172 and the Great Smoky Mountains in the State of Tennessee. 

 Nitrogen nutrient enrichment is a major environmental problem facing all U.S. coastal 

regions, but especially the Eastern, mid-Atlantic, and Gulf Coast regions, as excess nitrogen 

leads to eutrophication. There is broad scientific consensus that nitrogen-driven eutrophication of 

shallow estuaries in the U.S. has increased over the past several decades and that environmental 

degradation of coastal ecosystems is now a widespread occurrence.173,174,175 A recent national 

assessment of eutrophic conditions in U.S. estuaries found that 65% of the assessed systems had 

moderate to high overall eutrophic conditions.176 Estuaries and coastal waters tend to be 

nitrogen-limited and are therefore inherently sensitive to increased atmospheric nitrogen 

deposition.177 Of 138 estuaries examined in the National Assessment, 44 were identified as 

showing symptoms of nutrient enrichment. Of the 23 estuaries examined in the Northeast U.S., 

61% were classified as moderately to severely degraded. Other regions of the U.S. had mixtures 

of low, moderate, and high degree of eutrophication.178 The contribution from atmospheric 

nitrogen deposition can be greater than 30% of total nitrogen loads in some of the most highly 

eutrophic estuaries in the U.S., including the Chesapeake Bay.

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Regulatory Impact Analysis 

Figure 2-10 Areas Potentially Sensitive to Aquatic Nutrient Enrichment 

2-30 
Chapter 2: Air Quality, Health and Welfare Effects 

The most extreme effects of nitrogen deposition on U.S. aquatic ecosystems result in 

severe nitrogen-loading to these ecosystems that contribute to hypoxic zones devoid of life. 

Three hypoxia zones of special concern in the U.S. are (1) the zone located in the Gulf of Mexico 

straddling the States of Louisiana and Texas, (2) The Chesapeake Bay located between the States

of Maryland and Virginia, and (3) Long Island Sound located between the States of New York 

and Connecticut. The largest hypoxia zone in the U.S. is in the northern Gulf of Mexico along 

the continental shelf. During midsummer, this zone has regularly been larger than 16,000km2.

179

Figures 2-11, 2-12, and 2-13 depict the location of these three hypoxic zones. 

Figure 2-11 Hypoxia Zone in 2007 for the Gulf of Mexico

Figure 2-12 Hypoxia Zone in 2007 for Long Island Sound 

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Regulatory Impact Analysis 

Figures 2-13 Hypoxia Zone for Chesapeake Bay in 2003

Science of Nitrogen and Sulfur Deposition 

Nitrogen and sulfur interactions in the environment are highly complex. Both are 

essential and sometimes limiting nutrients needed for growth and productivity. Excess of 

nitrogen or sulfur can lead to acidification, nutrient enrichment, and eutrophication. 

Ships release emissions over a wide area, and depending on prevailing winds and other 

meteorological conditions, these emissions may be transported hundreds and even thousands of 

kilometers across North America.180 Section 2.4 discusses the results of U.S. air quality 

modeling which documents this phenomenon. Overall, these engines emit a large amount of 

NOX, SOX, and direct PM, which impact not only ambient air concentrations but also contribute 

to deposition of nitrogen and sulfur in many sensitive ecological areas throughout the U.S. 

Sulfur in marine fuel is primarily emitted as SO2, with a small fraction (about 2 percent) 

being converted to SO3.

181 SO3 almost immediately forms sulfate and is emitted as primary PM 

by the engine and consists of carbonaceous material, sulfuric acid, and ash (trace metals). The 

vast majority of the primary PM is less than or equal to 2.5 μm in diameter, and accounts for the 

majority of the number of particles in exhaust, but only a small fraction of the mass of DPM. 

2.3.1.2

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Chapter 2: Air Quality, Health and Welfare Effects 

These particles also react in the atmosphere to form secondary PM, which exist there as a carbon 

core with a coating of organic carbon compounds, nitrate particles, or as sulfuric acid and ash, 

sulfuric acid aerosols, or sulfate particles associated with organic carbon. 

At the same time, ships emit large amounts of NO and NO2 (NOX) emissions which are 

carried into the atmosphere where they may be chemically altered and transformed into new 

compounds. For example, NO2 can also be further oxidized to nitric acid (HNO3) and can 

contribute in that form to the acidity of clouds, fog, and rain water and can also form ambient 

particulate nitrate (pNO3) which may be deposited either directly onto terrestrial and aquatic 

ecosystems (“direct deposition”) or deposited onto land surfaces where it subsequently runs off

and is transferred into downstream waters (“indirect deposition”). 

Deposition of nitrogen and sulfur resulting from ship operations can occur either in a wet 

or dry form. Wet deposition includes rain, snow, sleet, hail, clouds, or fog. Dry deposition 

includes gases, dust, and minute particulate matters. Wet and dry atmospheric deposition of 

PM2.5 delivers a complex mixture of metals (such as mercury, zinc, lead, nickel, arsenic, 

aluminum, and cadmium), organic compounds (such as polycyclic organic matter, dioxins, and 

furans) and inorganic compounds (such as nitrate and sulfate). Together these emissions from

ships are deposited onto terrestrial and aquatic ecosystems across the U.S., contributing to the 

problems of acidification and nutrient enrichment. 

The chemical form of deposition is determined by ambient conditions (e.g., temperature, 

humidity, oxidant levels) and the pollutant source. Chemical and physical transformations of 

ambient particles occur in the atmosphere and in the media (terrestrial or aquatic) on which they 

deposit. These transformations influence the fate, bioavailability and potential toxicity of these 

compounds. The atmospheric deposition of metals and toxic compounds is implicated in severe 

ecosystem effects.182

 Ships also emit primary PM. In addition, secondary PM is formed from NOX and SOX

gaseous emissions and associated chemical reactions in the atmosphere. The major constituents 

of secondary PM are sulfate, nitrate, ammonium, and hydrogen ions. Secondary aerosol 

formation depends on numerous factors including the concentrations of precursors; the

concentrations of other gaseous reactive species such as ozone, hydroxyl radical, peroxy radicals, 

and hydrogen peroxide; atmospheric conditions, including solar radiation and relative humidity; 

and the interactions of precursors and preexisting particles within cloud or fog droplets or on or 

in the liquid film on solid particles.183

The lifetimes of particles vary with particle size. Accumulation-mode particles such as 

the sulfates and nitrates are kept in suspension by normal air motions and have a lower 

deposition velocity than coarse-mode particles; they can be transported thousands of kilometers 

and remain in the atmosphere for a number of days. They are removed from the atmosphere 

primarily by cloud processes. Dry deposition rates are expressed in terms of deposition velocity 

that varies with the particle size, reaching a minimum between 0.1 and 1.0 μm Da.

184

Particulate matter is a factor in acid deposition. Particles serve as cloud condensation 

nuclei and contribute directly to the acidification of rain. In addition, the gas-phase species that 

lead to the dry deposition of acidity are also precursors of particles. Therefore, reductions in 

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Regulatory Impact Analysis 

NOX and SO2 emissions will decrease both acid deposition and PM concentrations, but not 

necessarily in a linear fashion. Sulfuric acid, ammonium nitrate, and organic particles also are 

deposited on surfaces by dry deposition and can contribute to environmental effects.185

Computing Atmospheric Nitrogen and Sulfur Deposition to Specific Locations 

Inputs of new nitrogen, i.e., non-recycled mostly anthropogenic in origin, are often key 

factors controlling primary productivity in nitrogen-sensitive estuarine and coastal waters.186

Increasing trends in urbanization, agricultural intensity, and industrial expansion have led to 

increases in nitrogen deposited from the atmosphere on the order of a factor of 10 in the previous 

100 years.187 Direct fluxes of atmospheric nitrogen to ocean and gulf waters along the Northeast 

and Southeast U.S. are now roughly equal to or exceed the load of new nitrogen from riverine 

inputs at 11, 5.6, and 5.6 kg N/ha for the Northeast Atlantic coast of the U.S., the Southeast 

Atlantic coast of the U.S., and the U.S. Eastern Gulf of Mexico, respectively.188 Atmospheric 

nitrogen is dominated by a number of sources, most importantly transportation sources, including 

ships. 

Nitrogen deposition takes different forms physically. Physically, deposition can be 

direct, with the loads resulting from air pollutants depositing directly to the surface of a body of 

water, usually a large body of water like an estuary or lake. In addition, there is an indirect 

deposition component derived from deposition of nitrogen or sulfur to the rest of the watershed, 

both land and water, of which some fraction is transported through runoff, rivers, streams, and 

groundwater to the water body of concern. 

Direct and indirect deposition of nitrogen and sulfur to watersheds depend on air 

pollutant concentrations in the airshed above the watershed. The shape and extent of the airshed 

is quite different from that of the watershed. In a watershed, everything that falls in its area, by 

definition, flows into a single body of water. An airshed, by contrast, is a theoretical concept 

that defines the source area containing the emissions contributing a given level, often 75%, to the 

deposition in a particular watershed or to a given water body. Hence, airsheds are modeled 

domains containing the sources estimated to contribute a given level of deposition from each 

pollutant of concern. The principal NOX airsheds and corresponding watersheds for several 

regions in the eastern U.S. are shown in Figure 2-14.189 These airsheds extend well into U.S. 

coastal waters where ships operate. 

2.3.1.3

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Chapter 2: Air Quality, Health and Welfare Effects 

Figure 2-14 Principal Airsheds and Watersheds for Oxides of Nitrogen for Estuaries. Hudson/Raritan Bay;

Chesapeake Bay; Pamlico Sound; and Altamaha Sound (listed from north to south).

Nitrogen inputs have been studied in several U.S. Gulf Coast estuaries, as well, owing to 

concerns about eutrophication there. Nitrogen from atmospheric deposition in these locations is 

estimated to be 10 to 40% of the total input of nitrogen to many of these estuaries, and could be 

higher for some. Estimates of total nitrogen loadings to estuaries or to other large-scale elements 

in the landscape are then computed using measurements of wet and dry deposition, where these 

are available, and interpolated with or without a set of air quality model predictions such as the 

Extended Regional Acid Deposition Model (Ext-RADM).190

Table 2-3 lists several water bodies for which atmospheric nitrogen inputs have been 

computed and the ratio to total nitrogen loads is given. The contribution from the atmosphere 

ranges from a low of 2–8% for the Guadalupe Estuary in the southern part of the State of Texas 

to highs of ~38% in the New York State Bight and the Albemarle-Pamlico Sound in the State of 

North Carolina. 

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Regulatory Impact Analysis 

Table 2-3 Atmospheric Nitrogen Loads Relative to Total Nitrogen Loads in Selected U.S. Great

Waters.*

Summary of Ecological Effects Associated with Nitrogen and Sulfur Deposition 

Deposition of reduced and oxidized nitrogen and sulfur species cause acidification, 

altering biogeochemistry and affecting animal and plant life in terrestrial and aquatic ecosystems 

across the U.S. Major effects include a decline in sensitive tree species, such as red spruce and 

sugar maple; and a loss of biodiversity of fishes, zooplankton, and macro invertebrates. The 

sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur 

deposition is predominantly governed by geological characteristics (bedrock, weathering rates, 

etc.). 

Biological effects of acidification in terrestrial ecosystems are generally linked to 

aluminum toxicity and decreased ability of plant roots to take up base cations. Decreases in acid 

neutralizing capacity and increases in inorganic aluminum concentration contribute to declines in 

zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems. Across the 

U.S., ecosystems continue to be acidified by current emissions from both stationary sources, area 

sources, and mobile sources. For example, in the Adirondack Mountains of New York State, the 

2.3.1.4

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Chapter 2: Air Quality, Health and Welfare Effects 

current rates of nitrogen and sulfur deposition exceed the amount that would allow recovery of 

the most acid sensitive lakes to a sustainable acid neutralizing capacity (ANC) level.191

In addition to the role nitrogen deposition plays in acidification, nitrogen deposition also 

causes ecosystem nutrient enrichment leading to eutrophication that alters biogeochemical 

cycles. Excess nitrogen also leads to the loss of nitrogen sensitive lichen species as they are 

outcompeted by invasive grasses as well as altering the biodiversity of terrestrial ecosystems, 

such as grasslands and meadows. Nitrogen deposition contributes to eutrophication of estuaries 

and the associated effects including toxic algal blooms and fish kills. For example, the 

Chesapeake Bay Estuary is highly eutrophic and 21 - 30% of total nitrogen load comes from

deposition.192 Eutrophication also occurs in freshwater ecosystems. Symptoms, such as altered 

algal communities occur in western U.S. high elevation lakes at nitrogen deposition rates a low 

as 2 kg/ha/yr.193 Across the U.S., there are many terrestrial and aquatic ecosystems that have 

been identified as particularly sensitive to nitrogen deposition. 

The addition of nitrogen to most ecosystems causes changes in primary productivity and 

growth of plants and algae, which can alter competitive interactions among species. Some

species grow more than others, leading to shifts in population dynamics, species composition, 

and community structure. The most extreme effects of nitrogen deposition include a shift of 

ecosystem types in terrestrial ecosystems, and hypoxic zones that are devoid of life in aquatic 

ecosystems.

194

 There are a number of important quantified relationships between nitrogen deposition 

levels and ecological effects. Certain lichen species are the most sensitive terrestrial taxa to 

nitrogen with species losses occurring at just 3 kg N/ha/yr in the U.S. Pacific Northwest and in 

the southern portion of the State of California. The onset of declining biodiversity was found to 

occur at levels of 5 kg N/ha/yr and above within grasslands in both the State of Minnesota and in 

Europe. Altered species composition of Alpine ecosystems and forest encroachment into 

temperate grasslands was found at 10 kg N/ha/yr and above in both the U.S. and Canada.195

A United States Forest Service study conducted in areas within the Tongass Forest in 

Southeast Alaska found evidence of sulfur emissions impacting lichen communities. The 

authors concluded that the main source of sulfur and nitrogen found in lichens from Mt. Roberts 

is likely the burning of fossil fuels by cruise ships and other vehicles and equipment in 

downtown Juneau.196

Lichen are an important food source for caribou. This is causing concern about the 

potential role damage to lichens may be having on the Southern Alaska Peninsula Caribou Herd, 

which is an important food source to local subsistence based cultures. This herd has been 

decreasing in size, exhibiting both poor calf survival and low pregnancy rates, which are signs of 

dietary stress. Currently there is a complete caribou hunting ban, including a ban on subsistence 

hunting. If regulation of marine fuels could potentially enhance lichen biomass in the area, it 

would contribute in turn to maintenance of an important subsistence resource for local human 

populations. 

The biogeochemical cycle of mercury, a well-known neurotoxin, is closely tied to the 

sulfur cycle. Mercury is taken up by living organisms in the methylated form, which is easily 

2-37 
Regulatory Impact Analysis 

bioaccumulated in the food web. Sulfate-reducing bacteria in wetland and lake sediments play a 

key role in mercury methylation. Changes in sulfate deposition have resulted in changes in both 

the rate of mercury methylation and the corresponding mercury concentrations in fish. In 2006, 

3,080 fish advisories were issued in the U.S. due to the presence of methyl mercury in fish. 

Although sulfur deposition is important to mercury methylation, several other interrelated 

factors seem to also be related to mercury uptake, including low lake water pH, dissolved 

organic carbon, suspended particulate matter concentrations in the water column, temperature, 

and dissolved oxygen. In addition, the proportion of upland to wetland land area within a 

watershed, as well as wetland type and annual water yield, appear to be important. 

Ecological Effects Nutrient Enrichment 

In general, ecosystems that are most responsive to nutrient enrichment from atmospheric 

nitrogen deposition are those that receive high levels of nitrogen loading, are nitrogen-limited, or 

contain species that have evolved in nutrient-poor environments. Species that are adapted to low 

nitrogen supply will often be more readily outcompeted by species that have higher nitrogen 

demands when the availability of nitrogen is increased.197,198 199,200 As a consequence, some

native species can be eliminated by nitrogen deposition.201,202 203,204, 205 Note the terms “low” 

and “high” are relative to the amount of bioavailable nitrogen in the ecosystem and the level

deposition. 

Eutrophication effects resulting from excess nitrogen are more widespread than 

acidification effects in western North America. Figure 2-15 highlights areas in the Western U.S. 

where nitrogen effects have been extensively reported. The discussion of ecological effects of 

nutrient enrichment that follows is organized around three types of ecosystem categories which 

experience impacts from nutrient enrichment: terrestrial, transitional, and aquatic. 

2.3.1.5

 of 

2-38 
Chapter 2: Air Quality, Health and Welfare Effects 

Figure 2-15 Map of the Western U.S. Showing the Primary Geographic Areas where Nitrogen Deposition 

Effects have been Reported 

2.3.1.5.1 Terrestrial 

Ecological effects of nitrogen deposition occur in a variety of taxa and ecosystem types 

including: forests, grasslands, arid and semi-arid areas, deserts, lichens, alpine, and mycorrhizae. 

Atmospheric inputs of nitrogen can alleviate deficiencies and increase growth of some plants at 

the expense of others. Nitrogen deposition alters the competitive relationships among terrestrial 

plant species and therefore alters species composition and diversity.206,207,208 Wholesale shifts in 

species composition are easier to detect in short-lived terrestrial ecosystems such as annual 

grasslands, in the forest understory, or mycorrhizal associations, than for long-lived forest trees 

where changes are evident on a decade or longer time scale. Note species shifts and ecosystem

changes can occur even if the ecosystem does not exhibit signs of nitrogen saturation.

There are a number of important quantified relationships between nitrogen deposition 

levels and ecological effects.209 Certain lichen species are the most sensitive terrestrial taxa to 

nitrogen in the U.S. with clear adverse effects occurring at just 3 kg N/ha/yr. Figure 2-9 shows 

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Regulatory Impact Analysis 

the geographic distribution of lichens in the U.S. Among the most sensitive U.S. ecosystems are 

Alpine ecosystems where alteration of plant covers of an individual species (Carex rupestris) 

was estimated to occur at deposition levels near 4 kg N/ha/yr and modeling indicates that 

deposition levels near 10 kg/N/ha/yr alter plant community assemblages.210 Within grasslands, 

the onset of declining biodiversity was found to occur at levels of 5 kg N/ha/yr. Forest 

encroachment into temperate grasslands was found at 10 kg N/ha/yr and above in the U.S. Table 

2-4 provides a brief list of nitrogen deposition levels and associated ecological effects. 

Table 2-4 Examples of Quantified Relationship between Nitrogen Deposition Levels and Ecological Effects

a

Note: a EPA, Integrated Science Assessment for Oxides of Nitrogen and SulfurEcological criteria 

Most terrestrial ecosystems are nitrogen-limited, therefore they are sensitive to 

perturbation caused by nitrogen additions.211 The factors that govern the vulnerability of 

terrestrial ecosystems to nutrient enrichment from nitrogen deposition include the degree of 

nitrogen limitation, rates and form of nitrogen deposition, elevation, species composition, length 

of growing season, and soil nitrogen retention capacity. 

Regions and ecosystems in the western U.S. where nitrogen nutrient enrichment effects 

have been documented in terrestrial ecosystems are shown on Figure 2-15.212 The alpine 

ecosystems of the Colorado Front Range, chaparral watersheds of the Sierra Nevada, lichen and 

vascular plant communities in the San Bernardino Mountains and the Pacific Northwest, and the 

southern California coastal sage scrub community are among the most sensitive terrestrial 

ecosystems in the western U.S. 

In the eastern U.S., the degree of nitrogen saturation of the terrestrial ecosystem is often 

assessed in terms of the degree of nitrate leaching from watershed soils into ground water or 

surface water. Studies have estimated the number of surface waters at different stages of 

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Chapter 2: Air Quality, Health and Welfare Effects 

K In Stage 0, nitrogen inputs are low and there are strong nitrogen limitations on growth. Stage 1 is characterized by 

high nitrogen rentention and fertilization effect of added nitrogen on tree growth. Stage 2 includes the induction of 

nitrification and some nitrate leaching, though growth may still be high. In Stage 3 tree growth declines, 

nitrification and nitrate loss continue to increase, but nitrogen mineralization rates begin to decline. 

saturation across several regions in the eastern U.S.213 Of the 85 northeastern watersheds 

examined, 40% were in nitrogen-saturation Stage 0,K 52% in Stage 1, and 8% in Stage 2. Of the 

northeastern sites for which adequate data were available for assessment, those in Stage 1 or 2 

were most prevalent in the Adirondack and Catskill Mountains in the State of New York. 

2.3.1.5.2 Transitional 

About 107.7 million acres of wetlands are widely distributed in the conterminous U.S., 

95% of which are freshwater wetlands and 5% are estuarine or marine wetlands214 (Figure 2-16). 

At one end of the spectrum, bogs or peatland are very sensitive to nitrogen deposition because 

they receive nutrients exclusively from precipitation, and the species in them are adapted to low 

levels of nitrogen.215,216,217 Intertidal wetlands are at the other end of the spectrum; in these 

ecosystems, marine/estuarine water sources generally exceed atmospheric inputs by one or two 

orders of magnitude.218 Wetlands are widely distributed, including some areas that receive 

moderate to high levels of nitrogen deposition. 

Nitrogen deposition alters species richness, species composition and biodiversity in U.S. 

wetland ecosystems.219 The effect of nitrogen deposition on these ecosystems depends on the 

fraction of rainfall in its total water budget. Excess nitrogen deposition can cause shifts in 

wetland community composition by altering competitive relationships among species, which 

potentially leads to effects such as decreasing biodiversity, increasing non-native species 

establishment, and increasing the risk of extinction for sensitive and rare species. 

U.S. wetlands contain a high number of rare plant species.220,221, 222 High levels of 

atmospheric nitrogen deposition increase the risk of decline and extinction of these species that 

are adapted to low nitrogen conditions. In general, these include the genus Isoetes sp., of which 

three species are federally endangered; insectivorous plants like the endangered green pitcher 

Sarracenia oreophila; and the genus Sphagnum, of which there are 15 species listed as

endangered by eastern U.S. Roundleaf sundew (Drosera rotundifolia) is also susceptible to 

elevated atmospheric nitrogen deposition.223 This plant is native to, and broadly distributed 

across, the U.S. and is federally listed as endangered in Illinois and Iowa, threatened in 

Tennessee, and vulnerable in New York.224 In the U.S., Sarracenia purpurea can be used as a 

biological indicator of local nitrogen deposition in some locations.225

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Regulatory Impact Analysis 

Figure 2-16 Location of Wetlands in Continental U.S.

2.3.1.5.3 Freshwater Aquatic 

Nitrogen deposition alters species richness, species composition and biodiversity in 

freshwater aquatic ecosystems across the U.S.226 Evidence from multiple lines of research and 

experimental approaches support this observation, including paleolimnological reconstructions, 

bioassays, mesocosm and laboratory experiments. Increased nitrogen deposition can cause a 

shift in community composition and reduce algal biodiversity. Elevated nitrogen deposition 

results in changes in algal species composition, especially in sensitive oligotrophic lakes. In the 

West, a hindcasting exercise determined that the change in Rocky Mountain National Park lake 

algae that occurred between 1850 and 1964 was associated with an increase in wet nitrogen 

deposition that was only about 1.5 kg N/ha.227 Similar changes inferred from lake sediment cores 

of the Beartooth Mountains of Wyoming also occurred at about 1.5 kg N/ha deposition.228

Some freshwater algae are particularly sensitive to added nutrient nitrogen and 

experience shifts in community composition and biodiversity with increased nitrogen deposition. 

For example, two species of diatom (a taxanomic group of algae), Asterionella formosa and 

Fragilaria crotonensis, now dominate the flora of at least several alpine and montane Rocky 

Mountain lakes. Sharp increases have occurred in Lake Tahoe.229,230,231,232,233,234 The timing of

this shift has varied, with changes beginning in the 1950s in the southern Rocky Mountains and 

in the 1970s or later in the central Rocky Mountains. These species are opportunistic algae that 

have been observed to respond rapidly to disturbance and slight nutrient enrichment in many 

parts of the world. 

2.3.1.5.4 Estuarine Aquatic 

Nitrogen deposition also alters species richness, species composition and biodiversity in 

estuarine ecosystems throughout the U.S.235 Nitrogen is an essential nutrient for estuarine and 

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Chapter 2: Air Quality, Health and Welfare Effects 

marine fertility. However, excessive nitrogen contributes to habitat degradation, algal blooms, 

toxicity, hypoxia (reduced dissolved oxygen), anoxia (absence of dissolved oxygen), reduction of 

sea grass habitats, fish kills, and decrease in biodiversity.236,237,238,239,240 241 Each of these

potential impacts carries ecological and economic consequences. Ecosystem services provided 

by estuaries include fish and shellfish harvest, waste assimilation, and recreational activities.242

Increased nitrogen deposition can cause shifts in community composition, reduced 

hypolimnetic DO, reduced biodiversity, and mortality of submerged aquatic vegetation. The 

form of deposited nitrogen can significantly affect phytoplankton community composition in 

estuarine and marine environments. Small diatoms are more efficient in using nitrate than NH4

+

. 

Increasing NH4

+

 deposition relative to nitrate in the eastern U.S. favors small diatoms at the 

expense of large diatoms. This alters the foundation of the food web. Submerged aquatic 

vegetation is important to the quality of estuarine ecosystem habitats because it provides habitat 

for a variety of aquatic organisms, absorbs excess nutrients, and traps sediments. Nutrient 

enrichment is the major driving factor contributing to declines in submerged aquatic vegetation 

coverage. The Mid-Atlantic region is the most heavily impacted area in terms of moderate or 

high loss of submerged aquatic vegetation due to eutrophication. 

2.3.1.5.5 Estuarine and Coastal Aquatic 

Estuaries and coastal waters tend to be nitrogen-limited and are therefore inherently 

sensitive to increased atmospheric nitrogen loading.243,244 The U.S. national estuary condition 

assessment completed in 2007245 found that the most impacted estuaries in the U.S. occurred in 

the mid- Atlantic region and the estuaries with the lowest symptoms of eutrophication were in 

the North Atlantic. Nitrogen nutrient enrichment is a major environmental problem for coastal 

regions of the U.S., especially in the eastern and Gulf Coast regions. Of 138 estuaries examined 

in the national estuary assessment, 44 were identified as showing symptoms of nutrient overenrichment. Estuaries are among the most biologically productive ecosystems on Earth and 

provide critical habitat for an enormous diversity of life forms, especially fish. Of the 23 

estuaries examined in the national assessment in the Northeast, 61% were classified as 

moderately to severely degraded.246 Other regions had mixtures of low, moderate, and high 

degree of eutrophication (See Figure 2-17). 

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Regulatory Impact Analysis 

Figure 2-17 Overall Eutrophication Condition on a National Scale 

The national assessment also evaluated the future outlook of the nation’s estuaries based 

on population growth and future management plans. They predicted that trophic conditions 

would worsen in 48 estuaries, stay the same in 11, and improve in only 14 by the year 2020. 

Between 1999 and 2007, an equal number of estuary systems have improved their trophic status 

as have worsened. The assessed estuarine surface area with high to moderate/high eutrophic 

conditions have stayed roughly the same, from 72% in1999,247 to 78% in the 2007 

248

 The U.S. EPA’s Integrated Science Assessment for Oxides of Nitrogen and SulfurEcological Criteria found that the principal factor governing the sensitivity of terrestrial and 

aquatic ecosystems to acidification from nitrogen and sulfur deposition is geology (particularly 

surficial geology).249 Geologic formations having low base cation supply generally underlie th

watersheds of acid-sensitive lakes and streams. Bedrock geology has been used in numerous 

acidification studies.250,251,252,253,254 Other factors contribute to the sensitivity of soils and 

surface wat

assessment.

2.3.1.6 Ecological Effects of Acidification 

e 

ers to acidifying deposition, including topography, soil chemistry, land use, and 

hydrologic flow path. 

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Chapter 2: Air Quality, Health and Welfare Effects 

2.3.1.6.1 Terrestrial 

Acidifying deposition has altered major biogeochemical processes in the U.S. by 

increasing the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching from

soil to drainage waters, depleting base cations (especially calcium and magnesium) from soils, 

and increasing the mobility of aluminum. Inorganic aluminum is toxic to some tree roots. Plants 

affected by high levels of aluminum from the soil often have reduced root growth, which restricts 

the ability of the plant to take up water and nutrients, especially calcium.

255 These direct effects 

can, in turn, influence the response of these plants to climatic stresses such as droughts and cold 

temperatures. They can also influence the sensitivity of plants to other stresses, including insect 

pests and disease256 leading to increased mortality of canopy trees. In the U.S., terrestrial effects 

of acidification are best described for forested ecosystems (especially red spruce and sugar maple 

ecosystems) with additional information on other plant communities, including shrubs and 

lichen.257 There are several indicators of stress to terrestrial vegetation including percent 

dieback of canopy trees, dead tree basal area (as a percent), crown vigor index and fine twig 

258

Both coniferous and deciduous forests throughout the eastern U.S. are experiencing 

gradual losses of base cation nutrients from the soil due to accelerated leaching for acidifying 

deposition. This change in nutrient availability may reduce the quality of forest nutrition over 

the long term. Evidence suggests that red spruce and sugar maple in some areas in the eastern

U.S. have experienced declining health as a consequence of this deposition. For red spruce, 

(Picea rubens) dieback or decline has been observed across high elevation landscapes of the 

northeastern U.S., and to a lesser extent, the southeastern U.S. Acidifying deposition has been

implicated as a causal factor.259 Since the 1980s, red spruce growth has increased at both the 

higher- and lower-elevation sites corresponding to a decrease in SO2 emissions in the U.S. (to 

about 20 million tons/year by 2000), while NOX emissions held fairly steady (at about 25 million 

tons/year). Research indicates that annual emissions of sulfur plus NOX explained about 43% of

the variability in red spruce tree ring growth between 1940 and 1998, while climatic variability 

accounted for about 8% of the growth variation for that period.260 The observed dieback i

spruce has been linked, in part, to reduced cold tolerance of the spruce needles, caused by 

acidifying deposition. Results of controlled exposure studies showed that acidic mist or cloud 

water reduced the cold tolerance of current-year needles by 3 to 10° F.261 More recently, studie

have found a link between availability of soil calcium and winter injury.262 Figure 

dieback.

2.3.1.6.1.1 Health, Vigor, and Reproduction of Tree Species in Forests 

n red 

s 

2-18 shows 

the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S. 

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Regulatory Impact Analysis 

Figure 2-18 Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern U.S.263

In hardwood forests, species nutrient needs, soil conditions, and additional stressors work 

together to determine sensitivity to acidifying deposition. Stand age and successional stage also 

can affect the susceptibility of hardwood forests to acidification effects. In northeastern 

hardwood forests, older stands exhibit greater potential for calcium depletion in response to 

acidifying deposition than younger stands. Thus, with the successional change from pin cherry 

(Prunus pensylvanica), striped maple (Acer pensylvanicum), white ash (Fraxinus americana), 

yellow birch (Betula alleghaniensis) and white birch (Betula papyrifera) in younger stands to 

beech (Fagus grandifolia) and red maple (Acer rubrum) in older stands, there is an increase in 

sensitivity to acidification.264

Sugar maple (Acer saccharum) is the deciduous tree species of the northeastern U.S. and 

central Appalachian Mountain region (See Figure 2-18) that is most commonly associated with 

adverse acidification-related effects of nitrogen and sulfur deposition.265 In general, evidence 

indicates that acidifying deposition in combination with other stressors is a likely contributor to 

the decline of sugar maple trees that occur at higher elevation, on geologies dominated by 

sandstone or other base-poor substrate, and that have base-poor soils having high percentages of 

rock fragments.266

Loss of calcium ions in the base cations has also been implicated in increased 

susceptibility of flowering dogwood (Cornus florida) to its most destructive disease, dogwood 

anthracnose, a mostly fatal disease. Figure 2-19 shows the native range of flowering dogwood in 

the U.S. (dark gray) as well as the range of the anthracnose disease as of 2002 in the eastern U.S. 

(red). Flowering dogwood is a dominant understory species of hardwood forests in the eastern 

U.S.267

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Chapter 2: Air Quality, Health and Welfare Effects 

Figure 2-19 Native Range of Flowering Dogwood (dark gray) and the Documented Range of Dogwood

Anthracnose (red)268

The NOXSOX ISA269 found limited data on the possible effects of nitrogen and sulfur 

deposition on the acid-based characteristics of forests in the U.S., other than spruce-fire and 

northern hardwood forests ecosystems as described above. 

2.3.1.6.1.2 Health and Biodiversity of Other Plant Communities 

Shrubs 

The ISA found that available data suggest that it is likely that a variety of shrub and 

herbaceous species are sensitive to base cation depletion and/or aluminum toxicity. However, 

conclusive evidence is generally lacking. 

Lichens

The U.S. EPA NOXSOX ISA found that lichens and bryophytes are among the first 

components of the terrestrial ecosystem to be affected by acidifying deposition. Vulnerability of 

lichens to increased nitrogen input is generally greater than that of vascular plants.270 Even in 

the Pacific Northwest, which receives uniformly low levels of nitrogen deposition, changes from 

acid-sensitive and nitrogen-sensitive to pollution tolerant nitrophillic lichen taxa are occurring in 

some areas.271 Lichens remaining in areas affected by acidifying deposition were found to 

contain almost exclusively the families Candelariaccae, Physciaceae, and Teloschistaceae.272

Effects of sulfur dioxide exposure to lichens includes: reduced photosynthesis and 

respiration, damage to the algal component of the lichen, leakage of electrolytes, inhibition of 

nitrogen fixation, reduced K absorption, and structural changes.273,274 Additional research has 

concluded that the sulfur:nitrogen exposure ratio is as important as pH in causing toxic effects on 

2-47 
Regulatory Impact Analysis 

lichens. Thus, it is not clear to what extent acidity may be the principal stressor under high 

levels of air pollution exposure. The toxicity of sulfur dioxide to several lichen species is greater 

under acidic conditions than under neutral conditions.275,276 The effects of excess nitrogen 

deposition to lichen communities are discussed in Section 2.3.1.4. 

Arctic and Alpine Tundra

 The NOXSOX ISA found that the possible effects of acidifying deposition on arctic and 

alpine plant communities are also of concern. Especially important in this regard is the role of

nitrogen deposition in regulating ecosystem nitrogen supply and plant species composition. Soil 

acidification and base cation depletion in response to acidifying deposition have not been 

documented in arctic or alpine terrestrial ecosystems in the U.S. Such ecosystems are rare and 

spatially limited in the eastern U.S., where acidifying deposition levels have been high. These 

ecosystems are more widely distributed in the western U.S. and throughout much of Alaska, but 

acidifying deposition levels are generally low in these areas. Key concerns are for listed 

threatened or endangered species and species diversity. 

2.3.1.6.1.3 Aquatic Ecosystems 

Aquatic effects of acidification have been well studied in the U.S. and elsewhere at 

various trophic levels. These studies indicate that aquatic biota have been affected by 

acidification at virtually all levels of the food web in acid sensitive aquatic ecosystems. Effects 

have been most clearly documented for fish, aquatic insects, other invertebrates, and algae. 

Biological effects are primarily attributable to a combination of low pH and high 

inorganic aluminum concentrations. Such conditions occur more frequently during rainfall and 

snowmelt that cause high flows of water and less commonly during low-flow conditions, except 

where chronic acidity conditions are severe. Biological effects of episodes include reduced fish 

condition factor, changes in species composition and declines in aquatic species richness across 

multiple taxa, ecosystems and regions. These conditions may also result in direct mortality.277

Biological effects in aquatic ecosystems can be divided into two major categories: effects on 

health, vigor, and reproductive success; and effects on biodiversity. 

Case Study: Critical Load Modeling in the Adirondack Mountains of New York 

State and the Blue Ridge Mountains in the State of Virginia 

The Adirondack Mountains of New York and the Blue Ridge Mountains of Virginia have 

long been a locus for awareness of the environmental issues related to acidifying deposition. 

Soils and water bodies, such as lakes and streams, usually buffer the acidity from natural rain 

with "bases," the opposite of acids, from the environment. The poor buffering capability of the 

soils in both these regions make the lakes and streams particularly susceptible to acidification 

from anthropogenic nitrogen and sulfur atmospheric deposition resulting from nitrogen and 

sulfur oxides emissions. Consequently, acidic deposition has affected hundreds of lakes and 

thousands of miles of headwater streams in both of these regions. The diversity of life in these 

acidic waters has been reduced as a result of acidic deposition. 

2.3.1.7

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Chapter 2: Air Quality, Health and Welfare Effects 

The critical load approach provides a quantitative estimate of the exposure to one or more 

pollutants below which significant harmful effects on specific sensitive elements of the 

environment do not occur according to present knowledge. The critical load for a lake or stream

provides a means to gauge the extent to which a water body has recovered from past acid 

deposition, or is potentially at risk due to current deposition levels. Acid neutralizing capacity 

(ANC) is an excellent indicator of the health of aquatic organisms such as fish, insects, and 

invertebrates.

Figure 2-20 Locations of Lakes and Streams where Critical Loads were Calculated 

In this case study, the focus is on the combined load of nitrogen and sulfur deposition 

below which the ANC level would still support healthy aquatic ecosystems. Critical loads were 

calculated for 169 lakes in the Adirondack region and 60 streams in Virginia (Figure 2-20). The 

Steady-State Water Chemistry (SSWC) model was used to calculate the critical load, relying on 

water chemistry data from the USEPA Temporal Intergraded Monitoring of Ecosystems (TIME) 

and Long-term Monitoring (LTM) programs and model assumptions well supported by the 

scientific literature. Research studies have shown that surface water with ANC values greater 

than 50 μeq/L tend to protect most fish (i.e., brook trout, others) and other aquatic organisms 

(Table 2-5 ). In this case, the critical load represents the combined deposition load of nitrogen 

and sulfur to which a lake or stream could be subjected and still have an ANC of 50 μeq/L. 

Critical loads of combined total nitrogen and sulfur are expressed in terms of ionic charge 

balance as milliequivalent per square meter per year (meq/m2

/yr). 

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Regulatory Impact Analysis 

Table 2-5 Aquatic Status Categories 

Acute 

Concern 

<0 micro 

equivalent

per Liter 

(μeq/L)

Severe 

Concern 

0 – 20 

μeq/L 

Elevated 

Concern 

20 – 50

μeq/L 

Moderate

Concern 

50 – 100

μeq/L 

Low 

Concern 

>100 μeq/L

CATEGORY LABEL ANC LEVELS* EXPECTED ECOLOGICAL EFFECTS 

Complete loss of fish populations is expected. Planktonic 

communities have extremely low diversity and are dominated by

acidophilic forms. The numbers of individuals in plankton species 

that are present are greatly reduced. 

Highly sensitive to episodic acidification. During episodes of high

acid deposition, brook trout populations may experience lethal 

effects. Diversity and distribution of zooplankton communities 

decline sharply.

Fish species richness is greatly reduced (more than half of expected

species are missing). On average, brook trout populations experience 

sub-lethal effects, including loss of health and reproduction (fitness).

Diversity and distribution of zooplankton communities also decline. 

Fish species richness begins to decline (sensitive species are lost 

from lakes). Brook trout populations are sensitive and variable, with

possible sub-lethal effects. Diversity and distribution of zooplankton 

communities begin to decline as species that are sensitive to acid

deposition are affected. 

Fish species richness may be unaffected. Reproducing brook trout

populations are expected where habitat is suitable. Zooplankton

communities are unaffected and exhibit expected diversity and 

range. 

When the critical load is “exceeded,” it means that the amount of combined nitrogen and 

sulfur atmospheric deposition is greater than the critical load for a particular lake or stream, 

preventing the water body from reaching or maintaining an ANC concentration of 50 μeq/L. 

Exceedances were calculated from deposition for years 2002 and 2020 with and without 

emissions from shipping. In year 2002, there was no difference in the percent of lakes or streams 

in both regions that exceeded the critical load for the case with and without ship emissions 

(Table 2-6). For the year 2020, when ship emissions are present, 33% of lakes in the Adirondack 

Mountains and 52% of streams in the Virginia Blue Ridge Mountains received greater acid 

deposition than could be neutralized. When ship emissions were removed from the modeling 

domain for the year 2020, 31- and 50% of lakes and streams, respectively, received greater acid 

deposition than could be neutralized a 2% improvement. 

2.3.1.7.1 Regional Assessment 

A regional estimate of the benefits of the reduction in international shipping emissions in 

2020 can be derived from scaling up the results from 169 lakes to a larger population of lakes in 

the Adirondack Mountains. One hundred fifteen lakes of the 169 lakes modeled for critical loads 

are part of a subset of 1,842 lakes in the Adirondacks, which include all lakes from 0.5 to 2,000 

ha in size and at least 0.5 meters in depth. Using weighting factors derived from the EMAP 

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Chapter 2: Air Quality, Health and Welfare Effects 

probability survey and the critical load calculations from the 115 lakes, exceedance estimates 

were derived for the entire 1,842 lakes in the Adirondacks. Based on this approach, 66 fewer 

lakes in the Adirondack Mountains are predicted to receive nitrogen and sulfur deposition loads 

below the critical load and would be protected as a result of removing international shipping 

emissions in 2020. 

Currently, no probability survey has been completed for the study area in Virginia. 

However, the 60 trout streams modeled are characteristic of first and second order streams on 

non-limestone bedrock in the Blue Ridge Mountains of Virginia. Because of the strong 

relationship between bedrock geology and ANC in this region, it is possible to consider the 

results in the context of similar trout streams in the Southern Appalachians that have the same

bedrock geology and size. In addition, the 60 streams are a subset of 344 streams sampled by the 

Virginia Trout Stream Sensitivity Study, which can be applied to a population of 304 out of the 

original 344 streams. Using the 304 streams to which the analysis applies directly as the total, 6 

additional streams in this group would be protected as a result of removing international shipping 

emissions in 2020. However, it is likely that many more of the ~12,000 trout streams in Virginia 

would benefit from reduced international shipping emissions given the extent of similar bedrock 

geology outside the study area. 

Table 2-6 Percent of Modeled Lakes that Exceed the Critical Load for Years 2002 and 2020 with and without 

International Shipping Emissions. “Zero” Indicates without International Shipping Emissions 

2002 2002 ZERO 2020

Exceeded Critical Load

(%. Lakes)

45 45 33 

Non-Exceeded Critical Load (%. Lakes) 55 55 73 

Exceeded Critical Load

(%. Lakes)

82 82 52 

Non-Exceeded Critical Load (%. Lakes) 18 18 48 

2020 ZERO

Adirondack Mountains 

31 

71 

Virginia Blue Ridge Mountains 

50 

50 

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Regulatory Impact Analysis 

Figure 2-21 a. 2002 

Figure 2-21 b. 2020; Critical Load Exceedance for ANC Concentration of 50 μeq/L. Green dots represent 

lakes in the Adirondack Mountains where current nitrogen and sulfur deposition is below their critical load 

and maintains an ANC concentration of 50 μeq/L. Red dots are lakes where current nitrogen and sulfur 

deposition exceeds their limit and the biota are likely impacted. 

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Chapter 2: Air Quality, Health and Welfare Effects 

Figure 2-22 a. 2002 

Figure 2-22 b. 2020; Critical Load Exceedances for ANC Concentration of 50 μeq/L. Green dots represent 

streams in the Virginia Blue Ridge Mountains where current nitrogen and sulfur deposition is below their 

critical load and maintains an ANC concentration of 50 μeq/L. Red dots are streams where current nitrogen 

and sulfur deposition exceeds their limit and the biota are likely impacted.

2.3.2 Deposition of Particulate Matter 

Current international shipping emissions of PM2.5 contain small amounts of 

metals:nickel, vanadium, cadmium, iron, lead, copper, zinc, aluminum.

278,279,280 Investigations 

of trace metals near roadways and industrial facilities indicate that a substantial burden of heav

metals can accumulate on vegetative surfaces. Copper, zinc, and nickel are shown to be directly 

y 

2-53 
Regulatory Impact Analysis 

toxic to vegetation under field conditions.281 While metals typically exhibit low solubility, 

limiting their bioavailability and direct toxicity, chemical transformations of metal compounds 

occur in the environment, particularly in the presence of acidic or other oxidizing species. These 

chemical changes influence the mobility and toxicity of metals in the environment. Once taken 

up into plant tissue, a metal compound can undergo chemical changes, accumulate and be passed 

along to herbivores or can re-enter the soil and further cycle in the environment. 

Although there has been no direct evidence of a physiological association between tree 

injury and heavy metal exposures, heavy metals have been implicated because of similarities 

between metal deposition patterns and forest decline.282 This hypothesized correlation was 

further explored in high elevation forests in the northeastern U.S. These studies measured levels 

of a group of intracellular compounds found in plants that bind with metals and are produced by 

plants as a response to sublethal concentrations of heavy metals. These studies indicated a 

systematic and significant increase in concentrations of these compounds associated with the 

extent of tree injury. These data strongly imply that metal stress causes tree injury and 

contributes to forest decline in Northeast U.S.283 Contamination of plant leaves by heavy metals 

can lead to elevated soil levels. Trace metals absorbed into the plant, frequently bind to the leaf

tissue, and then are lost when the leaf drops. As the fallen leaves decompose, the heavy metals 

are transferred into the soil.284,285

Ships also emit air toxics, including polycyclic aromatic hydrocarbons (PAHs) -- a class 

of polycyclic organic matter (POM) that contain compounds which are known or suspected 

carcinogens. Since the majority of PAHs are adsorbed onto particles less than 1.0 μm in

diameter, long range transport is possible. Particles of this size can remain airborne for days or 

even months and travel distances up to 10,000 km before being deposited on terrestrial or aquatic 

surfaces.286,287,288,289,290 Atmospheric deposition of particles is believed to be the major source of

PAHs to the sediments of Lake Michigan in the Great Lakes, Chesapeake Bay which is 

surrounded by the States of Maryland and Virginia, Tampa Bay in the central part of the State of 

Florida and in other coastal areas of the U.S.291 PAHs tend to accumulate in sediments and reach 

high enough concentrations in some coastal environments to pose an environmental health threat 

that includes cancer in fish populations, toxicity to organisms living in the sediment and risks to 

those (e.g., migratory birds) that consume these organisms.292,293 PAHs tend to accumulate in 

sediments and bioaccumulate in freshwater, flora and fauna. 

The effects of the deposition of atmospheric pollution, including ambient PM, on 

materials are related to both physical damage and impaired aesthetic qualities. The deposition of 

PM (especially sulfates and nitrates) can physically affect materials, adding to the effects of 

natural weathering processes, by potentially promoting or accelerating the corrosion of metals, 

by degrading paints, and by deteriorating building materials such as concrete and limestone. 

Only chemically active fine particles or hygroscopic coarse particles contribute to these physical 

effects. In addition, the deposition of ambient PM can reduce the aesthetic appeal of buildings 

and culturally important articles through soiling. Particles consisting primarily of carbonaceous 

compounds cause soiling of commonly used building materials and culturally important items 

such as statues and works of art. 

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Chapter 2: Air Quality, Health and Welfare Effects 

L

 There are 156 Federally mandated class I areas which, under the Regional Haze Rule, are required to achieve 

natural background visibility levels by 2064. These mandatory class I federal areas are mostly national parks, 

national monuments, and wilderness areas. There are currently 116 IMPROVE monitoring sites (representing all 

156 mandatory class I federal areas) collecting ambient PM2.5 data at mandatory class I federal areas, but not all of 

these sites have complete data for 2002. 

2.3.3 Visibility Degradation 

Emissions from international shipping activity contribute to poor visibility in the U.S. 

through their primary PM2.5 and NOX emissions (which contribute to the formation of secondary 

PM2.5). These airborne particles degrade visibility by scattering and absorbing light. Good 

visibility increases the quality of life where individuals live and work, and where they engage in 

recreational activities. 

Modeling undertaken for this coordinated strategy shows that international shipping 

activities negatively impact visibility by contributing to urban haze in U.S. cities which are 

located near major deep sea ports and also as regional haze in national parks and wilderness 

areas throughout the U.S. The U.S. Government places special emphasis on protecting visibility 

in national parks and wilderness areas. Section 169 of the Clean Air Act requires the U.S. 

Government to address existing visibility impairment and future visibility impairment in the 156 

national parks (see figure 2-23) exceeding 6,000 acres, and wilderness areas exceeding 5,000 

acres, which are categorized as mandatory class I federal areas (62 FR 38680, July 18, 1997). 

Based on modeling for this coordinated strategy, international shipping activities in 2002 

contributed to visibility degradation at all of the 133 class I federal areas which have complete 

Interagency Monitoring of Protected Visual Environments (IMPROVE) ambient data for 2002 or 

are represented by IMPROVE monitors with complete data.L Absent further emission controls, 

by 2020, international shipping activities will have an even larger impact on visibility 

impairment in these class I federal areas. For example, in 2002, approximately 4% of visibility 

impairment in southern California’s Agua Tibia Wilderness was due to shipping activity. U.S. 

modeling, conducted for this coordinated strategy, indicates that by 2020 approximately 12.5% 

of visibility impairment in Agua Tibia will be due to shipping. Likewise, in 2002, 2.7% of 

visibility impairment in southern Florida’s Everglades National Park was due to international 

shipping, and this will double to 6% by 2020. Even in inland class I federal areas, shipping 

activity is contributing to visibility degradation. In 2020, about 2.5% of visibility degradation in 

the Grand Canyon National Park located in the state of Arizona will be from international 

shipping, while almost 6% of visibility degradation in the State of Washington’s North Cascades 

National Park will be from shipping emissions. 

Visibility Monitoring 

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other Federal 

land managers, and State organizations in the U.S., the U.S. EPA has supported visibility 

monitoring in national parks and wilderness areas since 1988. The monitoring network was 

originally established at 20 sites, but it has now been expanded to 110 sites that represent all but 

2.3.3.1

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Regulatory Impact Analysis 

one of the 156 mandatory Federal Class I areas across the country (see figure 2-23). This longterm visibility monitoring network is known as IMPROVE (Interagency Monitoring of Protected 

Visual Environments).

IMPROVE provides direct measurement of fine particles that contribute to visibility 

impairment. The IMPROVE network employs aerosol measurements at all sites, and optical and 

scene measurements at some of the sites. Aerosol measurements are taken for PM10 and PM2.5 

mass, and for key constituents of PM2.5, such as sulfate, nitrate, organic and elemental carbon, 

soil dust, and several other elements. Measurements for specific aerosol constituents are used to 

calculate "reconstructed" aerosol light extinction by multiplying the mass for each constituent by 

its empirically-derived scattering and/or absorption efficiency, with adjustment for the relative 

humidity. Knowledge of the main constituents of a site's light extinction "budget" is critical for 

source apportionment and control strategy development. Optical measurements are used to 

directly measure light extinction or its components. Such measurements are taken principally 

with either a transmissometer, which measures total light extinction, or a nephelometer, which 

measures particle scattering (the largest human-caused component of total extinction). Scene 

characteristics are typically recorded three times daily with 35 millimeter photography and are 

used to determine the quality of visibility conditions (such as effects on color and contrast) 

associated with specific levels of light extinction as measured under both direct and aerosolrelated methods. Directly measured light extinction is used under the IMPROVE protocol to 

cross check that the aerosol-derived light extinction levels are reasonable in establishing current

visibility conditions. Aerosol-derived light extinction is used to document spatial and temporal 

trends and to determine how proposed changes in atmospheric constituents would affect future 

visibility conditions. 

Annual average visibility conditions (reflecting light extinction due to both anthropogenic 

and non-anthropogenic sources) vary regionally across the U.S. The rural East generally has 

higher levels of impairment than remote sites in the West, with the exception of urban-influenced 

sites such as San Gorgonio Wilderness (CA) and Point Reyes National Seashore (CA), which 

have annual average levels comparable to certain sites in the Northeast. Regional differences are 

illustrated by Figures 4-39a and 4-39b in the Air Quality Criteria Document for Particulate 

Matter, which show that, for Class I areas, visibility levels on the 20% haziest days in the West 

are about equal to levels on the 20% best days in the East.294

Higher visibility impairment levels in the East are due to generally higher concentrations 

of anthropogenic fine particles, particularly sulfates, and higher average relative humidity levels. 

In fact, sulfates account for 60-86% of the haziness in eastern sites.295 Aerosol light extinction 

due to sulfate on the 20% haziest days is significantly larger in eastern class I areas as compared 

to western areas (Figures 4-40a and 4-40b in the Air Quality Criteria Document for Particulate 

Matter).296 With the exception of remote sites in the northwestern U.S., visibility is typically 

worse in the summer months. This is particularly true in the Appalachian region, where average 

light extinction in the summer exceeds the annual average by 40%.297

Addressing Visibility in the U.S. 

The U.S. EPA has two programmatic approaches to address visibility. First, to address 

the welfare effects of PM on visibility, EPA set secondary PM2.5 standards which would act in 

2.3.3.2

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Chapter 2: Air Quality, Health and Welfare Effects 

M These areas are defined in section 162 of the Act as those national parks exceeding 6,000 acres, wilderness areas 

and memorial parks exceeding 5,000 acres, and all international parks which were in existence on August 7, 1977. N As mentioned above, the EPA has recently proposed to amend the PM NAAQS (71 FR 2620, Jan. 17, 2006). The

proposal would set the secondary NAAQS equal to the primary standards for both PM2.5 and PM10-2.5. EPA also is

taking comment on whether to set a separate PM2.5 standard, designed to address visibility (principally in urban

areas), on potential levels for that standard within a range of 20 to 30 μg/m3

, and on averaging times for the standard 

within a range of four to eight daylight hours. 

conjunction with the establishment of a regional haze program. In setting this secondary 

standard, EPA concluded that PM2.5 causes adverse effects on visibility in various locations, 

depending on PM concentrations and factors such as chemical composition and average relative 

humidity. Second, section 169 of the Clean Air Act provides additional authority to address 

existing visibility impairment and prevent future visibility impairment in the 156 national parks, 

forests and wilderness areas categorized as mandatory class I federal areas (62 FR 38680-81, 

July 18, 1997).M Figure 2-23 below identifies where each of these parks are located in the U.S. 

In July 1999 the regional haze rule (64 FR 35714) was put in place to protect the visibility in 

mandatory class I federal areas. Visibility can be said to be impaired in both PM2.5

nonattainment areas and mandatory class I federal areas.N OGVs, powered by Category 3 

engines, contribute to visibility concerns in these areas through their primary PM2.5 emissions 

and their NOX and SOX emissions, which contribute to the formation of secondary PM2.5. 

Figure 2-23 Mandatory Class I Areas in the U.S. 

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Regulatory Impact Analysis 

2.3.4 Ozone Impacts on Plants and Ecosystems 

There are a number of environmental or public welfare effects associated with the 

presence of ozone in the ambient air.298 In this section, we discuss the impact of ozone on 

plants, including trees, agronomic crops and urban orname

The Air Quality Criteria Document for Ozone and related Photochemical Oxidants notes 

that, “ozone affects vegetation throughout the United States, impairing crops, native vegetation, 

and ecosystems more than any other air pollutant”.299 Like carbon dioxide (CO2) and other 

gaseous substances, ozone enters plant tissues primarily through apertures (stomata) in leaves in 

a process called “uptake”.300 Once sufficient levels of ozone(a highly reactive substance), or its 

reaction products, reaches the interior of plant cells, it can inhibit or damage essential cellular 

components and functions, including enzyme activities, lipids, and cellular membranes, 

disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns.301,302 If 

enough tissue becomes damaged from these effects, a plant’s capacity to fix carbon to form

carbohydrates, which are the primary form of energy used by plants is reduced,303 while plant 

respiration increases. With fewer resources available, the plant reallocates existing resources 

away from root growth and storage, above ground growth or yield, and reproductive processes, 

toward leaf repair and maintenance, leading to reduced growth and/or reproduction. Studies 

have shown that plants stressed in these ways may exhibit a general loss of vigor, which can lead 

to secondary impacts that modify plants' responses to other environmental factors. Specifically, 

plants may become more sensitive to other air pollutants, more susceptible to disease, insect 

attack, harsh weather (e.g., drought, frost) and other environmental stresses. Furthermore, there 

is evidence that ozone can interfere with the formation of mycorrhiza, essential symbiotic fungi 

associated with the roots of most terrestrial plants, by reducing the amount of carbon available 

for transfer from the host to the symbiont.304,305

This ozone damage may or may not be accompanied by visible injury on leaves, and 

likewise, visible foliar injury may or may not be a symptom of the other types of plant damage 

described above. When visible injury is present, it is commonly manifested as chlorotic or 

necrotic spots, and/or increased leaf senescence (accelerated leaf aging). Because ozone damage 

can consist of visible injury to leaves, it can also reduce the aesthetic value of ornamental 

vegetation and trees in urban landscapes, and negatively affects scenic vistas in protected natural 

areas. 

Ozone can produce both acute and chronic injury in sensitive species depending on the 

concentration level and the duration of the exposure. Ozone effects also tend to accumulate over 

the growing season of the plant, so that even lower concentrations experienced for a longer 

duration have the potential to create chronic stress on sensitive vegetation. Not all plants, 

however, are equally sensitive to ozone. Much of the variation in sensitivity between individual 

plants or whole species is related to the plant’s ability to regulate the extent of gas exchange via 

leaf stomata (e.g., avoidance of ozone uptake through closure of stomata)306,307,308 Other 

resistance mechanisms may involve the intercellular production of detoxifying substances. 

Several biochemical substances capable of detoxifying ozone have been reported to occur in 

plants, including the antioxidants ascorbate and glutathione. After injuries have occurred, plants 

may be capable of repairing the damage to a limited extent.309

ntals. 

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Chapter 2: Air Quality, Health and Welfare Effects 

Because of the differing sensitivities among plants to ozone, ozone pollution can also 

exert a selective pressure that leads to changes in plant community composition. Given the range 

of plant sensitivities and the fact that numerous other environmental factors modify plant uptake 

and response to ozone, it is not possible to identify threshold values above which ozone is 

consistently toxic for all plants. The next few paragraphs present additional information on 

ozone damage to trees, ecosystems, agronomic crops and urban ornamentals. 

Ozone also has been conclusively shown to cause discernible injury to forest trees.310,311

In terms of forest productivity and ecosystem diversity, ozone may be the pollutant with the 

greatest potential for regional-scale forest impacts. Studies have demonstrated repeatedly that 

ozone concentrations commonly observed in polluted areas can have substantial impacts on plant 

function.312,313 

Because plants are at the base of the food web in many ecosystems, changes to the plant 

community can affect associated organisms and ecosystems (including the suitability of habitats

that support threatened or endangered species and below ground organisms living in the root 

zone). Ozone impacts at the community and ecosystem level vary widely depending upon 

numerous factors, including concentration and temporal variation of tropospheric ozone, species 

composition, soil properties and climatic factors.314 In most instances, responses to chronic or 

recurrent exposure in forested ecosystems are subtle and not observable for many years. These 

injuries can cause stand-level forest decline in sensitive ecosystems.315,316,317 It is not yet 

possible to predict ecosystem responses to ozone with much certainty; however, considerable 

knowledge of potential ecosystem responses has been acquired through long-term observations 

in highly damaged forests in the United States. 

Laboratory and field experiments have also shown reductions in yields for agronomic 

crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and 

wheat). The most extensive field experiments, conducted under the National Crop Loss 

Assessment Network (NCLAN) examined 15 species and numerous cultivars. The NCLAN 

results show that “several economically important crop species are sensitive to ozone levels 

typical of those found in the United States.”318 In addition, economic studies have shown 

reduced economic benefits as a result of predicted reductions in crop yields associated with 

observed ozone levels.319,320,321 

Urban ornamentals represent an additional vegetation category likely to experience some 

degree of negative effects associated with exposure to ambient ozone levels. It is estimated that 

more than $20 billion (1990 dollars) are spent annually on landscaping using ornamentals, both 

by private property owners/tenants and by governmental units responsible for public areas.322

This is therefore a potentially costly environmental effect. However, in the absence of adequate 

exposure-response functions and economic damage functions for the potential range of effects 

relevant to these types of vegetation, no direct quantitative analysis has been conducted. 

Air pollution can have noteworthy cumulative impacts on forested ecosystems by 

affecting regeneration, productivity, and species composition.323 In the U.S., ozone in the lower 

atmosphere is one of the pollutants of primary concern. Ozone injury to forest plants can be 

diagnosed by examination of plant leaves. Foliar injury is usually the first visible sign of injury 

to plants from ozone exposure and indicates impaired physiological processes in the leaves.324

2-59 
Regulatory Impact Analysis 

In the U.S. this indicator is based on data from the U.S. Department of Agriculture 

(USDA) Forest Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 

program, formerly known as Forest Health Monitoring, FIA examines ozone injury to ozonesensitive plant species at ground monitoring sites in forest land across the country. For this 

indicator, forest land does not include woodlots and urban trees. Sites are selected using a 

systematic sampling grid, based on a global sampling design.325,326 At each site that has at least 

30 individual plants of at least three ozone-sensitive species and enough open space to ensure 

that sensitive plants are not protected from ozone exposure by the forest canopy, FIA looks for 

damage on the foliage of ozone-sensitive forest plant species. Because ozone injury is 

cumulative over the course of the growing season, examinations are conducted in July and 

August, when ozone injury is typically highest. 

Monitoring of ozone injury to plants by the USDA Forest Service has expanded over the 

last 10 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 41 

states in 2002. The data underlying this indicator are based on averages of all observations 

collected in 2002, the latest year for which data are publicly available at the time the study was 

conducted, and are broken down by EPA Region. Ozone damage to forest plants is classified 

using a subjective five-category biosite index based on expert opinion, but designed to be 

equivalent from site to site. Ranges of biosite values translate to no injury, low or moderate 

foliar injury (visible foliar injury to highly sensitive or moderately sensitive plants, respectively), 

and high or severe foliar injury, which would be expected to result in tree-level or ecosystemlevel responses, respectively.327, 328

Recent Ozone Data for the U.S. 

There is considerable regional variation in ozone-related visible foliar injury to sensitive 

plants in the U.S. The U.S. EPA has developed an environmental indicator based on data from

the U.S. Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) 

program which examines ozone injury to ozone-sensitive plant species at ground monitoring 

sites in forest land across the country (this indicator does not include woodlots and urban trees). 

Sites are selected using a systematic sampling grid, based on a global sampling design.329, 330

Because ozone injury is cumulative over the course of the growing season, examinations are 

conducted in July and August, when ozone injury is typically highest. The data underlying the 

indictor in Figure 2-24 are based on averages of all observations collected in 2002, the latest year 

for which data are publicly available at the time the study was conducted, and are broken down 

by U.S. EPA Regions. Ozone damage to forest plants is classified using a subjective fivecategory biosite index based on expert opinion, but designed to be equivalent from site to site. 

Ranges of biosite values translate to no injury, low or moderate foliar injury visible foliar injury 

to highly sensitive or moderately sensitive plants, respectively, and high or severe foliar injury, 

which would be expected to result in tree-level or ecosystem-level responses, respectively.331

 The highest percentages of observed high and severe foliar injury, those which are most 

likely to be associated with tree or ecosystem-level responses, are primarily found in the MidAtlantic and Southeast regions. In EPA Region 3 (which comprises the States of Pennsylvania, 

West Virginia, Virginia, Delaware, Maryland and Washington D.C.), 12% of ozone-sensitive 

plants showed signs of high or severe foliar damage, and in Regions 2 (States of New York, New 

Jersey) and 4 (States of North Carolina, South Carolina, Kentucky, Tennessee, Georgia, Florida, 

2.3.4.1

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Chapter 2: Air Quality, Health and Welfare Effects 

Alabama, and Mississippi), the values were 10% and 7%, respectively. The sum of high and 

severe ozone injury ranged from 2% to 4% in EPA Region 1 (the six New England States), 

Region 7 (States of Missouri, Iowa, Nebraska and Kansas), and Region 9 (States of California, 

Nevada, Hawaii and Arizona). The percentage of sites showing some ozone damage was about 

45% in each of these EPA Regions. 

Figure 2-24 Ozone Injury to Forest Plants in U.S. by EPA Regions, 2002ab 

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Regulatory Impact Analysis 

2.3.4.1.1 Indicator Limitations 

Field and laboratory studies were reviewed to identify the forest plant species in each

region that are highly sensitive to ozone air pollution. Other forest plant species, or even 

genetic variants of the same species, may not be harmed at ozone levels that cause effects on 

the selected ozone-sensitive species. 

Because species distributions vary regionally, different ozone-sensitive plant species 

were examined in different parts of the country. These target species could vary with respect to 

ozone sensitivity, which might account for some of the apparent differences in ozone injury 

among regions of the U.S. 

Ozone damage to foliage is considerably reduced under conditions of low soil moisture, 

but most of the variability in the index (70%) was explained by ozone concentration.332 Ozone 

may have other adverse impacts on plants (e.g., reduced productivity) that do not show signs of 

visible foliar injury.333

Though FIA has extensive spatial coverage based on a robust sample design, not all 

forested areas in the U.S. are monitored for ozone injury. Even though the biosite data have 

been collected over multiple years, most biosites were not monitored over the entire period, so 

these data cannot provide more than a baseline for future trends. 

2.3.4.1.2 Ozone Impacts on Forest Health 

Air pollution can impact the environment and affect ecological systems, leading to 

changes in the biological community (both in the diversity of species and the health and vigor of 

individual species). As an example, many studies have shown that ground-level ozone reduces 

the health of plants including many commercial and ecologically important forest tree species 

throughout the United States.334

When ozone is present in the air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Since photosynthesis occurs in cells within leaves, the ability of the 

plant to produce energy by photosynthesis can be compromised if enough damage occurs to 

these cells. If enough tissue becomes damaged, it can reduce carbon fixation and increase plant 

respiration, leading to reduced growth and/or reproduction in young and mature trees. Ozone 

stress also increases the susceptibility of plants to disease, insects, fungus, and other 

environmental stressors (e.g., harsh weather). Because ozone damage can consist of visible 

injury to leaves, it also reduces the aesthetic value of ornamental vegetation and trees in urban 

landscapes, and negatively affects scenic vistas in protected natural areas. 

Assessing the impact of ground-level ozone on forests in the eastern United States 

involves understanding the risks to sensitive tree species from ambient ozone concentrations and 

accounting for the prevalence of those species within the forest. As a way to quantify the risks to 

particular plants from ground-level ozone, scientists have developed ozone-exposure/treeresponse functions by exposing tree seedlings to different ozone levels and measuring reductions 

in growth as “biomass loss.” Typically, seedlings are used because they are easy to manipulate 

and measure their growth loss from ozone pollution. The mechanisms of susceptibility to ozone 

within the leaves of seedlings and mature trees are identical, and the decreases predicted using 

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Chapter 2: Air Quality, Health and Welfare Effects 

the seedlings should be related to the decrease in overall plant fitness for mature trees, but the 

magnitude of the effect may be higher or lower depending on the tree species. 335

Some of the common tree species in the United States that are sensitive to ozone are 

black cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), eastern white pine (Pinus 

strobus). Ozone-exposure/tree-response functions have been developed for each of these tree 

species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus ponderosa). 

Other common tree species, such as oak (Quercus spp.) and hickory (Carya spp.), are not nearly 

as sensitive to ozone. Consequently, with knowledge of the distribution of sensitive species and 

the level of ozone at particular locations, it is possible to estimate a “biomass loss” for each 

species across their range. 

W126 Modeling and Projected Impact of Ship Emissions on U.S. Forests Biomass 

To estimate the biomass loss for the tree species listed above across the eastern United 

States, the biomass loss for each of the five tree species was calculated using the three-month 12-

hour W126 exposure metric at each location and its individual ozone-exposure/tree-response 

functions. The W126 exposure metric was calculated using monitored data from the AQS air 

quality monitoring sites. This analysis was done for 2020 with and without international 

shipping emissions to determine the benefit of lowering shipping emissions on these sensitive 

tree species in the eastern half of the U.S. 

The biomass loss in the eastern U.S. attributable to international shipping appears to 

range from 0-6.5 % depending on the particular species. The most sensitive species in the U.S. 

to ozone-related biomass loss is black cherry; the area of its range with more than 10% biomass 

loss in 2020 decreased by 8.5% when emissions from ships were removed. Likewise, Table 2-7 

indicates that yellow-poplar, eastern white pine, aspen, and ponderosa pine saw areas with more 

then 2% biomass loss reduced by 2.1% to 3.8% in 2020. The 2% level of biomass loss is 

important, because a scientific consensus workshop on ozone effects reported that a 2% annual 

biomass loss causes long term ecological harm due to the potential for compounding effects over 

multiple years as short-term negative effects on seedlings affect long-term forest health. 336,337

Figure 2-25 shows ship emissions’ adverse impact on U.S. forest biomass loss in 2020. 

2.3.4.2

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Regulatory Impact Analysis 

Table 2-7 The Percent Improvement in Area of the Tree Species Range Between the “Base Case” and “Zero 

Out” Marine Emissions with Biomass Loss of Greater than 2, 4, 6, and 10% due to Ozone for Year 2020. 

Units are % Improvement of Area of Species Range. 

Figure 2-25 U.S. Geographic Areas where the Coordinated Strategy would Reduce Biomass Loss by More 

than 2%

2.3.4.2.1 Methodology 

Outputs from the CMAQ modeling were used to calculate a longer-term ozone exposure 

metric known as "W126”.338 Previous EPA analyses have concluded that the cumulative, 

seasonal W126 index is the most appropriate index for relating vegetation response to ambient 

ozone exposures. The metric is a sigmoidally weighted 3-month sum of all hourly ozone 

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Chapter 2: Air Quality, Health and Welfare Effects 

concentrations observed during the daily 12-hr period between 8 am to 8 pm. The three months 

are the maximum consecutive three months during the ozone season, defined for this modeling 

as May through September. 

As in the ozone and PM2.5 modeling, the CMAQ model was used in a relative sense to 

estimate how ambient W126 levels would change as a result of future growth and/or emissions 

reductions associated with our coordinated program. The resultant W126 outputs were fed into a 

separate model which calculated biomass loss from certain tree species as a result of prolonged 

exposure to ozone. The results of that analysis are discussed below. The CMAQ modeling 

estimated that ship emissions contributed to high levels of W126 in some coastal areas. This 

contribution was estimated to range from as much as 30- to 40% in parts of California and 

Florida. The average contribution from all ship emissions was 8% nationally. 

2.4 Impacts of the Coordinated Strategy on Air Quality 

The controls from the coordinated strategy (Section 3.4.3 of the DRIA) would 

significantly reduce emissions of NOX, SOX and PM from C3 vessels. Air quality modeling and 

monitoring data presented in this section indicate that a large number of people live in counties 

that are designated as nonattainment for either or both of the PM2.5 or 8-hour ozone NAAQS. 

Figure 2-26 illustrates the widespread nature of the ozone and PM2.5 nonattainment areas as well 

as the mandatory class I federal areas. Air quality modeling was performed for the coordinated 

strategy which illustrates the changes in ambient concentrations of PM2.5 and ozone as well as 

changes in deposition of nitrogen and sulfur and levels of visibility which are expected to occur 

with the emission reductions from the coordinated strategy. 

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, the emission control scenarios used to characterize the air quality impacts of the 

proposal, and the benefits modeling presented in Chapter 6, are slightly different than the 

coordinated strategy. For example, the 2020 air quality impacts are based on inventory estimates 

that were modeled using incorrect ECA boundary information off of the western coast of the 

U.S. A calculation error placed the western 200 nautical mile (nm) ECA boundary 

approximately 50 nm closer to shore. Additionally, the 2020 air quality control case does not 

reflect emission reductions related to global controls for areas that are beyond 200 nm but within 

the CMAQ air quality modeling domain. The impact of these differences is expected to be 

minimal. In total, while the inventory and air quality modeling discrepancies are modest, they 

result in a conservative estimate of the 2020 air quality impacts that are presented in this chapter. 

The modeling for 2030 was based on inventories that reflected an ECA distance closer to 

shore than what we are proposing. The air quality modeling for 2030 therefore reflects the 

impacts associated with approximately 80% of the emission reductions achieved by the 

coordinated strategy. As a result, the 2030 air quality impacts presented in this chapter should be 

considered conservative estimates of the improvements in air quality associated with the 

proposal. For the final RIA, we plan to model the 2030 coordinated strategy to control ship 

emissions with a 200 nm boundary and global controls beyond. 

Please refer to Chapter 3 for a comparison of the inventories used to support the air 

quality modeling and the inventories of the coordinated strategy. 

2-65 
Regulatory Impact Analysis 

 

Class I areas

shown in black

8hr Ozone nonattainment areas

as designated

shown in Blue 

PM2.5 nonattainment areas

as designated

shown in green

Figure 2-26 PM2.5 and 8-hour Ozone Nonattainment Areas and Mandatory Class I Federal Areas 

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Chapter 2: Air Quality, Health and Welfare Effects 

O A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating an ambient standard or is

contributing to a nearby area that is violating the standard. 

2.4.1 Particulate Matter 

The emission reductions from the coordinated strategy would assist PM nonattainment 

areas in reaching the standard by each area’s respective attainment date and assist PM 

maintenance areas in maintaining the PM standards in the future. In this section we present 

information on current and model-projected future PM levels. 

Current Levels of PM2.5

As described in Section 2.2.1, PM causes adverse health effects, and the U.S. 

Government has set national standards to protect against those health effects. There are two U.S. 

National Ambient Air Quality Standards (NAAQS) for PM2.5: an annual standard (15 μg/m3

) and 

a 24-hour standard (35 μg/m3). The most recent revisions to these standards were in 1997 and 

2006. In 2005, the U.S. EPA designated nonattainment areas for the 1997 PM2.5 NAAQS (70 FR 

19844, April 14, 2005).O As of March 4, 2009 there are 39 1997 PM2.5 nonattainment areas 

composed of 208 full or partial counties with a total population exceeding 88 million. Area 

designations for the 2006 24-hour PM2.5 NAAQS are expected to be promulgated in 2009 and 

become effective 90 days after publication in the Federal Register. 

States with PM2.5 nonattainment areas will be required to take action to bring those areas 

into compliance in the future. Most 1997 PM2.5 nonattainment areas are required to attain the 

1997 PM2.5 NAAQS in the 2010 to 2015 time frame and then be required to maintain the 1997 

PM2.5 NAAQS thereafter.339 The 2006 24-hour PM2.5 nonattainment areas will be required to 

attain the 2006 24-hour PM2.5 NAAQS in the 2014 to 2019 time frame and then be required to 

maintain the 2006 24-hour PM2.5 NAAQS thereafter.340 The IMO, the U.S. Government and 

individual states and local areas have already put in place many PM2.5 and PM2.5 precursor 

emission reduction programs. However, C3 vessels are significant contributors to PM2.5 in many 

areas and states will need additional reductions in a timely manner to help them meet their air 

quality goals. The fuel sulfur emission standards will become effective in 2010 and 2015, and 

the NOX engine emission standards will become effective in 2016. Therefore the fuel and engine 

emission reductions associated with the coordinated strategy will assist PM2.5 nonattainment 

areas in reaching the standard by each area’s respective attainment date and/or assist in

maintaining the PM standard in the future. 

Projected Levels of PM2.5

In conjunction with the coordinated strategy, we performed a series of air quality 

modeling simulations for the continental U.S. The model simulations were performed for 

several emissions scenarios including the following: 2002 baseline projection, 2020 baseline 

projection, 2020 baseline projection with C3 fuel and engine controls, 2030 baseline projection, 

and 2030 baseline projection with C3 fuel and engine controls. Information on the air quality 

2.4.1.1

2.4.1.2

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Regulatory Impact Analysis 

modeling methodology is contained in Section 2.4.5. In the following sections we describe 

projected PM2.5 levels in the future, with and without the controls described in this proposed 

action. 

2.4.1.2.1 Projected PM2.5 Levels without the Coordinated Strategy 

Even with the implementation of all current state and federal regulations, including the 

Small SI Engine Rule (73 FR 59034, October 8, 2008), the Locomotive and Marine Rule (73 FR 

25098, May 6, 2008), the Clean Air Nonroad Diesel rule (69 FR 38957, June 29, 2004), and the 

Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control 

Requirements (66 FR 5002, Jan. 18, 2001), there are projected to be U.S. counties violating the 

PM2.5 NAAQS well into the future. The model outputs from the 2002, 2020 and 2030 baselines, 

combined with current air quality data, were used to identify areas expected to exceed the annual 

PM2.5 NAAQS in the future. 

The baseline air quality modeling conducted for the coordinated strategy projects that in 

2020, with all current controls in effect but excluding the reductions expected to occur as a result 

of the coordinated strategy, at least 13 counties, with a projected population of nearly 30 million 

people, may not attain the annual standard of 15 μg/m3. Ten of these 13 projected nonattainment 

areas are in California, which has been shown to be strongly impacted by emissions from C3 

vessels. These numbers do not account for additional areas that may have future air quality 

measurements above the 24-hour standard of 35 μg/m3. The numbers also do not account for 

those areas that are within 10% of the PM2.5 standard. These areas, although not violating the 

standard, will also benefit from the emissions reductions which will help ensure long term

maintenance of the PM2.5 NAAQS. For example, in 2020, an additional 13 million people are 

projected to live in 12 counties that have air quality measurements within 10% of the annual 

PM2.5 NAAQS. This modeling supports the conclusion that there are a substantial number of

counties across the U.S. projected to experience PM2.5 concentrations at or above the PM2.5 

NAAQS into the future. Emission reductions from C3 vessels will be helpful for these counties 

in attaining and maintaining the PM2.5 NAAQS.

2.4.1.2.2 Projected PM2.5 Levels with the Coordinated Strategy 

This section summarizes the results of our modeling of PM2.5 air quality impacts in the 

future due to the reductions in C3 vessel emissions described in this proposed action. 

Specifically, we compare baseline scenarios to scenarios with controls. Our modeling indicates 

that the reductions from the coordinated strategy will provide nationwide improvements in 

ambient PM2.5 concentrations and minimize the risk of exposures in future years. Since the 

emission reductions from this rule go into effect during the period when some areas are still 

working to attain the PM2.5 NAAQS, the projected emission reductions will assist state and local 

agencies in their effort to attain the PM2.5 standard and help others maintain the standard. 

On a population-weighted basis, the average modeled future-year PM2.5 design values 

would decrease by 0.51 μg/m3 in 2020 and 0.98 μg/m3 in 2030. In addition, those counties that 

are projected to be above the 2006 annual PM2.5 standard in 2020 and 2030 would have even 

larger decreases from the emission controls associated with the coordinated strategy. On a 

population-weighted basis, the average modeled future-year PM2.5 design values for counties 

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Chapter 2: Air Quality, Health and Welfare Effects 

whose design values were greater than 15 μg/m3 would decrease by 1.56 μg/m3 in 2020 and 3.26 

μg/m3 in 2030. Table 2-8 shows the average change in future year PM2.5 design values for: (1) 

all counties with 2002 baseline design values, (2) counties with baseline design values that 

exceeded the standard in 2000-2004 (“violating” counties), (3) counties that did not exceed the 

standard, but were within 10% of it in 2000-2004, (4) counties with future year design values 

that exceeded the standard, and (5) counties with future year design values that did not exceed 

the standard, but were within 10% of it in 2020 and 2030. Counties within 10% of the standard 

are intended to reflect counties that meet the standard, but will likely benefit from help in 

maintaining that status in the face of growth. All of these metrics show a decrease in 2020 and 

2030, indicating in five different ways the overall improvement in ozone air quality. 

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Regulatory Impact Analysis 

Table 2-8 Average Change in Projected Future Year PM2.5 Design Value as a Result of the C3 Fuel and 

Engine Controls 

Averagea Number 

of US 

Counties 

Change in 

2020 design 

value 

(μg/m3) 

All 556 -0.22 

All, population-weighted 556 -0.51 

Counties whose base year is violating the 2006 

annual PM2.5 standard 

82 -0.28 

Counties whose base year is violating the 2006 

annual PM2.5 standard, population-weighted 

82 -0.81 

Counties whose base year is within 10 percent of 

the 2006 annual PM2.5 standard 

113 -0.20 

Counties whose base year is within 10 percent of 

the 2006 annual PM2.5 standard, populationweighted 

113 -0.36 

Counties whose 2020 base year is violating the 

2006 annual PM2.5 standard 

13 -0.99 

Counties whose 2020 base year is violating the 

2006 annual PM2.5 standard, populationweighted 

13 -1.56 

Counties whose 2030 base year is violating the 

2006 annual PM2.5 standard 

14 -1.06 

Counties whose 2030 base year is violating the 

2006 annual PM2.5 standard, populationweighted 

14 -1.57 

Counties whose 2020 base year is within 10 

percent of the 2006 annual PM2.5 standard 

12 -0.35 

Counties whose 2020 base year is within 10 

percent of the 2006 annual PM2.5 standard, 

population-weighted 

12 -0.30 

Counties whose 2030 base year is within 10 

percent of the 2006 annual PM2.5 standard 

12 -0.29 

Counties whose 2030 base year is within 10 

percent of the 2006 annual PM2.5 standard, 

population-weighted 

12 -0.17 

Change in 

2030 design 

value 

(μg/m3) 

-0.41 

-0.98 

-0.52 

-1.68 

-0.34 

-0.60 

-1.92 

-3.26 

-2.03 

-3.27 

-0.62 

-0.54 

-0.51 

-0.30 

Note: a

 Averages are over counties with 2002 modeled design values 

Figure 2-27 illustrates the geographic impact of the C3 engine and fuel controls on annual 

PM2.5 design values in 2020. The most significant decreases occur along all of the coastlines 

with the maximum decrease in a 2020 design value being 3.13 μg/m3

 in Miami, FL. 

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Chapter 2: Air Quality, Health and Welfare Effects 

Figure 2-27 Impact of C3 Fuel and Engine Controls on Annual PM2.5 Design Values (DV) in 2020 (units

are μg/m3

) 

Table 2-9 lists the counties with projected annual PM2.5 design values that violate or are 

within 10% of the annual PM2.5 standard in 2020 and 2030. Counties are marked with a “V” in 

the table if their projected design values are greater than or equal to 15.05 μg/m3. Counties are 

marked with an “X” in the table if their projected annual design values are greater than or equal 

to 13.55 μg/m3, but less than 15.05 μg/m3. The counties marked “X” are not projected to violate 

the standard, but to be close to it, so the rule will help assure that these counties continue to meet 

the standard. The current design values are also presented in Table 2-9. Recall that we project 

future design values only for counties that have current design values, so this list is limited to 

those counties with ambient monitoring data sufficient to calculate current 3-year design values. 

2-71 
Regulatory Impact Analysis 

T oun or 2030 cted Annua M2.5 Design Val s in Violation o 0% of 

e Annual P tandard in the Base and Con l Cases 

2000-2004 

AVERAGE 

A

P

2020 

MODELING 

PROJECTIONS 

OF BASE 

ANNU PM2.5

DV (μg/m3

) 

2020 

MODELING 

PROJECTIONS 

OF CONTROL 

ANNU PM2.5

DV (μg/m3

) 

2020 TED 

POPULATION341

able 2-9 C ties with 2020 Proje l P ue

th M2.5 S tro

STATE COUNTY

NNUAL

M2.5 DV

(μ/3

) AL AL

Alabama Jefferson Co 18.37 V X 

California Fresno Co 20.03 V V 

California Imperial Co 14.45 X X 

California Kern Co 21.77 V 

California Kings Co 18.77 V V 

California Los Angele 23.17 V V 

California Merced Co 16.48 V 

California Orange Co 18.27 V V 

Californ Riverside 27.15 V V 

Californ San Bernardino 24.63 V V 

California San Diego Co 15.65 V X 

California San Joaquin 14.84 X 

California Stanislaus Co 16.50 X X 

California Tulare Co 21.33 V V 

Califo Ventura Co 14.35 X 

Georgia Fulton Co 18.29 X X 

Illinois 

Illinois Madison 

Cook Co 17.07 X 

r Within 1

PROJEC

681,549 

1,066,878 

161,555 

876,131 

173,390 

277,863 

3,900,599 

2,252,510 

2,424,764 

3,863,460 

743,469 

607,766 

477,296 

1,023,136 

929,278 

Montana Lincoln

New York New York Co 

Ohio Cuyahoga Co 18.37 X X 1,326,680 

Pennsylvania Allegheny Co 21.00 V V 1,242,587 

West

Virginia 

Hancock Co 17.31 X 30,539 

zone 

The emission reductions from the coordinated strategy described in this proposal wo

also assist ozone nonattainment areas in reaching the standard by each area’s respective 

attainment date as well as assist ozone maintenance areas in maintaining the ozone standards in

the future. In this section, we present information on current and model-projected future ozone 

levels. 

V 

s Co 10,376,013 

X 

ia Co 

ia Co 

 Co 

rnia 

X 5,669,479 

Co 17.27 X X 278,167 

Kentucky Jefferson Co 16.78 X X 726,257 

Michigan Wayne Co 19.32 V V 1,908,196 

Co 15.85 X X 20,147 

17.16 X X 1,700,384 

2.4.2 O

uld 

2.4.2.1 Current Levels of Ozone 

As described in Section 2.2.2, ozone causes adverse health effects, and the U.S. 

Government has set national standards to protect against those health effects. The national 

2-72 
Chapter 2: Air Quality, Health and Welfare Effects 

P

 A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating an ambient standard or is

contributing to a nearby area that is violating the standard. 

Q The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area is designated as severe and will have to

attain before June 15, 2021. The South Coast Air Basin has requested to be reclassified as an extreme nonattainment

area which will make their attainment date June 15, 2024. The San Joaquin Valley Air Basin 8-hour ozone

nonattainment area is designated as serious and will have to attain before June 15, 2013. The San Joaquin Valley 

Air Basin has requested to be reclassified as an extreme nonattainment area which will make their attainment date 

June 15, 2024. 

ambient air quality standard (NAAQS) for ozone is an 8-hour standard set at 0.075 ppm. The 

most recent revision to this standard was in 2008, the previous 8-hour ozone standard, set in 

1997, had been 0.08 ppm. In 2004, the U.S. EPA designated nonattainment areas for the 1997

hour ozone NAAQS (69 FR 23858, A P

st 132 million.342

Q

 Nonattainment designations for the 2008 8-hour ozone standard are 

expected to be final in March 2010. 

States with ozone nonattainment areas are required to take action to bring those a

compliance in the future. The attainment date assigned to an ozone nonattainment area is based 

on the area’s classification. Most ozone nonattainment areas are required to attain the 1997 8

hour ozone NAAQS in the 2007 to 2013 time frame and then be required to maintain it 

thereafter. The attainment dates associated with the potential nonattainment areas based on the 

2008 8-hour ozone NAAQS will likely be in the 2013 to 2021 timeframe, depending on th

severity of the problem in each area. Table 2-10 provides an estimate, based on 2004-06 air 

quality data, of the counties with design values greater than the 2008 ozone NAAQS. We expec

many of th

X

posed standards would be useful to states as they seek to either attain or maintain the ozone 

 8-

pril 30, 2004). As of March 5, 2009, there are 57 1997 8-

hour ozone nonattainment areas composed of 293 full or partial counties with a total population 

of almo

reas into 

-

e 

t 

e ozone nonattainment areas will need to adopt additional emissions reduction 

programs to attain and maintain the ozone NAAQS. The expected NO reductions from these 

pro

NAAQS. 

2-73 
Regulatory Impact Analysis 

Table 2-10 Counties with Design Values Greater Than the 2008 Ozone NAAQS Based on 2005-2007 Air 

Quality Data

 Number of Counties Populationa

1997 Ozone Standard: counties within the 57 

areas currently designated as nonattainment 

293 131,977,890 

2008 Ozone Standard: additional counties that 

would not meet the 2008 NAAQS

Total 520 

a

 Population numbers are from 2000 census data. b

 Attainment designations for the 2008 ozone NAAQS have not yet been made. Nonattainment for the 2008 Ozone 

NAAQS will be based on three years of air quality data from later years. Also, the county numbers in the table 

include only the counties with monitors violating the 2008 Ozone NAAQS. The numbers in this table may be an

underestimate of the number of counties and populations that will eventually be included in areas with multiple 

counties designated nonattainment. 

2.4.2.2 Projected Levels of Ozone 

In conjunction with the coordinated strategy, we performed a series of air quality 

modeling simulations for the continental U.S. (described further in Section 3.4.3 of the DRIA). 

The model simulations were performed for several emissions scenarios including the following: 

2002 baseline projection, 2020 baseline projection, 2020 baseline projection with C3 fuel and 

engine controls, 2030 baseline projection, and 2030 baseline projection with C3 fuel and engine 

controls. Information on the air quality modeling methodology is contained in Section 2.4.5. In

the following sections, we describe our modeling of 8-hour ozone levels in the future with and 

without the controls described in this proposed action. 

2.4.2.2.1 Projected Ozone Levels without the Coordinated Strategy 

EPA has already adopted many emission control programs th

ambient ozone levels. These control programs include the Locomotive and Marine Rule (73 FR 

 May 6, 2008), Clean Air Interstate Rule (70 FR 25162, May 12, 2005), the Clean Air 

Nonroad Diesel rule (69 FR 38957, June 29, 2004), and the Heavy Duty Engine and Vehicle 

Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001). 

As a result of these programs, 8-hour ozone levels are expected to improve in the future. 

The baseline air quality modeling conducted for the coordinated strategy projects that in 

2020, with all current controls in effect but excluding the reductions achieved through the 

coordinated strategy, up to 50 counties, with a population of almost 50 million people, may not 

attain the 8-hour standard of 0.075 ppm. These numbers do not account for those areas that ar

within 10% of the 2008 ozone standard. These areas, although not violating the standards, wil

also benefit from the additional reductions from this rule, ensuring long term maintenance of th

ozone NAAQS. For example, in 2020, an additional 80 m on

nties that have air quality measurements within 10% of the 2008 ozone NAAQS. This 

modeling supports the conclusion that there are a substantial number of counties across the U.S. 

projected to experience 8-hour ozone concentrations at or above the ozone NAAQS into the 

future. Emission reductions from C3 vessels will be helpful for these counties in attaining and 

maintaining the ozone NAAQS. 

b

227 41,285,262 

173,263,152 

Notes: 

at are expected to reduce 

25098,

e 

l 

e 

illi people are projected to live in 

135 cou

2-74 
Chapter 2: Air Quality, Health and Welfare Effects 

2.4.2.2.2 Projected Ozone Levels with the Coordinated Strategy 

This section summarizes the results of our modeling of ozone air quality impacts in the 

future due to the reductions in C3 vessel emissions proposed in this action. Specifically, w

compare baseline scenarios to scenarios with controls (Section 2.4.2.2 and 3.4.3 of the DRIA). 

Our modeling indicates that the reductions from this rule will provide nationwide improvem

in ambient ozone concentrations and minimize the risk of exposures in future years. Since some

of the NOX emission reductions from this rule go into effect during the period when some areas

are still working to attain the 8-hour ozone NAAQS, the projected emission reductions will 

state and local agencies in their effort to attain the 8-hour ozone standard and help others 

maintain the standard. Emissions reductions f

e 

ents 

assist

rom this rule will also help to counter potential 

ozone increases due to climate change, which are expected in many urban areas in the United 

States, but are not reflected in the modeling shown here.343,344

On a population-weighted basis, the average modeled future-year 8-hour ozone design 

values will decrease by 0.30 ppb in 2020 and 0.95 ppb in 2030. In addition, those counties that 

are projected to be above the 2008 ozone standard in 2020 and 2030 would have even larger 

decreases from these proposed controls. On a population-weighted basis, the average modeled 

future-year 8-hour ozone design values for counties whose design values were greater than 75 

ppb would decrease by 0.46 ppb in 2020 and 1.58 ppb in 2030. Table 2-11 shows the average 

change in future year 8-hour ozone design values for: (1) all counties with 2002 baseline design 

values, (2) counties with baseline design values that exceeded the standard in 2000-2004 

(“violating” counties), (3) counties that did not exceed the standard, but were within 10% of it in 

2000-2004, (4) counties with future year design values that exceeded the standard, and (5) 

counties with future year design values that did not exceed the standard, but were within 10% of 

it in 2020 and 2030. Counties within 10% of the standard are intended to reflect counties that 

meet the standard, but will likely benefit from help in maintaining that status in the face of

growth. All of these metrics show a decrease in 2020 and 2030, indicating in five different ways 

the overall improvement in ozone air quality. 

2-75 
Regulatory Impact Analysis 

Table 2-11 Average Change in Projected Future Year 8-hour Ozone Design Value as a Result of the C3 Fuel

and Engine Controls

Averagea Number 

of US 

Counties 

Change in 

2020 design 

valueb (ppb) 

All 661 -0.22 

All, population-weighted 661 -0.30 

Counties whose base year is violating the 2008 

8-hour ozone standard 

497 -0.21 

Counties whose base year is violating the 2008 

8-hour ozone standard, population-weighted 

497 -0.27 

Counties whose base year is within 10 percent of 

the 2008 8-hour ozone standard 

99 -0.21 

Counties whose base year is within 10 percent of 

the 2008 8-hour ozone standard, populationweighted 

99 -0.29 

Counties whose 2020 base year is violating the 

2008 8-hour ozone standard 

50 -0.52 

Counties whose 2020 base year is violating the 

2008 8-hour ozone standard, populationweighted 

50 -0.46 

Counties whose 2030 base year is violating the 

2008 8-hour ozone standard 

33 -0.61 

Counties whose 2030 base year is violating the 

2008 8-hour ozone standard, populationweighted 

33 -0.49 

Counties whose 2020 base year is within 10 

percent of the 2008 8-hour ozone standard 

135 -0.30 

Counties whose 2020 base year is within 10 

percent of the 2008 8-hour ozone standard, 

population-weighted 

135 -0.24 

Counties whose 2030 base year is within 10 

percent of the 2008 8-hour ozone standard 

105 -0.38 

Counties whose 2030 base year is within 10 

percent of the 2008 8-hour ozone standard, 

population-weighted 

105 -0.27 

Change in 

2030 design 

valueb (ppb) 

-0.66 

-0.95 

-0.64 

-0.85 

-0.64 

-0.85 

-1.61 

-1.48 

-1.92 

-1.58 

-0.90 

-0.75 

-1.14 

-0.82 

Notes: 

a Averages are over counties with 2002 modeled design values b

 Ozone design values are reported in parts per million (ppm) as specified in 40 CFR Part 50. Due to the scale of

the design value changes in this action results have been presented in parts per billion (ppb) format. 

Figure 2-28 illustrates the geographic impact of the C3 engine and fuel controls on 8-hour 

ozone design values in 2020. The most significant decreases occur along all of the coastlines 

with the maximum decrease in a 2020 design value being 2.0 ppb in Bristol, Massachusetts. As 

can be seen from Table 2-11 and Figure 2-28 the air quality modeling performed for the 

coordinated strategy indicates that the C3 engine standards provide improvements in ozone 

2-76 
Chapter 2: Air Quality, Health and Welfare Effects 

levels for the vast majority of areas. However, there are two counties in Washington, Clallam

County (0.7 ppb) and Clark County (0.2 ppb), and two counties in southern California, Orange 

County (3.0 ppb) and San Bernardino County (0.1 ppb), which will experience 8-hour ozone 

design value increases in 2030 due to the NOX disbenefits which occur in these VOC-limited 

ozone nonattainment areas. 

While the impact of the C3 engine and fuel controls would reduce ozone levels generally 

and provide national ozone-related health benefits, this is not always the case at the local level. 

The air quality modeling projects that in a few areas ozone levels will get higher because of the 

NOX disbenefit phenomenon. Due to the complex photochemistry of ozone production, NOX

emissions lead to both the formation and destruction of ozone, depending on the relative 

quantities of NOX, VOC, and ozone formation catalysts such as the OH and HO2 radicals. In 

areas dominated by fresh emissions of NOX, ozone catalysts are removed via the production of 

nitric acid which slows the ozone formation rate. Because NOX is generally depleted more 

rapidly than VOC, this effect is usually short-lived and the emitted NOX can lead to ozone 

formation later and further downwind. The terms “NOX disbenefits” or “ozone disbenefits” refer 

to the ozone increases that result when reducing NOX emissions in localized areas. According to 

the NARSTO Ozone Assessment, disbenefits are generally limited to small regions within 

specific urban cores and are surrounded by larger regions in which NOX control is beneficial.345

It is important to note the following as well: there is a level of NOX control where enough NOX

will have been reduced to result in decreases in ambient ozone concentrations, this modeling 

does not include future VOC or NOX controls that local areas are planning, and reductions in 

NOX are not only important to help reduce ozone but also to help reduce PM2.5. 

2-77 
Regulatory Impact Analysis 

Figure 2-28 Impact of C3 Fuel and Engine Controls on 8-hour Ozone Design Values in 2020 (units are ppb)

2.4.3 Deposition of Nitrogen and Sulfur 

Current Levels of Nitrogen and Sulfur Deposition 

Over the past two decades, the EPA has undertaken numerous efforts to reduce nitrogen 

and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show 

that deposition of both nitrogen and sulfur compounds has decreased over the last 17 years 

although many areas continue to be negatively impacted by deposition. Deposition of inorganic 

nitrogen and sulfur species routinely measured in the U.S. between 2004 and 2006 were as high 

as 9.6 kg N/ha/yr and 21.3 kg S/ha/yr. Figures 2-29 and 2-30 show that annual total deposition 

(the sum of wet and dry deposition) decreased between 1989-1999 and 2004-2006 due to sulfur 

and NOX controls on power plants, motor vehicles and fuels in the U.S. The data shows that 

reductions were more substantial for sulfur compounds than for nitrogen compounds. These 

numbers are generated by the U.S. national monitoring network and they likely underestimate 

nitrogen deposition because NH3 is not measured. In the eastern U.S., where data are most 

abundant, total sulfur deposition decreased by about 36 % between 1990 and 2005 while total 

nitrogen deposition decreased by 19% over the same time frame.346

The EPA is concerned that both current ship emissions and projected future ship 

emissions will seriously erode environmental improvements that have been achieved in these 

ecologically sensitive areas. As the air quality modeling results in Section 2.4.3.2 show, both 

2.4.3.1

2-78 
Chapter 2: Air Quality, Health and Welfare Effects 

nitrogen and sulfur deposition resulting from ship emissions impact a significant portion of 

ecologically sensitive areas in the U.S. 

Figure 2-29 Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2004 -2006 

2-79 
Regulatory Impact Analysis 

Figure 2-30 Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2004-2006

2-80 
Chapter 2: Air Quality, Health and Welfare Effects 

2.4.3.2 Projected Levels of Nitrogen and Sulfur Deposition 

If the coordinated strategy were adopted, reductions in nitrogen deposition would result 

by 2020, benefiting many sensitive ecological areas throughout the U.S. Areas benefiting are 

described in detail in Section 2.3.1.1 and include sensitive forests, wetlands such as freshwater 

bogs and marshes, lakes and streams throughout the entire U.S. Figure 2-31 illustrates the 

nitrogen deposition reductions that would occur along U.S. coastlines in 2020 as well as 

reductions occurring within the interior of the U.S. Reductions would range from 3% to 7% 

along the entire Atlantic and Gulf Coasts while along the Pacific Coast nitrogen deposition 

reductions would be higher, ranging from 3% to 13%. 

Figure 2-31 Percent Change in Annual Total Nitrogen over the U.S. Modeling Domain 

With respect to sulfur deposition, adopting the coordinated strategy would result in 

reducing sulfur deposition levels in 2020; in some regions by more than 25%. Figure 2-32 

illustrates the sulfur deposition reductions occurring throughout the U.S. In some individual 

U.S. watersheds, consisting of offshore islands or close to coastal areas, sulfur deposition levels 

would be reduced by up to 80%. More generally, the Northeast Atlantic Coastal region would 

experience sulfur deposition reductions from C3 vessels ranging from 7% to 25% while the 

Southeast Atlantic Coastal region would experience reductions ranging from 7% to more than 

25%. Sulfur deposition would be reduced in the Gulf Coast region from 3% to more than 25%. 

2-81 
Regulatory Impact Analysis 

Along the West Coast of the U.S. sulfur deposition reductions exceeding 25% would occur in the 

entire Los Angeles Basin in the State of California. The Pacific Northwest would also see 

significant sulfur deposition reductions ranging from 4% to more than 25%. The Great Lakes 

would experience reductions of 1-3%. As importantly, sulfur deposition reductions due to the 

coordinated strategy would also impact the entire U.S. land mass with even interior sections of 

the U.S. experiencing reductions of 1%. Together, these reductions would assist the U.S. in its 

efforts to reduce acidification impacts associated with nitrogen and sulfur depositions in both 

terrestrial and aquatic ecosystems in coastal areas of the U.S. as well as within the interior of the 

U.S. 

Figure 2-32 Percent Change in Annual Total Sulfur over the U.S. Modeling Domain 

Appendix 3B presents the range as well as the average total nitrogen and total sulfur 

deposition changes in 2020 for CMAQ modeling scenarios over 18 specific U.S. subregions. In 

the case of the coordinated strategy, sulfur deposition levels were reduced by on average from 0 

to 19% over these large drainage regions. In individual hydrological unit codes (HUCs) 

consisting of offshore islands or close to coastal areas, sulfur deposition levels in 2020 were 

improved by as much as 78% while nitrogen deposition levels were improved by as much as 

13% in some coastal areas. 

2-82 
Chapter 2: Air Quality, Health and Welfare Effects 

R

 The level of visibility impairment in an area is based on the light-extinction coefficient and a unit less visibility 

index, called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a scale for 

perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the 

average person can generally perceive a change of one deciview. The higher the deciview value, the worse the 

visibility. Thus, an improvement in visibility is a decrease in deciview value. 

2.4.4 Visibility Degradation 

Current Visibility Levels 

Recently designated PM2.5 nonattainment areas indicate that, as of December 2008, over 

88 million people live in nonattainment areas for the 1997 PM2.5 NAAQS. Thus, at least these 

populations would likely be experiencing visibility impairment, as well as many thousands of 

individuals who travel to these areas. In addition, while visibility trends have improved in 

mandatory class I federal areas, the most recent data show that these areas continue to suffer 

from visibility impairment. In eastern parks, average visual range has decreased from 90 miles 

to 15-25 miles. In the West, visual range has decreased from 140 miles to 35-90 miles. In 

summary, visibility impairment is experienced throughout the U.S., in multi-state regions, urban 

areas, and remote mandatory class I federal areas.347,348 The mandatory federal class I areas are 

listed in Figure 2-23 and in Table 2-12. 

Projected Visibility Levels 

Based on modeling for the coordinated strategy, international shipping activities in 2002 

contributed to visibility degradation at all 133 mandatory class I federal areas monitored by the 

U.S. Government. Absent further emission controls, by 2020, international shipping activities 

will have an even larger impact on visibility impairment in these class I federal areas. The 

results suggest that controlling emissions from C3 vessels would result in improved visibility in 

all 133 class I federal areas, although areas would continue to have annual average deciview 

(DV) levels above background in 2020. 

The results indicate that reductions in regional haze would occur in all 133 of the areas 

analyzed as a result of an ECA adoption. The model projects that for all mandatory class I 

federal areas combined, average visibility on the 20% worst days at these scenic locales would 

improve by 0.21 deciviews,R or 1.2%. The greatest improvement in visibilities would be seen in 

coastal areas. For instance, the Agua Tibia Wilderness area (near Los Angeles) would see a 9% 

improvement (2.17 DV) in 2020. National parks and national wilderness areas in other parts of 

the country would also see improvements as a result of the controls from the coordinated 

strategy. For example, the Cape Romain National Wildlife Refuge (South Carolina) would see a 

5% improvement in visibility (1.16 DV); and Acadia National Park (Maine) would see a 4% 

improvement (0.76 DV) with a 200 nm ECA. Likewise, in 2002, about 3% of visibility 

impairment in southern Florida’s Everglades National Park was due to international shipping 

(0.61 DV), and this will double to 6% (1.35 DV) by 2020. Even in inland class I federal areas, 

international shipping activity is contributing to visibility degradation. In 2020, about 2.5% 

(0.28 DV) of visibility degradation in the Grand Canyon National Park located in the state of 

Arizona will be from international shipping, while almost 6% (0.81 DV) of visibility degradation 

2.4.4.1

2.4.4.2

2-83 
Regulatory Impact Analysis 

in the State of Washington’s North Cascades National Park will be from international shipping

emissions. Table 2-12 which follows contains the full visibility results from three 2020 

scenarios over the 133 analyzed areas. 

Table 2-12 Visibility Levels in Deciviews for Individual U.S. Class I Areas on the 20% Worst Days for Several 

Scenarios 

CLASS 1 AREA 

(20% WORST 

DAYS) 

STATE BASELINE 

VISIBILIT

Y 

2020

BASE 

200NM 

CONTROL 

ZERO C3

EMISSIONS 

Sipsey Wilderness AL 29.03 23.67 23.42 23.32 

Caney Creek Wilderness AR 26.36 22.20 22.01 21.88 

Upper Buffalo 

Wilderness 

AR 26.27 22.25 22.15 22.11 

Chiricahua NM AZ 13.43 13.15 13.07 13.00 

Chiricahua Wilderness AZ 13.43 13.17 13.09 13.02 

Galiuro Wilderness AZ 13.43 13.18 13.09 13.00 

Grand Canyon NP AZ 11.66 11.24 11.04 10.96 

Mazatzal Wilderness AZ 13.35 12.88 12.73 12.61 

Petrified Forest NP AZ 13.21 12.88 12.76 12.70 

Pine Mountain 

Wilderness 

AZ 13.35 12.74 12.59 12.48 

Saguaro NM AZ 14.83 14.39 14.31 14.22 

Sierra Ancha Wilderness AZ 13.67 13.33 13.21 13.10 

Sycamore Canyon 

Wilderness 

AZ 15.25 15.00 14.90 14.84 

Agua Tibia Wilderness CA 23.50 22.99 20.82 20.11 

Caribou Wilderness CA 14.15 13.73 13.51 13.43 

Cucamonga Wilderness CA 19.94 18.34 17.57 17.27 

Desolation Wilderness CA 12.63 12.29 12.11 12.07 

Dome Land Wilderness CA 19.43 18.59 18.23 18.14 

Emigrant Wilderness CA 17.63 17.35 17.14 17.08 

Hoover Wilderness CA 12.87 12.79 12.68 12.65 

Joshua Tree NM CA 19.62 17.95 17.30 17.21 

Lassen Volcanic NP CA 14.15 13.71 13.46 13.37 

Lava Beds NM CA 15.05 14.47 14.32 14.24 

NATURAL 

BACKGROUND

10.99 

11.58 

11.57 

7.21 

7.21 

7.21 

7.14 

6.68 

6.49 

6.68 

6.46 

6.59 

6.69 

7.64 

7.31 

7.06 

6.12 

7.46 

7.64 

7.91 

7.19 

7.31 

7.86 

2-84 
Chapter 2: Air Quality, Health and Welfare Effects 

Mokelumne Wilderness CA 12.63 12.40 12.21 12.16 

Pinnacles NM CA 18.46 17.86 17.11 16.89 

Point Reyes NS CA 22.81 22.38 21.71 21.54 

Redwood NP CA 18.45 18.26 17.81 17.48 

San Gabriel Wilderness CA 19.94 17.92 17.12 16.84 

San Gorgonio Wilderness CA 22.17 20.66 20.45 20.35 

San Jacinto Wilderness CA 22.17 20.25 19.86 19.55 

South Warner Wilderness CA 15.05 14.70 14.57 14.51 

Thousand Lakes 

Wilderness 

CA 14.15 13.68 13.42 13.33 

Ventana Wilderness CA 18.46 18.36 17.72 17.57 

Yosemite NP CA 17.63 17.32 17.13 17.08 

Black Canyon of the 

Gunnison NM

CO 10.33 9.77 9.69 9.66 

Eagles Nest Wilderness CO 9.61 9.05 9.00 8.98 

Flat Tops Wilderness CO 9.61 9.25 9.20 9.18 

Great Sand Dunes NM CO 12.78 12.41 12.36 12.34 

La Garita Wilderness CO 10.33 9.91 9.84 9.81 

Maroon Bells-Snowmass 

Wilderness 

CO 9.61 9.23 9.19 9.16 

Mesa Verde NP CO 13.03 12.42 12.33 12.28 

Mount Zirkel Wilderness CO 10.52 10.02 9.99 9.98 

Rawah Wilderness CO 10.52 10.00 9.97 9.95 

Rocky Mountain NP CO 13.83 13.09 13.06 13.05 

Weminuche Wilderness CO 10.33 9.88 9.80 9.77 

West Elk Wilderness CO 9.61 9.20 9.15 9.12 

Chassahowitzka FL 26.09 22.37 21.97 21.75 

Everglades NP FL 22.30 21.75 21.14 20.40 

St. Marks FL 26.03 22.37 21.96 21.65 

Cohutta Wilderness GA 30.30 23.29 23.13 23.07 

Okefenokee GA 27.13 23.86 23.30 23.07 

6.12 

7.99 

15.77 

13.91 

7.06 

7.30 

7.30 

7.86 

7.31 

7.99 

7.64 

6.24 

6.54 

6.54 

6.66 

6.24 

6.54 

6.83 

6.44 

6.44 

7.24 

6.24 

6.54 

11.21 

12.15 

11.53 

11.14 

11.44 

2-85 
Regulatory Impact Analysis 

Wolf Island GA 27.13 23.76 22.97 22.75 

Craters of the Moon NM ID 14.00 13.00 12.97 12.94 

Sawtooth Wilderness ID 13.78 13.66 13.63 13.61 

Mammoth Cave NP KY 31.37 25.43 25.33 25.30 

Acadia NP ME 22.89 20.55 19.79 19.62 

Moosehorn ME 21.72 19.02 18.55 18.38 

Roosevelt Campobello 

International Park

ME 21.72 19.25 18.58 18.23 

Isle Royale NP MI 20.74 18.99 18.84 18.81 

Seney MI 24.16 21.54 21.49 21.47 

Voyageurs NP MN 19.27 17.55 17.52 17.51 

Hercules-Glades 

Wilderness 

MO 26.75 22.84 22.74 22.72 

Anaconda-Pintler

Wilderness 

MT 13.41 13.14 13.10 13.07 

Bob Marshall Wilderness MT 14.48 14.13 14.11 14.09 

Cabinet Mountains

Wilderness 

MT 14.09 13.55 13.50 13.47 

Gates of the Mountains 

Wilderness 

MT 11.29 10.90 10.87 10.85 

Medicine Lake MT 17.72 16.20 16.18 16.17 

Mission Mountains 

Wilderness 

MT 14.48 14.02 13.99 13.97 

Scapegoat Wilderness MT 14.48 14.15 14.12 14.11 

Selway-Bitterroot 

Wilderness 

MT 13.41 13.08 13.02 12.98 

UL Bend MT 15.14 14.65 14.63 14.62 

Linville Gorge 

Wilderness 

NC 28.77 22.63 22.43 22.34 

Swanquarter NC 25.49 21.79 21.11 20.99 

Lostwood ND 19.57 17.45 17.43 17.41 

Theodore Roosevelt NP ND 17.74 16.44 16.42 16.41 

Great Gulf Wilderness NH 22.82 19.53 19.34 19.29 

Presidential Range-Dry 

River Wilderness 

NH 22.82 19.53 19.33 19.28 

Brigantine NJ 29.01 25.27 24.46 24.31 

Bandelier NM NM 12.22 11.45 11.39 11.36 

11.44 

7.53 

6.43 

11.08 

12.43 

12.01 

12.01 

12.37 

12.65 

12.06 

11.30 

7.43 

7.74 

7.53 

6.45 

7.90 

7.74 

7.74 

7.43 

8.16 

11.22 

11.94 

8.00 

7.79 

11.99 

11.99 

12.24 

6.26 

2-86 
Chapter 2: Air Quality, Health and Welfare Effects 

Bosque del Apache NM 13.80 12.93 12.89 12.87 

Gila Wilderness NM 13.11 12.59 12.52 12.48 

Pecos Wilderness NM 10.41 10.00 9.93 9.90 

Salt Creek NM 18.03 16.70 16.66 16.63 

San Pedro Parks 

Wilderness 

NM 10.17 9.52 9.44 9.41 

Wheeler Peak Wilderness NM 10.41 9.91 9.85 9.82 

White Mountain 

Wilderness 

NM 13.70 12.87 12.82 12.79 

Jarbidge Wilderness NV 12.07 11.88 11.81 11.78 

Wichita Mountains OK 23.81 20.45 20.31 20.24 

Crater Lake NP OR 13.74 13.33 13.20 13.13 

Diamond Peak 

Wilderness 

OR 13.74 13.26 13.11 13.03 

Eagle Cap Wilderness OR 18.57 17.73 17.69 17.65 

Gearhart Mountain 

Wilderness 

OR 13.74 13.41 13.30 13.25 

Hells Canyon Wilderness OR 18.55 17.16 17.12 17.07 

Kalmiopsis Wilderness OR 15.51 15.24 14.85 14.66 

Mount Hood Wilderness OR 14.86 14.30 13.93 13.64 

Mount Jefferson 

Wilderness 

OR 15.33 14.90 14.62 14.46 

Mount Washington 

Wilderness 

OR 15.33 14.88 14.62 14.46 

Mountain Lakes 

Wilderness 

OR 13.74 13.28 13.14 13.07 

Strawberry Mountain 

Wilderness 

OR 18.57 17.71 17.66 17.62 

Three Sisters Wilderness OR 15.33 14.93 14.69 14.54 

Cape Romain SC 26.48 23.51 22.35 22.14 

Badlands NP SD 17.14 15.63 15.59 15.57 

Wind Cave NP SD 15.84 14.78 14.75 14.73 

Great Smoky Mountains 

NP 

TN 30.28 24.01 23.81 23.72 

Joyce-Kilmer-Slickrock 

Wilderness 

TN 30.28 23.56 23.35 23.26 

Big Bend NP TX 17.30 16.25 16.11 16.01 

Carlsbad Caverns NP TX 17.19 16.05 15.98 15.93 

6.73 

6.69 

6.44 

6.81 

6.08 

6.44 

6.86 

7.87 

7.53 

7.84 

7.84 

8.92 

7.84 

8.32 

9.44 

8.44 

8.79 

8.79 

7.84 

8.92 

8.79 

12.12 

8.06 

7.71 

11.24 

11.24 

7.16 

6.68 

2-87 
Regulatory Impact Analysis 

Guadalupe Mountains 

NP 

TX 17.19 16.03 15.95 15.90 

Arches NP UT 11.24 10.94 10.86 10.83 

Bryce Canyon NP UT 11.65 11.41 11.28 11.22 

Canyonlands NP UT 11.24 10.96 10.90 10.89 

Zion NP UT 13.24 12.91 12.80 12.73 

James River Face

Wilderness 

VA 29.12 23.31 23.16 23.12 

Shenandoah NP VA 29.31 22.77 22.61 22.57 

Lye Brook Wilderness VT 24.45 21.02 20.77 20.72 

Alpine Lake Wilderness WA 17.84 16.85 16.56 16.26 

Glacier Peak Wilderness WA 13.96 13.85 13.53 13.19 

Goat Rocks Wilderness WA 12.76 12.23 11.95 11.70 

Mount Adams 

Wilderness 

WA 12.76 12.16 11.88 11.67 

Mount Rainier NP WA 18.24 17.47 17.02 16.66 

North Cascades NP WA 13.96 13.85 13.46 13.04 

Olympic NP WA 16.74 16.18 15.87 15.39 

Pasayten Wilderness WA 15.23 14.89 14.82 14.72 

Dolly Sods Wilderness WV 29.04 22.46 22.31 22.26 

Otter Creek Wilderness WV 29.04 22.45 22.30 22.26 

Bridger Wilderness WY 11.12 10.83 10.78 10.76 

Fitzpatrick Wilderness WY 11.12 10.87 10.81 10.79 

Grand Teton NP WY 11.76 11.37 11.32 11.30 

North Absaroka 

Wilderness 

WY 11.45 11.17 11.14 11.13 

Red Rock Lakes WY 11.76 11.45 11.40 11.38 

Teton Wilderness WY 11.76 11.43 11.38 11.36 

Washakie Wilderness WY 11.45 11.19 11.16 11.15 

Yellowstone NP WY 11.76 11.40 11.35 11.33 

6.68 

6.43 

6.86 

6.43 

6.99 

11.13 

11.35 

11.73 

8.43 

8.01 

8.36 

8.36 

8.55 

8.01 

8.44 

8.26 

10.39 

10.39 

6.58 

6.58 

6.51 

6.86 

6.51 

6.51 

6.86 

6.51 

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Chapter 2: Air Quality, Health and Welfare Effects 

S

 Community Modeling & Analysis System (CMAS) – Reports from the CMAQ Review Process can be found at:

http://www.cmascenter.org/r_and_d/cmaq_review_process.cfm?temp_id=99999 . T

 CMAQ version 4.6 was released on September 30, 2006. It is available from the Community Modeling and

Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org. 

2.4.5 Air Quality Modeling Methodology 

In this section, we present information on the air quality modeling, including the model 

domain and modeling inputs. Further discussion of the modeling methodology is included in the 

AQM TSD for the coordinated strategy.349

Air Quality Modeling Overview

A national scale air quality modeling analysis was performed to estimate future year 

annual PM2.5 concentrations, 8-hour ozone concentrations, nitrogen and sulfur deposition, and 

visibility levels. The 2002-based CMAQ modeling platform was used as the tool for the air 

quality modeling of future baseline emissions and control scenarios for the coordinated strategy. 

This platform represents a structured system of connected modeling-related tools and data that 

provide a consistent and transparent basis for assessing the air quality response to changes in 

emissions, meteorology, and/or model formulation. The base year of data used to construct this 

platform includes emissions and meteorology for 2002. The platform was developed by the U.S. 

EPA’s Office of Air Quality Planning and Standards in collaboration with the Office of Research 

and Development and is intended to support a variety of regulatory and research model 

applications and analyses. 

The CMAQ modeling system is a non-proprietary comprehensive three-dimensional, 

grid-based Eulerian air quality model designed to estimate the formation and fate of oxidant 

precursors, primary and secondary PM concentrations and deposition, over regional and urban 

spatial scales for given input sets of meteorological conditions and emissions.350,351,352 CMAQ 

is a publicly available, peer reviewed,S state-of-the-science model consisting of a number o

science attributes that are critical for simulating the oxidant precursors and non-linear organic 

and inorganic chemical relationships associated with the formation of sulfate, nitrate, and organic 

aerosols. CMAQ also simulates the transport and removal of directly emitted particles which are 

speciated as elemental carbon, crustal material, nitrate, sulfate, and organic aerosols. The 

CMAQ model version 4.6 was most recently peer-reviewed in February of 2007 for the U.S. 

EPA as reported in the “Third Peer Review of the CMAQ Model.”353{ XE "Seigneur et al., 

1999" } The CMAQ model is a well-known and well-respected tool and has been used in 

numerous national and international applications 354,355,356

This 2002 multi-pollutant modeling platform used the latest publicly-released CMAQ 

version 4.6T

 with a few minor changes and new features made internally by the U.S. EPA 

CMAQ model developers, all of which reflects updates to earlier versions in a number of areas to 

improve the underlying science. The model enhancements in CMAQ v4.6.1 include: (1) an incloud sulfate chemistry module that accounts for the nonlinear sensitivity of sulfate formation to 

varying pH; (2) an improved vertical asymmetric convective mixing module (ACM2) that allows 

in-cloud transport from a source layer to all other-in cloud layers (combined non-local and local 

2.4.5.1

f 

.

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Regulatory Impact Analysis 

closure scheme); (3) a heterogeneous reaction involving nitrate formation (gas-phase reactions 

involving N2O5 and H2O); (4) the heterogeneous N2O5 reaction probability is now temperatureand humidity-dependent, (5) an updated version of the ISORROPIA aerosol thermodynamics 

module including improved representation of aerosol liquid water content and correction in 

activity coefficients for temperature other than 298K, and (6) an updated gas-phase chemistry 

mechanism, Carbon Bond 05 (CB05) and associated Euler Backward Iterative (EBI) solver, with 

extensions to model explicit concentrations of air toxic species. 

Model Domain and Configuration 

The CMAQ modeling domain encompasses all of the lower 48 States and portions of 

Canada and Mexico. The modeling domain is made up of a large continental U.S. 36 km grid 

and two 12 km grids (an Eastern US and a Western US domain), as shown in Figure 2-33. The 

modeling domain contains 14 vertical layers with the top of the modeling domain at about 

16,200 meters, or 100 millibars (mb). 

Figure 2-33 Map of the CMAQ Modeling Domain

Model Inputs 

The key inputs to the CMAQ model include emissions from anthropogenic and biogenic 

sources, meteorological data, and initial and boundary conditions. The CMAQ meteorological 

input files were derived from a simulation of the Pennsylvania State University/National Center 

2.4.5.2

2.4.5.3

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Chapter 2: Air Quality, Health and Welfare Effects 

U Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANE-VU), Midwest 

Regional Planning Organization – Lake Michigan Air Directors Consortium (MWRPO-LADCO), Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air Partnership

(CENRAP), and Western Regional Air Partnership (WRAP). V These other modeling studies represent a wide range of modeling analyses which cover various models, model

configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules. 

for Atmospheric Research Mesoscale Model357 for the entire year of 2002. This model, 

commonly referred to as MM5, is a limited-area, nonhydrostatic, terrain-following system that 

solves for the full set of physical and thermodynamic equations which govern atmospheric 

motions.358 The meteorology for the national 36 km grid and the 12 km Eastern U.S. grid were 

developed by EPA and are described in more detail within the AQM TSD. The meteorology for 

the 12 km Western U.S. grid was developed by the Western Regional Air Partnership (WRAP) 

Regional Planning Organization. The meteorological outputs from MM5 were processed to 

create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor 

(MCIP) version 3.1 to derive the specific inputs to CMAQ, for example: horizontal wind 

components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and 

rainfall rates for each grid cell in each vertical layer.359

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model.360 The global 

GEOS-CHEM model simulates atmospheric chemical and physical processes driven by 

assimilated meteorological observations from the NASA’s Goddard Earth Observing System

(GEOS). This model was run for 2002 with a grid resolution of 2 degree x 2.5 degree (latitudelongitude) and 20 vertical layers. The predictions were used to provide one-way dynamic 

boundary conditions at three-hour intervals and an initial concentration field for the 36 km

CMAQ simulations. The future base conditions from the 36 km coarse grid modeling were used 

as the initial/boundary state for all subsequent 12 km finer grid modeling. 

The emissions inputs used for the 2002 base year and each of the future year base cases 

and control scenarios analyzed for the coordinated strategy are summarized in Chapter 3 of this 

DRIA. 

CMAQ Evaluation 

An operational model performance evaluation for PM2.5 and its related speciated 

components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.) was conducted using 

2002 state/local monitoring data in order to estimate the ability of the CMAQ modeling system

to replicate base year concentrations. In summary, model performance statistics were calculated 

for observed/predicted pairs of daily/monthly/seasonal/annual concentrations. Statistics were 

generated for the following geographic groupings: domain wide, Eastern vs. Western (divided 

along the 100th meridian), and each Regional Planning Organization (RPO) region.U The 

“acceptability” of model performance was judged by comparing our results to those found in 

recent regional PM2.5 model applications for other, non-EPA studies.V Overall, the performance 

for the 2002 modeling platform is within the range or close to that of these other applications. 

The performance of the CMAQ modeling was evaluated over a 2002 base case. The model was 

2.4.5.4

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Regulatory Impact Analysis 

able to reproduce historical concentrations of ozone and PM2.5 over the land with low amounts of 

bias and error. While we are not able to evaluate the model's performance over the ocean, there 

is no evidence to suggest that model performance is unsatisfactory over the ocean. A more 

detailed summary of the 2002 CMAQ model performance evaluation is available within the 

AQM TSD.

Model Simulation Scenarios 

As part of our analysis for this rulemaking, the CMAQ modeling system was used to 

calculate annual PM2.5 concentrations, 8-hour ozone concentrations, nitrogen and sulfur 

deposition levels and visibility estimates for each of the following emissions scenarios: 

2002 base year 

2020 base line projection 

2020 base line projection with coordinated strategy emission reductions 

2030 base line projection 

2030 base line projection with coordinated strategy emission reductions 

It should be noted that the emission control scenarios used in the air quality and benefits 

modeling are slightly different than the coordinated strategy. The differences reflect further 

refinements of the regulatory program since we performed the air quality modeling for this rule. 

Chapter 3 of this DRIA describes the changes in the inputs and resulting emission inventories 

between the preliminary assumptions used for the air quality modeling and the final regulatory 

scenario. These refinements to the program would not significantly change the results 

summarized here or our conclusions drawn from this analysis. 

We use the predictions from the model in a relative sense by combining the 2002 baseyear predictions with predictions from each future-year scenario and applying these modeled 

ratios to ambient air quality observations to estimate annual PM2.5 concentrations, 8-hour ozone 

concentrations, nitrogen and sulfur deposition levels, and visibility levels for each of the 2020 

and 2030 scenarios. The ambient air quality observations are average conditions, on a site by 

site basis, for a period centered around the model base year (i.e., 2000-2004). 

The projected annual PM2.5 design values were calculated using the Speciated Modeled 

Attainment Test (SMAT) approach. The SMAT uses an Federal Reference Method FRM mass 

construction methodology that results in reduced nitrates (relative to the amount measured by 

routine speciation networks), higher mass associated with sulfates (reflecting water included in 

FRM measurements), and a measure of organic carbonaceous mass that is derived from the 

difference between measured PM2.5 and its non-carbon components. This characterization of 

PM2.5 mass also reflects crustal material and other minor constituents. The resulting 

characterization provides a complete mass balance. It does not have any unknown mass that is 

sometimes presented as the difference between measured PM2.5 mass and the characterized 

chemical components derived from routine speciation measurements. However, the assumption 

that all mass difference is organic carbon has not been validated in many areas of the U.S. The 

2.4.5.5

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Chapter 2: Air Quality, Health and Welfare Effects 

SMAT methodology uses the following PM2.5 species components: sulfates, nitrates, ammonium, 

organic carbon mass, elemental carbon, crustal, water, and blank mass (a fixed value of 0.5 

μg/m3). More complete details of the SMAT procedures can be found in the report "Procedures 

for Estimating Future PM2.5 Values for the CAIR Final Rule by Application of the (Revised) 

Speciated Modeled Attainment Test (SMAT)".361 For this latest analysis, several datasets and 

techniques were updated. These changes are fully described within the technical support 

document for the Small SI Engine Rule modeling AQM TSD.362 The projected 8-hour ozone 

design values were calculated using the approach identified in EPA's guidance on air quality 

modeling attainment demonstrations.363

Deposition Modeling Methodology 

The CMAQ model provides estimates of the amount of nitrogen and sulfur deposition in 

each of the simulated scenarios. Additionally, we conducted analyses using a separate 

methodology in which the CMAQ outputs were used to estimate the impacts on deposition levels 

in a manner similar to how the model is used for ozone and fine particulate matter. In this 

methodology, CMAQ outputs of annual wet deposition from the 2002 base year model run are 

used in conjunction with annual wet deposition predictions from the control or future case 

scenarios to calculate relative reduction factors (RRFs) for wet deposition. Separate wet 

deposition RRFs are calculated for reduced nitrogen, oxidized nitrogen, and sulfur. These RRFs 

are multiplied by the corresponding measured annual wet deposition of reduced nitrogen, 

oxidized nitrogen, and sulfur from the National Atmospheric Deposition Program (NADP) 

network. The result is a projection of the NADP wet deposition for the control or future case 

scenarios. The projected wet deposition for each of the three species is added to the CMAQpredicted dry deposition for each of these species to produce total reduced nitrogen, total 

oxidized nitrogen, and total sulfur deposition for the control/future case scenario. The reduced 

and oxidized nitrogen depositions are summed to calculate total nitrogen deposition. 

This analysis was completed for each individual 8-digit hydrological unit code (HUC) 

within the U.S. modeling domain. Each 8-digit HUC represents a local drainage basin. There 

were 2,108 8-digit HUCs considered as part of this analysis. This assessment corroborated the 

absolute deposition modeling results. 

Visibility Modeling Methodology 

The modeling platform described in this section was also used to project changes in 

visibility. The estimate of visibility benefits was based on the projected improvement in annual 

average visibility at mandatory class I federal areas. There are 156 Federally mandated class I 

areas which, under the Regional Haze Rule, are required to achieve natural background visibility 

levels by 2064. These mandatory class I federal areas are mostly national parks, national 

monuments, and wilderness areas. There are currently 116 Interagency Monitoring of Protected 

Visual Environments (IMPROVE) monitoring sites (representing all 156 mandatory class I 

federal areas) collecting ambient PM2.5 data at mandatory class I federal areas, but not all of 

these sites have complete data for 2002. For this analysis, we quantified visibility improvement 

2.4.5.6

2.4.5.7

2-93 
Regulatory Impact Analysis 

W There are 100 IMPROVE sites with complete data for 2002. Many of these sites collect data that is 

“representative” of other nearby unmonitored mandatory class I federal areas. There are a total of 133 mandatory

class I federal areas that are represented by the 100 sites. The matching of sites to monitors is taken from “Guidance 

for Tracking Progress Under the Regional Haze Rule”. 

at the 133 mandatory class I federal areas which have complete IMPROVE ambient data for 

2002 or are represented by IMPROVE monitors with complete data.W

Visibility impairment is quantified in extinction units. Visibility degradation is directly 

proportional to decreases in light transmittal in the atmosphere. Scattering and absorption by 

both gases and particles decrease light transmittance. To quantify changes in visibility, our 

analysis computes a light-extinction coefficient (bext) and visual range. The light extinction 

coefficient is based on the work of Sisler, which shows the total fraction of light that is decreased 

per unit distance. This coefficient accounts for the scattering and absorption of light by both 

particles and gases and accounts for the higher extinction efficiency of fine particles compared to 

coarse particles. Fine particles with significant light-extinction efficiencies include sulfates, 

nitrates, organic carbon, elemental carbon, and soil.364

Visual range is a measure of visibility that is inversely related to the extinction 

coefficient. Visual range can be defined as the maximum distance at which one can identify a 

black object against the horizon sky. Visual range (in units of kilometers) can be calculated from

bext using the formula: Visual Range (km) = 3912/bext (bext units are inverse megameters [Mm-1]) 

The future year visibility impairment was calculated using a methodology which applies 

modeling results in a relative sense similar to the Speciated Modeled Attainment Test (SMAT). 

In calculating visibility impairment, the extinction coefficient is made up of individual 

component species (sulfate, nitrate, organics, etc). The predicted change in visibility is 

calculated as the percent change in the extinction coefficient for each of the PM species (on a 

daily average basis). The individual daily species extinction coefficients are summed to get a 

daily total extinction value. The daily extinction coefficients are converted to visual range and 

then averaged across all days. In this way, we can calculate annual average extinction and visual 

range at each IMPROVE site. Subtracting the annual average control case visual range from the 

base case visual range gives a projected improvement in visual range (in km) at each mandatory 

class I federal area. This serves as the visibility input for the benefits analysis (See Chapter 6 of 

this DRIA).

For visibility calculations, we are continuing to use the IMPROVE program species 

definitions and visibility formulas which are recommended in the modeling guidance.365 Each 

IMPROVE site has measurements of PM2.5 species and therefore we do not need to estimate the 

species fractions in the same way that we did for FRM sites (using interpolation techniques and 

other assumptions concerning volatilization of species). 

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Chapter 2: Air Quality, Health and Welfare Effects 

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41 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants 

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42 Devlin, R. B., McDonnell, W. F., Mann, R., Becker, S., House, D. E., Schreinemachers, D., 

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cellullar and biochemical changes in the lung. Am. J. Respir. Cell Mol. Biol., 4, 72-81. 

43 Koren, H. S., Devlin, R. B., Becker, S., Perez, R., McDonnell, W. F. (1991). Time-dependent 

changes of markers associated with inflammation in the lungs of humans exposed to ambient 

levels of ozone. Toxicol. Pathol., 19, 406-411. 

44 Koren, H. S., Devlin, R. B., Graham, D. E., Mann, R., McGee, M. P., Horstman, D. H., 

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45 Schelegle, E.S., Siefkin, A.D., McDonald, R.J. (1991). Time course of ozone-induced 

neutrophilia in normal humans. Am. Rev. Respir. Dis., 143,1353-1358. 

46 U.S. EPA. (1996). Air Quality Criteria for Ozone and Related Photochemical Oxidants. 

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47 Hodgkin, J.E., Abbey, D.E., Euler, G.L., Magie, A.R. (1984). COPD prevalence in 

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48 Euler, G.L., Abbey, D.E., Hodgkin, J.E., Magie, A.R. (1988). Chronic obstructive pulmonary 

disease symptom effects of long-term cumulative exposure to ambient levels of total oxidants 

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49 Abbey, D.E., Petersen, F., Mills, P.K., Beeson, W.L. (1993). Long-term ambient 

concentrations of total suspended particulates, ozone, and sulfur dioxide and respiratory 

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50 U.S. EPA. (2007). Review of the National Ambient Air Quality Standards for Ozone: Policy 

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Washington, DC, U.S. EPA. Retrieved on March 19, 2009 from Docket EPA-HQ-OAR-2003­

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51 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants 

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from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. 

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52 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants 

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53 Avol, E.L., Trim, S. C., Little, D.E., Spier, C.E., Smith, M. N., Peng, R.-C., Linn, W.S., 

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55 Raizenne, M.E., Burnett, R.T., Stern, B., Franklin, C.A., Spengler, J.D. (1989) Acute lung 

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56 Raizenne, M.; Stern, B.; Burnett, R.; Spengler, J. (1987 June) Acute respiratory function and 

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57 Spektor, D. M., Lippmann, M. (1991). Health effects of ambient ozone on healthy children at a 

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58 Spektor, D. M., Thurston, G.D., Mao, J., He, D., Hayes, C., Lippmann, M. (1991). Effects of 

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59 Spektor, D. M., Lippman, M., Lioy, P. J., Thurston, G. D., Citak, K., James, D. J., Bock, N., 

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normal children. Am. Rev. Respir. Dis., 137, 313-320. 

60 U.S. EPA. (2006). Air Quality Criteria for Ozone and Related Photochemical Oxidants 

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from Docket EPA-HQ-OAR-2003-0190 at http://www.regulations.gov/. 

61 Hazucha, M. J., Folinsbee, L. J., Seal, E., Jr. (1992). Effects of steady-state and variable ozone 

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62 Horstman, D.H., Ball, B.A., Folinsbee, L.J., Brown, J., Gerrity, T. (1995) Comparison of 

pulmonary responses of asthmatic and nonasthmatic subjects performing light exercise while 

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63 Horstman, D.H.,; Folinsbee, L.J., Ives, P.J., Abdul-Salaam, S., McDonnell, W.F. (1990). 

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2-99 
Regulatory Impact Analysis 

64 U.S. EPA (2008). Integrated Science Assessment (ISA) for Sulfur Oxides – Health Criteria 

(Final Report). EPA/600/R-08/047F. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843. 

65 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. 

66 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 3.1.7 and 5.3.2.1. 

67 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.4. 

68 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.4. 

69 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.4. 

70 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.3.2.1. 

71 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.3.2.1 and Figure 

3.1-2. 

72 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 3.1.4.2. 

73 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.3.2.1. 

74 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.4. 

75 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 3.3.1, Figure 3.3-2, 

Section 5.3.2.3. 

76 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 5.3.2.2. 

2-100 
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77 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 3.4.1, Figures 3.4-1 

and 3.4-2. 

78 Gauderman W.J., Avol E., Gilliland F., et al. (2004). The effect of air pollution on lung 

development from 10 to 18 years of age. N Engl J Med., 351, 1057-1067. 

79 Rojas-Martinez R., Perez-Padilla R., Olaiz-Fernandez G., Mendoza-Alvarado L., MorenoMacias H., Fortoul T., McDonnell W., Loomis D., Romieu I. (2007) Lung function growth in 

children with long-term exposure to air pollutants in Mexico City. Am J Respir Crit Care Med., 

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80 Oftedal, B. Brunekreef, B., Nystad, W., Madsen, C., Walker, S., Nafstad, P. (2008). 

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129-137. 

81 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Sections 3.4.5 and 5.3.2.4. 

82 U.S. EPA (2008). Integrated Science Assessment for Oxides of Nitrogen – Health Criteria 

(Final Report). EPA/600/R-08/071. Washington, DC,: U.S.EPA. Retrieved on March 19, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645. Section 3.4.5. 

83 U.S. EPA. (2006). Summary of Results for the 1999 National-Scale Assessment. NationalScale Air Toxics Assessment for 1999. This material is available electronically at 

http://www.epa.gov/ttn/atw/nata1999/risksum.html. 

84 U.S. EPA. (2006). National-Scale Air Toxics Assessment for 1999. This material is available 

electronically at http://www.epa.gov/ttn/atw/nata1999/. 

85 U.S. EPA (2002). Health Assessment Document for Diesel Engine Exhaust. EPA/600/8­

90/057F Office of Research and Development, Washington DC. Retrieved on March 17, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. pp. 1-1 1-2. 

86 U.S. EPA. (1999). Guidelines for Carcinogen Risk Assessment. Review Draft. NCEA-F-0644, 

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87 U.S. EPA. (1986).Guidelines for carcinogen risk assessment. EPA/630/R-00/004. Washington, 

DC: U.S. EPA. Retrieved on March 19, 2009 from 

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88 National Institute for Occupational Safety and Health (NIOSH). (1988). Carcinogenic effects 

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89 International Agency for Research on Cancer - IARC. (1997). Silica, some silicates, coal dust 

and para-aramid fibrils. In Monographs on the evaluation of carcinogenic risks to humans. Vol. 

68. Lyon, France: IARC, pp. 362-375. 

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90 National Institute for Occupational Safety and Health (NIOSH). (1988). Carcinogenic effects 

of exposure to diesel exhaust. NIOSH Current Intelligence Bulletin 50. DHHS (NIOSH) 

Publication No. 88-116. Atlanta, GA: Centers for Disease Control. Retrieved March 19, 2009 

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91 World Health Organization International Program on Chemical Safety (1996). Diesel fuel and 

exhaust emissions. In Environmental Health Criteria Vol. 171. Geneva: World Health 

Organization. Retrieved March 19, 2009 from 

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92 California Environmental Protection Agency (Cal EPA, OEHHA). (1998). Health risk 

assessment for diesel exhaust. Public and Scientific Review Draft. Sacramento, CA: Cal EPA. 

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93 National Toxicology Program (NTP). (2000). 9th Report on Carcinogens. Research Triangle 

Park, NC: Public Health Service, U.S. Department of Health and Human Services. Available 

from: http://ntp-server.niehs.nih.gov. 

94 Health Effects Institute (HEI). (1995). Diesel exhaust: a critical analysis of emissions, 

exposure, and health effects. A Special Report of the Institute's Diesel Working Group. 

Cambridge, MA: Health Effects Institute. 

95 Health Effects Institute (HEI). (1999). Diesel emissions and lung cancer: epidemiology and 

quantitative risk assessment. A special report of the Institute's Diesel Epidemiology Expert 

Panel. Cambridge, MA: Health Effects Institute (HEI). 

96 Health Effects Institute (HEI). (2002). Research directions to improve estimates of human 

exposure and risk assessment. A special report of the Institute's Diesel Epidemiology Working 

Group. Cambridge, MA: Health Effects Institute. 

97 U.S. EPA (2002). Health Assessment Document for Diesel Engine Exhaust. EPA/600/8­

90/057F Office of Research and Development, Washington DC. Retrieved on March 17, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. pp. 9-11. 

98 Bhatia, R., Lopipero, P., Smith, A. (1998). Diesel exposure and lung cancer. Epidemiology, 

9(1), 84-91. 

99 Lipsett, M. Campleman, S. (1999). Occupational exposure to diesel exhaust and lung cancer: 

a meta-analysis. Am J Public Health, 80(7), 1009-1017. 

100 U.S. EPA (2002), National-Scale Air Toxics Assessment for 1996. This material is available 

electronically at http://www.epa.gov/ttn/atw/nata/. 

101 U.S. EPA. (2006). National-Scale Air Toxics Assessment for 1999. This material is available 

electronically at http://www.epa.gov/ttn/atw/nata1999/. 

102 Ishinishi, N. Kuwabara, N. Takaki, Y., et al. (1988). Long-term inhalation experiments on 

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103 Heinrich, U., Fuhst, R., Rittinghausen, S., et al. (1995). Chronic inhalation exposure of Wistar 

rats and two different strains of mice to diesel engine exhaust, carbon black, and titanium 

dioxide. Inhal Toxicol, 7, 553-556. 

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104 Mauderly, J.L., Jones, R.K., Griffith, W.C., et al. (1987). Diesel exhaust is a pulmonary 

carcinogen in rats exposed chronically by inhalation. Fundam. Appl. Toxicol., 9, 208-221. 

105 Nikula, K.J., Snipes, M.B., Barr, E.B., et al. (1995). Comparative pulmonary toxicities and 

carcinogenicities of chronically inhaled diesel exhaust and carbon black in F344 rats. Fundam. 

Appl. Toxicol, 25,80-94. 

106 U.S. EPA (2002). Health Assessment Document for Diesel Engine Exhaust. EPA/600/8­

90/057F Office of Research and Development, Washington DC. Retrieved on March 17, 2009 

from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=29060. p. 9-9. 

107 Reger, R., Hancock, J., Hankinson, J., et al. (1982). Coal miners exposed to diesel exhaust 

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108 Attfield, MD. (1978). The effect of exposure to silica and diesel exhaust in underground metal 

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109 Wade, J.F., III, Newman, L.S. (1993) Diesel asthma: reactive airways disease following 

overexposure to locomotive exhaust. J Occup Med, 35, 149-154. 

110 U.S. EPA. (2006). National-Scale Air Toxics Assessment for 1999. This material is available 

electronically at http://www.epa.gov/ttn/atw/nata1999/. 

111 U.S. EPA. (2007). Chapter 3: Air Quality and Resulting Health and Welfare Effects of Air 

Pollution from Mobile Sources. In Mobile Source Air Toxics Rule (Control of Hazardous Air 

Pollutants from Mobile Sources; 72 FR 8428, February 26, 2007) Regulatory Impact Analysis. 

Retrieved March 19, 2009 from http://www.epa.gov/otaq/regs/toxics/420r07002.pdf. 

112 State of California Air Resources Board. (2009 March). Rail Yard Health Risk Assessments 

and Mitigation Measures. Retrieved March 19, 2009 from 

http://www.arb.ca.gov/railyard/hra/hra.htm. 

113 Di, P., Servin, A., Rosenkranz, K., Schwehr, B., Tran, H., (2006). Diesel Particulate Matter 

Exposure Assessment Study for the Ports of Los Angeles and Long Beach. Sacramento, CA: 

California EPA, California Air Resources Board (CARB). Retrieved March 19, 2009 from 

http://www.arb.ca.gov/regact/marine2005/portstudy0406.pdf. 

114 Di, P., Servin, A., Rosenkranz, K., Schwehr, B., Tran, H., (2006). Diesel Particulate Matter 

Exposure Assessment Study for the Ports of Los Angeles and Long Beach. Sacramento, CA: 

California EPA, California Air Resources Board (CARB). Retrieved March 19, 2009 from 

http://www.arb.ca.gov/regact/marine2005/portstudy0406.pdf. 

115 ICF International. September 28, 2007. Estimation of diesel particulate matter concentration 

isopleths for marine harbor areas and rail yards. Memorandum to EPA under Work Assignment 

Number 0-3, Contract Number EP-C-06-094. This memo is available in Docket EPA-HQ-OAR2007-0121. 

116 ICF International. September 28, 2007. Estimation of diesel particulate matter population 

exposure near selected harbor areas and rail yards. Memorandum to EPA under Work 

Assignment Number 0-3, Contract Number EP-C-06-094. This memo is available in Docket 

EPA-HQ-OAR-2007-0121. 

2-103 
Regulatory Impact Analysis 

117 ICF International. December 1, 2008. Estimation of diesel particulate matter concentration 

isopleths near selected harbor areas with revised emissions (revised). Memorandum to EPA 

under Work Assignment Number 1-9, Contract Number EP-C-06-094. This memo is available 

in Docket EPA-HQ-OAR-2007-0121. 

118 ICF International. December 10, 2008. Estimation of diesel particulate matter population 

exposure near selected harbor areas with revised harbor emissions (revised). Memorandum to 

EPA under Work Assignment Number 2-9, Contract Number EP-C-06-094. This memo is 

available in Docket EPA-HQ-OAR-2007-0121. 

119 State of California Air Resources Board. Diesel Particulate Matter Exposure Assessment 

Study for the Ports of Los Angeles and Long Beach, April 2006. This study found that oceangoing vessels transiting within California Coastal Waters, maneuvering or hoteling contributed a 

disproportional amount of the regional diesel PM emissions (about 10 percent of the total) for the 

California South Coast Air Basin in 2002. This study also found that about 96 percent of the 

ocean-going vessel diesel PM emissions were released in the offshore shipping lanes leading into 

the Ports of Long Beach and Los Angeles 

120 U.S. EPA 2004, Lyyränen et al., 1999 

121 U.S. EPA (2008). Nitrogen Dioxide/Sulfur Dioxide Secondary NAAQS Review: Integrated 

Science Assessment (ISA).(Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F. 

122 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and SulfurEcological Criteria (Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F. Retrieved on 

March 24, 2009, from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485 

123 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and SulfurEcological Criteria (Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F. Retrieved on 

March 24, 2009, from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485 

124 U.S. EPA, 2008. Integrated Science Assessment for Oxides of Nitrogen and SulfurEcological Criteria (Final). U.S. EPA, Washington D.C., EPA/600/R-08/082F. Retrieved on 

March 24, 2009, from http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485 

125 U.S. EPA. (2008). Risk and exposure assessment for the review of the secondary national 

ambient air quality standards for oxides of nitrogen and oxides of sulfur (Draft). Research 

Triangle Park, NC; Office of Air Quality Planning and Standards; U.S. Environmental Protection 

Agency. 

126 U.S. EPA. (2006). Wadeable streams assessment: A collaborative survey of the nation's 

streams. (Report no EPA-841-B-06-002). Washington, DC; Office of Water; Office of Research 

and Development; U.S. Environmental Protection Agency. 

127 Lawrence, G.B., Roy, K.M., Baldigo, B.P., Simonin, H.A., Capone, S.B., Sutherland, J.W., 

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128 Baker, J.P., Bernard, D.P., Christensen, S.W., & Sale, M.J. (1990b). Biological effects of 

changes in surface water acid-base chemistry. (State of science / technology report #13). 

Washington DC: National Acid Precipitation Assessment Program (NAPAP). 

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129 Kaufmann, P.R., Herlihy, A.T., Elwood, J.W., Mitch, M.E., Overton, W.S., Sale, M.J., 

Messer, J.J., Cougan, K.A., Peck, D.V., Reckhow, K.H., Kinney, A.J., Christie, S.J., Brown, 

D.D., Hagley, C.A., & Jager, H.I. (1988). Chemical characteristics of streams in the Mid-Atlantic 

and Southeastern United States. Volume I: Population descriptions and physico-chemical 

relationships. (EPA/600/3-88/021a). Washington, DC: U.S. Environmental Protection Agency. 

130 Kaufmann, P.R., Herlihy, A.T., Mitch, M.E., Messer, J.J., & Overton, W.S. (1991). Stream 

chemistry in the eastern United States 1. synoptic survey design, acid-base status, and regional 

patterns. Water Resources Research, 27, 611-627. 

131 Landers, D.H., Eilers, J.M., Brakke, D.F., Overton, W.S., Kellar, P.E., Silverstein, W.E., 

Schonbrod, R.D., Crowe, R.E., Linthurst, R.A., Omernik, J.M., Teague, S.A., & Meier, E.P. 

(1987). Western lake survey phase I: Characteristics of lakes in the western United States. 

Volume I: Population descriptions and physico-chemical relationships. (EPA/600/3-86/054a). 

Washington, DC: U.S. Environmental Protection Agency. 

132 Linthurst, R.A., Landers, D.H., Eilers, J.M., Brakke, D.F., Overton, W.S., Meier, E.P., & 

Crowe, R.E. (1986). Characteristics of Lakes in the eastern United States. Volume I. Population 

descriptions and physico-chemical relationships. (EPA-600/4-86-007a). Washington, DC: Office 

of Acid Deposition; Environmental Monitoring, and Quality Assurance; U.S. Environmental 

Protection Agency. 

133 Linthurst, R.A., Landers, D.H., Eilers, J.M., Kellar, P.E., Brakke, D.F., Overton, W.S., 

Crowe, R., Meier, E.P., Kanciruk, P., & Jeffries, D.S. (1986). Regional chemical characteristics 

of lakes in North America Part II: eastern United States. Water, Air, & Soil Pollution, 31, 577­

591. 

134 Stoddard, J., Kahl, J.S., Deviney, F.A., DeWalle, D.R., Driscoll, C.T., Herlihy, A.T., Kellogg, 

J.H., Murdoch, P.S., Webb, J.R., & Webster, K.E. (2003). Response of surface water chemistry 

to the Clean Air Act Amendments of 1990 (EPA/620/R-03/001). Research Triangle Park, NC: 

National Health and Environmental Effects Research Laboratory; Office of Research and 

Development; U.S. Environmental Protection Agency. 

135 Charles, D.F. (1991). Christie, S. (Eds.). Acidic deposition and aquatic ecosystems: Regional 

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358 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth-Generation Penn 

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for 

Atmospheric Research, Boulder CO. 

359 U.S. Environmental Protection Agency, Byun, D.W., and Ching, J.K.S., Eds, 1999. Science 

algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system, 

EPA/600/R-99/030, Office of Research and Development). Please also see: 

http://www.cmascenter.org/ 

360 Yantosca, B. (2004). GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling 

Group, Harvard University, Cambridge, MA, October 15, 2004. 

361 U.S. EPA, (2004), Procedures for Estimating Future PM2.5 Values for the CAIR Final Rule 

by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)- Updated 11/8/04. 

362 U.S. EPA, (2008), Control of Emissions from Nonroad Spark-Ignition Engines and 

Equipment, Technical Support Document 

363 EPA's guidance on air quality modeling attainment demonstrations 

2-121 
Regulatory Impact Analysis 

364 Sisler, J.F. (1996) Spatial and seasonal patterns and long term variability of the composition 

of the haze in the United States: an analysis of data from the IMPROVE network. CIRA Report, 

ISSN 0737-5352-32, Colorado State University. 

365 U.S. EPA, Guidance on the Use of Models and Other Analyses For Demonstrating Attainment 

of Air Quality Goals for Ozone, PM2.5, and Regional Haze; EPA-454/B-07-002; Research 

Triangle Park, NC; April 2007. 

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Chapter 3: Emission Inventory 

CHAPTER 3: EMISSION INVENTORY 

3.1 Introduction................................................................................................................3-2

3.2 Modeling Domain and Geographic Regions............................................................ 3-3 

3.3 Development of 2002 Baseline Inventory.................................................................3-5 

3.3.1 Outline of Methodology..................................................................................... 3-5 

3.3.2 Near Port Emissions .......................................................................................... 3-6 

3.3.3 Interport Emissions ......................................................................................... 3-79 

3.3.4 2002 Baseline Emission Inventories ............................................................... 3-97 

3.4 Development of 2020 and 2030 Scenarios..............................................................3-99 

3.4.1 Outline of Methodology................................................................................... 3-99 

3.4.2 Growth Factors by Geographic Region ......................................................... 3-99 

3.4.3 Emission Controls in Baseline and Control Scenarios ...............................3-118 

3.4.4 Calculation of Near Port and Interport Inventories................................... 3-124 

3.4.5 2020 and 2030 Baseline Inventories.............................................................. 3-131 

3.4.6 2020 and 2030 Control Inventories .............................................................. 3-133 

3.5 Estimated Category 3 Inventory Contribution................................................... 3-135 

3.5.1 Baseline Contribution of C3 Vessels to National Level Inventory ............ 3-135 

3.5.2 Contribution to Mobile Source Inventories for Selected Cities.................3-139 

3.6 Projected Emission Reductions ............................................................................ 3-140 

3.7 Inventories Used for Air Quality Modeling.........................................................3-141 

3-1 
Regulatory Impact Analysis 

CHAPTER 3 Emission Inventory 

3.1 Introduction 

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

(U.S.) mobile source emission inventory. The U.S. ship inventory reported here focuses on 

Category 3 (C3) vessels, which use C3 engines for propulsion. C3 engines are defined as having 

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

from both propulsion and auxiliary engines used on these vessels, as well as those on gas and 

steam turbine vessels. 

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

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

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

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

discussed in Sections 3.3.2.5 and 3.3.3.2, this inventory is primarily based on activity data for 

ships that carry foreign cargo. Category 3 vessels carrying domestic cargo that operate only 

between U.S. ports are only partially accounted for in this inventory.1 Emissions due to military 

vessels are also excluded. 

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

independently constructed port and interport emissions inventories. Port inventories were 

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

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

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

activity for each port. Interport emissions were obtained using the Waterway Network Ship 

Traffic, Energy and Environment Model (STEEM).3,4 STEEM also uses a “bottom-up” 

approach, estimating emissions from C3 vessels using historical North American shipping 

activity, ship characteristics, and activity-based emission factors. STEEM was used to quantify 

and geographically (i.e., spatially) represent interport vessel traffic and emissions for vessels 

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

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

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

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

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

the near-port portion of the STEEM inventory was simply retained. This was done for a base 

year of 2002. Inventories for 2020 were then projected using regional growth rates5,6

 and 

adjustment factors to account for the International Maritime Organization (IMO) Tier 1 and Tier 

2 NOX standards and NOX retrofit program.

2

 Inventories incorporating additional Tier 3 NOX 

and fuel sulfur controls within the proposed Emission Control Area (ECA) were also developed 

for 2020 and 2030. 

This chapter details the methodologies used to create the baseline and future year 

inventories and presents the resulting inventories for the U.S. Section 3.2 describes the modeling 

domain and geographic regions used in this analysis. Section 3.3 describes the methodology and 

3-2 
Chapter 3: Emission Inventory 

results for the 2002 base year inventory. Section 3.4 follows with a discussion of the growth 

rates and methodology used to create the 2020 and 2030 baseline and control inventories. 

Section 3.5 presents the estimated contribution of Category 3 vessels to U.S. national and local 

inventories. Section 3.6 follows with estimates of the projected emission reductions due to the 

proposed control program. Section 3.7 concludes the chapter by describing the changes in the 

inventories between the baseline scenarios used for the air quality modeling and the updated 

baseline scenarios in this proposed rule. 

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

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

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

oxides of nitrogen (NOX), particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), 

hydrocarbons (HC), carbon monoxide (CO), and carbon dioxide (CO2). The PM inventories 

include directly emitted PM only, although secondary sulfates are taken into account in the air 

quality modeling. 

3.2 Modeling Domain and Geographic Regions 

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

that extends 200 nautical miles (nm) from the official U.S. baseline, 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. This boundary is roughly equivalent to the border of the U.S 

Exclusive Economic Zone. The U.S. region was then clipped to the boundaries of the U.S. 

Exclusive Economic Zone. The boundary was divided into regions using geographic 

information system (GIS) shapefiles obtained from the National Oceanic and Atmospheric 

Administration, Office of Coast Survey.7 The accuracy of the NOAA shapefiles was verified 

with images obtained from the U.S. Geological Survey. The confirmed NOAA shapefiles were 

then combined with a shapefile of the U.S. international border from the National Atlas.8 

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

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

emission inventories for the years 2020 and 2030, as described later in this document. 

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

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

North). 

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

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

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

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

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

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

the U.S. West Coast. 

3-3 
Regulatory Impact Analysis 

 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. 

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

southeastern islands of Hawaii, Maui, Oahu, Molokai, Niihau, Kauai, Lanai, and 

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

then designated Hawaii West. 

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

below and shown in Figure 3-1. U.S. territories are not included in this analysis. 

 South Pacific (SP) 

 North Pacific (NP) 

 East Coast (EC) 

 Gulf Coast (GC) 

 Alaska Southeast (AE) 

 Alaska West (AW)

 Hawaii East (HE) 

 Hawaii West (HW)

 Great Lakes (GL) 

AE AW 

HW 

HE 

SP 

NP 

GL 

GC 

EC 

Figure 3-1 Regional Modeling Domains 

3-4 
Chapter 3: Emission Inventory 

3.3 Development of 2002 Baseline Inventory 

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

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

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

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

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

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

as for nine geographic regions within the U.S., are then presented. 

3.3.1 Outline of Methodology 

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

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

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

Based on an ICF International analysis,9

 these 117 commercial ports encompass nearly all U.S. 

C3 vessel calls.10 

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

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

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

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

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

(e.g., emission factors). 

The interport inventory was estimated using the Waterway Network Ship Traffic, Energy, 

and Environmental Model (STEEM).3,4 The model geographically characterizes emissions from 

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

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

positioning reports. The model then uses detailed information about ship destinations, ship 

attributes (e.g., vessel speed and engine horsepower), and emission factors to produce spatially 

allocated (i.e., gridded) emission estimates for ships engaged in foreign commerce. 

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

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

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

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

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

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

ship is traveling at reduced speeds varies by port. Also, the distance a ship traverses at reduced 

speeds often consists of two operational modes: a reduced speed zone (RSZ) as a ship enters or 

leaves the port area and actual maneuvering at a very low speed near the dock. Third, the 

STEEM model assumes that the maneuvering distance occurs at an engine load of 20 percent, 

which represents a vessel speed of approximately 60 percent of cruise speed. This is 

considerably faster than ships would maneuver near the docks. The single maneuvering speed 

assumed by STEEM also does not reflect the fact that the reduced speed zone, and therefore 

emissions, may vary by port. Fourth, and finally, the STEEM model does not include the 

3-5 
Regulatory Impact Analysis 

emissions from auxiliary engines during hotelling operations at the port. The near-port 

inventories correct these issues. 

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

most accurate for vessels while cruising in ocean or Great Lakes shipping lanes, and the near 

port inventories, which use more detailed local port information, are significantly more accurate 

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

the near port inventories, which extend 25 nautical miles and 7 nautical miles from the terminus 

of the RSZ for deep water ports and Great Lake ports, respectively, and (2) the remaining 

interport portion of the STEEM inventory, which extends from the endpoint of the near port 

inventories to the 200 nautical mile boundary or international border with Canada, as 

appropriate. Near some ports, a portion of the underlying STEEM emissions were retained if it 

was determined that the STEEM emissions included ships traversing the area near a port, but not 

actually entering or exiting the port. 

3.3.2 Near Port Emissions 

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

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

propulsion and auxiliary engines on these vessels. 

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

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

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

summarized here was conducted by ICF International under contract to EPA.2 The ICF 

documentation provides more detailed information.2 

3.3.2.1 Selection of Individual Ports to be Analyzed 

All 150 deep sea and Great Lake ports in the Principal Ports of the United States dataset11 

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

there is no information in the U.S. Army Corps of Engineers (USACE) entrances and clearances 

data about domestic traffic. (See Section 3.3.2.5 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 ARB provided the necessary data and estimates for those 

ports. This is discussed in Section 3.3.2.4.1. Also, a conglomerate port in the Puget Sound area 

was added as discussed in Section 3.3.2.4.2. The final list of 117 deep sea and Great Lake ports, 

along with their coordinates, is given in the Appendix, Table 3-102. 

3.3.2.2 Port Methodology 

Near port emissions for each port are calculated for four modes of operation: (1) 

hotelling, (2) maneuvering, (3) reduced speed zone (RSZ), and (4) cruise. Hotelling, or 

dwelling, occurs while the vessel is docked or anchored near a dock, and only the auxiliary 

engine(s) are being used to provide power to meet the ship’s energy needs. Maneuvering occurs 

within a very short distance of the docks. The RSZ varies from port to port, though generally the 

3-6 
Chapter 3: Emission Inventory 

RSZ would begin and end when the pilots board or disembark, and typically occurs when the 

near port shipping lanes reach unconstrained ocean shipping lanes. The cruise mode emissions 

in the near ports analysis extend 25 nautical miles beyond the end of the RSZ lanes for deep 

water ports and 7 nautical miles for Great Lake ports. 

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

used is as follows: 

Equation 3-1 

Emissionsmode[eng] (calls)(P[eng] )(hrs/ callmode)(LFmode[eng] )(EF[eng] )(Adj)(106 tonnes/ g) 

Where: 

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

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

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

hrs/callmode = Hours per call by mode 

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

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

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

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

10-6 = Conversion factor from grams to metric tonnes 

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

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

In addition, cruise mode activity is based on cruise distance and speed inputs. The following 

sections provide the specific equations used to calculate propulsion and auxiliary emissions for 

each activity mode. 

3.3.2.2.1 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 3-2 

Emissionscruise[main]  (calls)(P[main])(hrs/ callcruise)(LFcruise[main])(EF[main])(106 tonnes/ g) 

Where: 

Emissionscruise [main] = Metric tonnes emitted from main engines in cruise mode 

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

P[main] = Total main engine power, in kilowatts 

hrs/callcruise = Hours per call for cruise mode 

LFcruise [main] = Load factor for main engines in cruise mode (unitless) 

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these 

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

10-6 = Conversion factor from grams to metric tonnes 

3-7 
Regulatory Impact Analysis 

In addition, the time in cruise is calculated as follows: 

Equation 3-3 

Hrs / callcruise  Cruise Distance[nmiles]/ Cruise Speed [knots] 2 trips / 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 

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 3-4 

LoadFactorcruise[main]  Cruise Speed[knots]/ MaximumSpeed[knots]

3 

Since cruise speed is estimated at 94 percent of maximum speed12, the load factor for 

main engines at cruise is 0.83. 

Substituting Equation 3-3 for time in cruise into Equation 3-2, and using the load factor 

of 0.83, the equation used to calculate cruise mode emissions for the main engines becomes the 

following: 

Equation 3-5 Cruise Mode Emissions for Main Engines 

Emissions (calls)(P )(CruiseDistance/CruiseSpeed)(2trips/call)0.83(EF )(106 tonnes/g) cruise[main] [main] [main] 

Where: 

Emissionscruise [main] = Metric tonnes emitted from main engines in cruise mode 

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

P[main] = Total main engine power, in kilowatts 

Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles 

for Great Lake ports) 

Cruise speed = vessel service speed, in knots 

2 trips/call = Used to calculate round trip cruise distance 

0.83 = Load factor for main engines in cruise mode, unitless 

EF [main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these 

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

10-6 = Conversion factor from grams to metric tonnes 

The equation used to calculate cruise mode emissions for the auxiliary engines is: 

Equation 3-6 Cruise Mode Emissions for Auxiliary Engines 

Emissionscruise[aux] (calls)(P[aux])(CruiseDistance/CruiseSpeed)(2trips/call)(LF [ ])(EF[aux])(106 tonnes/ g) cruiseaux 

3-8 
Chapter 3: Emission Inventory 

Where: 

Emissionscruise[aux] = Metric tonnes emitted from auxiliary engines in cruise mode 

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

P[aux] = Total auxiliary engine power, in kilowatts 

Cruise distance = one way distance (25 nautical miles for deep sea ports, and 7 nautical miles 

for Great Lake ports) 

Cruise speed = vessel service speed, in knots 

2 trips/call = Used to calculate round trip cruise distance 

LFcruise [aux] = Load factor for auxiliary engines in cruise mode, unitless (these vary by ship 

type and activity mode) 

EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

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

10-6 = Conversion factor from grams to metric tonnes 

The inputs of calls, cruise distance, and vessel speed are the same for main and auxiliary 

engines. Relative to the main engines, auxiliary engines have separate inputs for engine power, 

load factor, and emission factors. The activity-related inputs, such as engine power, vessel 

speed, and calls, can be unique to each ship calling on a port, if ship-specific information is 

available. For this analysis, as discussed in Section 3.3.2.3.1.1, these inputs were developed by 

port for bins that varied by ship type, engine type, and dead weight tonnage (DWT) range. 

3.3.2.2.2 Reduced Speed Zone 

RSZ emissions are calculated for both propulsion (main) and auxiliary engines. The 

basic equation used to calculate RSZ mode emissions for the main engines is: 

Equation 3-7 

EmissionsRSZ[main] (calls)(P[main])(hrs/callRSZ)(LFRSZ[main])(EF[main])(Adj)(106 tonnes/ g) 

Where: 

EmissionsRSZ[main] = Metric tonnes emitted from main engines in RSZ mode 

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

P[main] = Total main engine power, in kilowatts 

hrs/callRSZ = Hours per call for RSZ mode 

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

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these 

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

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

10-6 = Conversion factor from grams to metric tonnes 

In addition, the time in RSZ mode is calculated as follows: 

Equation 3-8 

Hrs / callRSZ  RSZ Distance[nmiles]/ RSZ Speed [knots] 2trips / call 

Load factor during the RSZ mode is calculated as follows: 

3-9 
Regulatory Impact Analysis 

Equation 3-9 

LoadFactorRSZ [main]  RSZ Speed / MaximumSpeed 

3 

In addition: 

Equation 3-10 

MaximumSpeed  Cruise Speed / 0.94

Where: 

0.94 = Fraction of cruise speed to maximum speed 

Substituting 

Equation 3-10 into Equation 3-9, the equation to calculate load factor becomes: 

Equation 3-11 

LoadFactorRSZ [main]  RSZ Speed  0.94 / Cruise Speed

3 

Where: 

0.94 = Fraction of cruise speed to maximum speed 

Load factors below 2 percent were set to 2 percent as a minimum. 

Substituting Equation 3-8 for time in mode and Equation 3-11 for load factor into 

Equation 3-7 , the expression used to calculate RSZ mode emissions for the main engines 

becomes: 

Equation 3-12 RSZ Mode Emissions for Main Engines 

EmissionsRSZ[aux]  (calls)(P[aux])(RSZDistance/ RSZSpeed)(2trips/call)RSZSpeed0.94/CruiseSpeed

3 

(EF[aux])Adj(106 tonnes/ g) 

Where: 

EmissionsRSZ[main] = Metric tonnes emitted from main engines in RSZ mode 

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

P[main] = Total main engine power, in kilowatts 

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 significantly as the load decreases. 

During the RSZ mode, load factors can fall below 20 percent. Low load multiplicative 

3-10 
Chapter 3: Emission Inventory 

adjustment factors were developed and applied when the load falls below 20 percent (0.20). If 

the load factor is 0.20 or greater, the low load adjustment factor is set to 1.0. 

The equation used to calculate RSZ mode emissions for the auxiliary engines is: 

Equation 3-13 RSZ Mode Emissions for Auxiliary Engines 

EmissionsRSZ[aux]  (calls)(P[aux] )(RSZDistance/ RSZSpeed)(2trips/call)(LFRSZ[aux] )(EF[aux] )(106 tonnes/ g) 

Where: 

EmissionsRSZ[aux] = Metric tonnes emitted from auxiliary engines in RSZ mode 

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

P[aux] = Total auxiliary engine power, in kilowatts 

RSZ distance = one way distance, in nautical miles (specific to each port) 

RSZ speed = speed, in knots (specific to each port) 

2 trips/call = Used to calculate round trip cruise distance 

LFRSZ [aux] = Load factor for auxiliary engines in RSZ mode, unitless (these vary by ship type 

and activity mode) 

EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

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

10-6 = Conversion factor from grams to metric tonnes 

Unlike main engines, there is no need for a low load adjustment factor for auxiliary 

engines, because of the way they are generally operated. When only low loads are needed, one 

or more engines are shut off, allowing the remaining engines to maintain operation at a more 

efficient level. 

The inputs of calls, RSZ distance, and RSZ speed are the same for main and auxiliary 

engines. Relative to the main engines, auxiliary engines have separate inputs for engine power, 

load factor, and emission factors. The RSZ distances vary by port rather than vessel or engine 

type. Some RSZ speeds vary by ship type, while others vary by DWT. Mostly, however, RSZ 

speed is constant for all ships entering the harbor area. All Great Lake ports have reduced speed 

zone distances of three nautical miles occurring at halfway between cruise speed and 

maneuvering speed. 

3.3.2.2.3 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 3-14 

Emissionsman[main]  (calls)(P[main])(hrs/callman)(LFman[main])(EF[main])(Adj)(106 tonnes/ g) 

Where: 

Emissionsman[main] = Metric tonnes emitted from main engines in maneuvering mode 

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

P[main] = Total main engine power, in kilowatts 

hrs/callman = Hours per call for maneuvering mode 

3-11 
Regulatory Impact Analysis 

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

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these 

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

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

10-6 = Conversion factor from grams to metric tonnes 

Maneuvering time-in-mode is estimated based on the distance a ship travels from the 

breakwater or port entrance to the pier/wharf/dock (PWD). Maneuvering times also include 

shifts from one PWD to another or from one port within a greater port area to another. Average 

maneuvering speeds vary from 3 to 8 knots depending on direction and ship type. For 

consistency, maneuvering speeds were assumed to be the dead slow setting of approximately 5.8 

knots. 

Load factor during maneuvering is calculated as follows: 

Equation 3-15 

LoadFactorman[main]  Man Speed[knots]/ MaximumSpeed[knots]

3 

In addition: 

Equation 3-16 

MaximumSpeed  Cruise Speed[knots]/ 0.94 

Where: 

0.94 = Fraction of cruise speed to maximum speed 

Also, the maneuvering speed is 5.8 knots. Substituting Equation 3-16 into Equation 3-15, and 

using a maneuvering speed of 5.8 knots, the equation to calculate load factor becomes: 

Equation 3-17 

LoadFactorman[main]  5.45/ Cruise Speed 

3 

Load factors below 2 percent were set to 2 percent as a minimum. 

Substituting Equation 3-17 for load factor into Equation 3-14, the expression used to 

calculate maneuvering mode emissions for the main engines becomes: 

Equation 3-18 Maneuvering Mode Emissions for Main Engines 

Emissionsman[main] (calls)(P[main])(hrs/ callman)(5.45/CruiseSpeed)

3 (EF[main])(Adj)(106 tonnes/ g) 

Where: 

Emissionsman[main] = Metric tonnes emitted from main engines in maneuvering mode 

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

P[main] = Total main engine power, in kilowatts 

hrs/callman = Hours per call for maneuvering mode 

Cruise speed = Vessel service speed, in knots 

3-12 
Chapter 3: Emission Inventory 

EF[main] = Emission factor for main engines for the pollutant of interest, in g/kW-hr (these 

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

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

10-6 = Conversion factor from grams to metric tonnes 

Since the load factor during maneuvering usually falls below 20 percent, low load 

adjustment factors are also applied accordingly. Maneuvering times are not readily available for 

all 117 ports. For this analysis, maneuvering times and load factors available for a subset of the 

ports were used to calculate maneuvering emissions for the remaining ports. This is discussed in 

more detail in Section 3.3.2.3.8. 

The equation used to calculate maneuvering mode emissions for the auxiliary engines is: 

Equation 3-19 Maneuvering Mode Emissions for Auxiliary Engines 

Emissionsman[aux]  (calls) (P[aux] ) (hrs/ callman )(LFman[aux] ) (EF[aux] )(106 tonnes/ g) 

Where: 

Emissionsman[aux] = Metric tonnes emitted from auxiliary engines in maneuvering mode 

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

P[aux] = Total auxiliary engine power, in kilowatts 

hrs/callman = Hours per call for maneuvering mode 

LFman [aux] = Load factor for auxiliary engines in maneuvering mode, unitless (these vary by 

ship type and activity mode) 

EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

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

10-6 = Conversion factor from grams to metric tonnes 

Low load adjustment factors are not applied for auxiliary engines. 

3.3.2.2.4 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 3-20 Hotelling Mode Emissions for Auxiliary Engines 

Emissionshotel[aux]  (calls)(P[aux] )(hrs/ callhotel )(LFhotel[aux] )(EF[aux] )(106 tonnes/ g) 

Where: 

Emissionshotel[aux] = Metric tonnes emitted from auxiliary engines in hotelling mode 

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

P[aux] = Total auxiliary engine power, in kilowatts 

hrs/callhotel = Hours per call for hotelling mode 

LFhotel [aux] = Load factor for auxiliary engines in hotelling mode, unitless (these vary by ship 

type and activity mode) 

3-13 
Regulatory Impact Analysis 

EF[aux] = Emission factor for auxiliary engines for the pollutant of interest, in g/kW-hr (these 

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

10-6 = Conversion factor from grams to metric tonnes 

Hotelling times are not readily available for all 117 ports. For this analysis, hotelling 

times available for a subset of the ports were used to calculate hotelling emissions for the 

remaining ports. This is discussed in more detail in Section 3.3.2.3.8. 

3.3.2.3 Inputs for Port Emission Calculations 

From a review of the equations described in Section 3.3.2.2, 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. 

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

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

for 2002,13 together with Lloyd’s data for ship characteristics,14 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. 

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

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

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

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

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

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

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

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

3-14 
Chapter 3: Emission Inventory 

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 3.3.2.3.4. 

Table 3-1 Bins and Average Ship Characteristics for Deep Sea Ports 

Ship Type 

Main 

Engine a DWT Range Calls 

Engine Power (kW) Cruise 

Speed 

(kts) 

DWT 

Main Auxiliary 

AUTO CARRIER 

MSD 

< 10,000 35 6,527 1,736 16.0 6,211 

10,000 – 20,000 224 10,499 2,793 18.2 13,003 

20,000 – 30,000 28 6,620 1,761 13.0 22,268 

MSD Total 286 9,640 2,564 17.4 13,063 

SSD 

<10,000 84 7,927 2,109 17.7 8,845 

10,000 – 20,000 2,316 10,899 2,899 18.7 14,959 

20,000 – 30,000 621 13,239 3,522 19.5 24,860 

SSD Total 3,020 11,298 3,005 18.8 16,826 

AUTO CARRIER Total 3,306 11,155 2,967 18.7 16,500 

BARGE CARRIER 

MSD < 25,000 1 4,461 1,200 13.3 4,393 

MSD Total 1 4,461 1,200 13.3 4,393 

SSD 

< 25,000 1 3,916 1,053 14.0 11,783 

35,000 – 45,000 20 19,463 5,236 18.0 44,799 

45,000 – 90,000 19 25,041 6,736 20.0 48,093 

SSD Total 40 21,724 5,844 18.9 45,538 

ST 35,000 – 45,000 5 24,196 6,509 21.7 41,294 

ST Total 5 24,196 6,509 21.7 41,294 

BARGE CARRIER Total 45 21,779 5,859 19.1 44,657 

BULK CARRIER 

MSD 

< 25,000 213 4,867 1,080 14.0 15,819 

25,000 – 35,000 6 8,948 1,986 14.0 29,984 

35,000 – 45,000 44 9,148 2,031 15.2 39,128 

45,000 – 90,000 51 9,705 2,155 14.3 71,242 

> 90,000 1 16,109 3,576 15.8 105,550 

MSD Total 314 6,360 1,412 14.2 28,621 

3-15 
Regulatory Impact Analysis 

Table 3-1 Bins and Average Ship Characteristics for Deep Sea Ports (continued) 

Ship Type 

Main 

Engine a DWT Range Calls 

Engine Power (kW) Cruise 

Speed 

(kts) 

DWT 

Main Auxiliary 

BULK CARRIER 

SSD 

< 25,000 1,194 5,650 1,254 14.2 19,913 

25,000 – 35,000 2,192 7,191 1,596 14.6 29,323 

35,000 – 45,000 1,742 8,515 1,890 14.7 39,875 

45,000 – 90,000 3,733 9,484 2,105 14.4 62,573 

> 90,000 352 14,071 3,124 14.5 112,396 

SSD Total 9,212 8,434 1,872 14.5 46,746 

ST < 25,000 72 6,290 1,396 15.0 18,314 

25,000 – 35,000 3 8,948 1,986 15.0 33,373 

ST Total 75 6,379 1,416 15.0 18,819 

BULK CARRIER Total 9,600 8,350 1,854 14.5 45,936 

CONTAINER SHIP 

MSD 

< 25,000 1,005 6,846 1,506 17.2 8,638 

25,000 – 35,000 53 22,304 4,907 20.6 28,500 

35,000 – 45,000 59 26,102 5,742 22.3 39,932 

45,000 – 90,000 248 37,650 8,283 24.0 56,264 

MSD Total 1,365 13,878 3,053 18.8 19,419 

SSD 

< 25,000 2,054 12,381 2,724 19.1 18,776 

25,000 – 35,000 2,360 19,247 4,234 20.5 31,205 

35,000 – 45,000 2,443 24,755 5,446 21.8 40,765 

45,000 – 90,000 6,209 36,151 7,953 23.3 58,604 

> 90,000 98 57,325 12,612 25.0 105,231 

SSD Total 13,163 27,454 6,040 21.9 44,513 

ST 

< 25,000 46 20,396 4,487 20.8 19,963 

25,000 – 35,000 89 21,066 4,635 21.0 30,804 

35,000 – 45,000 41 23,562 5,184 21.0 40,949 

ST Total 176 21,472 4,724 21.0 30,334 

CONTAINER SHIP Total 14,703 26,122 5,747 21.6 42,014 

GENERAL CARGO 

MSD 

< 25,000 2,937 5,080 1,316 15.1 8,268 

25,000 – 35,000 38 9,458 2,450 15.4 30,746 

35,000 – 45,000 1 13,728 3,556 14.3 40,910 

45,000 – 90,000 9 11,932 3,090 16.0 50,250 

MSD Total 2,984 5,159 1,336 15.1 8,688 

SSD 

< 25,000 2,357 6,726 1,742 15.4 14,409 

25,000 – 35,000 500 7,575 1,962 14.9 29,713 

35,000 – 45,000 1,122 9,269 2,401 15.2 41,568 

45,000 – 90,000 405 9,336 2,418 15.1 47,712 

> 90,000 6 10,628 2,753 14.5 134,981 

SSD Total 4,389 7,718 1,999 15.3 26,326 

ST < 25,000 18 17,897 4,635 21.0 22,548 

ST Total 18 17,897 4,635 21.0 22,548 

GENERAL CARGO Total 7,391 6,709 1,738 15.2 19,196 

3-16 
Chapter 3: Emission Inventory 

Table 3-1 Bins and Average Ship Characteristics for Deep Sea Ports (continued) 

Ship Type 

Main 

Engine a DWT Range Calls 

Engine Power (kW) Cruise 

Speed 

(kts) 

DWT 

Main Auxiliary 

MISCELLANEOUS 

MSD All 51 9,405 2,530 12.7 6,083 

MSD Total 51 9,405 2,530 12.7 6,083 

MSD-ED All 6 16,968 4,565 12.7 15,795 

MSD-ED Total 6 16,968 4,565 12.7 15,795 

SSD All 7 4,659 1,253 14.2 8,840 

SSD Total 7 4,659 1,253 14.2 8,840 

ST All 1 12,871 3,462 21.0 16,605 

ST Total 1 12,871 3,462 21.0 16,605 

MISCELLANEOUS Total 64 9,564 2,573 13.0 7,311 

PASSENGER 

MSD <10,000 1,011 22,024 6,123 20.2 5,976 

10,000 - 20,000 24 96,945 26,951 28.5 15,521 

MSD Total 1,035 23,762 6,606 20.4 6,197 

MSD-ED <10,000 1,964 39,095 10,868 20.9 7,345 

10,000 - 20,000 228 53,236 14,800 22.0 10,924 

MSD-ED Total 2,192 40,566 11,277 21.1 7,717 

SSD <10,000 189 23,595 6,559 20.1 6,235 

SSD Total 189 23,595 6,559 20.1 6,235 

GT-ED 10,000 - 20,000 143 44,428 12,351 24.0 11,511 

GT-ED Total 143 44,428 12,351 24.0 11,511 

ST <10,000 13 16,858 4,687 21.2 6,981 

10,000 - 20,000 52 29,982 8,335 18.0 13,960 

ST Total 65 27,357 7,605 18.6 12,564 

PASSENGER Total 3,623 34,800 9,674 20.9 7,443 

REEFER 

MSD <10,000 122 4,829 1,961 16.3 5,646 

10,000 - 20,000 60 12,506 5,077 20.0 11,632 

MSD Total 182 7,360 2,988 17.5 7,619 

SSD <10,000 464 6,539 2,655 18.0 7,267 

10,000 - 20,000 801 12,711 5,161 20.8 13,138 

SSD Total 1,265 10,449 4,242 19.7 10,986 

REEFER Total 1,447 10,060 4,084 19.5 10,562 

RORO 

MSD 

<10,000 892 7,840 2,031 15.5 6,641 

10,000 - 20,000 286 9,312 2,412 17.0 11,338 

> 30,000 31 22,386 5,798 21.0 31,508 

MSD Total 1,208 8,561 2,217 16.0 8,389 

SSD 

<10,000 132 7,240 1,875 15.0 4,695 

10,000 - 20,000 208 9,062 2,347 16.9 14,293 

20,000 - 30,000 31 12,781 3,310 18.9 22,146 

> 30,000 555 20,362 5,274 18.9 42,867 

SSD Total 925 15,702 4,067 17.9 30,321 

GT > 30,000 1 47,076 12,193 24.0 36,827 

GT Total 1 47,076 12,193 24.0 36,827 

ST 10,000 – 20,000 2 22,373 5,795 25.0 16,144 

20,000 – 30,000 1 22,373 5,795 25.0 22,501 

ST Total 3 22,373 5,795 25.0 18,687 

RORO Total 2,137 11,687 3,027 16.8 17,910 

3-17 
Regulatory Impact Analysis 

Table 3-1 Bins and Average Ship Characteristics for Deep Sea Ports (continued) 

Ship Type 

Main 

Engine a DWT Range Calls 

Engine Power (kW) Cruise 

Speed 

(kts) 

DWT 

Main Auxiliary 

TANKER 

MSD 

<30,000 650 4,888 1,031 14.3 11,415 

30,000 - 60,000 181 10,533 2,222 15.3 42,153 

60,000 - 90,000 148 9,782 2,064 14.7 74,245 

90,000 - 120,000 3 15,139 3,194 14.1 113,957 

MSD Total 981 6,697 1,413 14.6 26,847 

SSD 

<30,000 3,050 6,303 1,330 14.6 17,145 

30,000 - 60,000 3,752 9,021 1,903 14.9 41,677 

60,000 - 90,000 1,766 10,310 2,175 14.6 74,595 

90,000 - 120,000 2,835 12,318 2,599 14.6 101,116 

120,000 - 150,000 258 15,840 3,342 14.7 144,405 

> 150,000 487 16,888 3,563 15.2 166,394 

SSD Total 12,147 9,755 2,058 14.7 61,353 

GT-ED 30,000 - 60,000 13 7,592 1,602 14.5 39,839 

GT-ED Total 13 7,592 1,602 14.5 39,839 

ST 

< 30,000 2 13,534 2,856 18.0 27,235 

30,000 - 60,000 87 15,818 3,338 17.9 43,982 

60,000 - 90,000 73 26,848 5,665 18.9 70,108 

90,000 - 120,000 4 17,660 3,726 16.3 91,868 

120,000 - 150,000 3 19,125 4,035 16.0 122,409 

> 150,000 2 20,785 4,386 14.3 190,111 

ST Total 170 20,678 4,363 18.2 58,616 

TANKER Total 13,310 9,667 2,040 14.8 58,754 

TUG MSD All 48 7,579 2,039 14.5 626 

MSD Total 48 7,579 2,039 14.5 626 

TUG Total 48 7,579 2,039 14.5 626 

Grand Total 55,672 15,212 3,593 17.4 38,083 

Note: a

 Engine Types: MSD = medium speed engine; SSD = slow speed engine; ST = steam turbine; GT = gas turbine 

3-18 
Chapter 3: Emission Inventory 

Table 3-2 Bins and Average Ship Characteristics for Great Lake Ports 

Ship Type 

Main 

Engine a DWT Range Calls 

Engine Power (kW) Cruise 

Speed 

Main Auxiliary (kts) DWT

BULK CARRIER 

MSD 

10,000 - 20,000 9 4,413 980 15.3 11,693 

20,000 - 30,000 4 8,826 1,959 14.0 28,481 

30,000 - 40,000 11 6,001 1,332 13.5 32,713 

MSD Total 24 5,876 1,305 14.2 24,125 

SSD 

10,000 - 20,000 18 4,844 1,075 13.6 14,392 

20,000 - 30,000 208 6,995 1,553 14.6 27,486 

30,000 - 40,000 223 8,284 1,839 14.1 34,172 

SSD Total 449 7,549 1,676 14.3 30,282 

ST 20,000 - 30,000 23 6,910 1,534 15.5 26,513 

ST Total 23 6,910 1,534 15.5 26,513 

BULK CARRIER Total 496 7,438 1,651 14.4 29,809 

SELF UNLOADING 

BULK CARRIER 

MSD 

10,000 - 20,000 5 3,114 691 10.5 12,513 

20,000 - 30,000 12 6,436 1,429 15.0 28,591 

30,000 - 40,000 771 6,881 1,528 13.2 33,531 

> 40,000 67 12,140 2,695 13.5 65,089 

MSD Total 855 7,265 1,613 13.3 35,812 

SSD 20,000 - 30,000 275 6,659 1,478 15.0 26,504 

30,000 - 40,000 122 7,574 1,681 14.9 34,476 

SSD Total 397 6,940 1,541 14.9 28,954 

ST 

< 10,000 26 3,236 718 12.3 4,538 

10,000 - 20,000 93 4,750 1,055 13.6 16,830 

20,000 - 30,000 79 6,679 1,483 16.6 28,847 

ST Total 198 5,321 1,181 14.6 20,011 

SELF UNLOADING BULK CARRIER Total 1,450 6,910 1,534 13.9 31,776 

GENERAL CARGO 

MSD < 10,000 87 4,436 847 15.1 6,755 

10,000 - 20,000 6 5,939 1,134 16.5 12,497 

MSD Total 93 4,533 866 15.2 7,125 

SSD 

< 10,000 3 4,763 910 16.4 6,708 

10,000 - 20,000 7 6,280 1,199 14.1 16,993 

20,000 - 30,000 1 7,099 1,356 16.0 24,432 

30,000 - 40,000 6 8,827 1,686 15.0 30,900 

SSD Total 17 6,959 1,329 14.9 20,524 

GENERAL CARGO Total 110 4,908 937 15.1 9,196 

INTEGRATED 

TUG-BARGE 

MSD All 24 5,364 1,443 13.8 672 

MSD Total 24 5,364 1,443 13.8 672 

INTEGRATED TUG-BARGE Total 24 5,364 1,443 13.8 672 

TANKER 

MSD 10,000 - 20,000 42 3,972 838 13.5 10,475 

MSD Total 42 3,972 838 13.5 10,475 

SSD 10,000 - 20,000 5 5,160 1,089 14.3 13,735 

SSD Total 5 5,160 1,089 14.3 13,735 

TANKER Total 47 4,098 865 13.6 10,822 

Grand Total 2,127 6,850 1,515 14.1 29,336 

Note: a

 Engine Types: MSD = medium speed engine; SSD = slow speed engine; ST = steam turbine 

3-19 
Regulatory Impact Analysis 

3.3.2.3.1.2 Removal of Category 1 and 2 Ships 

Since these inventories were intended to cover ships with Category 3 propulsion engines 

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-Fairplay Ltd.14 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 Guide15 and displacement per cylinder was calculated. 

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

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

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

current inventory. 

3.3.2.3.1.3 Treatment of Electric-Drive Ships 

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

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

current inventory. 

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. 

3.3.2.3.2 Cruise Distance 

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

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

outside of the reduced speed and maneuvering zones. 

3.3.2.3.3 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.16 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, intracoastal 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 

3-20 
Chapter 3: Emission Inventory 

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 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 and endpoints for each port are listed in the Appendix, Table 3-103. 

The RSZ for each Great Lake port was fixed at three nautical miles, as previously discussed in 

Section 3.3.2.2.2. 

The RSZ speeds were primarily taken from previous studies by ICF17,18 or from an 

ENVIRON report19 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 the Appendix, Table 3-103. The RSZ speeds for the Great Lake 

ports vary by vessel type and are the average of the vessel service speed and the maneuvering 

speed. 

3.3.2.3.4 Auxiliary Engine Power and Load Factors 

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

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

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

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

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

analysis. 

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

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

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

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

January 2005 that was principally used for this analysis.20 Average auxiliary engine power to 

propulsion power ratios were estimated by ship type and are presented in Table 3-3. These ratios 

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

types at each port. 

3-21 
Regulatory Impact Analysis 

Table 3-3 Auxiliary Engine Power Ratios (ARB Survey, except as noted) 

Ship Type 

Average 

Propulsion 

Engine (kW) 

Average Auxiliary Engines 

Auxiliary to 

Propulsion 

Number Ratio 

Power 

Each 

(kW) 

Total 

Power 

(kW) Engine Speed 

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

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

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

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

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

Miscellaneousb 6,250 2.9 580 1,680 Medium 0.269 

RORO 11,000 2.9 983 2,850 Medium 0.259 

Reefer 9,600 4.0 975 3,900 Medium 0.406 

Tanker 9,400 2.7 735 1,985 Medium 0.211 

Notes: 

a

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

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

from the Starcrest Vessel Boarding Program. 

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

Program were used. 

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 Program12 showed that auxiliary engines are on all of the 

time, except when using shoreside power during hotelling. 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 from total propulsion power 

and the ratios from Table 3-3) to calculate auxiliary engine emissions. 

3-22 
Chapter 3: Emission Inventory 

Table 3-4 Auxiliary Engine Load Factor Assumptions 

Ship-Type Cruise RSZ Maneuver Hotel 

Auto Carrier 0.13 0.30 0.67 0.24 

Bulk Carrier 0.17 0.27 0.45 0.22 

Container Ship 0.13 0.25 0.50 0.17 

Passenger Ship 0.80 0.80 0.80 0.64 

General Cargo 0.17 0.27 0.45 0.22 

Miscellaneous 0.17 0.27 0.45 0.22 

RORO 0.15 0.30 0.45 0.30 

Reefer 0.20 0.34 0.67 0.34 

Tanker 0.13 0.27 0.45 0.67 

3.3.2.3.5 Fuel Types and Fuel Sulfur Levels 

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

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

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

assumed 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. 

An ARB survey20 found that almost all ships used RM 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 RM. However, only 8 percent of passenger ships 

used distillate in their auxiliary engines, while the other 92 percent used RM. 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 RM in their auxiliary engines. 

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

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

percent was used for MDO.21 While a more realistic value for MDO used in the U.S. appears to 

be 0.4 percent, given the small proportion of distillate fuel used by ships relative to RM, the 

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

higher for RM. Table 3-5 provides the assumed mix of fuel types used for propulsion and 

auxiliary engines by ship type. 

3-23 
Regulatory Impact Analysis 

Table 3-5 Estimated Mix of Fuel Types Used by Ships 

Ship Type 

Fuel Used 

Propulsion Auxiliary 

Passenger 100% RM 92% RM/8% MDO 

Other 100% RM 71% RM/29% MDO 

3.3.2.3.6 Propulsion and Auxiliary Engine Emission Factors 

An analysis of emission data was prepared and published in 2002 by Entec.21 The 

resulting Entec emission factors include individual factors for three speeds of diesel engines 

(slow-speed diesel (SSD), medium-speed diesel (MSD), and high-speed diesel (HSD)), steam 

turbines (ST), gas turbines (GT), and two types of fuel used here (RM and MDO). Table 3-6 

lists the propulsion engine emission factors for NOX and HC that were used for the 2002 port 

inventory development. The CO, PM, SO2 and CO2 emission factors shown in the table come 

from other data sources as explained below. 

Table 3-6 Emission Factors for OGV Main Engines using RM, g/kWh 

Engine 

All Ports West Coast Ports Other Ports 

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

SSD 18.1 1.40 0.60 620.62 1.4 1.3 9.53 1.4 1.3 10.29 

MSD 14.0 1.10 0.50 668.36 1.4 1.3 10.26 1.4 1.3 11.09 

ST 2.1 0.20 0.10 970.71 1.4 1.3 14.91 1.5 1.4 16.10 

GT 6.1 0.20 0.10 970.71 1.4 1.3 14.91 1.5 1.4 16.10 

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

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

with a recent EPA review.22

 PM10 values were determined by EPA based on existing engine test data in consultation 

with ARB.23 GT PM10 emission factors were not part of the EPA analysis but assumed here to 

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

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

sulfur) and 1.35 g/kw-hr with residual fuel (2.46% sulfur).24 The equation used to generate 

emission factors based on sulfur content is shown below. 

Equation 3-21 Calculation of PM10 Emission Factors Based on Fuel Sulfur Levels 

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

Where: 

PMEF = PM emission factor adjusted for fuel sulfur 

PMNom= PM emission rate at nominal fuel sulfur level 

3-24 
Chapter 3: Emission Inventory 

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

SAct= Actual fuel sulfur level (weight percent) 

SNom= nominal fuel sulfur level (weight percent) 

= 0.24 for distillate fuel, 2.46 for residual fuel 

BSFC = fuel consumption in g/kW-hr 

= 200 g/kW-hr used for this analysis 

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

= 2.247% used for this analysis 

MWR = molecular weight ratio of sulfate PM to sulfur 

= 224/32 = 7 used for this analysis 

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 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 formula which was 

supplied by ENVIRON.25 Emission factors for SO2 emissions were calculated using the formula 

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

26 The brake specific fuel 

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

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

was 305 g/kWh based upon Entec.21 

Equation 3-22 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 weight21 and a ratio of molecular weights of CO2 and C at 3.667. 

Equation 3-23 Calculation of CO2 Emission Factors, g/kWh 

CO2 EF = BSFC x 3.667 x 0.867 

Fuel consumption was calculated from CO2 emissions based on a 1:3.183 ratio. 3.183 

tons of CO2 emissions are assumed produced from one metric ton of fuel. 

The most current set of auxiliary engine emission factors comes from Entec except as 

noted below. Table 3-7 provides these auxiliary engine emission factors. 

Table 3-7 Auxiliary Engine Emission Factors by Fuel Type, g/kWh 

Engine Fuel 

All Ports West Coast Ports Other Ports 

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

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

MDO 13.90 1.10 0.40 668.36 0.6 0.55 6.16 0.6 0.55 6.16 

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

3-25 
Regulatory Impact Analysis 

It should be noted that Entec used 2.7 percent fuel sulfur content for RM, and 1.0 percent 

for MDO which is consistent with the RM 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 assumed sulfur levels and the methodology suggested by ENVIRON25 while PM 

emissions were determined by EPA based on existing engine test data in consultation with 

ARB.23 

Using the ratios of RM versus MDO use determined by the ARB study20 as given in 

Table 3-5 together with the emission factors shown in Table 3-7, the auxiliary engine emission 

factor averages by ship type are listed in Table 3-8. As discussed above, this fuel sulfur level 

may be too high for the U.S. However, we do not believe this emission factor has a significant 

effect on the total emission inventory estimates. 

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 RM fuel used in 

auxiliary engines. The difference for PM is within the round off error of the emission factor. 

Table 3-8 Auxiliary Engine Emission Factors by Ship Type, g/kWh 

Ship Type 

All Ports West Coast Ports Other Ports 

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

Passenger 14.64 1.10 0.40 668.36 1.3 1.2 9.93 1.4 1.3 10.70 

Others 14.47 1.10 0.40 668.36 1.1 1.0 9.07 1.2 1.1 9.66 

3.3.2.3.7 Low Load Adjustment Factors for Propulsion Engines 

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

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

diesel engines are less efficient at low loads and the 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 2000.27 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 3-24 

Fuel Consumption (g/kWh) = 14.1205 (1/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: 

3-26 
Chapter 3: Emission Inventory 

Equation 3-25 

Emission Rate (g/kWh) = a (Fractional Load)

-x + b 

For SO2 emissions, however, EEA developed a slightly different equation: 

Equation 3-26 

Emission Rate (g/kWh) = a (Fuel Consumption x Fuel Sulfur Fraction) + b 

The coefficients for the above equations are given in Table 3-9 below. 

Table 3-9 Emission Factor Algorithm Coefficients for OGV Main Engines using RM 

Coefficient NOX HC CO PM SO2 CO2 

a 0.1255 0.0667 0.8378 0.0059 2.3735 44.1 

x 1.5 1.5 1.0 1.5 n/a 1.0 

b 10.4496 0.3859 0.1548 0.2551 -0.4792 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 3-10 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. 

3-27 
Regulatory Impact Analysis 

Table 3-10 Calculated Low Load Multiplicative Adjustment Factors 

Load (%) NOX HC CO PM SO2 CO2 

1 11.47 59.28 19.32 19.17 5.99 5.82 

2 4.63 21.18 9.68 7.29 3.36 3.28 

3 2.92 11.68 6.46 4.33 2.49 2.44 

4 2.21 7.71 4.86 3.09 2.05 2.01 

5 1.83 5.61 3.89 2.44 1.79 1.76 

6 1.60 4.35 3.25 2.04 1.61 1.59 

7 1.45 3.52 2.79 1.79 1.49 1.47 

8 1.35 2.95 2.45 1.61 1.39 1.38 

9 1.27 2.52 2.18 1.48 1.32 1.31 

10 1.22 2.20 1.96 1.38 1.26 1.25 

11 1.17 1.96 1.79 1.30 1.21 1.21 

12 1.14 1.76 1.64 1.24 1.18 1.17 

13 1.11 1.60 1.52 1.19 1.14 1.14 

14 1.08 1.47 1.41 1.15 1.11 1.11 

15 1.06 1.36 1.32 1.11 1.09 1.08 

16 1.05 1.26 1.24 1.08 1.07 1.06 

17 1.03 1.18 1.17 1.06 1.05 1.04 

18 1.02 1.11 1.11 1.04 1.03 1.03 

19 1.01 1.05 1.05 1.02 1.01 1.01 

20 1.00 1.00 1.00 1.00 1.00 1.00 

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 an efficient level. 

3.3.2.3.8 Use of Detailed Typical Port Data for Other Inputs 

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

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

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

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

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

for the propulsion engines) was then used for the typical ports and also assigned to the other 

modeled ports. A modeled port is the port in which emissions are to be estimated. The 

methodology that was used to select the typical ports and match these ports to the other modeled 

ports is briefly described in this section, and more fully described in the ICF documentation.2 

3.3.2.3.8.1 Selection of Typical Ports 

In 1999, EPA published two guidance documents17,18 to calculate marine vessel activity 

at ports. These documents contained detailed port inventories of eight deep sea ports, two Great 

3-28 
Chapter 3: Emission Inventory 

Lake ports and two inland river ports. The detailed inventories were developed by obtaining ship 

call data from Marine Exchanges/Port Authorities (MEPA) at the various ports for 1996 and 

matching the various ship calls to data from Lloyds Maritime Information Services to provide 

ship characteristics. The ports for which detailed inventories were developed are shown in Table 

3-11 for deep sea ports and Table 3-12 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 U.S. Army Corps of Engineers (USACE) Waterborne Commerce Statistics 

Center data. 

Table 3-11 Deep Sea MEPA Vessel Movement and Shifting Details 

MEPA Area and Ports MEPA Data Includes 

Lower Mississippi River 

including the ports of New 

Orleans, South Louisiana, 

Plaquemines, and Baton Rouge 

Information on the first and last pier/wharf/dock (PWD) for the 

vessel (gives information for at most one shift per vessel). No 

information on intermediate PWDs, the time of arrival at the first 

destination PWD, or the time of departure from the River. 

Consolidated Port of New York 

and New Jersey and other ports 

on the Hudson and Elizabeth 

Rivers 

All PWDs or anchorages for shifting are named. Shifting arrival 

and departure times are not given. Hotelling time is based upon the 

entrance and clearance times and dates, subtracting out 

maneuvering times. Maneuvering times were calculated based 

upon the distance the ship traveled at a given maneuvering speed. 

Delaware River Ports including 

the ports of Philadelphia, 

Camden, Wilmington and others 

All PWDs or anchorages for shifting are named. Shifting arrival 

and departure times are not given. Hotelling time is based upon the 

entrance and clearance times and dates, subtracting out 

maneuvering times. Maneuvering times were calculated based 

upon the distance the ship traveled at a given maneuvering speed. 

Puget Sound Area Ports including 

the ports of Seattle, Tacoma, 

Olympia, Bellingham, Anacortes, 

and Grays Harbor 

All PWDs or anchorages for shifting are named. Arrival and 

departure dates and times are noted for all movements, allowing 

calculation of maneuvering and hotelling both for individual shifts 

and the overall call on port. 

The Port of Corpus Christi, TX Only has information on destination PWD and date and time in 

and out of the port area. No shifting details. 

The Port of Coos Bay, OR Only has information on destination PWD and date and time in 

and out of the port area. No shifting details. 

Patapsco River Ports including 

the port of Baltimore Harbor, MD 

All PWDs or anchorages for shifting are named. Shifting arrival 

and departure times are not given. Hotelling time is based upon the 

entrance and clearance times and dates, subtracting out 

maneuvering times. Maneuvering times were calculated based 

upon the distance the ship traveled at a given maneuvering speed. 

The Port of Tampa, FL 

All PWDs or anchorages for shifting are named. Arrival and 

departure dates and times are noted for all movements, allowing 

calculation of maneuvering and hotelling both for individual shifts 

and the overall call. 

3-29 
Regulatory Impact Analysis 

Table 3-12 Great Lake MEPA movements and shifts 

MEPA Area and Ports MEPA Data Includes 

Port of Cleveland, OH 

Information on the first and last PWD for the vessel (gives 

information for at most one shift per vessel). No information on 

intermediate PWDs.. 

Port of Burns Harbor, IN No shifting details, No PWDs listed.. 

Since 1999, several new detailed emissions inventories have been developed and were 

reviewed for use as additional or replacement typical ports: These included: 

 Port of Los Angeles12,28 

 Puget Sound Ports29 

 Port of New York/New Jersey30 

 Port of Houston/Galveston31 

 Port of Beaumont/Port Arthur32 

 Port of Corpus Christi33 

 Port of Portland34 

 Ports of Cleveland, OH and Duluth-Superior, MN&WI

35 

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 Ports29 was used to 

calculate RSZ speeds, load factors, and times for all Puget Sound ports. As described in Section 

3.3.2.4.2, an additional modeled port was also added to account for the considerable amount of 

Jones Act tanker ship activity in the Puget Sound area that is not contained in the original 

inventory. 

The newer Port of New York/New Jersey inventory provided a check against estimates 

made using the 1996 data. All other new inventory information was found to lack sufficient 

detail to prepare the detailed typical port inventories needed for this project. 

The final list of nine deep sea and two Great Lake typical ports used in this analysis and 

their data year is as follows: 

 Lower Mississippi River Ports [1996] 

3-30 
Chapter 3: Emission Inventory 

 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 3.3.2.3.1.1. 

3.3.2.3.8.2 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,B

 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 3.3.2.4. The 

matched ports for the deep sea ports are provided in Table 3-13. 

Table 3-13 Matched Ports for the Deep Sea Ports 

Modeled Port Name Typical Like Port

 Anacortes, WA Puget Sound 

 Barbers Point, HI Puget Sound 

 Everett, WA Puget Sound 

 Grays Harbor, WA Puget Sound 

Honolulu, HI Puget Sound 

Kalama, WA Puget Sound 

Longview, WA Puget Sound 

 Olympia, WA Puget Sound 

B

 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. 

3-31 
Regulatory Impact Analysis 

Modeled Port Name Typical Like Port

 Port Angeles, WA Puget Sound 

 Portland, OR Puget Sound 

Seattle, WA Puget Sound 

Tacoma, WA Puget Sound 

Vancouver, WA Puget Sound 

Valdez, AK Puget Sound 

 Other Puget Sound Puget Sound 

 Anchorage, AK Coos Bay

 Coos Bay, OR Coos Bay

 Hilo, HI Coos Bay

 Kahului, HI Coos Bay

 Nawiliwili, HI Coos Bay

 Nikishka, AK Coos Bay

 Beaumont, TX Houston 

 Freeport, TX Houston 

Galveston, TX Houston 

 Houston, TX Houston 

 Port Arthur, TX Houston 

 Texas City, TX Houston 

 Corpus Christi, TX Corpus Christi 

 Lake Charles, LA Corpus Christi 

 Mobile, AL Corpus Christi 

 Brownsville, TX Tampa 

 Gulfport, MS Tampa 

 Manatee, FL Tampa

 Matagorda Ship Tampa 

 Panama City, FL Tampa

 Pascagoula, MS Tampa 

 Pensacola, FL Tampa 

 Tampa, FL Tampa 

 Everglades, FL Tampa 

 New Orleans, LA Lower Mississippi 

 Baton Rouge, LA Lower Mississippi 

 South Louisiana, LA Lower Mississippi 

 Plaquemines, LA Lower Mississippi 

 Albany, NY New York/New Jersey

 New York/New Jersey New York/New Jersey

 Portland, ME New York/New Jersey

 Georgetown, SC Delaware River 

 Hopewell, VA Delaware River 

Marcus Hook, PA Delaware River 

3-32 
Chapter 3: Emission Inventory 

Modeled Port Name Typical Like Port

 Morehead City, NC Delaware River 

Paulsboro, NJ Delaware River 

 Chester, PA Delaware River 

 Fall River, MA Delaware River 

New Castle, DE Delaware River 

 Penn Manor, PA Delaware River 

Providence, RI Delaware River 

Brunswick, GA Delaware River 

 Canaveral, FL Delaware River 

Charleston, SC Delaware River 

 New Haven, CT Delaware River 

 Palm Beach, FL Delaware River 

 Bridgeport, CT Delaware River 

Camden, NJ Delaware River 

 Philadelphia, PA Delaware River 

Wilmington, DE Delaware River 

Wilmington, NC Delaware River 

Richmond, VA Delaware River 

 Jacksonville, FL Delaware River 

 Miami, FL Delaware River 

 Searsport, ME Delaware River 

 Boston, MA Delaware River 

 New Bedford/Fairhaven, MA Delaware River 

 Baltimore, MD Patapsco River 

 Newport News, VA Patapsco River 

 Savannah, GA Patapsco River 

 Catalina, CA ARB Supplied 

 Carquinez, CA ARB Supplied 

El Segundo, CA ARB Supplied 

 Eureka, CA ARB Supplied 

 Hueneme, CA ARB Supplied 

 Long Beach, CA ARB Supplied 

Los Angeles, CA ARB Supplied 

 Oakland, CA ARB Supplied 

 Redwood City, CA ARB Supplied 

Richmond, CA ARB Supplied 

 Sacramento, CA ARB Supplied 

 San Diego, CA ARB Supplied 

 San Francisco, CA ARB Supplied 

 Stockton, CA ARB Supplied 

3-33 
Regulatory Impact Analysis 

Great Lake ports were matched to either Cleveland or Duluth as shown in Table 3-14. 

Table 3-14 Great Lake Match Ports 

Port Name Typical Like Port 

Alpena, MI Cleveland 

 Buffalo, NY Cleveland 

 Burns Waterway, IN Cleveland 

 Calcite, MI Cleveland 

 Cleveland, OH Cleveland 

 Dolomite, MI Cleveland 

 Erie, PA Cleveland 

 Escanaba, MI Cleveland 

Fairport, OH Cleveland 

 Gary, IN Cleveland 

 Lorain, OH Cleveland 

 Marblehead, OH Cleveland 

 Milwaukee, WI Cleveland 

Muskegon, MI Cleveland 

 Presque Isle, MI Cleveland 

 St Clair, MI Cleveland 

Stoneport, MI Cleveland 

 Two Harbors, MN Cleveland 

Ashtabula, OH Duluth-Superior

 Chicago, IL Duluth-Superior

 Conneaut, OH Duluth-Superior

 Detroit, MI Duluth-Superior

 Duluth-Superior, MN&WI Duluth-Superior

 Indiana, IN Duluth-Superior

 Inland Harbor, MI Duluth-Superior

 Manistee, MI Duluth-Superior

 Sandusky, OH Duluth-Superior

 Toledo, OH Duluth-Superior 

Once a modeled port was matched to a typical port, the maneuvering and hotelling timein-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 3.3.2.3. 

3.3.2.3.8.3 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 

3-34 
Chapter 3: Emission Inventory 

cases, the closest engine type at the typical like port was used. Also in a few cases, a specific 

ship type in the modeled port data was not in the typical like port data. In this case, the nearest 

like ship type at the typical port was chosen to calculate emissions at the modeled port. 

3.3.2.4 Stand Alone Ports 

In a few cases, the USACE entrances and clearances data was not used to calculate 

emissions at the modeled port. These include the California ports for which we received data 

from ARB, the Port of Valdez, Alaska, and a conglomerate port within the Puget Sound area, as 

described below. 

3.3.2.4.1 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 document36 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 3.3.2.3.1.3 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 PM10 and PM2.5 

respectively. Since an emission factor of 1.4 g/kWh was used in our calculations for PM10 and 

an emission factor of 1.3 g/kWh for PM2.5, ARB PM10 and PM2.5 emissions were multiplied by 

factors of 0.972 and 0.925, respectively to get consistent PM10 and PM2.5 emissions for 

propulsion engines. 

For auxiliary engines, ARB used the same emission factors as above, while we used 

PM10 and PM2.5 emission factors of 1.3 and 1.2 g/kWh, respectively for passenger ships and 1.1 

and 1.0 g/kWh, respectively for all other ships. In the ARB inventory, all passenger ships are 

treated as electric drive and all emissions are allocated to auxiliary engines. ARB auxiliary 

engine emissions were thus multiplied by factors of 0.903 and 0.854 respectively for passenger 

ships and 0.764 and 0.711 respectively for other ships to provide consistent PM emission 

calculations.

 SO2 emissions were also different between the ARB and these analyses. ARB used a 

compositeC

 propulsion engine SO2 emission factor of 10.55 g/kWh while we used a composite 

SO2 emission factor of 9.57 g/kWh. Thus, ARB SO2 propulsion emissions were multiplied by a 

C

 Based upon ARB assuming 95 percent of the engines were SSD and 5 percent were MSD. The composite SO2 EF 

of 9.57 g/kW-hr was calculated using this weighting, along with the SSD and MSD SO2 EFs for the West Coast 

ports reported in Table 3-6. 

3-35 
Regulatory Impact Analysis 

factor of 0.907 to be consistent with our emission calculations. For auxiliary engines, ARB used 

SO2 emission factors of 11.48 and 9.34 g/kWh, respectively for passenger and other ships, while 

we use emission factors of 9.93 and 9.07 g/kWh, respectively. Thus, ARB auxiliary SO2 

emissions were multiplied by factors of 0.865 and 0.971, respectively for passenger and other 

ships to provide consistent SO2 emissions. 

3.3.2.4.2 Port in Puget Sound 

In the newest Puget Sound inventory29, it was found that a considerable amount of tanker 

ships stop at Cherry Point, Ferndale, March Point and other areas which are not within the top 89 

U.S. deep sea ports analyzed in this analysis. In addition, since they are ships carrying U.S. 

cargo (oil from Alaska) from one U.S. port to another, they are not documented in the USACE 

entrances and clearances data. To compensate for this anomaly, an additional port was added 

which encompassed these tanker ships stopping within the Puget Sound area but not at one of the 

Puget Sound ports analyzed in this analysis. Ship calls in the 1996 typical port data to ports 

other than those in the top 89 U.S. deep sea ports were analyzed separately. There were 363 ship 

calls by tankers to those areas in 1996. In the inventory report for 2005, there were 468 calls. 

For 2002, it was estimated there were 432 calls. The same ship types and ship characteristics 

were used as in the 1996 data, but the number of calls was proportionally increased to 432 calls 

to represent these ships. The location of the “Other Puget Sound” port was approximately at 

Cherry Point near Aberdeen. 

3.3.2.4.3 Port of Valdez 

In a recent Alaska port inventory,37 it was found that significant Category 3 domestic 

tanker traffic enters and leaves the Port of Valdez on destination to West Coast ports. Since the 

USACE entrances and clearances data did not contain any tanker calls at Valdez in 2002, the 

recent Alaska inventory data was used to calculate emissions at that port. In this case, the 

number of calls and ship characteristics for 2002 were taken directly from the Alaska inventory 

and used in determining emissions for the modeled port with the Puget Sound area typical port 

being used as the like port. 

3.3.2.5 Domestic Traffic 

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

contains foreign cargo movements moved by either a foreign flag vessel or a U.S. flag vessel. 

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 Act 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 USACE entrances and clearances database, as these are 

considered foreign cargo movements. 

3-36 
Chapter 3: Emission Inventory 

Under the Jones Act, domestic cargo movements from one U.S. port to another U.S. port 

must be carried by a U.S. flag ship. The Jones Act also requires ships traveling between United 

States ports to be constructed by United States companies and owned by a United States 

company or citizen. Members of the ships’ crews must be United States citizens or legal aliens. 

Because of the use of USACE data, in the present baseline and future year inventories, only 

limited Jones Act ships were counted. These ships included those servicing California ports, 

those serving the Port of Valdez and those serving other Puget Sound ports. At all other ports, 

Jones Act ships were not counted. 

ICF conducted an analysis to estimate the amount of Category 3 Jones Act ships calling 

at the 117 U.S. ports. This was done by analyzing marine exchange data obtained from port 

authorities for eleven typical ports and using this information to estimate the Jones Act ship 

contribution for the remaining ports. Based on this limited analysis, Jones Act ships are 

estimated to account for 9.2% of the total installed power calling on U.S. ports. Approximately 

30% of these ships, largely in the Alaska and Pacific regions, have been included in the 2002 

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

constitute roughly 6.5% of total installed power.38 This results in an underestimation of the port 

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

also underestimated. 

3.3.2.6 2002 Near Port Inventories 

This section presents a summary 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. 

3.3.2.6.1 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 3-15. Auxiliary only emissions by ports 

are given in Table 3-16. Emissions by mode are given in Table 3-17 for cruise, Table 3-18 for 

reduced speed zone, Table 3-19 for maneuvering, and Table 3-20 for hotelling. Emissions by 

ship type by port are given in Table 3-21 through Table 3-31. 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. 

3.3.2.6.2 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 

3-37 
Regulatory Impact Analysis 

within the Great Lakes only. Other ships travel down the St. Lawrence River from the open 

ocean. Integrated tug-barges (ITB) are also used on the Great Lakes. 

Total emissions by port for Great Lakes Ports are shown in Table 3-32. Auxiliary engine 

emissions for Great Lake ports are shown in Table 3-33. Emissions by mode for Great Lake 

ports are shown in Table 3-34 for cruise, Table 3-35 for reduced speed zone, Table 3-36 for 

maneuvering, and Table 3-37 for hotelling. Emissions by ship type are shown in Table 3-38 

through Table 3-42. 

3-38 
Chapter 3: Emission Inventory 

Table 3-15 Total Emissions by Deep Sea Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Anacortes, WA 545 403 32 29 14 32 225 15,462 

 Barbers Point, HI 472 122 10 9 4 10 71 5,034 

 Everett, WA 186 82 7 6 3 7 46 3,125 

 Grays Harbor, WA 360 50 4 4 2 4 30 2,066 

Honolulu, HI 8,037 1,268 116 102 47 102 800 54,385 

 Kalama, WA 1,190 359 30 26 13 30 210 14,555 

Longview, WA 1,619 413 34 30 15 35 239 16,495 

 Olympia, WA 97 56 4 4 2 4 31 2,047 

 Port Angeles, WA 556 151 13 11 5 12 89 6,042 

 Portland, OR 11,198 2,307 206 182 117 223 1,320 90,558 

 Seattle, WA 26,292 6,669 573 513 265 551 3,789 253,190 

 Tacoma, WA 19,130 5,742 477 428 217 464 3,211 215,754 

Vancouver, WA 1,946 446 37 33 17 39 259 17,821 

Valdez, AK 6,676 343 37 33 11 27 299 20,789 

 Other Puget Sound 5,678 2,111 219 197 71 169 1,745 118,629 

 Anchorage, AK 537 221 18 16 7 17 133 8,236 

 Coos Bay, OR 399 46 4 3 2 4 27 1,810 

 Hilo, HI 4,514 929 77 70 27 72 626 44,368 

Kahului, HI 2,323 474 39 35 14 37 312 22,094 

 Nawiliwili, HI 591 122 10 9 4 9 83 5,884 

Nikishka, AK 1,110 270 26 24 8 21 209 13,794 

Beaumont, TX 12,699 2,106 261 240 91 189 1,972 83,736 

 Freeport, TX 7,411 714 92 85 25 54 716 28,422 

Galveston, TX 6,572 1,014 118 102 35 69 873 43,643 

 Houston, TX 47,147 4,625 546 491 158 347 4,136 183,952 

 Port Arthur, TX 3,531 436 52 47 17 37 388 17,342 

 Texas City, TX 7,382 970 127 117 33 74 986 38,575 

 Corpus Christi, TX 11,452 1,758 143 132 59 401 1,090 70,240 

 Lake Charles, LA 6,382 850 80 74 35 239 594 38,409 

 Mobile, AL 8,200 1,144 95 88 39 303 724 46,155 

 Brownsville, TX 1,213 175 14 13 6 14 108 7,057 

 Gulfport, MS 3,556 607 51 46 20 48 414 26,382 

 Manatee, FL 2,903 667 56 49 22 53 450 28,904 

 Matagorda Ship 2,504 389 32 28 14 33 239 15,827 

 Panama City, FL 662 70 6 5 2 6 44 2,789 

Pascagoula, MS 3,566 518 44 40 17 42 344 22,223 

 Pensacola, FL 351 40 3 3 1 3 27 1,726 

 Tampa, FL 10,941 1,507 129 109 50 121 988 63,033 

 Everglades, FL 38,304 4,287 402 372 134 334 3,123 198,127 

 New Orleans, LA 27,575 6,603 556 513 221 536 4,245 272,794 

 Baton Rouge, LA 4,627 1,985 160 148 63 155 1,223 78,568 

 South Louisiana, LA 18,366 6,428 519 479 203 502 3,976 257,346 

Plaquemines, LA 4,230 1,045 85 78 33 82 658 43,258 

 Albany, NY 396 103 9 8 4 9 65 4,167 

 New York/New Jersey 86,980 7,364 622 575 274 621 4,620 296,780 

 Portland, ME 3,968 722 60 55 23 57 466 30,836 

 Georgetown, SC 609 89 7 7 3 7 152 3,668 

 Hopewell, VA 185 45 4 3 2 4 211 1,764 

3-39 
Regulatory Impact Analysis 

Table 3-15 Total Emissions by Deep Sea Port in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Marcus Hook, PA 2,754 965 79 73 30 76 2,462 40,563 

 Morehead City, NC 967 121 10 9 4 10 94 5,196 

Paulsboro, NJ 3,272 668 55 50 22 54 2,103 26,676 

 Chester, PA 1,467 196 16 15 7 16 411 7,648 

 Fall River, MA 290 35 3 3 1 3 52 1,748 

New Castle, DE 765 199 16 15 6 16 394 8,257 

 Penn Manor, PA 721 174 14 13 6 14 656 6,878 

Providence, RI 1,097 198 16 15 6 16 334 8,222 

Brunswick, GA 5,184 670 54 50 22 53 1,297 26,273 

 Canaveral, FL 17,801 3,060 281 261 89 233 2,279 139,768 

Charleston, SC 46,233 3,809 311 288 133 310 4,519 150,424 

 New Haven, CT 1,801 287 23 22 9 22 207 12,116 

Palm Beach, FL 2,277 219 19 18 7 17 162 9,869 

 Bridgeport, CT 1,452 247 20 19 8 19 164 10,692 

Camden, NJ 4,209 994 82 76 34 83 1,625 41,540 

 Philadelphia, PA 7,644 1,684 140 129 55 140 3,363 70,523 

Wilmington, DE 4,444 627 52 48 23 54 1,011 25,319 

Wilmington, NC 4,888 641 53 49 22 52 956 26,264 

Richmond, VA 596 86 7 7 3 8 206 3,333 

 Jacksonville, FL 13,908 1,507 125 116 51 122 1,652 62,457 

Miami, FL 57,415 7,155 650 602 218 551 5,340 322,880 

Searsport, ME 543 110 9 8 3 9 124 4,769 

Boston, MA 13,290 1,647 146 135 53 131 1,572 74,625 

 New Bedford/Fairhaven, MA 181 39 3 3 1 3 33 1,700 

Baltimore, MD 25,197 6,412 519 481 212 502 3,918 244,560 

 Newport News, VA 5,529 505 41 38 17 41 316 19,760 

 Savannah, GA 37,523 3,594 289 267 126 291 2,174 137,046 

 Catalina, CA 928 78 7 7 2 6 53 3,639 

 Carquinez, CA 3,442 537 39 36 17 42 309 20,535 

El Segundo, CA 1,685 192 14 13 6 15 108 7,095 

 Eureka, CA 409 82 6 5 2 6 51 3,486 

Hueneme, CA 3,334 319 22 21 10 280 190 12,820 

 Long Beach, CA 56,935 5,303 389 357 166 417 3,141 213,005 

Los Angeles, CA 50,489 4,793 352 324 150 378 2,839 192,430 

 Oakland, CA 48,762 3,022 222 205 100 239 1,638 110,003 

 Redwood City, CA 456 107 8 7 3 8 64 4,317 

Richmond, CA 3,956 484 35 33 15 37 277 18,361 

 Sacramento, CA 455 138 10 9 4 11 81 5,417 

 San Diego, CA 8,255 840 68 63 25 65 536 36,609 

 San Francisco, CA 6,260 684 53 49 21 53 419 28,356 

 Stockton, CA 1,210 332 24 22 10 26 192 12,830 

 Total Port Emissions 863,191 121,606 10,530 9,631 4,148 10,635 93,908 4,995,871 

Total Port Emissions (short 

tons) 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005 

3-40 
Chapter 3: Emission Inventory 

Table 3-16 Auxiliary Engine Emissions by Deep Sea Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Anacortes, WA 115 147 11 10 4 11 92 6,798 

 Barbers Point, HI 101 77 6 5 2 6 48 3,568 

 Everett, WA 40 21 2 1 1 2 13 977 

 Grays Harbor, WA 73 25 2 2 1 2 16 1,176 

Honolulu, HI 2,043 793 67 61 22 60 522 36,366 

Kalama, WA 260 172 13 12 5 13 108 7,930 

Longview, WA 346 183 14 13 5 14 115 8,445 

 Olympia, WA 21 9 1 1 0 1 6 410 

 Port Angeles, WA 111 42 3 3 1 3 26 1,922 

 Portland, OR 2,560 924 70 64 26 70 580 42,675 

Seattle, WA 5,947 1,472 116 106 41 112 939 67,795 

Tacoma, WA 4,305 1,279 97 88 35 97 802 59,093 

Vancouver, WA 427 182 14 13 5 14 114 8,402 

Valdez, AK 1,411 256 20 18 7 19 161 11,836 

 Other Puget Sound 1,198 951 72 66 26 72 596 43,927 

 Anchorage, AK 158 99 8 7 3 8 63 3,683 

 Coos Bay, OR 78 21 2 2 1 2 14 949 

Hilo, HI 1,251 815 64 58 23 64 529 38,048 

Kahului, HI 642 412 32 29 12 32 267 19,178 

 Nawiliwili, HI 164 108 8 8 3 8 70 5,023 

Nikishka, AK 235 132 10 9 4 10 83 5,623 

Beaumont, TX 2,415 873 149 135 31 63 1,188 40,334 

 Freeport, TX 1,342 321 58 53 11 24 461 14,819 

Galveston, TX 1,645 674 89 75 24 42 660 31,135 

 Houston, TX 8,410 1,827 305 268 64 129 2,352 84,373 

 Port Arthur, TX 640 173 29 25 6 12 220 8,002 

 Texas City, TX 1,414 418 78 71 15 31 626 19,301 

 Corpus Christi, TX 2,486 770 64 59 21 59 514 35,563 

 Lake Charles, LA 1,347 457 38 35 13 35 305 21,105 

Mobile, AL 1,816 423 35 32 12 32 282 19,529 

 Brownsville, TX 260 84 7 6 2 6 56 3,899 

Gulfport, MS 878 415 34 30 11 31 292 19,017 

Manatee, FL 902 491 41 35 13 37 343 22,448 

 Matagorda Ship 535 202 17 14 6 15 131 9,318 

 Panama City, FL 130 28 2 2 1 2 19 1,315 

Pascagoula, MS 795 277 23 20 8 21 187 12,772 

Pensacola, FL 87 20 2 1 1 1 14 906 

Tampa, FL 2,639 777 67 51 21 59 534 35,735 

 Everglades, FL 9,813 3,032 277 256 84 230 2,158 140,039 

 New Orleans, LA 6,376 3,426 295 271 95 260 2,343 158,234 

 Baton Rouge, LA 988 813 67 62 22 62 543 37,544 

 South Louisiana, LA 3,988 2,969 246 226 82 226 1,982 137,151 

Plaquemines, LA 919 607 50 46 17 46 406 28,058 

 Albany, NY 85 46 4 3 1 3 31 2,111 

 New York/New Jersey 20,036 3,467 294 270 96 263 2,343 159,839 

 Portland, ME 883 477 40 37 13 36 320 22,034 

 Georgetown, SC 129 42 3 3 1 3 28 1,960 

Hopewell, VA 40 16 1 1 0 1 11 757 

3-41 
Regulatory Impact Analysis 

Table 3-16 Auxiliary Engine Emissions by Deep Sea Port in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Marcus Hook, PA 583 617 51 47 17 47 412 28,518 

 Morehead City, NC 203 74 6 6 2 6 49 3,421 

Paulsboro, NJ 701 294 25 23 8 22 198 13,584 

 Chester, PA 318 63 5 5 2 5 42 2,897 

 Fall River, MA 61 17 2 2 1 2 15 1,035 

New Castle, DE 164 120 10 9 3 9 80 5,532 

 Penn Manor, PA 159 69 6 5 2 5 46 3,204 

Providence, RI 236 118 10 9 3 9 79 5,436 

Brunswick, GA 1,302 263 22 20 7 20 176 12,160 

 Canaveral, FL 4,916 2,486 225 209 68 187 1,804 113,582 

Charleston, SC 10,277 1,630 136 124 45 124 1,093 75,271 

 New Haven, CT 379 188 16 14 5 14 125 8,664 

Palm Beach, FL 506 132 11 10 4 10 89 6,082 

 Bridgeport, CT 522 187 15 14 5 14 125 8,625 

Camden, NJ 1,286 579 48 44 16 44 387 26,754 

 Philadelphia, PA 1,803 976 81 74 27 74 652 45,081 

Wilmington, DE 1,155 303 25 23 8 23 202 13,982 

Wilmington, NC 1,045 333 28 25 9 25 223 15,397 

Richmond, VA 130 26 2 2 1 2 18 1,216 

 Jacksonville, FL 3,242 776 64 59 21 59 516 35,693 

Miami, FL 14,504 5,171 462 428 142 389 3,711 236,659 

Searsport, ME 116 73 6 6 2 6 49 3,380 

Boston, MA 3,100 1,105 94 87 30 84 759 50,846 

 New Bedford/Fairhaven, MA 53 28 2 2 1 2 19 1,280 

Baltimore, MD 5,924 1,632 137 126 45 52 1,111 75,309 

 Newport News, VA 1,216 170 14 13 5 13 122 8,063 

 Savannah, GA 8,297 1,035 83 76 29 79 691 47,804 

 Catalina, CA 257 45 4 4 1 3 28 2,043 

 Carquinez, CA 772 193 13 11 5 15 128 8,706 

El Segundo, CA 355 47 3 3 1 4 32 2,117 

 Eureka, CA 88 59 4 4 2 5 38 2,661 

Hueneme, CA 1,010 177 11 10 5 47 115 7,955 

 Long Beach, CA 13,007 2,632 178 162 72 205 1,704 119,333 

Los Angeles, CA 11,535 2,356 160 145 65 184 1,525 106,855 

 Oakland, CA 10,759 860 57 52 24 67 551 39,102 

 Redwood City, CA 101 59 4 3 2 5 39 2,665 

Richmond, CA 866 164 11 10 5 13 109 7,403 

 Sacramento, CA 95 61 4 4 2 5 40 2,754 

 San Diego, CA 2,164 483 37 34 13 37 311 21,942 

 San Francisco, CA 1,480 345 25 23 9 27 224 15,630 

 Stockton, CA 259 125 8 7 3 10 82 5,673 

 Total Auxiliary Emissions 197,430 57,317 5,052 4,597 1,615 4,306 41,232 2,635,436

 Total Auxiliary Emissions 

(short tons) 63,181 5,569 5,067 1,781 4,746 45,450 2,905,071 

3-42 
Chapter 3: Emission Inventory 

Table 3-17 Cruise Emissions by Deep Sea Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Anacortes, WA 545 50 4 4 2 4 29 1,871 

 Barbers Point, HI 472 28 2 2 1 2 16 1,039 

 Everett, WA 186 9 1 1 0 1 6 385 

 Grays Harbor, WA 360 15 1 1 1 1 10 627 

Honolulu, HI 8,037 300 28 26 10 23 206 13,469 

Kalama, WA 1,190 72 6 6 2 6 45 2,949 

Longview, WA 1,619 89 8 7 3 7 55 3,597 

 Olympia, WA 97 5 0 0 0 0 3 184 

 Port Angeles, WA 556 27 2 2 1 2 17 1,134 

 Portland, OR 11,198 424 40 37 15 33 291 19,040 

Seattle, WA 26,292 775 74 69 27 59 544 35,599 

Tacoma, WA 19,130 622 59 55 22 49 428 28,010 

Vancouver, WA 1,946 88 8 7 3 7 56 3,650 

Valdez, AK 6,676 45 8 8 2 4 75 4,904 

 Other Puget Sound 5,678 197 24 22 7 15 202 13,218 

 Anchorage, AK 537 22 2 2 1 2 14 934 

 Coos Bay, OR 399 21 2 2 1 2 12 758 

Hilo, HI 4,514 108 14 13 4 9 109 7,278 

Kahului, HI 2,323 58 7 6 2 5 51 3,382 

 Nawiliwili, HI 591 14 2 2 1 1 15 984 

Nikishka, AK 1,110 32 4 4 1 3 34 2,220 

Beaumont, TX 12,699 665 52 48 22 51 384 23,253 

 Freeport, TX 7,411 362 28 26 12 28 209 12,624 

Galveston, TX 6,572 283 23 22 9 22 175 10,741 

 Houston, TX 47,147 2,180 173 161 72 169 1,290 78,115 

 Port Arthur, TX 3,531 184 15 13 6 14 108 6,521 

 Texas City, TX 7,382 386 30 28 13 30 224 13,579 

 Corpus Christi, TX 11,452 584 46 43 19 45 341 20,702 

 Lake Charles, LA 6,382 266 25 23 9 21 195 11,811 

Mobile, AL 8,200 402 33 30 13 31 247 14,961 

 Brownsville, TX 1,213 69 5 5 2 5 40 2,453 

Gulfport, MS 3,556 148 13 12 5 12 95 5,765 

Manatee, FL 2,903 132 11 10 4 10 82 4,991 

 Matagorda Ship 2,504 143 11 10 5 11 83 5,021 

 Panama City, FL 662 35 3 3 1 3 20 1,240 

Pascagoula, MS 3,566 181 15 14 6 15 118 7,155 

Pensacola, FL 351 16 1 1 1 1 10 635 

Tampa, FL 10,941 539 45 42 18 42 341 20,705 

 Everglades, FL 38,304 1,348 131 121 45 104 1,038 62,951 

 New Orleans, LA 27,575 1,249 102 94 41 97 761 46,164 

 Baton Rouge, LA 4,627 238 19 17 8 18 139 8,439 

 South Louisiana, LA 18,366 961 75 70 32 74 557 33,789 

Plaquemines, LA 4,230 221 17 16 7 17 128 7,766 

 Albany, NY 396 20 2 2 1 2 12 734 

 New York/New Jersey 86,980 3,266 261 242 108 253 1,940 117,641 

 Portland, ME 3,968 195 16 15 6 15 118 7,131 

 Georgetown, SC 609 31 3 2 1 2 19 1,153 

Hopewell, VA 185 10 1 1 0 1 6 356 

3-43 
Regulatory Impact Analysis 

Table 3-17 Cruise Emissions by Deep Sea Port in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Marcus Hook, PA 2,754 143 11 10 5 11 82 4,974 

 Morehead City, NC 967 44 4 3 1 3 28 1,687 

Paulsboro, NJ 3,272 166 13 12 5 13 97 5,887 

 Chester, PA 1,467 63 5 5 2 5 37 2,261 

 Fall River, MA 290 13 1 1 0 1 9 540 

New Castle, DE 765 41 3 3 1 3 23 1,415 

 Penn Manor, PA 721 38 3 3 1 3 22 1,351 

Providence, RI 1,097 58 4 4 2 4 33 2,007 

Brunswick, GA 5,184 222 17 16 7 17 129 7,816 

 Canaveral, FL 17,801 665 54 50 22 52 501 30,423 

Charleston, SC 46,233 1,702 133 123 56 132 986 59,738 

 New Haven, CT 1,801 92 7 7 3 7 54 3,259 

Palm Beach, FL 2,277 83 8 7 3 6 60 3,623 

 Bridgeport, CT 1,452 58 4 4 2 5 34 2,073 

Camden, NJ 4,209 191 15 14 6 15 113 6,874 

 Philadelphia, PA 7,644 326 26 24 11 25 194 11,761 

Wilmington, DE 4,444 178 14 13 6 14 104 6,283 

Wilmington, NC 4,888 213 17 16 7 16 125 7,597 

Richmond, VA 596 25 2 2 1 2 15 891 

 Jacksonville, FL 13,908 571 46 43 19 44 349 21,139 

Miami, FL 57,415 2,068 173 161 70 161 1,497 90,831 

Searsport, ME 543 27 2 2 1 2 17 1,018 

Boston, MA 13,290 465 41 38 16 36 340 20,603 

 New Bedford/Fairhaven, MA 181 8 1 1 0 1 5 331 

Baltimore, MD 25,197 1,013 81 75 34 78 600 36,410 

 Newport News, VA 5,529 214 17 16 7 17 125 7,560 

Savannah, GA 37,523 1,400 110 102 46 108 815 49,371 

 Catalina, CA 928 36 4 3 1 3 26 1,700 

Carquinez, CA 3,442 171 13 12 6 13 92 6,025 

El Segundo, CA 1,685 87 7 6 3 7 47 3,068 

 Eureka, CA 409 19 2 1 1 1 11 699 

Hueneme, CA 3,334 137 11 10 5 11 74 4,862 

 Long Beach, CA 56,935 2,093 168 156 69 162 1,165 76,254 

Los Angeles, CA 50,489 1,856 149 138 62 144 1,033 67,622 

 Oakland, CA 48,762 1,676 131 122 55 130 900 58,866 

 Redwood City, CA 456 24 2 2 1 2 13 851 

Richmond, CA 3,956 197 15 14 7 15 106 6,936 

 Sacramento, CA 455 23 2 2 1 2 13 821 

 San Diego, CA 8,255 336 30 28 11 26 217 14,243 

 San Francisco, CA 6,260 273 23 21 9 21 162 10,632 

 Stockton, CA 1,210 63 5 5 2 5 34 2,216 

 Total Cruise Emissions 863,191 34,193 2,826 2,623 1,141 2,651 21,186 1,314,146 

Total Cruise Emissions (short 

tons) 37,691 3,115 2,891 1,258 2,922 23,353 1,448,598 

3-44 
Chapter 3: Emission Inventory 

Table 3-18 Reduced Speed Zone Emissions by Deep Sea Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Anacortes, WA 545 191 15 14 6 15 103 6,773 

 Barbers Point, HI 472 3 0 0 0 0 2 125 

 Everett, WA 186 49 4 4 2 4 27 1,785 

 Grays Harbor, WA 360 3 0 0 0 0 2 109 

Honolulu, HI 8,037 75 7 6 3 6 48 3,223 

Kalama, WA 1,190 101 8 7 4 9 57 3,800 

Longview, WA 1,619 125 10 9 5 11 70 4,645 

 Olympia, WA 97 43 3 3 1 3 23 1,509 

 Port Angeles, WA 556 77 6 6 3 6 45 2,924 

 Portland, OR 11,198 969 86 79 58 108 539 36,288 

Seattle, WA 26,292 4,289 349 323 151 347 2,402 157,988 

Tacoma, WA 19,130 3,685 290 269 121 285 2,023 133,271 

Vancouver, WA 1,946 175 14 13 7 16 100 6,661 

Valdez, AK 6,676 33 5 5 1 3 46 3,044 

 Other Puget Sound 5,678 963 112 104 32 75 942 61,929 

 Anchorage, AK 537 121 10 9 4 10 71 3,721 

 Coos Bay, OR 399 5 0 0 0 1 3 123 

Hilo, HI 4,514 27 2 2 1 2 18 339 

Kahului, HI 2,323 14 1 1 0 1 9 156 

 Nawiliwili, HI 591 4 0 0 0 0 2 47 

Nikishka, AK 1,110 117 12 12 4 9 99 5,979 

Beaumont, TX 12,699 771 81 75 45 88 574 29,868 

 Freeport, TX 7,411 28 2 2 1 2 18 1,016 

Galveston, TX 6,572 101 10 9 4 8 73 3,958 

 Houston, TX 47,147 656 57 53 22 50 429 24,233 

 Port Arthur, TX 3,531 97 10 9 6 11 71 3,760 

 Texas City, TX 7,382 181 16 14 6 14 117 6,581 

 Corpus Christi, TX 11,452 419 33 31 14 293 250 15,432 

 Lake Charles, LA 6,382 175 20 19 13 185 124 7,805 

Mobile, AL 8,200 352 29 27 12 239 219 13,537 

 Brownsville, TX 1,213 23 2 1 1 2 12 879 

Gulfport, MS 3,556 50 4 3 2 5 27 2,070 

Manatee, FL 2,903 78 7 4 3 7 36 3,183 

 Matagorda Ship 2,504 55 5 3 3 6 27 2,117 

 Panama City, FL 662 7 1 0 0 1 4 263 

Pascagoula, MS 3,566 68 6 5 2 6 40 2,788 

Pensacola, FL 351 5 0 0 0 0 3 225 

Tampa, FL 10,941 329 29 16 12 28 159 13,321 

 Everglades, FL 38,304 71 7 7 3 7 52 3,225 

 New Orleans, LA 27,575 2,670 224 208 98 227 1,678 103,988 

 Baton Rouge, LA 4,627 1,091 87 80 36 85 648 40,082 

 South Louisiana, LA 18,366 2,897 229 212 95 225 1,712 105,846 

Plaquemines, LA 4,230 244 19 18 8 19 144 8,910 

 Albany, NY 396 48 4 4 2 5 30 1,845 

 New York/New Jersey 86,980 881 83 76 54 105 547 34,706 

 Portland, ME 3,968 48 4 4 2 4 30 1,839 

 Georgetown, SC 609 16 1 1 1 1 105 615 

Hopewell, VA 185 22 2 2 1 2 196 781 

3-45 
Regulatory Impact Analysis 

Table 3-18 Reduced Speed Zone Emissions by Deep Sea Port in 2002(continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Marcus Hook, PA 2,754 245 20 18 9 20 1,996 9,058 

 Morehead City, NC 967 2 0 0 0 0 16 75 

Paulsboro, NJ 3,272 254 21 19 9 21 1,841 9,527 

 Chester, PA 1,467 86 7 7 3 8 343 3,292 

 Fall River, MA 290 5 0 0 0 0 29 231 

New Castle, DE 765 45 4 3 1 4 295 1,671 

 Penn Manor, PA 721 82 7 6 3 7 598 3,045 

Providence, RI 1,097 26 2 2 1 2 225 971 

Brunswick, GA 5,184 215 17 16 7 17 1,015 7,867 

 Canaveral, FL 17,801 73 7 7 2 6 94 3,316 

Charleston, SC 46,233 539 44 41 22 50 2,504 20,265 

 New Haven, CT 1,801 4 0 0 0 0 27 146 

Palm Beach, FL 2,277 5 0 0 0 0 14 235 

Bridgeport, CT 1,452 2 0 0 0 0 6 98 

Camden, NJ 4,209 346 29 27 14 32 1,208 13,693 

 Philadelphia, PA 7,644 505 43 40 19 48 2,603 19,709 

Wilmington, DE 4,444 206 17 16 10 20 747 7,996 

Wilmington, NC 4,888 110 9 9 5 10 620 4,169 

Richmond, VA 596 44 4 3 2 4 180 1,688 

 Jacksonville, FL 13,908 206 17 16 8 19 820 8,030 

Miami, FL 57,415 182 17 16 6 15 331 8,194 

Searsport, ME 543 11 1 1 0 1 59 442 

Boston, MA 13,290 135 13 12 6 13 514 6,009 

 New Bedford/Fairhaven, MA 181 4 0 0 0 0 10 158 

Baltimore, MD 25,197 4,325 347 321 142 336 2,596 159,626 

 Newport News, VA 5,529 131 11 10 5 11 86 4,998 

 Savannah, GA 37,523 1,333 107 99 46 110 802 49,492 

 Catalina, CA 928 11 1 1 0 1 8 523 

 Carquinez, CA 3,442 183 14 13 6 14 100 6,591 

El Segundo, CA 1,685 58 5 4 2 5 32 2,093 

 Eureka, CA 409 4 0 0 0 0 2 165 

Hueneme, CA 3,334 8 1 1 0 256 5 251 

 Long Beach, CA 56,935 748 62 58 30 69 436 29,056 

Los Angeles, CA 50,489 755 63 58 30 69 440 29,305 

 Oakland, CA 48,762 524 43 40 23 53 272 18,380 

 Redwood City, CA 456 25 2 2 1 2 14 905 

Richmond, CA 3,956 123 10 9 4 10 67 4,427 

 Sacramento, CA 455 58 5 4 2 4 32 2,088 

 San Diego, CA 8,255 98 9 8 3 8 63 4,198 

 San Francisco, CA 6,260 101 8 8 3 8 61 4,015 

 Stockton, CA 1,210 156 12 11 5 12 85 5,586 

 Total RSZ Emissions 863,191 34,427 2,887 2,657 1,280 3,804 35,148 1,318,897 

Total RSZ Emissions (short 

tons) 37,949 3,182 2,929 1,410 4,193 38,744 1,453,835 

3-46 
Chapter 3: Emission Inventory 

Table 3-19 Maneuvering Emissions by Deep Sea Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Anacortes, WA 545 50 5 3 3 5 23 1,610 

 Barbers Point, HI 472 25 2 2 1 2 12 806 

 Everett, WA 186 9 1 1 1 1 4 301 

 Grays Harbor, WA 360 12 1 1 1 1 6 412 

Honolulu, HI 8,037 360 36 28 19 32 194 13,248 

Kalama, WA 1,190 63 6 4 4 6 31 2,122 

Longview, WA 1,619 72 7 5 4 7 35 2,411 

 Olympia, WA 97 3 0 0 0 0 2 109 

 Port Angeles, WA 556 19 2 1 1 2 10 666 

 Portland, OR 11,198 501 49 37 33 50 232 16,173 

Seattle, WA 26,292 980 100 76 70 98 445 30,829 

Tacoma, WA 19,130 810 81 62 57 82 368 25,644 

Vancouver, WA 1,946 75 7 5 4 8 37 2,538 

Valdez, AK 6,676 55 8 6 3 5 46 3,156 

 Other Puget Sound 5,678 252 29 22 13 25 163 11,182 

 Anchorage, AK 537 1 0 0 0 0 1 54 

 Coos Bay, OR 399 1 0 0 0 0 0 26 

Hilo, HI 4,514 12 1 1 1 1 8 557 

Kahului, HI 2,323 6 1 1 0 1 4 283 

 Nawiliwili, HI 591 1 0 0 0 0 1 73 

Nikishka, AK 1,110 2 0 0 0 0 1 90 

Beaumont, TX 12,699 49 14 12 2 4 95 1,909 

 Freeport, TX 7,411 23 7 6 1 2 45 898 

Galveston, TX 6,572 40 12 5 1 3 38 1,676 

 Houston, TX 47,147 169 47 31 6 13 255 6,754 

 Port Arthur, TX 3,531 17 5 3 1 1 25 683 

 Texas City, TX 7,382 28 8 7 1 2 59 1,063 

 Corpus Christi, TX 11,452 112 11 10 8 14 68 4,385 

 Lake Charles, LA 6,382 54 6 5 4 6 38 2,414 

Mobile, AL 8,200 70 7 6 5 8 44 2,835 

 Brownsville, TX 1,213 8 1 1 1 1 7 323 

Gulfport, MS 3,556 27 3 2 2 3 20 1,025 

Manatee, FL 2,903 33 3 3 2 4 25 1,301 

 Matagorda Ship 2,504 16 2 1 1 2 13 609 

 Panama City, FL 662 4 0 0 0 0 3 144 

Pascagoula, MS 3,566 20 2 2 1 2 18 829 

Pensacola, FL 351 2 0 0 0 0 2 68 

Tampa, FL 10,941 66 7 6 4 8 95 2,637 

 Everglades, FL 38,304 233 24 23 12 23 163 10,273 

 New Orleans, LA 27,575 192 19 17 13 22 118 7,540 

 Baton Rouge, LA 4,627 35 3 3 2 4 21 1,371 

 South Louisiana, LA 18,366 143 14 12 10 18 87 5,606 

Plaquemines, LA 4,230 33 3 3 2 4 20 1,297 

 Albany, NY 396 3 0 0 0 0 2 120 

 New York/New Jersey 86,980 455 46 42 36 54 265 17,069 

 Portland, ME 3,968 37 4 3 2 4 23 1,472 

 Georgetown, SC 609 3 0 0 0 0 2 126 

Hopewell, VA 185 1 0 0 0 0 1 39 

3-47 
Regulatory Impact Analysis 

Table 3-19 Maneuvering Emissions by Deep Sea Port in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Marcus Hook, PA 2,754 22 2 2 2 3 14 874 

 Morehead City, NC 967 5 0 0 0 1 3 204 

Paulsboro, NJ 3,272 24 2 2 2 3 15 953 

 Chester, PA 1,467 5 1 1 0 1 3 204 

 Fall River, MA 290 1 0 0 0 0 1 60 

New Castle, DE 765 5 0 0 0 1 3 196 

 Penn Manor, PA 721 4 0 0 0 0 2 159 

Providence, RI 1,097 7 1 1 0 1 4 269 

Brunswick, GA 5,184 25 2 2 2 3 15 974 

 Canaveral, FL 17,801 70 7 6 3 6 50 3,118 

Charleston, SC 46,233 199 20 19 17 24 112 7,263 

 New Haven, CT 1,801 11 1 1 1 1 7 435 

Palm Beach, FL 2,277 9 1 1 1 1 6 388 

Bridgeport, CT 1,452 10 1 1 1 1 6 419 

Camden, NJ 4,209 27 3 2 2 3 17 1,090 

 Philadelphia, PA 7,644 46 4 4 3 6 28 1,790 

Wilmington, DE 4,444 22 2 2 2 3 13 861 

Wilmington, NC 4,888 24 2 2 2 3 14 922 

Richmond, VA 596 2 0 0 0 0 1 79 

 Jacksonville, FL 13,908 66 6 6 5 8 40 2,587 

Miami, FL 57,415 241 25 23 14 24 164 10,379 

Searsport, ME 543 4 0 0 0 0 2 147 

Boston, MA 13,290 65 7 6 4 7 44 2,812 

 New Bedford/Fairhaven, MA 181 1 0 0 0 0 1 52 

Baltimore, MD 25,197 130 13 12 10 15 76 4,931 

 Newport News, VA 5,529 25 3 2 2 3 14 929 

Savannah, GA 37,523 164 17 15 14 20 91 5,936 

 Catalina, CA 928 10 1 1 0 1 6 455 

 Carquinez, CA 3,442 23 1 1 1 1 11 740 

El Segundo, CA 1,685 9 1 1 0 1 4 287 

 Eureka, CA 409 4 0 0 0 0 2 133 

Hueneme, CA 3,334 9 0 0 0 1 4 294 

 Long Beach, CA 56,935 272 15 13 6 15 120 8,669 

Los Angeles, CA 50,489 242 13 12 5 13 106 7,687 

 Oakland, CA 48,762 241 10 9 5 11 89 6,472 

 Redwood City, CA 456 3 0 0 0 0 1 83 

Richmond, CA 3,956 26 2 1 1 2 12 838 

 Sacramento, CA 455 3 0 0 0 0 1 84 

 San Diego, CA 8,255 80 6 6 2 6 46 3,409 

 San Francisco, CA 6,260 54 4 4 1 4 29 2,105 

 Stockton, CA 1,210 7 0 0 0 0 3 220 

 Total Maneuver Emissions 863,191 7,383 758 625 440 724 4,356 266,262 

Total Maneuver Emissions 

(short tons) 8,138 835 689 485 799 4,802 293,504 

3-48 
Chapter 3: Emission Inventory 

Table 3-20 Hotelling Emissions by Deep Sea Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Anacortes, WA 545 113 9 8 3 9 71 5,207 

 Barbers Point, HI 472 66 5 5 2 5 42 3,064 

 Everett, WA 186 14 1 1 0 1 9 653 

 Grays Harbor, WA 360 20 2 1 1 2 12 918 

Honolulu, HI 8,037 533 45 41 15 40 352 24,445 

Kalama, WA 1,190 123 9 9 3 9 77 5,684 

Longview, WA 1,619 126 10 9 3 10 79 5,842 

 Olympia, WA 97 5 0 0 0 0 3 245 

 Port Angeles, WA 556 29 2 2 1 2 18 1,319 

 Portland, OR 11,198 413 31 29 11 31 259 19,057 

Seattle, WA 26,292 625 49 45 17 47 399 28,774 

Tacoma, WA 19,130 624 47 43 17 47 391 28,829 

Vancouver, WA 1,946 108 8 7 3 8 67 4,972 

Valdez, AK 6,676 210 16 15 6 16 132 9,685 

 Other Puget Sound 5,678 699 53 48 19 53 438 32,299 

 Anchorage, AK 537 76 6 5 2 6 47 3,527 

 Coos Bay, OR 399 20 1 1 1 1 12 903 

Hilo, HI 4,514 784 60 54 22 60 491 36,194 

Kahului, HI 2,323 396 30 27 11 30 248 18,273 

 Nawiliwili, HI 591 103 8 7 3 8 65 4,780 

Nikishka, AK 1,110 119 9 8 3 9 75 5,505 

Beaumont, TX 12,699 622 114 105 22 46 919 28,707 

 Freeport, TX 7,411 301 55 51 11 22 445 13,884 

Galveston, TX 6,572 590 73 67 21 36 587 27,267 

 Houston, TX 47,147 1,621 269 246 57 115 2,162 74,850 

 Port Arthur, TX 3,531 138 23 21 5 10 184 6,379 

 Texas City, TX 7,382 376 73 67 13 28 585 17,352 

 Corpus Christi, TX 11,452 643 53 49 18 49 430 29,720 

 Lake Charles, LA 6,382 355 29 27 10 27 237 16,379 

Mobile, AL 8,200 321 27 24 9 24 214 14,822 

 Brownsville, TX 1,213 74 6 6 2 6 49 3,402 

Gulfport, MS 3,556 382 31 29 10 29 272 17,521 

Manatee, FL 2,903 425 35 32 12 32 307 19,428 

 Matagorda Ship 2,504 175 15 13 5 13 117 8,080 

 Panama City, FL 662 25 2 2 1 2 16 1,141 

Pascagoula, MS 3,566 248 21 19 7 19 168 11,451 

Pensacola, FL 351 17 1 1 0 1 12 797 

Tampa, FL 10,941 573 49 45 16 43 392 26,370 

 Everglades, FL 38,304 2,634 240 222 73 200 1,870 121,678 

 New Orleans, LA 27,575 2,492 211 194 69 189 1,688 115,102 

 Baton Rouge, LA 4,627 621 51 47 17 47 414 28,676 

 South Louisiana, LA 18,366 2,427 201 185 67 185 1,620 112,104 

Plaquemines, LA 4,230 547 45 42 15 42 365 25,286 

 Albany, NY 396 32 3 2 1 2 21 1,467 

 New York/New Jersey 86,980 2,762 234 215 76 210 1,867 127,364 

 Portland, ME 3,968 442 37 34 12 34 296 20,394 

 Georgetown, SC 609 38 3 3 1 3 26 1,773 

Hopewell, VA 185 13 1 1 0 1 9 589 

3-49 
Regulatory Impact Analysis 

Table 3-20 Hotelling Emissions by Deep Sea Port in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Marcus Hook, PA 2,754 555 46 42 15 42 371 25,657 

 Morehead City, NC 967 70 6 5 2 5 47 3,230 

Paulsboro, NJ 3,272 223 19 17 6 17 150 10,309 

 Chester, PA 1,467 41 3 3 1 3 27 1,891 

 Fall River, MA 290 15 2 2 1 2 13 918 

New Castle, DE 765 108 9 8 3 8 72 4,975 

 Penn Manor, PA 721 50 4 4 1 4 34 2,323 

Providence, RI 1,097 108 9 8 3 8 72 4,975 

Brunswick, GA 5,184 208 17 16 6 16 139 9,616 

 Canaveral, FL 17,801 2,252 213 198 62 169 1,634 102,912 

Charleston, SC 46,233 1,368 114 105 38 104 917 63,159 

 New Haven, CT 1,801 179 15 14 5 14 120 8,276 

Palm Beach, FL 2,277 122 10 9 3 9 82 5,623 

 Bridgeport, CT 1,452 175 15 13 5 13 117 8,102 

Camden, NJ 4,209 430 36 33 12 33 287 19,882 

 Philadelphia, PA 7,644 807 67 61 22 61 539 37,262 

Wilmington, DE 4,444 220 18 17 6 17 147 10,180 

Wilmington, NC 4,888 294 24 22 8 22 196 13,576 

Richmond, VA 596 15 1 1 0 1 10 675 

 Jacksonville, FL 13,908 665 55 51 18 51 444 30,702 

Miami, FL 57,415 4,665 434 402 128 351 3,348 213,476 

Searsport, ME 543 68 6 5 2 5 46 3,163 

Boston, MA 13,290 982 85 78 27 74 673 45,202 

 New Bedford/Fairhaven, MA 181 25 2 2 1 2 17 1,160 

Baltimore, MD 25,197 944 79 73 26 72 646 43,593 

 Newport News, VA 5,529 136 11 10 4 10 91 6,274 

 Savannah, GA 37,523 698 55 50 19 53 466 32,248 

 Catalina, CA 928 21 2 2 1 2 13 961 

 Carquinez, CA 3,442 159 10 9 4 13 107 7,178 

El Segundo, CA 1,685 37 2 2 1 3 25 1,646 

 Eureka, CA 409 55 4 3 2 4 36 2,489 

Hueneme, CA 3,334 164 11 10 5 13 107 7,413 

 Long Beach, CA 56,935 2,189 144 130 60 172 1,420 99,027 

Los Angeles, CA 50,489 1,941 127 116 53 152 1,259 87,816 

 Oakland, CA 48,762 581 37 34 16 46 376 26,285 

 Redwood City, CA 456 55 4 3 2 4 36 2,479 

Richmond, CA 3,956 137 9 8 4 11 92 6,160 

 Sacramento, CA 455 54 3 3 1 4 35 2,424 

 San Diego, CA 8,255 326 23 21 9 25 209 14,758 

 San Francisco, CA 6,260 257 18 16 7 20 167 11,604 

 Stockton, CA 1,210 107 7 6 3 8 70 4,808 

Total Hotel Emissions 863,191 45,603 4,060 3,726 1,287 3,456 33,218 2,096,566 

Total Hotel Emissions (short 

tons) 50,268 4,475 4,107 1,419 3,809 36,617 2,311,068 

3-50 
Chapter 3: Emission Inventory 

Table 3-21 Auto Carrier Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Honolulu, HI 539 59 5 5 3 5 35 2,397 

Port Angeles, WA 6 1 0 0 0 0 1 47 

Portland, OR 2,331 416 38 33 21 42 246 16,911 

Seattle, WA 9 3 0 0 0 0 2 109 

Tacoma, WA 2,123 733 61 55 27 59 414 27,690 

Vancouver, WA 278 48 4 4 2 5 28 1,946 

Beaumont, TX 31 4 1 1 0 0 4 195 

Galveston, TX 560 59 6 5 2 4 43 2,372 

Houston, TX 1,141 122 12 11 4 8 92 5,019 

Mobile, AL 692 72 6 6 2 16 47 2,993 

Manatee, FL 4 0 0 0 0 0 0 14 

Matagorda Ship 16 1 0 0 0 0 1 48 

Pensacola, FL 169 13 1 1 0 1 8 520 

Tampa, FL 284 24 2 2 1 2 15 994 

Everglades, FL 136 22 2 2 1 2 14 938 

New Orleans, LA 225 50 4 4 2 4 32 2,089 

South Louisiana, LA 16 3 0 0 0 0 2 129 

New York/New Jersey 4,588 361 30 28 15 32 218 13,923 

Morehead City, NC 35 3 0 0 0 0 2 102 

Chester, PA 9 2 0 0 0 0 4 65 

Brunswick, GA 3,350 368 30 28 12 29 499 14,351 

Canaveral, FL 53 4 0 0 0 0 3 153 

Charleston, SC 1,922 182 15 14 6 14 169 7,234 

Bridgeport, CT 40 3 0 0 0 0 2 133 

Camden, NJ 0 0 0 0 0 0 0 0 

Philadelphia, PA 111 16 1 1 1 1 27 604 

Wilmington, DE 1,012 126 10 10 5 11 180 5,014 

Jacksonville, FL 4,420 389 32 29 14 32 362 15,430 

Miami, FL 131 10 1 1 0 1 7 395 

Boston, MA 744 62 5 5 2 5 54 2,495 

Baltimore, MD 5,458 1,290 103 95 43 101 768 48,152 

Newport News, VA 270 27 2 2 1 2 20 1,127 

Savannah, GA 644 76 6 6 3 6 46 2,898 

Carquinez, CA 682 84 6 6 3 6 49 3,246 

Hueneme, CA 2,036 125 9 8 4 157 71 4,650 

Long Beach, CA 1,068 96 7 6 3 7 55 3,681 

Los Angeles, CA 947 87 6 6 3 7 50 3,339 

Oakland, CA 10 1 0 0 0 0 1 42 

Richmond, CA 468 51 4 3 2 4 30 1,986 

San Diego, CA 1,374 131 9 9 4 10 77 5,123 

San Francisco, CA 20 2 0 0 0 0 1 81 

Total Auto Carrier 37,954 5,125 421 384 185 577 3,676 198,637 

Total Auto Carrier (short tons) 5,649 464 424 204 636 4,052 218,960 

3-51 
Regulatory Impact Analysis 

Table 3-22 Barge Carrier Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Mobile, AL 2 0 0 0 0 0 0 17 

New Orleans, LA 472 87 8 7 3 8 57 3,738 

Morehead City, NC 73 6 1 1 0 0 5 330 

Charleston, SC 420 55 4 4 2 4 78 2,279 

 Total Barge Carrier 967 148 13 12 5 12 141 6,364 

Total Barge Carrier (short 

tons) 163 14 13 6 14 156 7,015 

3-52 
Chapter 3: Emission Inventory 

Table 3-23 Bulk Carrier Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Anacortes, WA 67 28 2 2 1 2 15 1,033 

 Barbers Point, HI 82 14 1 1 1 1 9 599 

 Everett, WA 71 33 3 2 1 3 18 1,206 

 Grays Harbor, WA 140 24 2 2 1 2 14 974 

Honolulu, HI 158 29 2 2 1 2 17 1,188 

Kalama, WA 1,007 233 19 17 8 20 136 9,408 

Longview, WA 1,142 265 22 19 10 22 154 10,659 

 Olympia, WA 73 45 4 3 2 4 24 1,628 

 Port Angeles, WA 72 22 2 2 1 2 12 848 

 Portland, OR 2,351 633 51 46 23 53 364 25,061 

Seattle, WA 523 244 19 18 8 19 135 9,103 

Tacoma, WA 872 445 35 32 15 35 247 16,617 

Vancouver, WA 1,003 256 21 19 9 22 147 10,127 

Valdez, AK 7 1 0 0 0 0 1 59 

 Anchorage, AK 52 22 2 2 1 2 12 763 

 Coos Bay, OR 87 10 1 1 0 1 6 389 

Hilo, HI 31 3 0 0 0 0 2 125 

Kahului, HI 34 4 0 0 0 0 2 145 

Nikishka, AK 246 74 6 5 2 6 41 2,609 

Beaumont, TX 1,055 185 19 16 9 18 129 6,998 

 Freeport, TX 392 35 4 3 1 3 25 1,347 

Galveston, TX 1,063 114 11 9 4 8 78 4,285 

 Houston, TX 5,996 655 66 54 22 48 446 24,640 

 Port Arthur, TX 890 106 11 9 4 9 74 4,025 

 Texas City, TX 481 60 6 5 2 4 40 2,221 

 Corpus Christi, TX 3,359 460 37 34 16 121 278 17,665 

 Lake Charles, LA 1,116 147 13 12 6 46 91 5,870 

Mobile, AL 2,752 401 32 30 14 115 241 15,258 

 Brownsville, TX 685 106 9 8 3 8 65 4,234 

Gulfport, MS 120 21 2 2 1 2 13 834 

Manatee, FL 322 60 5 4 2 5 36 2,364 

 Matagorda Ship 586 118 10 9 4 10 71 4,713 

 Panama City, FL 79 13 1 1 0 1 8 515 

Pascagoula, MS 586 116 9 8 4 9 70 4,586 

Pensacola, FL 25 4 0 0 0 0 3 178 

Tampa, FL 3,380 604 49 43 20 48 365 23,968 

 Everglades, FL 626 109 9 8 3 9 70 4,652 

 New Orleans, LA 8,311 2,511 202 187 79 196 1,550 100,577 

3-53 
Regulatory Impact Analysis 

Table 3-23 Bulk Carrier Deep Sea Port Emissions in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Baton Rouge, LA 1,668 722 58 53 23 56 439 28,070 

 South Louisiana, LA 11,606 4,014 323 298 127 313 2,470 159,561 

Plaquemines, LA 2,714 665 54 50 21 52 417 27,385 

 Albany, NY 280 79 6 6 3 7 49 3,152 

 New York/New Jersey 3,168 482 41 37 16 39 317 20,791 

 Portland, ME 470 62 5 5 2 5 38 2,458 

 Georgetown, SC 408 63 5 5 2 5 116 2,606 

Hopewell, VA 127 30 2 2 1 2 144 1,167 

Marcus Hook, PA 243 54 4 4 2 4 192 2,146 

 Morehead City, NC 130 17 1 1 1 1 13 692 

Paulsboro, NJ 168 38 3 3 1 3 57 1,522 

 Chester, PA 35 7 1 1 0 1 26 289 

 Fall River, MA 127 13 2 1 1 1 30 792 

New Castle, DE 240 51 4 4 2 4 37 2,080 

 Penn Manor, PA 659 161 13 12 5 13 637 6,326 

Providence, RI 511 78 6 6 3 6 154 3,157 

Brunswick, GA 370 75 6 6 2 6 276 2,934 

 Canaveral, FL 464 59 5 4 2 5 54 2,453 

Charleston, SC 1,589 238 19 18 8 19 449 9,729 

 New Haven, CT 424 55 4 4 2 4 43 2,282 

Palm Beach, FL 83 11 1 1 0 1 9 442 

 Bridgeport, CT 98 13 1 1 0 1 10 547 

Camden, NJ 775 176 14 13 6 14 714 6,918 

 Philadelphia, PA 473 105 8 8 3 8 296 4,161 

Wilmington, DE 345 66 5 5 2 5 215 2,611 

Wilmington, NC 422 68 5 5 2 5 160 2,718 

Richmond, VA 11 3 0 0 0 0 18 117 

 Jacksonville, FL 1,394 203 17 15 6 16 337 8,436 

Miami, FL 122 16 1 1 1 1 17 653 

Searsport, ME 37 6 0 0 0 0 9 227 

Boston, MA 450 59 5 5 2 5 90 2,652 

Baltimore, MD 2,851 1,273 102 95 41 99 773 48,126 

 Newport News, VA 692 118 10 9 4 9 78 4,815 

 Savannah, GA 1,474 334 27 25 11 26 205 13,237 

 Carquinez, CA 717 172 12 11 5 13 103 6,934 

 Eureka, CA 114 28 2 2 1 2 18 1,201 

 Long Beach, CA 2,297 468 33 30 14 36 283 19,185 

Los Angeles, CA 2,037 423 29 27 13 33 255 17,295 

 Oakland, CA 280 40 3 3 1 3 23 1,568 

 Redwood City, CA 437 103 7 7 3 8 61 4,155 

Richmond, CA 385 82 6 5 2 6 50 3,371 

 Sacramento, CA 218 72 5 5 2 6 42 2,842 

 San Diego, CA 350 64 4 4 2 5 39 2,638 

 San Francisco, CA 498 101 7 6 3 8 61 4,139 

 Stockton, CA 638 198 14 13 6 15 116 7,780 

 Total Bulk Carrier 82,437 19,373 1,570 1,431 633 1,732 14,945 767,825 

Total Bulk Carrier (short tons) 21,355 1,731 1,577 697 1,909 16,474 846,382 

3-54 
Chapter 3: Emission Inventory 

Table 3-24 Container Ship Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Everett, WA 24 6 0 0 0 0 3 210 

Honolulu, HI 2,190 308 30 25 15 27 181 12,403 

Port Angeles, WA 14 2 0 0 0 0 1 78 

Portland, OR 5,227 879 85 74 59 96 486 33,142 

Seattle, WA 21,749 5,230 445 396 218 441 2,857 191,094 

Tacoma, WA 15,446 3,109 264 236 124 253 1,741 116,552 

Vancouver, WA 7 4 0 0 0 0 2 143 

Freeport, TX 1,575 74 6 6 2 6 46 2,679 

Galveston, TX 427 22 2 2 1 2 14 792 

Houston, TX 13,441 698 59 55 23 53 446 25,617 

Corpus Christi, TX 24 2 0 0 0 0 1 84 

Lake Charles, LA 36 4 0 0 0 1 2 135 

Mobile, AL 39 4 0 0 0 1 2 155 

Gulfport, MS 1,538 181 15 14 7 15 110 7,411 

Everglades, FL 7,732 658 56 52 23 53 426 27,826 

New Orleans, LA 5,756 788 65 60 35 76 482 30,940 

South Louisiana, LA 36 5 0 0 0 1 3 197 

Plaquemines, LA 12 1 0 0 0 0 1 41 

New York/New Jersey 56,253 3,246 268 248 130 281 1,934 122,010 

Morehead City, NC 24 2 0 0 0 0 1 58 

Chester, PA 1,140 139 11 10 5 11 306 5,313 

Charleston, SC 37,982 2,691 219 202 97 222 3,001 103,968 

New Haven, CT 14 1 0 0 0 0 1 34 

Palm Beach, FL 752 44 4 4 2 3 32 1,861 

Philadelphia, PA 2,696 306 25 23 13 28 671 11,715 

Wilmington, DE 1,999 197 16 15 8 18 379 7,555 

Wilmington, NC 1,779 130 11 10 5 12 162 5,115 

Richmond, VA 539 74 6 6 3 7 182 2,807 

Jacksonville, FL 3,997 279 24 22 11 24 279 11,419 

Miami, FL 20,834 1,310 107 99 46 105 961 51,282 

Boston, MA 5,016 325 27 25 13 28 305 12,667 

Baltimore, MD 9,224 1,411 112 104 49 113 828 51,462 

Newport News, VA 3,797 251 20 19 9 20 148 9,311 

Savannah, GA 28,209 2,088 168 156 77 173 1,230 76,805 

Carquinez, CA 27 3 0 0 0 0 2 105 

Eureka, CA 55 6 0 0 0 0 4 245 

Hueneme, CA 82 6 0 0 0 6 4 250 

Long Beach, CA 42,292 3,434 244 225 109 272 1,986 134,894 

Los Angeles, CA 37,505 3,097 221 203 99 247 1,791 121,601 

Oakland, CA 47,109 2,833 208 192 94 224 1,532 102,880 

Richmond, CA 165 15 1 1 0 1 8 571 

San Diego, CA 385 30 2 2 1 2 17 1,168 

San Francisco, CA 1,209 102 7 7 3 8 59 4,003 

 Total Container Ship 378,355 33,990 2,733 2,494 1,282 2,833 22,628 1,288,596 

Total Container Ship (short 

tons) 37,468 3,012 2,749 1,413 3,123 24,944 1,420,434 

3-55 
Regulatory Impact Analysis 

Table 3-25 General Cargo Ship Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Anacortes, WA 23 5 0 0 0 0 3 218 

Everett, WA 58 19 2 1 1 1 11 764 

Grays Harbor, WA 220 26 2 2 1 2 16 1,093 

Honolulu, HI 43 6 1 1 0 1 4 294 

Kalama, WA 116 15 1 1 1 1 8 568 

Longview, WA 441 61 5 5 2 5 35 2,376 

Olympia, WA 24 11 1 1 0 1 6 419 

Port Angeles, WA 390 90 7 7 3 7 49 3,291 

Portland, OR 771 123 11 9 5 11 71 4,812 

Seattle, WA 841 261 21 19 9 21 145 9,653 

Tacoma, WA 264 105 9 8 4 8 61 4,096 

Vancouver, WA 514 73 6 6 3 7 43 2,924 

Valdez, AK 6 1 0 0 0 0 1 39 

Anchorage, AK 4 1 0 0 0 0 1 48 

Coos Bay, OR 312 36 3 3 1 3 21 1,421 

Hilo, HI 5 1 0 0 0 0 0 21 

Kahului, HI 7 1 0 0 0 0 0 29 

Nikishka, AK 24 7 1 0 0 1 4 247 

Beaumont, TX 744 113 12 12 5 11 89 4,691 

Freeport, TX 238 22 2 2 1 2 16 845 

Galveston, TX 111 12 1 1 0 1 9 486 

Houston, TX 5,806 560 59 52 19 42 439 23,458 

Port Arthur, TX 890 100 11 9 4 9 77 4,085 

Texas City, TX 46 6 1 1 0 0 4 232 

Corpus Christi, TX 188 20 2 2 1 5 14 876 

Lake Charles, LA 670 71 7 6 3 22 49 3,150 

Mobile, AL 2,529 297 25 23 10 85 190 11,928 

Brownsville, TX 206 23 2 2 1 2 15 949 

Gulfport, MS 496 51 4 4 2 4 32 2,048 

Manatee, FL 301 36 3 3 1 3 22 1,430 

Matagorda Ship 27 2 0 0 0 0 2 104 

Panama City, FL 545 52 4 4 2 4 33 2,070 

Pascagoula, MS 466 45 4 4 2 4 30 1,915 

Pensacola, FL 71 7 1 1 0 1 5 287 

Tampa, FL 986 118 10 9 4 9 75 4,784 

Everglades, FL 1,813 197 18 16 6 16 138 9,057 

New Orleans, LA 2,925 601 50 46 20 48 384 24,538 

Baton Rouge, LA 356 111 10 9 4 9 73 4,624 

South Louisiana, LA 810 216 18 16 7 17 134 8,502 

Plaquemines, LA 178 29 2 2 1 2 19 1,192 

Albany, NY 83 15 1 1 1 1 10 639 

New York/New Jersey 1,841 153 13 12 6 13 95 5,957 

Georgetown, SC 202 26 2 2 1 2 35 1,062 

Hopewell, VA 44 12 1 1 0 1 42 444 

Marcus Hook, PA 39 7 1 1 0 1 16 299 

Morehead City, NC 387 40 3 3 1 3 30 1,684 

Paulsboro, NJ 22 3 0 0 0 0 2 145 

Chester, PA 237 40 3 3 1 3 71 1,679 

3-56 
Chapter 3: Emission Inventory 

Table 3-25 General Cargo Ship Deep Sea Port Emissions in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Fall River, MA 139 17 2 1 1 1 16 774 

Penn Manor, PA 56 12 1 1 0 1 18 500 

Providence, RI 32 4 0 0 0 0 7 158 

Brunswick, GA 1,066 168 14 12 6 14 475 6,535 

Canaveral, FL 549 61 5 5 2 5 52 2,509 

Charleston, SC 1,814 223 18 17 7 18 343 9,045 

New Haven, CT 382 43 4 3 1 3 30 1,791 

Palm Beach, FL 722 76 7 6 2 6 54 3,524 

Camden, NJ 974 180 15 14 6 15 349 7,471 

Philadelphia, PA 960 164 14 13 6 14 315 6,907 

Wilmington, DE 185 28 2 2 1 2 43 1,165 

Wilmington, NC 1,178 155 13 12 5 12 237 6,288 

Richmond, VA 38 7 1 1 0 1 5 322 

Jacksonville, FL 1,419 152 13 12 5 12 160 6,422 

Miami, FL 2,941 354 31 29 11 28 272 16,024 

Searsport, ME 3 0 0 0 0 0 1 17 

Boston, MA 122 14 1 1 0 1 13 606 

Baltimore, MD 2,275 673 56 52 22 52 430 26,796 

Newport News, VA 568 74 6 6 2 6 47 3,033 

Savannah, GA 2,521 415 34 32 14 32 261 16,543 

Carquinez, CA 39 8 1 1 0 1 5 331 

Eureka, CA 183 42 3 3 1 3 26 1,750 

Hueneme, CA 77 7 0 0 0 10 4 262 

Long Beach, CA 996 158 11 10 5 12 94 6,364 

Los Angeles, CA 883 143 10 9 4 11 85 5,742 

Oakland, CA 462 43 3 3 1 3 23 1,579 

Redwood City, CA 19 4 0 0 0 0 2 163 

Richmond, CA 67 13 1 1 0 1 8 530 

Sacramento, CA 202 58 4 4 2 5 34 2,292 

San Diego, CA 867 144 10 9 4 11 87 5,901 

San Francisco, CA 453 82 6 5 2 6 50 3,375 

Stockton, CA 202 55 4 4 2 4 32 2,147 

 Total General Cargo 49,711 7,402 630 576 251 684 6,208 302,338 

Total General Cargo (short 

tons) 8,159 694 635 277 754 6,843 333,270 

3-57 
Regulatory Impact Analysis 

Table 3-26 Miscellaneous Ship Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Honolulu, HI 16 4 0 0 0 0 2 149 

Portland, OR 21 7 1 0 0 0 4 269 

Seattle, WA 9 5 0 0 0 0 3 180 

Anchorage, AK 58 22 2 2 1 2 15 992 

Kahului, HI 1 0 0 0 0 0 0 11 

Houston, TX 13 1 0 0 0 0 1 49 

Corpus Christi, TX 119 16 2 1 1 5 12 759 

Lake Charles, LA 3 0 0 0 0 0 0 18 

Mobile, AL 604 83 8 7 3 24 62 3,903 

Pensacola, FL 65 11 1 1 0 1 8 497 

New Orleans, LA 12 7 1 1 0 1 4 281 

New York/New Jersey 26 7 1 1 0 1 5 325 

Baltimore, MD 23 14 1 1 0 1 10 674 

Newport News, VA 6 2 0 0 0 0 2 103 

 Total Miscellaneous 976 179 16 15 6 35 128 8,209 

Total Miscellaneous (short 

tons) 197 18 17 7 39 141 9,049 

3-58 
Chapter 3: Emission Inventory 

Table 3-27 Passenger Ship Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Honolulu, HI 4,359 637 58 53 19 48 427 28,546 

Portland, OR 60 12 1 1 1 1 8 558 

Seattle, WA 3,017 739 72 66 23 54 540 35,669 

Valdez, AK 31 2 0 0 0 0 2 110 

Anchorage, AK 200 66 5 5 2 5 43 2,495 

Hilo, HI 4,467 923 76 70 27 72 622 44,123 

Kahului, HI 2,256 466 38 35 14 36 307 21,755 

Nawiliwili, HI 583 120 10 9 4 9 82 5,810 

Galveston, TX 3,248 644 76 64 23 42 559 28,782 

Houston, TX 751 143 19 15 5 9 131 6,539 

Corpus Christi, TX 113 21 2 2 1 3 14 954 

Mobile, AL 330 80 7 6 2 11 52 3,538 

Manatee, FL 634 66 7 5 2 5 52 3,064 

Tampa, FL 3,599 352 34 25 12 28 271 16,166 

Everglades, FL 22,083 2,447 244 227 73 187 1,897 117,326 

New Orleans, LA 5,401 1,133 110 102 37 91 835 51,550 

New York/New Jersey 6,841 745 74 68 25 59 551 34,382 

Portland, ME 380 31 3 3 1 2 25 1,523 

Paulsboro, NJ 126 30 3 3 1 2 23 1,395 

Fall River, MA 11 1 0 0 0 0 1 63 

Canaveral, FL 15,756 2,758 256 238 80 209 2,044 126,856 

Charleston, SC 758 101 9 9 3 8 75 4,652 

Palm Beach, FL 146 15 1 1 0 1 11 684 

Philadelphia, PA 44 11 1 1 0 1 8 508 

Miami, FL 28,808 4,919 463 430 142 373 3,712 230,290 

Boston, MA 2,878 431 41 38 13 33 327 20,219 

New Bedford/Fairhaven, MA 16 2 0 0 0 0 2 94 

Baltimore, MD 1,058 427 42 39 13 33 320 19,829 

Savannah, GA 16 5 1 0 0 0 4 243 

Catalina, CA 919 78 7 7 2 6 53 3,608 

Eureka, CA 57 6 1 1 0 0 4 290 

Hueneme, CA 29 2 0 0 0 4 1 100 

Long Beach, CA 5,756 567 52 48 17 43 382 26,353 

Los Angeles, CA 5,105 516 47 43 16 40 348 23,970 

San Diego, CA 5,172 456 42 38 14 35 307 21,178 

San Francisco, CA 2,241 214 19 18 7 16 144 9,935 

 Total Passenger 127,251 19,165 1,819 1,668 578 1,470 14,184 893,157 

Total Passenger (short tons) 21,126 2,005 1,838 638 1,620 15,635 984,538 

3-59 
Regulatory Impact Analysis 

Table 3-28 Refrigerated Cargo Ship Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Honolulu, HI 6 3 0 0 0 0 2 113 

Port Angeles, WA 3 1 0 0 0 0 1 57 

Seattle, WA 55 30 2 2 1 2 17 1,203 

Anchorage, AK 140 62 5 4 2 5 36 2,256 

Galveston, TX 532 87 9 8 3 6 70 3,724 

Houston, TX 78 13 1 1 0 1 11 563 

Corpus Christi, TX 97 21 2 2 1 3 13 897 

Mobile, AL 22 5 0 0 0 1 3 209 

Gulfport, MS 374 56 5 4 2 4 37 2,320 

Manatee, FL 1,277 453 37 33 14 36 307 19,845 

Pascagoula, MS 232 54 5 4 2 4 38 2,387 

Pensacola, FL 2 0 0 0 0 0 0 13 

Tampa, FL 245 38 3 3 1 3 25 1,599 

Everglades, FL 116 71 6 5 2 5 47 3,223 

New Orleans, LA 163 109 9 8 3 9 72 4,907 

New York/New Jersey 1,575 195 16 15 7 16 123 8,151 

Morehead City, NC 6 1 0 0 0 0 1 56 

Paulsboro, NJ 4 1 0 0 0 0 1 53 

Brunswick, GA 158 32 3 2 1 2 20 1,373 

Canaveral, FL 525 96 8 7 3 7 63 4,212 

Charleston, SC 82 16 1 1 0 1 10 684 

Bridgeport, CT 1,086 188 15 14 6 15 121 8,196 

Camden, NJ 2,088 531 44 41 19 45 341 22,716 

Philadelphia, PA 833 206 17 16 7 18 132 8,874 

Wilmington, DE 733 171 14 13 6 14 110 7,319 

Jacksonville, FL 173 34 3 3 1 3 22 1,483 

Miami, FL 742 130 11 10 4 10 84 5,666 

Searsport, ME 5 1 0 0 0 0 1 44 

New Bedford/Fairhaven, MA 69 15 1 1 0 1 10 682 

Baltimore, MD 45 58 5 4 2 4 38 2,641 

Hueneme, CA 963 161 11 10 5 81 99 6,839 

Long Beach, CA 662 94 6 6 3 7 56 3,884 

Los Angeles, CA 587 84 6 5 2 7 51 3,494 

San Diego, CA 48 9 1 1 0 1 5 378 

 Total Reefer 13,724 3,027 247 226 98 313 1,968 130,060 

Total Reefer (short tons) 3,337 273 249 108 345 2,170 143,367 

3-60 
Chapter 3: Emission Inventory 

Table 3-29 Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Barbers Point, HI 4 0 0 0 0 0 0 3 

Everett, WA 27 2 0 0 0 0 1 64 

Honolulu, HI 39 3 0 0 0 0 2 129 

Longview, WA 5 1 0 0 0 0 1 39 

Portland, OR 110 8 1 1 0 1 5 325 

Seattle, WA 11 4 0 0 0 0 2 150 

Tacoma, WA 148 45 4 3 2 4 25 1,654 

Vancouver, WA 11 2 0 0 0 0 1 81 

Anchorage, AK 5 2 0 0 0 0 1 75 

Beaumont, TX 62 13 1 1 1 1 9 521 

Galveston, TX 59 7 1 1 0 0 5 290 

Houston, TX 810 99 10 9 3 7 75 4,089 

Corpus Christi, TX 6 1 0 0 0 0 1 33 

Lake Charles, LA 6 1 0 0 0 0 1 33 

Mobile, AL 69 11 1 1 0 2 7 454 

Gulfport, MS 1,028 299 26 23 9 23 222 13,769 

Manatee, FL 3 1 0 0 0 0 1 36 

Pensacola, FL 18 5 0 0 0 0 4 231 

Tampa, FL 166 48 4 4 1 4 36 2,230 

Everglades, FL 3,734 285 27 25 10 23 209 13,387 

New Orleans, LA 783 132 11 10 5 12 81 5,237 

South Louisiana, LA 4 1 0 0 0 0 1 34 

Albany, NY 6 1 0 0 0 0 1 50 

New York/New Jersey 3,323 290 24 22 11 25 176 11,292 

Portland, ME 305 28 3 2 1 2 20 1,316 

Morehead City, NC 27 2 0 0 0 0 2 104 

Chester, PA 47 8 1 1 0 1 5 302 

Penn Manor, PA 6 1 0 0 0 0 1 53 

Brunswick, GA 219 23 2 2 1 2 14 880 

Canaveral, FL 75 9 1 1 0 1 6 418 

Charleston, SC 455 43 4 3 1 3 28 1,777 

New Haven, CT 32 3 0 0 0 0 2 163 

Palm Beach, FL 423 49 4 4 2 4 35 2,302 

Bridgeport, CT 23 3 0 0 0 0 2 110 

Camden, NJ 22 4 0 0 0 0 3 178 

Philadelphia, PA 175 31 3 3 1 3 22 1,456 

Wilmington, DE 10 1 0 0 0 0 1 48 

Wilmington, NC 342 35 3 3 1 3 22 1,390 

Richmond, VA 8 2 0 0 0 0 1 87 

Jacksonville, FL 855 102 9 8 3 8 71 4,617 

Miami, FL 3,646 380 33 30 12 30 258 16,915 

Boston, MA 301 37 3 3 1 3 26 1,734 

Baltimore, MD 3,284 841 65 60 28 65 495 30,930 

Newport News, VA 77 12 1 1 0 1 8 532 

Savannah, GA 2,578 281 22 20 9 22 169 10,774 

Hueneme, CA 52 5 0 0 0 8 3 173 

Long Beach, CA 483 67 5 4 2 5 39 2,617 

3-61 
Regulatory Impact Analysis 

Table 3-29 Roll-On/Roll-Off Ship Deep Sea Port Emissions in 2002 (Continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Los Angeles, CA 428 61 4 4 2 5 36 2,379 

Oakland, CA 901 104 8 7 3 8 59 3,935 

Total RoRo 25,210 3,391 281 259 113 278 2,193 139,396 

Total RoRo (short tons) 3,738 310 286 125 306 2,418 153,658 

3-62 
Chapter 3: Emission Inventory 

Table 3-30 Tanker Ship Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Anacortes, WA 455 370 29 26 13 29 207 14,211 

Barbers Point, HI 387 108 9 8 4 9 62 4,432 

Everett, WA 6 23 2 2 1 2 13 881 

Honolulu, HI 687 221 18 16 8 18 130 9,165 

Kalama, WA 67 112 9 8 4 9 66 4,579 

Longview, WA 30 86 7 6 3 7 49 3,421 

Port Angeles, WA 72 34 3 3 1 3 25 1,721 

Portland, OR 309 222 19 17 8 18 133 9,246 

Seattle, WA 74 152 12 11 5 12 87 5,952 

Tacoma, WA 277 1,306 104 94 45 105 723 49,146 

Vancouver, WA 133 64 5 5 2 5 37 2,600 

Valdez, AK 6,632 338 36 32 11 27 296 20,581 

Other Puget Sound 5,678 2,111 219 197 71 169 1,745 118,629 

Anchorage, AK 78 45 4 3 2 4 25 1,608 

Hilo, HI 11 2 0 0 0 0 1 99 

Kahului, HI 9 2 0 0 0 0 1 77 

Nawiliwili, HI 8 2 0 0 0 0 1 74 

Nikishka, AK 840 189 19 18 6 15 165 10,938 

Beaumont, TX 10,807 1,791 228 210 76 159 1,742 71,331 

Freeport, TX 5,206 584 80 74 20 45 630 23,551 

Galveston, TX 552 68 12 11 2 5 93 2,832 

Houston, TX 19,096 2,334 319 294 80 178 2,494 93,908 

Port Arthur, TX 1,751 230 31 28 9 19 237 9,232 

Texas City, TX 6,856 905 121 111 31 69 942 36,122 

Corpus Christi, TX 7,498 1,213 99 91 40 263 752 48,747 

Lake Charles, LA 4,544 627 60 55 26 168 450 29,166 

Mobile, AL 1,114 187 15 14 6 46 115 7,432 

Brownsville, TX 320 45 4 3 1 4 28 1,859 

Manatee, FL 355 51 4 4 2 4 32 2,101 

Matagorda Ship 1,875 268 22 20 9 22 166 10,961 

Panama City, FL 38 5 0 0 0 0 3 204 

Pascagoula, MS 2,275 301 26 24 10 25 205 13,293 

Tampa, FL 2,282 323 27 24 11 26 202 13,293 

Everglades, FL 2,036 495 41 37 15 39 320 21,592 

New Orleans, LA 3,506 1,181 96 89 37 92 744 48,739 

Baton Rouge, LA 2,603 1,153 93 86 36 90 711 45,874 

South Louisiana, LA 5,886 2,187 177 164 68 170 1,365 88,846 

Plaquemines, LA 1,322 349 28 26 11 27 221 14,598 

Albany, NY 28 8 1 1 0 1 5 326 

New York/New Jersey 9,361 1,885 157 144 65 156 1,202 79,931 

Portland, ME 2,813 601 49 45 19 47 383 25,538 

Hopewell, VA 14 4 0 0 0 0 25 153 

Marcus Hook, PA 2,472 904 74 68 28 71 2,255 38,119 

3-63 
Regulatory Impact Analysis 

Table 3-30 Tanker Ship Deep Sea Port Emissions in 2002 (continued) 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Morehead City, NC 286 51 4 4 2 4 41 2,169 

Paulsboro, NJ 2,952 595 48 45 20 48 2,021 23,560 

Fall River, MA 13 3 0 0 0 0 5 120 

New Castle, DE 524 147 12 11 5 12 357 6,177 

Providence, RI 554 116 10 9 4 9 173 4,907 

Brunswick, GA 21 5 0 0 0 0 13 200 

Canaveral, FL 351 71 6 5 2 6 55 3,032 

Charleston, SC 1,213 260 21 20 8 20 366 11,054 

New Haven, CT 951 184 15 14 6 14 131 7,846 

Palm Beach, FL 124 22 2 2 1 2 19 923 

Bridgeport, CT 206 40 3 3 1 3 29 1,705 

Camden, NJ 349 103 8 8 3 8 218 4,257 

Philadelphia, PA 2,352 845 70 64 24 67 1,891 36,299 

Wilmington, DE 159 38 3 3 1 3 83 1,606 

Wilmington, NC 1,167 253 21 19 8 20 375 10,753 

Jacksonville, FL 1,633 346 28 26 11 27 419 14,554 

Miami, FL 161 33 3 3 1 3 26 1,502 

Searsport, ME 498 103 9 8 3 8 114 4,482 

Boston, MA 3,775 719 64 59 22 56 757 34,227 

New Bedford/Fairhaven, MA 96 22 2 2 1 2 22 924 

Baltimore, MD 979 424 33 31 14 33 256 15,951 

Newport News, VA 118 21 2 1 1 2 13 840 

Savannah, GA 2,083 395 31 29 13 31 258 16,547 

Catalina, CA 9 1 0 0 0 0 0 31 

Carquinez, CA 1,977 270 20 19 9 21 151 9,920 

El Segundo, CA 1,685 192 14 13 6 15 108 7,095 

Hueneme, CA 95 13 1 1 0 14 8 547 

Long Beach, CA 3,380 419 31 28 13 33 245 16,028 

Los Angeles, CA 2,998 383 28 26 12 30 223 14,610 

Richmond, CA 2,871 323 24 22 10 25 181 11,904 

Sacramento, CA 34 8 1 1 0 1 4 282 

San Diego, CA 60 6 0 0 0 0 3 224 

San Francisco, CA 1,839 184 14 13 6 14 104 6,823 

Stockton, CA 370 79 6 6 3 6 44 2,903 

 Total Tanker 146,245 29,758 2,796 2,562 994.23952 2,695 27,802 1,259,107 

Total Tanker (short tons) 32,802 3,082 2,824 1,096 2,971 30,646 1,387,928 

3-64 
Chapter 3: Emission Inventory 

Table 3-31 Ocean Going Tug Deep Sea Port Emissions in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Portland, OR 18 6 1 0 0 0 3 234 

Seattle, WA 4 2 0 0 0 0 1 78 

Kahului, HI 16 2 0 0 0 0 1 77 

Galveston, TX 19 2 0 0 0 0 1 80 

Houston, TX 16 1 0 0 0 0 1 70 

Corpus Christi, TX 47 5 0 0 0 1 4 226 

Lake Charles, LA 7 1 0 0 0 0 1 37 

Mobile, AL 46 6 1 0 0 2 4 269 

Brownsville, TX 3 0 0 0 0 0 0 16 

Manatee, FL 7 1 0 0 0 0 1 49 

Pascagoula, MS 7 1 0 0 0 0 1 42 

Everglades, FL 28 3 0 0 0 0 2 126 

New Orleans, LA 21 4 0 0 0 0 3 198 

South Louisiana, LA 7 2 0 0 0 0 1 77 

Plaquemines, LA 4 1 0 0 0 0 1 43 

New York/New Jersey 3 0 0 0 0 0 0 18 

Canaveral, FL 28 3 0 0 0 0 2 135 

Palm Beach, FL 28 3 0 0 0 0 2 133 

Jacksonville, FL 17 2 0 0 0 0 2 97 

Miami, FL 31 3 0 0 0 0 2 152 

Boston, MA 4 1 0 0 0 0 0 24 

Total Ocean Going Tug 361 48 5 4 2 6 34 2,182 

Total Ocean Going Tug (short 

tons) 53 5 4 2 6 37 2,405 

3-65 
Regulatory Impact Analysis 

Table 3-32 Total Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Alpena, MI 89 1.5 0.3 0.2 0.0 0.1 2.5 156 

Buffalo, NY 84 2.9 0.3 0.3 0.1 0.2 2.3 150 

Burns Waterway, IN 819 45.5 3.9 3.6 1.5 3.7 30.0 1,982 

Calcite, MI 126 3.4 0.3 0.3 0.1 0.3 2.5 158 

Cleveland, OH 560 32.6 2.8 2.5 1.0 2.6 21.8 1,448 

Dolomite, MI 67 1.9 0.2 0.1 0.1 0.2 1.1 73 

Erie, PA 55 2.2 0.2 0.2 0.1 0.2 1.7 112 

Escanaba, MI 118 3.1 0.3 0.3 0.1 0.3 2.3 146 

Fairport, OH 114 3.0 0.3 0.3 0.1 0.3 2.5 156 

Gary, IN 84 3.2 0.3 0.3 0.1 0.3 2.2 141 

Lorain, OH 64 1.5 0.2 0.2 0.1 0.1 1.3 84 

Marblehead, OH 26 0.5 0.1 0.1 0.0 0.0 0.5 34 

Milwaukee, WI 495 26.1 2.3 2.1 0.8 2.1 17.8 1,177 

Muskegon, MI 37 0.9 0.1 0.1 0.0 0.1 0.7 47 

Presque Isle, MI 562 16.2 1.4 1.3 0.7 1.4 10.0 637 

St Clair, MI 156 4.2 0.4 0.4 0.2 0.4 3.0 193 

Stoneport, MI 22 0.7 0.1 0.1 0.0 0.1 0.4 28 

Two Harbors, MN 48 1.2 0.1 0.1 0.0 0.1 0.9 56 

Ashtabula, OH 1,179 36.8 3.4 3.1 1.3 3.1 26.4 1,688 

Chicago, IL 492 22.1 1.9 1.8 0.7 1.8 15.3 1,003 

Conneaut, OH 1,863 52.6 5.0 4.7 1.9 4.4 39.5 2,501 

Detroit, MI 1,359 51.4 4.7 4.4 1.7 4.2 37.5 2,432 

Duluth-Superior, MN&WI 3,441 131.8 12.0 11.1 4.5 10.7 94.5 6,130 

Indiana, IN 140 5.9 0.5 0.5 0.2 0.5 4.1 272 

Inland Harbor, MI 56 1.5 0.1 0.1 0.1 0.1 1.1 69 

Manistee, MI 164 17.8 1.5 1.4 0.5 1.4 12.2 827 

Sandusky, OH 742 21.0 2.0 1.8 0.8 1.8 15.2 962 

Toledo, OH 1,517 57.9 5.1 4.7 2.0 4.7 39.3 2,550 

Total Emissions 14,476 549 50 46 19 45 389 25,210 

Total Emissions (short tons) 606 55 50 21 50 429 27,790 

3-66 
Chapter 3: Emission Inventory 

Table 3-33 Auxiliary Engine Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Alpena, MI 20 1.2 0.1 0.1 0.0 0.1 0.8 57 

 Buffalo, NY 19 1.5 0.1 0.1 0.0 0.1 1.0 71 

 Burns Waterway, IN 181 29.6 2.5 2.2 0.8 2.2 19.7 1,366 

Calcite, MI 28 1.4 0.1 0.1 0.0 0.1 0.9 63 

 Cleveland, OH 122 22.5 1.9 1.7 0.6 1.7 15.0 1,039 

Dolomite, MI 15 0.6 0.1 0.0 0.0 0.0 0.4 30 

Erie, PA 12 1.5 0.1 0.1 0.0 0.1 1.0 68 

 Escanaba, MI 26 1.2 0.1 0.1 0.0 0.1 0.8 56 

Fairport, OH 25 1.3 0.1 0.1 0.0 0.1 0.9 60 

 Gary, IN 18 1.5 0.1 0.1 0.0 0.1 1.0 70 

Lorain, OH 14 0.7 0.1 0.1 0.0 0.1 0.5 32 

 Marblehead, OH 6 0.3 0.0 0.0 0.0 0.0 0.2 12 

Milwaukee, WI 109 17.5 1.4 1.3 0.5 1.3 11.7 806 

Muskegon, MI 8 0.4 0.0 0.0 0.0 0.0 0.3 18 

Presque Isle, MI 125 5.6 0.5 0.4 0.2 0.4 3.7 257 

St Clair, MI 35 1.6 0.1 0.1 0.0 0.1 1.1 76 

Stoneport, MI 5 0.2 0.0 0.0 0.0 0.0 0.2 11 

 Two Harbors, MN 11 0.5 0.0 0.0 0.0 0.0 0.3 21 

Ashtabula, OH 262 16.3 1.3 1.2 0.4 1.2 10.9 752 

Chicago, IL 108 13.7 1.1 1.0 0.4 1.0 9.1 633 

 Conneaut, OH 414 20.9 1.7 1.6 0.6 1.6 13.9 964 

Detroit, MI 303 29.6 2.5 2.2 0.8 2.2 19.8 1,367 

 Duluth-Superior, MN&WI 760 74.0 6.1 5.6 2.0 5.6 49.4 3,418 

 Indiana, IN 31 3.7 0.3 0.3 0.1 0.3 2.5 170 

 Inland Harbor, MI 12 0.6 0.0 0.0 0.0 0.0 0.4 27 

Manistee, MI 35 15.1 1.3 1.2 0.4 1.2 10.1 699 

 Sandusky, OH 165 8.1 0.7 0.6 0.2 0.6 5.4 376 

Toledo, OH 336 30.9 2.6 2.3 0.9 2.3 20.6 1,426 

Total Auxiliary Emissions 3,202 302 25 23 8 23 202 13,944 

Total Auxiliary Emissions 

(short tons) 333 28 25 9 25 222 15,370 

3-67 
Regulatory Impact Analysis 

Table 3-34 Cruise Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Alpena, MI 89 0.3 0.1 0.1 0.0 0.0 1.2 75 

 Buffalo, NY 84 0.9 0.1 0.1 0.0 0.1 0.9 55 

 Burns Waterway, IN 819 11.7 1.0 0.9 0.4 0.9 7.5 453 

Calcite, MI 126 1.4 0.1 0.1 0.0 0.1 1.1 66 

 Cleveland, OH 560 7.7 0.7 0.6 0.3 0.6 5.2 314 

Dolomite, MI 67 0.8 0.1 0.1 0.0 0.1 0.5 30 

Erie, PA 55 0.6 0.1 0.1 0.0 0.0 0.5 33 

 Escanaba, MI 118 1.5 0.1 0.1 0.1 0.1 1.2 71 

Fairport, OH 114 1.2 0.1 0.1 0.0 0.1 1.1 66 

 Gary, IN 84 1.1 0.1 0.1 0.0 0.1 0.8 48 

Lorain, OH 64 0.6 0.1 0.1 0.0 0.0 0.6 37 

 Marblehead, OH 26 0.1 0.0 0.0 0.0 0.0 0.3 16 

Milwaukee, WI 495 6.4 0.6 0.5 0.2 0.5 4.5 275 

Muskegon, MI 37 0.4 0.0 0.0 0.0 0.0 0.3 21 

Presque Isle, MI 562 7.3 0.6 0.5 0.2 0.6 4.4 265 

St Clair, MI 156 1.7 0.2 0.2 0.1 0.1 1.4 82 

Stoneport, MI 22 0.3 0.0 0.0 0.0 0.0 0.2 12 

 Two Harbors, MN 48 0.6 0.1 0.0 0.0 0.0 0.4 26 

Ashtabula, OH 1,179 15.1 1.4 1.3 0.5 1.2 11.3 684 

Chicago, IL 492 6.4 0.6 0.5 0.2 0.5 4.6 281 

 Conneaut, OH 1,863 23.1 2.3 2.1 0.8 1.8 18.4 1,113 

Detroit, MI 1,359 16.5 1.6 1.5 0.6 1.3 13.1 793 

 Duluth-Superior, MN&WI 3,441 43.3 4.2 3.9 1.5 3.4 33.3 2,019 

 Indiana, IN 140 1.7 0.2 0.2 0.1 0.1 1.3 78 

 Inland Harbor, MI 56 0.7 0.1 0.1 0.0 0.1 0.5 31 

Manistee, MI 164 2.0 0.2 0.2 0.1 0.2 1.6 97 

 Sandusky, OH 742 9.4 0.9 0.8 0.3 0.7 7.1 428 

Toledo, OH 1,517 20.2 1.8 1.7 0.7 1.6 14.0 846 

Total Cruise Emissions 14,476 183 17 16 6 14 137 8,313 

Total Cruise Emissions (short 

tons) 202 19 18 7 16 151 9,164 

3-68 
Chapter 3: Emission Inventory 

Table 3-35 Reduced Speed Zone Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Alpena, MI 89 0.1 0.0 0.0 0.0 0.0 0.3 19 

 Buffalo, NY 84 0.2 0.0 0.0 0.0 0.0 0.2 14 

 Burns Waterway, IN 819 2.8 0.2 0.2 0.1 0.2 1.8 112 

Calcite, MI 126 0.3 0.0 0.0 0.0 0.0 0.3 16 

 Cleveland, OH 560 1.9 0.2 0.1 0.1 0.1 1.3 78 

Dolomite, MI 67 0.2 0.0 0.0 0.0 0.0 0.1 7 

Erie, PA 55 0.1 0.0 0.0 0.0 0.0 0.1 8 

 Escanaba, MI 118 0.4 0.0 0.0 0.0 0.0 0.3 18 

Fairport, OH 114 0.3 0.0 0.0 0.0 0.0 0.3 16 

 Gary, IN 84 0.3 0.0 0.0 0.0 0.0 0.2 12 

Lorain, OH 64 0.1 0.0 0.0 0.0 0.0 0.1 9 

 Marblehead, OH 26 0.0 0.0 0.0 0.0 0.0 0.1 4 

Milwaukee, WI 495 1.6 0.1 0.1 0.1 0.1 1.1 67 

Muskegon, MI 37 0.1 0.0 0.0 0.0 0.0 0.1 5 

Presque Isle, MI 562 1.7 0.1 0.1 0.1 0.1 1.0 65 

St Clair, MI 156 0.4 0.0 0.0 0.0 0.0 0.3 20 

Stoneport, MI 22 0.1 0.0 0.0 0.0 0.0 0.0 3 

 Two Harbors, MN 48 0.1 0.0 0.0 0.0 0.0 0.1 6 

Ashtabula, OH 1,179 3.7 0.3 0.3 0.1 0.3 2.8 170 

Chicago, IL 492 1.5 0.1 0.1 0.1 0.1 1.1 69 

 Conneaut, OH 1,863 5.8 0.6 0.5 0.2 0.5 4.5 277 

Detroit, MI 1,359 4.1 0.4 0.4 0.1 0.3 3.1 193 

 Duluth-Superior, MN&WI 3,441 10.8 1.0 0.9 0.4 0.8 8.1 502 

 Indiana, IN 140 0.4 0.0 0.0 0.0 0.0 0.3 19 

 Inland Harbor, MI 56 0.2 0.0 0.0 0.0 0.0 0.1 7 

Manistee, MI 164 0.5 0.0 0.0 0.0 0.0 0.4 24 

 Sandusky, OH 742 2.3 0.2 0.2 0.1 0.2 1.7 106 

Toledo, OH 1,517 4.9 0.4 0.4 0.2 0.4 3.4 208 

Total RSZ Emissions 14,476 45 4 4 2 4 33 2,052 

Total RSZ Emissions (short 

tons) 50 5 4 2 4 37 2,262 

3-69 
Regulatory Impact Analysis 

Table 3-36 Maneuvering Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Alpena, MI 89 0.2 0.0 0.0 0.0 0.0 0.3 17 

 Buffalo, NY 84 0.6 0.1 0.1 0.0 0.1 0.4 28 

 Burns Waterway, IN 819 4.4 0.4 0.4 0.3 0.5 3.0 190 

Calcite, MI 126 0.9 0.1 0.1 0.1 0.1 0.6 41 

 Cleveland, OH 560 2.0 0.2 0.2 0.1 0.2 1.4 89 

Dolomite, MI 67 0.5 0.0 0.0 0.0 0.1 0.3 19 

Erie, PA 55 0.2 0.0 0.0 0.0 0.0 0.2 11 

 Escanaba, MI 118 0.3 0.0 0.0 0.0 0.0 0.2 15 

Fairport, OH 114 0.8 0.1 0.1 0.0 0.1 0.6 38 

 Gary, IN 84 0.7 0.1 0.1 0.0 0.1 0.5 32 

Lorain, OH 64 0.4 0.0 0.0 0.0 0.0 0.3 18 

 Marblehead, OH 26 0.1 0.0 0.0 0.0 0.0 0.1 6 

Milwaukee, WI 495 2.3 0.2 0.2 0.1 0.3 1.6 105 

Muskegon, MI 37 0.2 0.0 0.0 0.0 0.0 0.2 11 

Presque Isle, MI 562 4.2 0.4 0.4 0.3 0.5 2.6 167 

St Clair, MI 156 1.1 0.1 0.1 0.1 0.1 0.7 47 

Stoneport, MI 22 0.2 0.0 0.0 0.0 0.0 0.1 7 

 Two Harbors, MN 48 0.2 0.0 0.0 0.0 0.0 0.2 10 

Ashtabula, OH 1,179 6.0 0.6 0.6 0.3 0.7 4.4 277 

Chicago, IL 492 2.0 0.2 0.2 0.1 0.2 1.4 87 

 Conneaut, OH 1,863 9.7 1.0 0.9 0.6 1.1 7.3 466 

Detroit, MI 1,359 5.7 0.6 0.6 0.3 0.6 4.5 284 

 Duluth-Superior, MN&WI 3,441 15.2 1.6 1.5 0.9 1.7 11.3 718 

 Indiana, IN 140 0.5 0.1 0.0 0.0 0.1 0.4 24 

 Inland Harbor, MI 56 0.3 0.0 0.0 0.0 0.0 0.2 13 

Manistee, MI 164 0.6 0.1 0.1 0.0 0.1 0.4 27 

 Sandusky, OH 742 3.8 0.4 0.4 0.2 0.4 2.7 174 

Toledo, OH 1,517 6.6 0.7 0.6 0.4 0.8 4.6 291 

Total Maneuver Emissions 14,476 70 7 7 4 8 50 3,213 

Total Maneuver Emissions 

(short tons) 77 8 7 5 9 56 3,542 

3-70 
Chapter 3: Emission Inventory 

Table 3-37 Hotelling Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Alpena, MI 89 1.0 0.1 0.1 0.0 0.1 0.7 46 

 Buffalo, NY 84 1.2 0.1 0.1 0.0 0.1 0.8 53 

 Burns Waterway, IN 819 26.6 2.2 2.0 0.7 2.0 17.7 1,227 

Calcite, MI 126 0.8 0.1 0.1 0.0 0.1 0.5 36 

 Cleveland, OH 560 20.9 1.7 1.6 0.6 1.6 14.0 967 

Dolomite, MI 67 0.4 0.0 0.0 0.0 0.0 0.2 16 

Erie, PA 55 1.3 0.1 0.1 0.0 0.1 0.9 60 

 Escanaba, MI 118 0.9 0.1 0.1 0.0 0.1 0.6 42 

Fairport, OH 114 0.8 0.1 0.1 0.0 0.1 0.5 36 

 Gary, IN 84 1.1 0.1 0.1 0.0 0.1 0.7 49 

Lorain, OH 64 0.4 0.0 0.0 0.0 0.0 0.3 20 

 Marblehead, OH 26 0.2 0.0 0.0 0.0 0.0 0.1 8 

Milwaukee, WI 495 15.8 1.3 1.2 0.4 1.2 10.5 730 

Muskegon, MI 37 0.2 0.0 0.0 0.0 0.0 0.2 11 

Presque Isle, MI 562 3.0 0.3 0.2 0.1 0.2 2.0 140 

St Clair, MI 156 1.0 0.1 0.1 0.0 0.1 0.6 44 

Stoneport, MI 22 0.1 0.0 0.0 0.0 0.0 0.1 6 

 Two Harbors, MN 48 0.3 0.0 0.0 0.0 0.0 0.2 13 

Ashtabula, OH 1,179 12.1 1.0 0.9 0.3 0.9 8.0 557 

Chicago, IL 492 12.2 1.0 0.9 0.3 0.9 8.2 565 

 Conneaut, OH 1,863 14.0 1.2 1.1 0.4 1.1 9.3 645 

Detroit, MI 1,359 25.1 2.1 1.9 0.7 1.9 16.8 1,162 

 Duluth-Superior, MN&WI 3,441 62.6 5.2 4.8 1.7 4.8 41.8 2,891 

 Indiana, IN 140 3.3 0.3 0.2 0.1 0.2 2.2 152 

 Inland Harbor, MI 56 0.4 0.0 0.0 0.0 0.0 0.3 18 

Manistee, MI 164 14.7 1.2 1.1 0.4 1.1 9.8 679 

 Sandusky, OH 742 5.5 0.5 0.4 0.2 0.4 3.7 254 

Toledo, OH 1,517 26.1 2.2 2.0 0.7 2.0 17.4 1,205 

Total Hotel Emissions 14,476 252 21 19 7 19 168 11,631 

Total Hotel Emissions (short 

tons) 278 23 21 8 21 185 12,821 

3-71 
Regulatory Impact Analysis 

Table 3-38 Self-Unloading Bulk Carrier Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Alpena, MI 89 1.5 0.3 0.2 0.0 0.1 2.5 156 

Buffalo, NY 71 2.0 0.2 0.2 0.1 0.2 1.8 112 

Burns Waterway, IN 236 7.6 0.7 0.7 0.3 0.7 5.7 362 

Calcite, MI 126 3.4 0.3 0.3 0.1 0.3 2.5 158 

Cleveland, OH 75 1.3 0.2 0.2 0.0 0.1 1.7 109 

Dolomite, MI 67 1.9 0.2 0.1 0.1 0.2 1.1 73 

Erie, PA 27 0.5 0.1 0.1 0.0 0.0 0.6 37 

Escanaba, MI 118 3.1 0.3 0.3 0.1 0.3 2.3 146 

Fairport, OH 114 3.0 0.3 0.3 0.1 0.3 2.5 156 

Gary, IN 71 2.3 0.2 0.2 0.1 0.2 1.6 104 

Lorain, OH 64 1.5 0.2 0.2 0.1 0.1 1.3 84 

Marblehead, OH 26 0.5 0.1 0.1 0.0 0.0 0.5 34 

Milwaukee, WI 169 4.3 0.5 0.4 0.2 0.4 3.8 238 

Muskegon, MI 37 0.9 0.1 0.1 0.0 0.1 0.7 47 

Presque Isle, MI 562 16.2 1.4 1.3 0.7 1.4 10.0 637 

St Clair, MI 156 4.2 0.4 0.4 0.2 0.4 3.0 193 

Stoneport, MI 22 0.7 0.1 0.1 0.0 0.1 0.4 28 

Two Harbors, MN 48 1.2 0.1 0.1 0.0 0.1 0.9 56 

Ashtabula, OH 1,047 29.2 2.8 2.6 1.1 2.5 21.6 1,363 

Chicago, IL 208 5.5 0.5 0.5 0.2 0.5 4.4 282 

Conneaut, OH 1,843 51.3 4.9 4.6 1.9 4.3 38.7 2,449 

Detroit, MI 802 20.3 2.1 2.0 0.7 1.7 17.5 1,106 

Duluth-Superior, MN&WI 2,201 61.8 6.0 5.6 2.3 5.2 47.9 3,034 

Indiana, IN 52 1.3 0.1 0.1 0.0 0.1 1.1 68 

Inland Harbor, MI 56 1.5 0.1 0.1 0.1 0.1 1.1 69 

Manistee, MI 9 0.2 0.0 0.0 0.0 0.0 0.2 11 

Sandusky, OH 735 20.6 1.9 1.8 0.8 1.7 14.8 935 

Toledo, OH 987 28.1 2.5 2.3 1.0 2.4 19.3 1,226 

Total SU Bulk Carrier 10,015 276 27 25 10 23 210 13,273 

Total SU Bulk Carrier (short 

tons) 304 29 27 11 26 231 14,631 

3-72 
Chapter 3: Emission Inventory 

Table 3-39 Bulk Carrier Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Buffalo, NY 13 0.9 0.1 0.1 0.0 0.1 0.6 38 

Burns Waterway, IN 562 36.8 3.0 2.8 1.1 2.9 23.5 1,567 

Cleveland, OH 427 27.7 2.3 2.1 0.9 2.2 17.7 1,179 

Erie, PA 17 1.1 0.1 0.1 0.0 0.1 0.7 46 

Gary, IN 7 0.5 0.0 0.0 0.0 0.0 0.3 22 

Milwaukee, WI 292 19.9 1.6 1.5 0.6 1.6 12.7 852 

Ashtabula, OH 126 7.4 0.6 0.6 0.2 0.6 4.7 313 

Chicago, IL 219 12.8 1.1 1.0 0.4 1.0 8.3 550 

Conneaut, OH 20 1.2 0.1 0.1 0.0 0.1 0.8 52 

Detroit, MI 458 27.2 2.2 2.1 0.9 2.2 17.3 1,149 

Duluth-Superior, MN&WI 1,032 61.1 5.2 4.7 1.9 4.8 40.5 2,692 

Indiana, IN 88 4.6 0.4 0.4 0.1 0.4 3.1 203 

Sandusky, OH 7 0.4 0.0 0.0 0.0 0.0 0.4 27 

Toledo, OH 421 25.1 2.1 2.0 0.8 2.0 16.8 1,116 

Total Bulk Carrier 3,689 227 19 17 7 18 147 9,807 

Total Bulk Carrier (short tons) 250 21 19 8 20 162 10,811 

Table 3-40 General Cargo Ship Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Burns Waterway, IN 21 1.2 0.1 0.1 0.0 0.1 0.8 53 

Cleveland, OH 58 3.5 0.3 0.3 0.1 0.3 2.4 160 

Erie, PA 11 0.6 0.1 0.1 0.0 0.0 0.4 29 

Milwaukee, WI 34 1.9 0.2 0.2 0.1 0.2 1.3 87 

Ashtabula, OH 6 0.2 0.0 0.0 0.0 0.0 0.2 12 

Chicago, IL 44 1.9 0.2 0.2 0.1 0.2 1.3 84 

Detroit, MI 44 2.0 0.2 0.2 0.1 0.2 1.3 88 

Duluth-Superior, MN&WI 167 6.7 0.6 0.5 0.2 0.5 4.7 305 

Toledo, OH 77 3.5 0.3 0.3 0.1 0.3 2.3 152 

Total General Cargo 462 22 2 2 1 2 15 969 

Total General Cargo (short 

tons) 24 2 2 1 2 16 1,068 

Table 3-41 Tanker Ship Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Chicago, IL 15 1.8 0.1 0.1 0.1 0.1 1.2 80 

Detroit, MI 6 0.7 0.1 0.1 0.0 0.1 0.4 30 

Duluth-Superior, MN&WI 12 1.4 0.1 0.1 0.0 0.1 0.9 63 

Manistee, MI 155 17.6 1.5 1.4 0.5 1.4 12.1 816 

Toledo, OH 5 0.5 0.0 0.0 0.0 0.0 0.3 24 

Total Tanker 193 22 2 2 1 2 15 1,012 

Total Tanker (short tons) 24 2 2 1 2 16 1,116 

3-73 
Regulatory Impact Analysis 

Table 3-42 Integrated Tug-Barge Emissions by Great Lake Port in 2002 

Port Name 

Installed 

Power 

(MW) 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2

 Gary, IN 6 0.3 0.0 0.0 0.0 0.0 0.2 15 

Chicago, IL 6 0.1 0.0 0.0 0.0 0.0 0.1 7 

Detroit, MI 49 1.2 0.1 0.1 0.0 0.1 0.9 59 

Duluth-Superior, MN&WI 29 0.7 0.1 0.1 0.0 0.1 0.6 35 

Toledo, OH 27 0.7 0.1 0.1 0.0 0.1 0.5 32 

Total ITB 117 3 0 0 0 0 2 149 

Total ITB (short tons) 3 0 0 0 0 3 164 

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. 

3.3.2.6.3 Summary 

This section provides a summary of the total port emissions for 2002. Table 3-43 and 

Table 3-44 provide a breakout of the total port emissions by auxiliary and propulsion engines, in 

units of metric tonnes and short tons, respectively. Table 3-45 and Table 3-46 provide the 

breakout by mode of operation, while Table 3-47 and Table 3-48 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 3-43 2002 Port Emissions Summary by Engine and Port Type (metric tonnes) 

Engine Type Port Type 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Propulsion 

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

Great Lakes 248 25 23 11 22 187 11,267 

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

Auxiliary 

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

Great Lakes 302 25 23 8 23 202 13,944 

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

All 

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

Great Lakes 549 50 46 19 45 389 25,210 

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

3-74 
Chapter 3: Emission Inventory 

Table 3-44 2002 Port Emissions Summary by Engine and Port Type (short tons) 

Engine Type Port Type 

Short Tons 

NOX PM10 PM2.5 HC CO SO2 CO2 

Deep Sea 70,866 6,039 5,549 2,792 6,977 58,065 2,601,934 

Propulsion Great Lakes 273 27 25 12 24 207 12,419 

Total 71,139 6,066 5,575 2,803 7,001 58,272 2,614,353 

Deep Sea 63,181 5,569 5,067 1,781 4,746 45,450 2,905,071 

Auxiliary Great Lakes 333 28 25 9 25 222 15,370 

Total 63,514 5,597 5,092 1,790 4,771 45,672 2,920,442 

Deep Sea 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005 

All Great Lakes 606 55 50 21 50 429 27,790 

Grand Total 134,653 11,662 10,667 4,593 11,772 103,944 5,534,795 

Table 3-45 2002 Port Emissions Summary by Mode and Port Type (metric tonnes) 

Mode Port Type 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3-75 
Regulatory Impact Analysis 

Table 3-46 2002 Port Emissions Summary by Mode and Port Type (short tons) 

Mode Port Type 

Short Tons 

NOX PM10 PM2.5 HC CO SO2 CO2 

Deep Sea 37,691 3,115 2,891 1,258 2,922 23,353 1,448,598 

Cruise Great Lakes 202 19 18 7 16 151 9,164 

Total 37,893 3,134 2,909 1,265 2,938 23,504 1,457,762 

Deep Sea 37,949 3,182 2,929 1,410 4,193 38,744 1,453,835 

RSZ Great Lakes 50 5 4 2 4 37 2,262 

Total 37,999 3,187 2,934 1,412 4,197 38,781 1,456,098 

Deep Sea 8,138 835 689 485 799 4,802 293,504 

Maneuvering Great Lakes 77 8 7 5 9 56 3,542 

Total 8,215 843 696 490 807 4,857 297,046 

Deep Sea 50,268 4,475 4,107 1,419 3,809 36,617 2,311,068 

Hotelling Great Lakes 278 23 21 8 21 185 12,821 

Total 50,546 4,498 4,128 1,426 3,830 36,802 2,323,889 

Deep Sea 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005 

All Great Lakes 606 55 50 21 50 429 27,790 

Grand Total 134,653 11,662 10,667 4,593 11,772 103,944 5,534,795 

3-76 
Chapter 3: Emission Inventory 

Table 3-47 2002 Port Emissions Summary by Ship Type and Port Type (metric tonnes) 

Ship Type Port Type 

Metric Tonnes 

NOX PM10 PM2.5 HC CO SO2 CO2 

Auto Carrier 

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

Great Lakes 0 0 0 0 0 0 0 

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

Barge Carrier 

Deep Sea 148 13 12 5 12 141 6,364 

Great Lakes 0 0 0 0 0 0 0 

Total 148 13 12 5 12 141 6,364 

Self-Unloading 

Bulk Carrier 

Deep Sea 0 0 0 0 0 0 0 

Great Lakes 276 27 25 10 23 210 13,273 

Total 276 27 25 10 23 210 13,273 

Other Bulk 

Carrier 

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

Great Lakes 227 19 17 7 18 147 9,807 

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

Container 

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

Great Lakes 0 0 0 0 0 0 0 

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

General Cargo 

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

Great Lakes 22 2 2 1 2 15 969 

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

Miscellaneous 

Deep Sea 179 16 15 6 35 128 8,209 

Great Lakes 0 0 0 0 0 0 0 

Total 179 16 15 6 35 128 8,209 

Passenger 

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

Great Lakes 0 0 0 0 0 0 0 

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

Refrigerated 

Cargo 

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

Great Lakes 0 0 0 0 0 0 0 

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

Roll-On/RollOff 

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

Great Lakes 0 0 0 0 0 0 0 

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

Tanker 

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

Great Lakes 22 2 2 1 2 15 1,012 

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

Ocean Going 

Tug 

Deep Sea 48 5 4 2 6 34 2,182 

Great Lakes 0 0 0 0 0 0 0 

Total 48 5 4 2 6 34 2,182 

Integrated TugBarge 

Deep Sea 0 0 0 0 0 0 0 

Great Lakes 3 0 0 0 0 2 149 

Total 3 0 0 0 0 2 149 

All 

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

Great Lakes 549 50 46 19 45 389 25,210 

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

3-77 
Regulatory Impact Analysis 

Table 3-48 2002 Port Emissions Summary by Ship Type and Port Type (short tons) 

Ship Type Port Type 

Short Tons 

NOX PM10 PM2.5 HC CO SO2 CO2 

Deep Sea 5,649 464 424 204 636 4,052 218,960 

Auto Carrier Great Lakes 0 0 0 0 0 0 0 

Total 5,649 464 424 204 636 4,052 218,960 

Deep Sea 163 14 13 6 14 156 7,015 

Barge Carrier Great Lakes 0 0 0 0 0 0 0 

Total 163 14 13 6 14 156 7,015 

Self-Unloading 

Bulk Carrier 

Deep Sea 0 0 0 0 0 0 0 

Great Lakes 304 29 27 11 26 231 14,631 

Total 304 29 27 11 26 231 14,631 

Other Bulk 

Carrier 

Deep Sea 21,355 1,731 1,577 697 1,909 16,474 846,382 

Great Lakes 250 21 19 8 20 162 10,811 

Total 21,605 1,752 1,597 705 1,929 16,636 857,193 

Deep Sea 37,468 3,012 2,749 1,413 3,123 24,944 1,420,434 

Container Great Lakes 0 0 0 0 0 0 0 

Total 37,468 3,012 2,749 1,413 3,123 24,944 1,420,434 

Deep Sea 8,159 694 635 277 754 6,843 333,270 

General Cargo Great Lakes 24 2 2 1 2 16 1,068 

Total 8,183 696 637 278 756 6,860 334,338 

Deep Sea 197 18 17 7 39 141 9,049 

Miscellaneous Great Lakes 0 0 0 0 0 0 0 

Total 197 18 17 7 39 141 9,049 

Deep Sea 21,126 2,005 1,838 638 1,620 15,635 984,538 

Passenger Great Lakes 0 0 0 0 0 0 0 

Total 21,126 2,005 1,838 638 1,620 15,635 984,538 

Refrigerated 

Cargo 

Deep Sea 3,337 273 249 108 345 2,170 143,367 

Great Lakes 0 0 0 0 0 0 0 

Total 3,337 273 249 108 345 2,170 143,367 

Roll-On/RollOff 

Deep Sea 3,738 310 286 125 306 2,418 153,658 

Great Lakes 0 0 0 0 0 0 0 

Total 3,738 310 286 125 306 2,418 153,658 

Deep Sea 32,802 3,082 2,824 1,096 2,971 30,646 1,387,928 

Tanker Great Lakes 24 2 2 1 2 16 1,116 

Total 32,826 3,084 2,826 1,097 2,973 30,663 1,389,044 

Ocean Going 

Tug 

Deep Sea 53 5 4 2 6 37 2,405 

Great Lakes 0 0 0 0 0 0 0 

Total 53 5 4 2 6 37 2,405 

Integrated TugBarge 

Deep Sea 0 0 0 0 0 0 0 

Great Lakes 3 0 0 0 0 3 164 

Total 3 0 0 0 0 3 164 

Deep Sea 134,047 11,608 10,616 4,572 11,723 103,515 5,507,005 

All Great Lakes 606 55 50 21 50 429 27,790 

Grand Total 134,653 11,662 10,667 4,593 11,772 103,944 5,534,795 

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Chapter 3: Emission Inventory 

3.3.3 Interport Emissions 

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

estimate interport emissions from main propulsion and auxiliary engines used by Category 3 

ocean-going vessels for the 2002 calendar year. The modeling domain for vessels operating in 

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

in the Great Lakes, it extends out to the international boundary with Canada. The emission 

results are divided into nine geographic regions of the U.S. (including Alaska and Hawaii), and 

then totaled to provide a national inventory. 

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

to any adjustments made to incorporate near-port inventories. The approach used to replace the 

near-port portion of the interport emissions is provided in Section 3.3.3.3. The final adjusted 

interport emissions are provided in Section 3.3.4. 

3.3.3.1 Methodology 

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

Energy, and Environmental Model (STEEM).3,4

 STEEM was developed by the University of 

Delaware as a comprehensive approach to quantify and geographically represent interport ship 

traffic, emissions, and energy consumption from large vessels calling on U.S. ports or transiting 

the U.S. coastline to other destinations, and shipping activity in Canada and Mexico. The model 

estimates emissions from main propulsion and auxiliary marine engines used on Category 3 

vessels that engage in 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 geographic information system (GIS) platform that also contains an 

empirically derived network of shipping lanes. It includes the emissions for all ship operational 

modes from cruise in unconstrained shipping lanes to maneuvering in a port. The model, 

however, excludes hotelling operations while the vessel is docked or anchored, and very low 

speed maneuvering close to a dock. For that reason, STEEM is referred to as an “interport” 

model, to easily distinguish it from the near ports analysis. 

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

way.39 The model begins by building a spatially-defined waterway network based on empirical 

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

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

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

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

sponsored by the National Oceanic and Atmospheric Administration and National Science 

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

Automated Mutual-Assistance Vessel Rescue (AMVER) system. 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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Regulatory Impact Analysis 

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

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

the waterway network. Each statistical lane (route and segment) is given a unique identification 

number for computational purposes. For the current analysis, STEEM used 20 years of ICOADS 

data (1983-2002) and about one year of AMVER data (part of 2004 and part of 2005) (Figure 

3-2). 

Figure 3-2 AMVER and ICOADS data 

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

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

USACE report on vessel entrances and clearances.13 There are 251 U.S. ports in the USACE 

entrances and clearances report. Each port also has a unique identification number for 

computational purposes. 

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

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

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

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

traffic flow can enter and leave a segment only through a junction or at a port. The figure 

represents only a sample of the many routes contained in the model. 

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Chapter 3: Emission Inventory 

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

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 USACE).13 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. 

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) 

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Regulatory Impact Analysis 

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

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

during cruise conditions. In STEEM, maneuvering is assumed to occur for the first and last 20 

kilometers of each trip when a ship is entering or leaving a port. A ship is assumed to move at 

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

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

proportioned among the respective cells that are represented by the gridded modeling domain. 

For this work, emissions estimates were produced at a cell resolution of 4 kilometers by 4 

kilometers, which is appropriate for most atmospheric air quality models. The results for each 

cell are then summed, as appropriate, to produce emission inventories for the various geographic 

regions of interest in this analysis. 

3.3.3.1.1 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 is described separately below. 

3.3.3.1.1.1 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 USACE report 

for the U.S. and the Lloyd’s Maritime Intelligence Unit (LMIU) for Canada and Mexico.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. 

3.3.3.1.1.2 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 USACE entrances and clearances report for 

each ship identification number: 

 Ship type 

 Gross registered tonnage (GRT) 

 Net registered tonnage (NRT) 

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Chapter 3: Emission Inventory 

The ship attributes data set contains the following information from Lloyd’s RegisterFairplay for each ship identification number.14 

 Main propulsion engine installed power (horsepower) 

 Service speed (cruise speed) 

 Ship size (length, wide, and draft) 

Sometimes data was lacking from the above references for ship speed. In these instances, 

the missing information was developed for each of nine vessel types and the appropriate value 

was applied to each individual ship of that type. Specifically, the missing ship speeds for each 

ship category were obtained from the average speeds used in a Lloyd’s Register study of the 

Baltic Sea and from an Entec UK Limited study for the European Commission.41,21 The 

resulting vessel cruise speeds for ships with missing data are shown in Table 3-49. 

Table 3-49 Average Vessel Cruise Speed by Ship Type a 

Ship Type Average Cruise Speed (knots) 

Bulk Carrier 14.1 

Container Ship 19.9 

General Cargo 12.3 

Passenger Ship 22.4 

Refrigerated Cargo 16.4 

Roll On-Roll Off 16.9 

Tanker 13.2 

Fishing 11.7 

Miscellaneous 12.7 

Note: a Used only when ship specific data were missing from the 

commercial database references. 

The average speed during maneuvering is approximately 60 percent of a ship’s cruise 

speed based on using the propeller law described in Section 3.3.2 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. In short, the STEEM 

model uses a ratio of main engine horsepower to auxiliary engine horsepower that was 

determined for eight different vessel types using information primarily from ICF International.42 

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Regulatory Impact Analysis 

(The ICF report attributed these power values to a study for the Port of Los Angeles by Starcrest 

Consulting.12) 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 3-50. 

Table 3-50 Auxiliary Engine Power Ratios 

Vessel Type 

Average Main 

Engine Power 

(kW) 

Average Auxiliary 

Engine Power (kW) 

Auxiliary to Main 

Engine Power 

Ratio 

Bulk Carrier 7,954 1,169 0.147 

Container Ship 30,885 5,746 0.186 

General Cargo 9,331 1,777 0.190 

Passenger Ship 39,563 39,563 a 1.000 

Refrigerated Cargo 9,567 3,900 b 0.136 

Roll On-Roll Off 10,696 c 2,156 c 0.202 

Tanker 9,409 1,985 0.211 

Miscellaneous 6,252 1,680 0.269 

Notes: 

a The ICF reference reported a value of 11,000 for auxiliary engines used on passenger vessels.42 

b The STEEM used auxiliary engine power as reported in the ARB methodology document.36 

c

 The STEEM purportedly used values for Roll On-Roll Off main and auxiliary engines that 

represent a trip weighted average of the Auto Carrier and Cruise Ship power values from the ICF 

reference. 

Finally, the ship attributes database provides information on the load factors for main 

engines during cruise and maneuvering operation, in addition to load factors for auxiliary marine 

engines. Main engine load factors for cruise operation were taken from a study of international 

shipping for all ship types, except passenger vessels.43 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.21 The main engine load factors at cruise 

speed by ship type are shown in Table 3-51. 

Auxiliary engine load factors, except for passenger ships, were obtained from the ICF 

International study referenced above. These values are also shown in Table 3-51. For cruise 

mode, neither port nor interport portions of the inventory were adjusted for low load operation, 

as the low load adjustments are only applied to propulsion engines with load factors below 20%. 

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Chapter 3: Emission Inventory 

Table 3-51 Main and Auxiliary Engine Load Factors at Cruise Speed by Ship Type 

Ship Type 

Average Main Engine 

Load Factor (%) 

Average Auxiliary Engine 

Load Factor (%) 

Bulk Carrier 75 17 

Container Ship 80 13 

General Cargo 80 17 

Passenger Ship 55 25 

Refrigerated Cargo 80 20 

Roll On-Roll Off 80 15 

Tanker 75 13 

Miscellaneous 70 17 

3.3.3.1.1.3 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 3-52 . 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. 21,36,42,43, 45 The PM factor was 

based on discussions with the California Air Resources Board (ARB) staff. The fuel specific CO 

emission factor was taken from a report by ENVIRON International.19 The STEEM study used 

the composite emission factors shown in the table because the voyage data used in the model do 

not explicitly identify main engine speed ratings, i.e., slow or medium, or the auxiliary engine 

fuel type, i.e., marine distillate or residual marine. The composite factor for each pollutant is 

determined by weighting individual emission factors by vessel engine population data from a 

2005 survey of ocean-going vessels that was performed by ARB.20 Fuel consumption was 

calculated from CO2 emissions using the same ratio (1:3.183) as used in the near-port analysis. 

Table 3-52 Main Engine Emission Factors by Ship and Fuel Type 

Engine Type 

Main Engine Emission Factors (g/kW-hr) 

Fuel Type NOX PM10 PM 2.5

 a HC CO SO2 

Slow Speed 

Residual 

Marine 18.1 1.5 1.4 0.6 1.4 10.5 

Medium Speed 

Residual 

Marine 14 1.5 1.4 0.5 1.1 11.5 

Composite EF 

Residual 

Marine 17.9 1.5 1.4 0.6 1.4 10.6 

Note: a Estimated from PM10 using a multiplicative adjustment factor of 0.92. 

The emission factors for auxiliary engines are shown in Table 3-53. The fuel specific 

main emission factors for NOX and HC were taken from several recent analyses of ship 

emissions in the U.S., Canada, and Europe, as referenced above for the main engine load factors. 

The PM factor for marine distillate was taken from a report by ENVIRON International, which 

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Regulatory Impact Analysis 

was also referenced above. The PM factor for residual marine was based on discussions with the 

California Air Resources Board (ARB) staff. The CO factors are from the Starcrest Consulting 

study of the Port of Los Angeles.12 For SO2, the fuel specific emission factors were obtained 

from Entec and Corbett and Koehler.21,43

 The composite emission factors displayed in the table are discussed below. 

Table 3-53 Auxiliary Engine Emission Factors by Ship and Fuel Type 

Engine Type 

Auxiliary Engine Emission Factors (g/kW-hr) 

Fuel Type NOX PM10 PM 2.5

 a HC CO SO2 

Medium Speed 

Marine 

Distillate 13.9 0.3 0.3 0.4 1.1 4.3 

Medium Speed 

Residual 

Marine 14.7 1.5 1.4 0.4 1.1 12.3 

Composite EF 

Residual 

Marine 14.5 1.2 1.1 0.4 1.1 ** 

Note: a Estimated from PM10 using a multiplicative adjustment factor of 0.92. b See Table 3-54 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 marine. Again, 

the composite factor for those pollutants was determined by weighting individual emission 

factors by vessel engine population data from a 2005 survey of ocean-going vessels that was 

performed by ARB.20

 For SO2, composite emission factors for auxiliary engines were calculated for each vessel 

type. These composite factors were determined by taking the fuel specific emission factors from 

Table 3-53 and weighting them with an estimate of the amount of marine distillate and residual 

marine that is used by these engines. The relative amount of each fuel type consumed was taken 

from the 2005 ARB survey. The relative amounts of each fuel type for each vessel type and the 

resulting SO2 emission factors are shown in Table 3-54. 

Table 3-54 Auxiliary Engine SO2 Composite Emission Factors by Vessel Type 

Vessel Type 

Residual Marine 

(%) 

Marine Distillate 

(%) 

Composite 

Emission Factor 

(g/kW-hr) 

Bulk Carrier 71 29 9.98 

Container Ship 71 29 9.98 

General Cargo 71 29 9.98 

Passenger Ship 92 8 11.66 

Refrigerated Cargo 71 29 9.98 

Roll On-Roll Off 71 29 9.98 

Tanker 71 29 9.98 

Miscellaneous 0 100 4.3 

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Chapter 3: Emission Inventory 

3.3.3.1.1.4 EPA Adjustments to STEEM PM and SO2 Emission Inventories 

The interport emission results contained in this study for PM10 and SO2 were taken from 

the STEEM inventories and then adjusted to reflect EPA’s recent review of available engine test 

data and fuel sulfur levels as described Section 3.3.2.3 for the near port analysis. In the near 

ports work, a PM emission factor of 1.4 g/kW-hr was used for most main engines, e.g., slow 

speed diesel and medium speed diesel engines, all of which are assumed to use residual marine. 

A slightly higher value was used for steam turbine and gas turbine engines, and a slightly lower 

value was used for most auxiliary engines. However, these engines represent only a small 

fraction of the total emissions inventory. As shown in Section 3.3.3.1.1.3, 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 SO2 emission inventories were similarly adjusted using SO2 emission 

factors from the interport analysis (Section 3.3.3.1.1.3) and the near ports analysis (Section 

3.3.2.3). This information is displayed in Table 3-55. 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.20 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 3-55 SO2 Emission Factors Used to Adjust STEEM Emission Inventories 

Engine Type Fuel Type 

STEEM 

(g/ kW -hr) 

Near Ports 

(g/ kW -hr) 

Composite 

(g/ kW -hr) 

Slow Speed 

Residual 

Marine 10.50 10.29 n/a 

Medium Speed 

Residual 

Marine 11.50 11.09 n/a 

Composite 

Residual 

Marine 10.6 a 10.33 a 0.975 

Note: a Weighted by ship populations from 2005 ARB survey: 95 percent slow speed and 5 percent 

medium speed. 

3.3.3.2 Interport Domestic Traffic 

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

engaged in foreign commerce. As a result, U.S.-flagged vessels carrying domestic cargo (Jones 

Act ships) are not included. The STEEM interport analysis also roughly estimated the emissions 

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

 Specifically, the 

interport analysis estimated that the large ocean-going vessels carrying only domestic cargo 

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

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Regulatory Impact Analysis 

In Section 3.3.2.5, in the estimation of port inventories, the estimate of excluded installed 

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

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

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

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

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

underpredict the benefits of the proposed ECA. 

3.3.3.3 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 3.3.2 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 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. 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 ICF.2 

3.3.3.3.1 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. 

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Chapter 3: Emission Inventory 

3.3.3.3.1.1 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 dataset.11 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 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 the Appendix, Table 

3-102. 

3.3.3.3.1.2 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 3.3.2 and 

the Army Corp of Engineers National Waterway Network (NWN) geographic database of 

navigable waterways in and around the U.S.16 The coordinates of RSZ endpoints for all of the 

117 ports used in this work are shown in the Appendix, Table 3-103. 

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 3-4 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 3-5 shows an 

example of a port with multiple RSZs. 

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Regulatory Impact Analysis 

Figure 3-4 Example of Gridded RSZ Lane (Hopewell, Virginia) 

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Chapter 3: Emission Inventory 

Figure 3-5 Example of Multiple RSZ Lanes (Brunswick, Georgia) 

3.3.3.3.1.3 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 3-6 provides an example of multiple cruise lanes. 

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Regulatory Impact Analysis 

Figure 3-6 Example of Multiple Cruise Lanes 

(Tampa and Port Manatee, Florida) 

3.3.3.3.2 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 3-6, 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 

3-7). 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 

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Chapter 3: Emission Inventory 

emission results (Figure 3-8). The methodology for determining the emissions from transient 

ship operation is described below. 

Figure 3-7 Example of Complete Replacement of STEEM Emissions 

(Panama City, Florida) 

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Regulatory Impact Analysis 

Figure 3-8 Example of Partial Replacement of STEEM Emissions 

(Coos Bay, Oregon) 

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 3-8 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. 

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Chapter 3: Emission Inventory 

Figure 3-9 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 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. 

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Regulatory Impact Analysis 

Original 

New 

Figure 3-9 Spatial Comparison of the Original STEEM and New Combined Gridded Inventories—

Southeast United States 

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Chapter 3: Emission Inventory 

3.3.4 2002 Baseline 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, 

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

specified in Section 3.2 were created using ArcGIS software to intersect the regional shapefiles 

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

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

emissions in each of the cells defined within a region were then summed. The final emission 

inventories for 2002 are shown in Table 3-56 for each of the nine geographic regions and the 

nation. The geographic scope of these regions was previously displayed in Figure 3-1. The fuel 

consumption by fuel type associated with each region is also provided in Table 3-57. 

Table 3-56 2002 Regional and National Emissions from Category 3 Vessel Main and Auxiliary Engines 

Region 

Metric Tonnes 

NOX PM10 PM2.5 a HC CO SO2 CO2 

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

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

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

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

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

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

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

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

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

Total Metric Tonnes 670,137 53,358 49,089 22,322 53,445 411,512 24,573,605 

Total Short Tons

 b 738,700 58,817 54,112 24,606 58,913 453,614 27,087,763 

Notes: 

a

 Estimated from PM10 using a multiplicative adjustment factor of 0.92. b

 Converted from metric tonnes using a multiplicative conversion factor of 1.102 short tons per metric tonne. 

Table 3-57 2002 Regional and National Fuel Consumption 

Region 

Metric Tonnes Fuel 

Distillate Residual Total 

Alaska East (AE) 1,887 204,725 206,612 

Alaska West (AW) 0 673,490 673,490 

East Coast (EC) 91,529 2,463,153 2,554,682 

Gulf Coast (GC) 63,876 1,928,628 1,992,504 

Hawaii East (HE) 4,375 252,794 257,170 

Hawaii West (HW) 0 361,836 361,836 

North Pacific (NP) 15,905 295,230 311,135 

South Pacific (SP) 35,052 1,157,714 1,192,765 

Great Lakes (GL) 1,270 168,801 170,071 

Total Metric Tonnes 213,894 7,506,371 7,720,265 

Total Short Tons

 b 235,778 8,274,358 8,510,136 

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Regulatory Impact Analysis 

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.1,4

 These vessels are sometimes referred to as Jones Act 

ships, as explained in Section 3.3.2.5. 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 3-56. This is less than the 6.5 percent estimate 

based on calls to U.S. ports, since the interport traffic includes transiting traffic in U.S. waters. 

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

U.S. emissions are presented in Table 3-58 and Table 3-59. 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 3-58 2002 Contribution of Near Ports and Interport Emissions to the Total C3 Inventory 

Region and 

Port Type 

Metric Tonnes 

NOX PM10 PM2.5 a 

Total 

% 

Region 

% 

Type Total 

% 

Region 

% 

Type Total 

% 

Region 

% 

Type 

Interport 

Deep Sea 

Great Lakes 

549,852 82.1 100 42,945 80.5 100 39,510 80.5 100 

535,325 -- 97.4 41,811 -- 97.4 38,465 -- 97.4 

14,528 -­ 2.6 1,135 -­ 2.6 1,044 -­ 2.6 

Near Port 

Deep Sea 

Great Lakes 

120,285 17.9 100 10,413 19.5 100 9,580 19.5 100 

119,793 -- 99.6 10,368 -- 99.6 9,539 -- 99.6 

491 -- 0.4 44 -- 0.4 41 -- 0.4 

All Regions 

Deep Sea 

Great Lakes 

670,137 100 -- 53,358 100 -- 49,089 100 -- 

655,118 -- 97.8 52,179 -- 97.8 48,004 -- 97.8 

15,019 -­ 2.2 1,179 -­ 2.2 1,085 -­ 2.2 

All Region 

Short Tons b 738,700 -- -- 58,817 -- -- 54,112 -- -- 

 Notes: a

 Estimated from PM10 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. 

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Chapter 3: Emission Inventory 

Table 3-59 2002 Contribution of Near Ports and Interport Emissions to the Total C3 Inventory (Cont’d) 

Region and 

Port Type 

Metric Tonnes 

HC CO SO2 

Total 

% 

Region 

% 

Type Total 

% 

Region 

% 

Type Total 

% 

Region 

% 

Type 

Interport 

Deep Sea 

Great Lakes 

18,219 81.6 100 42,912 80.3 100 318,450 77.4 100 

17,738 -- 97.4 41,778 -- 97.4 310,030 -- 97.4 

481 -­ 2.6 1,134 -­ 2.6 8,420 -­ 2.6 

Near Port 

Deep Sea 

Great Lakes 

4,103 18.4 100 10,533 19.7 100 93,062 22.6 100 

4,086 -- 99.6 10,493 -- 99.6 92,716 -- 99.6 

17 -- 0.4 40 -- 0.4 346 -- 0.4 

All Regions 

Deep Sea 

Great Lakes 

22,322 100 -- 53,445 100 -- 411,512 100 -- 

21,824 -- 97.8 52,271 -- 97.8 402,746 -- 97.9 

498 -­ 2.2 1,174 -­ 2.2 8,766 -­ 2.1 

All Region Short 

Tons a 24,606 -- -- 58,913 -- -- 453,614 -- -- 

Note: a

 Converted from metric tonnes using a multiplicative adjustment factor of 1.102 short tons per metric tonne. 

3.4 Development of 2020 and 2030 Scenarios 

3.4.1 Outline 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 to account for the change in ship traffic over these time periods, i.e., 

growth, and the effect of the current controls and the NOX and fuel controls described in the 

proposed rule. 

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

of the modeling regions described in Section 3.2, the emission adjustment factors, and the 

resulting 2020 and 2030 emission inventories. 

The final section describes the baseline and control emission inventories that were 

developed for calendar years 2020 and 2030. The 2030 inventories were used for air quality 

modeling, although the 2020 control inventories reported here have been updated relative to 

those used for the air quality modeling. A comparison of the 2020 control case inventories 

reported here with those used for the air quality modeling is provided in Section 3.7. 

3.4.2 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 nine 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 

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Regulatory Impact Analysis 

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 3.4.2.1).5,6

 This is followed by the derivation of the growth factors that are used in this 

study for the nine geographic regions of interest (Section 3.4.2.9). 

3.4.2.1 Summary of Regional Growth Rate Development 

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

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

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

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

demand for commodities that are produced in one location and consumed in another, as predicted 

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

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

consumption, and engine load factors). Typical voyage parameters are then assigned to the trade 

routes that include average ship speed, round trip mileage, tons of cargo shipped, and days in 

port. Fuel consumption for each trade route and commodity type thus depends on commodity 

projections, ship characteristics, and voyage characteristics. Figure 3-10 from RTI illustrates the 

approach to developing baseline projections of marine fuel consumption. 

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

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

to 3.7%.46 RTI’s overall U.S. growth rate was projected at 3.4%, which is consistent with that 

range. 

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Chapter 3: Emission Inventory 

Figure 3-10 Illustration of Method for Estimating Bunker Fuel Demand 

3.4.2.2 Trade Analysis 

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

portion of Figure 3-10, were defined for the following eight general types of commodities: 

- liquid bulk – crude oil 

- liquid bulk – refined petroleum products 

- liquid bulk – residual petroleum products 

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Regulatory Impact Analysis 

- liquid bulk – chemicals (organic and inorganic) 

- liquid bulk –gas (including LNG and LPG) 

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

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

- containerized cargo 

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

3-60 shows the countries associated with each region. 

Table 3-60 Aggregate Regions and Associated Countries 

Aggregate Regions Base Countries / Regions 

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

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

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

E. Canadaa Canadaa 

W. Canadaa Canadaa 

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

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

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

South America 

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

West Coast of S. America 

Africa – West Western Africa 

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

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

Europe-North 

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

Netherlands, Norway, Sweden, Switzerland, United Kingdom 

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

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

Caspian Region Southeast CIS 

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

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

Australia/NZ Australia, New Zealand 

Japan Japan 

Pacific-High Growth 

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

Korea, Taiwan, Thailand 

China China 

Rest of Asia Viet Nam, India, Pakistan, Other Indian Subcontinent 

Note: a Canada is treated as a single destination in the GI model. Shares of Canadian imports from and exports to 

regions of the world in 2004 are used to divide Canada trade into shipments to/from Eastern Canada ports and 

shipments to/from Western Canada ports.

47 

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Chapter 3: Emission Inventory 

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

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

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

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

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

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

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

country’s import cost for the commodity. The model then estimates demand forces, countryspecific 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 3-61 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 3-61 Illustration of World Trade Estimates for Composite Commodities, 2005, 2012, and 2020 

Cargo (millions of tons) 

Commodity Type 2005 2012 2020 

Dry Bulk 2,473 3,051 3,453 

Crude Oil 1,703 2,011 2,243 

Container 714 1,048 1,517 

Refined Petroleum 416 471 510 

General Cargo 281 363 452 

Residual Petroleum and Other Liquids 190 213 223 

Chemicals 122 175 228 

Natural Gas 79 91 105 

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

3.4.2.3 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 3-10, 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. 

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Regulatory Impact Analysis 

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.48 These 

assignments are shown in Table 3-62. 

Table 3-62 Assignment of Commodities to Vessel Types 

Commodity Ship Category Vessel Type 

Liquid bulk – crude oil Crude Oil Tankers Tanker 

Liquid bulk – refined 

petroleum products 

Product Tankers Product Carrier 

Liquid bulk – residual 

petroleum products 

Product Tankers Product Carrier 

Liquid bulk – chemicals 

(organic and inorganic) 

Chemical Tankers Chemical & Oil Carrier 

Liquid bulk – natural gas 

(including LNG and LPG) 

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

Ethylene/LPG, Ethylene/LPG/Chemical, 

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

LPG/Oil, Oil & Liquid Gas Carrier 

Dry bulk (e.g. grain, coal, 

steel, ores and scrap) 

Dry Bulk Carriers Bulk Carrier 

General cargo (including 

neobulk, lumber/forest 

products) 

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

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

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

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

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

Containerizable cargo Container Ships Fully Cellular Container 

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

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

size breaks within the data. Table 3-63 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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Chapter 3: Emission Inventory 

Table 3-63 Fleet Characteristics 

Ship Type Size by DWT 

Minimum 

Size

 (DWT) 

Maximum 

Size 

(DWT) 

Number 

of Ships 

Total 

DWT 

(millions) 

Total 

Horse 

Power 

(millions) 

Suezmax 83,000 140,000 101 9.83 8.56 

PostPanamax 56,500 83,000 465 30.96 29.30 

Container Panamax 42,100 56,500 375 18.04 15.04 

Intermediate 14,000 42,100 1,507 39.80 32.38 

Feeder 0 14,000 1,100 8.84 7.91 

General Cargo All All 3,214 26.65 27.07 

Capesize 79,000 0 715 114.22 13.81 

Dry Bulk Panamax 54,000 79,000 1,287 90.17 16.71 

Handymax 40,000 54,000 991 46.50 10.69 

Handy 0 40,000 2,155 58.09 19.58 

VLCC 180,000 0 470 136.75 15.29 

Suezmax 120,000 180,000 268 40.63 5.82 

Crude Oil Tanker 

AFRAmax 75,000 120,000 511 51.83 8.58 

Panamax 43,000 75,000 164 10.32 2.17 

Handymax 27,000 43,000 100 3.45 1.13 

Coastal 0 27,000 377 3.85 1.98 

Chemical Tanker All All 2,391 38.80 15.54 

AFRAmax 68,000 0 226 19.94 3.60 

Petroleum Product Panamax 40,000 68,000 352 16.92 4.19 

Tanker Handy 27,000 40,000 236 7.90 2.56 

Coastal 0 27,000 349 3.15 1.54 

Natural Gas 

Carrier 

VLGC 60,000 0 157 11.57 5.63 

LGC 35,000 60,000 140 6.88 2.55 

Midsize 0 35,000 863 4.79 3.74 

Other All All 7,675 88.51 53.60 

Total -- -- -- 26,189 888.40 308.96 

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). 

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Regulatory Impact Analysis 

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.49 

Figure 3-11 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. 

200

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

180 

160 

140 

120 

100 

80 

60 

40 

20 

0 

Figure 3-11 Diesel Engine Specific Fuel Consumption 

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: Specific Fuel Consumption (g/bhp-hour) 

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Chapter 3: Emission Inventory 

Equation 3-27 

Fleet AFCv,s  1 SFCv,s  HPv,s 106 tonnes / g N 

 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 in 

use(g/HP-hr) 

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

106

 tonnes/g = Conversion from grams to metric tonnes 

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

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

percent of the horsepower rating, i.e., actual engine load. Table 3-64 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.50 

Table 3-64 Main and Auxiliary Engine Load Factors 

Vessel Type 

Main 

Engine 

Load Factor 

(%) 

Auxiliary Engine as 

Percent of Main 

Engine 

Auxiliary Engine as 

Percent of Main Engine at 

Sea 

Container Vessels 80 22.0 11.0 

General Cargo Carriers 80 19.1 9.5 

Dry Bulk Carriers 75 22.2 11.1 

Crude Oil Tankers 75 21.1 10.6 

Chemical Tankers 75 21.1 10.6 

Petroleum Product Tankers 75 21.1 10.6 

Natural Gas Carrier 75 21.1 10.6 

Other 70 20.0 10.0 

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

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

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

following change in fleet characteristics over the projection period: 

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

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

will improve over time through retirement and replacement. 

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Regulatory Impact Analysis 

 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. 

3.4.2.4 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 3-10, 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 3.4.2.5.) 

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

speed: 

Equation 3-28 

Days at Sea Per Voyagev,s,route  round trip distance route 

speedv,s  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 

 Table 3-65 presents the speeds by vessel type that were used in the analysis.

50 These 

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

period. 

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Chapter 3: Emission Inventory 

Table 3-65 Vessel Speed by Type 

Vessel Type Speed (knots) 

Crude Oil Tankers 13.2 

Petroleum Product Tankers 13.2 

Chemical Tankers 13.2 

Natural Gas Carriers 13.2 

Dry Bulk Carriers 14.1 

General Cargo Vessels 12.3 

Container Vessels 19.9 

Other 12.7 

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

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

amount of cargo (DTW) per voyage: 

Equation 3-29 

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

fleet average DWTv,s  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. 

3.4.2.5 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 

3-109 
Regulatory Impact Analysis 

and auxiliary engines when operated at sea and in port. These steps are illustrated by the 

following equations: 

Equation 3-30 

FCy    FCtrade,route,year trade route 

   

AFC trade,route,yatsea x DaysatSea trade,route,y 

AFC trade,route,yat port xDaysat Port trade,route,y 

 trade route 

 Where: 

FC = Fuel consumed in metric tonnes 

y = calendar year 

trade = Commodity type 

route = Unique trip itinerary 

AFC = Average daily fuel consumption in metric tonnes 

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

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

Equations 3-31 

AFC trade,route, yatsea   (Percent of tradealong route) v,s 

Fleet AFC v,s x MELF AEatsea LF  v,s,t,r 

AFC trade,route, yat port   (Percent of tradealong route) v,s 

Fleet AFC v,s x AE import LF  v,s,t,r 

DaysatSea   (Percent of tradealong route) Daysatsea per voyage x Number of voyages  trade,route,y v,s,t,r v,s  v,s v,s 

Daysat Port trade,route,y   (Percent of tradealong route) v,s Days at port per voyage x Number of voyages  v,s,t,r 

 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

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 3.4.2.3 

and the trade analysis in Section 3.4.2.2. 

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Chapter 3: Emission Inventory 

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 3-12 shows 

estimated world-wide bunker fuel consumption by vessel type. 

0 

100 

200 

300 

400 

500 

600 

Million 

Tons of F

uel 

199520002005201020152020 

Container General Cargo Dry Bulk Crude Oil 

Chemicals Petroleum Natural Gas Other 

Fishing Vessels Passenger Ships Military Vessels 

Figure 3-12 Worldwide Bunker Fuel Consumption 

Figure 3-13 shows the annual growth rates by vessel-type/cargo that are used in the 

projections shown in Figure 3-12. 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. 

3-111 
Regulatory Impact Analysis 

-2% 

0% 

2% 

4% 

6% 

8% 

10% 

Percent C

hange i

n B

u

n

ker F

uel 

20062008201020122014201620182020 

Total Container General Cargo Dry Bulk 

Crude Oil Chemicals Petroleum Natural Gas 

Other Fishing Vessels Passenger Ships Military Vessels 

Figure 3-13 Annual Growth Rate in World-Wide Bunker Fuel Use by Commodity Type 

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

The methodology described above provides an estimate of fuel consumption for 

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

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

 North Pacific 

 South Pacific 

 Gulf

 East Coast 

 Great Lakes

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

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

regions of the U.S. 

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

Worldscale Association and Maritime Chain.51 The data from Worldscale is considered to be the 

3-112 
Chapter 3: Emission Inventory 

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-toport 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 3-65 to find the length of a voyage in days. Table 3-66 presents the 

day lengths for non-containerized vessel types and Table 3-67 shows the same information for 

container vessels. 

Table 3-66 Day Length for Voyages for Non-Container Cargo Ship 

(approximate average) 

Global Insights Trade Regions 

Days per Voyage 

US South 

Pacific 

US North 

Pacific 

US East 

Coast 

US Great 

Lakes US Gulf 

Africa East-South 68 75 57 62 54 

Africa North-Mediterranean 49 56 37 43 47 

Africa West 56 63 36 46 43 

Australia-New Zealand 48 47 65 81 63 

Canada East 37 46 7 18 19 

Canada West 11 5 40 58 39 

Caspian Region 95 89 41 46 48 

China 41 36 73 87 69 

Europe Eastern 61 68 38 45 46 

Europe Western-North 53 60 24 32 34 

Europe Western-South 54 61 30 37 37 

Greater Caribbean 26 33 16 29 17 

Japan 35 31 65 81 62 

Middle East Gulf 77 72 56 65 83 

Pacific High Growth 52 48 67 76 88 

Rest of Asia 68 64 66 64 73 

Russia-FSU 64 71 38 46 48 

Rest of South America 51 30 41 46 44 

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Regulatory Impact Analysis 

Table 3-67 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 

3.4.2.8 Bunker Fuel Consumption for the United States 

Figure 3-14 and Figure 3-15 present the estimates of fuel use for delivering trade goods 

to and from the U.S. The results in Figure 3-14 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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Chapter 3: Emission Inventory 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

Million Tons of B

u

n

ker Fuel 

199520002005201020152020 

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

Figure 3-14 Bunker Fuel Used to Import and Export Cargo by Region of the United States 

Figure 3-15 shows the estimated annual growth rates for the fuel consumption that are 

used in the projections shown in Figure 3-14. Overall, the average annual growth rate in marine 

bunkers associated with future U.S. trade flows is 3.4 percent between 2005 and 2020. 

3-115 
Regulatory Impact Analysis 

-2% 

0% 

2% 

4% 

6% 

8% 

10% 

Percent Change in Bunker Fuel 

20062008201020122014201620182020 

United States US South Pacific US North Pacific 

US Great Lakes US Gulf US East Coast 

Figure 3-15 Annual Growth Rates for Bunker Fuel Used to Import and Export Cargo by Region of the 

United States

3.4.2.9 2020 and 2030 Growth Factors for Nine Geographic Regions 

The results of the RTI analysis described above are used to develop the growth factors 

that are necessary to project the 2002 base year emissions inventory to 2020 and 2030. The next 

two sections describe how the five RTI regions were associated with the nine regions analyzed in 

this report, and how the specific growth rates for each of the nine regions were developed. 

3.4.2.9.1 Mapping the RTI Regional Results to the Nine Region Analysis 

As described in Section 3.3.4, the nine geographic regions analyzed in this study were 

designed to be consistent with the five RTI regional modeling domains. More specifically, four 

of the nine geographic areas in this study, i.e., Alaska East, Alaska West, Hawaii East, and 

Hawaii West are actually subsets of two broader regional areas that were analyzed by RTI, i.e., 

the North Pacific for both Alaska regions and South Pacific for Hawaii. Therefore, the growth 

rate information from the related larger region was assumed to be representative for that state. 

The nine 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 3-68. 

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Chapter 3: Emission Inventory 

Table 3-68 Association of the RTI Regions to the Nine Emission 

Inventory Regions 

Consumption Region 

Corresponding Emission 

Inventory Region 

North Pacific North Pacific (NP) 

North Pacific Alaska East (AE) 

North Pacific Alaska West (AW) 

South Pacific South Pacific (SP) 

South Pacific Hawaii East (HE) 

South Pacific Hawaii West (HW) 

Gulf Gulf Coast (GC) 

East Coast East Coast (EC) 

Great Lakes Great Lakes (GL) 

3.4.2.9.2 Growth Factors for the Emission Inventory Analysis 

Emission inventories for 2020 and 2030 are estimated in Section 3.4.5 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 3-69 for each projection year. 

Table 3-69 Regional Emission Inventory Growth Factors for 2020 and 2030 

2002-2020 Multiplicative Growth 

Average Factor Relative to 2002

Annualized 

Growth Rate 

Emission Inventory Region (%) 2020 2030 

Alaska East (AE) 3.3 1.79 2.48 

Alaska West (AW) 3.3 1.79 2.48 

East Coast (EC) 4.5 2.21 3.43 

Gulf Coast (GC) 2.9 1.67 2.23 

Hawaii East (HE) 5.0 2.41 3.92 

Hawaii West (HW) 5.0 2.41 3.92 

North Pacific (NP) 3.3 1.79 2.48 

South Pacific (SP) 5.0 2.41 3.92 

Great Lakes (GL) 1.7 1.35 1.60 

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Regulatory Impact Analysis 

3.4.3 Emission Controls in Baseline and Control Scenarios 

This section describes the control programs present in the baseline and control scenarios, 

as well as the resulting emission factors. 

The baseline scenario includes the International Marine Organization’s (IMO) Tier 1 

NOX standard for marine diesel engines that became effective in 2000. The control scenario 

applies global controls as well as additional ECA controls within the ECA boundaries 

The global NOX controls include a retrofit program for Tier 0 (pre-control) engines, 

which was modeled as 11 percent control from Tier 0 for 80 percent of 1990 thru 1999 model 

year (MY) engines greater than 90 liters per cylinder (L/cyl) starting in 2011. The retrofit 

program was also modeled with a five year phase-in. The current Tier 1 controls, which also are 

modeled as achieving an 11 percent reduction from Tier 0, apply to the 2000 thru 2010 MY 

engines. In 2011 thru 2015, Tier 2 controls are applied. Tier 2 controls are modeled as a 2.5 

g/kW-hr reduction from Tier 1. Fuel sulfur content for the global control area is assumed to be 

controlled to 5,000 ppm. No controls are assumed for HC or CO. 

Within the ECA areas, additional Tier 3 NOX controls are applied for 2016 MY engines 

and beyond. Tier 3 controls are modeled as achieving an 80 percent reduction from Tier 1 

levels. In addition to the NOX control program, fuel sulfur content is also assumed to be 

controlled to 1,000 ppm within the ECA in 2020 and 2030. Fuel sulfur content affects SO2 and 

PM emissions. Note that gas and steam turbine engines are not subject to any of the NOX 

standards; however, these engines are not a large part of the inventory. 

Within the control scenario, global controls are applied for the Alaska West and Hawaii 

West regions. Global controls are also applied beyond 200 nm from shore for the 48 contiguous 

states, Alaska East, and Hawaii East. The ECA controls are applied within 200 nm from shore 

for the 48 contiguous states as well as the Alaska East and Hawaii East regions. 

3.4.3.1 2020 and 2030 Emission Factors 

The baseline scenario described in the previous section includes Tier 1 NOX control. The 

control scenario includes additional NOX controls and fuel sulfur controls, the latter affecting PM 

and SO2 emissions. The switch to lower sulfur distillate fuel use is also assumed to lower CO2 

emissions slightly. HC and CO are assumed to remain unchanged. 

 The NOX emission factors (EFs) by engine/ship type and tier are provided in Table 3-70. 

Tier 0 refers to pre-control. There are separate entries for Tier 0/1 base and Tier 0/1 control, 

since the Tier 0/1 control engines would be using distillate fuel, and there are small NOX 

emission reductions assumed when switching from residual to distillate fuel.21 The NOX control 

EFs by tier were derived using the assumptions described in Section 3.4.3. 

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Chapter 3: Emission Inventory 

Table 3-70 Modeled NOX Emission Factors by Tier 

Engine/ 

Ship Type 

NOX EF (g/kW-hr) 

Baseline Control Areas 

Tier 0 Tier 1 Tier 0 

T0 

retrofit a Tier 1 Tier 2 Tier 3 

Main 

SSD 18.1 16.1 17 15.1 15.1 12.6 3 

MSD 14 12.5 13.2 11.7 11.7 9.2 2.3 

ST 2.1 n/a 2 n/a n/a n/a n/a 

GT 6.1 n/a 5.7 n/a n/a n/a n/a 

Aux 

Pass 14.6 13.0 14.6 n/a* 13 10.5 2.6 

Other 14.5 12.9 14.5 n/a* 12.9 10.4 2.6 

Note: a

 The retrofit program applies to engines over 90 L/cyl; auxiliary engines are smaller than this 

cutpoint and would therefore not be subject to the program.

 The NOX EFs by tier were then used with the age distributions in Table 3-71 and Table 

3-72 below to generate calendar year NOX EFs by engine/ship type for the base and control areas 

included in the scenarios. These calendar year NOX EFs are provided in Table 3-73. Since the 

age distributions are different for vessels in the Great Lakes, NOX EFs were determined 

separately for the Great Lakes. 

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Regulatory Impact Analysis 

Table 3-71 Vessel Age Distribution for Deep Sea Ports by Engine Type 

Age Group 

(years old) 

Propulsion Engine Type

 a (Fraction of Total) All 

Auxiliary 

Engines 

MSD SSD GT ST 

0 0.00570 0.02667 0.00000 0.00447 0.01958 

1 0.07693 0.07741 0.07189 0.12194 0.07670 

2 0.10202 0.07512 0.14045 0.16464 0.08426 

3 0.08456 0.07195 0.05608 0.05321 0.07489 

4 0.08590 0.05504 0.67963 0.00000 0.07831 

5 0.06427 0.05563 0.04165 0.00000 0.05685 

6 0.06024 0.04042 0.00000 0.00000 0.04455 

7 0.07867 0.07266 0.00626 0.00000 0.07150 

8 0.06730 0.05763 0.00000 0.00000 0.05764 

9 0.04181 0.04871 0.00000 0.00000 0.04475 

10 0.04106 0.04777 0.00000 0.00000 0.04364 

11 0.03100 0.03828 0.00000 0.00000 0.03538 

12 0.04527 0.03888 0.00000 0.04873 0.04160 

13 0.03583 0.02787 0.00000 0.00000 0.02909 

14 0.03519 0.02824 0.00000 0.00000 0.02935 

15 0.02921 0.01466 0.00000 0.00000 0.01869 

16 0.00089 0.01660 0.00000 0.00000 0.01189 

17 0.01326 0.01582 0.00000 0.00000 0.01462 

18 0.00847 0.02414 0.00000 0.00000 0.01966 

19 0.00805 0.01982 0.00000 0.00000 0.01550 

20 0.00566 0.02258 0.00000 0.00000 0.01756 

21 0.00495 0.02945 0.00000 0.00000 0.02260 

22 0.00503 0.01883 0.00000 0.00875 0.01467 

23 0.00676 0.01080 0.00000 0.00883 0.00943 

24 0.00539 0.01091 0.00000 0.00883 0.00900 

25 0.01175 0.01099 0.00000 0.18029 0.01224 

26 0.00803 0.01045 0.00000 0.11065 0.01130 

27 0.00522 0.00835 0.00000 0.01395 0.00738 

28 0.00294 0.00788 0.00000 0.08657 0.00659 

29 0.00285 0.00370 0.00034 0.02907 0.00349 

30 0.00254 0.00106 0.00370 0.05126 0.00193 

31 0.00084 0.00113 0.00000 0.00605 0.00096 

32 0.00023 0.00367 0.00000 0.07105 0.00322 

33 0.00117 0.00582 0.00000 0.00000 0.00419 

34 0.00132 0.00092 0.00000 0.00000 0.00098 

35+ 0.01967 0.00013 0.00000 0.03172 0.00598 

Note: a

 MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam turbine.

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Chapter 3: Emission Inventory 

Table 3-72 Vessel Age Distribution for Great Lake Ports by Engine Type 

Age Group 

(years old) 

Propulsion Engine Typea

 (Fraction of Total) 

MSD SSD ST All Auxiliary 

Engines 

0 0.01610 0.03913 0.00000 0.02399 

1 0.02097 0.03489 0.00000 0.02243 

2 0.01370 0.04644 0.00000 0.02544 

3 0.02695 0.03040 0.00000 0.02511 

4 0.01571 0.04547 0.00000 0.02497 

5 0.04584 0.01498 0.00000 0.02442 

6 0.01494 0.02180 0.00000 0.01528 

7 0.01327 0.01857 0.00000 0.01391 

8 0.00099 0.04842 0.00000 0.02107 

9 0.00027 0.03376 0.00000 0.01454 

10 0.01085 0.01177 0.00000 0.01076 

11 0.00553 0.01183 0.00000 0.00782 

12 0.00739 0.00546 0.00000 0.00626 

13 0.02289 0.02557 0.00000 0.02242 

14 0.00000 0.00286 0.00000 0.00121 

15 0.00275 0.00510 0.00000 0.00361 

16 0.00069 0.00073 0.00000 0.00078 

17 0.00000 0.00104 0.00000 0.00041 

18 0.00342 0.01967 0.00000 0.01059 

19 0.00219 0.01220 0.00000 0.00645 

20 0.00867 0.06140 0.00000 0.03034 

21 0.00000 0.05638 0.00000 0.02503 

22 0.03375 0.02108 0.00000 0.02279 

23 0.04270 0.02051 0.00000 0.02606 

24 0.08161 0.01010 0.00000 0.03744 

25 0.02935 0.05217 0.00000 0.03480 

26 0.18511 0.00522 0.00000 0.07701 

27 0.01870 0.00389 0.00000 0.01083 

28 0.13815 0.01438 0.00000 0.06181 

29 0.05487 0.01160 0.00000 0.02697 

30 0.00000 0.00114 0.00000 0.00047 

31 0.03986 0.00000 0.00000 0.01611 

32 0.03654 0.00282 0.00000 0.01631 

33 0.03358 0.00000 0.00000 0.01358 

34 0.00295 0.00123 0.00000 0.00165 

35+ 0.06974 0.30796 1.00000 0.31734 

Notes: 

a

 MSD is medium speed diesel, SSD is slow speed diesel, GT is gas turbine, ST is steam 

turbine. b

 Fleet average weighted by installed power (ship port calls x main propulsion engine power). 

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Regulatory Impact Analysis 

Table 3-73 Modeled NOX Emission Factors by Calendar Year and Control Type 

Engine/ 

Ship 

Type 

CY NOX EF (g/kW-hr) 

2002 

2020 Base 

2020 ECA 

Control 

2020 Global 

Control 2030 Base 

2030 ECA 

Control 

2030 Global 

Control 

DSPa

 GLb DSP GL DSP GL DSP GL DSP GL DSP GL 

Main 

SSD 18.1 16.36 17.12 10.80 13.07 13.74 14.95 16.13 16.73 5.68 10.44 13.00 14.20 

MSD 14 12.58 13.64 7.72 11.79 10.17 12.44 12.50 12.74 3.58 9.95 9.49 11.44 

ST 2.1 2.1 2.1 2.0 2.0 2.0 2.0 2.1 2.1 2.0 2.0 2.0 2.0 

GT 6.1 6.1 n/a

c 5.7 n/a 5.7 n/a 6.1 n/a 5.7 n/a 5.7 n/a 

Aux 

Pass 14.6 13.21 14.13 8.59 11.99 n/a n/a 13.05 13.61 4.39 10.30 n/a n/a 

Other 14.5 13.06 13.97 8.59 11.99 n/a n/a 12.90 13.46 4.39 10.30 n/a n/a 

Notes: 

a

 DSP = Deep sea ports and areas other than the Great Lakes b

 GL = Great Lakes c

 n/a = not applicable. There are no GT engines assumed to be operating in the Great Lakes. Auxiliary engines are assumed to be 

operating in ports and therefore not subject to global controls. 

For PM and SO2, there are no proposed standards; however, the control of fuel sulfur 

affects these pollutants. Therefore, the PM and SO2 EFs are strictly a function of fuel sulfur 

level. For the baseline portions of the inventory, there are two residual fuel sulfur levels 

modeled: 25,000 ppm for the West Coast and 27,000 ppm for the rest of the U.S. The baseline 

distillate fuel sulfur level assumed for all areas is 15,000 ppm. As discussed in Section 3.3.2.3.5, 

for the baseline, main engines use residual fuel and auxiliary engines use a mix of residual and 

distillate fuel. For the control areas, there are two levels of distillate fuel sulfur assumed to be 

used by all engines: 5,000 ppm for the global control areas and 1,000 ppm for the ECA control 

areas. 

Table 3-74 provides the PM10 EFs by engine/ship type and fuel sulfur level. For 

modeling purposes, PM2.5 is assumed to be 92 percent of PM10. The PM EFs are adjusted to 

reflect the appropriate fuel sulfur levels using the equation described in Section 3.3.2.3.6. 

 Table 3-75 provides the modeled SO2 EFs. SO2 emission reductions are directly 

proportional to reductions in fuel sulfur content. 

CO2 is directly proportional to fuel consumed. Table 3-76 provides the modeled CO2 and 

brake specific fuel consumption (BSFC) EFs. Due to the higher energy content of distillate fuel 

on a mass basis, the switch to distillate fuel for the control areas results in a small reduction to 

BSFC and, correspondingly, CO2 emissions.21 

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Chapter 3: Emission Inventory 

Table 3-74 Modeled PM10 Emission Factors* 

Engine/ 

Ship Type 

PM10 EF (g/kW-hr) 

Baseline Control Areas 

Other than 

West Coast 

27,000 ppm S 

West Coast

a 

25,000 ppm S 

ECA 

5,000 ppm S 

Global Control 

1,000 ppm S 

Main 

SSD 1.40 1.40 0.31 0.19 

MSD 1.40 1.40 0.31 0.19 

ST 1.50 1.40 0.35 0.17 

GT 1.50 1.40 0.35 0.17 

Aux 

Pass 1.40 1.30 0.31 0.19 

Other 1.20 1.10 0.31 0.19 

Note: a For the base cases, the West Coast fuel is assumed to be used in the following

regions: Alaska East (AE), Alaska West (AW), Hawaii East (HE), Hawaii West 

(HW), North Pacific (NP), and South Pacific (SP). 

Table 3-75 Modeled SO2 Emission Factors* 

Engine/ 

Ship Type 

SO2 EF (g/kW-hr) 

Baseline Control Areas 

Other than 

West Coast 

27,000 ppm S 

West Coast

a 

25,000 ppm S 

ECA 

5,000 ppm S 

Global 

Control 

1,000 ppm S 

Main 

SSD 10.29 9.53 1.81 0.36 

MSD 11.09 10.26 1.96 0.39 

ST 16.10 14.91 2.83 0.57 

GT 16.10 14.91 2.83 0.57 

Aux 

Pass 10.70 9.93 1.96 0.39 

Other 9.66 9.07 1.96 0.39 

Note: a For the base cases, the West Coast fuel is assumed to be used in the following

regions: Alaska East (AE), Alaska West (AW), Hawaii East (HE), Hawaii West 

(HW), North Pacific (NP), and South Pacific (SP). 

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Regulatory Impact Analysis 

Table 3-76 Modeled Fuel Consumption and CO2 Emission Factors 

Engine/ 

Ship Type 

EF (g/kW-hr) 

Baseline Control Areas 

BSFC CO2 BSFC CO2 

Main 

SSD 195 620.62 185 588.86 

MSD 210 668.36 200 637.05 

ST 305 970.71 290 923.07 

GT 305 970.71 290 923.07 

Aux 

Pass 210 668.36 200 636.60 

Other 210 668.36 200 636.60 

3.4.4 Calculation of Near Port and Interport Inventories 

Based on the emission factors described in Section 3.4.3.1, appropriate growth factors 

and emission adjustment factors were applied to the 2002 baseline inventory to obtain the NOX, 

PM (PM10 and PM2.5), SO2, and CO2 inventory of each 2020 and 2030 scenario. Adjustment 

factors are ratios of the 2020 or 2030 calendar year EFs to the 2002 calendar year EFs. 

Adjustment factors are derived separately by engine type for propulsion and auxiliary engines. 

The adjustment factors for propulsion engines are applied to the propulsion portion of the port 

inventory and the interport portion of the inventory. The adjustment factors for auxiliary engines 

are applied to the auxiliary portion of the port inventory. This section describes the development 

and application of the adjustment factors to the port and interport inventories, and the 

methodology for combining the port and interport portions. 

3.4.4.1 Port Methodology 

3.4.4.1.1 Non-California Ports 

For the non-California ports, 2002 emissions for each port are summed by engine/ship 

type. Propulsion and auxiliary emissions are summed separately, since the EF adjustment factors 

differ. The appropriate regional growth factor, as provided in Table 3-69, is then applied, along 

with EF adjustment factors by engine/ship type. The EF adjustment factors are a ratio of the 

control EF to the 2002 EF. Table 3-77 thru Table 3-81 provide the EF adjustment factors for 

each pollutant and control area. The ports will be subject to ECA controls in the control 

scenarios. These tables are also used as input for the California ports and interport control 

inventory development, discussed in subsequent sections. Since the control scenario assumes a 

portion of the inventory is subject to global controls, the adjustment factors for the 2020 and 

2030 global controls are also provided. The baseline adjustment factors are also provided. 

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Chapter 3: Emission Inventory 

Table 3-77 NOX EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ 

Ship 

Type 

2020 Base 

2020 ECA 

Control 

2020 Global 

Control 2030 Base 

2030 ECA 

Control 

2030 Global 

Control 

DSPb

 GLc DSP GL DSP GL DSP GL DSP GL DSP GL 

Main 

SSD 0.9037 0.9459 0.5967 0.7219 0.7592 0.8261 0.8913 0.9243 0.3138 0.5771 0.7183 0.7847 

MSD 0.8987 0.9744 0.5515 0.8423 0.7265 0.8883 0.8926 0.9101 0.2559 0.7109 0.6776 0.8170 

ST 1.0000 1.0000 0.9524 0.9524 0.9524 0.9524 1.0000 1.0000 0.9524 0.9524 0.9524 0.9524 

GT 1.0000 n/a 0.9344 n/a 0.9344 n/a 1.0000 n/a 0.9344 n/a 0.9344 n/a 

Aux 

Pass 0.9025 0.9657 0.5869 0.8196 n/a n/a 0.8917 0.9301 0.3003 0.7042 n/a n/a 

Other 0.9025 0.9657 0.5940 0.8295 n/a n/a 0.8917 0.9301 0.3039 0.7127 n/a n/a 

Notes: 

a

 NOX adjustment factors are a ratio of future base or control EFs to 2002 EFs b

 DSP = deep sea ports and areas other than the Great Lakes c

 GL = Great Lakes 

Table 3-78 PM10 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ 

Ship Type 

Base ECA Control Global Control 

Otherb

 WCc Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.1352 0.1352 0.2183 0.2183 

MSD 1.0000 1.0000 0.1328 0.1328 0.2227 0.2227 

ST 1.0000 1.0000 0.1108 0.1187 0.2324 0.2490 

GT 1.0000 1.0000 0.1108 0.1187 0.2324 0.2490 

Aux 

Pass 1.0000 1.0000 0.1328 0.1430 0.2227 0.2398 

Other 1.0000 1.0000 0.1550 0.1691 0.2598 0.2834 

Notes: 

a

 PM10 adjustment factors are a ratio of the control EFs to the baseline EFs. PM is not 

adjusted for the future baselines because fuel sulfur levels are only assumed to

change within the ECA and global control areas. b

 Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, 

Hawaii, North Pacific, and South Pacific. 

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Table 3-79 PM2.5 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ 

Ship Type 

Base ECA Control Global Control 

Otherb

 WCc Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.1339 0.1339 0.2163 0.2163 

MSD 1.0000 1.0000 0.1316 0.1316 0.2207 0.2207 

ST 1.0000 1.0000 0.1092 0.1176 0.2291 0.2467 

GT 1.0000 1.0000 0.1092 0.1176 0.2291 0.2467 

Aux 

Pass 1.0000 1.0000 0.1316 0.1426 0.2207 0.2390 

Other 1.0000 1.0000 0.1555 0.1711 0.2608 0.2868 

Notes: 

a

 PM2.5 adjustment factors are a ratio of the control EFs to the baseline EFs. PM is 

not adjusted for the future baselines because fuel sulfur levels are only assumed to

change within the ECA and global control areas. The PM2.5 adjustment factors are 

slightly different from those for PM10 due to rounding. b

 Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, 

Hawaii, North Pacific, and South Pacific. 

Table 3-80 SO2 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ 

Ship Type 

Base ECA Control Global Control 

Otherb

 WCc Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.0351 0.0380 0.1757 0.1898 

MSD 1.0000 1.0000 0.0353 0.0381 0.1764 0.1907 

ST 1.0000 1.0000 0.0352 0.0380 0.1761 0.1901 

GT 1.0000 1.0000 0.0352 0.0380 0.1761 0.1901 

Aux 

Pass 1.0000 1.0000 0.0365 0.0394 0.1827 0.1969 

Other 1.0000 1.0000 0.0405 0.0431 0.2024 0.2156 

Notes: 

a

 SO2 adjustment factors are a ratio of the control EFs to the baseline EFs. SO2 is 

not adjusted for the future baselines because fuel sulfur levels are only assumed to

change within the ECA and global control areas. b

 Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, 

Hawaii, North Pacific, and South Pacific. 

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Table 3-81 CO2 EF Adjustment Factors by Engine/Ship Type and Control Typea 

Engine/ 

Ship Type 

Base ECA Control Global Control 

Otherb

 WCc Other WC Other WC 

Main 

SSD 1.0000 1.0000 0.9488 0.9488 0.9488 0.9488 

MSD 1.0000 1.0000 0.9531 0.9531 0.9531 0.9531 

ST 1.0000 1.0000 0.9509 0.9509 0.9509 0.9509 

GT 1.0000 1.0000 0.9509 0.9509 0.9509 0.9509 

Aux 

Pass 1.0000 1.0000 0.9525 0.9593 0.9525 0.9593 

Other 1.0000 1.0000 0.9525 0.9683 0.9525 0.9683 

Notes: 

a

 CO2 adjustment factors are a ratio of the control EFs to the baseline EFs. CO2 is not 

adjusted for the future baselines because fuel consumption (BSFC) is only assumed 

to change within the ECA and global control areas. b

 Other = Other than West Coast c WC = Ports/areas within the West Coast. This includes the regions of Alaska, 

Hawaii, North Pacific, and South Pacific. 

3.4.4.1.2 California Ports 

For the California ports, 2002 emissions for each port are summed by ship type. 

Propulsion and auxiliary emissions are summed separately, since the EF adjustment factors 

differ. The EF adjustment factors by engine/ship type, provided in the previous section, are 

consolidated by ship type, using the CARB assumption that engines on all ships except 

passenger ships are 95 percent slow speed diesel (SSD) engines and 5 percent medium speed 

diesel engines (MSD) based upon a 2005 ARB survey.52 All passenger ships were assumed to be 

medium speed diesel engines with electric drive propulsion (MSD-ED). Steam turbines (ST) and 

gas-turbines (GT) are not included in the CARB inventory. The EF adjustment factors by ship 

type are then applied, along with ship-specific growth factors supplied by CARB. The shipspecific growth factors relative to 2002 are provided in Table 3-82 below. 

Table 3-82 Growth Factors by Ship Type for California Ports Relative to 2002 

Ship Type 

Calendar Year 

2002 2020 2030 

Auto 1.0000 1.5010 1.8478 

Bulk 1.0000 0.2918 0.1428 

Container 1.0000 2.5861 4.2828 

General 1.0000 0.7331 0.5985 

Passenger 1.0000 7.5764 26.4448 

Reefer 1.0000 1.0339 1.0532 

RoRo 1.0000 1.5010 1.8478 

Tanker 1.0000 2.0979 3.0806 

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Regulatory Impact Analysis 

3.4.4.2 Interport Methodology 

The interport portion of the inventory is not segregated by engine or ship type. As a 

result, regional EF adjustment factors were developed based on the assumed mix of main 

(propulsion) engine types in each region. The mix of main engine types by region was 

developed using the ship call and power data and is presented in Table 3-83 and Figure 3-16. 

Main engines are considered a good surrogate for interport emissions, since the majority of 

emissions while underway are due to the main engines. The EF adjustment factors by main 

engine type in Section 3.4.4.1.1 were used together with the mix of main engine types by region 

to develop the EF regional adjustment factors for each control area. The resulting EF regional 

adjustment factors for each pollutant and control area are provided in Table 3-84 thru Table 3-88 

below. These EF regional adjustment factors, together with the regional growth factors in Table 

3-69, were applied to calculate the future inventories for each control area. 

Table 3-83 Installed Power by Main Engine Type for Deep Sea Ports a 

Region 

2020 Installed Power (%) 2030 Installed Power (%) 

MSD SSD GT ST Total MSD SSD GT ST Total 

Alaska East (AE) 19.1% 18.4% 0.3% 62.2% 0.8% 19.1% 18.4% 0.3% 62.2% 0.6% 

Alaska West (AW) 19.1% 18.4% 0.3% 62.2% 0.8% 19.1% 18.4% 0.3% 62.2% 0.6% 

East Coast (EC) 25.6% 72.5% 0.9% 1.0% 45.4% 25.6% 72.5% 0.9% 1.0% 42.3% 

Gulf Coast (GC) 13.7% 85.5% 0.0% 0.8% 16.8% 13.7% 85.5% 0.0% 0.8% 13.4% 

Hawaii East(HE) 66.2% 18.5% 7.4% 8.0% 2.0% 66.2% 18.5% 7.4% 8.0% 2.0% 

Hawaii West (HW) 66.2% 18.5% 7.4% 8.0% 2.0% 66.2% 18.5% 7.4% 8.0% 2.0% 

North Pacific (NP) 5.1% 83.5% 1.6% 9.7% 5.0% 5.1% 83.5% 1.6% 9.7% 4.1% 

South Pacific (SP) 29.2% 70.8% 0.0% 0.0% 30.0% 45.5% 54.5% 0.0% 0.0% 37.6% 

Note: 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. 

ST 

8% 

MSD 

48% 

SSD 

44% 

Figure 3-16 Installed Power by Main Engine Type for Great Lake Ports D 

D 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. 

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Table 3-84 NOX EF Adjustment Factors by Region and Control Type

a 

U.S. Region 2002 

2020 2030 

Base 

ECA 

Control 

Global 

Control Base 

ECA 

Control 

Global 

Control 

Alaska East (AE) 1.0000 0.9629 0.8104 n/a 0.9595 0.7019 n/a 

Alaska West (AW) 1.0000 0.9629 n/a 0.8737 0.9595 n/a 0.8568 

East Coast (EC) 1.0000 0.9042 0.5917 n/a 0.8937 0.3110 n/a 

Gulf Coast (GC) 1.0000 0.9038 0.5935 n/a 0.8924 0.3113 n/a 

Hawaii East (HE) 1.0000 0.9152 0.6201 n/a 0.9088 0.3723 n/a 

Hawaii West (HW) 1.0000 0.9152 n/a 0.7659 0.9088 n/a 0.7260 

North Pacific (NP) 1.0000 0.9143 0.6343 n/a 0.9036 0.3828 n/a 

South Pacific (SP) 1.0000 0.9022 0.5837 n/a 0.8919 0.2877 n/a 

Great Lakes (GL) 1.0000 0.9641 0.7989 n/a 0.9238 0.6726 n/a 

Out of Regionb 1.0000 0.8942 n/a 0.7557 0.8940 n/a 0.7103 

Notes: 

a

 NOX adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment 

factors are used to adjust the interport portion of the 2002 inventory. b

 Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of

region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion

power in each region. ECA control is only assumed within 200nm.

Table 3-85 PM10 EF Adjustment Factors by Region and Control Type

a 

U.S. Region 2002 

2020 2030 

Base 

ECA 

Control 

Global 

Control Base 

ECA 

Control 

Global 

Control 

Alaska East (AE) 1.0000 1.0000 0.1244 n/a 1.0000 0.1244 n/a 

Alaska West (AW) 1.0000 1.0000 n/a 0.2280 1.0000 n/a 0.2280 

East Coast (EC) 1.0000 1.0000 0.1341 n/a 1.0000 0.1341 n/a 

Gulf Coast (GC) 1.0000 1.0000 0.1347 n/a 1.0000 0.1347 n/a 

Hawaii East (HE) 1.0000 1.0000 0.1311 n/a 1.0000 0.1311 n/a 

Hawaii West (HW) 1.0000 1.0000 n/a 0.2246 1.0000 n/a 0.2246 

North Pacific (NP) 1.0000 1.0000 0.1332 n/a 1.0000 0.1332 n/a 

South Pacific (SP) 1.0000 1.0000 0.1345 n/a 1.0000 0.1341 n/a 

Great Lakes (GL) 1.0000 1.0000 0.1320 n/a 1.0000 0.1320 n/a 

Out of Regionb 1.0000 1.0000 n/a 0.2198 1.0000 n/a 0.2200 

Notes: 

a

 PM10 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment 

factors are used to adjust the interport portion of the 2002 inventory. b

 Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of

region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion

power in each region. ECA control is only assumed within 200nm.

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Regulatory Impact Analysis 

Table 3-86 PM2.5 EF Adjustment Factors by Region and Control Typea 

U.S. Region 2002 

2020 2030 

Base 

ECA 

Control 

Global 

Control Base 

ECA 

Control 

Global 

Control 

Alaska East (AE) 1.0000 1.0000 0.1233 n/a 1.0000 0.1233 n/a 

Alaska West (AW) 1.0000 1.0000 n/a 0.2252 1.0000 n/a 0.2252 

East Coast (EC) 1.0000 1.0000 0.1329 n/a 1.0000 0.1329 n/a 

Gulf Coast (GC) 1.0000 1.0000 0.1334 n/a 1.0000 0.1334 n/a 

Hawaii East (HE) 1.0000 1.0000 0.1299 n/a 1.0000 0.1299 n/a 

Hawaii West (HW) 1.0000 1.0000 n/a 0.2225 1.0000 n/a 0.2225 

North Pacific (NP) 1.0000 1.0000 0.1320 n/a 1.0000 0.1320 n/a 

South Pacific (SP) 1.0000 1.0000 0.1332 n/a 1.0000 0.1329 n/a 

Great Lakes (GL) 1.0000 1.0000 0.1307 n/a 1.0000 0.1307 n/a 

Out of Regionb 1.0000 1.0000 n/a 0.2177 1.0000 n/a 0.2180 

Notes: 

a

 PM2.5 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment

factors are used to adjust the interport portion of the 2002 inventory. b

 Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of

region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion

power in each region. ECA control is only assumed within 200nm.

Table 3-87 SO2 EF Adjustment Factors by Region and Control Typea 

U.S. Region 2002 

2020 2030 

Base 

ECA 

Control 

Global 

Control Base 

ECA 

Control 

Global 

Control 

Alaska East (AE) 1.0000 1.0000 0.0380 n/a 1.0000 0.0380 n/a 

Alaska West (AW) 1.0000 1.0000 n/a 0.1814 1.0000 n/a 0.1814 

East Coast (EC) 1.0000 1.0000 0.0352 n/a 1.0000 0.0352 n/a 

Gulf Coast (GC) 1.0000 1.0000 0.0352 n/a 1.0000 0.0352 n/a 

Hawaii East (HE) 1.0000 1.0000 0.0381 n/a 1.0000 0.0381 n/a 

Hawaii West (HW) 1.0000 1.0000 n/a 0.1893 1.0000 n/a 0.1893 

North Pacific (NP) 1.0000 1.0000 0.0380 n/a 1.0000 0.0380 n/a 

South Pacific (SP) 1.0000 1.0000 0.0380 n/a 1.0000 0.0380 n/a 

Great Lakes (GL) 1.0000 1.0000 0.0352 n/a 1.0000 0.0352 n/a 

Out of Regionb 1.0000 1.0000 n/a 0.1811 1.0000 n/a 0.1821 

Notes: 

a

 SO2 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment

factors are used to adjust the interport portion of the 2002 inventory. b

 Out of Region refers to areas outside the 200nm, but within the air quality modeling domain. The out of

region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion

power in each region. ECA control is only assumed within 200nm.

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Chapter 3: Emission Inventory 

Table 3-88 CO2 EF Adjustment Factors by Region and Control Type

a 

U.S. Region 2002 

2020 2030 

Base 

ECA 

Control 

Global 

Control Base 

ECA 

Control 

Global 

Control 

Alaska East (AE) 1.0000 1.0000 0.9509 n/a 1.0000 0.9509 n/a 

Alaska West (AW) 1.0000 1.0000 n/a 0.9509 1.0000 n/a 0.9509 

East Coast (EC) 1.0000 1.0000 0.9499 n/a 1.0000 0.9499 n/a 

Gulf Coast (GC) 1.0000 1.0000 0.9494 n/a 1.0000 0.9494 n/a 

Hawaii East (HE) 1.0000 1.0000 0.9519 n/a 1.0000 0.9519 n/a 

Hawaii West (HW) 1.0000 1.0000 n/a 0.9519 1.0000 n/a 0.9519 

North Pacific (NP) 1.0000 1.0000 0.9493 n/a 1.0000 0.9493 n/a 

South Pacific (SP) 1.0000 1.0000 0.9501 n/a 1.0000 0.9507 n/a 

Great Lakes (GL) 1.0000 1.0000 0.9510 n/a 1.0000 0.9510 n/a 

Out of Regionb 1.0000 1.0000 n/a 0.9499 1.0000 n/a 0.9502 

Notes: 

a

 CO2 adjustment factors are a ratio of future base or control EFs to 2002 EFs. These regional adjustment

factors are used to adjust the interport portion of the 2002 inventory. b

 Out of Region refers to areas outside 200nm, but within the air quality modeling domain. The out of

region adjustment factors are derived by weighting the regional adjustment factors by the main propulsion

power in each region. ECA control is only assumed within 200nm.

3.4.4.3 Estimating and Combining the Near Port and Interport Inventories 

To produce future year control scenarios, the interport inventories were scaled by a 

growth factor to 2020 and 2030, as previously described. An ECA boundary line was drawn so 

that each point on it was at a 200 nm distance from the nearest point on land. Adjustment 

factors, as described in Section 3.4.3.1, were then applied to interport emissions within the ECA 

boundary. 

To create control scenarios in the near port inventories, growth and control factors were 

applied to the 2002 near port inventories (described in Sections 3.4.2 and 3.4.3.1). The near port 

inventories were then converted into a gridded format (Section 3.3.3.3). Using this grid, STEEM 

values were removed from near port cells and near port emissions were used as replacement 

values. In cases where the emissions near ports were only partially attributable to port traffic, the 

STEEM inventory was reduced rather than removed. 

Interport and near port emissions were then aggregated to form regional totals. 

3.4.5 2020 and 2030 Baseline Inventories 

The resulting 2020 and 2030 estimated emission inventories by region and the nation are 

shown in Table 3-89 and Table 3-90. These baseline inventories account for growth as well as 

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Regulatory Impact Analysis 

implementation of the Tier 1 NOX standard. Estimated fuel consumption for the baseline 

inventories by region and fuel type is given in Table 3-91. 

Table 3-89 2020 Baseline Emissions Inventory 

U.S. Region 

Metric Tonnes per Year 

NOX PM10 PM2.5 a

 HC CO SO2 CO2 

Alaska East (AE) 29,242 2,561 2,356 1,073 2,534 19,084 1,182,047 

Alaska West (AW) 93,685 8,118 7,469 3,444 8,112 60,227 3,711,596 

East Coast (EC) 439,604 39,003 35,882 16,216 38,382 323,038 18,121,202 

Gulf Coast (GC) 259,295 23,403 21,531 9,590 23,628 174,751 10,567,512 

Hawaii East (HE) 48,026 4,185 3,850 1,765 4,161 31,075 1,930,172 

Hawaii West (HW) 67,573 5,888 5,417 2,483 5,855 43,722 2,715,741 

North Pacific (NP) 42,644 3,916 3,603 1,706 3,799 27,807 1,800,743 

South Pacific (SP) 234,968 20,148 18,536 8,585 20,686 149,751 9,490,502 

Great Lakes (GL) 19,842 1,613 1,484 681.914 1,607 11,993 740,624 

Total U.S. Metric 

Tonnes 1,234,879 108,835 100,128 45,544 108,762 841,447 50,260,140 

Total U.S. Short Tons

 b 1,361,221 119,970 110,372 50,204 119,890 927,537 55,402,321 

Notes: 

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 3-90 2030 Baseline Emissions Inventory 

U.S. Region 

Metric Tonnes per Year 

NOX PM10 PM2.5 a

 HC CO SO2 CO2 

Alaska East (AE) 42,930 3,544 3,260 1,485 3,505 26,404 1,635,479 

Alaska West (AW) 137,951 11,232 10,333 4,765 11,223 83,329 5,135,278 

East Coast (EC) 679,271 60,615 55,766 25,207 59,678 502,305 28,163,780 

Gulf Coast (GC) 341,903 31,142 28,651 12,761 31,427 232,547 14,062,207 

Hawaii East (HE) 78,806 6,818 6,273 2,875 6,780 50,630 3,144,932 

Hawaii West (HW) 110,880 9,593 8,825 4,045 9,539 71,237 4,424,900 

North Pacific (NP) 58,937 5,433 4,999 2,372 5,278 38,556 2,497,078 

South Pacific (SP) 394,335 34,948 32,152 14,635 35,208 259,982 16,470,350 

Great Lakes (GL) 22,471 1,910 1,757 807 1,902 14,196 876,636 

Total U.S. Metric 

Tonnes 1,867,484 165,235 152,016 68,951 164,539 1,279,185 76,410,639 

Total U.S. Short Tons

 b 2,058,549 182,140 167,569 76,006 181,373 1,410,061 84,228,311 

Notes: 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. 

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Chapter 3: Emission Inventory 

Table 3-91 Fuel Consumption by Category 3 Vessels in Baseline Scenarios 

U.S. Region 

Metric Tonnes Fuel 

2020 Baseline 2030 Baseline 

Distillate Residual Total Distillate Residual Total 

Alaska East (AE) 3,386 367,977 371,363 4,685 509,132 513,817 

Alaska West (AW) 0 1,166,068 1,166,068 0 1,613,345 1,613,345 

East Coast (EC) 202,139 5,490,981 5,693,120 313,916 8,534,271 8,848,187 

Gulf Coast (GC) 96,428 3,223,557 3,319,985 128,338 4,289,571 4,417,910 

Hawaii East (HE) 10,529 595,871 606,400 17,151 970,889 988,040 

Hawaii West (HW) 0 853,202 853,202 0 1,390,166 1,390,166 

North Pacific (NP) 28,532 537,206 565,738 39,476 745,028 784,505 

South Pacific (SP) 83,576 2,898,045 2,981,622 157,878 5,016,595 5,174,474 

Great Lakes (GL) 1,269 231,412 232,681 2,037 273,375 275,412 

Total U.S. Metric 

Tonnes 425,860 15,364,319 15,790,179 663,482 23,342,374 24,005,856 

Total U.S. Short Tons 469,431 16,936,262 17,405,693 731,364 25,730,563 26,461,926 

3.4.6 2020 and 2030 Control Inventories 

For the control scenario, the inventories for each of the nine geographic regions, the U.S. 

total, and the 48-state total are presented in Table 3-92 and Table 3-93. The regional and total 

inventories include all emissions within 200nm of shore. For the purposes of this analysis, ECA 

controls are assumed to apply to all regions, except Alaska West and Hawaii West. For the 

Alaska West and Hawaii West regions, global controls apply. Estimated fuel consumption for 

the control inventories by region and fuel type is given in Table 3-94. 

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Regulatory Impact Analysis 

Table 3-92 Category 3 Vessel Inventories for 2020 Control Casea 

U.S. Region 

Metric Tonnes per Year 

NOX PM10 PM2.5 a

 HC CO SO2 CO2 

Alaska East (AE) 25,978 322 296 1,072 2,534 728 1,124,652 

Alaska West (AW) 90,787 1,851 1,703 3,444 8,112 10,927 3,529,505 

East Coast (EC) 289,671 5,286 4,863 16,231 38,421 11,514 17,233,800 

Gulf Coast (GC) 170,861 3,201 2,945 9,581 23,615 6,255 10,034,946 

Hawaii East (HE) 32,952 551 507 1,764 4,162 1,187 1,838,832 

Hawaii West (HW) 57,406 1,323 1,217 2,483 5,855 8,277 2,585,222 

North Pacific (NP) 29,105 539 496 1,709 3,803 1,076 1,715,210 

South Pacific (SP) 150,461 2,753 2,533 8,546 20,585 5,786 9,009,986 

Great Lakes (GL) 16,420 207 190 676 1,602 420 704,390 

Total U.S. Metric 

Tonnes 863,642 16,032 14,750 45,507 108,688 46,168 47,776,542 

Total U.S. Short Tons

 b 952,002 17,673 16,259 50,163 119,808 50,892 52,664,623 

Note: a

 This scenario assumes ECA controls apply within 200 nautical miles of all U.S. regions except Alaska West 

and Hawaii West, with global controls applied in all other areas. Corrected boundaries are used. 

Table 3-93 Category 3 Vessel Inventories for 2030 Control Casea 

U.S. Region 

Metric Tonnes per Year 

NOX PM10 PM2.5 a

 HC CO SO2 CO2 

Alaska East (AE) 30,722 445 410 1,485 3,505 1,008 1,556,045 

Alaska West (AW) 123,187 2,677 2,463 4,765 11,223 15,847 4,883,341 

East Coast (EC) 235,378 8,221 7,563 25,207 59,678 17,896 26,763,558 

Gulf Coast (GC) 118,930 4,261 3,920 12,761 31,426 8,325 13,355,741 

Hawaii East (HE) 31,992 899 827 2,875 6,780 1,933 2,995,263 

Hawaii West (HW) 88,502 2,175 2,001 4,045 9,539 13,596 4,214,197 

North Pacific (NP) 22,758 751 691 2,372 5,278 1,494 2,378,683 

South Pacific (SP) 128,302 4,769 4,388 14,635 35,202 10,030 15,713,679 

Great Lakes (GL) 16,369 253 233 807 1,902 501 833,733 

Total U.S. Metric 

Tonnes 796,140 24,451 22,495 68,951 164,539 70,630 72,694,239 

Total U.S. Short Tons

 b 877,594 26,953 24,797 76,006 181,373 77,856 80,131,682 

Note: a

 This scenario assumes ECA controls apply within 200 nautical miles of all U.S. regions, except Alaska 

West and Hawaii West, with global controls elsewhere. Corrected boundaries are used. 

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Table 3-94 Fuel Consumption by Category 3 Vessels in Control Scenarios 

U.S. Region 

Metric Tonnes Fuel 

2020 Control 2030 Control 

Distillate Residual Total Distillate Residual Total 

Alaska East (AE) 353,331 0 353,331 488,861 0 488,861 

Alaska West (AW) 1,108,861 0 1,108,861 1,534,194 0 1,534,194 

East Coast (EC) 5,414,326 0 5,414,326 8,408,281 0 8,408,281 

Gulf Coast (GC) 3,152,669 0 3,152,669 4,195,960 0 4,195,960 

Hawaii East (HE) 577,704 0 577,704 941,019 0 941,019 

Hawaii West (HW) 812,197 0 812,197 1,323,970 0 1,323,970 

North Pacific (NP) 538,866 0 538,866 747,309 0 747,309 

South Pacific (SP) 2,830,658 0 2,830,658 4,936,751 0 4,936,751 

Great Lakes (GL) 221,297 0 221,297 261,933 0 261,933 

Total U.S. Metric 

Tonnes 15,009,910 0 15,009,910 22,838,278 0 22,838,278 

Total U.S. Short Tons 16,545,593 0 16,545,593 25,174,892 0 25,174,892 

3.5 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. 

3.5.1 Baseline Contribution of C3 Vessels 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 SO2 in 

2002. We estimate that their contribution will increase to about 40 percent of mobile source 

NOX, 48 percent of mobile source PM2.5, and 95 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 the following tables. Inventory projections for 2020 and 2030 include the effect of 

existing emission mobile source and stationary source control programs previously adopted by 

EPA. 

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Regulatory Impact Analysis 

Table 3-95 50 State Annual NOX Baseline Emission Levels for Mobile and Other Source Categories 

Category 

2002 2020 2030 

short tons 

% of 

mobile 

source 

% of 

total short tons 

% of 

mobile 

source 

% of 

total short tons 

% of 

mobile 

source 

% of 

total 

Commercial Marine (C3) 738,700 5.8 3.5 1,361,221 24.4 12.0 2,058,549 39.8 18.8 

Locomotive 1,118,786 8.8 5.2 669,405 12.0 5.9 437,245 8.4 4.0 

Recreational Marine Diesel 40,437 0.3 0.2 43,579 0.8 0.4 43,665 0.8 0.4 

Commercial Marine (C1 & C2) 834,025 6.6 3.9 499,798 8.9 4.4 308,614 6.0 2.8 

Land-Based Nonroad Diesel 1,555,812 12.2 7.3 683,481 12.2 6.0 435,774 8.4 4.0 

Small Nonroad SI 119,833 0.9 0.6 80,901 1.4 0.7 91,913 1.8 0.8 

Recreational Marine SI 49,902 0.4 0.2 87,709 1.6 0.8 73,961 1.4 0.7 

SI Recreational Vehicles 10,614 0.1 0.0 30,108 0.5 0.3 34,318 0.7 0.3 

Large Nonroad SI (>25hp) 336,292 2.6 1.6 48,270 0.9 0.4 47,766 0.9 0.4 

Aircraft 103,591 0.8 0.5 132,278 2.4 1.2 143,986 2.8 1.3 

Total Off Highway 4,907,990 38.6 23.0 3,636,750 65.1 32.0 3,675,790 71.0 33.6 

Highway Diesel 3,529,046 27.7 16.5 681,142 12.2 6.0 355,817 6.9 3.2 

Highway non-diesel 4,293,733 33.7 20.1 1,270,269 22.7 11.2 1,144,199 22.1 10.4 

Total Highway 7,822,779 61.4 36.7 1,951,411 34.9 17.2 1,500,016 29.0 13.7 

Total Mobile Sources 12,730,769 100.0 59.6 5,588,160 100.0 49.2 5,175,806 100.0 47.3 

Stationary Point & Area 

Sources 8,613,718 - 40.4 5,773,927 - 50.8 5,773,927 - 52.7 

Total Man-Made Sources 21,344,488 - 100 11,362,088 - 100 10,949,734 - 100 

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Table 3-96 50 State Annual PM2.5 Baseline Emission Levels for Mobile and Other Source Categories 

Category 

2002 2020 2030 

short tons 

% of 

diesel 

mobile 

% of 

total short tons 

% of 

diesel 

mobile 

% of 

total short tons 

% of 

diesel 

mobile 

% of 

total 

Commercial Marine (C3) 54,112 14.7 1.5 110,372 52.9 3.3 167,569 74.8 4.9 

Locomotive 29,660 8.1 0.8 15,145 7.3 0.4 8,584 3.8 0.3 

Recreational Marine Diesel 1,096 0.3 0.0 973 0.5 0.0 1,053 0.5 0.0 

Commercial Marine (C1 & C2) 28,730 7.8 0.8 15,787 7.6 0.5 10,017 4.5 0.3 

Land-Based Nonroad Diesel 159,111 43.3 4.5 46,056 22.1 1.4 17,902 8.0 0.5 

Small Nonroad SI 25,700 0.7 31,981 0.9 36,795 1.1 

Recreational Marine SI 16,262 0.5 2,845 0.1 1,225 0.0 

SI Recreational Vehicles 13,710 0.4 11,901 0.4 10,090 0.3 

Large Nonroad SI (>25hp) 1,652 0.0 2,421 0.1 2,844 0.1 

Aircraft 17,979 0.5 22,176 0.7 24,058 0.7 

Total Off Highway 348,013 9.9 259,656 7.7 280,136 8.2 

Highway Diesel 94,982 25.8 2.7 20,145 9.7 0.6 18,802 8.4 0.6 

Highway non-diesel 51,694 1.5 45,329 1.3 51,621 1.5 

Total Highway 146,676 4.2 65,474 1.9 70,423 2.1 

Total Mobile Sources 494,690 14.1 325,131 9.6 350,559 10.3 

Stationary Point & Area Sources 3,025,244 85.9 3,047,714 90.4 3,047,714 89.7 

Total Man-Made Sources 3,519,933 100 3,372,845 100 3,398,274 100 

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Regulatory Impact Analysis 

Table 3-97 50 State Annual SO2 Baseline Emission Levels for Mobile and Other Source Categories 

Category 

2002 2020 2030 

short tons 

% of 

mobile 

source 

% of 

total short tons 

% of 

mobile 

source 

% of 

total short tons 

% of 

mobile 

source 

% of 

total 

Commercial Marine (C3) 453,614 43.2 3.0 927,537 93.3 10.5 1,410,061 94.9 15.1 

Locomotive 75,385 7.2 0.5 396 0.0 0.0 464 0.0 0.0 

Recreational Marine Diesel 5,145 0.5 0.0 162 0.0 0.0 192 0.0 0.0 

Commercial Marine (C1 & C2) 80,353 7.6 0.5 2,961 0.3 0.0 3,002 0.2 0.0 

Land-Based Nonroad Diesel 172,304 16.4 1.2 999 0.1 0.0 1,079 0.1 0.0 

Small Nonroad SI 6,742 0.6 0.0 8,870 0.9 0.1 10,282 0.7 0.1 

Recreational Marine SI 2,755 0.3 0.0 2,995 0.3 0.0 3,184 0.2 0.0 

SI Recreational Vehicles 1,530 0.1 0.0 2,862 0.3 0.0 3,019 0.2 0.0 

Large Nonroad SI (>25hp) 933 0.1 0.0 905 0.1 0.0 1,020 0.1 0.0 

Aircraft 8,701 0.8 0.1 11,171 1.1 0.1 12,197 0.8 0.1 

Total Off Highway 807,463 76.9 5.4 958,857 96.5 10.8 1,444,498 97.2 15.4 

Highway Diesel 71,147 6.8 0.5 4,218 0.4 0.0 5,478 0.4 0.1 

Highway non-diesel 171,866 16.4 1.1 30,922 3.1 0.3 36,011 2.4 0.4 

Total Highway 243,013 23.1 1.6 35,140 3.5 0.4 41,489 2.8 0.4 

Total Mobile Sources 1,050,475 100.0 7.0 993,998 100.0 11.2 1,485,986 100.0 15.9 

Stationary Point & Area 

Sources 13,897,968 - 93.0 7,864,681 - 88.8 7,864,681 - 84.1 

Total Man-Made Sources 14,948,443 - 100 8,858,678 - 100 9,350,667 - 100 

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Chapter 3: Emission Inventory 

3.5.2 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 3-98, 

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.53 

Table 3-98 Contribution of Commercial Marine Vessels to Mobile Source Inventories 

for Selected Ports in 2002a 

Port Area 

% of tot

NOX 

al % of total 

PM2.5 

% of tot

SO2 

al 

Valdez, AK 4 10 43 

Seattle, WA 10 20 56 

Tacoma, WA 20 38 74 

San Francisco, CA 1 1 31 

Oakland, CA 8 14 80 

LA/Long Beach, CA 5 10 71 

Beaumont, TX 6 20 55 

Galveston, TX 5 12 47 

Houston, TX 3 10 41 

New Orleans, LA 14 24 59 

South Louisiana, LA 12 24 58 

Miami, FL 13 25 66 

Port Everglades, FL 9 20 56 

Jacksonville, FL 5 11 52 

Savannah, GA 24 39 80 

Charleston, SC 22 33 87 

Wilmington, NC 7 16 73 

Baltimore, MD 12 27 69 

New York/New Jersey 4 9 39 

Boston, MA 4 5 30 

Note: a

 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.54 These 

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Regulatory Impact Analysis 

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. 

3.6 Projected Emission Reductions 

The projected tons reductions for each of the 2020 control cases relative to the 2020 

baseline, as well as the tons reductions for the 2030 control case relative to the 2030 baseline, are 

presented in Table 3-99 thru Table 3-100. Reductions by region, for the total U.S., and for the 

total 48-states, are provided by pollutant in each table. 

Table 3-99 Reductions for 2020 Control Casea 

U.S. Region 

Metric Tonnes per Year 

NOX PM10 PM2.5 a

 HC CO SO2 CO2 

Alaska East (AE) 3,264 2,239 2,060 0 0 18,356 57,395 

Alaska West (AW) 2,897 6,267 5,766 0 0 49,300 182,091 

East Coast (EC) 149,933 33,717 31,020 0 0 311,523 887,402 

Gulf Coast (GC) 88,434 20,202 18,586 0 0 168,496 532,567 

Hawaii East (HE) 15,074 3,634 3,343 0 0 29,888 91,340 

Hawaii West (HW) 10,166 4,565 4,200 0 0 35,445 130,519 

North Pacific (NP) 13,539 3,377 3,107 0 0 26,731 85,533 

South Pacific (SP) 84,507 17,395 16,003 0 0 143,965 480,516 

Great Lakes (GL) 3,422 1,406 1,294 0 0 11,574 36,235 

Total U.S. Metric 

Tonnes 371,237 92,803 85,378 0 0 795,279 2,483,598 

Total U.S. Short Tons 409,219 102,297 94,114 0 0 876,645 2,737,698 

Note: a

 The emission reductions are relative to the 2020 baseline. 

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Chapter 3: Emission Inventory 

Table 3-100 Reductions for 2030 Control Casea 

U.S. Region 

Metric Tonnes per Year 

NOX PM10 PM2.5 a

 HC CO SO2 CO2 

Alaska East (AE) 12,208 3,099 2,851 0 0 25,397 79,434 

Alaska West (AW) 14,764 8,555 7,870 0 0 67,482 251,937 

East Coast (EC) 443,893 52,394 48,203 0 0 484,409 1,400,222 

Gulf Coast (GC) 222,973 26,881 24,731 0 0 224,221 706,466 

Hawaii East (HE) 46,814 5,919 5,446 0 0 48,698 149,669 

Hawaii West (HW) 22,377 7,417 6,824 0 0 57,641 210,703 

North Pacific (NP) 36,179 4,683 4,308 0 0 37,062 118,395 

South Pacific (SP) 266,033 30,179 27,764 0 6 249,952 756,671 

Great Lakes (GL) 6,102 1,657 1,524 0 0 13,694 42,904 

Total U.S. Metric 

Tonnes 1,071,344 140,783 129,521 0 0 1,208,555 3,716,400 

Total U.S. Short Tons 1,180,955 155,187 142,772 0 0 1,332,204 4,096,630 

Note: a

 The emission reductions are relative to the 2030 baseline. 

3.7 Inventories Used for Air Quality Modeling 

The emission inventories for 2020 presented in this chapter are slightly different from the 

emissions inventories used in the air quality modeling presented in Chapter 2. Specifically, the 

2020 inventories used in the air quality modeling reflect a slightly different boundary for the 

proposed ECA that was based on a measurement error. Due to the nature of the measurement 

error, the corrections to the ECA boundaries are not uniform, but are different by coastal area. 

The measurement error affects only those portions that are farthest from shore. The 2030 

inventories are not affected by this error. 

A comparison of the air quality and final inventories by region for the 2020 baseline 

scenario is provided in Table 3-101. Results are provided only for NOX, PM2.5, and SO2, since 

the air quality modeling is focused on ozone and PM2.5. In addition, Alaska and Hawaii are not 

included, since the air quality modeling domain does not include these states. As seen in Table 

3-101, the changes due to the boundary error are not expected to have a significant impact on the 

results of our analysis. 

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Regulatory Impact Analysis 

Table 3-101 Comparison of Air Quality versus Final Inventories for 2020 Baseline Case 

U.S. Region 

Metric Tonnes per Year 

NOX PM2.5 SO2 

AQ Final 

% 

Diff AQ Final 

% 

Diff AQ Final 

% 

Diff 

East Coast (EC) 439,713 439,604 0% 35,891 35,882 0% 323,108 323,038 0% 

Gulf Coast (GC) 261,024 259,295 1% 21,669 21,531 1% 175,862 174,751 1% 

North Pacific 

(NP) 42,291 42,644 -1% 3,575 3,603 -1% 27,580 27,807 -1% 

South Pacific 

(SP) 216,849 234,968 -8% 17,092 18,536 -8% 138,102 149,751 -8% 

Great Lakes (GL) 19,842 19,842 0% 1,484 1,484 0% 11,993 11,993 0% 

Total 48-State 979,719 996,353 -2% 79,711 81,036 -2% 676,645 687,340 -2% 

The 2020 control inventories are also subject to the boundary error. In addition, the 2020 

air quality control case does not include global controls for areas that are beyond 200 nm but 

within the air quality modeling domain. The impact of this latter difference is expected to be 

minimal. 

The modeling for 2030 was based on inventories that reflected an ECA distance closer to 

shore than what we are proposing. The air quality modeling, and related estimates of benefits, 

therefore reflect the impacts associated with approximately 80% of the emission reductions 

achieved by the proposed coordinated strategy. As a result, the 2030 air quality impacts and 

health benefits presented in Chapters 2 and 6, respectively, should be considered conservative 

estimates of the improvements in air quality associated with the proposal. For the final RIA, we 

plan to model the 2030 coordinated strategy to control ship emissions with a 200 nm boundary 

and global controls beyond. 

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Chapter 3: Emission Inventory 

APPENDIX 3A 

Port Coordinates and Reduced Speed Zone Information 

3-143 
Regulatory Impact Analysis 

Table 3-102 Port Coordinates 

Port Name 

US ACE 

Code 

Port Coordinates 

Longitude Latitude 

Albany, NY C0505 -73.7482 42.64271 

Alpena, MI L3617 -83.4223 45.0556 

Anacortes, WA C4730 -122.6 48.49617 

Anchorage, AK C4820 -149.895 61.23778 

Ashtabula, OH L3219 -80.7917 41.91873 

Baltimore, MD C0700 -76.5171 39.20899 

Barbers Point, Oahu, HI C4458 -158.109 21.29723 

Baton Rouge, LA C2252 -91.1993 30.42292 

Beaumont, TX C2395 -94.0881 30.08716 

Boston, MA C0149 -71.0523 42.35094 

Bridgeport, CT C0311 -73.1789 41.172 

Brownsville, TX C2420 -97.3981 25.9522 

Brunswick, GA C0780 -81.4999 31.15856 

Buffalo, NY L3230 -78.8953 42.8783 

Burns Waterway Harbor, IN L3739 -87.1552 41.64325 

Calcite, MI L3620 -83.7756 45.39293 

Camden-Gloucester, NJ C0551 -75.1043 39.94305 

Carquinez, CA CCA01 -122.123 38.03556 

Catalina, CA CCA02 -118.496 33.43943 

Charleston, SC C0773 -79.9216 32.78878 

Chester, PA C0297 -75.3222 39.85423 

Chicago, IL L3749 -87.638 41.88662 

Cleveland, OH L3217 -81.6719 41.47852 

Conneaut, OH L3220 -80.5486 41.96671 

Coos Bay, OR C4660 -124.21 43.36351 

Corpus Christi, TX C2423 -97.3979 27.81277 

Detroit, MI L3321 -83.1096 42.26909 

Duluth-Superior, MN and 

WI L3924 -92.0964 46.77836 

El Segundo, CA CCA03 -118.425 33.91354 

Erie, PA L3221 -80.0679 42.15154 

Escanaba, MI L3795 -87.025 45.73351 

Eureka, CA CCA04 -124.186 40.79528 

Everett, WA C4725 -122.229 47.98476 

Fairport Harbor, OH L3218 -81.2941 41.76666 

Fall River, MA C0189 -71.1588 41.72166 

Freeport, TX C2408 -95.3304 28.9384 

Galveston, TX C2417 -94.8127 29.31049 

Gary, IN L3736 -87.3251 41.61202 

Georgetown, SC C0772 -79.2896 33.36682 

Grays Harbor, WA C4702 -124.122 46.91167 

Gulfport, MS C2083 -89.0853 30.35216 

Hilo, HI C4400 -155.076 19.72861 

Honolulu, HI C4420 -157.872 21.31111 

Hopewell, VA C0738 -77.2763 37.32231 

3-144 
Chapter 3: Emission Inventory 

Port Name 

US ACE 

Code 

Port Coordinates 

Longitude Latitude 

Houston, TX C2012 -95.2677 29.72538 

Indiana Harbor, IN L3738 -87.4455 41.67586 

Jacksonville, FL C2017 -81.6201 30.34804 

Kahului, Maui, HI C4410 -156.473 20.89861 

Kalama, WA C4626 -122.863 46.02048 

Lake Charles, LA C2254 -93.2221 30.22358 

Long Beach, CA C4110 -118.21 33.73957 

Longview, WA C4622 -122.914 46.14222 

Lorain, OH L3216 -82.1951 41.48248 

Los Angeles, CA C4120 -118.241 33.77728 

Manistee, MI L3720 -86.3443 44.25082 

Marblehead, OH L3212 -82.7091 41.52962 

Marcus Hook, PA C5251 -75.4042 39.81544 

Matagorda Ship Channel, 

TX C2410 -96.5641 28.5954 

Miami, FL C2164 -80.1832 25.78354 

Milwaukee, WI L3756 -87.8997 42.98824 

Mobile, AL C2005 -88.0411 30.72527 

Morehead City, NC C0764 -76.6947 34.71669 

Muskegon, MI L3725 -86.3501 43.19492 

Nawiliwili, Kauai, HI C4430 -159.353 21.96111 

New Bedford, MA C0187 -70.9162 41.63641 

New Castle, DE C0299 -75.5616 39.65668 

New Haven, CT C1507 -72.9047 41.29883 

New Orleans, LA C2251 -90.0853 29.91414 

New York, NY and NJ C0398 -74.0384 40.67395 

Newport News, VA C0736 -76.4582 36.98522 

Nikishka, AK C4831 -151.314 60.74793 

Oakland, CA C4345 -122.308 37.82152 

Olympia, WA C4718 -122.909 47.06827 

Other Puget Sound, WA C4754 -122.72 48.84099 

Palm Beach, FL C2162 -80.0527 26.76904 

Panama City, FL C2016 -84.1993 30.19009 

Pascagoula, MS C2004 -88.5588 30.34802 

Paulsboro, NJ C5252 -75.2266 39.82689 

Penn Manor, PA C0298 -74.7408 40.13598 

Pensacola, FL C2007 -87.2579 30.40785 

Philadelphia, PA C0552 -75.2022 39.91882 

Plaquemines, LA, Port of C2255 -89.6875 29.48 

Port Angeles, WA C4708 -123.453 48.1305 

Port Arthur, TX C2416 -93.9607 29.83142 

Port Canaveral, FL C2160 -80.6082 28.41409 

Port Dolomite, MI L3627 -84.3128 45.99139 

Port Everglades, FL C2163 -80.1178 26.09339 

Port Hueneme, CA C4150 -119.208 34.14824 

Port Inland, MI L3803 -85.8628 45.95508 

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Regulatory Impact Analysis 

Port Name 

US ACE 

Code 

Port Coordinates 

Longitude Latitude 

Port Manatee, FL C2023 -82.5613 27.63376 

Portland, ME C0128 -70.2513 43.64951 

Portland, OR C4644 -122.665 45.47881 

Presque Isle, MI L3845 -87.3852 46.57737 

Providence, RI C0191 -71.3984 41.81178 

Redwood City, CA CCA05 -122.21 37.51306 

Richmond, CA C4350 -122.374 37.92424 

Richmond, VA C0737 -77.4194 37.45701 

Sacramento, CA CCA06 -121.544 38.56167 

San Diego, CA C4100 -117.178 32.70821 

San Francisco, CA C4335 -122.399 37.80667 

Sandusky, OH L3213 -82.7123 41.47022 

Savannah, GA C0776 -81.0954 32.08471 

Searsport, ME C0112 -68.925 44.45285 

Seattle, WA C4722 -122.359 47.58771 

South Louisiana, LA, Port 

of C2253 -90.6179 30.03345 

St. Clair, MI L3509 -82.4941 42.82663 

Stockton, CA C4270 -121.316 37.9527 

Stoneport, MI L3619 -83.4703 45.28073 

Tacoma, WA C4720 -122.452 47.28966 

Tampa, FL C2021 -82.5224 27.78534 

Texas City, TX C2404 -94.9181 29.36307 

Toledo, OH L3204 -83.5075 41.66294 

Two Harbors, MN L3926 -91.6626 47.00428 

Valdez, AK C4816 -146.346 61.12473 

Vancouver, WA C4636 -122.681 45.62244 

Wilmington, DE C0554 -75.507 39.71589 

Wilmington, NC C0766 -77.954 34.23928 

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Chapter 3: Emission Inventory 

Table 3-103 Port RSZ Information 

Port Name 

RSZ 

Speed 

(knts) 

RSZ 

distance 

(naut mi) 

Final RSZ End Point(s) 

Longitude Latitude 

Albany, NY c 142.5 -73.8929 40.47993 

Alpena, MI e 3 -83.2037 44.99298 

Anacortes, WA a 108.3 -124.771 48.49074 

Anchorage, AK 14.5 143.6 -152.309 59.5608 

Ashtabula, OH e 3 -80.8097 42.08549 

Baltimore, MD c 157.1 -75.8067 36.8468 

Barbers Point, Oahu, HI 10 5.1 -158.132 21.21756 

-89.4248 28.91161 

Baton Rouge, LA 10 219.8 -89.137 28.98883 

Beaumont, TX 7 53.5 -93.7552 29.55417 

Boston, MA 10 14.3 -70.7832 42.37881 

Bridgeport, CT 10 2 -73.1863 41.13906 

Brownsville, TX 8.8 18.7 -97.0921 26.06129 

-80.9345 31.29955 

Brunswick, GA 13 38.8 -81.1357 30.68935 

Buffalo, NY e 3 -79.0996 42.81683 

Burns Waterway Harbor, IN e 3 -87.1032 41.80625 

Calcite, MI e 3 -83.5383 45.39496 

Camden-Gloucester, NJ c 94 -75.0095 38.79004 

Carquinez, CA 12 39 -122.632 37.76094 

Catalina, CA 12 11.9 -118.465 33.63641 

Charleston, SC 12 17.3 -79.6452 32.62557 

Chester, PA c 78.2 -75.0095 38.79004 

Chicago, IL e 3 -87.4141 41.86971 

Cleveland, OH e 3 -81.765 41.63079 

Conneaut, OH e 3 -80.5639 42.13361 

Coos Bay, OR 6.5 13 -124.359 43.35977 

Corpus Christi, TX d 30.1 -96.8753 27.74433 

Detroit, MI e 3 -83.1384 42.10308 

Duluth-Superior, MN and 

WI e 3 -91.8536 46.78916 

-118.926 33.91252 

El Segundo, CA 12 23.3 -118.465 33.63641 

Erie, PA e 3 -80.115 42.3151 

Escanaba, MI e 3 -86.9224 45.58297 

Eureka, CA 12 9 -124.347 40.75925 

Everett, WA a 123.3 -124.771 48.49074 

Fairport Harbor, OH e 3 -81.3917 41.91401 

Fall River, MA 9 22.7 -71.3334 41.41708 

Freeport, TX c 2.6 -95.2949 28.93323 

Galveston, TX c 9.3 -94.6611 29.3247 

Gary, IN e 3 -87.2824 41.77658 

Georgetown, SC 12 17.6 -79.0779 33.1924 

3-147 
Regulatory Impact Analysis 

Port Name 

RSZ 

Speed 

(knts) 

RSZ 

distance 

(naut mi) 

Final RSZ End Point(s) 

Longitude Latitude 

Grays Harbor, WA a 4.9 -124.24 46.89509 

Gulfport, MS 10 17.4 -88.9263 30.11401 

Hilo, HI 10 7.1 -154.985 19.76978 

-157.956 21.17658 

Honolulu, HI 10 10 -157.785 21.23827 

Hopewell, VA 10 91.8 -75.8067 36.8468 

Houston, TX c 49.6 -94.6611 29.3247 

Indiana Harbor, IN e 3 -87.4007 41.8401 

Jacksonville, FL 10 18.6 -81.3649 30.39769 

Kahului, Maui, HI 10 7.5 -156.44 21.01066 

Kalama, WA b 68.2 -124.137 46.22011 

Lake Charles, LA 6 38 -93.3389 29.73094 

-118.465 33.63641 

Long Beach, CA 12 18.1 -118.13 33.45211 

Longview, WA b 67.3 -124.137 46.22011 

Lorain, OH e 3 -82.2701 41.64023 

-118.465 33.63641 

Los Angeles, CA 12 20.6 -118.13 33.45211 

Manistee, MI e 3 -86.3819 44.41573 

Marblehead, OH e 3 -82.7293 41.69638 

Marcus Hook, PA c 94.7 -75.0095 38.79004 

Matagorda Ship Channel, TX 7.3 24 -96.2287 28.33472 

Miami, FL 12 3.8 -80.1201 25.75787 

Milwaukee, WI e 3 -87.6718 42.97343 

Mobile, AL 11 36.1 -88.0644 30.1457 

Morehead City, NC 10 2.2 -76.6679 34.68999 

Muskegon, MI e 3 -86.5377 43.29151 

Nawiliwili, Kauai, HI 10 7.3 -159.266 21.87705 

New Bedford, MA 9 22.4 -71.1013 41.38499 

New Castle, DE c 60.5 -75.0095 38.79004 

New Haven, CT 10 2.1 -72.9121 41.26588 

-89.4248 28.91161 

New Orleans, LA 10 104.2 -89.137 28.98883 

New York, NY and NJ c 15.7 -73.8929 40.47993 

Newport News, VA 14 24.3 -75.8067 36.8468 

Nikishka, AK 14.5 90.7 -152.309 59.5608 

Oakland, CA 12 18.4 -122.632 37.76094 

Olympia, WA a 185.9 -124.771 48.49074 

Other Puget Sound, WA a 106 -124.771 48.49074 

Palm Beach, FL 3 3.1 -79.9973 26.77129 

Panama City, FL 10 10 -84.1797 30.0818 

Pascagoula, MS 10 17.5 -88.4804 30.09597 

Paulsboro, NJ c 83.5 -75.0095 38.79004 

Penn Manor, PA c 114.5 -75.0095 38.79004 

Pensacola, FL 12 12.7 -87.298 30.27777 

3-148 
Chapter 3: Emission Inventory 

Port Name 

RSZ 

Speed 

(knts) 

RSZ 

distance 

(naut mi) 

Final RSZ End Point(s) 

Longitude Latitude 

Philadelphia, PA c 88.1 -75.0095 38.79004 

-89.4248 28.91161 

Plaquemines, LA, Port of 10 52.4 -89.137 28.98883 

Port Angeles, WA a 65 -124.771 48.49074 

Port Arthur, TX 7 21 -93.7552 29.55417 

Port Canaveral, FL 10 4.4 -80.5328 28.41439 

Port Dolomite, MI e 3 -84.2445 45.83181 

Port Everglades, FL 7.5 2.1 -80.082 26.08627 

Port Hueneme, CA 12 2.8 -119.238 34.10859 

Port Inland, MI e 3 -85.6524 45.87553 

Port Manatee, FL 9 27.4 -83.0364 27.59078 

Portland, ME 10 11.4 -70.1077 43.54224 

Portland, OR b 105.1 -124.137 46.22011 

Presque Isle, MI e 3 -87.082 46.5804 

Providence, RI 9 24.9 -71.3334 41.41708 

Redwood City, CA 12 36 -122.632 37.76094 

Richmond, CA 12 22.6 -122.632 37.76094 

Richmond, VA 10 106.4 -75.8067 36.8468 

Sacramento, CA 12 90.5 -122.632 37.76094 

San Diego, CA 12 11.7 -117.315 32.62184 

San Francisco, CA 12 14.4 -122.632 37.76094 

Sandusky, OH e 3 -82.5251 41.56193 

Savannah, GA 13 45.5 -78.0498 33.83598 

Searsport, ME 9 22.2 -68.7645 44.1179 

Seattle, WA a 133.3 -124.771 48.49074 

South Louisiana, LA, Port 

of 10 142.8 

-89.4248 28.91161 

-89.137 28.98883 

St. Clair, MI e 3 -82.5838 42.55923 

Stockton, CA 12 86.9 -122.632 37.76094 

Stoneport, MI e 3 -83.2355 45.25919 

Tacoma, WA a 150.5 -124.771 48.49074 

Tampa, FL 9 30 -83.0364 27.59078 

Texas City, TX c 15.1 -94.6611 29.3247 

Toledo, OH e 3 -83.3034 41.7323 

Two Harbors, MN e 3 -91.4414 46.93391 

Valdez, AK 10 27.2 -146.881 60.86513 

Vancouver, WA b 95.7 -124.137 46.22011 

Wilmington, DE c 65.3 -75.0095 38.79004 

Wilmington, NC 10 27.6 -80.325 31.84669 a Cruise speed through Strait of Juan de Fuca, then varies by ship type for remaining journey b

 Inbound on Columbia River at 6.5 knots, outbound at 12 knots c

 Speed varies by ship type similar to typical like port d Speed varies by ship DWTs e All Great Lake ports have reduced speed zone distances of 3 nautical miles with 

speeds halfway between service speed and maneuvering speed. 

3-149 
Regulatory Impact Analysis 

APPENDIX 3B 

Inventory Impacts of Alternative Program 

The proposed program represents a comprehensive approach to reduce emissions from 

Category 3 marine diesel engines. As we developed this proposal, we evaluated an alternative, 

which considers the possibility of pulling ahead the CAA Tier 3 NOX standard from 2016 to 

2014. NOX emissions were calculated for the year 2023 under three scenarios: Tier 1 only NOX 

standards (the base case), the coordinated strategy as proposed in NPRM which includes the 

2016 NOX standards in effect 2016 (the primary case), and NOX standards for U.S.-vessels only 

pulled ahead to 2014 (the alternative case). This appendix describes the methodology that was 

used to estimate the NOX inventories for the proposed and alternative program scenarios in 2023. 

The inventories described in this chapter are for calendar years 2002, 2020, and 2030. To 

calculate inventories for 2023, a spreadsheet model was developed and used. For both the 

proposed and alternative scenarios, it was assumed that the proposed ECA controls apply within 

200 nautical miles for all 48 contiguous states. The only difference modeled was the different 

start dates for Tier 3. Note that only emissions from U.S. vessels are impacted by the proposed 

alternative. 

Under the proposed base scenario, 48-state NOX emissions in 2023 are 10,494,636 short 

tons. With the coordinated strategy in effect (the primary case), 48-state NOX emissions in 2023 

are 7,515,389 short tons, a 28.4 % reduction from Tier 1 only standards. Under the alternative 

scenario, 48-state NOX emissions in 2023 are 7,444,866 short tons, a difference of 0.9 percent 

from the primary case (Figure 3B-1). 

2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 

Calendar Year 

Figure 3B-1 NOX Emissions with the Primary Case (Tier 3 in 2016) versus the Alternative Case (Tier 3 in 

2014 for U.S. vessels only) 

0 

100,000 

200,000 

300,000 

400,000 

500,000 

600,000 

700,000 

800,000 

900,000 

1,000,000 

NOX Emissions (short tons per year) 

Tier 3 in 2016 

Tier 3 in 2014 for U.S. ships 

3-150 
Chapter 3: Emission Inventory 

References 

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Regulatory Impact Analysis 

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16 U.S. Army Corps of Engineers (2006). National Waterway Network, Available online at 

http://www.iwr.usace.army.mil/ndc/data/datanwn.htm, Downloaded April 2006. 

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18 ARCADIS Geraghty & Miller, Inc. (September 1999). Commercial Marine Activity for Great 

Lake and Inland River Ports in the United States, prepared for the U.S. Environmental Protection 

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19 ENVIRON International Corporation (2002). Commercial Marine Emission Inventory 

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20 California Air Resources Board (2005), 2005 Oceangoing Ship Survey, Summary of Results, 

Docket ID EPA-HQ-OAR-2007-0121-0149. 

21 Entec UK Limited (2002). Quantification of Emissions from Ships Associated with Ship 

Movements between Ports in the European Community, prepared for the European Commission, 

Docket ID EPA-HQ-OAR-2007-0121-0059. 

22 U.S. Environmental Protection Agency (January 2009). Category 3 Marine Engine CO and HC 

Emission Factors, Memorandum from Ari Kahan to Docket EPA-HQ-OAR-2007-0121, Docket 

ID EPA-HQ-OAR-2007-0121-0173. 

23 U.S. Environmental Protection Agency (September 2007). Estimation of Particulate Matter 

Emission Factors for Diesel Engines on Ocean-Going Vessels, Memorandum from Mike 

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24 U.S. Environmental Protection Agency (September 2007). Estimation of Particulate Matter 

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Samulski to Docket EPA-HQ-OAR-2007-0121, Docket ID EPA-HQ-OAR-2007-0121-0060. 

25 Memo from Chris Lindhjem of ENVIRON (2005). PM Emission Factors, December 15, 2005, 

Docket ID EPA-HQ-OAR-2007-0121-0162. 

26 U.S. Environmental Protection Agency, Exhaust and Crankcase Emission Factors for Nonroad 

Engine Modeling – Compression Ignition (April 2004). Appendix C, EPA- 420-P-04-009, 

3-152 
Chapter 3: Emission Inventory 

available online at http://www.epa.gov/otaq/models/nonrdmdl/nonrdmdl2004/420p04009.pdf, 

Docket ID EPA-HQ-OAR-2003-0190-0411. 

27 Energy and Environmental Analysis Inc. (February 2000). Analysis of Commercial Marine 

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28 Starcrest Consulting Group (January 2007). Draft Port of Los Angeles Air Emissions 

Inventory for Calendar Year 2005, Docket ID EPA-HQ-OAR-2007-0121-0145. 

29 Starcrest Consulting Group (April 2007). Puget Sound Maritime Air Forum Maritime Air 

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30 Starcrest Consulting Group, LLC (April 2003). The New York, Northern New Jersey, Long 

Island Nonattainment Area Commercial Marine Vessel Emission Inventory, Vol 1 - Report, 

Prepared for the Port Authority of New York & New Jersey, United States and the Army Corps 

of Engineers, New York District, Docket ID EPA-HQ-OAR-2007-0121-0152. 

31 Starcrest Consulting Group, LLC (November 2000). Houston-Galveston Area Vessel 

Emissions Inventory, Prepared for the Port of Houston Authority and the Texas Natural Resource 

Conservation Commission, Docket ID EPA-HQ-OAR-2007-0121-0147. 

32 Eastern Research Group and Starcrest Consulting Group, LLC (January 2004). Update To The 

Commercial Marine Inventory For Texas To Review Emissions Factors, Consider A Ton-Mile 

EI Method, And Revise Emissions For The Beaumont-Port Arthur Non-Attainment Area Final 

Report, Submitted to the Houston Advanced Research Center, Docket ID EPA-HQ-OAR-2007­

0121-0153. 

33 Zuber M. Farooqui and Kuruvilla John (June 2004). Refinement of the Marine Emissions 

Inventory for the Corpus Christi Urban Airshed, Department of Environmental Engineering, 

Texas A&M University – Kingsville, Proceedings of the 97th Annual A&WMA Conf. & 

Exhibition, Docket ID EPA-HQ-OAR-2007-0121-0160. 

34 ICF International (October 2007). Commercial Marine Port Inventory Development, prepared 

for the U.S. Environmental Protection Agency, EPA Report Number EPA-420-R-07-012c, 

October 2007, Docket ID EPA-HQ-OAR-2007-0121-0063.1. 

35 ENVIRON International Corporation (March 2007). LADCO 2005 Commercial Marine 

Emissions, Docket ID EPA-HQ-OAR-2007-0121-0158. 

36 California Air Resources Board (October 2005). Emissions Estimation Methodology for 

Ocean-Going Vessels, Docket ID EPA-HQ-OAR-2007-0121-0164. 

37 E.H. Pechan & Associates Inc. (June 2005), Commercial Marine Inventories for Select 

Alaskan Ports, Final Report, Prepared for the Alaska Department of Conservation, Docket ID 

EPA-HQ-OAR-2007-0121-0165. 

38 ICF International (March 2009). Inventory Contribution of U.S. Flagged Vessels, prepared for 

the U.S. Environmental Protection Agency, EPA Report Number EPA-420-R-09-005, Docket ID 

EPA-HQ-OAR-2007-0121-0154. 

3-153 
Regulatory Impact Analysis 

39 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial 

Marine Vessel Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final 

Report, prepared by University of Delaware for the California Air Resource Board, Contract 

Number 04-346, and the Commission for Environmental Cooperation in North America, 

Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

40 Corbett, J. et al. (May 2006). Estimation, Validation and Forecasts of Regional Commercial 

Marine Vessel Inventories, Tasks 1 and 2: Baseline Inventory and Ports Comparison, Final 

Report, prepared by University of Delaware for the California Air Resource Board, Contract 

Number 04-346, and the Commission for Environmental Cooperation in North America, 

Contract Number 113.111, May 2006, Docket ID EPA-HQ-OAR-2007-0121-0013. 

41 Lloyd’s Register and International Maritime Organization, Marine Exhaust Emission 

Quantification Study – Baltic Sea, in MEPC 45/INF.7. 1998. 

42 ICF International (January 5, 2006). Current Methodologies and Best Practices in Preparing 

Port Emission Inventories, Final Report, prepared for the U.S. Environmental Protection Agency, 

available online at http://www.epa.gov/sectors/ports/bp_portemissionsfinal.pdf, Docket ID EPAHQ-OAR-2007-0121-0148. 

43 Corbett, J.J. and H.W. Koehler (2003). Updated Emissions from Ocean Shipping, Journal of 

Geophysical Research, 108(D20); p. 4650, Docket ID EPA-HQ-OAR-2007-0121-0176. 

44 Corbett, J.J. and H.W. Koehler (2004). Considering Alternative Input Parameters in an 

Activity-Based Ship Fuel Consumption and Emissions Model: Reply to Comment by Oyvind 

Endresen et al. on “Updated Emissions from Ocean Shipping,” Journal of Geophysical 

Research. 109(D23303), Docket ID EPA-HQ-OAR-2007-0121-0159. 

45 Levelton Consultants Ltd. (2006). Marine Emission Inventory Study Eastern Canada and Great 

Lakes – Interim Report 4: Gridding Results, prepared for Transportation Development Centre, 

Transport Canada, Docket ID EPA-HQ-OAR-2007-0121-0161. 

46 IMO. Revision of MARPOL Annex VI and the NOx technical code. Input from the four 

subgroups and individual experts to the final report of the Informal Cross Government/Industry 

Scientific Group of Experts. BLG/INF.10 12/28/2007, Docket ID EPA-HQ-OAR-2007-0121­

0118. 

47 Transport Canada (2004). Transportation in Canada Annual Report 2004. (Tables 3-26 and 8­

27). http://www.tc.gc.ca/pol/en/report/anre2004/8F_e.htm, Docket ID EPA-HQ-OAR-2007­

0121-0169. 

48 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and 

Effects of Designation Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft 

Report, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA420­

D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

49 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and 

Effects of Designation Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft 

Report, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA420­

D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

3-154 
Chapter 3: Emission Inventory 

50 Corbett, James and Chengfeng Wang (October 26, 2005). Emission Inventory Review SECA 

Inventory Progress Discussion, p 11, memorandum to California Air Resources Board, Docket 

ID EPA-HQ-OAR-2007-0121-0168. 

51 RTI International (December 2006). Global Trade and Fuels Assessment – Future Trends and 

Effects of Designation Requiring Clean Fuels in the Marine Sector: Task Order No. 1, Draft 

Report, prepared for the U.S. Environmental Protection Agency, EPA Report Number EPA420­

D-07-006, Docket ID EPA-HQ-OAR-2007-0121-0063.3. 

52 California Air Resources Board ( September 2005). 2005 Oceangoing Ship Survey, Summary 

of Results, Docket ID EPA-HQ-OAR-2007-0121-0149. 

53 U.S. Maritime Administration, Office of Statistical and Economic Analysis (April 2006). 

Vessel Calls at U.S. & World Ports, 2005, Docket ID EPA-HQ-OAR-2007-0121-0040. 

54 Santa Barbara County Air Quality News, Issue 62, July-August 2001, Docket ID EPA-HQOAR-2007-0121-0167. 

3-155 
Chapter 4: Technological Feasibility 

CHAPTER 4: TECHNOLOGICAL FEASIBILITY 

4.1 Overview of Emissions Standards and Emission Control Technologies..................... 4-2

4.2 Emission Control Technologies for Tier 2 Standards...................................................4-3

4.2.1 In-Cylinder NOX Controls ....................................................................................4-3

4.3 Emission Control Technologies for Tier 3 Standards...................................................4-5

4.3.1 Selective Catalytic Reduction................................................................................4-6

4.3.2 Water-Based Technologies....................................................................................4-9

4.4 Vessel Technologies for Low Sulfur Fuel Standards ..................................................4-11

4.4.1 Fuel Switching on Vessels....................................................................................4-11

4.4.2 Exhaust Gas Cleaning Systems...........................................................................4-20

4.5 Technology for Producing/Distributing Lower Sulfur Fuel.......................................4-23

4.5.1 Production of Lower Sulfur Marine Fuel..........................................................4-23

4.5.2 Fuel Distribution Considerations .......................................................................4-25

4.6 Impact on Safety, Noise, and Energy............................................................................4-29

4-1 
Regulatory Impact Analysis 

CHAPTER 4: Technological Feasibility 

In this chapter, we describe in detail the analysis of emission control technologies we 

used to develop the standards we are proposing. Section 4.1 presents an overview of the 

proposed standards and the emission control technologies we expect will be used in meeting 

these standards. Section 4.2 describes the in-cylinder, or engine design-based, emission control 

technologies that can be used to meet the proposed Tier 2 standards. Section 4.3 describes the 

exhaust aftertreatment and water-based emission control technologies that can be used to meet 

the proposed Tier 3 standards. Section 4.4 describes technologies associated with switching to 

low sulfur distillate fuel or, alternatively, using exhaust gas cleaning devices to remove sulfur 

from the exhaust. Section 4.5 presents technology that can be used to produce and distribute 

additional low sulfur distillate fuel. Section 4.6 discusses the potential impact of the proposed 

standards on safety, noise, and energy. 

4.1 Overview of Emissions Standards and Emission Control Technologies 

Our current emission standards for Category 3 (C3) marine engines are equivalent to the 

NOX limits in Annex VI to the Convention for the Prevention of Pollution from Ships 

(MARPOL). These standards, referred to as “Tier 1”, were adopted by the EPA in 2003 and 

went into effect in 2004. Globally, these standards went into effect 2003 and became retroactive 

for vessels built from 2000 to 2002. The Tier 1 standards rely on engine-based technologies to 

reduce emissions. The International Maritime Organization recently amended Annex VI to 

include new tiers of NOX standards for new engines that reflect the use of advanced emission 

control technologies, including exhaust aftertreatment; these Tier 2 and Tier 3 standards will go 

into effect in 2011 and 2016, respectively. The Annex VI amendments also include limits on the 

sulfur content of fuel that will reduce SOX and PM emissions, and NOX limits for existing 

engines that will take effect as soon as certified Approved Methods are available. 

To meet the proposed Tier 2 standards (which require approximately 15 to 21% 

reductions in NOX relative to Tier 1, depending on rated engine speed), advanced, engine-based 

improvements will be needed. These engine-based approaches for Tier 2 can include changes 

and/or advancements to turbocharger, valve timing, compression ratio, combustion chamber, and 

common-rail fuel injection system designs. The extent to which any or all of these engine-based 

improvements are used is dependent upon the level of emission reduction needed for a given 

engine. The fuel injection approaches to reducing engine-out NOX emissions are described in 

detail in Section 4.2. 

To meet the proposed Tier 3 standards (which require an 80% reduction in NOX, relative 

to Tier 1), further engine-based approaches, such as Exhaust Gas Recirculation (EGR), direct 

water injection, fuel-water emulsification, and intake air humidification are under development. 

We anticipate that exhaust aftertreatment approaches, such as Selective Catalytic Reduction 

(SCR), will be used to achieve the necessary NOX reductions. SCR is a common catalytic 

exhaust emission control used for meeting more stringent NOX emissions standards in worldwide 

diesel applications. Stationary, coal-fired power plants have used SCR for three decades as a

means of controlling NOX emissions, and currently, U.S. and European heavy-duty truck 

manufacturers are using this technology to meet the more-stringent NOX limits. In the Category 

2 and Category 3 marine sector, at least 300 vessels are currently equipped with SCR systems to 

4-2 
Chapter 4: Technological Feasibility 

control NOX emissions. Our analysis , described in detail in Section 4.3, projects that SCR will 

be a viable technology available to C3 engine manufacturers to meet the Tier 3 NOX standard we 

are proposing. 

4.2 Emission Control Technologies for Tier 2 Standards 

4.2.1 In-Cylinder NOX Controls 

The engine-out, or in-cylinder, NOX emissions of a diesel engine can be controlled by 

utilizing engine design and calibration parameters (e.g. fuel delivery and valve timing) to limit 

the formation of NOX. The formation rate of NOX has a strong exponential relationship to 

combustion temperature; high combustion temperatures result in high NOX formation rates.1,2

Any changes to the engine design or combustion process which can lower the peak temperature 

will reduce NOX emissions. Most of the engine-out NOX emission control technologies 

discussed in the following in this Section reduce NOX emissions by reducing the level and 

duration peak combustion temperatures, while balancing the impact on PM emissions, fuel 

consumption, and torque output. 

Control of diesel emissions by modifying the combustion processes is often characterized 

by trade-offs in NOX emission control versus other parameters such as PM emissions, and fuel 

consumption. For example, lower oxygen content (through exhaust gas recirculation, or EGR) 

lowers NOX formation but may increase PM formation. Advanced (earlier) injection timing 

reduces PM emissions but increases NOX formation, while retarded (later) injection timing 

reduces NOX formation but increases PM formation, increases fuel consumption, and at high 

torque output levels, can increase soot accumulation within the lubricating oil. During engine 

development, these trade-offs are balanced against each other in order to obtain effective NOX

and PM control while maintaining acceptable power output, fuel efficiency, and engine 

durability. The introduction of more-advanced electronic fuel injection systems and the 

flexibility these systems provide in terms of injection timing, fuel delivery rate, number of 

injection events per combustion cycle can improve these tradeoffs, allowing for reduced 

emissions of both NOX and PM, while minimizing the impact on fuel efficiency. 

Electronic control of injection timing has been used by highway, nonroad, locomotive, 

and marine diesel engine manufacturers to balance NOX emissions, PM emissions, fuel 

efficiency, engine performance and engine durability. While in-line, unit-injector, and commonrail injection systems can all benefit from electronic controls, it is the common-rail system which 

provides the greatest flexibility of controlling the injection timing, pressure, flow rate, as well as 

the number of injection events for each combustion cycle. Engine manufactures, such as MAN 

B&W and Wärtsilä, have already incorporated common rail systems into their Tier 1 engine 

designs, and we expect that manufacturers will continue to improve these systems to further 

reduce NOX emissions while minimizing the effect on PM emissions and fuel consumption. 

4.2.1.1 Fuel Injection Pressure and Timing 

Delaying the start of fuel injection, and thus the start of combustion, can significantly 

reduce NOX emissions from a diesel engine. The effect of injection timing on emissions and 

performance is well established.3,4,5,6

 Delaying the start of combustion by retarding injection 

4-3 
Regulatory Impact Analysis 

timing aligns the heat release from the fuel combustion with the portion of the power (or 

combustion) stroke of the engine cycle after the piston has begun to move down. This means 

that the cylinder volume is increasing and that work (and therefore heat) is being extracted from

the hot gases. The removal of this heat through expansion lowers the temperature in the 

combustion gases. NOX is reduced because the premixed burning phase is shortened and 

because cylinder temperature and pressure are lowered. 

Injection timing retard typically reduces NOX while increasing HC, CO, PM, and fuel 

consumption because the end of injection comes later in the combustion stroke, where the time

for extracting energy from fuel combustion is shortened and the cylinder temperature and 

pressure are too low for more complete oxidation of PM. The increases in HC, CO, and PM can 

be offset by increasing injection pressure, allowing an earlier end of injection at the same torque 

output (i.e., shorter injection duration for the same quantity of fuel injected), and by using 

multiple injection events following the primary combustion event to enhance soot oxidation. 

While injection timing retard can achieve the 20% reduction in NOX required for Tier 2, and HC, 

CO, and PM increases can be eliminated, or minimized, through optimization for the injection 

strategy. 

We expect that electronic control of the fuel injection timing and pressure will be used by 

manufacturers of Tier 2 and Tier 3 engines to reduce engine-out NOX emissions. 

4.2.1.2 Common Rail Fuel Injection Systems 

The most recent advances in fuel injection technology for marine use are high-pressure 

common rail injection systems with the ability to use multiple injections and rate shaping (i.e., 

adjusting the flow rate of fuel delivered throughout the injection event as a function of crank 

angle) to control the timing and quantity of fuel delivered to the engine over the course of a 

single combustion event. Common rail systems can provide both NOX and PM reductions and 

are in widespread use in heavy-duty on-highway diesel engines, and are also used in many 

current nonroad diesel engines. These common rail systems provide precise control of the fuel 

injection event, allowing it to be broken up into discrete, multiple phases. Injecting a small 

quantity of fuel early in the compression stroke (or well before the piston reaches top-deadcenter) is known as “pilot” injection. The ignition of this smaller quantity of fuel limits the rapid 

increase in pressure and temperature (and the associated NOX formation) which is characteristic 

of premixed diesel combustion. Injecting the remainder of the fuel quantity into the established 

flame resulting from the pilot injection then allows for a steady burn which limits the combustion 

temperature, and hence NOX emissions. Rate shaping of the fuel injection event can be done 

either mechanically or electronically, and has been shown to reduce NOX emissions by up to 20 

percent diesel engines.7

A further splitting of the injection event, using a late cycle, or post-main, injection pulse 

has been shown to significantly reduce particulate emissions, most notably in cases where 

retarded injection timing, or a combination of injection timing retard and EGR, is used to control 

NOX.

8,9,10,11 With this approach, the typical diffusion-burn combustion event is broken up into 

two events; a main injection which is terminated, followed by a short dwell period with no 

injection, and a short, post-main injection event, see Figure 4-1. The second pulse of injected 

fuel induces late-combustion turbulent mixing. The splitting of the injection event into two 

4-4 
Chapter 4: Technological Feasibility 

events aids in breaking up and entraining the “soot cloud” formed from the first injection event 

into the bulk cylinder contents, allowing further combustion of the soot can occur. 

Figure 4-1 An Example of Using Multiple Fuel Injection Events to Induce Late-Combustion Mixing and

Increase Soot Oxidation for PM Control (adapted from Pierpont, Montgomery, and Rietz, 1995) 

By utilizing a fuel delivery strategy which incorporates retarded injection timing (for 

reduced NOX emissions), multiple injections (to reduce the PM which would typically increase 

with retarded injection timing), and rate shaping (to control the level and duration of peak 

combustion temperatures), an engine can be operated in a manner which balances NOX

emissions, PM emissions, and fuel consumption under all operating conditions. As in the case of 

Tier 1 engines, the application of common-rail technology (which allows a broad range of 

control over the fuel injection and combustion process) can reduce fuel consumption, but the 

ultimate fuel saving potential is limited by the NOX standard that the engine must comply with; 

the lower the NOX standard, the less potential there is to reduce fuel consumption through 

control of the fuel injection and combustion parameters.12 We project that fuel delivery 

strategies which result in decreases in peak cylinder temperature and pressure to meet the 

proposed Tier 2 NOX standard may increase fuel consumption by as much as 2%. However, 

engine manufacturers may be able to reduce NOx emissions and reduce fuel consumption if the 

compression ratio is increased while simultaneously reducing the excess air ratio (to maintain an 

equivalent ‘effective’ compression ratio).13 In addition, Miller-cycle supercharging (in which 

higher intake charge pressures and early closing of the intake valve can result in lower

combustion temperatures) can be used to reduce NOx emissions without increasing fuel 

consumption.14

4.3 Emission Control Technologies for Tier 3 Standards 

In this section we describe the emission control technologies that we believe will be used 

to meet the Tier 3 standards. In general, these technologies involve the use of SCR exhaust 

aftertreatment, water-based approaches (e.g., fuel-water emulsification, intake air humidification, 

and direct water injection), and EGR to reduce NOX emissions. These technologies may be used 

individually, or in combination with other technologies, to achieve the level of NOX reduction a 

given manufacturer or engine design requires. SCR is a commonly-used aftertreatment 

technology for diesel engines that can achieve a 90 to 95% reduction in NOX emissions in marine 

applications.

15 Light-duty, heavy-duty (both highway and nonroad), and marine diesel 

applications have already begun using SCR technology to meet more stringent, aftertreatment4-5 
Regulatory Impact Analysis 

forcing NOX standards and water-based technologies have been demonstrated in C3 marine 

applications. Given the preponderance of studies and data and our analysis summarized in this 

section, we believe that these technologies are appropriate for C3 marine applications. 

4.3.1 Selective Catalytic Reduction 

NOX emissions can be reduced substantially using SCR, a commonly-used technology 

used to comply with NOX emissions standards in diesel applications worldwide. An SCR 

catalyst reduces nitrogen oxides to N2 and water by using ammonia (NH3) as the reducing agent. 

The most-common method for supplying ammonia to the SCR catalyst is to inject an aqueous 

urea-water solution into the exhaust stream. In the presence of high-temperature exhaust gas 

(greater than 250 °C), the urea hydrolyzes to form NH3 and CO2; the NH3 is stored on the surface 

of the SCR catalyst where it is used to complete the NOX-reduction reaction. In theory, it is 

possible to achieve 100% NOX conversion if the NH3-to-NOX ratio (α) is 1:1 and the space 

velocity within the catalyst is not excessive (i.e., there is ample time for the reactions to occur). 

The urea dosing strategy and the desired α are dependent on the conditions present in the 

exhaust; namely gas temperature and the quantity of NOX present (which can be determined by 

engine mapping, temperature sensors, and NOX sensors). However, given the space limitations 

in packaging exhaust aftertreatment devices mobile and marine applications, an α of 0.85-1.0 is 

often used to balance the need for high NOX conversion rates against the potential for NH3 slip 

(where NH3 passes through the catalyst unreacted). 

Stationary power plants fueled with coal, diesel, and natural gas have used SCR for three 

decades as a means of controlling NOX emissions. European heavy-duty truck manufacturers are 

using this technology to meet Euro 5 emissions limits and several heavy-duty truck engine 

manufacturers have indicated that they will use SCR technology to meet stringent U.S. NOX

limits beginning in 2010. Studies have shown that a selective catalytic reduction (SCR) system

is capable of providing well in excess of 80% NOX reduction efficiency in high-power, heavyduty diesel applications.16,17,18 SCR has also been demonstrated for use with marine diesel 

engines. To date, more than 300 SCR systems, developed by Argillon, Wärtsilä, Munters, and 

other companies, have been installed on marine vessels. Some of which have been in operation 

for more than 10 years and have accumulated 80,000 hours of operation.19,20,21,22 These systems

are used in a wide range of ship types including ferries, supply ships, RoRos (roll-on roll-off), 

tankers, container ships, icebreakers, cargo ships, workboats, cruise ships, and foreign navy 

vessels for both propulsion and auxiliary engines. These SCR units are being used successfully 

on low- and medium-speed Category 3 propulsion engines and on Category 2 propulsion and 

auxiliary engines. The fuel used on ships with SCR systems ranges from low sulfur distillate 

fuel to high sulfur residual fuel. In marine applications, SCR is capable of reducing NOX

emissions more than 90 percent.23,24,25,26 An example of the performance capability of SCR in a 

medium-speed diesel marine application is the Staten Island Ferry Alice Austen. This 

demonstration project reports that 90 to 95% NOX reduction is possible under steady-state 

conditions where the exhaust gas temperature is above 270 °C.27

 Marine engine manufacturers report that the minimum exhaust temperature for SCR 

operation ranges from 250 to 300°C, depending on the catalyst system design and fuel sulfur 

level.28,29,30 Below this temperature, the SCR catalyst unit would not be hot enough to 

efficiently reduce NOX. An example of the effect of exhaust gas temperature on the NOX

4-6 
Chapter 4: Technological Feasibility 

conversion efficiency of an SCR catalyst is shown in Figure 4-3. If the engine is able to use fuel

with very low sulfur levels, a highly reactive oxidation catalyst can be used upstream of the SC

unit to convert NO to NO2, improving the low temperature efficiency of the SCR. NO2 reacts 

the SCR catalyst at lower temperatures than NO and therefore, use of an oxidation catalyst can 

lower the exhaust temperature at which an SCR unit is effective. However, as the sulfur 

concentration in the fuel increases, a less reactive oxidation catalyst must be used to prev

excessive formation of sulfates and poisoning of the oxidation catalyst. When operating o

marine distillate fuel with a sulfur level of 1,000 ppm, the minimum exhaust temperature for 

effective reductions through a current SCR system would be on the order of 270°C. On typical 

heavy fuel oils, which have sulfur concentrations on the order of 2.5 percent, the exhaust 

temperature would need to be about 300°C due to high sulfur concentrations. Sea trial test 

from a vessel equipped with a 55 MW, low-speed main engine indicates that turbine inle

exhaust gas temperatures, illustrated in Figure 4-2, will be near, or above, the minimum leve

needed to achieve greater than 80% NOX reduction for all operating loads of the E3 test cycl

(which includes power levels from 25 to 100% 31

0

50

100

150

200

250

300

350

400

0 10 20 30 40 50 60 70 80 90

Engine Load (% of rated power)

Exhaust Gas Temperature (°C)

Turbine Inlet

Turbine Outlet

Figure 4-2 Example of Exhaust Gas Temperature as a Function of Engine Load on a 55 MW, 2-stroke, LowSpeed Main Propulsion Engine31

As shown in Figure 4-3, the NOX conversion efficiency of an SCR catalyst can be greater 

than 90% (for the ‘turbine inlet’ exhaust gas temperatures observed in the sea trial data shown in 

Figure 4-2). And even at the relatively cooler turbine temperatures, it is possible to achieve NOX

conversion efficiencies greater than 80%. We believe that modern SCR systems will be able to 

achieve the NOX reduction levels sufficient to meet or exceed the proposed Tier 3 standard. 

R 

in 

ent 

n 

data 

t 

l 

e 

).

4-7 
Regulatory Impact Analysis 

0

20

40

60

80

100

150 200 250 300 350 400 450

Exhaust Gas Temperature @ Catalyst Inlet (°C)

NOx Conversion Efficiency (

%)

Example of

pre-turbo 

exhaust gas 

temperature 

range for lowspeed main 

engine (@ 25-

to-85% load)

Figure 4-3 SCR NOX Conversion Efficiency versus Exhaust Temperature Using an Ammonia-to-NOX Ratio of

1:132

In cases such as low power operation (less than 25% engine load), where exhaust 

temperatures could fall below the minimum required to ensure proper SCR functioning, we 

believe there are several approaches to ensure that exhaust temperatures remain high. An 

example of such approach, proposed by Munters, is to position the SCR system ahead of the 

turbocharger inlet .33 On turbocharged engines, the exhaust gas temperature is always higher at 

the inlet (or before the turbine stage) than at the outlet. When exhaust gasses pass through the 

turbine stage, heat energy from the exhaust gas is converted into shaft work, where it is then used 

to compress the intake air. By positioning the SCR before the turbocharger inlet, where exhaust 

gas temperatures are higher, the engine load range over which the SCR can operate is extended. 

For example, during sea trials on a 55 MW low-speed main engine, the exhaust gas temperature 

conditions at the turbine inlet would allow SCR operation at 12% engine load, whereas the 

turbine outlet temperature conditions would only SCR operation over a narrower much narrower 

load range (approximately 15 to 50% engine load), unless other measures are taken to increase 

exhaust heat. Such measures to increase exhaust heat may include reducing the level of charge 

air cooling or modifying the injection timing. Another approach to increase the exhaust 

temperature would be to use burner systems during low power operation. The “preturbocharger” SCR approach has been used on vessels equipped with slow-speed engines which 

require NOX control when operating at low loads near a coastal areas.34 In one case, SCR was 

used on a short passenger car ferry which originally had exhaust temperatures below 200°C 

when the engine was operated at low load.35 When the SCR unit was installed, controls were 

placed on the intercooler in the air intake system. By reducing the amount of cooling on the 

4-8 
Chapter 4: Technological Feasibility 

intake air, the exhaust temperature was increased to be within the operating range of the SCR 

unit, even during low power operation. On a ship using multiple propulsion engines, one or 

more engines could be shut down such that the remaining engine (or engines) operating at higher 

power. Whichever approach is used, we believe that engine manufacturers will be able to 

design systems which allow the SCR to function at engine loads below 25%, yet still remain 

below the upper temperature limit (500 °C) of the SCR unit during high-load operation. 

The onboard storage of the aqueous urea solution on marine vessels can be accomplished 

through segmenting of the existing fuel tanks or the fitment of a separate stainless steel or plastic 

urea tank. To assure consistent SCR operation between refueling stops, the volume of ureawater solution carried onboard will need to be sufficient; the amount of solution required is 

dependent on the expected NH3-to-NOX ratio (α) of the engine under the normal operating 

conditions. At the appropriate intervals, the vessel operator will need to refill the urea tank. The 

distribution and dispensing of urea is already established for on-road sectors, and is being 

developed for the nonroad, railroad, and marine sectors as well. We expect that the distribution 

and dispensing of urea for C3 marine vessels will benefit from any solutions put in place by 

these other sectors, and should be in place well in advance of the proposed Tier 3 regulations. 

SCR- or emissions-grade urea is a widely used industrial chemical around the world. 

Although an infrastructure for widespread transportation, storage, and dispensing of SCR-grade 

urea does not currently exist in most shipping ports, we believe that it will develop as-needed, 

based on market forces. Concerning urea production capacity, the U.S. has more than sufficient 

capacity to meet the additional needs of the marine sector. Currently, the U.S. consumes 14.7 

million tons of ammonia resources per year, and relies on imports for 41 percent of that total (of

which, urea is the principal derivative). In 2005, domestic ammonia producers operated their 

plants at 66 percent of rated capacity, which provides 4.5 million tons of reserve production 

capacity.36 Thus we do not project that urea cost, supply, or infrastructure will be an issue in the 

2016 timeframe for implementation of the proposed Tier 3 standards. 

4.3.2 Water-Based Technologies 

In this Section we describe the “water-based” technologies which can be used to reduce 

NOX emissions. All of these approaches to reducing engine-out NOX are based on limiting the 

formation of NOX by limiting the peak combustion temperature. It is the heat capacity of water, 

its ability to absorb combustion energy, which limits the peak combustion temperature, and 

hence NOX formation. Whether this water is emulsified with fuel, injected directly into the 

combustion chamber, or in the form of humidity within the intake air, its purpose is to limit the 

peak combustion temperature. These water-based approaches to controlling NOX emissions, 

when used in combination with the engine design-based approaches, such as fuel injection 

controls, EGR, and variable valve timing, are also capable of providing significant (up to 60%) 

NOX reductions.13 Whichever approach or combination of approaches is employed by engine 

manufacturers reduce Tier 2 and Tier 3 NOX emissions, we believe that these water-based 

technologies are feasible and can be implemented within the timeframe of this proposed 

rulemaking. 

4-9 
Regulatory Impact Analysis 

4.3.2.1 Fuel-Water Emulsions 

Fuel-water emulsions for marine engines can be either diesel fuel-water mixtures, with 

emulsifying and/or stabilizing agents added, or a heavy fuel oil-water mixtures. When a fuelwater mixture is injected into the combustion chamber, vaporization of water within the mixture 

injection increases fuel dispersion (making the combustion of fuel more efficient) and absorbs 

combustion heat, which limits the formation of NOX. For each 0.7 to 1% of water added to the 

fuel, a 1% reduction in NOX emissions can be realized.Error! Bookmark not defined.,37 Engine 

manufacturers have demonstrated NOX reductions of up to 50% through the use of fuel-water 

emulsions alone.38,39 In many existing engine designs, the limiting factor for fuel-water 

emulsions is the delivery capacity of the fuel injection system; to maintain the same power level 

(i.e., keep the quantity of fuel injected constant), the injection system must have enough volume 

capacity to deliver the quantity of fuel normally injected, plus an additional volume of water 

emulsified within the fuel. We believe that future injection systems which utilize fuel-water 

emulsions will be designed to accommodate the amount of water in the fuel necessary to meet 

the applicable NOX standard while maintaining engine power at the same level observed when 

running on 100% fuel. 

4.3.2.2 Direct Water Injection

Direct water injection (DWI) technology involves introducing water into the combustion 

chamber during the combustion process. The injection of water, whether directing into the 

combustion chamber or into the intake manifold, can be controlled electronically, allowing 

precise calibration and control of the water-to-fuel ratio. In the case where water is injected 

directly into the combustion chamber (and separate from the fuel), electronic control also allows 

precise control over timing and quantity of water injected as well. This approach allows water to 

be injected at a point in the combustion process where it will provide the optimum NOX

reduction while minimizing the impact on other criteria pollutants (e.g. HC, CO, and PM) and 

fuel consumption.40 Engine manufacturers have reported that DWI, when using a water-to-fuel 

ratio of 40 to 70%, is capable of reducing NOX emissions by 50 to 60%, without affecting engine 

power.26,Error! Bookmark not defined.

4.3.2.3 Intake Air Humidification 

Similar to fuel-water emulsions and direct water injection, increasing the humidity of the 

intake air on a diesel engine reduces the peak temperature of combustion, and hence, reduces the 

formation of NOX. One approach to introducing water into the combustion process is to increase 

the humidity (water content) of the intake air through evaporation of a water mist, which is 

injected to the intake air as it exits the compressor stage of the turbocharger. As intake air is 

compressed by the turbocharger, its temperature increases, and it is this temperature increase 

which facilitates the evaporation of the injected water mist. To achieve a 50% reduction in NOX

emissions, the quantity of water that must be added to the intake air is roughly twice the quantity 

of fuel consumed by the engine (or double the amount of water consumed in fuel-water emulsion 

or DWI approaches for a similar NOX reduction).26

4-10 
Chapter 4: Technological Feasibility 

4.3.2.4 Exhaust Gas Recirculation 

Exhaust gas recirculation (EGR) is a strategy which reduces peak combustion 

temperature (and hence NOX formation, similar to water-based approaches) in which a noncombustible gas is added to the combustion process. In this strategy, exhaust gas is typically 

routed from the exhaust system and mixed with the incoming combustion air. The recycled 

exhaust gas has lower oxygen content and also absorbs some of the heat energy during 

combustion, both of which reduce the peak temperatures. MAN B&W has demonstrated that up 

to 70% NOX reduction can be achieved when using EGR in combination with intake air 

humidification.39 An alternative to routing/mixing exhaust gas with the incoming fresh air 

charge is use “internal” EGR, where early closing of the exhaust valve is used to trap a portion of 

the exhaust gas from the previous combustion event within the cylinder.41

4.4 Vessel Technologies for Low Sulfur Fuel Standards 

The MARPOL Annex VI fuel sulfur limit for ships operating in an ECA is 1.5% today 

and reduces to 1.0% in March 2010 and further to 0.1% in 2015. We anticipate that the 0.1% 

fuel sulfur limit, beginning in 2015, will likely result in the use of distillate fuel for operation in 

ECAs. This would require the vessel to switch from a higher sulfur fuel to 0.1% S fuel before 

entering the ECA. The practical implications of fuel switching are discussed below. As an 

alternative operating on low sulfur fuel, an exhaust gas cleaning device may be used to remove 

sulfur from the exhaust. These devices, which are colloquially known as SOX scrubbers, are also 

discussed below. 

4.4.1 Fuel Switching on Vessels 

4.4.1.1 Impact of fuel switching on emissions 

Currently, the majority of ocean-going vessels use residual fuel (also called ‘Heavy Fuel 

Oil (HFO) or ‘Intermediate Fuel Oil’ (IFO)) in their main propulsion engines, as this fuel is 

relatively inexpensive and has a good energy density. This fuel is relatively dense (‘heavy’) and 

is created as a refining by-product from typical petroleum distillation. Residual fuels typically 

are composed of heavy, residuum hydrocarbons and can contain various contaminants such as 

heavy metals, water and sulfur compounds. The current global average sulfur for residual 

marine fuel is approximately 2.7%.42 It is these sulfur compounds that cause the SOX emissions 

when the fuel is combusted. 

Switching from operating marine engines on residual fuel to distillate fuel can reduce 

exhaust PM emissions, both on a mass basis and on a particle basis. The sulfur in marine fuel is 

primarily emitted as SO2; however, a small fraction (about 2 percent) is converted to SO3. SO3

almost immediately forms sulfate and is emitted as direct PM by the engine. Consequently, 

emissions of SO2 and sulfate PM are very high for engines operating on residual fuel. Switching 

from high sulfur residual fuel to low sulfur distillate fuel results in large reductions in SO2 and 

sulfate PM emissions. 

In addition to high sulfur levels, residual fuel contains relatively high concentrations of

low volatility, high molecular weight organic compounds and metals. Organic compounds that 

4-11 
Regulatory Impact Analysis 

contribute to PM can be present either as a nucleation aerosol or as a material adsorbed on the 

surfaces of agglomerated elemental carbon soot particles and metallic ash particles. The sulfuric 

acid aerosol in the exhaust provides a nucleus for agglomeration of organic compounds. 

Operation on higher volatility distillate fuel reduces both nucleation and adsorption of organic 

compounds into particulate matter. Therefore, in addition to direct sulfate PM reductions, 

switching from residual fuel to distillate fuel reduces organic PM and metallic ash particles in the 

exhaust. 

The impact of switching from high-sulfur residual fuel to lower sulfur distillate fuel on 

PM levels has been investigated in a number of test programs.43,44,45,46,47 On a mass basis, PM

from marine engines has been shown to be reduced by 60 to 90 percent when switching from

residual to distillate fuel. Figure 4-4 presents the impact of fuel switching on direct PM

emissions for testing performed on one slow-speed two-stroke marine engine and four mediumspeed four-stroke marine engines. 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2-s SS 4-s MS 4-s MS 4-s MS 4-s MS

P

M [

g/kW-hr]

residual

distillate

6,000 ppm S

1,600 ppm S

29,000 ppm S

800 ppm S

22,00

0

ppm S

<100 pp

m S

24,000 pp

m S

7,600 ppm S

900 ppm S

1,600 pp

m S

26,000 ppm S

Figure 4-4 Effect of Fuel Switching on PM Emissions from Marine Engines 

The PM emissions reductions presented above were primarily due to reductions in direct 

sulfate PM. However, fuel switching also led to measured reductions in non-sulfate PM for 

these engines. Specifically, significant reductions were observed in the soluble organic fraction 

of the PM as well as metallic ash. This is demonstrated in the following charts, excerpted from

three of the papers referenced above, which present speciated PM reductions due to fuel 

switching. 

4-12 
Chapter 4: Technological Feasibility 

Figure 4-5 Speciated PM from 2-Stroke Slow-Speed Engine (Kasper et al, 2007) 

Distillate Fuel Residual Fuel 

Figure 4-6 Soluble Organic Fraction of PM from 4-Stroke Medium-Speed Engine (Nakajima et al, 2000) 

Figure 4-7 Speciated PM from 4-Stroke Medium-Speed Engine (MAN, 2007) 

4-13 
Regulatory Impact Analysis 

Operating on distillate fuel also reduces the particle count in the exhaust. Lowering the 

sulfur in the fuel reduces the relative fuel contribution to ultrafine nucleation aerosols by 

reducing nucleation sites for organic PM. These nucleation particles are the largest contributor 

to particle number, since the fine particle number count is approximately 1.5 times higher for 

operation on residual fuel than for operation on distillate fuel. This effect is shown in Figure 

4-8.

48,49

0.0E+00

1.0E+16

2.0E+16

3.0E+16

4.0E+16

5.0E+16

6.0E+16

7.0E+16

Ship 1: Residual Ship 2: Distillate

P

article

s per kg of fuel burned 

Figure 4-8 Exhaust Particle Concentration for Two Ships

4.4.1.2 Fuel Switching Procedures 

Marine distillate fuels are similar in composition and structure to other petroleum based 

middle distillate fuels such as diesel and No. 2 heating oil, but they have a much lower allowable 

sulfur content than residual fuels.50 This lower sulfur content means that by combusting marine 

distillate fuel in their propulsion engines, vessels operating within the ECA would meet the 

stricter SOX requirements. However, sulfur content is not the only difference between the 

marine residual and distillate fuels; they also have different densities, viscosities, and aromatic 

contents. 

In the majority of vessels today, marine distillate fuel is used for operation during routine 

maintenance, prior to and immediately after engine shut-down, or in emergencies. Standard 

procedures today have been established to ensure that this operational fuel switchover is 

performed safely and efficiently. Mainly, in order for the vessel to completely switch from one 

type of fuel to another, the fuel pumps and wetted lines will need to be completely purged by the

new fuel to ensure that the ship is burning the correct fuel for the area. This purging will vary 

from ship to ship due to engine capacity, design, operation, and efficiency. Provided the ship has 

separate service tanks for distillate and residual fuel (most, if not all, vessels do), fuel switching 

time should be limited only by maximum allowable rate of fuel temperature change,typically not 

4-14 
Chapter 4: Technological Feasibility 

more than 2°C change per minute. Figure 4-9 presents three common fuel system configurations 

recommended by a C3 engine manufacturer to facilitate fuel switching. 

A.

B.

4-15 
Regulatory Impact Analysis 

C.

Figure 4-9 Common C3 Fuel Tank Layouts. A. One MDO and one HFO settling tank, B. One MDO and two 

HFO settling tanks, C. One MDO settling tank and two sets of HFO settling and service tanks (Courtesy of

MAN B&W)

 This slow temperature increase will ensure that the fuel’s viscosity does not drastically 

change prior to injection and therefore protects the fuel injection equipment. If the fuel viscosity 

or temperature is increased too quickly, some of the fuel handling components, such as the fuel 

valves, pump plungers, or fuel suction valves, could become damaged or ‘sticky’ and not 

function correctly. One way to ensure that the fuels are changed out accordingly is to install an 

automatic system for handling the changeover of different viscosity fuels, as shown in Figure 4-

10 from MAN B&W.

34

4-16 
Chapter 4: Technological Feasibility 

Figure 4-10 Automatic System for Changeover between Fuels of Different Viscosity

The maritime industry has analyzed the differences between the residual and distillate 

fuel compositions to address any potential issues that could arise from switching operation of a 

C3 engine from residual fuel to distillate fuel. The results from this research has evolved into 

routine operational switching procedures that ensure a safe and efficient way for the C3 engines 

to switch operation between the residual and distillate fuels. A brief summary of the fuel 

differences, as well as any potential issues and their usual solutions are below. 

4.4.1.2.1 Fuel Density 

Due to its chemical composition, residual fuel has a slightly higher density than marine 

distillates. Using a less dense fuel could affect the ballast of a ship at sea and would have to 

require compensation. Therefore, when beginning to operate on the distillate fuel, the vessel 

operator would have to pay attention to the vessel’s ballast and may have to compensate for any 

changes that may occur. We anticipate that these procedures would be similar to operating the 

vessel with partially-full fuel tanks. 

Another consideration when switching to a lower density fuel is the change in volumetric 

energy content. Distillate fuel has a lower energy density content on a per gallon basis when 

4-17 
Regulatory Impact Analysis 

compared to the residual fuel; however, per ton, distillate fuel’s energy density is larger than the 

residual fuel. This means that when switching from residual fuel to distillate fuel, if the vessel’s 

tanks are volumetrically limited (i.e., the tanks can only hold a set quantity of fuel gallons), the 

distance a vessel can travel on the distillate fuel may be slightly shorter than the distance the 

vessel could travel on the residual fuel due to the lower volumetric energy content of distillate 

fuel, which could require compensation. This distance reduction would be approximately 5% 

and would only be of concern while the vessel was operating on the distillate fuel (i.e., while in 

the U.S. ECA) as the majority of the time the vessel will be operating on the residual fuel. 

However, if the vessel is limited by weight, the higher energy content per ton of fuel would 

provide an operational advantage. 

4.4.1.2.2 Kinematic Viscosity 

Residual fuel’s kinematic viscosity is much higher than marine distillate fuel’s viscosity. 

Viscosity is the ‘thickness’ of the fuel. If this parameter is lowered from the typical value used 

within a pump, some issues could arise. If a distillate fuel with a lower viscosity is used in a 

system that typically operates on residual fuel, the decrease in viscosity could quickly cause 

problems with high-pressure fuel injection pumps; whereas older, lower-pressure pumps can 

develop troubles over a period of time, especially if the pump in question has large clearances 

and cannot make up the pressure to pump the fuel through with the thinner fuel due to the 

increased potential for internal leakage of the thinner fuel through the clearances in the pumping 

elements. Internal leakage is part of the design of a fuel pump and is used in part to lubricate the 

pumping elements. However, if this leakage rate is too high, the fuel pump could produce less 

than optimal fuel injection pressures. If the distillate fuel’s lower viscosity becomes an issue, it 

is possible to cool the fuel and increase the viscosity above 2 centistokes, which is how most 

vessels operate today during routine fuel switchovers as was discussed above. 51,52

4.4.1.2.3 Flash Point 

Flash point is the temperature at which the vapors off the fuel ignite with an outside 

ignition source. This can be a safety concern if the owner/operator uses an onroad diesel fuel 

rather than a designated ‘marine distillate’ fuel for operation because marine fuels have a 

specified minimum flash point of 60°F to ensure onboard safety, whereas onroad diesel has a 

minimum specified flash point of 52°F.53,54 However, since most distillate fuels are created in 

the same fashion, typical flash points of onroad diesel are above 60°F and would meet the marine 

fuel specification for this property. If the flash point of the fuel being used on-board the vessel 

becomes a concern, the operator/bunker supplier would have to ensure that the vessel is 

obtaining fuel with a minimum flash point of 60°F via the bunker delivery note or through fuel 

testing. 

4.4.1.2.4 Lubricity 

 Lubricity is the ability of the fuel to lubricate the engine/pump during operation. If the 

distillate is more ‘harsh’ (from severely reduced sulfur content or removal of certain chemical 

structures) than the residual fuel typically used, there can be added friction to the engine/pump 

which could cause malfunctions and/or failures of equipment. Fuels with higher viscosity and 

high sulfur content tend to have very good lubricity without the use of specific lubricity 

4-18 
Chapter 4: Technological Feasibility 

improving additives. Refining processes that lower fuel sulfur levels and their viscosities can 

also remove some of the naturally-occurring lubricating compounds. Severe hydrotreating of fuel 

to obtain ultra-low sulfur levels can result in poor fuel lubricity. Therefore, refineries commonly 

add lubricity improvers to ultra low sulfur diesel. This will most likely become a concern when 

very low levels of sulfur are present in the fuel and/or the fuel has been hydrotreated to reduce 

sulfur, e.g., if ultra-low sulfur highway diesel (ULSD) is used in the engine. 55 Several groups 

have conducted studies on this subject, and for some systems where fuel lubricity has become an 

issue, lubricity additives can be utilized or the owner/operator can install a lubricating system for 

the fuel pump. 

4.4.1.2.5 Lube Oil 

Diesel engines require lubrication in order to operate efficiently, and these lubricating 

oils need to be compatible with the fuel used in the engine. Lube oil base numbers help to 

achieve a compatible lubricant between the fuel and the oil. If the lube oil base is too lubricating 

for the fuel, calcium and other deposits can develop on the surfaces. If the lube base oil is too 

little of a lubricant for use with the fuel, the fuel’s acidity can increase causing additional wear 

on parts as well as creating problems combusting the fuel. Lube oils are used to neutralize acids 

formed in combustion, most commonly sulfuric acids created from sulfur in the fuel. The 

quantity of acid neutralizing additives in lube oil should match the total sulfur content of the fuel. 

If excessive amounts of these additives are used, they may create deposits on engine 

components. Marine engine manufacturers have recommended that lube oil only needs to be 

adjusted if the fuel is switched for more than one week, but the oil feed rate may need to be 

reduced as well as engine operating power. Additional research has been conducted in this area 

and several oil companies have been working to create a lubricating oil that would be compatible 

with several different types of fuel. 56

4.4.1.2.6 Asphaltenes 

Asphaltenes are heavy, non-volatile, aromatic compounds which are contained naturally 

in some types of crude oil. Asphaltenes may precipitate out of the fuel solution when a fuel rich 

in carbon disulfide, such as residual fuel, is mixed with a lighter hydrocarbon fuel, such as npentane or n-heptane found in some distillate fuels. 57 When these heavy aromatic compounds 

fall out of the fuel solution, they can clog filters, create deposition along the fuel 

lines/combustion chamber, seize the fuel injection pump, or cause other system troubles. This 

risk can be minimized through onboard test kits and by purchasing distillate and residual fuel 

from the same refiner. However, according to the California Air Resources Board, the formation 

of asphaltenes is not seen as an issue based on data from previous maritime rules. 58

As can be seen, if vessel operators choose to operate on marine distillate fuel while in the 

ECA, some prudence is required. However, as described above, any issues that could arise with 

switching between residual and distillate fuel are minimal and are relatively easy to address 

through changes to operating procedures. To conduct a successful switchover between the 

residual and marine distillate fuels, vessel operators will need to keep the above issues in mind 

and follow the engine manufacturer’s standard fuel switching procedure. 

4-19 
Regulatory Impact Analysis 

4.4.2 Exhaust Gas Cleaning Systems 

Annex VI allows for alternative compliance strategies in including the use of exhaust gas 

cleaning systems (EGCS). EGCS systems used today for sulfur control are commonly known as 

SOX scrubbers. 

4.4.2.1 SOX Scrubber 

SOX scrubbers are capable of removing up to 95 percent of SOX from ship exhaust using 

the ability of seawater to absorb SOX. SOX scrubbers have been widely used in stationary source 

applications, where they are a well established SOX reduction technology. In these applications, 

lime or caustic soda are typically used to neutralize the sulfuric acid in the washwater. While 

SOX scrubbers are not widely used on ocean going vessels, there have been prototype 

installations to demonstrate their viability in this application such as the Krystallon systems 

installed on the P&O ferry Pride of Kent and the Holland America Line cruise ship the ms 

Zaandam.

59, 60 These demonstrations have shown scrubbers can replace and fit into the space 

occupied by the exhaust silencer units and can work well in marine applications. 

There are two main scrubber technologies. The first is an open-loop design which uses 

seawater as exhaust washwater and discharges the treated washwater back to the sea. Such open 

loop designs, such as those used on the Pride of Kent and ms Zaandam, discussed above, are also 

referred to as seawater scrubbers. In a seawater scrubber, the exhaust gases are brought into 

contact with seawater, either through spraying seawater into the exhaust stream or routing the 

exhaust gases through a water bath. The SO2 in the exhaust reacts with oxygen to produce sulfur 

trioxide which then reacts with water to form sulfuric acid. The sulfuric acid in the water then 

reacts with carbonate and other salts in the seawater to form sulfates which may be removed 

from the exhaust. The washwater is then treated to remove solids and raise the pH prior to 

discharge back to the sea. The solids are collected as sludge and held for proper disposal ashore. 

 A second type of SOX scrubber, using a closed loop design, is also feasible for use on 

marine vessels.61,62 In a closed loop system, fresh water is used as washwater, and caustic soda 

is injected into the washwater to neutralize the sulfur in the exhaust. A small portion of the 

washwater is bled off and treated to remove sludge, which is held and disposed of at port, as with 

the open loop design. The treated effluent is held onboard or discharged at open sea. Additional 

fresh water is added to the system as needed. While this design is not completely closed loop, 

strictly speaking, it can be operated in zero discharge mode for periods of time. 

Exhaust gas scrubbers can achieve reductions in particulate matter as well. By removing 

sulfur from the exhaust, the scrubber removes most of the direct sulfate PM. Sulfates are a large 

portion of the PM from ships operating on high sulfur fuels. By reducing the SOX emissions, the 

scrubber will also control much of the secondary PM formed in the atmosphere from SOX

emissions. However, simply mixing alkaline water in the exhaust does not necessarily remove 

much of the carbonaceous PM, ash, or metals in the exhaust. While SO2 associates with the 

wash water, particles can only be washed out of the exhaust through direct contact with the 

water. In simple scrubber designs, much of the mass of particles can hide in gas bubbles and 

escape out the exhaust. 

4-20 
Chapter 4: Technological Feasibility 

Manufacturers have been improving their scrubber designs to address carbonaceous soot 

and other fine particles. Finer water sprays, longer mixing times, and turbulent action would be 

expected to directionally reduce PM emissions through contact impactions. One scrubber design 

uses an electric charge on the water to attract particles in the exhaust to the water.63,64 In this 

design, the exhaust gas is first passed through a preconditioning chamber where a coarse water 

spray cools the exhaust, removes particles larger than 10 microns and causes very small particles 

to agglomerate into larger particles. The exhaust gas then moves successively through one or 

two cloud generation chambers, where highly charged water droplets form a cloud. These 

droplets serve to attract the particles, and as each water droplet has collected enough tiny 

particles and thus has its charge neutralized, it coagulates with other droplets and falls to a sump. 

This liquid is then re-circulated back to the cloud generator and used to form new charged 

droplets. Finally, the cleaned exhaust passes from the cloud chamber through a mist eliminator 

to remove excess moisture and out through the exhaust stack. In the dual cloud chamber design, 

the first chamber contains positively charged water droplets which collect neutral and negatively 

charged particles. Conversely, the second chamber contains negatively charged water droplets 

which collect positively charged and remaining neutral particles. Since most particles are 

neutral, this second chamber would only be utilized in designs requiring very high particle 

removal efficiency. 

In another design, demisters are used that help effectively wash out PM from the exhaust 

stream.

65 In this design, the exhaust gases are compressed and then expanded in a saturated 

environment. The expansion process in the supersaturated environment results in condensation 

and agglomeration of fine particulate, which is then washed from the exhaust stream using a 

water spray. In either of these PM control system designs, however, the systems would be 

effective at removing SO2 from the exhaust even if the additional hardware needed for nonsulfate PM reduction were not used. 

Water-soluble components of the exhaust gas such as SO2, SO3, and NO2 form sulfates 

and nitrates that are dissolved into the discharge water. Scrubber wash water also includes 

suspended solids, heavy metals, hydrocarbons and polycyclic aromatic hydrocarbons (PAHs). 

Before the scrubber water is discharged, it may be processed to remove solid particles through 

several approaches. Heavier particles may be trapped in a settling or sludge tank for disposal. 

The removal process may include cyclone technology similar to that used to separate water from

residual fuel prior to delivery to the engine. However, depending on particle size distribution 

and particle density, settling tanks and hydrodynamic separation may not effectively remove all 

suspended solids. Other approaches include filtration and flocculation techniques. Flocculation, 

which is used in many waste water treatment plants, refers to adding a chemical agent to the 

water that will cause the fine particles to aggregate so that they may be filtered out. Sludge 

separated from the scrubber water would be stored on board until it is disposed of at proper 

facilities. 

IMO is developing guidance criteria for the use of exhaust gas cleaning devices, such as 

SOX scrubbers, as alternative to operating on low sulfur fuel. This draft guidance, includes 

monitoring and water discharge practices.66 The washwater should be continuously monitored 

for pH, PAH and turbidity. Further, the IMO guidance include specifications for these same

items, as well as nitrate content when washwater is discharged in ports, harbors or estuaries. 

4-21 
Regulatory Impact Analysis 

Finally, the IMO guidance recommends that washwater residue (sludge) be delivered ashore to 

adequate reception facilities, and not discharged to the sea or burned on board. 

Finally, we would not consider an exhaust gas scrubber to be an acceptable control 

strategy for reducing NOX emissions. In a typical diesel exhaust gas mixture, NOX is composed 

of roughly 5-10% NO2, with the majority of the remainder in the form of NO. NO2 is soluble in 

water, and therefore may be removed by the water in the scrubber. It is possible to treat the 

exhaust upstream of the scrubber to convert more of the NOX to NO2, thereby facilitating the use 

of a scrubber to remove NO2. However we are concerned that this would add to nitrogen loading 

of the water in which the ship is operating. As discussed in Section 2.3.1, nitrogen loading can 

lead to serious water quality impacts. The issue of NOX scrubbing is addressed in the draft IMO 

EGCS guidelines by limiting the amount of NOX that may be removed by the scrubber. 

4.4.2.2 Equivalence to Fuel Switching 

MARPOL Annex VI does not present specific exhaust gas limits that are deemed to be 

equivalent to the primary standard of operating on low sulfur fuel. Prior to the recent 

amendments to Annex VI, regulation 13 included a limit of 6 g/kW-hr SO2 as an alternative to 

the 1.5% sulfur limit for sulfur emission control areas. Under the amended requirements, the 

specific SO2 limit was removed and more general language on alternative approaches was 

included. Specifically, regulation 4 of MARPOL Annex VI now states “The Administration of a 

Party may allow any fitting, material, appliance or apparatus to be fitted in a ship or other 

procedures, alternative fuel oils, or compliance methods used as a alternative to that required by 

this Annex if such fitting, material, appliance or apparatus or other procedures, alternative fuel 

oils, or compliance methods are at least as effective in terms of emissions reductions as that 

required by this Annex, including any of the standards set forth in regulations 13 and 14.” 

Based on the methodology that was used to determine the SO2 limit of 6.0 g/kW hr for 

existing ECAs, the corresponding limit, which is presented in the draft ECGS guidelines, would 

be 0.4 g/kW-hr SO2 for a 0.1% fuel S limit. This limit is based on an assumed fuel consumption 

rate of 200 g/kW-hr and the assumption that all sulfur in the fuel is converted to SO2 in the 

exhaust. This calculation is presented in the following equation: 

SO2 [g/kW-hr] = BSFC × fuel S × conversion × MWRSO2/S

= 200 × 0.1% × 100% × 64/32 

= 0.4 g/kW-hr, where: 

BSFC = brake specific fuel consumption = 200 g/kW-hr 

fuel S = fuel sulfur level (weight percent) = 0.1% 

conversion = percentage of sulfur in fuel that is converted to SO2 = 100% 

MWRSO2/S = molecular weight ratio of SO2 to sulfur = 64/32 

The draft EGCS guidelines also use an approach of basing the limit on a ratio of SO2 to 

CO2. This has the advantage of being easier to measure during in-use monitoring. In addition, 

this ratio holds more constant at lower loads than a brake-specific limit, which would approach 

infinity as power approaches zero. For the 1.5% fuel sulfur limit, a SO2 (ppm)/CO2(%) limit of 

65 was developed.67 As with the equation above, the simplifying assumption is made that all 

4-22 
Chapter 4: Technological Feasibility 

fuel sulfur is converted to SO2 and all carbon is converted to CO2. The equivalent limit for 0.1% 

fuel sulfur presented in the ECGS guidelines is 4.0 SO2 (ppm)/CO2(%). 

SO2/CO2 [ppm/%] = (fuel S / fuel C) × 10,000 × MWRC/S

= (0.1% / 86.3%) × 10,000 × 12/32 

= 4.0 ppm/%, where: 

fuel C = fuel carbon level (weight percent) for distillate fuel 

= (100% - 0.1%S – 0.03% other) × (MWH × H/C)/(MWC + MWH × H/C) A

= 86.3% 

10,000 = conversion from percent to ppm

MWRC/S = molecular weight ratio of carbon to sulfur = 12/32 

Scrubbers are effective at reducing SO2 emissions and sulfate PM emissions from the 

exhaust. However, as discussed above, the effectiveness of the scrubber at removing PM 

emissions, other than sulfates, is dependent on the scrubber design. In addition to sulfate PM 

reductions, switching from residual fuel to distillate fuel results in reductions in organic PM and 

metallic ash particles in the exhaust. Clearly, scrubbers can be designed to provide similar 

reductions in such non-sulfate PM emissions if need be to provide equivalent reductions 

compared to fuel switching. 

We would not consider an exhaust gas scrubber to be an acceptable control strategy for 

reducing NOX emissions. In a typical diesel exhaust gas mixture, NOX is composed of roughly 

5-10% NO2, with the majority of the remainder in the form of NO. NO2 is soluble in water, and 

therefore may be removed by the water in the scrubber. It is possible to treat the exhaust 

upstream of the scrubber to convert more of the NOX to NO2, thereby facilitating the use of a 

scrubber to remove NO2. However we are concerned that this would add to nitrogen loading of 

the water in which the ship is operating. As discussed in Chapter 2, nitrogen loading can lead to 

serious water quality impacts. The issue of NOX scrubbing is addressed in the draft IMO EGCS 

guidelines by limiting the amount of NOX that may be removed by the scrubber. 

4.5 Technology for Producing/Distributing Lower Sulfur Fuel 

4.5.1 Production of Lower Sulfur Marine Fuel 

We project that the 1,000 ppm fuel sulfur limit, beginning in 2015, will likely result in 

the increased use of distillate fuel for operation in ECAs. As such, additional distillate fuel will 

likely be necessary to replace the residual fuel that would have been used without an ECA. 

Some engines already operate on distillate fuel; however, this distillate fuel may need to be 

further refined to meet the 1,000 ppm S limit.

A Fuel properties are based on properties in the IMO NOX monitoring guidelines, MEPC.103(49) which includes a 

hydrogen to carbon (H/C) ratio for distillate fuel of 1.88 mol/mol. In addition, fuel is assumed to be composed of

carbon, hydrogen, sulfur, and other, where other is assumed to be 0.03 weight % for distillate fuel. (MWH =1.008, 

MWC = 12.01)

4-23 
Regulatory Impact Analysis 

4.5.1.1 Processing of Residual Stocks 

IFO bunker grades are primarily comprised of residual stocks, such as Vacuum

Residuals, Atmospheric Residuals, Visbreaker Residuals, and Fluidized Catalytic Cracking 

(FCC) clarified oil. These fuels also contain distillates that are added as cutter stocks, such as 

Light Cycle Oil (LCO), Vacuum Gas Oils (VGO), and kerosenes. As such, only the residual fuel 

blendstocks in IFO bunkers would need to be replaced or converted into distillate volumes to 

provide for additional lower sulfur distillate marine fuel. For converting residuals to distillates, 

refiners use two process technologies: Coking Units (Cokers) and Residual Hydrocrackers. 

Coking units are used to convert the poorer quality residual feedstocks in IFO bunkers, 

such as vacuum residuals. The coking units crack these resids into distillates, using heat and 

residence time to make the conversion. The process produces petroleum coke and off gas as 

byproducts. Residual hydrocrackers are used to convert low and medium sulfur residual streams 

into distillates. Residual hydrocracking uses fluidized catalyst, heat and hydrogen to 

catalytically convert residual feedstocks into distillates and other light fuel products. The 

hydrocracking process upgrades low value residual stocks into high value distillate transportation 

fuels consuming large amounts of hydrogen. 

For processing of residual blendstocks, vacuum tower distillation capacity is added to 

extract gas oils blendstocks that exist in residuals fuels used in current IFO bunker grades. The 

extracted gas oils are further processed in either distillate hydrotreaters or gas oil hydrocrackers 

to produce a distillate fuel that would meet a 1,000 ppm fuel sulfur limit. The use of additional 

vacuum towers capacity minimizes the volume of residual stocks which lowers processing costs, 

as less volume of fuel is processed in high cost residual coking and residual hydrocracker 

processes.

4.5.1.2 Distillate Stocks Processing 

Conventional distillate hydrotreating technology is used to lower the sulfur levels of high 

sulfur distillate stocks. This technology removes sulfur compounds from distillate stocks using 

catalyst, heat and hydrogen. Since the ECA sulfur standard is 1,000 ppm, conventional distillate 

hydrotreating would likely be the technology chosen by refiners to make this distillate, rather 

than the ultra low sulfur technology that is used to remove sulfur to levels below 15 ppm. 

Conventional distillate hydrotreating refers to the design and conditions in the process, such as 

catalyst type, catalyst volume, reactor pressure, feed and reactor flow scheme used to lower 

sulfur levels to 500 ppm or higher. 

Although the cutter stocks in IFO bunkers are distillate fuels, they would need to be 

desulfurized because the 1,000 ppm sulfur limit for the ECA is lower than the nominal sulfur 

levels for these blendstocks under the “business as usual” projections. The sulfur levels of 

distillate used directly as bunker fuel (MDO and MGO), are greater than 1,000 ppm, and thus 

would also need to be treated. Therefore, in addition to converting residuals to distillate fuels, 

existing distillates used as bunker fuel in MDO, MGO and IFO would also need to be 

hydrotreated. More distillate hydrotreating capacity would be required to lower the sulfur 

content of incremental distillate produced from cokers and residual hydrocrackers that do not 

meet low sulfur marine fuel standards. 

4-24 
Chapter 4: Technological Feasibility 

For distillate stocks that are highly aromatic and high in sulfur, the use of technology for 

hydrocracking low sulfur gas oil is used to convert these blendstocks into No 2. grade diesel 

streams. Gas oil hydrocracking is a high volume gain process which produces diesel blendstocks 

that typically meet ECA sulfur standards, eliminating the need for further processing in 

hydrotreaters. 

4.5.1.3 Supportive Processes 

The increase in hydrotreating and hydrocracking requires new hydrogen and sulfur plant 

capacity. Extra hydrogen is required to react with and remove sulfur compounds in refinery 

hydrotreating processes. It is also needed to improve the hydrogen to carbon ratio of products 

made from converting IFO blend components to distillates, via processing in cokers and 

hydrocrackers. 

4.5.2 Fuel Distribution Considerations 

The existing nonroad, locomotive, and marine (NRLM) diesel fuel program requires that 

all marine diesel fuel meet a 15 ppm sulfur standard by June 1, 2014 except fuel produced by 

transmix processors which is allowed to meet a 500 ppm sulfur standard indefinitely, and fuel 

with a T90 distillation point greater than 700 oF when used in Category 2 or 3 marine diesel 

engines to which no EPA sulfur standard currently applies.B The proposed provisions in today’s 

rule would adopt a 1,000 ppm sulfur standard for fuel sold for use in an emission control area 

(ECA) as defined by the International Maritime Organization under MARPOL Annex VI. The 

U.S. Government has proposed an amendment to MARPOL Annex VI to establish an ECA that 

would include the majority of U.S. coastal waters. Assuming the adoption of an amendment to 

MARPOL Annex VI establishing a U.S. ECA, the proposed 1,000 ppm C3 marine sulfur 

standard would become effective January 1, 2015. 

Due to the nature of the refinery options to reduce the sulfur content of fuel used in C3 

engines, we believe that the fuel manufactured to meet a 1,000 ppm sulfur specification would 

likely have a T90 below 700 oF, and thus would be subject to the requirements under the existing 

NRLM diesel program.

C Therefore, changes are needed to existing NRLM diesel program to 

facilitate the adoption of a 1,000 ppm sulfur standard for C3 marine under MARPOL Annex VI. 

Without such changes, the implementation of a 1,000 ppm C3 diesel sulfur standard would 

actually result in the requirement for the use of 15 ppm diesel fuel in C3 marine engines. 

The current provisions that allow transmix processors to continue to produce 500 ppm 

locomotive and marine (LM) diesel fuel after June 1, 2014 were put in place to allow an outlet 

for >15 ppm sulfur diesel fuel produced at transmix processors other than heating oil. These 

special provisions were deemed to be necessary due to challenges associated with desulfurizing 

diesel fuel produced at transmix processing facilities to a 15 ppm sulfur standard and the 

B

 The existing marine diesel fuel standards and the proposed C3 marine sulfur standards are discussed in more detail

in Section IV of today’s preamble. C

 The production of fuel meeting a 1,000 ppm sulfur standard for use in C3 marine engines is discussed in Section 

4.5.1 of this DRIA. 

4-25 
Regulatory Impact Analysis 

geographically limited and seasonal nature of the heating oil market. Transmix processing 

facilities consist of a simple distillation column with no other facilities for modifying the 

resulting gasoline and diesel fractions such as a hydrotreater to remove sulfur.D The small 

throughput of transmix processing facilities is not sufficient to justify the installation of current 

sulfur removal units (such as a hydrotreater). 

In the process of shipping products by pipeline, mixing takes place between batches of 

gasoline and distillate products that abut each other in the pipeline. This material (referred to as 

transmix) must be re-processed to make it suitable for use. The vast majority of transmix 

volume originates from pipeline shipments, although some is also generated during other fuel 

distribution activities such as when the same fuel handling and storage equipment is alternatively 

used for gasoline and distillate fuels. Transmix volumes typically gather towards the end of 

pipeline systems which are commonly distant from refineries. Transmix processors are typically 

located at these downstream pipeline locations to provide a means of coping with transmix 

volumes that would otherwise present logistical difficulties to return to refineries for 

reprocessing. Although transmix that is generated near refineries is sometimes returned to the 

refinery for reprocessing, introducing large volumes of transmix into the distillation column at a 

refinery can cause problems in the management of the output from this unit.E Hence, refiners 

would face difficulties in absorbing all of the transmix generated in the distribution system even 

absent the logistical hurdles. 

The use of 500 ppm LM diesel fuel was limited to outside of the Northeast Mid-Atlantic 

(NE/MA) and Alaska area after 2014 because it was concluded that heating oil provided a 

sufficient outlet for >15 ppm diesel fuel from transmix processors within the NE/MA area and 

Alaska. To support the continued use 500 ppm LM diesel fuel outside the NE/MA area and AK, 

additional requirements were put in place to prevent distillate initially produced as heating oil to 

be inappropriately shifted into the 500 ppm LM diesel pool during distribution. Specifically, 

heating oil and 500 ppm LM diesel are required to be designated and tracked (D&T) throughout 

the distribution system up to the point where the fuel leaves the terminal. Handlers of these fuels 

in the distribution chain are further required to file a report with EPA on an annual basis to 

demonstrate that heating oil was not inappropriately shifted into the 500 ppm LM diesel pool. 

These requirements continue indefinitely after 2014 for all parties in the distribution chain that 

handle heating oil and/or 500 ppm LM diesel fuel. We estimated that a many as 1,000 parties in 

the distribution system may be affected by these recordkeeping and reporting requirements at an 

annual cost of approximately $2.6 million.68

Before heating oil leaves the terminal, the solvent yellow 124 (SY-124) marker is 

required to be added in order to continue to prevent its introduction in the 500 ppm LM diesel 

pool given that the D&T and reporting requirements were not practical to implement 

downstream of the terminal level. Given that most heating oil use takes place in the NE/MA area 

D High octane gasoline blendstocks are sometimes blended into the gasoline fraction produced at a transmix 

processor to restore it to a marketable octane level. This is sometimes necessary because the heavier ends that 

normally exist in gasoline (which are high in octane) are typically cut into the distillate fraction during transmix

distillation. E

 Distillation columns at refineries are tuned to handle crude oil that has a much broader boiling range than transmix. 

4-26 
Chapter 4: Technological Feasibility 

and AK, the exclusion of 500 ppm LM diesel fuel from the NE/MA and AK after June 1, 2014 

and the accompanying exemption from heating oil marker requirement in these areas 

substantially limited the amount of heating oil that would need to be marked. This substantially 

limited the costs associated with installing equipment to store/inject the marker at the terminal 

and the cost of the marker itself. We estimated that 1.4 billion gallons of heating oil would need 

to be marked each year at an annual cost of $425 thousand.69

The accommodation of 1,000 ppm C3 diesel fuel within the framework of the NRLM 

program affords an opportunity to potentially simplify the requirements under the NRLM 

program. We believe that the creation of a 1,000 ppm C3 marine diesel grade in combination 

with the continued demand for heating oil may provide a sufficient outlet for >15 ppm diesel fuel 

produced by transmix processors. Thus, under the primary option in today’s notice, we are 

proposing to eliminate the allowance for the continued production of 500 ppm LM diesel fuel by 

transmix processors. This would allow the tracking, reporting, and marker requirements for 

heating oil to be eliminated after June 1, 2014, which would result in a significant reduction in 

the cost of compliance for a number of parties in the fuel distribution system. Since there would 

be no limitation on the amount of 1,000 ppm C3 diesel that could be produced, and given the 

absence of a limited 500 ppm LM diesel pool, the sulfur content alone would be sufficient to 

differentiate 1,000 C3 diesel fuel from other distillate fuels (in order to facilitate compliance 

oversight by EPA).F

Removing the potential outlet to the locomotive and C2 marine markets, while opening a 

new outlet to the C3 marine market would affect the distribution pathways for >15 ppm diesel 

fuel produced at transmix processors. We believe that transmix generated near the coasts would 

have ready access to marine applications, and transmix generated in the mid-continent could be 

shipped via rail, pipeline, or other means to C3 marine and heating oil markets on the coasts. We

requested comment on this, and plan to further evaluate whether some transmix processors might 

face difficulties in shipping their distillate product to market in the absence of an outlet to the 

locomotive and marine users. 

There may be some increase in the cost of distributing some portion of the >15 ppm

diesel fuel produced by transmix processors while in other cases there may be decrease in 

distribution costs. It is useful to compare the potential savings from the elimination of the D&T 

requirements needed to support the 500 ppm LM transmix provisions to the potential increase in 

distribution cost for such fuel if the outlet to the LM diesel fuel market was eliminated. To 

facilitate this comparison, we assumed that 430 million gallons a year of transmix generated 500 

ppm diesel fuel would be used in LM applications. This is 40% of the total annual transmix 

volume. The remaining transmix is assumed to be consumed in the heating oil market or 

returned to a refinery for reprocessing. Dividing the annual potential savings (~$3 million) by 

the annual volume of transmix-generated 500 ppm distillate estimated to be used in LM results in 

approximately 1 cent per gallon. Thus, if the distribution costs for 500 ppm diesel fuel produced 

at transmix processors increased by 1 cent per gallon as a result of the proposed amendments, the 

F

 Internal Revenue Service (IRS) red dye requirements to differentiate non-taxed diesel fuel will continue to apply. 

4-27 
Regulatory Impact Analysis 

overall net cost would be neutral. We believe that the overall impact to the distribution costs for 

500 ppm transmix-generated diesel fuel would be less than 1 cent per gallon. 

We anticipate that the introduction of a 1,000 ppm C3 marine fuel grade would not cause 

the need for a significant number of additional storage tanks or transport vessels (rail cars, tank 

trucks, and barges). Downstream of the producer, we expect the same distribution equipment 

would be used. In certain instances where the distribution pathway may need to be altered to 

accommodate a switch from the locomotive and C2 marine market to the C3 marine market, it 

may be necessary to introduce an additional trans-loading step from rail car to tank truck. 

However, we believe that such trans-loading could be accomplished at existing trans-loading 

facilities at rail yards. The improved flow-ability of 1,000 ppm diesel fuel over current C3 

marine fuels (which sometimes requires heating to maintain flow-ability), may simplify the 

handling of C3 marine fuels. The likely fungibility of 1,000 ppm C3 marine fuels with heating 

oil may also facilitate its distribution in areas where heating oil is shipped in bulk (primarily the 

Northeast). Based on the above discussion, we expect that the introduction of a 1,000 ppm C3 

marine fuel grade and the elimination of 500 ppm transmix processor LM diesel fuel post 2014 

with the associated streamlining of the diesel program compliance requirements would result in a 

reduced costs to the industry as a whole. 

We do not anticipate that the lack of access to 500 ppm LM diesel fuel produced at 

transmix processors would pose a difficulty to locomotive and C2 marine end users given the 

widespread availability of 15 ppm diesel fuel. Instead of being the consumers of fuel produced 

at transmix processors, locomotive and C2 marine operators would likely be an important means 

of bringing such fuel to the C3 market. 

Two potential alternative options were identified to the primary proposed option 

discussed above. Under the first option, the NE/MA area would be expanded to cover all U.S. 

coastal areas included in the emission control area defined under MARPOL Annex VI. 

Consistent with the current provisions in the NE/MA area, 500 ppm LM diesel fuel produced by 

transmix processors could not be used in the expanded NE/MA area. This would afford a greater 

potential for transmix processors to market their >15 ppm diesel fuel by maintaining a substantial 

outlet to the LM market. We would likely pursue such an option only if additional input from

industry indicates that the primary proposed option would substantially limit transmix processors 

ability to market >15 ppm diesel fuel. The adoption of this option would forgo the substantial 

simplification of EPA’s diesel program requirements under the primary proposed option. 

Furthermore, additional tracking and volume balance requirements (similar to those for heating 

oil) would be needed for 1,000 ppm C3 marine fuel to prevent the inappropriate shifting of fuel 

manufactured as 1,000 ppm C3 marine fuel (but actually meeting a 500 ppm sulfur standard) to 

the limited 500 ppm LM diesel pool. 

Under the second option, the existing provisions to facilitate the use of 500 ppm LM 

diesel fuel post 2014, including the existing NE/MA provisions, would be retained as is. This 

would require the most augmentation to the existing diesel fuel program in order to ensure that of 

fuel manufactured as 1,000 ppm C3 marine fuel would not be inappropriately shifted to the 500 

ppm LM diesel pool. In addition to the need for C3 marine fuel tracking and balance provisions 

(as under the first alternative option), a unique marker would be needed to differentiate C3 

marine fuel (or at least C3 marine fuel that had a sulfur content of 500 ppm and less) from 500 

4-28 
Chapter 4: Technological Feasibility 

ppm LM diesel fuel downstream of the terminal level. We are not aware of any suitable markers 

for this task. Such a marker would need to be compatible with, and distinguishable from, the red 

dye required by IRS as well as well as other in-use fuel and fuel system components. 

Furthermore, we anticipate that the added cost and complexity of a new marker for C3 marine 

diesel fuel might be a substantial deterrent to the 1,000 ppm marine diesel fuel. If this were the 

case, 15 ppm might largely be used for the purpose, and transmix processors would find a more 

limited market for their >15 ppm distillate. 

4.6 Impact on Safety, Noise, and Energy 

We do not anticipate any impact on vessel safety or noise due to the engine-based 

emission control technologies which we anticipate manufacturers will use to meet the proposed 

Tier 2 and Tier 3 standards. Some of these technologies are incremental improvements to 

existing engine components, and many of these improvements have already been applied to 

similar engines. Based on numerous data from automotive, truck, and marine industries, we do 

not anticipate that SCR technology will impact vessel safety or noise. 

No new impacts are anticipated on the energy supply due to the proposed rule. We

anticipate that the Tier II NOX standards required by Annex VI of the International Convention 

for the Prevention of Pollution from ships will result engine modifications which may result in 

approximately a 2% fuel penalty. The 2020 increase in fuel consumption (in U.S. inventory 

domain) due to 2% Tier II penalty is roughly 1,700 barrels per day (BPD) (250 BPD from U.S. 

vessels). The use of SCR to meet Tier III NOX standards may provide the opportunity to offset 

this fuel penalty when vessels are operating in an ECA by recalibrating the engine when the SCR 

is operating and relying on the SCR unit to achieve the full NOX reduction. Because we are not 

proposing requirements that would necessitate further engine or vessel modifications beyond 

what is anticipated to meet the Annex VI requirements, this rule would not significantly affect 

the energy use, production, or distribution beyond what is required by Annex VI. 

Similarly, we are not proposing to establish new fuel sulfur standards beyond what is 

necessary under the Annex VI requirements for marine fuels in this action; therefore no increase 

in energy use during fuel refining is anticipated. However, under the coordinated strategy, 

increased demand for distillate fuel in the ECA would increase the volume of crude oil needed in 

global refinery processes. This is discussed in Chapter 5. As shown in Table 5-36, the total 

refinery crude throughput in 2020 would increase by nearly 0.1 million BPD, leading to a 

corresponding increase in crude oil supply. 

4-29 
Regulatory Impact Analysis 

References 

1

 Flynn, P., et al, “Minimum Engine Flame Temperature Impacts on Diesel and Spark-Ignition 

Engine NOX Production”, SAE 2000-01-1177, 2000. 

2

 Heywood, John B., “Internal Combustion Engine Fundamentals”, McGraw-Hill, 1988. 

3

 Herzog, P., et al, "NOX Reduction Strategies for DI Diesel Engines," SAE 920470, 1992. 

4

 Uyehara, O., "Factors that Affect NOX and Particulates in Diesel Engine Exhaust," SAE 

920695, 1992. 

5

 Durnholz, M., G. Eifler, and H. Endres, "Exhaust-Gas Recirculation - A Measure to Reduce 

Exhaust Emission of DI Diesel Engines," SAE 920725, 1992. 

6

 Bazari, Z. and B. French, "Performance and Emissions Trade-Offs for a HSDI Diesel Engine - 

An Optimization Study," SAE 930592, 1993. 

7

 Ghaffarpour, M. and R. Baranescu, “NOX Reduction Using Injection Rate Shaping and 

Intercooling in Diesel Engines,” SAE 960845, 1996. 

8

 Tow, T.C., D.A. Pierpont, and R.D. Reitz, “Reducing Particulate and NOX Emissions by Using 

Multiple Injections in a Heavy Duty D.I. Diesel Engine,” SAE 940897, 1994. 

9

 Pierpont, D.A., D.T. Montgomery, and R.D. Reitz, “Reducing Particulate and NOX Emissions 

Using Multiple Injections and EGR in a D.I. Diesel Engine,” SAE 950217, 1995 

10 Ricart, L.M. and R.D. Reitz, “Visualization and Modeling of Pilot Injection and Combustion 

in Diesel Engines”, SAE 960833, 1996. 

11 Mather, D.K. and R.D. Reitz, “Modeling the Influence of Fuel Injection Parameters on Diesel 

Engine Emissions,” SAE 980789, 1998. 

12 “Experience with Sulzer Common-Rail Engines,” Wärtsilä, 

http://www.wartsila.com/wartsila/global/docs/en/ship_power/media_publications/technical 

_papers 

13 Geist, M.A. R. Holtbecker, and S. Chung, “Marine Diesel NOX Reduction – A New Sulzer 

Diesel Ltd Approach,” SAE 970321, 1997. 

14 Goldsworthy, L., “Design of Ship Engines for Reduced Emissions of Oxides of Nitrogen,” 

Australian Maritime College, presented at the Engineering a Sustainable Future Conference, July 

2002, http://www.amc.edu.au/system/files/shipNOx.pdf 

15 MAN B&W, “Exhaust Gas Emission Control Today and Tomorrow – Applications on MAN 

B&W Two-Stroke Marine Diesel Engines,” September 19, 2008, 

http://www.manbw.com/files/news/filesof9187/5510-0060-00ppr.pdf 

16 Walker, A.P. et al., “The Development and In-Field Demonstration of Highly Durable SCR 

Catalyst Systems,” SAE 2004-01-1289. 

17 Conway, R. et al., “Combined SCR and DPF Technology for Heavy Duty Diesel Retrofit,” 

SAE 2005-01-1862, 2005. 

4-30 
Chapter 4: Technological Feasibility 

18 “The Development and On-Road Performance and Durability of the Four-Way Emission 

Control SCRT® System,” presented by Andy Walker, 9th DEER Conference, August, 2003. 

19 “DEC SCR Convertor System,” Muenters, May 1, 2006, Docket ID EPA-HQ-OAR-2007­

0121-0013. 

20 Hagström, U., “Humid Air Motor (HAM) and Selective Catalytic Reduction (SCR),” Viking 

Line, presented at Air Pollution from Ships, May 24-26, 2005, Docket ID EPA-HQ-OAR-2007­

0121-0027. 

21 “Reference List - SI NOX ® Systems,” Argillon, December 2006, Docket ID EPA-HQ-OAR2007-0121-0035. 

22 “Reference List January 2005 Marine Applications,” Hug Engineering, January 2005, Docket 

ID EPA-HQ-OAR-2007-0121-0036. 

23 Heim, K., “Future Emission Legislation and Reduction Possibilities,” Wärtsilä, presented at 

CIMAC Circle 2006, September 28, 2006, Docket ID EPA-HQ-OAR-2007-0121-0017. 

24 Argillon, “Exhaust Gas Aftertreatment Systems; SCR – The Most Effective Technology for 

NOX Reduction,” presented at Motor Ship Marine Propulsion Conference, May 7-8, 2003, 

Docket ID EPA-HQ-OAR-2007-0121-0010. 

25 Holmström, Per, “Selective Catalytic Reduction,” presentation by Munters at Clean Ships: 

Advanced Technology for Clean Air, February 7-9, 2007, Docket ID EPA-HQ-OAR-2007-0121­

0013. 

26 Wärtsilä, “2005 Annual Report,” 

http://www.euroland.com/arinhtml/sf_wrt/2005/BY_ENG_2005.pdf. 

27 M.J. Bradley & Associates, "Alice Austen Vessel SCR Demonstration Project - Final Report," 

August 2006, http://www.mjbradley.com/documents/Austen_Alice_Report_Final_31Aug06.pdf. 

28 Rasmussen, K., Ellegasrd, L., Hanafusa, M., Shimada, K., “Large Scale SCR Application on 

Diesel Power Plant,” CIMAC paper number 179, presented at International Council on 

Combustion Engines Congress, 2004, Docket ID EPA-HQ-OAR-2007-0121-0007. 

29 “Munters SCR ConverterTM System,” downloaded from www.munters.com, November 21, 

2006, Docket ID EPA-HQ-OAR-2007-0121-0023. 

30 Argillon, “Exhaust Gas Aftertreatment Systems; SCR – The Most Effective Technology for 

NOX Reduction,” presented at Motor Ship Marine Propulsion Conference, May 7-8, 2003, 

Docket ID EPA-HQ-OAR-2007-0121-0010. 

31 From emission test results provided to EPA from sea trials on a Tier I, 55 MW, low-speed 

engine, Docket ID EPA-HQ-OAR-2007-0121-XXXX. 

32 Johnson Matthey, “SCRT® Technology for Retrofit of Heavy Duty Diesel Applications,” 

presented by Ray Conway, 11th DEER Conference, August, 2005. 

33 Munters, “Selective Catalytic Reduction,” presented at Clean Ships Conference, San Diego, 

CA, February 7, 2007. 

34 MAN B&W, “Emission Control Two-Stroke Low-Speed Diesel Engines,” December 1996, 

Docket ID EPA-HQ-OAR-2007-0121-0020. 

4-31 
35

Regulatory Impact Analysis 

“NOX Emissions from M/V Hamlet,” Data provided to W. Charmley, U.S. EPA. by P. 

Holmström, DEC Marine, February 5, 2007, Docket ID EPA-HQ-OAR-2007-0121-0015. 

36 U.S. Department of the Interior, "Mineral Commodity Summaries 2006," page 118, U.S. 

Geological Survey, January 13, 2006, Docket ID EPA-HQ-OAR-2007-0121-0022. 

37 Hountalas, D.T., “Use of Water Emulsion and Intake Water Injection as NOX Reduction 

Techniques for Heavy Duty Diesel Engines,” SAE 2006-01-1414, 2006. 

38 MAN B&W, “Emission Control: Two-Stroke Low-Speed Diesel Engines,” 1999, 

http://www.manbw.com/files/news/filesof1417/19993701.pdf. 

39 MAN B&W, “Emission Control: MAN B&W Two-Stroke Diesel Engines,” 2004, 

http://www.manbw.com/files/news/filesof4458/p9000.pdf. 

40 Wärtsilä, “Marine Technologies for Reduced Emissions,” presented by Heinrich Schmid and 

German Weisser, 2nd Annual Conference on Green Ship Technology, Amsterdam, Netherlands, 

April 13-14th, 2005. 

41 Weisser, G., “Emission Reduction Solutions for Marine Vessels – Wartsila Perspective” 

presentation by Wartsila at the Clean Ships: Advanced Technology for Clean Air Conference, 

February 8, 2007, Docket ID EPA-HQ-OAR-2007-0121-0032. 

42 International Maritime Organization, “Input from the four subgroups and individual experts to 

the final report of the Informal Cross Government/Industry Scientific Group of Experts,” 

Subcommittee on Bulk Liquids and Gases, 12th session, Agenda item 6, BLG12/INF.10, 

December 28, 2007. 

43 Kasper, A., Aufdenblatten, S., Forss, A., Mohr, M. and Burtscher, H., “Particulate Emissions 

from a Low-Speed Marine Diesel Engine,” Aerosol Science and Technology, 41:1, 24 – 32, 2007 

44 Nakajima et al, “Measurement and Analysis of Particulate Matter (PM) from Marine Diesel 

Engines,” Proceedings of the 6th International Symposium on Marine Engineering, October, 

2000. 

45 Email from Fritz Fleischer, MAN to Bryan Wood-Thomas, EPA, “Particulates,” October 02, 

2007. 

46 Agrawal, H., et al., “In-Use Gaseous and Particulate Matter Emissions from a Modern Ocean 

Going Container Vessel,” Atmospheric Environment, doi:10.1016/j.atmosenv.2008.02.053, 2008. 

47 Maeda et al, “Measurement of PM Emission from Marine Diesel Engines,” International 

Council on Combustion Engines, CIMAC Paper 107, 2004. 

48 Janhäll, S., “Particle Emissions from Ships,” Göteborg University, The Alliance for Global 

Sustainability, ISBN: 978-91-976534-3-5, 2007. 

49 Sinha et al., “Emissions of Trace Gases and Particles from Two Ships in the Southern Atlantic 

Ocean,” Atmospheric Environment 37 (2003) 2139–2148, 2003. 

50 International Standard Organization (ISO) “8217 Petroleum products -- Fuels (class F) -- 

Specifications of Marine Fuels,” Edition: 3 | Stage: 90.92 | TC 28/SC 4, ICS: 75.160.20, 

http://www.iso.org 

4-32 
Chapter 4: Technological Feasibility 

51 Wärtsilä, “CARB Workshop Switching Fuel,” presented by Leo Schnellmann, California 

Maritime Technical Working Group, Sacramento, CA, July 24, 2007, 

http://www.arb.ca.gov/ports/marinevess/presentations/072407/072407warpres.pdf 

52 Herbert Engineering Corp., “California Maritime Technical Working Group Focus on Fuel 

Switching Fuel Oil Systems,” prepared by Herbert Engineering Corp., California Maritime 

Technical Working Group, Sacramento, CA, July 24, 2007, 

http://www.arb.ca.gov/ports/marinevess/presentations/072407/072407herpres.pdf 

53 International Standard Organization (ISO) “8217 Petroleum products -- Fuels (class F) -- 

Specifications of Marine Fuels,” Edition: 3 | Stage: 90.92 | TC 28/SC 4, ICS: 75.160.20, 

http://www.iso.org 

54 ASTM International, “ASTM D975 - 09 Standard Specification for Diesel Fuel Oils,” 

Developed by Subcommittee: D02.E0.02 |Book of Standards Volume: 05.01, 

http://www.astm.org 

55 ExxonMobil Marine Lubricants, “Low Sulfur Fuel: Impacts on the Marine Industry,” 

http://www.exxonmobil.com/lubes/exxonmobil/marine/files/LSF_Bulletin.pdf 

56 Total Petrochemicals USA, Inc., Lubmarine, Talusia Universal, 

http://www.lubmarine.com/lub/content/NT000F9DB2.pdf 

57 Lloydminster Oilfield Technical Society (OTS), “OTS Heavy Oil Science Center, What are 

Asphaltenes?,” Lloydminster, Saskatchewan/Alberta, Canada, 

http://www.lloydminsterheavyoil.com/asphaltenes.htm 

58 California Air Resources Board, “Commercial Marine Vessels,” Sacramento, CA, 

http://www.arb.ca.gov/ports/marinevess/marinevess.htm 

59 “Holland America Line Krystallon Sea Water Scrubber Technology Demonstration Project,” 

July 23-25, 2008 

60 Hufnagl, M., et al (2005 March). Effects of Sea Water Scrubbing – Final Report. BP Marine. 

61 Heim, Klaus M. (2008 September 25). Engines and SOX Scrubber Technologies to Meet IMO 

Fuel Quality Requirements on Sulphur and SOX Paper presented at the CIMAC Circle SMM 

2008. 

62 Henriksson, T. (2007 September 28). SOX Scrubbing of Marine Exhaust Gases. Retrieved 

August 12, 2008 from http://www.shipgaz.com/magazine/issues/2007/18/1807_article1.php. 

63 Tri-Mer Corporation (2005, April 14). Cloud Chamber Scrubber Performance Results for 

Diesel Exhaust. 

64 Tri-Mer Corporation. Cloud Chamber Scrubbers Product Bulletin. Retrieved April 9, 2009 

from http://www.tri-mer.com/pdf-files/Tri-Mer-CCS-Brochure-03-09.pdf 

65 Dupont/Belco. Belco Technologies Corporation. Presentation on emission control 

technologies from Dupont/Belco receieved August 21, 2008. 

66 “Proposed amendments for resolution MEPC.170(57) – Guidelines for Exhaust Gas Cleaning 

Systems,” Submitted by the Institute of Marine Engineering, Science and Technology, to the 

59th session of the Marine Environment Protection Committee, International Maritime 

Organization, MEPC 59/10/5, April 10, 2009. 

4-33 
Regulatory Impact Analysis 

67 International Maritime Organization (2005 July 22). Guidelines for On-Board Exhaust GasSOx Cleaning Systems; Annex 12, Resolution MEPC.130(53). MEPC 53/24/Add.1. 

68 Supporting Statement for the Information Collection Request, Recordkeeping and Reporting 

Requirements for Motor Vehicle and Non-Road Diesel Fuel, EPA ICR 1718.08, January 2008. 

69 Section 7.4.4 “Fuel Marker Costs”, Final Regulation Analysis, Control of Emissions from 

Nonroad Engines, May 2004, EPA420-R-04-007, http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf 

4-34 
Chapter 5: Engineering Cost Estimates 

CHAPTER 5: ENGINEERING COST ESTIMATES 

5.1 Methodology for Estimating Engine and Equipment Engineering Costs................... 5-3

5.1.1 Engineering Cost Methodology ............................................................................ 5-7

5.1.2 Development of 2010-2040 Fleets ......................................................................... 5-9

5.2 Engineering Costs for Freshly Manufactured Engines .............................................. 5-15

5.2.1 Tier 2 Variable Hardware Costs ........................................................................ 5-15

5.2.2 Tier 2 Fixed Costs ................................................................................................ 5-18

5.2.3 Tier 3 Variable Hardware Costs ........................................................................ 5-19

5.2.4 Tier 3 Fixed Costs ................................................................................................ 5-28

5.3 Engineering Costs for Existing Engines....................................................................... 5-30

5.3.1 Variable Costs for the Existing Engine Program..............................................5-30

5.3.2 Fixed Costs for the Existing Engine Program................................................... 5-32

5.4 Engineering Costs for Vessels .......................................................................................5-33

5.4.1 Freshly Manufactured Vessels............................................................................ 5-33

5.4.2 Existing Vessels Hardware Costs ....................................................................... 5-39

5.5 Operating Costs.............................................................................................................. 5-42

5.5.1 Tier II Fuel Consumption Impacts..................................................................... 5-42

5.5.2 Tier III Urea Consumption................................................................................. 5-44

5.5.3 Operation on Lower-Sulfur Fuel........................................................................ 5-45

5.5.4 Projected Fuel Costs............................................................................................ 5-46

5.6 Summary of Final Program Engineering Costs ..........................................................5-59

5.6.1 Engineering Costs for Freshly Manufactured Engines .................................... 5-59

5.6.2 Engineering Costs for Vessels.............................................................................5-60

5.6.3 Total Increased Operating Costs........................................................................ 5-61

5.6.4 Total Engineering and Operating Costs Associated with the Final Program 5-62

5.7 Cost Effectiveness........................................................................................................... 5-63

5-1 
Regulatory Impact Analysis 

CHAPTER 5: Engineering Cost Estimates 

 In this chapter, we present the projected cost impacts associated with the coordinated 

emission control strategy for Category 3 vessels including the engine and fuel standards 

described in this proposal and those that would apply in the U.S. ECA.A 

We estimate the costs of the coordinated strategy to be approximately $1.85 billion in 

2020, increasing to $3.11 billion in 2030.B Of the 2020 costs, nearly 89 percent or $1.64 billion 

are attributable to ECA fuel sulfur provisions which include the costs incurred by both U.S. and 

foreign-flagged vessels. The total operational costs are estimated to be $1.82 billion in 2020, 

which include fuel sulfur controls, a two percent fuel consumption penalty associated with Tier 2 

and global Tier II NOX standards, and the use of urea on vessels equipped with selective catalytic 

reduction (SCR) to meet Tier 3 and global Tier III NOX standards. The costs to apply engine 

controls to U.S.-flagged vessels are expected to be $31.9 million in 2020, increasing to $47.4 

million in 2030 as more ships are built to comply with CAA Tier 3 NOX limits. 

When attributed by pollutant, at a discount rate of 3 percent from 2010 through 2040, the 

NOX controls are expected to cost about $510 per ton of NOX reduced, SOX controls are 

expected to cost about $930 per ton of SOX reduced, and the PM controls are expected to cost 

about $7,950 per ton of PM reduced ($500, $920, and $7,850 per ton of NOX, SOX, and PM 

respectively, at a net present value of 7 percent over the same period.) These costs are 

comparable to our other recently-adopted mobile source programs, and are one of the most cost 

effective programs in terms of NOX and PM when compared to recent mobile and stationary 

programs. The coordinated strategy also provides very cost effective SOX reductions comparable 

to the Heavy-Duty Nonroad diesel rulemaking. 

The estimated costs presented in this chapter are for the entire coordinated strategy, 

including those requirements that are the subject of this proposal and those that are associated 

with the proposed ECA designation. The costs of the coordinated strategy consists of the costs 

associated with the MARPOL Annex VI global standards that we are implementing through 

APPS, some of which we are also adding to our CAA emission control program for U.S. vessels 

(Tier 2 and Tier 3 NOX emission control hardware for U.S. vessels; operating costs for the Tier 2 

NOX requirements; controls for existing vessels; certain compliance requirements). Also 

included are the costs associated with the U.S. portion of the ECA package (Tier 3 operating 

costs; fuel sulfur hardware and operating costs). Note that the proposal to amend Annex VI to 

designate U.S. coasts is a separate action and is not part of this rulemaking. Also note that the 

costs associated with the Canadian portion of the ECA package are not included in the costs of 

the coordinated strategy. 

A We use the term “engineering costs” to differentiate from “social costs.” Social costs are discussed in Chapter 7 of 

this RIA. For simplicity, the terms “cost” and “costs” throughout the discussion in this Chapter 5 should be taken as 

referring to “engineering costs.” B

 The costs totals reported in this NPRM are slightly different than those reported in the ECA proposal. This is 

because the ECA proposal did not include costs associated with the Annex VI existing engine program, Tier II, or 

the costs associated with existing vessel modifications that may be required to accommodate the use of lower sulfur 

fuel. Further, the cost totals presented in the ECA package included Canadian cost estimates. 

5-2 
Chapter 5: Engineering Cost Estimates 

The regulatory changes proposed for Category 1 and 2 engines are not included in this 

cost analysis as they are intended to be compliance flexibilities and not result in increased 

compliance costs. Similarly, the technical amendments proposed for other engines, would not 

have significant economic impacts and are therefore not addressed here. Finally, compliance 

costs for gas turbine engines are not addressed separately because they would be similar to those 

for diesel marine engines. 

A more detailed description of the components of the coordinated strategy that are 

included in this cost analysis is presented in Section 5.1of this chapter. Section 5.2 describes the 

methodology used to estimate the hardware and operating costs, including the development of a 

representative future fleet and predicted sales volumes to which these hardware and operating 

costs are applied. Sections 5.3 and 5.4 present the estimated hardware costs of the individual 

engine technologies we expect manufacturers to use to comply with the emissions standards for 

new and existing engines, along with a discussion of the associated fixed costs of these 

technologies such as research and development, tooling, and certification. Section 5.5 describes 

vessel hardware and fixed costs that may be incurred by some vessels to accommodate the use of 

lower sulfur fuel. Section 5.6 presents our estimate of changes in vessel operating costs that 

may result from the coordinated strategy, including estimated fuel production costs. Section 5.7 

presents the total estimated cost of the coordinated strategy to U.S.- and foreign-flagged vessels, 

and finally Section 5.8presents the cost effectiveness of this program. 

This analysis relies on a number of assumptions about the prices of various engine and 

fuel hardware components, as well as fuel consumption, the number of affected vessels, and their 

operation. As noted in the preamble for this proposal, we seek comment on all aspects of this 

analysis, including all of these assumptions and the methodology we used to estimate the costs of 

the program. 

All costs presented in this chapter are in 2006 dollars. 

5.1 Components of Coordinated Strategy Included in this Analysis 

This analysis estimates the costs associated with all components of the coordinated 

strategy. These include the costs of the proposed Clean Air Act (CAA) Tier 2 and Tier 3 

emission standards for U.S.-flagged vessels, operational costs associated with the global Tier II 

and Tier III standards for foreign-flagged vessels operating in the ECA, and the ECA fuel sulfur 

requirements. We also include Clean Air Act compliance costs that will apply only to new U.S. 

vessels for verification testing after engine installation (PLT). The fuel program changes are 

implementation provisions and do not impose compliance costs but, instead, may reduce the 

costs for fuel distributors of complying with EPA’s distillate diesel standards. 

While there is significant overlap between the coordinated strategy and our proposal for 

ECA designation, the total costs associated with these two programs are not identical. The 

differences between the two programs are set out in Table 5-1. The estimated costs for the 

coordinated strategy include hardware costs for new U.S.-flagged vessels to comply with the 

CAA Tier 2 and Tier 3 engine standards, and for existing U.S.-flagged vessels to comply with 

the MARPOL Annex VI existing engine requirements. Costs are also included for hardware 

changes associated with switching to 1,000 ppm sulfur fuel for certain new and existing U.S.-

5-3 
Regulatory Impact Analysis 

flagged vessels. The cost analysis includes all of these hardware costs even though some of the 

benefits from using these emission control systems will occur outside the United States. 

Conversely, we do not include any new vessel Tier 3 or fuel hardware costs for foreign vessels 

that operate in U.S. waters even though a significant share of the benefits of the coordinated 

strategy will arise from foreign vessels that comply with the ECA engine and fuel sulfur limits 

while operating within the U.S. ECA. An alternative approach would be to allocate a portion of 

hardware costs of complying with the Tier 3 NOX standards and the fuel sulfur limits to the 

coordinated strategy. For example, analysis of MARAD port entrance data shows that about 30 

percent of the vessels that enter U.S. ports account for about 75 percent of the vessel entrances. 

This suggests it may be reasonable to allocate the hardware costs for 30 percent of the new 

foreign vessels to the coordinated strategy. Similarly, it may be reasonable to discount the share 

of estimated hardware costs included in the coordinated strategy costs for those U.S. vessels that 

do not operate primarily between two U.S. ports. As noted in the preamble for this proposal, we 

are requesting comment on the allocation of hardware costs and on whether the U.S. should 

adopt the alternative approach described above or some other method to allocate these costs. 

The estimated costs for the coordinated strategy also includes operating costs for U.S. 

and foreign vessels while operating in the inventory modeling domain, which includes the 

proposed ECA. These increased operating costs include changes in fuel consumption rates, the 

differential increase in fuel costs, and the use of urea for engines equipped with SCR. There are 

also Clean Air Act compliance costs that will apply only to new U.S. vessels for verification 

testing after engine installation (PLT). The fuel program changes are implementation provisions 

and do not impose compliance costs but, instead, may reduce the costs for fuel distributors of 

complying with EPA’s distillate diesel standards. 

Estimated costs for the proposed ECA designation, on the other hand, include hardware 

costs for both U.S. and foreign vessels to meet the ECA Tier III NOX and fuel sulfur limits and 

operating costs associated with using those systems within the ECA domain. Although we 

included the entire hardware cost for all vessels, these vessels will likely operate in other existing 

or yet-to-be-designated ECAs in Europe or Asia, and therefore it may have been more 

appropriate to allocate only a portion of those hardware costs. 

5-4 
Chapter 5: Engineering Cost Estimates 

Table 5-1 Costs Associated with the Coordinated Strategy and U.S./Canadian Proposal to IMO for ECA

Designation 

PROGRAM ELEMENT COORDINATED 

STRATEGY AND U.S. 

ECA 

CANADIAN ECA 

Hardware – T2 

(variable costs; fixed costs 

applied in 2010) 

US vessels $3,310,000 NA – not part of ECA 

Foreign Vessels N/A – global std NA – not part of ECA 

Hardware – T3 

(variable costs; fixed costs 

recovered in the year in which 

they occur: 2011-15) 

US vessels 

(variable costs; fixed costs 

recovered in the year in which 

they occur: 2011-15) 

$28,700,000 $100,000,000 

Foreign vessels: 30% of vessels 

making 75% of entrances to US 

portsa

 $296,700,000 

Foreign vessels: 70% of vessels 

making 25% of entrances to US 

portsa 

$692,200,000 

Hardware – Fuel US vessels 

(new vessel costs) 

$804,000 $10,000,000 

Foreign vessels 

(new vessel costs) 

$23,600,000 

Operating – T2 

(inside full inventory 

modeling domain) 

US vessels $5,630,000 NA – not part of ECA 

Foreign vessels $32,900,000 NA – not part of ECA 

Operating – T3 

(inside relevant part of ECA) 

US vessels $15,800,000 $30,000,000 

Foreign vessels $127,000,000 

Operating – Fuel 

(inside relevant part of ECA) 

US vessels $210,000,000 $260,000,000 

Foreign vessels $1,430,000,000 

Existing vessels – engine costs 

(all US vessels 1990-99 

retrofit during first 5 years of 

program, 2011-15) 

US vessels $0 NA – not part of ECA 

Foreign vessels N/A – global std NA – not part of ECA 

Existing vessels – vessel fuel 

switching costs 

(all US vessels 1999-90 

retrofit during first 5 years of 

program, 2011-15) 

US vessels $0 Canada did not provide 

Foreign vessels $0 Canada did not provide 

Note: a Note the RIA reports $1,010,000,000 in engine costs for foreign vessels; this includes the costs of production line 

testing which is not applicable to foreign vessels under our CAA program. This element is not included in the 

estimated costs for the Coordinated Strategy; we will revise the RIA. 

5-5 
Regulatory Impact Analysis 

The proposed CAA Tier 2 and Tier 3 NOX standards will not result in costs to U.S. 

vessels above or beyond those costs that will already be experienced through compliance with 

the Tier II and Tier III NOX standards contained in Annex VI. In addition, we are not proposing 

NOX standards for foreign-flagged vessels, nor are we proposing requirements for the use of 

lower sulfur fuel. However, the U.S. Government was a key participant in the development of 

the new Annex VI NOX emission standards and fuel sulfur limits. As such, we are interested in 

quantifying the costs and benefits associated with the coordinated strategy. 

5.2 Methodology for Estimating Engine and Equipment Engineering Costs 

To estimate the cost of the coordinated strategy for ensuring that all ships that affect U.S. 

air quality will be required to meet stringent NOX and fuel sulfur requirements, we estimated the 

hardware and operational costs to both U.S.- and affected foreign-flagged ships separately. The 

hardware costs are only applied to U.S.-flagged vessels, and include those associated with the 

Annex VI existing engine program, Tier 2, Tier 3, and the use of lower sulfur fuel. For the sake 

of completeness, however, estimated hardware costs for foreign-flagged vessels associated with 

global Annex VI Tier III standards and additional hardware that may be required to 

accommodate the use of lower sulfur fuel are presented here as a separate analysis. Tier 2 

hardware costs are expected to consist of changes to the engine block and the migration from 

mechanical fuel injection to common rail fuel injection systems. Tier 3 hardware costs include 

engine modifications, the migration from mechanical fuel injection to common rail fuel injection 

systems, and the installation of SCR. Hardware costs associated with the use of lower sulfur fuel 

are a result of applying additional tanks and equipment to enable a vessel to switch from residual 

fuel to lower sulfur fuel. These equipment costs were applied to those new vessels that may 

require such additional hardware, and also include the estimated cost of retrofitting the portion of 

the fleet that may require additional hardware to accommodate the use of lower sulfur fuel in 

2015, when the fuel sulfur standards take effect. The total hardware costs also include a per 

engine cost of $10,000 associated with the proposed requirement to test each production engine 

(§1042.302). 

The operational costs were applied to both U.S.- and foreign-flagged vessels and include 

additional operational costs associated with the applicable NOX limits, and the use of lower 

sulfur fuel. The operational costs for NOX controls consist of the additional fuel required due to 

an estimated two percent fuel penalty associated with the use of technology to meet Tier 2 

standards for U.S.-flagged vessels and global Annex VI Tier II standards for foreign-flagged 

vessels, and the use of urea for ships equipped with an SCR unit. The operational costs 

associated with the use of lower sulfur fuel include both the differential cost of using lower 

sulfur fuel that meets ECA standards instead of using marine distillate fuel, and the differential 

cost of using lower sulfur fuel that meets ECA standards instead of using residual fuel. 

To assess these potential cost impacts we must understand: the makeup of the fleet of 

ships expected to visit the U.S. when these requirements go into effect, the emission reduction 

technologies expected to be used, and the cost of these technologies. The total engine and vessel 

costs associated with the coordinated strategy are based on a cost per unit value applied to the 

number of affected vessels. Operational costs are based on fuel consumption values determined 

in the inventory analysis (Chapter 3). This section discusses an overview of the methodology 

used to develop the hardware and operational costs. This section also presents the methodology 

5-6 
Chapter 5: Engineering Cost Estimates 

used to develop a fleet of future vessels necessary to determine how to apply these hardware and 

engineering costs. 

5.2.1 Engineering Cost Methodology 

To determine the cost of applying emission reduction technology on a per vessel basis, 

ICF International (ICF) was contracted by the U.S. EPA to conduct a cost study of the various 

compliance strategies expected to be used to meet the new requirements. The resulting cost 

estimates were used to determine a $/kW equation which could be scaled according to engine 

speed and power to arrive at a per vessel cost. The per vessel hardware costs were then applied 

to the number of applicable new vessels estimated to be built in each year after the standards take 

effect. 

5.2.1.1 Overview 

There are a number of technologies available or expected to be available to meet CAA 

Tier 2 and Tier 3, global Tier II and Tier III, and Annex VI Existing Engine NOX standards, and 

to accommodate the use of lower sulfur fuel. We expect that each manufacturer will evaluate all 

possible technology avenues to determine how to best balance costs while ensuring compliance, 

however, this analysis makes certain assumptions regarding how manufacturers will comply with 

the new emission and fuel standards. First, Tier 2 assumes that compliance is met through a 

combination of fuel injection system changes for some engines and the use of engine 

modifications for all engines. These engine modifications do not require additional hardware 

and therefore do not have any related variable costs associated with them. The fuel injection 

change (the migration from mechanical fuel injection to common rail fuel injection systems) is 

expected to apply to a certain fraction of engines for Tier 2, and an additional fraction for Tier 3. 

Tier 3 NOX standards are projected to be met through the use of SCR systems. The fuel 

standards are assumed to be met through the use of lower sulfur fuel. An analysis was performed 

on the current global operating fleet to estimate the percentage of the fleet that may require 

additional hardware to accommodate the use of lower sulfur fuel, as some ships either already 

have or are expected to be built with the equipment necessary to accommodate the use of lower 

sulfur fuel. 

Through our background work for this rulemaking and for the ECA application, we 

sought input from the regulated community regarding the expected future costs of applying the 

emission control technologies associated with the coordinated strategy. EPA contracted with 

ICF to research the fixed and variable costs associated with the technologies expected to be used 

to meet engine and fuel sulfur requirements, as applied to different engine types and sizes. A 

series of both slow-speed and medium-speed engine configurations were selected and used to 

provide an understanding of the costs to apply emission control technologies associated with the 

coordinated strategy. The engine configurations were selected based on a review of 2005 U.S. 

Army Corps of Engineers ‘Entrances and Clearances’ data which was used to determine the 

characteristics of engines on those vessels that call on U.S. ports most frequently. This data 

represents a broad range of propulsion power for each engine type (slow and medium speed 

engines). The costs developed for these engine configurations were used to develop a $/kW 

value that could be applied to any slow or medium speed engine. Using the average propulsion 

power by ship type presented in the inventory analysis (Chapter 3), the per vessel hardware costs 

5-7 
Regulatory Impact Analysis 

were then applied to the estimated number of applicable vessels built after the standards take 

effect, Table 5-2 lists these engine configurations. After ICF developed their initial cost 

estimates, they provided surveys to several engine and emission control technology 

manufacturers to determine the reasonableness of their approach and cost estimates. Input 

received from those surveyed was incorporated into the final cost estimates used in this analysis. 

Table 5-2 Engine Configurations Used in the Cost Analyses 

ENGINE TYPE MEDIUM-SPEED LOW-SPEED 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

BSFC (g/kWh) 210 195 

5.2.1.2 Hardware Costs 

The hardware cost estimates include variable costs (components, assembly, and 

associated markup) and fixed costs (tooling, research and development, redesign efforts, and 

certification.) For technologies sold by a supplier to an engine manufacturer, cost estimates are 

based on a direct cost to manufacture the system components plus a 29 percent markup to 

account for the supplier’s overhead and profit.1 Variable costs also include a 29 percent markup 

to account for both manufacturer and dealer overhead and carrying costs. Fixed costs are 

estimated to be incurred over a five-year period preceding the introduction of the standard. 

Hardware costs associated with IMO’s existing engine standards were applied to the 

portion of the existing U.S.-flagged fleet built between 1990 and 1999 expected to be subject to 

these standards in 2011 when the standards go into effect. These costs were taken over a fiveyear period beginning in 2011 where 20 percent of the subject fleet was estimated to undergo a 

rebuild event in each respective year. The existing engine program fixed costs were phased in 

over a five year period beginning in 2010 and applied on a per vessel basis using the estimated 

applicable fleet in each respective sales year. 

Hardware costs associated with the CAA Tier 2 program were applied to all new U.S.-

flagged vessels beginning in the year 2011 when the standards take effect. The fixed costs 

associated with Tier 2 standards are expected to be incurred over a five year period, however, as 

the Tier 2 standards take effect in 2011, it is assumed that manufacturers are nearing the end of 

their research and development efforts. To capture all of these costs, the fixed costs that would 

have been incurred during that five year phase-in period were all taken in the year 2010 and 

applied on a per vessel basis using the applicable future fleet of new U.S.-flagged vessels in 

2010. 

Hardware costs associated with Tier 3 are estimated for U.S. vessels and are applied as of 

2016. Because of the global scope of the Tier III standards, and the fact that other ECAs exist 

today and more may exist in the future, we do not include hardware costs for Tier III emission 

controls on foreign-flagged vessels. However, for completeness, Section 5.2 presents these 

hardware cost estimates separately. The fixed costs associated with Tier 3 were phased in over a 

5-8 
Chapter 5: Engineering Cost Estimates 

five year period beginning in 2011. Hardware costs associated with the use of lower sulfur fuel 

are estimated separately for both new and existing vessels that may require additional hardware 

to accommodate the use of lower sulfur fuel. The fuel sulfur control related hardware costs for 

new vessels begin to apply in 2015, while all retrofit costs are expected to be incurred by 2015. 

The fixed costs for both new and existing vessels that may require additional hardware to 

accommodate the use of lower sulfur fuel are applied on a per vessel basis and are phased in over 

a five year period beginning as of 2010. 

5.2.1.3 Operational Costs 

 The operational costs estimated here are comprised of three parts, (1) the estimated 2 

percent increase in fuel consumption (see Chapter 4) expected to occur with the use of Tier 2 and 

global Tier II technologies on U.S.- and foreign-flagged vessels, (2) the differential cost of using 

lower sulfur, and (3) the use of urea with SCR as a Tier 3 and global Tier III NOX emission 

reduction technology on both U.S.- and foreign-flagged vessels. The fuel consumption values 

were determined in the inventory analysis (see Chapter 3). The differential cost of using lower 

sulfur fuel is discussed in Section 5.5.4. The estimated costs of using of urea associated with 

Tier III NOX standards are derived from a urea dosage rate that is 7.5 percent of the fuel 

consumption rate. 

Operating costs per vessel vary depending on what year the vessel was built, for example, 

vessels built as of 2016 will incur operating costs associated with the use of urea necessary when 

using SCR as a Tier 3 and global Tier III NOX emission control technology. Vessels built prior 

to 2016 will not incur the cost of using urea, but will incur operating costs associated with the 

differential cost of using lower sulfur fuel. Further, we have assumed that vessels built as of 

2011 that meet Tier 2 and global Tier II standards will incur a 2 percent fuel consumption 

penalty. Therefore, an estimated fleet had to be developed over a range of years, and provide a 

breakout of ships by age in each year. We use the fuel consumption rates from the inventory 

analysis in Chapter 3 as the basis for estimating additional operating costs incurred prior to 2016 

and for ships traveling in non-ECA areas within the U.S. after 2016 

5.2.2 Development of 2010-2040 Fleets 

To project future costs, we needed to first develop estimates of the number of ships that 

may visit U.S. ports in a baseline year. This baseline fleet was grown using the growth rates 

described in Chapter 3 to estimate an approximation of the fleet of ships by age and engine type 

that may visit U.S. ports in the future. 

5.2.2.1 Baseline Fleet 

 To characterize the fleet of ships visiting U.S. ports we used U.S. port call data collected 

in 2002 for the inventory port analysis (see Chapter 3 of the draft RIA) which included only 

vessels with C3 engines where the engine size and type was identified. We used this data with 

the growth rates developed in the inventory analysis to estimate how many ships by ship type 

and engine type would visit U.S. ports in future years. Due to the long life of these vessels, and 

the fact that there has been no significant event that would have changed the composition of the 

world fleet since this baseline data was taken, it is reasonable to use 2002 data as the basis for 

5-9 
Regulatory Impact Analysis 

modeling the future fleet upon which to base hardware cost estimates. An analysis is presented 

in Section 5.1.2.2 which confirms the reasonableness of this assumption using 2007 MARAD 

data. The ships that called on the U.S. in 2002 were cross referenced with Lloyd’s database 

using each ship’s IMO number to determine the actual propulsion power, engine type, and ship 

type information for each ship. This allowed for ships without Category 3 engines to be 

removed from the analysis. To separate slow speed engines from medium speed engines where 

that information was not explicitly available, 2-stroke engines were assumed to be slow speed 

engines (SSD), and 4-stroke engines were assumed to be medium speed engines (MSD). The 

research performed for this cost analysis differentiated between SSD and MSD engines, and 

separate $/kW values were developed for each engine type. The separation by engine type was 

also necessary to allow for the use of the age distribution formula developed in the inventory 

analysis (Chapter 3) which provided a method to estimate how many vessels the hardware costs 

are applicable to in each year. 

The ship type information gathered from this baseline data, for the purposes of both this 

analysis and the inventory, was categorized into one of the following ship types: Auto Carrier, 

Bulk Carrier, Container, General Cargo, Miscellaneous, Passenger, Refrigerated Cargo (Reefer), 

Roll-On Roll-Off (RoRo), and Tankers. The 2002 baseline fleet was also used to develop 

average ship characteristics shown in Tbale 5-3, these values were used to represent the 

characteristics of new (and future existing) vessels included in this cost analysis. 

The 2002 port call data were sorted by IMO number to determine the total number of 

unique ships that visited all included U.S. ports in 2002. Table 5-4 shows the breakout by ship 

type of these approximately 6,700 ships. Next, to be consistent with the inventory analysis 

which provides different regional growth rates, the original port call data were separated into the 

same regions used by the inventory (South Pacific (SP), North Pacific (NP), East Coast (EC), 

Gulf Coast (GC), Alaska East (AE), Alaska West (AW), Hawaii East (HE), and West Hawaii 

(HW)). This was done by matching each port-of-call entry in the original port call data file with 

the corresponding region containing that port as per the inventory analysis.2 This resulted in a 

fleet of ships for each region, each with a unique IMO number. 

5-10 
Chapter 5: Engineering Cost Estimates 

Table 5-3 Average Ship Characteristics 

SHIP TYPE ENGINE 

SPEED 

AVERAGE 

PROPULSION 

POWER (KW) 

AVERAGE 

AUXILIARY 

POWER 

(KW) 

SERVICE 

SPEED 

(KNOTS) 

AVERAGE 

DWT 

Auto Carrier Slow Speed 11,000 3,000 19 17,000 

Medium 

Speed 

9,600 2,600 17 13,000 

Bulk Carrier Slow Speed 8,400 1,900 15 47,000 

Medium 

Speed 

6,300 1,400 14 27,000 

Steam Turbine 6,400 1,400 15 19,000 

Container Slow Speed 27,000 6,000 22 45,000 

Medium 

Speed 

14,000 3,000 19 19,000 

Steam Turbine 21,000 4,700 21 30,000 

General 

Cargo 

Slow Speed 7,700 2,000 15 26,000 

Medium 

Speed 

5,200 1,300 15 8,700 

Steam Turbine 18,000 4,600 21 23,000 

Passenger Slow Speed 24,000 6,600 210 6,200 

Medium 

Speed 

24,000 6,600 20 6,200 

Steam Turbine 27,000 7,600 19 13,000 

Gas Turbine 44,000 12,000 24 12,000 

Reefer Slow Speed 10,000 4,200 20 11,000 

Medium 

Speed 

7,400 3,000 18 7,600 

RoRo Slow Speed 16,000 4,000 18 30,000 

Medium 

Speed 

8,600 2,200 16 8,400 

Gas Turbine 47,000 12,000 24 37,000 

Steam Turbine 22,000 5,800 25 19,000 

Tanker Slow Speed 9,800 2,100 15 61,000 

Medium 

Speed 

6,700 1,400 15 27,000 

Gas Turbine 7,600 1,600 15 40,000 

Steam Turbine 21,000 4,400 18 59,000 

Misc. Slow Speed 4,700 1,300 14 8,800 

Medium 

Speed 

9,400 2,500 13 6,000 

Steam Turbine 13,000 3,500 21 17,000 

Some ships may have visited ports in more than one region which could result in an 

overestimate of the hardware costs that are applied to each unique vessel as required. To prevent 

over-counting of vessels visiting the U.S., a factor was developed (see Equation 5-1) to account 

for this overlap. The number of unique ships in each region (identified by unique IMO numbers) 

was summed together to produce a total number of “unique” ships visiting all regions, this value 

was then reduced by the total number of unique ships that had visited U.S. ports in 2002 (from 

5-11 
Regulatory Impact Analysis 

the original baseline data) to eliminate the over-counting of ships that had visited multiple 

regions. 

Equation 5-1 Regional Fleet Overlap Reduction Factor 

#Unique _ Auto _ Carriers _ in _ Total _ Port _ Call _ Data 

Unique _ Auto _ Carriers _ by _ Re gion  % _ Actual _Unique _ Re gional _ Auto _ Carriers 

For example, a total of 300 unique auto carriers visited all included U.S. ports in 2002, 

yet when looking at unique ships on a regional basis and totaling all regions, 650 auto carriers 

appeared to visit. This implies that only 46 percent of auto carriers were “unique” and the 

additional 350 auto carriers were actually ships that had visited multiple regions. Therefore, only 

46 percent of the auto carriers in each regional fleet were assumed to be “unique.” The growth 

rates were only applied to the corrected count of “unique” ships in each region to estimate the 

regional fleet makeup in future years. 

Table 5-4 2002 Baseline Fleet of Ships and Regional Overlap Factor 

SHIP TYPE TOTAL UNIQUE 

SHIP VISITS TO 

U.S. PORTS IN 2020 

UNIQUE REGIONAL 

VISITING U.S. 

PORTS IN 2020 

REGIONAL 

OVERLAP FACTOR 

Auto Carrier 300 650 46% 

Bulk 2,500 3,600 68% 

Container 1,000 1,600 63% 

Gen. Cargo 980 1,700 57% 

Misc 24 50 49% 

Pass 110 200 57% 

Reefer 280 400 71% 

RoRo 120 200 58% 

Tanker 1,400 2,700 52% 

Total 6,700 11,000 62% 

5.2.2.2 Projected Fleet 

Within each region, the ship types were further broken down by engine type. The unique 

ship fleet within each region was then grown by ship type and engine type using the appropriate 

growth rate (Chapter 3) to estimate the makeup of the future fleet. To be consistent with the 

inventory, we used the same flag fractions to estimate how many of these vessels were U.S.-

flagged and how many were foreign-flagged.3 However, the flag fractions developed by the 

inventory are based on installed power, while the cost estimate is based on the number of vessels 

that called U.S. ports. According to MARAD, U.S.-flagged ships averaged 36 calls per vessel 

per year while foreign-flagged ships averaged only eight calls per vessel per year.4

 To eliminate 

the potential over-counting of the actual number of U.S.-flagged ships using the installed power 

method, which does not account for the disparity in the number of visits per year by ship, the 

U.S.-flagged vessel fraction presented in the inventory was reduced by 75 percent for this cost 

analysis and was applied to estimate the number of U.S. vessels in the future fleet. This resulted 

in a more representative estimated fleet in 2007 of 265 existing and 22 new U.S.-flagged vessels. 

According to MARAD, at the end of 2007 there were 189 U.S.-flagged vessels and another 25 

5-12 
Chapter 5: Engineering Cost Estimates 

on order that were over 10,000 deadweight tons.5,C Table 5-5 shows the estimated 2020 fleet of 

ships expected to visit U.S. ports. 

Table 5-5 Baseline 2002 and Estimated 2020 Fleet by Ship Type and Engine Type 

SHIP TYPE ENGINE 

SPEED 

US 

FLAG 

FOREIGN 

FLAG 

TOTAL US 

FLAG 

FOREIGN 

FLAG 

TOTAL 

2002 2020 

Auto 

Carrier 

SSD 8 220 228 20 510 530 

Auto 

Carrier 

MSD 1 50 51 4 120 124 

Bulk Carrier SSD 70 2,300 2,370 150 4,900 5,050 

Bulk Carrier MSD 20 300 320 30 660 690 

Bulk Carrier ST 8 60 68 15 120 135 

Container SSD 30 890 920 70 2,100 2,170 

Container MSD 3 120 123 7 290 298 

Container ST 2 30 32 4 70 74 

General 

Cargo 

SSD 20 630 650 40 1,400 1,440 

General 

Cargo 

MSD 3 120 123 8 300 308 

General 

Cargo 

ST 1 20 21 3 40 43 

Passenger SSD 2 70 72 4 150 154 

Passenger MSD 1 30 31 2 80 82 

Passenger ST 1 5 6 1 10 11 

Passenger GT 0 2 3 0 5 5 

Reefer SSD 5 120 195 10 130 140 

Reefer MSD 2 60 62 5 130 135 

RoRo SSD 2 60 62 4 140 144 

RoRo MSD 1 30 31 2 60 62 

RoRo GT 0 1 1 0 2 2 

RoRo ST 0 2 3 0 5 5 

Tanker SSD 30 1,000 1,030 60 2,200 2,260 

Tanker MSD 10 300 340 20 800 820 

Tanker GT 1 20 21 1 50 51 

Tanker ST 5 50 55 7 110 117 

Misc. SSD 0 7 8 1 15 16 

Misc. MSD 0 4 4 1 10 10 

Misc. ST 0 1 2 1 3 3 

Total: 220 6,500 6,730 470 14,410 14,880 

C Note that the number reported by MARAD includes only vessels greater than 10,000 DWT while the average 

vessel characteristics developed from the baseline fleet of ships visiting U.S. ports in 2002, and reported in 

Table 5-3, include some vessels less than 10,000 DWT assumed to have C3 propulsion engines, and are considered 

in this analysis. 

5-13 
Regulatory Impact Analysis 

5.2.2.3 Current Distillate Carrying Capacity of the Existing Global Fleet 

Although most ships primarily operate on residual fuel, they typically carry some amount 

of distillate fuel as well. Switching to the use of lower sulfur distillate fuel is the compliance 

strategy assumed here to be used by both new and existing ships in 2015 when the new lower 

sulfur fuel standards go into effect. To estimate the potential cost of this compliance, we first 

evaluated the distillate storage capacity of the current existing fleet to estimate how many ships 

may require additional hardware to accommodate the use of lower sulfur fuel. We performed 

this analysis on the entire global fleet listed in Lloyd’s database as of 2008.6 Of the nearly 

43,000 vessels listed, approximately 20,000 vessels had provided Lloyd’s with fuel tankage 

information, cruise speed, and propulsion engine power data. Using this information, we were 

able to estimate how far each individual vessel could travel on its existing distillate carrying 

capacity. 

The cruise speed provided by Lloyd’s was used to determine the vessel’s maximum 

speed using Equation 5-2 while transit speed was assumed to be 12 knots, and maneuver speed 

5.8 knots.7 The load factor used at cruise speed was 83 percent, while both the transit and 

maneuver load factors were estimated by cubing the ratio their respective speeds to the ship’s 

maximum speed. The same low load factors used in the inventory (for loads less than 20 

percent) were used here to adjust the brake specific fuel consumption (BSFC) because diesel 

engines are less efficient at low loads and the BSFC tends to increase. It was also assumed that 

ships spend a total of four hours per call in both transit and maneuver speeds. The BSFC values 

used here are the same as reported in the inventory section, 195 g/kWh for SSDs, 210 g/kWh for 

MSDs, and 305 g/kW for steam and gas turbines. The fuel consumed by auxiliary engines was 

also taken into account and the same ratios used in the inventory section (Chapter 3) of auxiliary 

power by ship type were used here to estimate the total installed auxiliary engine power and load 

factors at cruise, transit, and maneuver speeds for each ship. 

Equation 5-2: Maximum Speed 

Lloyds _ speed * 0.83  max imum _ speed 0.94 

To determine if the current distillate capacity of a particular ship was sufficient to call on 

the U.S. ECA without requiring additional hardware, we evaluated whether or not each ship 

could travel 1,140 nm, or the distance between the Port of Los Angeles and the Port of Tacoma. 

This distance was selected because it represents one of the longer trips a ship could travel 

without stopping at another port, and should overestimate the number of vessels that would 

require such a modification. The amount of fuel a ship will consume calling on a port and 

travelling a total distance of 1,140 nm was determined using the methodology described above. 

The total fuel used in each mode (cruise, transit and maneuver) by both main and auxiliary 

engines was summed and compared to the total amount of distillate fuel carried onboard. This 

provided an estimate of the number of ships that had sufficient distillate capacity onboard, and 

the number that did not, shown in Table 5-6. The resulting percentages of ships that were 

estimated to require a retrofit were then applied to the number of existing ships in the 2015 fleet 

to estimate the total cost of this compliance strategy for existing ships built prior to 2015. The 

same percentages were also applied to all new ships built as of 2015 to determine the number of 

5-14 
Chapter 5: Engineering Cost Estimates 

ships that may require additional hardware, beyond that of comparable new ships, to estimate the 

cost of this compliance strategy for new vessels. 

Table 5-6 Ships that Can Travel 1,140 nm on Existing Distillate Carrying Capacity 

SHIP TYPE TOTAL 

# 

SHIPS 

TOTAL # 

SHIPS THAT 

ONLY 

CARRY 

DISTILLATE 

DISTILLATE 

ONLY SHIPS 

THAT MAY 

NEED A 

MODIFICATION 

TOTAL # 

SHIPS THAT 

CARRY 

DISTILLATE 

+ ANOTHER 

FUEL 

SHIPS THAT 

CARRY 

DISTILLATE + 

ANOTHER 

FUEL THAT 

MAY NEED A 

MODIFICATION 

TOTAL # 

SHIPS THAT 

CARRY NO 

DISTILLATE 

% NO 

DISTILLATE 

TOTAL OF ALL 

SHIPS THAT 

MAY NEED A 

MODIFICATION 

# % # % # % 

General Cargo 4,600 1,900 9 0.5% 2,300 200 8.9% 370 8.2% 580 13% 

Tanker 5,900 740 60 8.7% 4,900 1,600 33% 280 4.7% 1,900 33% 

Container 1,900 45 1 2.2% 1,700 910 53% 140 7.3% 1000 55% 

Bulk Cargo 3,600 230 7 3.1% 3,000 1,600 53% 400 11% 2,000 55% 

RoRo 510 70 1 1.4% 380 30 7.6% 60 12% 90 18% 

Auto Carrier 360 20 0 0.0% 310 20 7.1% 40 10% 60 16% 

Misc. 1,600 1,100 4 0.4% 210 70 34% 210 14% 280 18% 

Passenger 710 170 10 6.0% 460 270 59% 85 12% 360 51% 

Reefer 530 60 0 0.0% 440 20 4.1% 25 4.8% 40 8.2% 

5.3 Engineering Costs for Freshly Manufactured Engines 

This section describes the projected variable and fixed costs to new engines. The 

component, tooling, labor and overhead costs are presented here separately for Tier 2 and Tier 3. 

First, the costs are presented as estimated for the six engine configurations described in Table 

5-2. Those values were then plotted by engine type and the resulting curve fit and $/kW 

equation is presented next. Finally, the stream of costs from 2010 through the year 2040 are 

presented with a three and seven percent discount rate. 

5.3.1 Tier 2 Variable Hardware Costs 

Tier 2 NOX standards are roughly 20 percent lower than the existing Tier 1 NOX 

standards. To meet these standards, in-cylinder emission control approaches such as 

electronically controlled high pressure common rail fuel systems, turbocharger optimization, 

compression ratio changes and electronically controlled exhaust valves could be used. There are 

no variable costs associated with the engine modifications (such as injection timing and valve 

timing adjustments, increased compression ratio, and nozzle optimization) as the changes are not 

expected to require any additional hardware.D However, the migration of some engines from 

mechanically controlled mechanical fuel injection (MFI) to common rail fuel systems will 

require additional hardware including a control unit, common rail accumulators, low and high 

pressure pumps, injectors and wiring harnesses. The cost of the Tier 2 technology presented here 

was developed using Tier 1 technology as the baseline. Table 5-7 shows the per engine variable 

cost estimates for the six engine configurations used in this analysis, Figure 5-1 shows the cost 

D MAN Diesel, “Exhaust Gas Emission Control Today and Tomorrow, August 19, 2008,” available at 

http://www.manbw.com/article_009187.html 

5-15 
Regulatory Impact Analysis 

curve developed from these data points to determine a $/kW equation applicable to other engine 

sizes. 

Table 5-7 Variable Costs for Going to Common Rail from Mechanical Fuel Injection Systems 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Hardware Cost to Engine Manufacturer 

Component Costs 

 Electronic Control Unit $3,500 $3,500 $3,500 $5,000 $5,000 $5,000

 Common Rail Accumulators (each) $2,000 $2,000 $2,000 $2,000 $2,000 $2,000

 Number of Accumulators 3 6 8 9 12 18

 Low Pressure Pump $2,000 $3,000 $4,000 $2,500 $3,500 $4,500

 High Pressure Pump $3,500 $4,500 $6,000 $4,500 $6,000 $8,000

 Modified injectors (each) $2,500 $2,500 $2,500 $3,500 $3,500 $3,500

 Number of injectors 9 12 16 18 24 36 

 Wiring Harness $2,500 $2,500 $2,500 $3,000 $3,000 $3,000 

Total Component Cost $40,000 $55,500 $72,000 $96,000 $125,500 $182,500 

Assembly 

Labor (hours) 120 160 200 200 250 300

 Cost ($23.85/hr) $2,900 $3,800 $4,800 $4,800 $5,900 $7,100

 Overhead @ 40% $1,100 $1,500 $1,900 $1,900 $2,400 $2,900 

Total Assembly Cost $4,000 $5,300 $6,700 $6,700 $8,300 $10,000 

Total Variable Cost $44,000 $60,800 $78,700 $102,700 $133,800 $192,500 

Markup @ 29% $12,800 $17,700 $22,800 $29,800 $38,800 $55,800 

Total Hardware RPE $56,800 $78,500 $101,500 $132,500 $172,600 $248,300 

5-16 
Chapter 5: Engineering Cost Estimates 

Costs of Migrating from Mechanical Fuel Injection 

to Common Rail

8 ,500, $132,000 

48,000, $2 48,000 

4 ,500 , $5 7 ,000 

9 ,50 0 , $ 78,000 

18,000 , $1 01,000 

y = 32,000Ln(x) - 215,000 

($) 

"S low S peed - M echanic al Injec tion" 

M edium S peed M ec hanic al Injec tion 

$300,000 

$250,000 

$200,000 

$150,000 

$100,000 

$50,000 

$0 

15,000, $173,00 0 

y = 67,000Ln(x) - 470,000 

0 10,000 20,000 30,000 40,000 50,000 60,000 (k W) 

Figure 5-1 Variable Cost Curve-Fit for Mechanically Controlled MFI to Common Rail Fuel Injection 

Systems 

It is estimated that approximately 20 percent of SSD and 60 percent of MSD will remain 

mechanically injected under the Tier 2 standards. We estimate that 5 percent of all SSD and 10 

percent of MSD are already equipped with common rail fuel systems.E Table 5-8 shows the 

expected migration from MFI to common rail for Tier 2 and Tier 3 NOX standards. Table 5-9 

shows the total cost estimate of the Tier 2 program per year from 2010 through 2040, these costs 

are included in the total cost of the coordinated strategy. Also included here are the costs 

associated with the proposed requirement to test each production engine (§1042.302). We 

estimate that, on average, this requirement would add a one-time cost of $10,000 for each new 

engine. 

Table 5-8 Mechanical Injection to Common Rail Technology Mix for Tier 2 and Tier 3 

TECHNOLOGY 

MIX 

 ENGINE 

SPEED 

PERCENT 

COMMON 

RAIL IN TIER 1 

PERCENT 

MECHANICAL MFI 

TO COMMON RAIL 

PERCENT ELECTRICAL 

MFI TO COMMON RAIL 

TOTAL 

PERCENT 

COMMON RAIL 

Tier 2 

(Tier 1 is baseline) 

SSD 5 75 0 80 

MSD 10 30 0 40 

Tier 3 SSD 80 5 15 100 

MSD 40 10 30 80 

E Conversations between Lou Browing of ICF and Amy Kopin of the U.S. EPA on 3/1/09. 

5-17 
Regulatory Impact Analysis 

Table 5-9 Estimated Tier 2 Variable Costs to U.S.-Flagged Vessels 2010-2040 

TOTAL US FLAG - TIER 2 COSTS 

Year SSD MSD Total 

Variable Variable 

2010 $0 $0 $0 

2011 $2,410,000 $164,000 $2,570,000 

2012 $2,510,000 $170,000 $2,680,000 

2013 $2,610,000 $176,000 $2,790,000 

2014 $2,710,000 $183,000 $2,890,000 

2015 $2,810,000 $190,000 $3,000,000 

2016 $2,700,000 $139,000 $2,840,000 

2017 $2,800,000 $144,000 $2,940,000 

2018 $2,910,000 $150,000 $3,060,000 

2019 $3,030,000 $155,000 $3,190,000 

2020 $3,150,000 $161,000 $3,310,000 

2021 $3,270,000 $167,000 $3,440,000 

2022 $3,400,000 $174,000 $3,570,000 

2023 $3,540,000 $181,000 $3,720,000 

2024 $3,680,000 $188,000 $3,870,000 

2025 $3,820,000 $195,000 $4,020,000 

2026 $3,980,000 $203,000 $4,180,000 

2027 $4,130,000 $211,000 $4,340,000 

2028 $4,300,000 $219,000 $4,520,000 

2029 $4,470,000 $227,000 $4,700,000 

2030 $4,650,000 $236,000 $4,890,000 

2031 $4,840,000 $246,000 $5,090,000 

2032 $5,040,000 $256,000 $5,300,000 

2033 $5,240,000 $266,000 $5,510,000 

2034 $5,460,000 $277,000 $5,740,000 

2035 $5,680,000 $288,000 $5,970,000 

2036 $5,910,000 $299,000 $6,210,000 

2037 $6,150,000 $311,000 $6,460,000 

2038 $6,400,000 $324,000 $6,720,000 

2039 $6,670,000 $337,000 $7,010,000 

2040 $6,940,000 $351,000 $7,290,000 

NPV @ 3% $75,400,000 $4,040,000 $79,400,000 

NPV @ 7% $43,200,000 $2,380,000 $45,600,000 

5.3.2 Tier 2 Fixed Costs 

Tier 2 fixed costs are comprised of those associated with engine modifications shown in 

Table 5-10, and those associated with the migration from MFI to common rail shown in Table 

5-11. The engine modification fixed cost estimates include modification of fuel injection timing, 

increasing the compression ratio, fuel injection nozzle optimization and Miller cycle effects. 

Retooling cost estimates include cylinder head and piston rod shim modifications to increase 

5-18 
Chapter 5: Engineering Cost Estimates 

compression ratios as well as to accommodate different injection nozzles. Differential costs for 

new common rail fuel injection systems that replace MFI systems include research and 

development, and retooling costs include modification of the cylinder head to accommodate the 

common rail fuel injection systems. The fixed costs associated with common rail are applied on 

a per vessel basis only to those engines expected to receive this technology; the fixed costs 

associated with the engine modifications are applied to all vessels. These costs are included in 

the total estimated cost of the coordinated strategy. 

Table 5-10 Fixed Costs Estimated for Tier 2 Engine Modifications 

ENGINE SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Fixed Costs 

R&D Costs (1 year R&D) $688,000 $688,000 $688,000 $688,000 $688,000 $688,000 

Retooling Costs $750,000 $750,000 $750,000 $1,000,000 $1,000,000 $1,000,000 

Marine Society Approval $5,000 $5,000 $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 40 40 

Years to recover 5 5 5 5 5 5 

Fixed cost/engine $7,200 $7,200 $7,200 $8,500 $8,500 $8,500 

Table 5-11 Fixed Costs for Mechanical Injection to Common Rail 

ENGINE SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed 

(rpm) 

650 550 500 130 110 100 

Fixed Costs 

R&D Costs (1 year 

R&D) 

$688,000 $688,000 $688,000 $688,000 $688,000 $688,000 

Retooling Costs $1,000,000 $1,000,000 $1,000,000 $1,000,000 $1,000,000 $1,000,000 

Marine Society 

Approval 

$5,000 $5,000 $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 40 40 

Years to recover 5 5 5 5 5 5 

Fixed cost/engine $8,500 $8,500 $8,500 $8,500 $8,500 $8,500 

5.3.3 Tier 3 Variable Hardware Costs 

Tier 3 NOX standards are approximately 80 percent lower than the existing Tier 1 NOX 

standards. To meet these standards, it is expected that SCR will be used along with the 

additional migration from mechanical injection systems to common rail, and engine 

modifications. The variable costs associated with Tier 3 include the continued migration to 

5-19 
Regulatory Impact Analysis 

common rail (see Table 5-8 for the expected percentages migrating for Tier 3). Table 5-7 shows 

these variable costs. Table 5-12 shows the variable costs associated with the migration from 

electronically controlled mechanical fuel injection (EFI) to common rail. A cost estimate is 

presented for each of the six engine configurations used in this analysis. Figure 5-2 shows the 

cost curve developed from these data points to determine a $/kW equation applicable to other 

engine sizes and types. 

The variable costs associated with the use of engine modifications for Tier 3 include the 

use of two stage turbochargers and electronic valve actuation, and are shown in Table 5-13, 

Figure 5-3 shows the cost curve used to determine a $/kW equation applicable to other engine 

sizes and types. The methodology used here to estimate the capacity of the SCR systems is 

based on the power rating of the propulsion engines only. Auxiliary engine power represents 

about 20 percent of the total installed power on a vessel; however, it would be unusual to operate 

both propulsion and auxiliary engines at 100 percent load. Typically, ships operate under full 

propulsion power only while at sea when the SCR is not operating; when nearing ports the 

auxiliary engine is operating at high loads while the propulsion engine is operating at very low 

loads. 

Table 5-14 shows the variable costs associated with the use of SCR, these costs include 

the urea tank, the reactor, dosage pump, urea injectors, piping, bypass valve, the acoustic horn, a 

cleaning probe and the control unit and wiring. Detailed costs for the urea tank are shown in 

Table 5-15 and are based on the storage of urea sufficient for up to 250 hours of normal 

operation of the SCR. It is envisioned that the urea tank is constructed of 304 stainless steel one 

mm thick due to the corrosive nature of urea, at a cost of approximately $2,700 per metric ton 

(tonne).F 

In this analysis, we estimated the average number of hours a ship may spend to call on a 

U.S. port: if the call was straight in and straight out at a distance of 200 nm, the average time 

spent was slightly over 35 hours. If the distance travelled was substantial, such as from the Port 

of Los Angeles to the Port of Tacoma, or 1140 nm, the average time spent travelling was 

approximately 75 hours. Therefore, the size of the tanks and corresponding $/kW value 

estimated here to carry enough urea for 250 hours of continuous operation may be an 

overestimate, and some owners may choose a smaller tank and to refill more often. Based on 

250 hours of operation, a range of urea tank sizes from 20 m3

 to approximately 260 m3 was 

estimated for the six different engine configurations used in this analysis. 

To understand what impacts this may have on the cargo hauling capacity of the ship, we 

looked at the ISO standard containers used today. Currently, over two-thirds of the containers in 

use today are 40 feet long, total slightly over 77m3

 and are the equivalent of two TEU.G The 

urea tank size range estimates provided here reflect a cargo equivalence of 0.5-2 TEUs, based on 

a capacity sufficient for 250 hours of operation. The TEU capacity of container ships, for 

example, continues to increase and can be as high as 13,000 TEUs; while not all ports are 

equipped to handle ships of this size, feeder ships (ships that carry containers to ocean-going 

F http://www.metalprices.com/FreeSite/metals/stainless_product/product.asp#Tables for 2006. G www.iicl.org, Institute of International Container Lessors 

5-20 
Chapter 5: Engineering Cost Estimates 

vessels in smaller ports) have also increased in size to carry as much as 2,000 TEUs.H Based on 

a rate of approximately $1,300 per TEU to ship a container from Asia to the US,I

a net profit 

margin of 10%, and an average of 16 trips per year, the estimated cost due to displaced cargo to 

call on a U.S.-Canada ECA may be $2,100.J,K,L The cost analysis presented here does not 

include displaced cargo costs due to the variability of tank sizes owners choose to install. 

The cost of Tier 3 technology as presented here was developed using Tier 2 as a baseline. 

Figure 5-4 shows the shows the cost curve used to determine a $/kW equation applicable to other 

engine types and sizes. The total variable hardware costs of Tier 3 estimated here include the 

fuel injection changes, engine modifications, SCR, and the costs associated with the proposed 

requirement to test each production engine (§1042.302). We estimate that, on average, this 

requirement would add a one-time cost of $10,000 for each new engine. Table 5-16 shows the 

total variable hardware costs estimated from 2010 through 2040 of Tier 3 for U.S.-flagged 

vessels, and Table 5-17 shows the total variable hardware costs estimated from 2010 through 

2040 of Tier 3 for foreign-flagged vessels. 

H Kristensen, Hans Otto Holmegaard, “Preliminary Ship Design of Container Ships, Bulk Carriers, Tankers, and 

Ro-Ro Ships. Assessment of Environmental Impact from Sea-Borne Transport Compared with Landbased 

Transport,” March, 2008. I

 http://people.hofstra.edu/geotrans/eng/ch2en/conc2en/maritimefreightrates.html J

 http://moneycentral.msn.com/investor/invsub/results/hilite.asp?Symbol=SSW K http://moneycentral.msn.com/investor/invsub/results/hilite.asp?Symbol=SSW L

 Based on a container ship carrying nearly 9,000 TEUs traveling from Hong Kong to the Port of Los Angeles 

(approximately 6,400 nm) with a cruise speed of 25 nm/hr, the round trip time is nearly 21 days and this trip could 

be made roughly 16 times per year. 

5-21 
Regulatory Impact Analysis 

Table 5-12 Variable Costs for EFI to Common Rail 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Hardware Costs to the Manufacturer 

Component Costs 

 Electronic Control Unit $500 $500 $500 $500 $500 $500 

 Common Rail Accumulators 

(each) 

$2,000 $2,000 $2,000 $2,000 $2,000 $2,000 

 Number of Accumulators 3 6 8 9 12 18

 Low Pressure Pump $1,000 $1,000 $1,000 $1,500 $1,500 $1,500 

 High Pressure Pump $1,500 $1,500 $1,500 $2,000 $2,000 $2,000 

 Modified injectors (each) $500 $500 $500 $750 $750 $750 

 Number of injectors 9 12 16 18 24 36

 Wiring Harness $500 $500 $500 $650 $650 $650 

Total Component Cost $14,000 $21,500 $27,500 $36,150 $46,650 $67,650 

Assembly 

Labor (hours) 40 60 80 40 60 80

 Cost ($23.85/hr) $950 $1,430 $1,910 $950 $1,430 $1,910 

Overhead @ 40% $380 $570 $760 $380 $570 $760 

Total Assembly Cost $1,330 $2,000 $2,670 $1,330 $2,000 $2,670 

Total Variable Cost $15,300 $23,500 $30,200 $37,500 $48,700 $70,300 

Markup @ 29% $4,400 $6,800 $8,800 $10,900 $14,100 $20,400 

Total Hardware RPE $19,700 $30,300 $39,000 $48,400 $62,800 $90,700 

5-22 
t t

Chapter 5: Engineering Cost Estimates 

C osts of Migrating from E lectronic Fuel Injection to 

C om m on Rail 

8 ,500, $48 ,0 00 

15,000, $63 ,0 00 

4 8,000, $91,000 

1 8,000, $39,000 

4 ,500, $20,000 

9,500, $30,000 

8 ,500, $48 ,0 00

15,000, $63 ,0 00

4 8,000, $91,000

1 8,000, $39,000

4 ,500, $20,00 0

9,500, $30,000

y = 24,000*Ln(x) - 170,000 

y = 14,000*Ln(x) - 96,000 

y = 24,000*Ln(x) - 170,000

y = 14,000*Ln(x) - 96,000

$0 

$10,000 

$20,000 

$30,000 

$40,000 

$50,000 

$60,000 

$70,000 

$80,000 

$90,000 

$100,000 

0 10,000 20,000 30,000 40,000 50,000 60,000 (k W) 

($) 

"Slow Speed - Elec tronic Injec tion" 

M edium Speed Elec tronic Injec tion "Sl S d El i I j i " 

Figure 5-2 Variable Cost Curve-Fit for Electronically Controlled MFI to Common Rail Fuel Injection 

Systems 

Table 5-13 Variable Costs for Engine Modifications Associated with Tier 3 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Hardware Costs to the Manufacturer 

Component Costs 

2 Stage Turbochargers 

(Incremental) 

$16,250 $20,900 $46,750 $28,000 $42,000 $61,000 

Electronic Intake Valves (each) $285 $285 $285 

Intake Valves per Cylinder 2 2 2 

Electronic Exhaust Valves 

(each) 

$285 $285 $285 $425 $425 $425 

Exhaust Valves per Cylinder 2 2 2 4 4 4 

Controller $3,750 $3,750 $3,750 $3,750 $3,750 $3,750 

Wiring $2,800 $2,800 $2,800 $2,800 $2,800 $2,800 

Total Component Cost $33,000 $41,000 $72,000 $45,000 $62,000 $88,000 

Markup @ 29% $10,000 $12,000 $21,000 $13,000 $18,000 $25,000 

Total Hardware RPE $43,000 $53,000 $93,000 $58,000 $80,000 $113,000 

5-23 
Regulatory Impact Analysis 

Estimated Costs for T ier 3 Engine Modifications 

48,0 00 , $113,4 56 

1 5,000, $80,174 

8 ,5 00 , $57,7 28 

18,000, $92,2 87 

9,500, $53,058 

4 ,500, $42,647 

y = 31661Ln(x) - 226946 

y = 3.7768x + 22378 

$0 

$20,000 

$40,000 

$60,000 

$80,000 

$100,000 

$120,000 

$140,000 

0 10,000 20,000 30,000 40,000 50,000 60,000 (k W) 

($) 

Slow Speed - Engine M odifications 

M edium Speed Engine M odifications 

Figure 5-3 Variable Cost Curve-Fit for Engine Modifications Associated with Tier 3 

5-24 
Chapter 5: Engineering Cost Estimates 

Table 5-14 Variable Costs Associated with the Use of SCR 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Hardware Costs to the Supplier 

Component Costs 

 Aqueous Urea Tank $1,200 $1,900 $2,800 $1,700 $2,400 $4,600 

Reactor $200,000 $295,000 $400,000 $345,000 $560,000 $1,400,000

 Dosage Pump $9,500 $11,300 $13,000 $11,300 $13,000 $15,000 

 Urea Injectors (each) $2,400 $2,400 $2,400 $2,400 $2,400 $2,400 

 Number of Urea 

Injectors 

3 6 8 12 16 24

 Piping $4,700 $5,600 $6,600 $5,600 $7,500 $9,500 

Bypass Valve $4,700 $5,600 $6,600 $5,600 $6,600 $7,500 

Acoustic Horn $9,500 $11,300 $13,000 $11,700 $14,000 $16,400 

 Cleaning Probe $575 $575 $575 $700 $700 $700 

Control Unit/Wiring $14,000 $14,000 $14,000 $19,000 $19,000 $19,000 

Total Component Cost $251,000 $360,000 $476,000 $429,000 $662,000 $1,530,000 

Assembly 

 Labor (hours) 1000 1200 1500 1200 1600 2000

 Cost ($23.85/hr) $23,900 $28,600 $35,800 $28,600 $38,200 $47,700 

Overhead @ 40% $9,500 $11,400 $14,300 $11,400 $15,300 $19,100 

Total Assembly Cost $33,400 $40,000 $50,100 $40,000 $53,500 $66,800 

Total Variable Cost $284,800 $399,700 $525,800 $469,400 $715,000 $1,597,100 

Markup @ 29% $82,600 $115,900 $152,500 $136,100 $207,300 $463,200 

Total Hardware RPE $367,400 $515,600 $678,300 $605,500 $922,300 $2,060,300 

5-25 
Regulatory Impact Analysis 

Estimated T ier 3 Selective C atalytic R edu ction 

C osts 

48,000, $2 ,100,000 

8 ,50 0 , $606 ,000 

4 ,500, $367,00 

9 ,500, $516,0 0 0 

y = 22.6x + 279,000 

$0 

$500,000 

$1,000,000 

$1,500,000 

$2,000,000 

$2,500,000 

($) 

S low S peed -S CR M edium S peed S CR 

1 5 ,000, $922,000 

18,000, $6 78,000 

y = -0.0004x2 + 57.2x + 145,000 

0 10,000 20,000 30,000 40,000 50,000 60,000 (k W ) 

Figure 5-4 Variable Cost Curve-Fit for SCR Systems 

Table 5-15 Variable Costs Associated with the Urea Tanks for use with SCR Systems

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Urea Tank Costs

 Urea Amount (kg) 12,910 27,255 51,642 22,645 39,961 127,875

 Density (kg/m^3) 1,090 1,090 1,090 1,090 1,090 1,090 

Tank Size (m^3) 14 30 57 21 37 117 

Tank Material (m^3) 0.04 0.06 0.09 0.05 0.07 0.14 

Tank Material Cost ($) $758 $1,248 $1,909 $977 $1,426 $3,093 

Assembly 

 Labor (hours) 5 6 7 10 12 15

 Cost ($23.85/hr) $119 $143 $167 $238 $286 $358 

Overhead @ 40% $48 $57 $67 $95 $114 $143 

Total Assembly Cost $167 $200 $234 $334 $401 $501 

Total Variable Cost $925 $1,448 $2,143 $1,310 $1,826 $3,594 

Markup @ 29% $268 $420 $621 $380 $530 $1,042 

Total Hardware RPE $1,194 $1,868 $2,765 $1,690 $2,356 $4,636 

5-26 
Chapter 5: Engineering Cost Estimates 

Table 5-16 Estimated Tier 3 Costs to U.S.-Flagged Vessels 2010-2040 ($Millions) 

TOTAL ESTIMATED US FLAG TIER 3 COSTS 

Year SSD MSD 

Total 

Variable Variable 

2010 $0.00 $0.00 $0.00 

2011 $0.00 $0.00 $0.00 

2012 $0.00 $0.00 $0.00 

2013 $0.00 $0.00 $0.00 

2014 $0.00 $0.00 $0.00 

2015 $0.00 $0.00 $0.00 

2016 $21.5 $2.98 $24.5 

2017 $22.4 $3.09 $25.5 

2018 $23.3 $3.21 $26.5 

2019 $24.2 $3.33 $27.5 

2020 $25.2 $3.46 $28.6 

2021 $26.2 $3.59 $29.8 

2022 $27.2 $3.73 $31.0 

2023 $28.3 $3.87 $32.2 

2024 $29.5 $4.02 $33.5 

2025 $30.6 $4.18 $34.8 

2026 $31.9 $4.35 $36.2 

2027 $33.2 $4.52 $37.7 

2028 $34.5 $4.69 $39.2 

2029 $35.9 $4.88 $40.8 

2030 $37.4 $5.07 $42.5 

2031 $38.9 $5.28 $44.2 

2032 $40.5 $5.49 $46.0 

2033 $42.2 $5.71 $47.9 

2034 $43.9 $5.94 $49.8 

2035 $45.7 $6.18 $51.9 

2036 $47.6 $6.43 $54.0 

2037 $49.6 $6.69 $56.3 

2038 $51.6 $6.96 $58.6 

2039 $53.8 $7.25 $61.0 

2040 $56.0 $7.54 $63.5 

NPV @ 3% $509 $69.3 $579 

NPV @ 7% $261 $35.6 $297 

5-27 
Regulatory Impact Analysis 

Table 5-17 Estimated Tier 3 Costs to Foreign Flagged Vessels 2010-2040 ($Millions) 

TOTAL FOREIGN FLAG 

Year SSD MSD Total 

Variable Variable 

2010 $0 $0 $0 

2011 $0 $0 $0 

2012 $0 $0 $0 

2013 $0 $0 $0 

2014 $0 $0 $0 

2015 $0 $0 $0 

2016 $754 $89 $843 

2017 $784 $93 $877 

2018 $817 $97 $914 

2019 $850 $101 $951 

2020 $885 $105 $991 

2021 $922 $110 $1,032 

2022 $960 $115 $1,075 

2023 $1,000 $120 $1,119 

2024 $1,040 $125 $1,165 

2025 $1,080 $130 $1,210 

2026 $1,130 $136 $1,266 

2027 $1,180 $142 $1,322 

2028 $1,230 $148 $1,378 

2029 $1,280 $154 $1,434 

2030 $1,330 $161 $1,491 

2031 $1,390 $168 $1,558 

2032 $1,450 $175 $1,625 

2033 $1,510 $183 $1,693 

2034 $1,570 $191 $1,761 

2035 $1,640 $199 $1,839 

2036 $1,710 $208 $1,918 

2037 $1,780 $217 $1,997 

2038 $1,850 $227 $2,077 

2039 $1,930 $237 $2,167 

2040 $2,020 $247 $2,267 

NPV @ 3% $18,100 $2,180 $20,280 

NPV @ 7% $9,260 $1,110 $10,370 

5.3.4 Tier 3 Fixed Costs 

The Tier 3 fixed costs presented here include those associated with the use of SCR, 

including research and development costs, marine society approval, and retooling for the 

redesign of the exhaust system to accommodate the SCR unit, and are shown in Table 5-18. The 

migration to common rail from for Tier 3 is primarily from EFI which includes modification of 

the cylinder head to accommodate common rail fuel injection systems, these costs are shows in 

5-28 
Chapter 5: Engineering Cost Estimates 

Table 5-18. The fixed costs associated with the migration from MFI to common rail are shown 

above in Table 5-11. Finally, Tier 3 also includes the fixed costs associated with the engine 

modifications which include the use of two stage turbochargers and electronic valve actuation; 

the retooling costs represent turbocharger redesign and valve actuation modifications. These 

costs are applied to U.S.-flagged vessels and are included in the total cost estimate of the 

coordinated strategy. 

Table 5-18 Fixed Costs Associated with the use of SCR for Tier 3 

ENGINE SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Fixed Costs 

R&D Costs (1 year 

R&D) 

$1,376,000 $1,376,000 $1,376,000 $1,376,000 $1,376,000 $1,376,000 

Retooling Costs $2,000,000 $2,000,000 $2,000,000 $2,000,000 $2,000,000 $2,000,000 

Marine Society 

Approval 

$5,000 $5,000 $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 40 40 

Years to recover 5 5 5 5 5 5 

Fixed cost/engine $16,900 $16,900 $16,900 $16,900 $16,900 $16,900 

Table 5-19 Fixed Costs Associated with the Migration of EFI to Common Rail 

ENGINE SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Fixed Costs 

R&D Costs (0.5 year 

R&D) 

$344,000 $344,000 $344,000 $344,000 $344,000 $344,000 

Retooling Costs $500,000 $500,000 $500,000 $500,000 $500,000 $500,000 

Marine Society Approval $5,000 $5,000 $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 40 40 

Years to recover 5 5 5 5 5 5 

Fixed cost/engine $4,200 $4,200 $4,200 $4,200 $4,200 $4,200 

5-29 
Regulatory Impact Analysis 

Table 5-20 Fixed Costs Associated with Engine Modifications Used for Tier 3 

ENGINE SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Fixed Costs 

R&D Costs (1 year 

R&D) 

$688,000 $688,000 $688,000 $688,000 $688,000 $688,000 

Retooling Costs $1,000,000 $1,000,000 $1,000,000 $1,320,000 $1,320,000 $1,320,000 

Marine Society 

Approval 

$5,000 $5,000 $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 40 40 

Years to recover 5 5 5 5 5 5 

Fixed cost/engine $8,500 $8,500 $8,500 $10,000 $10,000 $10,000 

5.4 Engineering Costs for Existing Engines 

The October 2008 amendments to MARPOL Annex VI include NOX standards that apply 

to existing engines on ships constructed on or after January 1, 1990 but prior to January 1, 2000 

for marine diesel engines with a per cylinder displacement of at least 90 liters and a power output 

of over 5,000 kW. Subject engines must be retrofit with components that reduce NOX 

approximately 20 percent and be certified to confirm the engine meets Tier I standards. 

5.4.1 Variable Costs for the Annex VI Existing Engine Program 

Most manufacturers will comply with the existing engine standard by providing retrofit 

kits which contain modified fuel injectors and possibly modified injection timing. The costs for 

the retrofit kit include new fuel injectors plus three months of research and development to 

modify the timing, and consist only of the incremental cost associated with the required emission 

reductions. A Marine Society approval certificate is also estimated to be included. As part of 

the IMO regulations, the retrofit kit cannot exceed $375 Special Drawing Rights (SDR)/metric 

ton of NOX reduced. The currency value of the SDR is determined by summing the values in 

U.S. dollars, based on market exchange rates, of a basket of major currencies (the U.S. dollar, 

Euro, Japanese yen, and pound sterling). The SDR currency value is calculated daily and the 

valuation basket is reviewed and adjusted every five years, the conversion rate used in this 

analysis is $1.49 per SDR. Table 5-21 presents the estimated variable costs associated with the 

existing engine program. The costs are presented only for the engine configurations used in this 

analysis that are subject to the program. 

5-30 
Chapter 5: Engineering Cost Estimates 

Table 5-21 Variable Costs Associated with the Existing Engine Program 

SPEED MEDIUM LOW LOW LOW 

Engine Power (kW) 18,000 8,500 15,000 48,000 

Cylinders 16 6 8 12 

Liters/cylinder 95 380 650 1400 

Engine Speed (rpm) 500 130 110 100 

Hardware Cost to Engine Manufacturer 

Component Costs 

 Number of Injectors 16 18 24 36 

 Improved Fuel Valves 

(each) 

$235 $235 $375 $470 

Total Component Cost $3,760 $4,230 $9,000 $16,920 

Assembly 

 Labor (hours) 120 168 216 312 

 Cost ($23.85/hr) $2,860 $4,010 $5,150 $7,440 

Overhead @ 40% $1,150 $1,600 $2,060 $2,980 

Total Assembly Cost $4,010 $5,610 $7,210 $10,420 

Total Variable Cost $7,770 $9,840 $16,210 $27,340 

Markup @ 29% $2,250 $2,850 $4,700 $7,930 

Total Hardware RPE $10,020 $12,690 $20,910 $35,270 

Existing Engine Program Costs 

48,000, $35,300 

8,500, $12,700 

15,000, $20,900 

18,000, $10,000 

y = 13,000*Ln(x) - 104,000 

$0 

$5,000 

$10,000 

$15,000 

$20,000 

$25,000 

$30,000 

$35,000 

$40,000 

0 10,000 20,000 30,000 40,000 50,000 60,000 (kW) 

($) 

Slow Speed Medium Speed 

Figure 5-5 Variable Cost Curve Fit for Existing Engine Program 

5-31 
Regulatory Impact Analysis 

5.4.2 Fixed Costs for the Annex VI Existing Engine Program 

The fixed costs associated with the existing engine program are presented below in Table 

5-22 and include the costs for research and development and marine society approval. These 

costs as applied to U.S.-flagged vessels are included in the total cost estimate of the coordinated 

strategy. 

Table 5-22 Fixed Costs Associated with the Existing Engine Program 

SPEED MEDIUM LOW LOW LOW 

Engine Power (kW) 18,000 8,500 15,000 48,000 

Cylinders 16 6 8 12 

Liters/cylinder 95 380 650 1400 

Engine Speed (rpm) 500 130 110 100 

Fixed Costs 

R&D Costs (0.25 year R&D) $172,000 $172,000 $172,000 $172,000 

Marine Society Approval $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 

Years to recover 5 5 5 5 

Fixed cost/engine $880 $880 $880 $880 

5.4.3 Total Estimated Costs of the Annex VI Existing Engine Program 

The costs for the existing engine program for Tier 0 (pre-control) engines were developed 

in the same manner modeled in the inventory (Chapter 3) as being applicable to 80 percent of 

1990 through 1999 model year engines greater than 90 liters per cylinder (L/cyl) and 5,000 kW 

starting in 2011, with a five year phase-in. In this cost analysis, the research and development 

fixed costs were phased in from 2010-2014, while the certification fixed costs were applied in 

2010. To estimate the cost of the existing engine program to U.S.-flagged vessels, we had to 

determine how many ships built between 1990-1999 there would be in each year from 2011 

through 2015 using the age distribution analysis from the inventory. The $/kW values were then 

applied to the portion of the fleet each year between 2011 and 2015 expected to be subject to 

these standards. Table 5-23 presents the total estimated costs of the existing engine program to 

U.S.-flagged vessels; these costs are included in the total cost estimate of the coordinated 

strategy. 

Table 5-23 Total Estimated Costs of the Existing Engine Program to U.S.-Flagged Vessels 

TOTAL US FLAG 

Year SSD MSD Total 

2010 $0 $0 $0 

2011 $145,000 $6,800 $152,000 

2012 $139,000 $5,500 $144,000 

2013 $132,000 $4,600 $137,000 

2014 $126,000 $3,600 $129,000 

2015 $128,000 $3,000 $131,000 

2016 $0 $0 $0 

5-32 
Chapter 5: Engineering Cost Estimates 

5.5 Engineering Costs for Vessels 

5.5.1 Freshly Manufactured Vessels 

5.5.1.1 Variable Costs 

The vessel costs associated with the coordinated strategy are those that may be incurred if 

additional hardware is required to accommodate the use of lower sulfur fuel. This section 

discusses the costs that may be incurred by some newly built ships if additional fuel tank 

equipment, beyond that installed on comparable new ships, is required to meet lower sulfur fuel 

standards in the ECA. Based on existing vessel fleet data, we estimate that nearly one-third of 

new vessels may need additional equipment installed to accommodate additional lower sulfur 

fuel storage capacity. The size of the tank is dependent on the frequency with which the 

individual ship owner prefers to fill the lower sulfur fuel tank. 

Costs include additional distillate fuel storage tanks, an LFO fuel separator, an HFO/LFO 

blending unit, a 3-way valve, an LFO cooler, filters, a viscosity meter, and various pumps and 

piping, these costs are shown in Table 5-24. The estimates of the additional tank costs are shown 

in Table 5-25. Distillate tanks are assumed to be constructed of cold rolled steel one mm thick, 

double walled, and estimated to carry capacity sufficient for 250 hours of propulsion and 

auxiliary engine operation. Similar to the urea tank size estimation presented in this analysis, 

this is most likely an overestimate of the amount of lower sulfur fuel a ship owner would need to 

carry, resulting in an overestimate of the total cost to existing and new vessels. The tank size 

based on 250 hours of operation and based on the six different engine configuration used in this 

analysis ranges from 240 m3

 to nearly 2,000 m3

. This would be the equivalent of 6-50 TEUs. 

This cost analysis does not reflect the costs of displaced cargo as there are other design options 

such as partitioning of a residual fuel tank to allow for lower sulfur fuel capacity which would 

reduce the amount of additional space required, nor does this analysis reflect the possibility that 

some ships may have already been designed to carry smaller amounts of distillate fuel in separate 

tanks for purposes other than continuous propulsion. 

The costs were developed for each of the six different engine sizes and types used in this 

analysis. These values were used to develop a curve fit, Figure 5-6, used to determine a $/kW 

equation applicable to other engine sizes and types. Table 5-27 presents the total estimated 

variable hardware costs for U.S.-flagged vessels associated with the installation of additional 

equipment to enable the use of lower sulfur fuel, these costs are included in the total estimated 

cost of the coordinated strategy. While the estimated costs to new foreign-flagged vessels are 

presented here in Table 5-28, they are not included as a part of the total cost of the coordinated 

strategy as this technology will be used globally and will result in emissions reductions in many 

other countries. 

5-33 
Regulatory Impact Analysis 

Table 5-24 Variable Costs Associated with the use of Lower Sulfur Fuel - New Vessels 

Speed Medium Medium Medium Low Low Low 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Hardware Cost to Supplier 

Component Costs 

 Additional Tanks $3,400 $5,500 $8,300 $4,600 $6,500 $13,700 

 LFO Separator $2,800 $3,300 $3,800 $3,800 $4,200 $4,700 

 HFO/LFO Blending Unit $4,200 $4,700 $5,600 $4,700 $5,600 $6,600 

 3-Way Valve $950 $1,400 $1,900 $1,400 $1,900 $2,800 

 LFO Cooler $2,400 $2,800 $3,300 $2,800 $3,800 $4,700 

Filters $950 $950 $950 $950 $950 $950 

Viscosity Meter $1,400 $1,400 $1,400 $1,400 $1,400 $1,400 

 Piping/Pumps $2,000 $2,000 $2,000 $2,000 $2,000 $2,000 

Total Component Cost $18,100 $22,100 $27,300 $21,600 $26,400 $36,900 

Assembly 

 Labor (hours) 240 320 480 320 480 600

 Cost ($23.85/hr) $5,700 $7,600 $11,400 $7,600 $11,400 $14,300 

Overhead @ 40% $2,300 $3,100 $4,600 $3,100 $4,600 $5,700 

Total Assembly Cost $8,000 $10,700 $16,000 $10,700 $16,000 $20,000 

Total Variable Cost $26,100 $32,700 $43,300 $32,300 $42,400 $56,900 

Markup @ 29% $7,600 $9,500 $12,600 $9,400 $12,300 $16,500 

Total Hardware RPE $33,700 $42,200 $55,900 $41,700 $54,700 $73,400 

5-34 
Chapter 5: Engineering Cost Estimates 

Fuel Sw itching Hardw are Costs - New Vessels 

4 8,000, $73,000 

8 ,500, $42,000 

15,0 00 , $55,0 00 

18,000, $56,000 

9,500, $42,000 

4 ,500, $34,000 

y = 18,000Ln(x) - 120,000 

y = 1.64x + 26,000 

$0 

$10,000 

$20,000 

$30,000 

$40,000 

$50,000 

$60,000 

$70,000 

$80,000 

0 10,000 20,000 30,000 40,000 50,000 60,000 (k W) 

($) 

Slow Speed - Fuel S witching Hardware Cos ts Ex is ting Ves sels 

M edium Speed Fuel S witching Cos ts -Ex is ting Ves sels 

Figure 5-6 Variable Cost Curve Fit for Fuel Switching Vessels Costs to New Vessels 

5-35 
Regulatory Impact Analysis 

Table 5-25 Variable Cost to New Vessels Associated with Fuel Switching - Extra Tankage 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power 

(kW) 

4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed 

(rpm) 

650 550 500 130 110 100 

Propulsion 

 BSFC (g/kWh) 210 210 210 195 195 195 

Load factor 73% 73% 73% 73% 73% 73% 

Auxiliary 

 Power (kW) 1,000 2,200 4,100 1,900 3,400 10,900 

 BSFC (g/kWh) 227 227 227 227 227 227 

Load factor 31% 31% 31% 31% 31% 31% 

Combined

 Fuel Amount (kg) 190,000 401,000 760,000 336,000 592,000 1,896,000 

 Density (kg/m^3) 960 960 960 960 960 960 

Tank Size (m^3) 238 501 950 350 617 1,975 

Tank Material 

(m^3) 

0.46 0.75 1.15 0.59 0.87 1.88 

Tank Material Cost 

($) 

$2,500 $4,100 $6,200 $3,200 $4,700 $10,100 

Assembly 

 Labor (hours) 5 6 7 10 12 15 

 Cost ($23.85/hr) $119 $143 $167 $238 $286 $358 

 Overhead@40% $48 $57 $67 $95 $114 $143 

Total Assembly 

Cost 

$167 $200 $234 $334 $401 $501 

Total Variable 

Cost 

$2,600 $4,300 $6,500 $3,500 $5,100 $10,600 

Markup @ 29% $800 $1,200 $1,900 $1,000 $1,500 $3,100 

Total Hardware 

RPE 

$3,400 $5,500 $8,400 $4,500 $6,600 $13,700 

5.5.1.2 Fixed Engineering Costs 

The fixed vessel costs associated with the use of switching to lower sulfur fuel are shown 

in Table 5-26. These costs include research and development, and marine society approval; it 

assumed there would not be any new retooling costs incurred. These costs as applied to U.S.-

flagged vessels are included in the total estimated cost of the coordinated strategy. 

5-36 
Chapter 5: Engineering Cost Estimates 

Table 5-26 Fixed Costs for Fuel Switching Hardware Costs on New Vessels 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Fixed Costs 

R&D Costs (0.25 year 

R&D) 

$172,000 $172,000 $172,000 $172,000 $172,000 $172,000 

Marine Society Approval $5,000 $5,000 $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 40 40 

Years to recover 5 5 5 5 5 5 

Fixed cost/engine $880 $880 $880 $880 $880 $880 

5.5.1.3 Total Cost to New Vessels 

Total vessel hardware cost estimates associated with the coordinated strategy were 

developed from the number of new ships expected to require additional hardware to 

accommodate the use of lower sulfur fuel (approximately one-third as discussed in Section 

5.2.2.3). All new vessels were considered to have the average characteristics (including 

propulsion power) shown in Table5-3. The variable and fixed cost estimates developed for the 

six engine configurations shown above were used to develop $/kW equations that were applied 

to the number of new ships, by ship and engine type, expected to require this additional 

hardware. The total estimated hardware costs to new U.S.-flagged vessels are shown below in 

Table5-27, these costs are included in the total cost associated with the coordinated strategy. 

The total estimated hardware costs to foreign-flagged vessels are shown in Table 5-27, however, 

these costs are only shown here for the sake of completeness and are not included in the total 

cost estimate of the coordinated strategy. 

5-37 
Regulatory Impact Analysis 

Table 5-27 Total Estimated New Vessel Hardware Costs - U.S.-Flagged 

TOTAL US FLAG 

Year SSD MSD Gas Turbine Steam Turbine Total 

2010 $0 $0 $0 $0 $0 

2011 $0 $0 $0 $0 $0 

2012 $0 $0 $0 $0 $0 

2013 $0 $0 $0 $0 $0 

2014 $0 $0 $0 $0 $0 

2015 $499,000 $38,100 $3,900 $125,000 $666,000 

2016 $518,000 $39,500 $4,100 $129,000 $691,000 

2017 $539,000 $41,000 $4,300 $133,000 $717,000 

2018 $560,000 $42,500 $4,500 $138,000 $745,000 

2019 $582,000 $44,100 $4,700 $143,000 $774,000 

2020 $605,000 $45,800 $4,900 $148,000 $804,000 

2021 $628,000 $47,500 $5,200 $153,000 $834,000 

2022 $653,000 $49,300 $5,400 $158,000 $866,000 

2023 $679,000 $51,200 $5,700 $163,000 $899,000 

2024 $706,000 $53,100 $5,900 $169,000 $934,000 

2025 $734,000 $55,200 $6,200 $175,000 $970,000 

2026 $764,000 $57,300 $6,500 $181,000 $1,010,000 

2027 $794,000 $59,500 $6,800 $187,000 $1,050,000 

2028 $826,000 $61,800 $7,200 $194,000 $1,090,000 

2029 $859,000 $64,200 $7,500 $200,000 $1,130,000 

2030 $894,000 $66,700 $7,900 $207,000 $1,180,000 

2031 $930,000 $69,300 $8,200 $215,000 $1,220,000 

2032 $968,000 $72,100 $8,600 $222,000 $1,270,000 

2033 $1,010,000 $74,900 $9,000 $230,000 $1,320,000 

2034 $1,050,000 $77,900 $9,500 $238,000 $1,380,000 

2035 $1,090,000 $81,000 $9,900 $246,000 $1,430,000 

2036 $1,140,000 $84,200 $10,400 $255,000 $1,490,000 

2037 $1,180,000 $87,600 $10,900 $264,000 $1,540,000 

2038 $1,230,000 $91,100 $11,400 $274,000 $1,610,000 

2039 $1,280,000 $94,800 $12,000 $283,000 $1,670,000 

2040 $1,330,000 $98,600 $12,600 $293,000 $1,730,000 

NPV @ 3% $12,600,000 $946,000 $109,000 $2,960,000 $16,600,000 

NPV @ 7% $6,610,000 $497,000 $56,000 $1,570,000 $8,730,000 

5-38 
Chapter 5: Engineering Cost Estimates 

Table 5-28 Total Estimated New Vessel Hardware Costs – Foreign Flagged 

TOTAL FOREIGN FLAG 

Year SSD MSD Gas Turbine Steam Turbine Total 

2010 $0 $0 $0 $0 $0 

2011 $0 $0 $0 $0 $0 

2012 $0 $0 $0 $0 $0 

2013 $0 $0 $0 $0 $0 

2014 $0 $0 $0 $0 $0 

2015 $16,500,000 $1,090,000 $127,000 $1,630,000 $19,300,000 

2016 $17,200,000 $1,140,000 $133,000 $1,690,000 $20,200,000 

2017 $17,800,000 $1,190,000 $140,000 $1,760,000 $20,900,000 

2018 $18,600,000 $1,240,000 $147,000 $1,830,000 $21,800,000 

2019 $19,300,000 $1,290,000 $154,000 $1,900,000 $22,600,000 

2020 $20,100,000 $1,350,000 $161,000 $1,970,000 $23,600,000 

2021 $20,900,000 $1,410,000 $169,000 $2,050,000 $24,500,000 

2022 $21,800,000 $1,470,000 $177,000 $2,130,000 $25,600,000 

2023 $22,700,000 $1,530,000 $186,000 $2,220,000 $26,600,000 

2024 $23,600,000 $1,600,000 $194,000 $2,310,000 $27,700,000 

2025 $24,600,000 $1,670,000 $204,000 $2,400,000 $28,900,000 

2026 $25,600,000 $1,740,000 $214,000 $2,490,000 $30,000,000 

2027 $26,600,000 $1,820,000 $224,000 $2,590,000 $31,200,000 

2028 $27,700,000 $1,890,000 $235,000 $2,700,000 $32,500,000 

2029 $28,900,000 $1,980,000 $246,000 $2,810,000 $33,900,000 

2030 $30,100,000 $2,060,000 $258,000 $2,920,000 $35,300,000 

2031 $31,300,000 $2,160,000 $271,000 $3,040,000 $36,800,000 

2032 $32,600,000 $2,250,000 $284,000 $3,160,000 $38,300,000 

2033 $34,000,000 $2,350,000 $298,000 $3,290,000 $39,900,000 

2034 $35,400,000 $2,450,000 $312,000 $3,420,000 $41,600,000 

2035 $36,900,000 $2,560,000 $327,000 $3,560,000 $43,300,000 

2036 $38,500,000 $2,670,000 $343,000 $3,710,000 $45,200,000 

2037 $40,100,000 $2,790,000 $360,000 $3,860,000 $47,100,000 

2038 $41,800,000 $2,920,000 $377,000 $4,010,000 $49,100,000 

2039 $43,500,000 $3,050,000 $395,000 $4,180,000 $51,100,000 

2040 $45,400,000 $3,180,000 $414,000 $4,350,000 $53,300,000 

NPV @ 3% $424,000,000 $28,900,000 $3,590,000 $41,200,000 $497,000,000 

NPV @ 7% $221,000,000 $15,000,000 $1,850,000 $21,600,000 $260,000,000 

5.5.2 Existing Vessels Hardware Costs 

5.5.2.1 Existing Vessel Variable Costs 

Existing vessels are required to meet the ECA lower sulfur fuel standards beginning in 

2015. The existing vessel hardware costs associated with the coordinated strategy are those that 

may be incurred if additional hardware is required to accommodate the use of lower sulfur. 

Based on the methodology described above in Section 5.1.2.2, it is estimated that over two-thirds 

of vessels would not require additional hardware. For the remaining vessels, the hardware 

requirements would be similar to those discussed in Section 5.4.1, and would most likely include 

5-39 
Regulatory Impact Analysis 

additional distillate fuel storage tanks, an LFO fuel separator, an HFO/LFO blending unit, a 3-

way valve, an LFO cooler, filters, a viscosity meter, and various pumps and piping. This cost 

analysis does not reflect other design options such as partitioning of a residual fuel tank to allow 

for lower sulfur fuel capacity which would reduce the amount of additional space required, nor 

does this analysis reflect the possibility that some ships may have already been designed to carry 

smaller amounts of distillate fuel in separate tanks for purposes other than continuous propulsion. 

Similar to the costs to new vessels, the existing vessel hardware cost analysis assumes 

sufficient capacity for 250 hours of main and auxiliary engine operation, which may be an 

overestimate of the amount of fuel necessary to call on U.S. ports. The variable costs associated 

with existing vessels are shown in Table 5-29. Retrofitting a vessel is expected to require more 

effort than making upgrades during new vessel construction, to address this, additional labor is 

allocated for installing equipment to accommodate the use of lower sulfur fuel on existing ships. 

The cost of the extra tanks is assumed to be the same as that for new vessels, shown in Table -24. 

The costs were developed for each of the six different engine sizes and types used in this 

analysis, and are shown plotted in Figure 5-7 from which a curve fit was developed to obtain an 

equation for the $/kW cost of this technology that could be applied to other engine types and 

sizes. 

Table 5-29 Variable Costs Associated with the use of Lower Sulfur Fuel - Existing Vessels 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Hardware Cost to Supplier 

Component Costs 

 Additional Tanks $3,400 $5,500 $8,300 $4,600 $6,500 $13,700 

 LFO Separator $2,800 $3,300 $3,800 $3,800 $4,200 $4,700 

 HFO/LFO Blending 

Unit 

$4,200 $4,700 $5,600 $4,700 $5,600 $6,600 

 3-Way Valve $950 $1,400 $1,900 $1,400 $1,900 $2,800 

 LFO Cooler $2,400 $2,800 $3,300 $2,800 $3,800 $4,700 

Filters $950 $950 $950 $950 $950 $950 

Viscosity Meter $1,400 $1,400 $1,400 $1,400 $1,400 $1,400 

 Piping/Pumps $2,000 $2,000 $2,000 $2,000 $2,000 $2,000 

Total Component 

Cost 

$18,100 $22,100 $27,300 $21,600 $26,400 $36,900 

Assembly 

 Labor (hours) 480 640 960 640 960 1200

 Cost ($23.85/hr) $11,400 $15,300 $22,900 $15,300 $22,900 $28,600

 Overhead @ 40% $4,600 $6,100 $9,200 $6,100 $9,200 $11,400 

Total Assembly Cost $16,000 $21,400 $32,100 $21,400 $32,100 $40,000 

Total Variable Cost $34,100 $43,400 $59,300 $43,000 $58,400 $77,000 

Markup @ 29% $9,900 $12,600 $17,200 $12,500 $17,000 $22,300 

Total Hardware RPE $44,000 $55,000 $76,500 $55,500 $75,400 $99,300 

5-40 
Chapter 5: Engineering Cost Estimates 

Fuel Sw itching Hardw are Costs - Existing Vessels 

48,000, $99,300 

8 ,500, $55,400 

4 ,500, $44,000 

9 ,5 00 , $56,000 

y = 24,600Ln(x) - 164,700 

y = 2.409x + 33,200 

($) 

Slow Speed Fuel S witching -New Ves sels 

M edium Speed Fuel S witching -New Ves sels 

$120,000 

$100,000 

$80,000 

$60,000 

$40,000 

$20,000 

$0 

15,000, $75,400 

18,0 00 , $76,500 

0 10,000 20,000 30,000 40,000 50,000 60,000 (k W ) 

Figure 5-7 Variable Cost Curve Fit for Fuel Switching Vessels Costs to Existing Vessels 

5.5.2.2 Fixed Engineering Costs 

The fixed costs associated with the use of switching to lower sulfur fuel for existing 

vessels are shown in Table 5-30, and are similar to the cost for new vessels; however, additional 

research and development is provided to test systems on existing ships. The fixed costs are 

applied to U.S.-flagged vessels and are included in the total estimated cost of the coordinated 

strategy. 

Table 5-30 Fixed Costs for Fuel Switching Hardware Costs on Existing Vessels 

SPEED MEDIUM MEDIUM MEDIUM LOW LOW LOW 

Engine Power (kW) 4,500 9,500 18,000 8,500 15,000 48,000 

Cylinders 9 12 16 6 8 12 

Liters/cylinder 35 65 95 380 650 1400 

Engine Speed (rpm) 650 550 500 130 110 100 

Fixed Costs 

R&D Costs (0.33 year 

R&D) 

$227,040 $227,040 $227,040 $227,040 $227,040 $227,040 

Marine Society Approval $5,000 $5,000 $5,000 $5,000 $5,000 $5,000 

Engines/yr. 40 40 40 40 40 40 

Years to recover 5 5 5 5 5 5 

Fixed cost/engine $1,160 $1,160 $1,160 $1,160 $1,160 $1,160 

5-41 
Regulatory Impact Analysis 

5.5.2.3 Total Costs to Existing Vessels 

The total estimated cost to existing vessels includes both variable and fixed costs. 

Analysis of these costs to both U.S.-flagged and foreign-flagged vessels that affect U.S. 

emissions was completed and while U.S.-flagged costs are included in the total estimated cost of 

the coordinated strategy, foreign-flagged costs are presented only for the benefit of ship owners. 

The fuel sulfur standards take effect in 2015, and for the purposes of simplification all vessels 

were assumed to be modified by 2015 therefore all hardware costs were applied in 2015. The 

cost to existing U.S.-flagged vessels is estimated to be $10.4 million in 2015. The cost to 

foreign-flagged vessels in 2015 is estimated to be $317 million. Table 5-31 shows the estimated 

costs to U.S- and foreign-flagged vessels through 2015 included all fixed and variable costs. 

Table 5-31 Estimated Costs to Existing Vessels - U.S. and Foreign Flagged 

TOTAL US FLAG 

Year Fixed Variable Total 

2010 $155,700 $0 $155,700 

2011 $161,500 $0 $161,500 

2012 $167,600 $0 $167,600 

2013 $173,800 $0 $173,800 

2014 $180,300 $0 $180,300 

2015 $0 $10,400,000 $10,400,000 

TOTAL FOREIGN FLAG 

Year Fixed Variable Total 

2010 $4,671,000 $0 $4,671,000 

2011 $4,855,000 $0 $4,855,000 

2012 $5,048,000 $0 $5,048,000 

2013 $5,249,000 $0 $5,249,000 

2014 $5,458,000 $0 $5,458,000 

2015 $0 $316,900,000 $316,900,000 

5.6 Operating Costs 

5.6.1 Tier II Fuel Consumption Impacts 

We estimate a two percent fuel consumption penalty associated with the engine 

modifications made to comply with Tier II standards. The two percent fuel penalty estimate is 

based on the use of modifications to the fuel delivery system to achieve Tier II NOX reductions, 

and does not reflect the possibility that there may be other technologies available to 

manufacturers that could offset this fuel penalty (see Chapter 4 for more details). Additionally, 

Tier III will provide an opportunity to re-optimize engines for fuel economy when using 

aftertreatment such as SCR to provide NOX reductions. To estimate the cost of this fuel penalty, 

we applied the two percent to the total fuel consumed presented in the inventory (Chapter 3) and 

the fuel prices described in Section 5.5.4. The engines must continue to meet Tier II standards 

globally after 2015; therefore, this fuel penalty is projected even for Tier III engines. However, 

for engines equipped with SCR, we assume that the fuel penalty will not be incurred when the 

engine is operating in the ECA where Tier III standards apply because the use of SCR 

5-42 
Chapter 5: Engineering Cost Estimates 

aftertreatment affords the opportunity to advance the fuel injection timing for fuel efficiency 

while using the exhaust aftertreatment technology to achieve Tier III NOX levels. These 

operational costs were applied to both U.S.- and foreign-flagged vessel operations estimated in 

the inventory analysis and are shown in Table 5-32 below, these costs are included in the total 

estimated cost of the coordinated strategy. 

Table 5-32 Operational Costs Associated with Tier II 

YEAR 

U.S. FLAG FOREIGN FLAG TOTAL 

Residual 

Fuel 

(Tonnes) 

Distillate 

Fuel 

(Tonnes) 

Residual 

Fuel 

(Tonnes) 

Distillate 

Fuel 

(Tonnes) 

Residual 

Fuel 

(Tonnes) 

Distillate 

Fuel 

(Tonnes) 

CO2 

Emissions 

(Tonnes) 

Distillate and 

Residual Fuel 

Costsa 

2010 0 0 0 0 0 0 0 $0 

2011 516 14 3,361 107 3,878 121 12,729 $1,306,556 

2012 2,519 63 16,596 515 19,115 577 62,682 $6,431,250 

2013 4,873 118 31,985 983 36,858 1,101 120,825 $13,249,217 

2014 7,184 174 47,257 1,449 54,441 1,623 178,450 $19,567,804 

2015 2,127 7,172 3,709 57,335 5,836 64,507 223,903 $32,201,585 

2016 2,604 8,302 4,591 66,590 7,196 74,892 261,286 $37,530,731 

2017 3,055 7,890 5,468 63,265 8,523 71,155 253,615 $36,240,842 

2018 3,706 7,971 6,692 64,206 10,399 72,176 262,837 $37,367,120 

2019 4,307 7,897 7,862 63,702 12,169 71,599 266,634 $37,708,847 

2020 0 12,035 0 70,273 0 82,309 261,988 $38,513,853 

2021 0 12,454 0 71,154 0 83,608 266,125 $39,121,913 

2022 0 12,905 0 72,466 0 85,371 271,737 $39,946,889 

2023 0 12,739 0 67,999 0 80,739 256,991 $37,779,189 

2024 0 12,578 0 63,722 0 76,301 242,865 $35,702,589 

2025 0 12,779 0 62,561 0 75,340 239,807 $35,253,126 

2026 0 12,564 0 58,117 0 70,681 224,979 $33,073,186 

2027 0 12,264 0 53,759 0 66,022 210,149 $30,893,155 

2028 0 11,918 0 48,529 0 60,447 192,404 $28,284,583 

2029 0 12,100 0 47,729 0 59,829 190,436 $27,995,257 

2030 0 12,173 0 45,646 0 57,819 184,039 $27,054,822 

2031 0 12,802 0 47,703 0 60,505 192,586 $28,311,283 

2032 0 13,980 0 54,026 0 68,006 216,464 $31,821,543 

2033 0 14,711 0 56,740 0 71,451 227,429 $33,433,355 

2034 0 14,967 0 55,524 0 70,491 224,371 $32,983,936 

2035 0 15,412 0 55,390 0 70,802 225,362 $33,129,589 

2036 0 15,844 0 55,446 0 71,290 226,916 $33,358,073 

2037 0 16,128 0 52,665 0 68,793 218,969 $32,189,713 

2038 0 16,367 0 51,713 0 68,080 216,698 $31,855,940 

2039 0 17,026 0 52,845 0 69,871 222,401 $32,694,254 

2040 0 17,320 0 52,368 0 69,688 221,816 $32,608,299 

NPV @ 3% 26,400 193,000 113,000 944,000 139,000 

119,000 

1,140,000 

653,000 

4,060,000 

2,460,000 

$580,000,000 

NPV @ 7% 21,800 103,000 97,100 549,000 $346,000,000 

Note: a

These fuel costs were estimated using $462/tonne of distillate through 2014, $468/tonne of distillate as of

2015, $322/tonne for residual through 2012, and $346/tonne for residual fuel as of 2013. 

The impacts of this estimated increase in fuel consumption on U.S. energy security are 

small. U.S. energy security is broadly defined as protecting the U.S. economy against 

circumstances that threaten significant short- and long-term increases in energy costs. Most 

discussion of U.S. energy security revolves around the topic of the economic costs of the U.S. 

dependence on oil imports. The Tier 2 standards modestly increase consumption of petroleum in 

5-43 
Regulatory Impact Analysis 

ocean-going vessels. This increase in petroleum consumption increases both financial and 

strategic risks associated with a potential disruption in supply or a spike in cost of oil. As a 

result, the Tier 2 standards have a modest adverse impact on U.S. energy security. In the recent 

RFS2 proposal, EPA estimated that the macroeconomic disruption component for energy 

security at $4.74 per barrel or oil, or alternatively, $0.11/gallon. In the case of ocean-going 

vessels, there is no energy security-related monopsony component since the oil consumed in the 

vessels in purchased outside the U.S. 

5.6.2 Tier III Urea Consumption 

In addition to the SCR hardware costs discussed above in Section 5.2.4, ships built asof 

2016 would also incur operating costs associated with SCR’s use of urea. The urea costs are 

based on a price of $1.52 per gallon with a density of 1.09 g/cc. The cost per gallon was derived 

for a 32.5 percent urea solution delivered in bulk to the ship through research completed by ICF 

combined with historical urea price information.8,9,10,11 This cost analysis uses a urea dosing rate 

that is 7.5 percent of the BSFC to estimate how much urea would be used by different engine 

types and sizes. These operational costs were applied to both U.S. and foreign-flagged vessels, 

and are shown in Table 5-33 below. These costs are included in the total estimated cost of the 

coordinated strategy. 

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Chapter 5: Engineering Cost Estimates 

Table 5-33 Operation Costs Associated with the use of Urea with SCR for Tier 3 –U.S.- and Foreign-Flagged 

YEAR U.S.-FLAG FOREIGN-FLAG TOTAL 

2010 $0 $0 $0 

2011 $0 $0 $0 

2012 $0 $0 $0 

2013 $0 $0 $0 

2014 $0 $0 $0 

2015 $0 $0 $0 

2016 $934,000 $7,110,000 $8,040,000 

2017 $4,320,000 $34,500,000 $38,800,000 

2018 $8,220,000 $66,100,000 $74,300,000 

2019 $12,100,000 $97,500,000 $110,000,000 

2020 $15,800,000 $127,000,000 $143,000,000 

2021 $19,500,000 $157,000,000 $177,000,000 

2022 $22,700,000 $183,000,000 $206,000,000 

2023 $27,700,000 $225,000,000 $253,000,000 

2024 $32,300,000 $263,000,000 $295,000,000 

2025 $36,500,000 $297,000,000 $334,000,000 

2026 $40,800,000 $333,000,000 $374,000,000 

2027 $44,900,000 $367,000,000 $412,000,000 

2028 $49,500,000 $406,000,000 $456,000,000 

2029 $53,900,000 $441,000,000 $495,000,000 

2030 $58,500,000 $480,000,000 $539,000,000 

2031 $62,300,000 $512,000,000 $574,000,000 

2032 $66,000,000 $543,000,000 $609,000,000 

2033 $70,100,000 $577,000,000 $647,000,000 

2034 $74,900,000 $618,000,000 $693,000,000 

2035 $79,700,000 $658,000,000 $738,000,000 

2036 $85,100,000 $702,000,000 $787,000,000 

2037 $91,300,000 $753,000,000 $844,000,000 

2038 $97,100,000 $801,000,000 $898,000,000 

2039 $103,000,000 $847,000,000 $950,000,000 

2040 $109,000,000 $896,000,000 $1,010,000,000 

NPV @ 3% $660,000,000 $5,410,000,000 $6,070,000,000 

NPV @ 7% $299,000,000 $2,450,000,000 $2,750,000,000 

5.6.3 Operation on Lower-Sulfur Fuel 

The increased operating costs associated with the use of lower sulfur fuel, as discussed in 

Section 5.5.4, were applied to the fuel consumption values presented in the inventory. The costs 

in 2014 and 2015 represent operation on 1.0 percent sulfur residual fuel in the proposed ECA. 

Costs for 2015 and later represent operation on 0.1 percent sulfur distillate fuel in the proposed 

ECA. These costs were applied to both U.S.- and foreign-flagged vessels and are presented in 

the Table 5-34 below, and are included in the total estimated cost of the coordinated strategy. 

5-45 
Regulatory Impact Analysis 

Table 5-34 Operational Costs Associated with the use of Lower Sulfur Fuel (in $thousands) 

YEAR US FLAG FOREIGN FLAG TOTAL 

2010 $0 $0 $0 

2011 $0 $0 $0 

2012 $0 $0 $0 

2013 $30,700 $201,000 $232,000 

2014 $31,800 $209,000 $241,000 

2015 $176,000 $1,160,000 $1,340,000 

2016 $180,000 $1,210,000 $1,390,000 

2017 $190,000 $1,260,000 $1,450,000 

2018 $200,000 $1,310,000 $1,510,000 

2019 $200,000 $1,370,000 $1,570,000 

2020 $210,000 $1,430,000 $1,640,000 

2021 $220,000 $1,490,000 $1,710,000 

2022 $230,000 $1,550,000 $1,780,000 

2023 $240,000 $1,620,000 $1,860,000 

2024 $250,000 $1,690,000 $1,940,000 

2025 $260,000 $1,760,000 $2,020,000 

2026 $270,000 $1,840,000 $2,110,000 

2027 $280,000 $1,920,000 $2,200,000 

2028 $290,000 $2,000,000 $2,290,000 

2029 $300,000 $2,090,000 $2,390,000 

2030 $310,000 $2,180,000 $2,500,000 

2031 $330,000 $2,280,000 $2,600,000 

2032 $340,000 $2,380,000 $2,720,000 

2033 $350,000 $2,480,000 $2,840,000 

2034 $370,000 $2,600,000 $2,960,000 

2035 $380,000 $2,710,000 $3,100,000 

2036 $400,000 $2,830,000 $3,240,000 

2037 $420,000 $2,960,000 $3,380,000 

2038 $440,000 $3,100,000 $3,540,000 

2039 $450,000 $3,240,000 $3,700,000 

2040 $470,000 $3,390,000 $3,870,000 

NPV @ 3% $4,500,000 $31,000,000 $35,500,000 

NPV @ 7% $2,370,000 $16,300,000 $18,600,000 

5.6.4 Projected Fuel Costs 

This section presents our analysis of the impact of the proposed ECA on marine fuel 

costs. We project that the 1,000 ppm fuel sulfur limit, beginning in 2015, will likely result in the 

use of distillate fuel for operation in ECAs.M As such, the primary cost of the fuel sulfur limit 

M As an alternative, an exhaust gas cleaning device (scrubber) may be used. This analysis does not include the 

effect on distillate fuel demand of this alternative approach. It is expected that scrubbers would only be used in the 

case where the operator determines that the use of a scrubber would result in a cost savings relative to using 

5-46 
Chapter 5: Engineering Cost Estimates 

will be that associated with switching from heavy fuel oil to higher-cost distillate fuel, when 

operating in the ECA. Some engines already operate on distillate fuel and would not be affected 

by fuel switching costs. On the other hand, distillate fuel costs may be affected by the need to 

further refine the distillate fuel to meet the 1,000 ppm S limit. To investigate these effects, 

studies were performed on the impact of a U.S/Canadian ECA on global fuel production and 

costs. These studies, which are summarized below, include economic modeling to project 

bunker fuel demand and refinery modeling to assess the impact of a U.S./Canada ECA on fuel 

costs. Detailed documentation of these studies may be found in the docket. 

5.6.4.1 Bunker Fuel Demand Modeling 

To assess the affect of an ECA on the refining industry, we needed to first understand and 

characterize the fuels market and more specifically the demand for the affected marine fuels both 

currently and in the future. Research Triangle Institute (RTI) was contracted to conduct a fuels 

study using an activity-based economic approach.12 The RTI study established baseline bunker 

fuel demand, projected a growth rate for bunker fuel demand, and established future bunker fuel 

demand volumes. The basis for this work was the Global Insights economic model which 

projects international trade for different categories of commodities. Demand for marine fuels 

was derived from the demand of transportation of various types of cargoes by ship, which, in 

turn, was derived from the demand for commodities produced in one region of the world and 

consumed in another. The flow of commodities was matched with typical vessels for that trade 

(characterized according to size, engine power, age, specific fuel consumption, and engine load 

factors). Typical voyage parameters were 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 was thus a function of commodity projections, ship characteristics, and voyage 

characteristics. 

The bunker demand model included operation off the coasts of the contiguous United 

States and southeastern Alaska. The bunker demand volumes for this modeling in the Canadian 

portion of the North American ECA was based on fuel consumed by ships en route to and from 

Canadian ports based on estimates from Environment Canada. 

The affected fuel volumes used in the WORLD model are slightly higher than what we 

now estimate for the proposed ECA. This difference is because the RTI evaluation of affected 

fuel volumes was performed before the ECA was defined and was performed independently of 

the emission inventory modeling described in Chapter 3. However, we believe it is reasonable to 

use the fuel cost increases, on a per-tonne basis, from the WORLD modeling to estimate the 

impact of the proposed ECA. In earlier work,13 EnSys modeled a number of fuel control 

scenarios where the volume of affected fuel was adjusted to represent (1) different ECAs or (2) 

various penetration scenarios of exhaust gas scrubbers (as an alternative to fuel switching). This 

work suggests that the differences in fuel volume between these scenarios have only a small 

effect on fuel cost. Although this earlier work was based on the older crude oil and refinery 

costs used in the expert group study, it is sufficient for observing the sensitivity of fuel cost 

distillate fuel. Therefore we are only estimating the cost of compliance using distillate fuel here as we believe this is 

the most likely approach. 

5-47 
Regulatory Impact Analysis 

increases to small changes (on a global scale) in affected fuel volume. In addition, the larger 

affected fuel volume, used in the WORLD modeling, directionally increases the projected fuel 

cost increases, and therefore allows for a conservative analysis. 

5.6.4.2 Bunker Fuel Cost Modeling 

5.6.4.2.1 Methodology 

To assess the impacts of the proposed ECA on fuel costs, the World Oil Refining 

Logistics and Demand (WORLD) model was run by Ensys Energy & Systems, the owner and 

developer of the refinery model. The WORLD model is the only such model currently 

developed for this purpose, and was developed by a team of international petroleum consultants. 

It has been widely used by industries, government agencies, and OPEC over the past 13 years, 

including the Cross Government/Industry Scientific Group of Experts, established to evaluate the 

effects of the different fuel options proposed under the revision of MARPOL Annex VI.14 The 

model incorporates crude sources, global regions, refinery operations, and world economics. 

The results of the WORLD model have been shown to be comparable to other independent 

predictions of global fuel, air pollutant emissions and economic predictions. 

WORLD is a comprehensive, bottom-up model of the global oil downstream that 

includes crude and noncrude supplies; refining operations and investments; crude, products, and 

intermediates trading and transport; and product blending/quality and demand. Its detailed 

simulations are capable of estimating how the global system can be expected to operate under a 

wide range of different circumstances, generating model outputs such as price effects and 

projections of refinery operations and investments. 

5.6.4.2.2 Assessment of the Impact of Marine Fuel Standards 

During the development of the amendments to MARPOL Annex VI, a Cross 

Government/Industry Scientific Group of Experts was established, by IMO, to evaluate the 

effects of the different fuel options that were under consideration at the time. This expert group 

engaged the services of EnSys to assess the impact of these fuel options using the WORLD 

model. The final report from this study presents great detail on the capabilities of the WORLD 

model and provides support for why the WORLD model was chosen as the appropriate tool for 

modeling the economic impacts of the different fuel options.15 The following description of the 

WORLD model is taken from the expert group study: 

WORLD is a linear programming model that simulates the activities and economics of 

the world regional petroleum industry against short, medium or long term horizons. It models 

and captures the interactions between: 

 crude supply; 

 non-crudes supply: Natural gas Liquids (NGLs), merchant MTBE, biofuels, 

petrochemical returns, Gas To Liquid fuels (GTLs), Coal to Liquid fuels (CTLs); 

 refining operations; 

 refining investment; 

 transportation of crudes, products and intermediates; 

5-48 
Chapter 5: Engineering Cost Estimates 

 product blending/quality; 

 product demand; and 

 market economics and pricing. 

The model includes a database representing over 180 world crude oils and holds detailed, 

tested, state-of-the-art representation of fifty-plus refinery processes. These representations 

include energy requirements based on today’s construction standards for new refinery units. It 

allows for advanced representation of processes for reformulated, ultra-lower sulfur/aromatics 

fuels and was extended for detailed modeling of marine fuels for the aforementioned EPA and 

API studies. The model contains detailed representations of the blending and key quality 

specifications for over 50 different products spread across the product spectrum and including 

multiple grades of gasolines, diesel fuels/gasoils (marine and non-marine) and residual fuels 

(marine and non-marine). 

The refining industry is a co-product industry. This means that changes in production of 

one product also affect production volume and/or production costs of other products. As 

necessary, the model will adjust refinery throughputs and operations, crude and product trade 

patterns to ensure that a specified product demand slate is met, without surplus or deficit of any 

product. 

To evaluate the impact of changes to marine fuels specifications as a result of any of the 

options under consideration, the model is run with a future demand scenario for all products. The 

first run, the base case, assumes marine fuels in line the current Annex VI regulation. The second 

run is done with marine fuel specifications in line with the option under consideration. Both runs 

are optimized independently. Since the only thing that is altered between the cases is the change 

in the projected marine fuels regulation, the difference between both cases is therefore a true 

assessment of the actual cost and other implications of the change to the marine fuels 

requirements under consideration. Thus, the incremental refining investment costs, incremental 

marine fuel costs and incremental refinery/net CO2 emissions are all directly attributable to, and 

must be allocated to, the change in regulation. 

Prior to the expert group study, EnSys made updates to the WORLD model to be able to 

perform the analysis of the impacts of different marine fuel options. As part of this effort, the 

refinery data, capacity additions, technology assumptions, and costs were reviewed. EnSys 

reviewed relevant regulations to ensure that the WORLD model was correctly positioned to 

undertake future analyses of marine fuels ECAs. In developing these updates, a number of 

issues had to be considered: 

 the costs of refining, including the capital expenditures required to reduce bunker fuel 

sulfur content and the potential for process technology improvements; 

 likely market reactions to increased bunker fuel costs, such as fuel grade availability, 

impacts on the overall transportation fuels balance, and competition with land-based 

diesel and residual fuels for feedstocks that can upgrade fuels; 

 the effects of emissions trading; and 

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Regulatory Impact Analysis 

 the potential for low- and high-sulfur grade bunker sources and consumption to 

partially shift location depending on supply volume, 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 ECA 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 bunker demand to be reallocated to marine diesel. Beyond 

any international specifications, the analytical system needed to be able to accommodate future 

consideration of regional, national, and local specifications. 

The primary approach taken to manage these issues was to 

 expand the number of bunker grades in the model to three distillates and four residual 

grades,N 

 allow for variation where necessary in (regional) sulfur standards on specific bunker 

grades, and 

 enable residual bunker demand to be switched to marine diesel. 

Other updates to the WORLD model included product transportation matrices covering 

tanker, interregional pipeline, and minor modes were expanded to embody the additional 

distillate and residual bunker grades, adjustments to the yield patterns of the residuum 

desulfurization, and blocking of paraffinic streams from residual fuel blends. The details of 

compliance in any particular region must be estimated external to the main WORLD model. As 

discussed above, we provided our estimates of affected fuel volumes to Ensys. 

5.6.4.2.3 Updates for ECA Analysis 

To determine the impact of a North American ECA, the WORLD model was employed 

using the same basic approach as for the IMO expert group study. Modeling was performed for 

2020 in which the control case included a fuel sulfur level of 1,000 ppm in the U.S. and 

Canadian EEZs.16 The baseline case was modeled as “business as usual” in which ships continue 

to use the same fuel as today. This approach was used for two primary reasons. First, significant 

emission benefits are expected in an ECA, beginning in 2015, due to the use of 1,000 ppm S fuel. 

These benefits, and costs, would be much higher in the early years of the program before the 

5,000 ppm S global standard goes into effect. By modeling this scenario, we are able to observe 

the impact of the proposed ECA in these early years. Second, there is no guarantee that the 

global 5,000 ppm S fuel sulfur standards will begin in 2020. The global standard may be 

delayed until 2025, subject to a fuel availability review in 2018. In addition, the 35,000 ppm S 

N Specifically, the following seven grades were implemented: MGO, plus distinct high- and low-sulfur blends for 

MDO and the main residual bunker 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 SECAs. 

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Chapter 5: Engineering Cost Estimates 

global standard, which begins in 2012, is higher than the current residual fuel sulfur average of 

27,000 ppm S. 

In the modeling for the expert group study, crude oil prices were based on projections 

released by the U.S. Energy Information Administration (EIA) in 2006.17 Since that time, oil 

prices have fluctuated greatly. Using new information, EIA has updated its projections of oil 

price for 2020.18,19 In response to this real-world effect, the ECA modeling was conducted using 

the updated oil price estimates. Specifically, we used a crude oil price of $51.55 for the reference 

case, and $88.14/bbl for the high price case, both expressed in real (2006) dollars. These crude 

oil prices were input to the WORLD model which then computed residual and distillate marine 

oil prices for 2020. The net refinery capital impacts are imputed based on the differences in the 

costs to the refining industry that occur between the Base Cases and ECA cases in 2020. The 

incremental global refining investment over the Base Case is projected to cost an additional 

$3.83 billion, with $1.48 billion being used for debottlenecking projects and $1.96 billion used 

for new units. For the high priced crude case, the incremental capital investments for an ECA 

is $3.44 billion over the base case, with new units accounting for $2.49 billion while 

debottlenecking costs are $0.72 billion. For both of the crude oil price cases, refinery 

investments represent a marginal increase of about 2% over the corresponding total base case 

investments required in 2020. Additionally, the majority of these ECA investments occur in the 

U.S./Canada refining regions, though smaller amounts also occur in other world regions. In 

addition to increased oil price estimates, the updated model accounts for increases in natural gas 

costs, capital costs for refinery upgrades, and product distribution costs. 

5.6.4.3 Results of Fuel Cost Study 

5.6.4.3.1 Incremental Refinery Capital Investments Associated with Desulfurization 

The primary refining cost of desulfurization is associated with converting IFO bunker oil 

into a distillate fuel with a DMA specification. The other significant refining costs are those 

related to desulfurizing distillate stocks. The bulk of the refinery investments occur in regions 

located outside of the U.S. and Canada, because capital investments in these regions are 

approximately 9 and 23 percent of the overall capital for the reference and high priced crude 

cases, respectively. Table 5-35 summarizes the overall capital investments made for both 

conversion of IFO bunker oil into distillate as well as desulfurization in refineries in the various 

U.S. regions (East Coast, West Coast and Gulf Coast) and overseas. These cost estimates are 

based on the WORLD modeling results. 

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Regulatory Impact Analysis 

Table 5-35 Incremental Refinery Capital Investment Made in 2020 (2006 dollars) 

REFINERY INVESTMENTS ($ BILLION) 

Base 

Case 

$52/bbl 

Crude 

NA ECA 

$52/bbl 

Crude 

Delta Base Case 

$88/bbl 

Crude 

NA ECA 

$88/bbl 

Crude 

Delta 

USEC 1.4 1.2 -0.2 1.0 0.9 -0.1 

USGCCE 14.5 14.8 0.3 26.2 27.3 1.2 

USWCCW 1.4 1.6 0.2 1.4 1.5 0.2 

Refinery 

Investments Total 

USA+Canada 

17.3 17.6 0.3 28.6 29.8 1.3 

Refinery 

Investments Total 

Other Regions 

85.2 88.1 2.9 110.5 115.0 4.4 

Total World 102.5 105.7 3.2 139.1 144.8 5.7 

Type of Modification 

Debottleneck 0.7 0.7 0.0 1.4 1.4 0.0 

Major New Units 97.8 100.8 3.0 132.1 138.0 6.0 

Total World 102.5 105.7 3.2 139.1 144.8 5.7 

Notes: 

USEC is United States East Coast, USGCCE is United States Gulf Coast and Eastern Canada, 

USWCCW is United States West Coast and Western Canada, $Bn is Billion U.S. Dollars. The 

results presented are investments made in 2020 to add new refinery processing capacity to what

exists in the 2008 base case plus known projects. 

Refinery investments in North America, Greater Caribbean and South American regions 

account for greater than half of all investments for the reference case, while investments made in 

China and Middle Eastern Gulf regions account for close to 40 percent of remaining investments. 

This accounts for greater than 90 percent of investments for the reference case. For the high 

price case, investments in the U.S., Canada, Greater Caribbean and South American refiner 

regions again account for greater than half of all investments made, while European north and 

China regions account for greater than 44 percent of the remaining investments. Table 5-36 

summarizes overall incremental investments made in all world refining regions for the reference 

and high price case. 

5-52 
Chapter 5: Engineering Cost Estimates 

Table 5-36 World Region Refining Investments for ECA Made in 2020 

REFERENCE CASE HIGH PRICED CASE 

 Capital, $ 

Billion 

% of Capital Capital, $ Billion % of Capital 

USEC -0.167 -5.2% -0.095 -1.7% 

USGICE 0.277 8.7% 1.159 20.3% 

USWCCW 0.176 5.5% 0.224 3.9% 

GrtCAR 0.253 7.9% 0.828 14.5% 

SthAM 0.810 25.4% 0.870 15.3% 

AfWest 0.004 0.1% 0.002 0.0% 

AfN-EM 0.143 4.5% -0.006 -0.1% 

Af-E-S 0.007 0.2% 0.006 0.1% 

EUR-No 0.011 0.4% 1.239 21.7% 

EUR-So -0.006 -0.2% -0.035 -0.6% 

EUR-Ea 0.021 0.7% -0.014 -0.2% 

CaspRg 0.157 4.9% -0.001 0.0% 

RusFSU 0.185 5.8% 0.036 0.6% 

MEGulf 0.754 23.6% 0.119 2.1% 

PacInd -0.115 -3.6% 0.069 1.2% 

PacHi 0.177 5.5% 0.000 0.0% 

China 0.490 15.3% 1.305 22.9% 

RoAsia 0.018 0.6% -0.002 0.0% 

Total 3.20 100.0% 5.70 100.0% 

Notes: 

USEC = US East Coast, USGICE= US Gulf Coast, Interior & Canada East, USWCCW= US West 

Coast & Canada West, GrtCAR= Greater Caribbean, SthAM= South America, AfWest=African 

West, AFN- EM= North Africa/Eastern Mediterranean, AF-E-S=Africa East and South, EurNo=Europe North, EUR-So= Europe South, EUR-EA= Europe East, CaspRg= Caspian Region, 

RusFSU= Russia & Other Former Soviet Union, MEGulf= Middle East Gulf, Pac Ind= Pacific 

Industrialized, PacHi= Pacific High Growth / Industrializing, RoAsia= Rest of Asia 

5.6.4.3.2 Capacity and Throughput Changes for the Reference Case 

The WORLD model used a total of 140 thousand barrels per stream day (KBPSD) of 

coking capacity to convert residual stocks to distillates. Of this amount, 110 KBPSD is existing 

spare or “slack” capacity available in U.S. and Canada refiner regions. This capacity is available 

based on projections that refiners add excess coking capacity in the base case. The remaining 

balance of coking capacity, or 30 KBPSD, is new capacity added to refiner regions outside of 

United States and Canada, equivalent to one additional coker. In addition to utilizing more 

coking capacity, the WORLD model also increased residual hydrocracking capacity by 50 

KBPSD to convert residual stocks into distillates. These one to two additional hydrocrackers 

were added to refiner regions located outside of United States and Canada. Overall, considering 

the use of cokers and residual hydrocrackers, the total refiner process capacity is 190 KBPSD for 

residual stocks processing, mirroring the amount needed to process the residual volumes 

contained in IFO180 and IFO 380 bunker grades. To remove any gas oils in residual blendstocks 

5-53 
Regulatory Impact Analysis 

such as atmospheric and vacuum tower residuals, the model utilized 60 KBPSD of existing 

vacuum tower capacity, 50 KBPSD in U.S. and Canada and 10 KBPSD in other refiner regions. 

Crude throughput is increased by 54 KBPSD, primarily to account for increased energy 

usage in refinery processes such as hydro crackers and hydrotreaters. Crude throughput is also 

increased to offset liquid volume loss from residual stocks that are converted to petroleum coke 

in coking units. Table 5-37 summarizes overall crude and non crude throughputs for the base 

and ECA cases. 

Table 5-37 Refiner Crude and Non Crude Throughputs

 REFERENCE 

BASE CASE 

REFERENCE 

ECA 

CASE 

DELTA HIGH 

BASE 

CASE 

HIGH 

ECA 

CASE 

DELTA 

Crude Throughput MMBPD 86.7 86.7 0.1 75.6 75.6 0.0 

Non Crude Supply 

NGL ETHANE MMBPD 1.7 1.7 0.0 1.7 1.7 0.0 

NGLs C3+ MMBPD 6.3 6.3 0.0 6.1 6.1 0.0 

PETCHEM RETURNS MMBPD 1.0 1.0 0.0 0.8 0.8 0.0 

BIOMASS MMBPD 1.5 1.5 0.0 3.0 3.0 0.0 

METHANOL (EX NGS) MMBPD 0.1 0.1 0.0 0.1 0.1 0.0 

GTL LIQUIDS (EX NGS) MMBPD 0.3 0.3 0.0 0.6 0.6 0.0 

CTL LIQUIDS (EX COAL) MMBPD 0.5 0.5 0.0 0.8 0.8 0.0 

HYDROGEN (EX NGS) MMBPD 1.0 1.0 0.0 0.8 0.9 0.1 

Total Non Crude Supply MMBPD 12.3 12.3 0.0 14.0 14.0 0.0 

TOTAL Supply MMBPD 99.3 99.4 0.1 90.2 90.3 0.1 

The model added 70 KBPSD of new ultra lower sulfur gas oil hydrocracking capacity in 

refiner regions outside of the U.S. and Canada. The distillate produced from these units has a sulfur 

content low enough to meet ECA standards and therefore does not require further processing in 

hydrotreaters. The model also reduced throughput by 40 KBPSD in existing base case capacity for 

Conventional Gas Oil Hydrocrackers located in U.S. and Canada refiner regions. 

The model added 160 KBPSD of new conventional distillate hydrotreating capacity, 140 

KBPSD to U.S. and Canada refiner regions and 20 KBPSD in refining regions in other areas of the 

world. In addition to new units, the model used 150 KBPSD of “slack” distillate conventional 

hydrotreating capacity, 90 KBPSD of this located in U.S. and Canada and 60 KBPSD in other world 

refiner regions. Considering this, the total net use of conventional distillate hydrotreating for the 

reference case is 310 KBPSD above the base case, mirroring incremental demand of lower sulfur 

distillate for ECA. The model used 70 KBPSD of slack capacity for vacuum gas oil/residual 

hydrotreating in addition to distillate hydrotreating. Of this amount, 40 KBPSD is in U.S. and 

Canada and 30 KBPSD in other world refiner regions. 

The increased hydrotreating and hydrocracking capacity requires new hydrogen and sulfur 

plant capacity and was added to refiner regions that use more distillate hydrotreating and 

5-54 
Chapter 5: Engineering Cost Estimates 

hydrocracking. Other minor refinery process modifications were required by the model in 2020, 

although these were not substantial (see Table 5-38). 

Table 5-38 Refinery Secondary Processing Capacity Additions in 2020 Reference Case (Million barrels per 

stream day) 

USE OF BASE CAPACITY NEW CAPACITY BASE PLUS NEW CAPACITY

 US/CAN Rest of 

World 

Total US/CAN Rest of 

World 

Total US/CAN Rest of 

World 

Total 

Total Additions 

Over Base 

0.00 0.05 0.05 0.00 0.05 0.05 0.00 0.05 0.05 

Total Crude 

Capacity Used 

2020 

0.02 0.04 0.05 0.02 0.04 0.05 0.017 0.037 0.054 

Vacuum 

Distillation 

0.05 0.01 0.06 0.00 (0.02) (0.02) 0.05 (0.01) 0.04 

Coking 0.11 0.00 0.12 0.00 0.02 0.02 0.11 0.03 0.14 

Catalytic 

Cracking 

(0.07) 0.01 (0.06) 0.00 (0.01) (0.01) (0.07) 0.00 (0.07) 

Hydro-Cracking 

(TOTAL) 

(0.04) 0.00 (0.04) 0.00 0.12 0.12 (0.04) 0.12 0.08 

- Gasoil 

Conventional 

(0.04) 0.00 (0.04) 0.00 0.00 0.00 (0.04) 0.00 (0.04) 

- Gasoil ULS 0.00 0.00 0.00 0.00 0.07 0.07 0.00 0.07 0.07 

- Resid LS 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.01 

- Resid MS 0.00 0.00 0.00 0.00 0.04 0.04 0.00 0.04 0.04 

Catalytic 

Reforming with 

Revamp 

0.01 0.00 0.02 0.00 0.07 0.07 0.01 0.07 0.08 

Hydrotreating 

(Total) 

0.13 0.08 0.21 0.11 0.05 0.17 0.24 0.14 0.37 

- Gasoline – ULS 0.00 (0.00) (0.00) (0.03) 0.03 (0.00) (0.03) 0.02 (0.01) 

Distillate

 -New Conv/LS 

0.09 0.06 0.15 0.14 0.02 0.16 0.23 0.08 0.31 

- VGO/Resid 0.04 0.03 0.06 0.00 0.00 0.00 0.04 0.03 0.07 

Hydrogen, 

(MMSCFD) 

0 70 70 8 211 218 8 280 288 

Sulfur Plant, 

(TPD) 

500 500 1000 10 130 140 510 630 1140 

While coking and hydrocracking (residual and gas oil) processes primarily produce 

distillates, to a lesser extent, some low octane gasoline blendstocks are also manufactured, requiring 

refiners to install additional catalytic reforming unit capacity. As such, in the U.S. and Canada 

regions approximately 10 KPBSD of existing spare CCR capacity is used while approximately 70 

BPSD of new CCR capacity is added to other WORLD refiner regions that added cokers and 

hydrocrackers. 

5.6.4.3.3 Capacity and Throughput Changes for the High Price Crude Oil Case 

For the high priced case, the high cost of crude and high capital costs for processing units 

push the model to reduce installation of new processing units. The price of natural gas is also 

reduced relative to the price of crude which induces the model to use more natural gas and 

reduce the use of crude. Under these conditions, the model uses less crude, more natural gas and 

installs less capital for refinery processing units. As a result, the model favors the use of more 

5-55 
Regulatory Impact Analysis 

hydrocracking processing which adds hydrogen (made from natural gas) to residual and gas oils, 

producing lower sulfur distillates stocks that do not require further processing in hydrotreaters. 

The model also uses more synthetic crudes and less heavy sour crudes, which reduce the 

amounts of residual stocks that need upgrading. 

Crude throughput is increased by 29 KBPSD, which is less than the reference case, as the 

model preferentially uses natural gas over crude and reduces the use of cokers and hydrotreating. 

Table 5-39 shows crude and non crude inputs for the high priced case. 

The WORLD model used a total of 80 KBPSD of “slack” coking capacity to convert residual 

stocks to distillates. Of this amount, 70 KBPSD was used in the U.S. and Canada regions and 10 

KBPSD in regions in other areas of the world. The model also added 80 KBPSD of new low and 

medium sulfur residual hydrocracking capacity to convert residual stocks into distillates—20 KBPSD 

in the U.S. and Canada and 60 KBPSD in other world refiner regions. Overall, considering the use 

of cokers and residual hydrocrackers, the total refiner process capacity for residual stocks processing 

for use in the ECA is 160 KBPSD for the high priced case. 

To extract gas oils from residual blendstocks, the model utilized 90 KBPSD of existing 

vacuum tower capacity—80 KBPSD in the U.S. and Canada and 10 KBPSD on other refiner regions. 

In addition, the model added 120 KBPSD of new ultra lower sulfur gas oil hydrocracking capacity in 

refiner regions outside of the U.S. and Canada. The distillate fuel produced from these units meet 

ECA sulfur standards. The model also used 30 KBPSD of slack capacity in the U.S. and Canada 

refiner regions for hydrocracking of conventional gas oil. 

The model added 40 KBPSD of new conventional distillate hydrotreating capacity to the U.S. 

and Canada refiner regions and 20 KBPSD of new capacity to refining regions in other areas of the 

world. While the model also used 40 KBPSD of “slack” conventional distillate hydrotreating 

capacity in the U.S. and Canada, other world refiner regions decreased use of base case or slack 

capacity by 80 KBPSD. Considering the use of the new and slack capacity, a total net use of 

capacity is 20 KBPSD of conventional distillate hydrotreating capacity. The model also used 60 

KBPSD of existing slack capacity for vacuum gas oil/residual distillate hydrotreaters, with 20 

KBPSD used in the U.S. and Canada refiner regions and 40 KBPSD in other world refining regions. 

The use of additional hydrocracking and hydrotreater capacity requires installation of new 

hydrogen plant capacity. New sulfur plant capacity is required in refiner regions to process the 

offgas produced from incremental use of hydro cracking and hydrotreating (see Table 5-39 below). 

5-56 
Chapter 5: Engineering Cost Estimates 

Table 5-39 Refinery Secondary Processing Capacity Additions in 2020 High Priced Case (Million barrels per 

stream day) 

USE OF BASE CAPACITY NEW CAPACITY BASE PLUS NEW CAPACITY 

US/CAN Rest of 

World 

Total US/CAN Rest of 

World 

Total US/CAN Rest of 

World 

Total 

Total Additions 

Over Base Case 

0.00 (0.05) (0.05) 0.00 (0.05) (0.05) 0.00 (0.05) (0.05) 

Total Crude 

Capacity Used in 

2020 

0.05 (0.02) 0.03 0.05 (0.02) 0.03 0.054 (0.024) 0.029 

Vacuum Distillation 0.08 0.10 0.18 0.00 0.00 0.00 0.08 0.10 0.18 

Coking 0.07 0.01 0.08 0.00 (0.00) (0.00) 0.07 0.00 0.08 

Catalytic Cracking (0.03) (0.05) (0.09) 0.00 0.00 0.00 (0.03) (0.05) (0.09) 

Hydro-Cracking 

(Total) 

0.03 0.00 0.03 0.02 0.18 0.20 0.05 0.18 0.22 

- Gasoil 

Conventional 

0.03 0.00 0.03 0.00 0.00 0.00 0.03 0.00 0.03 

- Gasoil ULS 0.00 0.00 0.00 0.00 0.12 0.12 0.00 0.12 0.12 

- Resid LS 0.00 0.00 0.00 0.02 0.03 0.05 0.02 0.03 0.05 

- Resid MS 0.00 0.00 0.00 0.00 0.03 0.03 0.00 0.03 0.03 

Catalytic Reforming 

with Revamp 

0.00 0.02 0.02 (0.05) 0.02 (0.03) (0.05) 0.04 (0.00) 

Hydrotreating 

(Total) 

0.06 (0.04) 0.02 0.04 0.02 0.06 0.11 (0.03) 0.08 

- Gasoline – ULS 0.00 0.00 0.00 0.00 (0.01) (0.01) 0.00 (0.01) (0.01) 

Distillate

 -New Conv/LS 

0.04 (0.08) (0.03) 0.04 0.02 0.06 0.08 (0.06) 0.02 

- VGO/Resid 0.02 0.03 0.05 0.00 0.00 0.00 0.02 0.04 0.06 

Hydrogen, 

(MMSCFD) 

0 0 0 243 325 568 243 325 568 

Sulfur Plant, (TPD) 580 300 880 0 120 120 580 420 1000 

5.6.4.3.4 Overall Increases Due to Fuel Switching and Desulfurization 

Global fuel use in 2020 by international shipping is projected to be 500 million metric 

tonnes per year (tonnes/yr). The main energy content effects of bunker grade shifts were 

captured in the WORLD modeling by altering the volume demand and, at the same time, 

consistency was maintained between the bunker demand figures in tonnes and in barrels. The 

result was that partial or total conversion of IFO to distillate was projected to lead to a reduction 

in the total global tonnes of bunker fuel required but also led to a projected increase in the barrels 

required. These effects are evident in the WORLD case results. Because only a small portion of 

global marine fuel is consumed in the ECA, the overall impact on global fuel production is small. 

Global fuel use in 2020 by ships is projected to be 500 million metric tonnes/yr. Of this amount, 

90 million metric tonnes of fuel is used for U.S./Canadian trade, or about 18 percent of total 

global fuel use. In the proposed ECA, less than 20 million metric tonnes of fuel will be 

consumed in 2020, which is less than 4 percent of total global marine fuel use. Of the amount of 

fuel to be consumed in the proposed ECA in 2020, about 4 million metric tonnes of distillate will 

be consumed in the Business as Usual (BAU) case, which is about 20 percent of the amount of 

5-57 
Regulatory Impact Analysis 

total fuel to be consumed in the proposed ECA.O As would be expected, since the shift in fuel 

volumes on a world scale is relatively small, the WORLD model predicts the overall global 

impact of an ECA to also be small. 

There are two main components to projected increased marine fuel cost associated with 

an ECA. The first component results from the shifting of operation on residual fuel to operation 

on higher cost distillate fuel. This is the dominant cost component. The WORLD model 

computed costs based on a split between the costs of residual and distillate fuels. However, there 

is a small cost associated with desulfurizing the distillate to meet the 1,000 ppm S standard in the 

ECA. Based on the WORLD modeling, the average increase in costs associated with switching 

from marine residual to distillate will be $145 per tonne.P

 This is the cost increase that will be 

borne by the shipping companies purchasing the fuel. Of this amount, $6 per tonne is the cost 

increase associated with distillate desulfurization. In other words, we estimate a cost increase of 

$6/tonne for distillate fuel used in an ECA. 

The above cost estimates are based on EIA’s “reference case” projections for crude oil 

price in 2020. We also performed a sensitivity analysis using EIA’s “high price” scenario. 

Under this scenario, the increase in fuel costs for switching from residual to distillate fuel is $237 

per tonne. The associated increase in distillate fuel cost is $7 per tonne. 

 Table 5-40 summarizes the reference and high price fuel cost estimates with and without 

an ECA. In the baseline case, fuel volumes for operation are 18% marine gas oil (MGO), 7% 

marine diesel oil (MDO), and 75% IFO. In the ECA, all fuel volumes are modeled as MGO. 

Table 5-40 Estimated Marine Fuel Costs 

FUEL UNITS REFERENCE CASE HIGH PRICE CASE 

Baseline ECA Baseline ECA 

MGO $/bbl $ 61.75 $ 62.23 $ 102.70 $ 103.03 

$/tonne $ 464 $ 468 $ 772 $ 775 

MDO $/bbl $ 61.89 $ 62.95 $ 102.38 $ 103.70 

$/tonne $ 458 $ 466 $ 757 $ 767 

IFO $/bbl $ 49.87 $ 49.63 $ 83.14 $ 82.52 

$/tonne $ 322 $ 321 $ 538 $ 534 

O For this analysis, the U.S. included the lower contiguous states and southeastern Alaska. P

 Note that distillate fuel has a higher energy content, on a per tonne basis, than residual fuel. As such, there is an 

offsetting cost savings, on a per tonne basis, for switching to distillate fuel. Based on a 5 percent higher energy 

content for distillate, the net equivalent cost increase is estimated as $123 for each tonne of residual fuel that is being 

replaced by distillate fuel ($200/tonne for the high price case). 

5-58 
Chapter 5: Engineering Cost Estimates 

5.7 Summary of Final Program Engineering Costs 

5.7.1 Engineering Costs for Freshly Manufactured Engines 

The total engine costs presented here include the fixed and variable costs of CAA Tier 2 

and Tier 3 technologies to U.S.-flagged vessels, and are included in the total estimated cost of 

the coordinated strategy. The costs associated with the existing engine program are not included 

here but are presented in Section 5.3.1. The engine related costs to new U.S.-flagged vessels 

through 2040 at a 3 percent discount rate is estimated to be $0.66 billion, and $0.35 billion at a 7 

percent discount rate. 

Table 5-41 Total U.S.-Flagged Engine Costs for Freshly Manufactured Engines 

YEAR FIXED VARIABLE TOTAL 

2010 $309,000 $0 $309,000 

2011 $837,000 $2,580,000 $3,420,000 

2012 $869,000 $2,680,000 $3,550,000 

2013 $902,000 $2,780,000 $3,680,000 

2014 $936,000 $2,890,000 $3,830,000 

2015 $972,000 $3,000,000 $3,970,000 

2016 $0 $27,400,000 $27,400,000 

2017 $0 $28,400,000 $28,400,000 

2018 $0 $29,600,000 $29,600,000 

2019 $0 $30,700,000 $30,700,000 

2020 $0 $31,900,000 $31,900,000 

2021 $0 $33,200,000 $33,200,000 

2022 $0 $34,500,000 $34,500,000 

2023 $0 $35,900,000 $35,900,000 

2024 $0 $37,400,000 $37,400,000 

2025 $0 $38,800,000 $38,800,000 

2026 $0 $40,400,000 $40,400,000 

2027 $0 $42,000,000 $42,000,000 

2028 $0 $43,700,000 $43,700,000 

2029 $0 $45,500,000 $45,500,000 

2030 $0 $47,400,000 $47,400,000 

2031 $0 $49,300,000 $49,300,000 

2032 $0 $51,300,000 $51,300,000 

2033 $0 $53,400,000 $53,400,000 

2034 $0 $55,600,000 $55,600,000 

2035 $0 $57,900,000 $57,900,000 

2036 $0 $60,200,000 $60,200,000 

2037 $0 $62,700,000 $62,700,000 

2038 $0 $65,300,000 $65,300,000 

2039 $0 $68,000,000 $68,000,000 

2040 $0 $70,800,000 $70,800,000 

NPV @ 3% $4,440,000 $658,000,000 $663,000,000 

NPV @ 7% $3,990,000 $342,000,000 $346,000,000 

5-59 
Regulatory Impact Analysis 

5.7.2 Engineering Costs for Vessels 

The vessel costs presented here are associated with the use of lower sulfur fuel to meet 

the 2015 fuel sulfur standards for U.S.-flagged vessels. The costs here include additional 

equipment that may be required to accommodate the use of lower sulfur fuel on both new and 

existing vessels. The total cost to U.S.-flagged ships through 2040 at a 3 percent discount rate is 

$0.26 billion, or $0.17 billion at a seven percent discount rate. 

Table 5-42 U.S.-Flagged Vessel Engineering Costs 

YEAR FIXED VARIABLE TOTAL 

2010 $166,000 $0 $166,000 

2011 $172,000 $0 $172,000 

2012 $179,000 $0 $179,000 

2013 $185,000 $0 $185,000 

2014 $192,000 $0 $192,000 

2015 $0 $11,100,000 $11,100,000 

2016 $0 $691,000 $691,000 

2017 $0 $717,000 $717,000 

2018 $0 $745,000 $745,000 

2019 $0 $773,000 $773,000 

2020 $0 $803,000 $803,000 

2021 $0 $834,000 $834,000 

2022 $0 $866,000 $866,000 

2023 $0 $899,000 $899,000 

2024 $0 $934,000 $934,000 

2025 $0 $970,000 $970,000 

2026 $0 $1,010,000 $1,010,000 

2027 $0 $1,050,000 $1,050,000 

2028 $0 $1,090,000 $1,090,000 

2029 $0 $1,130,000 $1,130,000 

2030 $0 $1,180,000 $1,180,000 

2031 $0 $1,220,000 $1,220,000 

2032 $0 $1,270,000 $1,270,000 

2033 $0 $1,320,000 $1,320,000 

2034 $0 $1,370,000 $1,370,000 

2035 $0 $1,430,000 $1,430,000 

2036 $0 $1,490,000 $1,490,000 

2037 $0 $1,540,000 $1,540,000 

2038 $0 $1,610,000 $1,610,000 

2039 $0 $1,670,000 $1,670,000 

2040 $0 $1,740,000 $1,740,000 

NPV @ 3% $842,000 $25,600,000 $26,500,000 

NPV @ 7% $781,000 $16,200,000 $16,900,000 

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Chapter 5: Engineering Cost Estimates 

5.7.3 Total Increased Operating Costs 

The total increase operating costs associated with the coordinated strategy are $42 billion 

at a 7 percent discount rate in 2040, and $22 billion at a 3 percent discount rate. The operational 

costs include Tier 2 and global Tier II fuel consumption increases, the use of urea with SCR for 

Tier 3 and Tier III equipped vessels, and the increased costs associated with the use of lower 

sulfur fuel. Table 5-43 presents the operating costs for both U.S.- and foreign-flagged vessels, 

and the total for the coordinated strategy. 

Table 5-43 Total Operational Costs ($Thousands) 

TOTAL OPERATING COSTS 

Year U.S.-Flag Foreign Flag Total 

2010 $0 $0 $0 

2011 $0 $1,000 $1,000 

2012 $1,000 $6,000 $7,000 

2013 $32,000 $213,000 $245,000 

2014 $34,000 $226,000 $260,000 

2015 $180,000 $1,188,000 $1,368,000 

2016 $189,000 $1,250,000 $1,439,000 

2017 $199,000 $1,326,000 $1,525,000 

2018 $210,000 $1,409,000 $1,619,000 

2019 $221,000 $1,500,000 $1,721,000 

2020 $233,000 $1,590,000 $1,823,000 

2021 $246,000 $1,680,000 $1,926,000 

2022 $258,000 $1,767,000 $2,025,000 

2023 $272,000 $1,877,000 $2,149,000 

2024 $286,000 $1,983,000 $2,269,000 

2025 $300,000 $2,086,000 $2,386,000 

2026 $315,000 $2,200,000 $2,515,000 

2027 $330,000 $2,312,000 $2,642,000 

2028 $345,000 $2,429,000 $2,774,000 

2029 $362,000 $2,553,000 $2,915,000 

2030 $378,000 $2,681,000 $3,059,000 

2031 $395,000 $2,814,000 $3,209,000 

2032 $413,000 $2,948,000 $3,361,000 

2033 $431,000 $3,084,000 $3,515,000 

2034 $451,000 $3,244,000 $3,695,000 

2035 $471,000 $3,394,000 $3,865,000 

2036 $494,000 $3,558,000 $4,052,000 

2037 $517,000 $3,738,000 $4,255,000 

2038 $541,000 $3,925,000 $4,466,000 

2039 $566,000 $4,112,000 $4,678,000 

2040 $591,000 $4,311,000 $4,902,000 

NPV @ 3% $5,260,000 $36,900,000 $42,200,000 

NPV @ 7% $2,730,000 $19,000,000 $21,700,000 

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Regulatory Impact Analysis 

5.7.4 Total Engineering and Operating Costs Associated with the Final Program 

The total engineering hardware and operational costs associated with the coordinated 

strategy and included in the total cost of the coordinated strategy are described in Table 5-44 and 

presented in Table 5-45. The total cost of the program through 2040 at a 3 percent discount rate 

is $43 billion, or $22 billion at a 7 percent discount rate. 

Table 5-44 U.S.- and Foreign-Flagged Costs Included in Total 

COSTS INCLUDED IN THE TOTAL COORDINATED STRATEGY COST ESTIMATEA,B 

U.S.-Flagged Vessels Foreign-Flagged Vessels 

Annex VI Existing Engine Program Hardware Costs 

 (No Estimated Fuel Consumption Penalty) Not Included 

Tier 2 Hardware Costs Not Included 

Tier 2 Operational Costs 

 (based on an estimated 2 percent fuel consumption penalty) 

Tier 2 Operational 

 (based on an estimated 2 percent fuel consumption penalty) 

Tier 3 Hardware Costs Not Included 

 (Presented as a separate analysis, not included in total cost) 

Tier 3 Operational Costs 

 (based on the use of urea with SCR) 

Tier 3 Operational 

 (based on the use of urea with SCR) 

Lower Sulfur Fuel Hardware Costs 

 (New and Existing Vessels) 

Not Included 

 (Presented as a separate analysis, not included in total cost) 

Lower Sulfur Fuel Operational Costs 

 (based on the differential price of lower sulfur fuel) 

Lower Sulfur Fuel Operational Costs 

 (based on the differential price of lower sulfur fuel) 

Notes: 

a

 The cost totals reported in this NPRM are slightly different than those reported in the ECA proposal, because the ECA proposal did not include 

costs associated with the Annex VI existing engine program, Tier II, or the costs associated with existing vessel modifications that may be 

required to accommodate the use of lower sulfur fuel. Further, the cost totals presented in the ECA package included Canadian cost estimates. b

 For a more detailed explanation of these costs, see Chapter 5 of the draft RIA. 

5-62 
Chapter 5: Engineering Cost Estimates 

Table 5-45 Total Costs Associated with the Coordinated Strategy ($Thousands) 

YEAR FIXED VARIABLE OPERATIONAL TOTAL 

2010 $485 $0 $0 $485 

2011 $1,020 $2,730 $1,310 $5,060 

2012 $1,060 $2,820 $6,430 $10,300 

2013 $1,100 $2,920 $245,000 $249,000 

2014 $1,140 $3,020 $261,000 $265,000 

2015 $972 $14,200 $1,370,000 $1,380,000 

2016 $0 $28,100 $1,440,000 $1,470,000 

2017 $0 $29,200 $1,530,000 $1,550,000 

2018 $0 $30,300 $1,620,000 $1,650,000 

2019 $0 $31,500 $1,720,000 $1,750,000 

2020 $0 $32,700 $1,820,000 $1,850,000 

2021 $0 $34,000 $1,930,000 $1,960,000 

2022 $0 $35,400 $2,030,000 $2,060,000 

2023 $0 $36,800 $2,150,000 $2,180,000 

2024 $0 $38,300 $2,270,000 $2,310,000 

2025 $0 $39,800 $2,390,000 $2,430,000 

2026 $0 $41,400 $2,510,000 $2,550,000 

2027 $0 $43,100 $2,640,000 $2,680,000 

2028 $0 $44,800 $2,770,000 $2,820,000 

2029 $0 $46,600 $2,910,000 $2,960,000 

2030 $0 $48,500 $3,060,000 $3,110,000 

2031 $0 $50,500 $3,210,000 $3,260,000 

2032 $0 $52,600 $3,360,000 $3,410,000 

2033 $0 $54,700 $3,520,000 $3,570,000 

2034 $0 $56,900 $3,690,000 $3,750,000 

2035 $0 $59,300 $3,870,000 $3,930,000 

2036 $0 $61,700 $4,060,000 $4,120,000 

2037 $0 $64,300 $4,260,000 $4,320,000 

2038 $0 $66,900 $4,470,000 $4,530,000 

2039 $0 $69,700 $4,680,000 $4,750,000 

2040 $0 $72,600 $4,910,000 $4,980,000 

NPV @ 3% $5,320 $685,000 $42,200,000 $42,900,000 

NPV @ 7% $4,800 $359,000 $21,700,000 $22,100,000 

5.8 Cost Effectiveness 

One tool that can be used to assess the value of the coordinated strategy is the measure of 

cost effectiveness; a ratio of engineering costs incurred per ton of emissions reduced. This 

analysis involves a comparison of our proposed program to other measures that have been or 

could be implemented. As summarized in this section, the coordinated strategy represents a 

highly cost effective mobile source control program for reducing NOX, PM and SOX emissions. 

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Regulatory Impact Analysis 

We have estimated the cost per ton based on the net present value of 3 percent and 7 

percent of all hardware costs incurred by U.S.-flagged vessels, and all operational costs incurred 

by both U.S.- and foreign-flagged vessels, and all emission reductions generated from the year 

2010 through the year 2040. The baseline case for these estimated reductions is the existing set 

of engine standards for C3 marine diesel engines and existing fuel sulfur limits. Note that PM2.5 

is estimated to be 92 percent of the more inclusive PM10 emission inventory for marine vessels. 

In Chapter 3, we generate and present PM2.5 inventories since recent research has determined 

that these are of greater health concern. Traditionally, we have used PM10 in our cost 

effectiveness calculations. Since cost effectiveness is a means of comparing control measures to 

one another, we use PM10 in our cost effectiveness calculations for comparisons to past control 

measures, Table5-46 shows the annual emissions reductions associated with the coordinated 

strategy, these annual tons are undiscounted. A description of the methodology used to estimate 

these annual reductions can be found in Chapter 3 of the draft RIA. 

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Chapter 5: Engineering Cost Estimates 

Table 5-46 Estimated Emissions Reductions Associated with the Coordinated Strategy (Short tons) 

CALENDAR 

YEAR 

REDUCTIONS (TONS) 

NOX SOX PM 

2010 47,000 0 0 

2011 54,000 0 0 

2012 70,000 0 0 

2013 88,000 390,000 48,400 

2014 105,000 406,000 50,400 

2015 123,000 641,000 68,000 

2016 150,000 668,000 70,800 

2017 209,000 695,000 73,700 

2018 279,000 724,000 76,800 

2019 349,000 755,000 80,000 

2020 409,000 877,000 94,100 

2021 488,000 916,000 98,200 

2022 547,000 954,000 102,000 

2023 634,000 995,000 107,000 

2024 714,000 1,040,000 111,000 

2025 790,000 1,080,000 116,000 

2026 866,000 1,130,000 121,000 

2027 938,000 1,170,000 126,000 

2028 1,020,000 1,220,000 131,000 

2029 1,100,000 1,280,000 137,000 

2030 1,180,000 1,330,000 143,000 

2031 1,260,000 1,390,000 149,000 

2032 1,330,000 1,450,000 155,000 

2033 1,410,000 1,510,000 162,000 

2034 1,500,000 1,580,000 169,000 

2035 1,590,000 1,650,000 177,000 

2036 1,690,000 1,720,000 184,000 

2037 1,810,000 1,800,000 193,000 

2038 1,920,000 1,880,000 201,000 

2039 2,020,000 1,970,000 210,000 

2040 2,130,000 2,050,000 220,000 

NPV at 3% 14,400,000 19,100,000 2,100,000 

NPV at 7% 6,920,000 10,100,000 1,090,000 

The net estimated reductions by pollutant, using a net present value of 3 percent from 

2010 through 2040 are 14.4 million tons of NOX, 19.1 million tons of SOX, and 2.1 million tons 

of PM (6.9 million, 10.1 million, and 1.1 million tons of NOX, SOX, and PM, respectively, at a 

net present value of 7 percent over the same period.) 

Using the above cost and emission reduction estimates, we estimated the lifetime (2010 

through 2040) cost per ton of pollutant reduced. For this analysis, all of the hardware costs 

associated with the Annex VI existing engine program, and Tier 2 and Tier 3 NOX standards as 

well as the operational costs associated with the CAA Tier 2 and Tier 3, and global Tier II and 

Tier III NOX standards were attributed to NOX reductions. The costs associated with lower 

5-65 
Regulatory Impact Analysis 

sulfur fuel operational costs as applied to all vessels visiting U.S. ports and the hardware costs 

associated with accommodating the use of lower sulfur fuel on U.S.-flagged vessels were 

associated with SOX and PM reductions. In this analysis, we have allocated half of the costs 

associated with the use of lower sulfur fuel to PM and half to SOX, because the costs incurred to 

reduce SOX emissions directly reduce emissions of PM as well. Using this allocation of costs 

and the emission reductions shown in Table5-46, we can estimate the lifetime cost per ton 

reduced associated with each pollutant. The resultant estimated cost effectiveness numbers are 

shown in Table 5-47. Using a net present value of 3 percent, the discounted lifetime cost per ton 

of pollutant reduced is $510 for NOX, $930 for SOX, and $7,950 for PM ($500, $920, and $7,850 

per ton of NOX, SOX, and PM, respectively, at a net present value of 7 percent.) As shown in 

Table 5-47, these estimated discounted lifetime costs are similar to the annual long-term (2030) 

cost per ton of pollutant reduced. 

Table 5-47 Coordinated Strategy Estiamted Aggregate Discounted Cost per Ton and Long-Term Annual

Cost per Tona, b

POLLUTANT 2010 THRU 2040 DISCOUNTED 

LIFETIME COST PER TON AT 

3% 

2010 THRU 2040 DISCOUNTED 

LIFETIME COST PER TON AT 

7% 

LONG-TERM 

COST PER TON 

(FOR 2030) 

NOX $510 $500 $520 

SOX $930 $920 $940 

PM $7,950 $7,850 $8,060 

Notes: 

a These costs are in 2006 U.S. dollars. b The $/ton numbers presented here vary from those presented in the ECA proposal due to the net present value of

the annualized reductions being applied from 2015-2020, and the use of tonnes rather than of short tons.

These results for the coordinated strategy compare favorably to other air emission control 

programs. Table 5-48 compares the coordinated strategy to other air programs. This comparison 

shows that the coordinated strategy will provide a cost-effective strategy for generating 

substantial NOX, SOX, and PM reductions from ocean-going vessels. The results presented in 

Table 5-48 are lifetime costs per ton discounted at a net present value of 3 percent, with the 

exception of the stationary source program and locomotive/marine retrofits, for which annualized 

costs are presented. While results at a net present value of 7 percent are not presented, the results 

would be similar. Specifically, the coordinated strategy falls within the range of values for other 

recent programs. 

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Chapter 5: Engineering Cost Estimates 

Table 5-48 Estimated $/ton for the Coordinated Strategy Compared to Previous Mobile Source Programs for 

NOX, SOX, and PM10

SOURCE CATEGORY20,a IMPLEMENTATION 

DATE 

NOX 

COST/TONNE 

SOX 

COST/TONNE 

PM10 

COST/TONNE 

OGV Coordinated Strategy NPRM, 2009 2011 510 930 7,950 

Nonroad Small Spark-Ignition Engines 

73 Fed Reg 59034, October 8, 2008 2010 330-1200b,c - -

Stationary Diesel (CI) Engines b 

71 Fed Reg 39154, July 11, 2006 2006 580 – 20,000 - 3,500 – 42,000 

Locomotives and C1/C2 (Both New and 

Retrofits) c 

73 Fed Reg 25097, May 6, 2008 

2015 730b

 - 8,400 (New) 

45,000 (Retrofit)b 

Heavy Duty Nonroad Diesel Enginesc 69 

Fed Reg 38957, June 29, 2004 2015 1,100b

 780 13,000 

Heavy Duty Onroad Diesel Engines c 

66 Fed Reg 5001, January 18, 2001 2010 2,200b

 5,800 14,000 

Notes: 

a

 Table presents aggregate program-wide cost/ton over 30 years, discounted at a 3 percent NPV, except for 

stationary CI Engines and LocoMarine retrofits, for which annualized costs of control for individual sources are 

presented. All figures are in 2006 U.S. dollars per short ton. b

 Includes NOX plus non-methane hydrocarbons (NMHC). NMHC are also ozone precursors, thus some rules set 

combined NOX +NMHC emissions standards. NMHC are a small fraction of NOX so aggregate cost/ton 

comparisons are still reasonable. c

 Low end of range represents costs for marine engines with credit for fuel savings, high end of range represents 

costs for other nonroad SI engines without credit for fuel savings. 

5-67 
Regulatory Impact Analysis 

References 

1

 ICF International, “Costs of Emission Reduction Technologies for Category 3 Marine 

Engines,” prepared for the U.S. Environmental Protection Agency, December 2008. EPA Report 

Number : EPA-420-R-09-008. 

2

 “Matched Typical Ports to Modeled Ports” Table 2-33 of Section 2.5.2 of “The Commercial 

Marine Port Inventory Development, 2002 and 2005 Draft Inventories” report to EPA from ICF 

International, September 2007. 

3

 ICF International, Inventory Contribution of U.S. Flagged Vessels, prepared for the U.S. 

Environmental Protection Agency, EPA Report Number EPA-420-R-09-005, March 2009. 

4

 http://www.marad.dot.gov/documents/Vessel_Calls_at_US_Ports_Snapshot.pdf 

5

 http://www.marad.dot.gov/documents/us-flag_fleet_10000_dwt_and_above.xls 

6

 Lloyd’s Register of Ships, can be found at www.sea-web.com 

7

 ICF International, “Commercial Marine Port Inventory Development 2002 and 2005 Draft 

Inventories” prepared by the U.S. Environmental Protection Agency, September 2007. 

8

 "Nonroad SCR-Urea Study Final Report" July 29, 2007 TIAX for Engine Manufacturers 

Association (EMA) can be found 

at:http://www.enginemanufacturers.org/admin/content/upload/198.pdf 

9

 http://www.adblueonline.co.uk/air_1/bulk_delivery 

10 http://www.factsaboutscr.com/documents/IntegerResearch-Ureapricesbackto2005levels.pdf 

11 http://www.fertilizerworks.com/fertreport/index.html 

12 Research Triangle Institute, 2008. “Global Trade and Fuels Assessment—Future Trends and 

Effects of Designating Requiring Clean Fuels in the Marine Sector”; Research Triangle Park, 

NC; EPA420-R-08-021; November. 

13 Research Triangle Institute, 2008. “Global Trade and Fuels Assessment—Future Trends and 

Effects of Designating Requiring Clean Fuels in the Marine Sector”; Research Triangle Park, 

NC; EPA420-R-08-021; November. 

14 International Maritime Organization, Note by the Secretariat, “Revision of MARPOL Annex 

VI and NOX Technical Code; Input from the four subgroups and individual experts to the final 

report of the Informal Cross Government/Industry Scientific Group of Experts,” Subcommittee 

on Bulk Liquids and Gases, 12th Session, Agenda Item 6, BLG 12/INF.10, December 28, 2007. 

15 International Maritime Organization, Note by the Secretariat, “Revision of MARPOL Annex 

VI and NOX Technical Code; Input from the four subgroups and individual experts to the final 

report of the Informal Cross Government/Industry Scientific Group of Experts,” Subcommittee 

on Bulk Liquids and Gases, 12th Session, Agenda Item 6, BLG 12/INF.10, December 28, 2007. 

16 EnSys Energy & Systems, Inc. and RTI International 2009. Global Trade and Fuels 

Assessment—Additional ECA Modeling Scenarios. prepared for the U.S. Environmental 

Protection Agency. 

5-68 
Chapter 5: Engineering Cost Estimates 

17 Energy Information Administration, 2006. “Annual Energy Outlook 2006” (DOE/EIA0383(2006)); Washington, DC. (Available at: http://www.eia.doe.gov/oiaf/aeo/archive.html ) 

18 Energy Information Administration, 2008a. “Annual Energy Outlook 2008” (DOE/EIA0383(2008)); Washington, DC. (Available at: http://www.eia.doe.gov/oiaf/aeo/ ) 

19 Energy Information Administration, 2008b. “International Energy Outlook 2008” (DOE/EIA0484(2008)); Washington, DC. (Available at: http://www.eia.doe.gov/oiaf/ieo/ ) 

20 Regulation of Fuels and Fuel Additives: Fuel Quality Regulations for Highway Diesel Fuel 

Sold in 1993 and Later Calendar Years, 55 Fed Reg 34120, August 21, 1990. 

Standards of Performance for Stationary Compression Ignition Internal Combustion Engines, 

71 Fed Reg 39154, July 11, 2006. 

Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition 

Engines Less Than 30 Liters per Cylinder, 73 Fed Reg 25097, May 6, 2008. 

Control of Emissions of Air Pollution From Nonroad Diesel Engines and Fuel 69 Fed Reg 

38957, June 29, 2004. 

Control of Air Pollution from New Motor Vehicles: Heavy-Duty Engine and Vehicle Standards 

and Highway Diesel Fuel Sulfur Control Requirements 66 Fed Reg 5001, January 18, 2001. 

Control of Air Pollution From New Motor Vehicles: Tier 2 Motor Vehicle Emissions Standards 

and Gasoline Sulfur Control Requirements 65 Fed Reg 6697, February 10, 2000. 

Acid Rain Program; General Provisions and Permits, Allowance System, Continuous 

Emissions Monitoring, Excess Emissions and Administrative Appeals, 58 Fed Reg 3590, 

January 11, 1993; Finding of Significant Contribution and Rulemaking for Certain States in 

the Ozone Transport Assessment Group Region for Purposes of Reducing Regional Transport 

of Ozone, 63 Fed Reg 57356, October 27, 1998. 

Prevention of Significant Deterioration (PSD) and Nonattainment New Source Review (NSR): 

Baseline Emissions Determination, Actual-to-Future-Actual Methodology, Plantwide 

Applicability Limitations, Clean Units, Pollution Control Projects, 67 Fed Reg 80186, 

December 31, 2002 

5-69 
 Chapter 6: Cost-Benefit Analysis 

CHAPTER 6: COST-BENEFIT ANALYSIS 

6.1 Overview ........................................................................................................................... 6-2

6.2 Quantified Human Health Impacts ................................................................................ 6-9

6.3 Monetized Benefits ......................................................................................................... 6-14

6.4 Methodology ................................................................................................................... 6-18

6.4.1 Human Health Impact Functions.......................................................................6-18

 6.4.2 Economic Values for Health Outcomes ............................................................. 6-23

6.4.3 Manipulating Air Quality Modeling Data for Health Impacts Analysis ........ 6-29

6.5 Methods for Describing Uncertainty ............................................................................ 6-31

6.6 Comparison of Costs and Benefits................................................................................6-32

6-1 
Regulatory Impact Analysis 

CHAPTER 6: Cost-Benefit Analysis 

6.1 Overview 

This chapter presents our analysis of the health and environmental benefits that are 

estimated to occur as a result of EPA’s coordinated strategy to address emissions from Category 

3 engines and ocean-going vessels throughout the period from initial implementation through 

2030. We provide estimated costs for the entire coordinated strategy, including the Annex VI 

Tier II NOX requirements and the ECA controls that will be mandatory for U.S. and foreign 

vessels through the Act to Prevent Pollution from Ships. However, unlike the cost analysis, this 

benefits analysis does not allocate benefits between the components of the program (the 

requirements in this rule and the requirements that would apply through MARPOL Annex VI 

and ECA implementation). This is because the benefits of the coordinated strategy will be fully 

realized only when the U.S. ECA is in place and both U.S. and foreign vessels are required to use 

lower sulfur fuel and operate their Tier 3 NOx controls while in the designated area, and 

therefore it makes more sense to consider the benefits of the coordinated strategy as a whole.. 

The components of the coordinated strategy would apply stringent NOX and SOX 

standards to virtually all vessels that affect U.S. air quality, and impacts on human health and 

welfare would be substantial. As presented in Chapter 2, the coordinated strategy for controlling 

emissions from Category 3 engines and ocean-going vessels is expected to provide very large 

reductions in NOX (a precursor to ozone formation and secondarily-formed PM2.5), SOX (a 

precursor to secondarily-formed PM2.5) and directly-emitted PM2.5. These pollutants contribute 

to on-land concentrations of PM2.5 and ozone that cause harm to human health and the 

environment. This chapter presents the reductions in adverse health impacts that can be expected 

to occur from the adoption of a coordinated strategy to control ship emissions that includes 

implementation of the proposed CAA standards and the ECA designation described in this 

proposal. 

Exposure to ozone and PM2.5 is linked to adverse human health impacts such as 

premature deaths as well as other important public health and environmental effects. The most 

conservative premature mortality estimates (Pope et al., 2002 for PM2.5 and Bell et al., 2004 for 

ozone)1,2

 suggest that implementation of the coordinated strategy would reduce approximately 

13,000 premature mortalities in 2030 and yield approximately $110 billion in total benefits. The 

upper end of the premature mortality estimates (Laden et al., 2006 for PM2.5 and Levy et al., 

2005 for ozone)3,4 suggest that implementation of the coordinated strategy would increase the 

estimate of avoided premature mortalities to approximately 32,000 in 2030 and yield 

approximately $280 billion in total benefits.A Thus, even taking the most conservative premature 

mortality assumptions, the health impacts of the coordinated strategy presented in this proposal 

are clearly substantial. 

The health impacts modeling presented in this chapter is based on peer-reviewed studies 

of air quality and health and welfare effects associated with improvements in air quality. The 

A These benefits use a 3% discount rate. Using a 7% discount rate, the benefits are approximately 10% less. 

6-2 
 Chapter 6: Cost-Benefit Analysis 

health impact estimates for the coordinated strategy are based on an analytical structure and 

sequence consistent with health impacts analyses performed by the United States Environmental 

Protection Agency (EPA) for its recent analyses in support of the final Ozone National Ambient 

Air Quality Standard (NAAQS) and the final PM NAAQS as well as all of its recent mobile 

source emission control programs.5,6 For a more detailed discussion of the principles of health 

impacts analysis used here, we refer the reader to those NAAQS documents. 

To model the ozone and PM air quality impacts of the coordinated strategy, we used the 

Community Multiscale Air Quality (CMAQ) model (see Chapter 2). The modeled ambient air 

quality data serves as an input to the Environmental Benefits Mapping and Analysis Program 

(BenMAP).B BenMAP is a computer program developed by the EPA that integrates a number of 

the modeling elements used in previous analyses (e.g., interpolation functions, population 

projections, health impact functions, valuation functions, analysis and pooling methods) to 

translate modeled air concentration estimates into health effects incidence estimates and 

monetized benefits estimates. 

The range of total ozone- and PM-related benefits associated with the coordinated 

strategy to control ship emissions is presented in Table 6-1. We present total benefits based on 

the PM- and ozone-related premature mortality function used. The benefits ranges therefore 

reflect the addition of each estimate of ozone-related premature mortality (each with its own row 

in Table 6-1) to estimates of PM-related premature mortality. These estimates represent EPA’s 

preferred approach to characterizing the best estimate of benefits associated with the coordinated 

strategy. As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods 

used to estimate air quality benefits evolve to reflect the Agency’s most current interpretation of 

the scientific and economic literature. This analysis, therefore, incorporates four important 

changes from recent RIAs released by the Office of Transportation and Air Quality (OTAQ): 

 As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods used to 

estimate air quality benefits evolve over time to reflect the Agency’s most current 

interpretation of the scientific and economic literature. For a period of time (2004-2008), the 

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of 

statistical life (VSL) estimate derived from a limited analysis of some of the available 

studies. OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent 

with two meta-analyses of the wage-risk literature. The $1 million value represented the 

lower end of the interquartile range from the Mrozek and Taylor (2002)7

 meta-analysis of 33 

studies and $10 million represented the upper end of the interquartile range from the Viscusi 

and Aldy (2003)8

 meta-analysis of 46 studies. The mean estimate of $5.5 million (2000$)C 

was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006)9 

meta-analysis. However, the Agency neither changed its official guidance on the use of VSL 

B

 Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ 

benmodels.html. C

 In this analysis, we adjust the VSL to account for a different currency year (2006$) and to account for income

growth to 2020 and 2030. After applying these adjustments to the $5.5 million value, the VSL is $7.7m in 2020 and 

$7.9 in 2030. 

6-3 
Regulatory Impact Analysis 

in rule-makings nor subjected the interim estimate to a scientific peer-review process through 

the Science Advisory Board (SAB) or other peer-review group. 

During this time, the Agency continued work to update its guidance on valuing mortality risk 

reductions, including commissioning a report from meta-analytic experts to evaluate 

methodological questions raised by EPA and the SAB on combining estimates from the 

various data sources. In addition, the Agency consulted several times with the Science 

Advisory Board Environmental Economics Advisory Committee (SAB-EEAC) on the issue. 

With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its 

guidance using specific, appropriate meta-analytic techniques to combine estimates from 

unique data sources and different studies, including those using different methodologies (i.e., 

wage-risk and stated preference) (U.S. EPA-SAB, 2007).10 

Until updated guidance is available, the Agency determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, 

the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the 

Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) while the Agency continues 

its efforts to update its guidance on this issue.D This approach calculates a mean value across 

VSL estimates derived from 26 labor market and contingent valuation studies published 

between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$).E 

The Agency is committed to using scientifically sound, appropriately reviewed evidence in 

valuing mortality risk reductions and has made significant progress in responding to the 

SAB-EEAC’s specific recommendations. The Agency anticipates presenting results from 

this effort to the SAB-EEAC in the Fall 2009 and that draft guidance will be available shortly 

thereafter. 

 Since 2004, the Office of Air and Radiation (OAR) has employed an interim approach to the 

Value of a Statistical Life (VSL) that differs from the default methodology defined in EPA's 

prevailing Economic Guidelines and was not reviewed by the Agency’s Science Advisory 

Board (SAB). At the time, OAR agreed to use this as an interim value while updates to the 

mortality risk valuation guidance were completed. OAR used a range of $1 million to $10 

million (2000$) to arrive at the VSL: the lower end of the range was based on a metaanalysis of 33 studies by Mrozek and Taylor (2002),11 and the upper end of the range was 

based on a meta-analysis of 46 studies by Viscusi and Aldy (2003).12 An average of these 

figures is $5.5 million (2000$). Based on recent SAB review and a desire for consistency in 

the application of the VSL within the Agency, we believe it is no longer appropriate to 

D In the (draft) update of the Economic Guidelines, EPA retained the VSL endorsed by the SAB with the 

understanding that further updates to the mortality risk valuation guidance would be forthcoming in the near future. 

Therefore, this report does not represent final agency policy. The 2000 guidelines can be downloaded here: 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html, and the draft updated version (2008) of the 

guidelines can be downloaded here: http://yosemite.epa.gov/ee/epa/eerm.nsf/vwRepNumLookup/EE0516?OpenDocument E

 In this analysis, we adjust the VSL to account for a different currency year (2006$) and to account for income 

growth to 2020 and 2030. After applying these adjustments to the $6.3 million value, the VSL is $8.9m in 2020 and 

$9.1m in 2030. 

6-4 
 Chapter 6: Cost-Benefit Analysis 

continue to use this as an interim value, especially while the Agency awaits a systematic 

review of the recent valuation literature to inform a revision to the mortality risk valuation 

guidance.F Instead, EPA has decided to apply the VSL that was vetted and endorsed by the 

Agency when the 2000 Guidelines for Preparing Economic Analyses were drafted. OAR 

will now apply a VSL that is consistent with the value used by all other branches of the 

Agency that quantify and monetize mortality incidence. This approach calculates a mean 

value across VSL estimates derived from 26 labor market and contingent valuation studies 

published between 1974 and 1991. The mean VSL across these studies is $6.3 million 

(2000$).G 

 In recent analyses, OTAQ has estimated PM2.5-related benefits assuming that a threshold 

exists in the PM-related concentration-response functions (at 10 μg/m3) below which there 

are no associations between exposure to PM2.5 and health impacts. For the benefits analysis 

of the coordinated strategy, however, we have revised this assumption. EPA strives to use 

the best available science to support our benefits analyses, and we recognize that 

interpretation of the science regarding air pollution and health is dynamic and evolving. 

Based on our review of the body of scientific literature, EPA applied the no-threshold model 

in this analysis, which is consistent with the approach taken in the recently published 

Portland Cement MACT RIA.H EPA's draft Integrated Science Assessment (ISA),13 which 

was recently reviewed by EPA’s Clean Air Scientific Advisory Committee (CASAC),14,15 

concluded that the scientific literature consistently finds that a no-threshold log-linear model 

most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response 

function. Although this document does not represent final agency policy that has undergone 

the full agency scientific review process, it provides a basis for reconsidering the application 

of thresholds in PM2.5 concentration-response functions used in EPA’s RIAs. It is important 

to note that while CASAC provides advice regarding the science associated with setting the 

National Ambient Air Quality Standards, typically other scientific advisory bodies provide 

specific advice regarding benefits analysis. Because the Portland Cement RIA was 

completed while CASAC was reviewing the PM ISA, we solicited comment on the use of the 

F

 EPA is in the process of revisiting this Guidance and has recently engaged the SAB-EEAC on several issues 

including the use of meta-analysis as a means of combining estimates and approaches for assessing mortality 

benefits when changes in longevity may vary widely (U.S. EPA, 2006). The Agency is committed to using the best 

available science in its analyses and will revise this guidance in response to SAB recommendations (see U.S. EPA, 

2007 for recent SAB recommendations). 

U.S. Environmental Protection Agency (U.S. EPA). 2006. Willingness to Pay for Environmental Health Risk 

Reductions when there are Varying Degrees of Life Expectancy: A White Paper. 

http://yosemite.epa.gov/ee/epa/eerm.nsf/vwRepNumLookup/EE-0495?OpenDocument 

U.S. Environmental Protection Agency (U.S. EPA). 2007. SAB Advisory on EPA’s Issues in Valuing 

Mortality Risk Reduction. 

http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$File/sab-08-001.pdf G For a description of both approaches, please refer to Appendix B of the 2008 draft of EPA's Guidelines for 

Preparing Economic Analyses. It can be downloaded at http://yosemite.epa.gov/ee/epa/eermfile.nsf/vwAN/EE0516-01.pdf/$File/EE-0516-01.pdf H U.S. Environmental Protection Agency. (2009). Regulatory Impact Analysis: National Emission Standards for 

Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air and Radiation. 

Retrieved on May 4, 2009, from http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf 

6-5 
Regulatory Impact Analysis 

no-threshold model for benefits analysis within the preamble of that proposed rule. The 

comment period for the Portland Cement proposed NESHAP has been extended until 

September 4, 2009.I As explained in the recently published Portland Cement MACT RIA,J 

EPA’s preferred benefits estimation approach assumes a no-threshold model that calculates 

incremental benefits down to the lowest modeled PM2.5 air quality levels. This approach 

reflects EPA’s most current interpretation of the scientific literature on PM2.5 and mortality. 

Please see Section 6.4.1.3 of the RIA for more discussion of the treatment of thresholds in 

this analysis. 

 For the coordinated strategy, we rely on two empirical (epidemiological) studies of the 

relationship between ambient PM2.5 and premature mortality (the extended analyses of the 

Harvard Six Cities study by Laden et al (2006) and the American Cancer Society (ACS) 

cohort by Pope et al (2002)) to anchor our benefits analysis, though we also present the 

PM2.5-related premature mortality benefits associated with the estimates supplied by the 

expert elicitation as a sensitivity analysis. This approach was recently adopted in the 

Portland Cement MACT RIA. Since 2006, EPA has calculated benefits based on these two 

empirical studies and derived the range of benefits, including the minimum and maximum 

results, from an expert elicitation of the relationship between exposure to PM2.5 and 

premature mortality (Roman et al., 2008).16 Using alternate relationships between PM2.5 and 

premature mortality supplied by experts, higher and lower benefits estimates are plausible, 

but most of the expert-based estimates have fallen between the two epidemiology-based 

estimates (Roman et al., 2008). Assuming no threshold in the empirically-derived premature 

mortality concentration response functions used in the analysis of the coordinated strategy, 

only one expert falls below the empirically-derived range while two of the experts are above 

this range (see Tables 6-5 and 6-6). Please refer to the Portland Cement MACT RIA for more 

information about the preferred approach and the evolution of the treatment of threshold 

assumptions within EPA’s regulatory analyses. 

 The range of ozone benefits associated with the coordinated strategy is estimated based on 

risk reductions derived from several sources of ozone-related mortality effect estimates. This 

analysis presents six alternative estimates for the association based upon different functions 

reported in the scientific literature. We use three multi-city studies,17,18,19 including the Bell, 

2004 National Morbidity, Mortality, and Air Pollution Study (NMMAPS) that was used as 

the primary basis for the risk analysis in the ozone Staff Paper20 and reviewed by the Clean 

Air Science Advisory Committee (CASAC).21 We also use three studies that synthesize 

ozone mortality data across a large number of individual studies.22,23,24 This approach is 

consistent with recommendations provided by the NRC in their ozone mortality report (NRC, 

2008),25 “The committee recommends that the greatest emphasis be placed on estimates from 

new systematic multicity analyses that use national databases of air pollution and mortality, 

such as in the NMMAPS, without excluding consideration of meta-analyses of previously 

I

Readers interested in commenting on the use of the no-threshold model for benefits analysis should direct their 

comments to Docket ID No. EPA–HQ–OAR–2002–0051 (available at http://www.regulations.gov) before the 

comment period closes. J

 U.S. Environmental Protection Agency. (2009). Regulatory Impact Analysis: National Emission Standards for 

Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air and Radiation. 

Retrieved on May 4, 2009, from http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf 

6-6 
 Chapter 6: Cost-Benefit Analysis 

published studies.” The NRC goes on to note that there are uncertainties within each study 

that are not fully captured by this range of estimates. 

Table 6-1 Estimated 2030 Monetized PM-and Ozone-Related Health Benefits of a Coordinated U.S. Strategy 

to Control Ship Emissionsa 

2030 TOTAL OZONE AND PM BENEFITS – PM MORTALITY DERIVED FROM AMERICAN 

CANCER SOCIETY ANALYSIS AND SIX-CITIES ANALYSISA 

Premature Ozone 

Mortality Function 

Reference Total Benefits 

(Billions, 2006$, 3% 

Discount Rate)b,c 

Total Benefits 

(Billions, 2006$, 7% 

Discount Rate) b,c 

Multi-city analyses Bell et al., 2004 $110 - $280 $100 - $250 

Huang et al., 2005 $120 - $280 $110 - $250 

Schwartz, 2005 $120 - $280 $110 - $250 

Meta-analyses Bell et al., 2005 $120 - $280 $110 - $250 

Ito et al., 2005 $120 - $280 $110 - $260 

Levy et al., 2005 $120 - $280 $110 - $260 

Notes: 

a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was 

developed by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related

premature mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 

2006). b

 Note that total benefits presented here do not include a number of unquantified benefits categories. A detailed

listing of unquantified health and welfare effects is provided in Table 6-2. c

 Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines for 

Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of 

presentation and computation.

The benefits in Table 6-1 include all of the human health impacts we are able to quantify 

and monetize at this time. However, the full complement of human health and welfare effects 

associated with PM and ozone remain unquantified because of current limitations in methods or 

available data. We have not quantified a number of known or suspected health effects linked 

with ozone and PM for which appropriate health impact functions are not available or which do 

not provide easily interpretable outcomes (i.e., changes in heart rate variability). Additionally, 

we are unable to quantify a number of known welfare effects, including reduced acid and 

particulate deposition damage to cultural monuments and other materials, and environmental 

benefits due to reductions of impacts of eutrophication in coastal areas. These are listed in Table 

6-2. As a result, the health benefits quantified in this chapter are likely underestimates of the 

total benefits attributable to the implementation of the coordinated strategy to control ship 

emissions. 

6-7 
Regulatory Impact Analysis 

Table 6-2 Unquantified and Non-Monetized Potential Effects of a Coordinated U.S. Strategy to Control Ship

Emissions 

POLLUTANT/ 

EFFECTS 

EFFECTS NOT INCLUDED IN ANALYSIS - CHANGES IN: 

Ozone Healtha Chronic respiratory damageb 

Premature aging of the lungsb 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e 

Ozone Welfare Yields for 

-commercial forests 

-some fruits and vegetables 

-non-commercial crops 

Damage to urban ornamental plants 

Impacts on recreational demand from damaged forest aesthetics 

Ecosystem functions 

Exposure to UVb (+/-)e 

PM Healthc Premature mortality - short term exposures

d 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic bronchitis 

Non-asthma respiratory emergency room visits 

Exposure to UVb (+/-)e 

PM Welfare Residential and recreational visibility in non-Class I areas 

Soiling and materials damage 

Damage to ecosystem functions 

Exposure to UVb (+/-)e 

Nitrogen and Sulfate 

Deposition Welfare 

Commercial forests due to acidic sulfate and nitrate deposition 

Commercial freshwater fishing due to acidic deposition 

Recreation in terrestrial ecosystems due to acidic deposition 

Existence values for currently healthy ecosystems 

Commercial fishing, agriculture, and forests due to nitrogen deposition 

Recreation in estuarine ecosystems due to nitrogen deposition 

Ecosystem functions 

Passive fertilization 

CO Health Behavioral effects 

HC/Toxics Healthf Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde) 

Anemia (benzene) 

Disruption of production of blood components (benzene) 

Reduction in the number of blood platelets (benzene) 

Excessive bone marrow formation (benzene) 

Depression of lymphocyte counts (benzene) 

Reproductive and developmental effects (1,3-butadiene) 

Irritation of eyes and mucus membranes (formaldehyde) 

Respiratory irritation (formaldehyde) 

Asthma attacks in asthmatics (formaldehyde) 

Asthma-like symptoms in non-asthmatics (formaldehyde) 

Irritation of the eyes, skin, and respiratory tract (acetaldehyde) 

Upper respiratory tract irritation and congestion (acrolein) 

6-8 
 Chapter 6: Cost-Benefit Analysis 

HC/Toxics Welfare Direct toxic effects to animals 

Bioaccumulation in the food chain 

Damage to ecosystem function 

Odor 

Notes: 

a

 The public health impact of biological responses such as increased airway responsiveness to stimuli, inflammation

in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection are 

likely partially represented by our quantified endpoints. b

 The public health impact of effects such as chronic respiratory damage and premature aging of the lungs may be 

partially represented by quantified endpoints such as hospital admissions or premature mortality, but a number of

other related health impacts, such as doctor visits and decreased athletic performance, remain unquantified. c

 In addition to primary economic endpoints, there are a number of biological responses that have been associated

with PM health effects including morphological changes and altered host defense mechanisms. The public health 

impact of these biological responses may be partly represented by our quantified endpoints. d While some of the effects of short-term exposures are likely to be captured in the estimates, there may be

premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis. 

However, the PM mortality results derived from the expert elicitation do take into account premature mortality 

effects of short term exposures. e

 May result in benefits or disbenefits. f

 Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the Clean Air Act.

6.2 Quantified Human Health Impacts 

Tables 6-3 and 6-4 present the annual PM2.5 and ozone health impacts in the 48 

contiguous U.S. states associated with the coordinated strategy for both 2020 and 2030. For each 

endpoint presented in Tables 6-3 and 6-4, we provide both the mean estimate and the 90% 

confidence interval. 

Using EPA’s preferred estimates, based on the ACS and Six-Cities studies and no 

threshold assumption in the model of mortality, we estimate that the coordinated strategy would 

result in between 5,300 and 14,000 cases of avoided PM2.5-related premature deaths annually in 

2020 and between 13,000 and 32,000 avoided premature deaths annually in 2030. As a 

sensitivity analysis, when the range of expert opinion is used, we estimate between 1,900 and 

18,000 fewer premature mortalities in 2020 and between 4,500 and 42,000 fewer premature 

mortalities in 2030. 

The range of ozone benefits associated with the coordinated strategy is based on risk 

reductions estimated using several sources of ozone-related mortality effect estimates. This 

analysis presents six alternative estimates for the association based upon different functions 

reported in the scientific literature, derived from both the National Morbidity, Mortality, and Air 

Pollution Study (NMMAPS) (Bell et al., 2004) and from a series of recent meta-analyses (Bell et 

al., 2005, Ito et al., 2005, and Levy et al., 2005). This approach is not inconsistent with 

recommendations provided by the NRC in their recent report (NRC, 2008) on the estimation of 

ozone-related mortality risk reductions, “The committee recommends that the greatest emphasis 

be placed on estimates from new systematic multicity analyses that use national databases of air 

pollution and mortality, such as in the NMMAPS, without excluding consideration of metaanalyses of previously published studies.” 

6-9 
Regulatory Impact Analysis 

For ozone-related premature mortality, we estimate a range of between 61 to 280 fewer 

premature mortalities as a result of the coordinated strategy in 2020 and between 220 to 980 in 

2030. The increase in annual benefits from 2020 to 2030 reflects additional emission reductions 

from coordinated strategy, as well as increases in total population and the average age (and thus 

baseline mortality risk) of the population. 

Following these tables, we also provide a more comprehensive presentation of the 

distributions of incidence generated using the available information from empirical studies and 

expert elicitation. Tables 6-5 and 6-6 present the distributions of the reduction in PM2.5-related 

premature mortality based on the C-R distributions provided by each expert, as well as that from 

the data-derived health impact functions, based on the statistical error associated with the ACS 

study (Pope et al., 2002) and the Six-cities study (Laden et al., 2006). The 90% confidence 

interval for each separate estimate of PM-related mortality is also provided. 

In 2020, the effect estimates of nine of the twelve experts included in the elicitation panel 

fall within the empirically-derived range provided by the ACS and Six-Cities studies. Only one 

expert falls below this range, while two of the experts are above this range. This same 

relationship occurs in 2030, as well. Although the overall range across experts is summarized in 

these tables, the full uncertainty in the estimates is reflected by the results for the full set of 12 

experts. The twelve experts’ judgments as to the likely mean effect estimate are not evenly 

distributed across the range illustrated by arraying the highest and lowest expert means. 

6-10 
 Chapter 6: Cost-Benefit Analysis 

Table 6-3 Estimated PM2.5-Related Health Impacts Associated with a Coordinated U.S. Strategy to Control 

Ship Emissions

a

Health Effect 

2020 Annual Reduction in 

Ship-Related Incidence 

(5th% - 95th%ile) 

2030 Annual Reduction in 

Ship-Related Incidence 

(5th% - 95th%ile) 

Premature Mortality – Derived from Epidemiology 

Literatureb

 Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

 Adult, age 25+, Six-Cities Study (Laden et al., 2006) 

 Infant, age <1 year (Woodruff et al., 1997) 

5,300 

(2,100 – 8,500) 

14,000 

(7,400 – 20,000) 

20 

(0 – 55) 

13,000 

(5,000 – 20,000) 

32,000 

(18,000 – 47,000) 

37 

(0 – 100) 

Chronic bronchitis (adult, age 26 and over) 3,800 

(700 – 6,900) 

8,500 

(1,600 – 15,000) 

Non-fatal myocardial infarction (adult, age 18 and 

over) 

8,800 

(3,200 – 14,000) 

22,000 

(8,100 – 35,000) 

Hospital admissions - respiratory (all ages)c 1,200 

(590 – 1,800) 

2,900 

1,400 – 4,200) 

Hospital admissions - cardiovascular (adults, age >18)d 2,700 

(2,000 – 3,200) 

7,100 

(5,000 – 8,300) 

Emergency room visits for asthma (age 18 years and 

younger) 

3,500 

(2,000 – 4,900) 

8,100 

(4,800 – 11,000) 

Acute bronchitis, (children, age 8-12) 8,500 

(0 – 17,000) 

19,000 

(0 – 37,000) 

Lower respiratory symptoms (children, age 7-14) 100,000 

(49,000 – 150,000) 

220,000 

(110,000 – 330,000) 

Upper respiratory symptoms (asthmatic children, age 

9-18) 

77,000 

(24,000 – 130,000) 

170,000 

(54,000 – 290,000) 

Asthma exacerbation (asthmatic children, age 6-18) 95,000 

(10,000 – 260,000) 

210,000 

(23,000 – 580,000) 

Work loss days 720,000 

(630,000 – 810,000) 

1,500,000 

(1,300,000 – 1,700,000) 

Minor restricted activity days (adults age 18-65) 4,300,000 

(3,600,000 – 4,900,000) 

9,000,000 

(7,600,000 – 10,000,000) 

Notes: 

a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United States. b

 PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and the Six-Cities 

Study (Laden et al., 2006). Note that these are two alternative estimates of adult mortality and should not be summed. PMrelated infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997). c Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), pneumonia and

asthma. d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease, 

dysrhythmias, and heart failure.

6-11 
Regulatory Impact Analysis 

Table 6-4 Estimated Ozone-Related Health Impacts Associated with a Coordinated U.S. Strategy to Control

Ship Emissions

a

Health Effect 

2020 Annual Reduction in ShipRelated Incidence 

(5th% - 95th%ile) 

2030 Annual Reduction in ShipRelated Incidence 

(5th% - 95th%ile) 

Premature Mortality, All agesb 

Multi-City Analyses

 Bell et al. (2004) – Non-accidental 

 Huang et al. (2005) – Cardiopulmonary

 Schwartz (2005) – Non-accidental 

Meta-analyses:

 Bell et al. (2005) – All cause

 Ito et al. (2005) – Non-accidental 

 Levy et al. (2005) – All cause 

61 

(23 – 98) 

100 

(43 – 160) 

93 

(34 – 150) 

200 

(100 – 290) 

270 

(170 – 370) 

280 

(200 – 360) 

220 

(71 – 370) 

370 

(140 – 610) 

340 

(100 – 570) 

690 

(330 – 1,100) 

980 

(580 – 1,400) 

980 

(670 – 1,300) 

Hospital admissions- respiratory causes (adult, 

65 and older)c 

470 

(46 – 830) 

2,000 

(97 – 3,600) 

Hospital admissions -respiratory causes 

(children, under 2) 

380 

(180 – 590) 

1,200 

(500 – 2,000) 

Emergency room visit for asthma (all ages) 210 

(0 – 550) 

740 

(0 – 1,900) 

Minor restricted activity days (adults, age 18­

65) 

360,000 

(160,000 – 570,000) 

1,200,000 

(440,000 – 1,900,000) 

School absence days 130,000 

(51,000 – 190,000) 

450,000 

(150,000 – 680,000) 

Notes: 

a Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United States. b

 Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative studies: Bell

et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005). The estimates of

ozone-related premature mortality should therefore not be summed. c

 Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for COPD and 

pneumonia. 

6-12 
 Chapter 6: Cost-Benefit Analysis 

Table 6-5 Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 

2020 Associated with a Coordinated U.S. Strategy to Control Ship Emissions 

Source of Mortality 

Estimate 

2020 Primary Option 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 2,100 5,300 8,500 

Laden et al. (2006) 7,400 14,000 20,000 

Expert A 2,600 14,000 26,000 

Expert B 1,400 11,000 23,000 

Expert C 2,000 11,000 23,000 

Expert D 1,600 7,600 12,000 

Expert E 9,000 18,000 27,000 

Expert F 6,800 9,800 14,000 

Expert G 0 6,300 12,000 

Expert H 30 8,100 18,000 

Expert I 1,700 11,000 19,000 

Expert J 2,600 8,700 19,000 

Expert K 0 1,900 8,800 

Expert L 1,100 7,500 15,000 

Table 6-6 Results of Application of Expert Elicitation: Annual Reductions in Premature Mortality in 

2030 Associated with a Coordinated U.S. Strategy to Control Ship Emissions 

Source of Mortality 

Estimate 

2030 Primary Option 

5th Percentile Mean 95th Percentile 

Pope et al. (2002) 5,000 13,000 20,000 

Laden et al. (2006) 18,000 32,000 47,000 

Expert A 6,300 34,000 61,000 

Expert B 3,400 26,000 55,000 

Expert C 4,700 26,000 55,000 

Expert D 3,800 18,000 30,000 

Expert E 22,000 42,000 64,000 

Expert F 17,000 24,000 34,000 

Expert G 0 15,000 28,000 

Expert H 71 19,000 44,000 

Expert I 4,100 26,000 45,000 

Expert J 6,200 21,000 46,000 

Expert K 0 4,500 21,000 

Expert L 2,600 18,000 35,000 

6-13 
Regulatory Impact Analysis 

6.3 Monetized Benefits 

Monetized values for each quantified health endpoint are presented in Table 6-7. For 

each endpoint presented in Table 6-7, we provide both the mean estimate and the 90% 

confidence interval. Total aggregate monetized benefits are presented in Tables 6-8 and 6-9 

using either a 3 percent or 7 percent discount rate, respectively. All of the monetary benefits are 

in constant-year 2006 dollars. 

In addition to omitted benefits categories such as air toxics and various welfare effects, 

not all known PM2.5- and ozone-related health and welfare effects could be quantified or 

monetized. The estimate of total monetized health benefits of the coordinated strategy is thus 

equal to the subset of monetized PM2.5- and ozone-related health benefits we are able to quantify 

plus the sum of the nonmonetized health and welfare benefits. We believe the total benefits are 

therefore likely underestimated. 

Our estimate of total monetized benefits in 2020 for the coordinated strategy, using the 

ACS and Six-Cities PM mortality studies and the range of ozone mortality assumptions, is 

between $47 billion and $110 billion, assuming a 3 percent discount rate, or between $42 billion 

and $100 billion, assuming a 7 percent discount rate. In 2030, we estimate the monetized 

benefits to be between $110 billion and $280 billion, assuming a 3 percent discount rate, or 

between $100 billion and $260 billion, assuming a 7 percent discount rate. The monetized 

benefit associated with reductions in the risk of both ozone- and PM2.5-related premature 

mortality ranges between 90 to 98 percent of total monetized health benefits, in part because we 

are unable to quantify a number of benefits categories (see Table 6-2). These unquantified 

benefits may be substantial, although their magnitude is highly uncertain. 

The next largest benefit is for reductions in chronic illness (chronic bronchitis and 

nonfatal heart attacks), although this value is more than an order of magnitude lower than for 

premature mortality. Hospital admissions for respiratory and cardiovascular causes, minor 

restricted activity days, and work loss days account for the majority of the remaining benefits. 

The remaining categories each account for a small percentage of total benefit; however, they 

represent a large number of avoided incidences affecting many individuals. A comparison of the 

incidence table to the monetary benefits table reveals that there is not always a close 

correspondence between the number of incidences avoided for a given endpoint and the 

monetary value associated with that endpoint. For example, there are over 100 times more work 

loss days than PM-related premature mortalities (based on the ACS study), yet work loss days 

account for only a very small fraction of total monetized benefits. This reflects the fact that 

many of the less severe health effects, while more common, are valued at a lower level than the 

more severe health effects. Also, some effects, such as hospital admissions, are valued using a 

proxy measure of willingness-to-pay (e.g., cost-of-illness). As such, the true value of these 

effects may be higher than that reported here. 

6-14 
 Chapter 6: Cost-Benefit Analysis 

Table 6-7 Estimated Monetary Value in Reductions in Incidence of Health and Welfare Effects (in millions of 

2006$) a,b

 2020 2030 

PM2.5-Related Health Effect Estimated Mean Value of Reductions 

(5th and 95th %ile) 

Premature Mortality 

– Derived from 

Epidemiology 

Studiesc,d, 

Adult, age 30+ - ACS study 

(Pope et al., 2002) 

3% discount rate 

7% discount rate 

$43,000 

($5,000 - $110,000) 

$38,000 

($4,500 - $100,000) 

$100,000 

($12,000 - $270,000) 

$94,000 

($11,000 - $250,000) 

Adult, age 25+ - Six-cities study 

(Laden et al., 2006) 

3% discount rate 

7% discount rate 

$110,000 

($14,000 - $270,000) 

$98,000 

($13,000 - $250,000) 

$270,000 

($35,000 - $670,000) 

$240,000 

($32,000 - $610,000) 

Infant Mortality, <1 year – 

(Woodruff et al. 1997) 

$180 

($0 - $670) 

$330 

($0 - $1,300) 

Chronic bronchitis (adults, 26 and over) $1,900 

($140 - $6,500) 

$4,300 

($340 - $15,000) 

Non-fatal acute myocardial infarctions 

3% discount rate 

7% discount rate 

$960 

($170 - $2,300) 

$930 

($160 - $2,300) 

$2,300 

($390 - $5,600) 

$2,200 

($360 - $5,500) 

Hospital admissions for respiratory causes $17 

($8.4 - $25) 

$41 

($21 - $61) 

Hospital admissions for cardiovascular causes $76 

($48 - $110) 

$190 

($120 - $270) 

Emergency room visits for asthma $1.3 

($0.70 - $1.9) 

$3.0 

($1.6 - $4.5) 

Acute bronchitis (children, age 8–12) $0.63 

($0 - $1.6) 

$1.4 

($0 - $3.4) 

Lower respiratory symptoms (children, 7–14) $2.0 

($0.75 - $3.7) 

$4.4 

($1.7 - $8.1) 

Upper respiratory symptoms (asthma, 9–11) $2.4 

($0.65 - $5.3) 

$5.3 

($1.5 - $12) 

Asthma exacerbations $5.1 

($0.51 - $15) 

$11 

($1.1 - $34) 

Work loss days $110 

($94 - $120) 

$230 

($200 - $260) 

Minor restricted-activity days (MRADs) $270 

($150 – $390) 

$570 

($330 - $830) 

Ozone-related Health Effect 

Premature Mortality, All ages – 

Derived from Multi-city 

analyses 

Bell et al., 2004 $540 

($63 - $1,400) 

$2,000 

($230 - $5,300) 

Huang et al., 2005 $910 

($110 - $2,300) 

$3,400 

($390 - $8,900) 

Schwartz, 2005 $830 

($94 - $2,200) 

$3,000 

($320 - $8,200) 

Premature Mortality, All ages – 

Derived from Meta-analyses 

Bell et al., 2005 $1,700 

($220 - $4,400) 

$6,300 

($810 - $16,000) 

6-15 
Regulatory Impact Analysis 

Ito et al., 2005 $2,400 

($330 - $5,900) 

$8,900 

($1,200 - $22,000) 

Levy et al., 2005 $2,400 

($340 - $5,900) 

$8,900 

($1,200 - $22,000) 

Hospital admissions- respiratory causes (adult, 65 and older) $11 

($1.1 - $20) 

$47 

($2.3 - $85) 

Hospital admissions- respiratory causes (children, under 2) $3.8 

($1.8 – $5.9) 

$12 

($5.0 - $20) 

Emergency room visit for asthma (all ages) $0.08 

($0.03 - $0.20) 

$0.27 

($0 - $0.68) 

Minor restricted activity days (adults, age 18-65) $23 

($9.8 - $41) 

$73 

($26 - $130) 

School absence days $12 

($4.6 - $17) 

$40 

($13 - $61) 

Notes: 

a

 Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM and ozone 

benefits are nationwide. b

 Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year (2020 

or 2030) c

 Valuation assumes discounting over the SAB recommended 20 year segmented lag structure. Results reflect the 

use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic

analyses (EPA, 2000; OMB, 2003). d

 The valuation of adult premature mortality derived from the epidemiology literature is not additive. Rather, the 

valuations represent a range of possible mortality benefits. 

6-16 
 Chapter 6: Cost-Benefit Analysis 

Table 6-8 Total Monetized Benefits Associated with a Coordinated U.S. Strategy to Control Ship Emissions – 

3% Discount Rate 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from the ACS and Six Cities Studies 

2020 2030 

Ozone Reference Mean Total Ozone Reference Mean Total 

Mortality Benefits Mortality Benefits 

Function Function 

Bell et al., $47 - $110 Bell et al., $110 - $280 

2004 2004 

Huang et al., $47 - $110 Huang et al., $120 - $280 Multi-city Multi-city 2005 2005 

Schwartz, $47 - $110 Schwartz, $120 - $280 

2005 2005 

Bell et al., $48 - $110 Bell et al., $120 - $280 

2005 2005 

Meta-analysis Ito et al., 2005 $48 - $110 Meta-analysis Ito et al., 2005 $120 - $280 

Levy et al., $48 - $110 Levy et al., $120 - $280 

2005 2005 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

2020 2030 

Ozone Reference Mean Total Ozone Reference Mean Total 

Mortality Benefits Mortality Benefits 

Function Function 

Bell et al., $19 - $150 Bell et al., $48 - $360 

2004 2004 

Huang et al., $19 - $150 Huang et al., $49 - $360 Multi-city Multi-city 2005 2005 

Schwartz, $19 - $150 Schwartz, $49 - $360 

2005 2005 

Bell et al., $20 - $150 Bell et al., $52 - $370 

2005 2005 

Meta-analysis Ito et al., 2005 $21 - $150 Meta-analysis Ito et al., 2005 $54 - $370 

Levy et al., $21 - $150 Levy et al., $54 - $370 

2005 2005 

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Regulatory Impact Analysis 

Table 6-9 Total Monetized Benefits Associated with a Coordinated U.S. Strategy to Control Ship Emissions – 

7% Discount Rate 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from the ACS and Six Cities Studies 

2020 2030 

Ozone Reference Mean Total Ozone Reference Mean Total 

Mortality Benefits Mortality Benefits 

Function Function 

Bell et al., $42 - $100 Bell et al., $100 - $250 

2004 2004 

Huang et al., $43 - $100 Huang et al., $110 - $250 Multi-city Multi-city 2005 2005 

Schwartz, $43 - $100 Schwartz, $110 - $250 

2005 2005 

Bell et al., $44 - $100 Bell et al., $110 - $250 

2005 2005 

Meta-analysis Ito et al., 2005 $44 - $100 Meta-analysis Ito et al., 2005 $110 - $260 

Levy et al., $44 - $100 Levy et al., $110 - $260 

2005 2005 

Total Ozone and PM Benefits (billions, 2006$) – 

PM Mortality Derived from Expert Elicitation (Lowest and Highest Estimate) 

2020 2030 

Ozone Reference Mean Total Ozone Reference Mean Total 

Mortality Benefits Mortality Benefits 

Function Function 

Bell et al., $18 - $130 Bell et al., $44 - $330 

2004 2004 

Huang et al., $18 - $130 Huang et al., $45 - $330 Multi-city Multi-city 2005 2005 

Schwartz, $18 - $130 Schwartz, $45 - $330 

2005 2005 

Bell et al., $19 - $130 Bell et al., $48 - $330 

2005 2005 

Meta-analysis Ito et al., 2005 $19 - $140 Meta-analysis Ito et al., 2005 $51 - $330 

Levy et al., $19 - $140 Levy et al., $51 - $330 

2005 2005 

6.4 Methodology 

6.4.1 Human Health Impact Functions 

Health impact functions measure the change in a health endpoint of interest, such as 

hospital admissions, for a given change in ambient ozone or PM concentration. Health impact 

functions are derived from primary epidemiology studies, meta-analyses of multiple 

epidemiology studies, or expert elicitations. A standard health impact function has four 

components: (1) an effect estimate from a particular study; (2) a baseline incidence rate for the 

health effect (obtained from either the epidemiology study or a source of public health statistics 

such as the Centers for Disease Control); (3) the size of the potentially affected population; and 

(4) the estimated change in the relevant ozone or PM summary measures. 

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 Chapter 6: Cost-Benefit Analysis 

A typical health impact function might look like: 

 x y  y0 e 1, 

where y0 is the baseline incidence (the product of the baseline incidence rate times the potentially 

affected population), β is the effect estimate, and Δx is the estimated change in the summary 

pollutant measure. There are other functional forms, but the basic elements remain the same. 

The following subsections describe the sources for each of the first three elements: size of the 

potentially affected populations; PM2.5 and ozone effect estimates; and baseline incidence rates. 

Section 4.2.2 describes the ozone and PM air quality inputs to the health impact functions. 

6.4.1.1 Potentially Affected Populations 

The starting point for estimating the size of potentially affected populations is the 2000 

U.S. Census block level dataset.26 Benefits Modeling and Analysis Program (BenMAP) 

incorporates 250 age/gender/race categories to match specific populations potentially affected by 

ozone and other air pollutants. The software constructs specific populations matching the 

populations in each epidemiological study by accessing the appropriate age-specific populations 

from the overall population database. BenMAP projects populations to 2020 using growth 

factors based on economic projections.27 

6.4.1.2 Effect Estimate Sources 

The most significant quantifiable benefits of reducing ambient concentrations of ozone 

and PM are attributable to reductions in human health risks. EPA’s Ozone and PM Criteria 

Documents28,29 and the World Health Organization’s 2003 and 200430,31 reports outline 

numerous human health effects known or suspected to be linked to exposure to ambient ozone 

and PM. EPA recently evaluated the ozone and PM literature for use in the benefits analysis for 

the final 2008 Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same literature 

in this analysis; for more information on the studies that underlie the health impacts quantified in 

this RIA, please refer to those documents. 

It is important to note that we are unable to separately quantify all of the possible PM and 

ozone health effects that have been reported in the literature for three reasons: (1) the possibility 

of double counting (such as hospital admissions for specific respiratory diseases versus hospital 

admissions for all or a sub-set of respiratory diseases); (2) uncertainties in applying effect 

relationships that are based on clinical studies to the potentially affected population; or (3) the 

lack of an established concentration-response (CR) relationship. Table 6-10 lists the health 

endpoints included in this analysis. 

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Regulatory Impact Analysis 

Table 6-10 Ozone- and PM-Related Health Endpoints 

ENDPOINT POLLUTANT STUDY STUDY POPULATION 

Premature Mortality 

Premature mortality – 

daily time series 

O3 Multi-city 

Bell et al (2004) (NMMAPS study)

32 – Nonaccidental 

Huang et al (2005)33 - Cardiopulmonary 

Schwartz (2005)

34 – Non-accidental 

Meta-analyses: 

Bell et al (2005)35 – All cause 

Ito et al (2005)

36 – Non-accidental 

Levy et al (2005)

37 – All cause 

All ages 

Premature mortality 

—cohort study, allcause 

PM2.5 Pope et al. (2002)38 

Laden et al. (2006)

39 

>29 years 

>25 years 

Premature mortality, 

total exposures 

PM2.5 Expert Elicitation (IEc, 2006)40 >24 years 

Premature mortality 

— all-cause 

PM2.5 Woodruff et al. (1997)

41 Infant (<1 year) 

Chronic Illness 

Chronic bronchitis PM2.5 Abbey et al. (1995)42 >26 years 

Nonfatal heart attacks PM2.5 Peters et al. (2001)43 Adults (>18 years) 

Hospital Admissions 

Respiratory 

O3 

Pooled estimate: 

Schwartz (1995) - ICD 460-519 (all resp)44 

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)45,46 

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)47 

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD) 

Moolgavkar et al. (1997) – ICD 490-496 

(COPD) 

>64 years 

Burnett et al. (2001)48 <2 years 

PM2.5 Pooled estimate: 

Moolgavkar (2003)—ICD 490-496 (COPD)49 

Ito (2003)—ICD 490-496 (COPD)

50 

>64 years 

PM2.5 Moolgavkar (2000)—ICD 490-496 (COPD)51 20–64 years 

PM2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years 

PM2.5 Sheppard (2003)—ICD 493 (asthma)52 <65 years 

Cardiovascular PM2.5 Pooled estimate: 

Moolgavkar (2003)—ICD 390-429 (all 

cardiovascular) 

Ito (2003)—ICD 410-414, 427-428 (ischemic 

heart disease, dysrhythmia, heart failure) 

>64 years 

PM2.5 Moolgavkar (2000)—ICD 390-429 (all 

cardiovascular) 

20–64 years 

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 Chapter 6: Cost-Benefit Analysis 

Asthma-related ER 

visits 

O3 Pooled estimate: 

Jaffe et al (2003)53 

Peel et al (2005)54 

Wilson et al (2005)55 

5–34 years 

All ages 

All ages 

Asthma-related ER 

visits (con’t) 

PM2.5 Norris et al. (1999)56 0–18 years 

Other Health Endpoints 

Acute bronchitis PM2.5 Dockery et al. (1996)57 8–12 years 

Upper respiratory 

symptoms 

PM2.5 Pope et al. (1991)58 Asthmatics, 9–11 

years 

Lower respiratory 

symptoms 

PM2.5 Schwartz and Neas (2000)59 7–14 years 

Asthma exacerbations PM2.5 Pooled estimate: 

Ostro et al. (2001)60 (cough, wheeze and 

shortness of breath) 

Vedal et al. (1998)61 (cough) 

6–18 years

a 

Work loss days PM2.5 Ostro (1987)

62 18–65 years 

School absence days 

O3 

Pooled estimate: 

Gilliland et al. (2001)63 

Chen et al. (2000)64 

5–17 years

b 

Minor Restricted 

Activity Days 

(MRADs) 

O3 Ostro and Rothschild (1989)65 18–65 years 

PM2.5 Ostro and Rothschild (1989) 18–65 years 

Notes: 

a

 The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) 

study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended 

the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age 

group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical Plan 

for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits 

of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. b

 Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent 

advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school 

absences for all school-aged children based on the biological similarity between children aged 5 to 17. 

In selecting epidemiological studies as sources of effect estimates, we applied several 

criteria to develop a set of studies that is likely to provide the best estimates of impacts in the 

U.S. To account for the potential impacts of different health care systems or underlying health 

status of populations, we give preference to U.S. studies over non-U.S. studies. In addition, due 

to the potential for confounding by co-pollutants, we give preference to effect estimates from 

models including both ozone and PM over effect estimates from single-pollutant models.66,67 

6.4.1.3 Treatment of Potential Thresholds in PM2.5-Related Health Impact Functions 

In recent analyses, OTAQ has estimated PM2.5-related benefits assuming that a threshold 

exists in the PM-related concentration-response functions (at 10 μg/m3

) below which there are no 

associations between exposure to PM2.5 and health impacts. For the benefits analysis of the 

coordinated strategy, however, we have revised this assumption. As explained in the recently 

published Portland Cement MACT RIA, EPA’s preferred benefits estimation approach assumes 

6-21 
Regulatory Impact Analysis 

a no-threshold model that calculates incremental benefits down to the lowest modeled PM2.5 air 

quality levels. 

EPA strives to use the best available science to support our benefits analyses, and we 

recognize that interpretation of the science regarding air pollution and health is dynamic and 

evolving. Based on our review of the body of scientific literature, EPA applied the no-threshold 

model in this analysis, which is consistent with the approach taken in the recently published 

Portland Cement MACT RIA.K EPA's draft Integrated Science Assessment (ISA),68 which was 

recently reviewed by EPA’s Clean Air Scientific Advisory Committee (CASAC),69,70 concluded 

that the scientific literature consistently finds that a no-threshold log-linear model most 

adequately portrays the PM-mortality concentration-response relationship while recognizing 

potential uncertainty about the exact shape of the concentration-response function. However, 

during this review, CASAC did not comment on thresholds, and an absence of comments on the 

ISA regarding thresholds generally implies that CASAC accepted the ISA conclusion. Although 

this document does not represent final agency policy that has undergone the full agency scientific 

review process, it provides a basis for reconsidering the application of thresholds in PM2.5 

concentration-response functions used in EPA’s RIAs. It is important to note that while CASAC 

provides advice regarding the science associated with setting the National Ambient Air Quality 

Standards, typically other scientific advisory bodies provide specific advice regarding benefits 

analysis. Because the Portland Cement RIA was completed while CASAC was reviewing the 

PM ISA, we solicited comment on the use of the no-threshold model for benefits analysis within 

the preamble of that proposed rule. The comment period for the Portland Cement proposed 

NESHAP has been extended until September 4, 2009.L As explained in the recently published 

Portland Cement MACT RIA,M EPA’s preferred benefits estimation approach assumes a nothreshold model that calculates incremental benefits down to the lowest modeled PM2.5 air 

quality levels. This approach reflects EPA’s most current interpretation of the scientific 

literature on PM2.5 and mortality. Please refer to the Portland Cement MACT RIA for a 

description of the history of the treatment of thresholds in our analyses.71 

As can be seen in Table 6-11, we conducted a sensitivity analysis for premature 

mortality, with alternative thresholds at 3 μg/m3

 (the “background,” or no-threshold, 

assumption), 7.5 μg/m3, 10 μg/m3, 12 μg/m3, and 14 μg/m3 . By replacing the no-threshold 

assumption in the ACS premature mortality function with a 10 μg/m3 threshold model, the 

number of avoided incidences of premature mortality would decrease by approximately 40 

percent. 

K U.S. Environmental Protection Agency. (2009). Regulatory Impact Analysis: National Emission Standards for 

Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air and Radiation. 

Retrieved on May 4, 2009, from http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf L Readers interested in commenting on the use of the no-threshold model for benefits analysis should direct their 

comments to Docket ID No. EPA–HQ–OAR–2002–0051 (available at http://www.regulations.gov) before the 

comment period closes. M U.S. Environmental Protection Agency. (2009). Regulatory Impact Analysis: National Emission Standards for 

Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Office of Air and Radiation. 

Retrieved on May 4, 2009, from http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf 

6-22 
 Chapter 6: Cost-Benefit Analysis 

Table 6-11 PM-Related Mortality Benefits Associated with a Coordinated U.S. Strategy to Control Ship

Emissions: Threshold Sensitivity Analysis Using the ACS Study (Pope et al., 2002)a

Level of 

Assumed 

Threshold 

PM Mortality Incidence 

2020 2030 

14 μg/m3 b 1,800 4,600 

12 μg/m3 2,100 5,100 

10 μg/m3 c 3,400 7,800 

7.5 μg/m3 d 4,500 11,000 

3 μg/m3 e 5,300 13,000 

Notes: 

a

 Note that this table only presents the effects of a threshold on PMrelated mortality incidence based on the ACS study. b

 Alternative annual PM NAAQS. c Previous threshold assumption d

 SAB-HES (2004)86 

e

 NAS (2002)87 

6.4.2 Economic Values for Health Outcomes 

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

willingness-to-pay (WTP) for changes in risk of a health effect rather than WTP for a health 

effect that would occur with certainty (Freeman, 1993). Epidemiological studies generally 

provide estimates of the relative risks of a particular health effect that is avoided because of a 

reduction in air pollution. We converted those to units of avoided statistical incidence for ease of 

presentation. We calculated the value of avoided statistical incidences by dividing individual 

WTP for a risk reduction by the related observed change in risk. For example, suppose a 

pollution-reduction regulation is able to reduce the risk of premature mortality from 2 in 10,000 

to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then 

the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk). 

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary 

estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing 

the risk of a health effect, because they reflect the direct expenditures related to treatment, but 

not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We 

provide unit values for health endpoints (along with information on the distribution of the unit 

value) in Table 6-12. All values are in constant year 2006 dollars, adjusted for growth in real 

income out to 2020 and 2030 using projections provided by Standard and Poor’s. Economic 

theory argues that WTP for most goods (such as environmental protection) will increase if real 

income increases. Many of the valuation studies used in this analysis were conducted in the late 

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Regulatory Impact Analysis 

1980s and early 1990s. Because real income has grown since the studies were conducted, 

people’s willingness to pay for reductions in the risk of premature death and disease likely has 

grown as well. We did not adjust cost of illness-based values because they are based on current 

costs. Similarly, we did not adjust the value of school absences, because that value is based on 

current wage rates. For details on valuation estimates for PM-related endpoints, see the 2006 

PM NAAQS RIA. For details on valuation estimates for ozone-related endpoints, see the 2008 

Ozone NAAQS RIA. 

6-24 
 Chapter 6

Table 6-12 Unit Values Used for Economic Valuation of Health Endpoints (2000$)a 

Health Endpoint 

Central Estimate of Value Per Statistical Incidence 

Derivation of Estimates 

1990 Income Level 

2020 Income Levelb 

2030 Income Levelb 

Premature Mortality (Value of a Statistical Life): PM2.5- and 

Ozone-related 

$6,320,000 $7,590,000 $7,800,000 EPA currently recommends a default central V

based on a Weibull distribution fitted to twentestimates (5 contingent valuation and 21 labor underlying studies, the distribution parametersinformation are available in Appendix B of EPfor Preparing Economic Analyses. The guideat: http://yosemite.epa.gov/ee/epa/eermfile.ns01.pdf/$File/EE-0516-01.pdf 

Chronic Bronchitis (CB) $340,000 $420,000 $430,000 Point estimate is the mean of a generated distr

avoid a case of pollution-related CB. WTP to pollution-related CB is derived by adjusting WViscusi et al., [1991]72) to avoid a severe case 

difference in severity and taking into account with respect to severity of CB. 

Nonfatal Myocardial Infarction (heart attack)  3% discount rate Age 0–24  Age 25–44 Age 45–54 Age 55–65 

Age 66 and over

 7% discount rate Age 0–24  Age 25–44 Age 45–54 Age 55–65 

Age 66 and over 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

$66,902 $74,676 $78,834 $140,649 $66,902 $65,293 $73,149 $76,871 $132,214 $65,293 

Age-specific cost-of-illness values reflect lost medical costs over a 5-year period following a earnings estimates are based on Cropper and K

Direct medical costs are based on simple averRussell et al. (1998)74 and Wittels et al. (1990

Lost earnings: Cropper and Krupnick (1990). Present discouof lost earnings: age of onset: at 3% at 7% 25-44 $8,774 $7,855 45-54 $12,932 $11,578 55-65 $74,746 $66,920 Direct medical expenses: An average of: 1. Wittels et al. (1990) ($102,658—no discou2. Russell et al. (1998), 5-year period ($22,33$21,113 at 7% discount rate) 

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Regulatory Impact Analysis 

Table 6-12 Unit Values Used for Economic Valuation of Health Endpoints (2006$)a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical Incidence 

Derivation of Estimates 

1990 Income Level 

2020 Income Levelb 

2030 Income Levelb 

Hospital Admissions Chronic Obstructive Pulmonary Disease (COPD) (ICD codes 490-492, 494-496) 

$12,378 $12,378 $12,378 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted category illnesses) reported in Agency for HQuality (2000)76 (www.ahrq.gov). 

Pneumonia (ICD codes 480-487) 

$14,693 $14,693 $14,693 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted pneumonia category illnesses) reported in AResearch and Quality (2000) (www.ahrq.gov

Asthma Admissions $6,634 $6,634 $6,634 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted category illnesses) reported in Agency for HQuality (2000) (www.ahrq.gov). 

All Cardiovascular (ICD codes 390-429) 

$18,387 $18,387 $18,387 The COI estimates (lost earnings plus direct 

on ICD-9 code-level information (e.g., averaaverage length of hospital stay, and weighted cardiovascular category illnesses) reported in Research and Quality (2000) (www.ahrq.gov

Emergency Room Visits for Asthma 

$286 $286 $286 Simple average of two unit COI values: 

(1) $311.55, from Smith et al. (1997)77 and 

(2) $260.67, from Stanford et al. (1999).78 

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 Chapter 6

Table 6-12 Unit Values Used for Economic Valuation of Health Endpoints (2006$)a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical Incidence 

Derivation of Estimates 

1990 Income Level 

2020 Income Levelb 

2030 Income Levelb 

Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) 

$25 $27 $27 Combinations of the three symptoms for whi

available that closely match those listed by Pdifferent “symptom clusters,” each describindollar value was derived for each type of URestimates of WTP (IEc, 1994)79 to avoid eac

and assuming additivity of WTPs. The dollaaverage of the dollar values for the seven dif

Lower Respiratory Symptoms (LRS) 

$16 $17 $17 Combinations of the four symptoms for whic

available that closely match those listed by S11 different “symptom clusters,” each descridollar value was derived for each type of LRestimates of WTP (IEc, 1994) to avoid each and assuming additivity of WTPs. The dollaaverage of the dollar values for the 11 differe

Asthma Exacerbations $42 $45 $45 Asthma exacerbations are valued at $42 per 

mean of average WTP estimates for the four “bad asthma day,” described in Rowe and Ch

study surveyed asthmatics to estimate WTP asthma day,” as defined by the subjects. For an asthma attack is assumed to be equivalent asthma is moderate or worse as reported in th(1986) study. 

Acute Bronchitis $360 $380 $390 Assumes a 6-day episode, with daily value e

low and high values for related respiratory syin Neumann et al. (1994).81 

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Regulatory Impact Analysis 

Table 6-12 Unit Values Used for Economic Valuation of Health Endpoints (2006$)a (continued) 

Health Endpoint 

Central Estimate of Value Per Statistical Incidence 

Derivation of Estimates 

1990 Income Level 

2020 Income Levelb 

2030 Income Levelb 

Restricted Activity and Work/School Loss Days Work Loss Days (WLDs) Variable 

(national median = ) 

County-specific median annual wages divid

of vacation) and then by 5—to get median dCensus, compiled by Geolytics, Inc. 

School Absence Days $75 $75 $75 Based on expected lost wages from parent s

Estimated daily lost wage (if a mother must child) is based on the median weekly wage older in 2000 (U.S. Census Bureau, StatisticStates: 2001, Section 12: Labor Force, EmTable No. 621). This median wage is $551estimated median daily wage of $103. The expected loss in wages due to a day of mother would have to stay home with her cprobability that the mother is in the workforwould lose if she missed a day = 72.85% of 

Worker Productivity $0.95 per 

worker per 10% change in ozone per day 

$0.95 per worker per 10% change in ozone per day 

$0.95 per worker per 10% change in ozone per day 

Based on $68 – median daily earnings of wand fishing – from Table 621, Statistical Ab(“Full-Time Wage and Salary Workers – N2000") (Source of data in table: U.S. BureaBulletin 2307 and Employment and Earning

Minor Restricted Activity Days (MRADs) 

$51 $54 $55 Median WTP estimate to avoid one MRAD 

a All monetized annual benefit estimates associated with the coordinated strategy have been inflated to reflect values in year 2006 dolla

Price Indexes to adjust both WTP- and COI-based benefits estimates to 2006 dollars from 2000 dollars.83 For WTP-based estimates, w

1.17 based on the CPI-U for “all items.” For COI-based estimates, we use an inflation factor of 1.29 based on the CPI-U for medical ca

b Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as enviincrease if real incomes increase. Benefits are therefore adjusted by multiplying the unadjusted benefits by the appropriate adjustment 

growth over time. For a complete discussion of how these adjustment factors were derived, we refer the reader to the PM NAAQS reguthat similar adjustments do not exist for cost-of-illness-based unit values. For these, we apply the same unit value regardless of the futu

6-28 
Chapter 6: Cost-Benefit Analysis 

6.4.3 Manipulating Air Quality Modeling Data for Health Impacts Analysis 

In Chapter 2, we summarized the methods for and results of estimating air quality for the 

coordinated strategy presented in this proposal. These air quality results are in turn associated 

with human populations to estimate changes in health effects. For the purposes of this analysis, 

we focus on the health effects that have been linked to ambient changes in ozone and PM2.5 

related to emission reductions estimated to occur due to the proposed ECA. We estimate 

ambient PM2.5 and ozone concentrations using the Community Multiscale Air Quality model 

(CMAQ). This section describes how we converted the CMAQ modeling output into full-season 

profiles suitable for the health impacts analysis. 

6.4.3.1 General Methodology 

First, we extracted hourly, surface-layer PM and ozone concentrations for each grid cell 

from the standard CMAQ output files. For ozone, these model predictions are used in 

conjunction with the observed concentrations obtained from the Aerometric Information 

Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.N,O The 

predicted changes in ozone concentrations from the future-year base case to future-year control 

scenario serve as inputs to the health and welfare impact functions of the benefits analysis (i.e., 

BenMAP). 

To estimate ozone-related health effects for the contiguous United States, full-season 

ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we 

generated full-season ozone profiles for each location in two steps: (1) we combined monitored 

observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid 

of 12-km by 12-km population grid cells for the contiguous 48 states, and (2) we converted these 

full-season hourly ozone profiles to an ozone measure of interest, such as the daily 8-hour 

maximum.P,Q 

For PM2.5, we also use the model predictions in conjunction with observed monitor data. 

CMAQ generates predictions of hourly PM species concentrations for every grid. The species 

include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a 

primary fine fraction (corresponding to PM less than 2.5 microns in diameter), and several 

secondary particles (e.g., sulfates, nitrates, and organics). PM2.5 is calculated as the sum of the 

primary fine fraction and all of the secondarily formed particles. Future-year estimates of PM2.5 

were calculated using relative reduction factors (RRFs) applied to 2002 ambient PM2.5 and PM2.5 

species concentrations. A gridded field of PM2.5 concentrations was created by interpolating 

Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM2.5 

N The ozone season for this analysis is defined as the 5-month period from May to September. O Based on AIRS, there were 961 ozone monitors with sufficient data (i.e., 50 percent or more days reporting at least 

nine hourly observations per day [8 am to 8 pm] during the ozone season). P

 The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP.

Q This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi

Neighbor Averaging (EVNA) spatial interpolation. See the BenMAP manual for technical details, available for

download at http://www.epa.gov/air/benmap.

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Regulatory Impact Analysis 

species concentrations were created by interpolating EPA speciation network (ESPN) ambient 

data and IMPROVE data. The ambient data were interpolated to the CMAQ 12 km grid. 

The procedures for determining the RRFs are similar to those in EPA’s draft guidance for 

modeling the PM2.5 standard (EPA, 1999). The guidance recommends that model predictions be 

used in a relative sense to estimate changes expected to occur in each major PM2.5 species. The 

procedure for calculating future-year PM2.5 design values is called the “Speciated Modeled 

Attainment Test (SMAT).” EPA used this procedure to estimate the ambient impacts of the 

coordinated strategy to control ship emissions. 

Table 6-13 provides those ozone and PM2.5 metrics for grid cells in the modeled domain 

that enter the health impact functions for health benefits endpoints. The population-weighted 

average reflects the baseline levels and predicted changes for more populated areas of the nation. 

This measure better reflects the potential benefits through exposure changes to these populations. 

Table 6-13. Summary of CMAQ-Derived Population-Weighted Ozone and PM2.5 Air Quality Metrics for 

Health Benefits Endpoints Associated with a Coordinated U.S. Strategy to Control Ship Emissions 

 2020 2030 

Statistica Baseline Change

b

 Baseline Change

b 

Ozone Metric: National Population-Weighted Average (ppb)

c 

Daily Maximum 8-Hour Average 

Concentration 

44.60 0.21 44.33 0.54 

PM2.5 Metric: National Population-Weighted Average (ug/m3) 

Annual Average Concentration 10.24 0.35 10.40 0.68 

Notes: 

a

 Ozone and PM2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects estimates. 

Ozone metrics are calculated over relevant time periods during the daylight hours of the “ozone season” 

(i.e., May through September). . b

 The change is defined as the base-case value minus the control-case value. c

 Calculated by summing the product of the projected CMAQ grid-cell population and the estimated

CMAQ grid cell seasonal ozone concentration and then dividing by the total population.

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, the emission control scenarios used in the air quality and benefits modeling are 

slightly different than the coordinated strategy. The discrepancies impact the benefits analysis in 

three ways: 

 As noted in Chapter 3, the air quality modeling used for the 2020 scenarios is based on 

inventory estimates that were modeled using incorrect ECA boundary information off of the 

western coast of the U.S. A calculation error placed the western 200nm ECA boundary 

approximately 50nm closer to shore. We believe the impact of this difference, while modest, 

likely leads to a small underestimate of the 2020 benefits presented in this chapter. 

 The 2020 air quality modeling scenarios do not include emission reductions associated with 

the implementation of global controls (set through IMO) beyond the assumed ECA boundary 

of 200 nm. Again, while we expect the impact of this difference is modest, the omission of 

these additional emission reductions likely leads to a small underestimate of the 2020 

benefits presented in this section. 

 Also noted in Chapter 3, the air quality modeling for the 2030 scenario reflects air quality 

impacts associated with an assumed ECA distance of 100nm with global controls (set 

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Chapter 6: Cost-Benefit Analysis 

through IMO) beyond the ECA boundary. To estimate the 2030 benefits associated with a 

200 nm ECA boundary, we transferred the relationship between modeled impacts between 

100 nm and 200 nm ECA boundaries observed in 2020. For each health endpoint and 

associated valuation, we calculated a ratio based on the national-level estimate for the 200 

nm and 100 nm scenario and applied that to the related 2030 100 nm estimate. For the final 

RIA, we plan to model the 2030 coordinated strategy to control ship emissions with a 200 nm 

boundary and global controls beyond. 

6.5 Methods for Describing Uncertainty 

The National Research Council (NRC)84 highlighted the need for EPA to conduct 

rigorous quantitative analysis of uncertainty in its benefits estimates and to present these 

estimates to decision makers in ways that foster an appropriate appreciation of their inherent 

uncertainty. In response to these comments, EPA’s Office of Air and Radiation (OAR) is 

developing a comprehensive strategy for characterizing the aggregate impact of uncertainty in 

key modeling elements on both health incidence and benefits estimates. Components of that 

process include emissions modeling, air quality modeling, health effects incidence estimation, 

and valuation. 

In benefit analyses of air pollution regulations conducted to date, the estimated impact of 

reductions in premature mortality has accounted for 85% to 95% of total benefits. Therefore, it 

is particularly important to characterize the uncertainties associated with reductions in premature 

mortality. The health impact functions used to estimate avoided premature deaths associated 

with reductions in ozone have associated standard errors that represent the statistical errors 

around the effect estimates in the underlying epidemiological studies.R In our results, we report 

credible intervals based on these standard errors, reflecting the uncertainty in the estimated 

change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect 

model uncertainty between alternative study designs. 

For premature mortality associated with exposure to PM, we follow the same approach 

that has been used in several recent RIAs.85,86,87 First, we use Monte Carlo methods for 

estimating random sampling error associated with the concentration response functions from 

epidemiological studies and economic valuation functions. Monte Carlo simulation uses random 

sampling from distributions of parameters to characterize the effects of uncertainty on output 

variables, such as incidence of premature mortality. Specifically, we used Monte Carlo methods 

to generate confidence intervals around the estimated health impact and dollar benefits. 

Distributions for individual effect estimates are based on the reported standard errors in the 

epidemiological studies. Distributions for unit values are described in Table 6-11. 

Second, as a sensitivity analysis, we use the results of our expert elicitation of the 

concentration response function describing the relationship between premature mortality and 

R

 Health impact functions measure the change in a health endpoint of interest, such as hospital admissions, for a 

given change in ambient ozone or PM concentration. 

6-31 
Regulatory Impact Analysis 

ambient PM2.5 concentration.S, 88 Incorporating only the uncertainty from random sampling error 

omits important sources of uncertainty (e.g., in the functional form of the model; whether or not 

a threshold may exist). This second approach attempts to incorporate these other sources of 

uncertainty. 

Use of the expert elicitation and incorporation of the standard errors approaches provide 

insights into the likelihood of different outcomes and about the state of knowledge regarding the 

benefits estimates. Both approaches have different strengths and weaknesses, which are fully 

described in Chapter 5 of the PM NAAQS RIA. 

These multiple characterizations, including confidence intervals, omit the contribution to 

overall uncertainty of uncertainty in air quality changes, baseline incidence rates, populations 

exposed and transferability of the effect estimate to diverse locations. Furthermore, the approach 

presented here does not yet include methods for addressing correlation between input parameters 

and the identification of reasonable upper and lower bounds for input distributions characterizing 

uncertainty in additional model elements. As a result, the reported confidence intervals and range 

of estimates give an incomplete picture about the overall uncertainty in the estimates. This 

information should be interpreted within the context of the larger uncertainty surrounding the 

entire analysis. 

6.6 Comparison of Costs and Benefits 

This section presents the cost-benefit comparison related to the expected impacts of our 

coordinated strategy for ocean-going vessels. In estimating the net benefits of the coordinated 

strategy, the appropriate cost measure is ‘social costs.’ Social costs represent the welfare costs 

of a rule to society and do not consider transfer payments (such as taxes) that are simply 

redistributions of wealth. For this analysis, we estimate that the social costs of the coordinated 

program are equivalent to the estimated compliance costs of the program. While vessel owners 

and operators will see their costs increase by the amount of those compliance costs, they are 

expected to pass them on in their entirety to consumers of marine transportation services in the 

form of increased freight rates. Ultimately, these costs will be borne by the final consumers of 

goods transported by ocean-going vessels in the form of higher prices for those goods. The 

social benefits of the coordinated strategy are represented by the monetized value of health and 

welfare improvements experienced by the U.S. population. Table 6-14 contains the estimated 

social costs and the estimated monetized benefits of the coordinated strategy. 

The results in Table 6-14 suggest that the 2020 monetized benefits of the coordinated 

strategy are greater than the expected costs. Specifically, the annual benefits of the total program 

will range between $47 to $110 billion annually in 2020 using a three percent discount rate, or 

between $42 to $100 billion assuming a 7 percent discount rate, compared to estimated social 

costs of approximately $1.9 billion in that same year. These benefits are expected to increase to 

between $110 and $280 billion annually in 2030 using a three percent discount rate, or between 

S

 Expert elicitation is a formal, highly structured and well documented process whereby expert judgments, usually of 

multiple experts, are obtained (Ayyb, 2002). 

6-32 
Chapter 6: Cost-Benefit Analysis 

$100 and $260 billion assuming a 7 percent discount rate, while the social costs are estimated to 

be approximately $3.1 billion. Though there are a number of health and environmental effects 

associated with the coordinated strategy that we are unable to quantify or monetize (see Table 6­

2), the benefits of the coordinated strategy far outweigh the projected costs. 

Using a conservative benefits estimate, the 2020 benefits outweigh the costs by a factor 

of 22. Using the upper end of the benefits range, the benefits could outweigh the costs by a 

factor of 58. Likewise, in 2030 benefits outweigh the costs by at least a factor of 32 and could be 

as much as a factor of 90. Thus, even taking the most conservative benefits assumptions, 

benefits of the coordinated strategy clearly outweigh the costs. 

Table 6-14 Summary of Annual Benefits and Costs Associated with a Coordinated U.S. Strategy to Control

Ship Emissions a

(Millions of 2006 dollars) 

Description 2020 2030 

Total Estimated Costsb $1,900 $3,100 

Total Estimated Health Benefitsc,d,e,f

 3 percent discount rate

 7 percent discount rate 

$47,000 to $110,000 

$42,000 to $100,000 

$110,000 to $280,000 

$100,000 to $260,000 

Annual Net Benefits (Total Benefits – Total Costs) 

 3 percent discount rate

 7 percent discount rate 

$45,000 to $110,000 

$40,000 to $98,000 

$110,000 to $280,000 

$97,000 to $260,000 

Notes: 

a

 All estimates represent annual benefits and costs anticipated for the years 2020 and 2030. Totals are rounded to

two significant digits and may not sum due to rounding. b

 The calculation of annual costs does not require amortization of costs over time. Therefore, the estimates of annual 

cost do not include a discount rate or rate of return assumption (see Chapter 7 of the RIA). In Chapter 7, however, 

we use both a 3 percent and 7 percent social discount rate to calculate the net present value of total social costs 

consistent with EPA and OMB guidelines for preparing economic analyses. c

 Total includes ozone and PM2.5 benefits. Range was developed by adding the estimate from the Bell et al., 2005 

ozone premature mortality function to PM2.5-related premature mortality derived from the ACS (Pope et al., 2002) 

and Six-Cities (Laden et al., 2006) studies. d Annual benefits analysis results reflect the use of a 3 percent and 7 percent discount rate in the valuation of 

premature mortality and nonfatal myocardial infarctions, consistent with EPA and OMB guidelines for preparing 

economic analyses. e Valuation of premature mortality based on long-term PM exposure assumes discounting over the SAB 

recommended 20-year segmented lag structure described in the Regulatory Impact Analysis for the Final Clean Air 

Interstate Rule (March, 2005). f Not all possible benefits or disbenefits are quantified and monetized in this analysis. Potential benefit categories 

that have not been quantified and monetized are listed in Table 6-2. 

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Regulatory Impact Analysis 

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78 Stanford, R., T. McLaughlin, and L.J. Okamoto. (1999). The Cost of Asthma in the 

Emergency Department and Hospital. American Journal of Respiratory and Critical Care 

Medicine 160(1):211-215. 

79 Industrial Economics, Incorporated (IEc). (1994). Memorandum to Jim DeMocker, Office of 

Air and Radiation, Office of Policy Analysis and Review, U.S. Environmental Protection 

Agency. March 31. 

80 Rowe, R.D., and L.G. Chestnut. (1986). Oxidants and Asthmatics in Los Angeles: A Benefits 

Analysis—Executive Summary. Prepared by Energy and Resource Consultants, Inc. Report to 

the U.S. Environmental Protection Agency, Office of Policy Analysis. EPA-230-09-86-018. 

Washington, DC. 

81 Neumann, J.E., M.T. Dickie, and R.E. Unsworth. (1994). Linkage Between Health Effects 

Estimation and Morbidity Valuation in the Section 812 Analysis—Draft Valuation Document. 

Industrial Economics Incorporated (IEc) Memorandum to Jim DeMocker, U.S. Environmental 

Protection Agency, Office of Air and Radiation, Office of Policy Analysis and Review. March 

31. 

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82 Tolley, G.S. et al. January (1986). Valuation of Reductions in Human Health Symptoms and 

Risks. University of Chicago. Final Report for the U.S. Environmental Protection Agency. 

83 Council of Economic Advisors. (2005). The Annual Report of the Council of Economic 

Advisors. In: Economic Report of the President. Table B-60. U.S. Government Printing Office: 

Washington, DC. 

84 National Research Council (NRC). (2002). Estimating the Public Health Benefits of Proposed 

Air Pollution Regulations. The National Academies Press: Washington, D.C. 

85 U.S. Environmental Protection Agency, (2004a). Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and 

Radiation. Retrieved on April 10, 2009, from http://www.epa.gov/nonroaddiesel/2004fr/420r04007.pdf 

86 U.S. Environmental Protection Agency, (2005). Regulatory Impact Analysis for the Clean Air 

Interstate Rule. EPA 452/-03-001. Prepared by Office of Air and Radiation. Retrieved on April 

10, 2009, from http://www.epa.gov/interstateairquality/tsd0175.pdf 

87 U.S. Environmental Protection Agency, (2006). Regulatory Impact Analysis for the PM 

NAAQS. EPA Prepared by Office of Air and Radiation. Retrieved on April 10, 2009, from 

http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf 

88 Industrial Economics, Inc. (2006). Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for 

EPA Office of Air Quality Planning and Standards, September. Retrieved on April 10, 2009, 

from http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf 

6-40 
Chapter 7: Economic Impact Analysis 

CHAPTER 7: ECONOMIC IMPACT ANALYSIS 

7.1 Overview and Results .......................................................................................................... 7-3

7.1.1 What is an Economic Impact Analysis?.................................................................... 7-3

7.1.2 What Methodology Did EPA Use in This Economic Impact Analysis?................. 7-3

7.1.3 What Economic Sectors are Included in This Economic Impact Analysis?.......... 7-4

7.1.4 Summary of Results.................................................................................................... 7-5

7.2 Economic Methodology ..................................................................................................... 7-12

7.2.1 What Is the Economic Theory Used to Estimate Economic Impacts?................. 7-12

7.2.2 How Is This Economic Theory Applied In This EIA? .......................................... 7-27

7.3 Estimating Market Impacts on the Marine Transportation Market............................ 7-30

7.3.1 Container Vessel........................................................................................................ 7-30

 7.3.2 Bulk Carrier .............................................................................................................. 7-35

7.3.3 Cruise Ship ................................................................................................................ 7-37

7-1 
Regulatory Impact Analysis 

CHAPTER 7:Economic Impact Analysis 

This chapter contains our analysis of the expected economic impacts of our coordinated 

strategy on the markets for Category 3 marine diesel engines, ocean-going vessels, and the 

marine transportation service sector. We examine the impacts of all components of the 

coordinated strategy on the markets for Category 3 marine diesel engines, ocean-going vessels, 

marine fuels, and international marine transportation services. This includes the cost of the 

Clean Air Act emission control program for Category 3 marine diesel engines for U.S. vessel 

owners and the costs of complying with the ECA emission and fuel sulfur controls for all ships 

operating in the area proposed by the U.S. Government to be designated as an Emission Control 

Area (ECA) under MARPOL Annex VI. We look at two aspects of the economic impacts: 

estimated social costs and how they are shared across stakeholders, and estimated market 

impacts in terms of changes in prices and quantities produced for directly affected markets. 

This economic impact analysis uses a competitive model approach for all affected 

markets. The competitive market assumption is discussed in section 7.1.2, below. 

The total estimated social costs of the coordinated strategy in 2030 are equivalent to the 

estimated compliance costs of the coordinated strategy, at approximately $3.1 billion.A,B These 

costs are expected to accrue initially to the owners and operators of affected vessels. These 

owners and operators are expected to pass their increased costs on to the entities that purchase 

international marine transportation services in the form of higher freight rates. Ultimately, these 

costs will be borne by the final consumers of goods transported by ocean-going vessels in the 

form of slightly higher prices for those goods. 

With regard to market-level impacts, we estimate that compliance with the coordinated 

strategy would increase the price of a new vessel by 0.5 to 2 percent, depending on the vessel. 

The impact of the coordinated strategy, including the ECA controls, on the price of ocean marine 

transportation services would vary, depending on the route and the amount of time spent in the 

proposed U.S. ECA. For example, we estimate that the cost of operating a ship in liner service 

between Singapore, Seattle, and Los Angeles/Long Beach, which includes about 1,700 nm of 

operation in the proposed ECA, would increase by about 3 percent. For a container ship, this 

represents a price increase of about $18 per container, assuming the total increase in operating 

costs is passed on to the purchaser of marine transportation services. This would be about a 3 

percent price increase. The per passenger price of a seven-day Alaska cruise operating entirely 

within the ECA is expected to increase about $7 per day. For ships that spend less time in the 

ECA, the expected increase in total operating costs and therefore the impact on freight prices, 

would be smaller. 

A All estimates presented in this section are in 2006 dollars. B

 The costs totals reported in this NPRM are slightly different than those reported in the ECA proposal. This is 

because the ECA proposal did not include costs associated with the Annex VI existing engine program, Tier II, or 

the costs associated with existing vessel modifications that may be required to accommodate the use of lower sulfur 

fuel. Further, the cost totals presented in the ECA package included Canadian cost estimates. 

7-2 
Chapter 7: Economic Impact Analysis 

7.1 Overview and Results 

7.1.1 What is an Economic Impact Analysis? 

In general, the purpose of an Economic Impact Analysis (EIA) is to provide information 

about the potential economic consequences of a regulatory action, such as the proposed 

coordinated strategy to reduce emissions from ocean-going vessels. Such an analysis consists of 

estimating the social costs of a regulatory program and the distribution of these costs across 

stakeholders. The estimated social costs can then be compared with estimated social benefits as 

presented in Chapter 6. 

In an economic impact analysis, social costs are the value of the goods and services lost 

by society resulting from (a) the use of resources to comply with and implement a regulation and 

(b) reductions in output. There are two parts to the analysis. In the market analysis, we estimate 

how prices and quantities of goods directly affected by the emission control program can be 

expected to change once the program goes into effect. In the economic welfare analysis, we look 

at the total social costs associated with the program and their distribution across key 

stakeholders.

1 

7.1.2 What Methodology Did EPA Use in This Economic Impact Analysis? 

Our analysis of the economic impacts of the coordinated strategy is based on the 

application of basic microeconomic theory. We use a competitive market model approach in 

which the interaction between supply and demand set equilibrium market prices and quantities. 

Specifically, we can use the relationships between supply and demand to simulate how markets 

can be expected to respond to increases in production costs that occur as a result of the new 

emission control program. Using that information, we can estimate the social costs of the 

program and identify how those costs will be shared across the markets and, thus, across 

stakeholders. 

There are two key assumptions about the affected markets that affect our analysis: the 

structure of the Category 3 engine market and the nature of demand in the ocean marine 

transportation market. 

With regard to the structure of the Category 3 engine market, we assume this market is 

competitive. This assumption is discussed in more detail in Section 7.2.1.3.3. 

With regard to the nature of demand for marine transportation services, we assume that 

demand is nearly perfectly inelastic. As explained in Section 7.2.1.1, this assumption is 

appropriate because there are no reasonable alternatives to transportation by ship for most goods. 

The assumption of nearly perfectly inelastic demand has three consequences for the analysis. 

First, with respect to market impacts, it means that equilibrium quantity in the affected markets 

will not change relative to the baseline, no-control scenario, and that the equilibrium prices for 

marine engines, vessels, and transportation services will increase by the amount of the 

compliance costs. This is explained more fully in Section 7.2.2. Second, it means that virtually 

all of the compliance costs will be borne by the users of marine transportation services. These 

costs are expected to be passed on to consumers of goods transported by sea in the form of 

7-3 
Regulatory Impact Analysis 

higher prices. Third, it means that it is not necessary to estimate the price elasticity of supply for 

the affected markets. As long as the supply curves can be assumed to be upward-sloping, the 

degree of the slope will not affect the estimated market impacts or social welfare impacts 

described below. 

It should be noted that this economic analysis holds all other aspects of the market 

constant except for elements of the coordinated strategy. It does not attempt to predict the future 

market equilibrium conditions, particularly with respect to how excess capacity in today’s 

market due to the current economic downturn will be absorbed. This approach is appropriate 

because the goal of an economic impact analysis is to explore the impacts of a specific program; 

allowing changes in other market conditions would confuse the impacts due to the proposed 

regulatory program. 

The economic impacts of the coordinated strategy presented below rely on the estimated 

engineering compliance costs described in Chapter 5. These costs include hardware costs for 

new U.S. vessels, to comply with the Tier 2 and Tier 3 engine standards, and for existing U.S. 

vessels, to comply with the MARPOL Annex VI requirements for existing engines. There are 

also hardware costs for fuel switching equipment on new and existing U.S. vessels to comply 

with the 1,000 ppm fuel sulfur limit; the cost analysis assumes that 32 percent of all vessels 

require fuel switching equipment to be added (new vessels) or retrofit (existing vessels). Also 

included are expected increases in operating costs for U.S. and foreign vessels operating in the 

ECA. These increased operating costs include changes in fuel consumption rates, increases in 

fuel costs, and the use of urea for engines equipped with SCR, as well as a small increase in 

operating costs for operation outside the ECA due to the fuel price impacts of the program. 

7.1.3 What Economic Sectors are Included in This Economic Impact Analysis? 

The coordinated strategy consists of two parts: Clean Air Act emission standards for 

Category 3 engines, and fuel sulfur requirements for vessels operating in the proposed U.S. ECA. 

Therefore, we examine the economic impacts on the following markets: the Category 3 engine 

market, the market for vessels that use these engines, and the marine transportation services 

market. The characteristics of the markets analyzed that are relevant to the EIA are summarized 

in Table 7-1, and described in more detail in Section 7.3. 

With respect to the fuels market, the market impacts were estimated through the cost 

analysis described in Chapter 5, using the WORLD model. 

7-4 
Chapter 7: Economic Impact Analysis 

Table 7-1 Summary of Markets in Economic Impact Model 

Description of Markets: Supply C3 Marine Diesel Engines : 

• slow-speed diesel (SSD) 

• medium-speed diesel (MSD) 

Ocean Marine Vessels: 9 types of vessel 

• Auto Carrier 

• Bulk Carrier 

• Container 

• General Cargo 

• Passenger 

• Reefer 

• RoRo 

• Tanker 

• Misc. 

Marine Transportation Services: U.S. and foreign entities that provide ocean marine 

transportation services that operate with the proposed U.S. ECA using affected 

engines and fuels 

All supply curves upward sloping (price increase leads to increase in amount 

produced) 

Description of Markets: Demand C3 Marine Engines: Vessel manufacturers 

Ocean Marine Vessels: Marine vessel users (owners of all types of ocean vessels) 

Marine Transportation Services: Entities that use marine transportation services 

(consumer goods, chemical, agricultural, oil companies; personal transportation; etc.) 

Demand for marine transportation services assumed to be nearly perfectly inelastic; 

demand for other markets derived from demand for marine transportation services 

Geographic Scope 50 states 

Note: USA ECA would apply in 48 states plus portions of Alaska, Hawaii 

Market Structure Competitive Market 

7.1.4 Summary of Results 

7.1.4.1 Market Impacts: Engine and Vessel Markets 

The estimated market impacts for engines and vessels are based on the variable costs 

associated with the engine and vessel compliance programs; fixed costs are not included in the 

market analysis. This is appropriate because in a competitive market the industry supply curve is 

generally based on the market's marginal cost curve; fixed costs do not influence production 

decisions at the margin. Therefore, the market analysis for a competitive market is based on 

variable costs only. 

The assumption of nearly perfectly inelastic demand for marine transportation services 

means that the quantity of these services purchased is not expected to change as a result of costs 

of complying with the ECA requirements. As a result, the demand for vessels and engines would 

7-5 
Regulatory Impact Analysis 

also not change compared to the no-control scenario, and the quantities produced would remain 

the same. 

The assumption of nearly perfectly inelastic demand for marine transportation services 

also means the price impacts of the coordinated strategy on new engines and vessels would be 

equivalent to the variable engineering compliance costs. Estimated price impacts for a sample of 

engine-vessel combinations are set out in Table 7-2, for medium speed engines, and Table 7-3, 

for slow speed engines. These are the estimated price impact associated with the Tier 3 engine 

standards on a vessel that will switch fuels to comply with the fuel sulfur requirements in the 

ECA. Because the standards do not phase in, the estimated price impacts are the same for all 

years, beginning in 2016. 

Table 7-2 Summary of Estimated Market Impacts –Medium Speed Tier 3 Engines and Vessels 

 ($2006) a 

SHIP TYPE AVERAGE 

PROPULSION 

POWER 

NEW VESSEL 

ENGINE PRICE 

IMPACT (NEW 

TIER 3 ENGINE 

PRICE IMPACT)b 

NEW VESSEL 

FUEL SWITCHING 

EQUIPMENT 

PRICE IMPACTc 

NEW VESSEL 

TOTAL PRICE 

IMPACT 

Auto Carrier 9,600 $573,200 $42,300 $615,500 

Bulk Carrier 6,400 $483,500 $36,900 $520,400 

Container 13,900 $687,800 $49,200 $736,000 

General Cargo 5,200 $450,300 $34,900 $475,200 

Passenger 23,800 $952,500 $65,400 $1,107,900 

Reefer 7,400 $511,000 $38,500 $549,500 

RoRo 8,600 $543,800 $40,500 $584,300 

Tanker 6,700 $492,800 $37,400 $530,200 

Misc. 9,400 $566,800 $41,900 $608,700 

Notes: 

a The new vessel engine price impacts listed here do not include a per engine cost of $10,000 for engines 

installed on U.S. vessels to comply with the proposed production testing requirement (§1042.302) b Medium speed engine price impacts are estimated from the cost information presented in Chapter 5 using the 

following formula: (10%*($/SHIP_MECH→CR))+(30%*($/SHIP_ELEC→CR))+(T3 ENGINE MODS)+(T3SCR)) c

 Assumes 32 percent of new vessels would require the fuel switching equipment.

7-6 
Chapter 7: Economic Impact Analysis 

Table 7-3 Summary of Estimated Market Impacts – Slow Speed Tier 3 Engines and Vessels ($2006)

 a 

SHIP TYPE AVERAGE 

PROPULSION 

POWER 

NEW VESSEL 

ENGINE PRICE 

IMPACT (NEW 

TIER 3 ENGINE 

PRICE IMPACT)b 

NEW VESSEL 

FUEL SWITCHING 

EQUIPMENT 

PRICE IMPACTc 

NEW VESSEL 

TOTAL PRICE 

IMPACT 

Auto Carrier 11,300 $825,000 $48,000 $873,000 

Bulk Carrier 8,400 $672,600 $42,700 $715,300 

Container 27,500 $1,533,100 $63,900 $1,597,000 

General Cargo 7,700 $632,900 $41,000 $673,900 

Passenger 23,600 $1,385,300 $61,200 $1,446,500 

Reefer 10,400 $781,000 $46,500 $827,500 

RoRo 15,700 $1,042,100 $53,900 $1,096,000 

Tanker 9,800 $744,200 $45,300 $789,500 

Misc. 4,700 $453,600 $32,000 $485,600 

Notes: 

a The new vessel engine price impacts listed here do not include a per engine cost of $10,000 for engines 

installed on U.S. vessels to comply with the proposed production testing requirement (§1042.302) b

 Slow speed engine price impacts are estimated from the cost information presented in Chapter 5 using the 

following formula: (5%*($/SHIP_MECH→CR))+(15%*($/SHIP_ELEC→CR))+(T3 ENGINE MODS)+(T3 SCR)) c Assumes 32 percent of new vessels would require the fuel switching equipment.

The estimated price impacts for Tier 2 vessels would be substantially lower, given the 

technology that will be used to meet the Tier 2 standards is much less expensive. The cost of 

complying with the Tier 2 standards ranges from about $56,000 to $100,000 for a medium speed 

engine, and from about $130,000 to $250,000 for a slow speed engine. Again, because the 

standards do not phase in, the estimated price impacts are the same for all years the Tier 2 

standards are required, 2011 through 2015. 

These estimated price impacts for Tier 2 and Tier 3 vessels are small when compared to 

the price of a new vessel. A selection of new vessel prices is provided in Table 7-4; these range 

from about $40 million to $480 million. The program price increases range from about $600,000 

to $1.5 million. A price increase of $600,000 to comply with the Tier 3 standards and fuel 

switching requirements would be an increase of approximately 2 percent for a $40 million 

vessel. The largest vessel price increase noted above, for a Tier 3 passenger vessel, is about $1.5 

million; this is a price increase of less than 1 percent for a $478 million passenger vessel. 

Independent of the nearly perfectly inelasticity of demand, price increases of this magnitude 

would be expected to have little, if any, effect on the sales of new vessels, all other economic 

conditions held constant. 

7-7 
Regulatory Impact Analysis 

Table 7-4 Newbuild Vessel Price by Ship Type and Size, Selected Vessels (Millions, $2008) 

Vessel Type Vessel Size 

Category 

Size Range (Mean) (DWT) Newbuild 

Bulk Carrier Handy 10,095 – 39,990 (27,593) $56.00 

Handymax 40,009 – 54,881 (47,616) $79.00 

Panamax 55,000 – 78,932 (69,691) $97.00 

Capesize 80,000 – 364,767 (157,804) $175.00 

Container Feeder 1,000-13,966 (9,053) $38.00 

Intermediate 14,003-36,937 (24,775) $70.00 

Panamax 37,042-54,700 (45,104) $130.00 

Post Panamax 55,238-84,900 (67,216) $165.00 

Gas carrier Midsize 1,001-34,800 (7,048) $79.70 

LGC 35,760-59,421 (50,796) $37.50 

VLGC 62,510-122,079 (77,898) $207.70 

General 

cargo 

Coastal Small 1,000-9,999 (3,789) $33.00 

Coastal Large 10,000-24,912 (15,673) $43.00 

Handy 25,082-37,865 (29,869) $52.00 

Panamax 41,600-49,370 (44,511) $58.00 

Passenger All 1,000–19,189 (6,010) $478.40 

Reefer All 1,000–19,126 (6,561) $17.30 

Ro-Ro All 1,000–19,126 (7,819) $41.20 

Tanker Coastal 1,000-23,853 (7,118) $20.80 

Handymax 25,000-39,999 (34,422) $59.00 

Panamax 40,000-75,992 (52,300) $63.00 

AFRAmax 76,000-117,153 (103,112) $77.00 

Suezmax 121,109-167,294 (153,445) $95.00 

VLCC 180,377-319,994 (294,475) $154.00 

Sources: Lloyd’s Shipping Economist (2008), Informa (2008), Lloyd’s Sea-Web (2008) 

7.1.4.2 Market Impacts: Fuel Market 

The market impacts for the fuel markets were estimated through the modeling performed 

to estimate the fuel compliance costs for the coordinated strategy. In the WORLD model, the 

total quantity of fuel used is held constant, which is consistent with the assumption that the 

demand for international shipping transportation would not be expected to change due to the lack 

of transportation alternatives. 

The expected price impacts of the coordinated strategy are set out in Table 7-5. Note that 

on a mass basis, less distillate than residual fuel is needed to go the same distance (5 percent 

less). The prices in Table 7-5 are adjusted for this impact. Table 7-5 shows that the coordinated 

strategy is expected to result in a small increase in the price of marine distillate fuel, about 1.3 

percent. The price of residual fuel is expected to decrease slightly, by less than one percent, due 

to a reduction in demand for that fuel. 

7-8 
Chapter 7: Economic Impact Analysis 

Table 7-5 Summary of Estimated Market Impacts - Fuel Markets 

FUEL UNITS BASELINE 

PRICE 

CONTROL 

PRICE 

ADJUSTED FOR 

ENERGY 

DENSITY 

% CHANGE 

Distillate $/tonne $462 $468 N/A +1.3% 

Residual $/tonne $322 $321 N/A -0.3% 

Fuel 

Switching 

$/tonne $322 $468 $444 +38.9% 

Because of the need to shift from residual fuel to distillate fuel in the ECA, ship owners 

are expected to see an increase in their total cost of fuel. This increase is because distillate fuel is 

more expensive than residual fuel. Factoring in the higher energy content of distillate fuel 

relative to residual fuel, the fuel cost increase would be about 39 percent. 

7.1.4.3 Market Impacts: Marine Transportation Services Market 

We used the above estimates of engine, vessel, and fuel price impacts to estimate the 

impacts on the prices of marine transportation services. This analysis, presented in Section 7.3, 

below, is limited to the impacts of increases in operating costs due to the fuel and emission 

requirements of the coordinated strategy. Operating costs would increase due to the increase in 

the price of fuel, the need to switch to fuel with a sulfur content not to exceed 1,000 ppm while 

operating in the ECA, and due to the need to dose the aftertreatment system with urea to meet the 

Tier 3 standards. Table 7-6 summarizes these price impacts for selected transportation markets. 

Table 7-6 also lists the vessel and engine parameters that were used in the calculations. 

Table 7-6 Summary of Impacts of Operational Fuel/Urea Cost Increases 

VESSEL TYPE VESSEL AND 

ENGINE 

PARAMETERS 

OPERATIONAL PRICE 

INCREASES 

Container 

North Pacific Circle Route 

36,540 kW 

50,814 DWT 

$17.53/TEU 

Bulk Carrier 

North Pacific Circle Route 

3,825 kW 

16,600 DWT 

$0.56/tonne 

Cruise Liner 

(Alaska) 

31,500 kW 

226,000 DWT 

1,886 passengers 

$6.60/per passenger per day 

This information suggests that the increase in marine transportation service prices would 

be small, both absolutely and when compared to the price charged by the ship owner per unit 

transported. For example, Stopford notes that the price of transporting a 20 foot container 

between the UK and Canada is estimated to be about $1,500; of that, $700 is the cost of the 

ocean freight; the rest is for port, terminal, and other charges.2 An increase of about $18 

represents an increase of less than 3 percent of ocean freight cost, and about one percent of 

transportation cost. Similarly, the price of a 7-day Alaska cruise varies from $100 to $400 per 

night or more. In that case, this price increase would range from 1.5 percent to about 6 percent. 

7-9 
Jul-08 

Aug-08 

Aug-08 

Sep-08 

Oct-08 

Oct-08 

Nov-08 

Dec-08 

Dec-08 

Jan-09 

Feb-09 

Regulatory Impact Analysis 

Our analysis also suggests that increases in operational costs of the magnitude expected 

to occur for vessels operating in the ECA are within the range of historic price variations for 

bunker fuel. This is illustrated in Figure 7-1. This figure is based on variation in fuel price 

among the ports of Singapore, Houston, Rotterdam, and Fujairah. 

$175 

$275 

$375 

$475 

$575 

$675 

$/MT 

Baseline Value (Cheapest) 

Most Expensive Fuel 

3% Increase due to ECA 

Date 

Figure 7-1 Range of Bunker Fuel Prices 

This graph illustrates the price differential between these ports, comparing the estimated 

3% ECA increase to the cheapest fuel for each month. We then plotted these calculated ECA 

increases (the 3% increases), the cheapest fuel (as a baseline) and the most expensive fuel for the 

same six month period. As can be observed from the previous calculations and the trends in 

Figure 7-1, there are both spatial and temporal price fluctuations in fuel prices. During this 

period (granted, a period of above-average fluctuations), the price of fuel varied both spatially 

and temporally. The variation over time is higher than the variation over ports; however, by 

either form of variation, the 3% increase in bunker fuel price due to the ECA is smaller than the 

normal price variation of the fuel. 

7.1.4.4 Social Welfare Impacts and Their Distribution across Stakeholders 

The total social costs of the coordinated strategy are based on both fixed and variable 

costs. This is because fixed costs are a cost to society: they displace other product development 

activities that may improve the quality or performance of engines and vessels. In this economic 

impact analysis, fixed costs are accounted for in the year in which they occur, with the fixed 

costs associated with the Tier 2 engine standards accounted for in 2010 and the fixed costs 

associated with the Tier 3 engine standards and the ECA controls accounted for in the five-year 

period beginning prior to their effective dates. 

7-10 
Chapter 7: Economic Impact Analysis 

The social costs of the coordinated strategy are estimated to be the same as the total 

engineering compliance costs. These costs for all years are presented in Table 5-44, copied 

below for convenience. For 2030, the social costs are estimated to be about $3.1 billion. Due to 

the nearly perfectly inelastic demand for marine transportation services, these costs are expected 

to be borne fully by consumers of marine transportation services. 

Table 7-7 Total Costs Associated with the Coordinated Strategy 

Year Fixed Variable Operational Total 

2010 $477,020 $0 $0 $477,020 

2011 $1,018,766 $2,497,657 $1,306,556 $4,822,979 

2012 $1,056,245 $2,580,365 $6,431,250 $10,067,860 

2013 $1,095,347 $2,667,173 $244,951,550 $248,714,070 

2014 $1,136,035 $2,757,514 $260,810,043 $264,703,592 

2015 $972,037 $13,954,191 $1,369,402,786 $1,384,329,013 

2016 $0 $28,052,583 $1,438,235,966 $1,466,288,549 

2017 $0 $29,154,639 $1,525,633,990 $1,554,788,630 

2018 $0 $30,302,933 $1,622,800,854 $1,653,103,787 

2019 $0 $31,499,499 $1,721,756,141 $1,753,255,640 

2020 $0 $32,746,463 $1,820,614,217 $1,853,360,680 

2021 $0 $34,046,049 $1,925,263,118 $1,959,309,168 

2022 $0 $35,400,583 $2,028,002,568 $2,063,403,150 

2023 $0 $36,812,494 $2,147,543,473 $2,184,355,967 

2024 $0 $38,284,325 $2,266,962,666 $2,305,246,991 

2025 $0 $39,818,735 $2,387,551,773 $2,427,370,508 

2026 $0 $41,418,504 $2,512,510,228 $2,553,928,732 

2027 $0 $43,086,539 $2,638,815,284 $2,681,901,822 

2028 $0 $44,825,880 $2,774,457,455 $2,819,283,335 

2029 $0 $46,639,709 $2,913,118,509 $2,959,758,218 

2030 $0 $48,531,352 $3,063,782,201 $3,112,313,554 

2031 $0 $50,504,289 $3,205,898,377 $3,256,402,666 

2032 $0 $52,562,159 $3,358,465,311 $3,411,027,470 

2033 $0 $54,708,770 $3,519,017,395 $3,573,726,165 

2034 $0 $56,948,104 $3,689,819,658 $3,746,767,762 

2035 $0 $59,284,330 $3,867,580,840 $3,926,865,170 

2036 $0 $61,721,806 $4,056,506,472 $4,118,228,278 

2037 $0 $64,265,093 $4,258,730,159 $4,322,995,252 

2038 $0 $66,918,964 $4,465,788,635 $4,532,707,599 

2039 $0 $69,688,411 $4,680,205,640 $4,749,894,051 

2040 $0 $72,578,659 $4,905,310,074 $4,977,888,733 

NPV @ 3% $5,311,963 $683,356,096 $42,179,757,713 $42,868,425,773 

NPV @ 7% $4,805,557 $358,019,816 $21,724,932,914 $22,087,758,287 

These social costs are small when compared to the total value of U.S. waterborne foreign 

trade. In 2007, waterborne trade for government and non-government shipments by vessel into 

7-11 
Regulatory Impact Analysis 

and out of U.S. foreign trade zones, the 50 states, the District of Columbia, and Puerto Rico was 

about $1.4 trillion. Of that, about $1 trillion was for imports.3 

If only U.S. vessels are considered, the social costs of the coordinated strategy in 2030 

would be about $427.5 million. Again, these social costs are small when compared to the annual 

revenue for this sector. In 2002, the annual revenue for this sector was about $19.8.4 

While users of marine transportation services are expected to bear the entire compliance 

costs of the program, these costs are expected to be passed on to consumers in the form of higher 

prices for the goods transported by sea. When these costs are spread across these goods, their 

impacts are expected to be very small. For example, an increase of $18 to transport a container 

from Singapore to Los Angeles would result in an increase of about one cent for a pair of shoes. 

In general, transportation costs are only a small portion of the costs of goods and materials. 

According to UNCTAD, freight costs in 2001 were only about 5.1 percent of import value in 

2001, for developed countries.5 

7.2 Economic Methodology 

The methodology used in this Economic Impact Analysis (EIA) is rooted in applied 

microeconomic theory and was developed following U.S. EPA’s recommendations.6 This 

section describes the economic theory underlying the analysis and how it was applied to the 

problem of estimating the economic impacts of the proposed ECA on shipping engaged in 

international trade. 

7.2.1 What Is the Economic Theory Used to Estimate Economic Impacts? 

The approach used to estimate the economic impacts of the proposed ECA relies on the 

basic relationships between production and consumption in competitive markets. 

7.2.1.1 Behavioral Model 

This economic impact analysis uses a behavioral approach in that it builds on the 

engineering cost analysis by incorporating economic theory related to producer and consumer 

behavior to estimate changes in market conditions. As Bingham and Fox7 note, this framework 

provides “a richer story” of the expected distribution of economic welfare changes across 

producers and consumers. In behavioral models, manufacturers of goods affected by a 

regulation are economic agents who can make adjustments, such as changing production rates or 

altering input mixes, which will generally affect the market environment in which they operate. 

In this case, implementation of a control program results in an increase in production 

costs by the amount of the compliance costs. This generates a “shock” to existing equilibrium 

market conditions (a change in supply). Producers of affected products will try to pass some or 

all of the increased production costs on to the consumers of these goods through price increases, 

without changing the quantity produced. In response to the price increases, consumers will 

decrease the quantity they buy of the affected good (a change in the quantity demanded). This 

creates surplus production at the new price. Producers will react to the decrease in quantity 

demanded by reducing the quantity they produce, and they will be willing to sell the remaining 

7-12 
Chapter 7: Economic Impact Analysis 

production at a lower price that does not cover the full amount of the compliance costs. 

Consumers will then react to this new price. These interactions continue until the surplus is 

removed and a new market equilibrium price and quantity combination is achieved. 

The amount of the compliance costs that will be borne by stakeholders is ultimately 

limited by the price sensitivity of consumers and producers in the relevant markets, represented 

by the price elasticities of demand and supply for each market. An “inelastic” price elasticity 

(less than one) means that supply or demand is not very responsive to price changes (a one 

percent change in price leads to less than one percent change in quantity). An “elastic” price 

elasticity (more than one) means that supply or demand is sensitive to price changes (a one 

percent change in price leads to more than one percent change in quantity). A price elasticity of 

one is unit elastic, meaning there is a one-to-one correspondence between a percent change in 

price and percent change in quantity. 

On the production side, price elasticity of supply depends on the time available to adjust 

production in response to a change in price, how easy it is to store goods, and the cost of 

increasing (or decreasing) output. In this analysis, we assume the supply for engines, vessels, 

and marine transportation services is elastic: an increase in the market price of an engine, vessel 

or freight rates will lead producers to want to produce more, while a decrease will lead them to 

produce less (this is the classic upward-sloping supply curve). It would be difficult to estimate 

the slope of the supply curve for each of these markets given the global nature of the sector. 

However, it is reasonable to assume that the supply elasticity for the ocean marine transportation 

services market is likely to be greater than one. This is because output can more easily be 

adjusted due to a change in price. For the same reason, the supply elasticity for the new 

Category 3 engine market is also likely to be greater than one, especially since these engines are 

often used in other land-based industries, notably in power plants. The supply elasticity for the 

vessel construction market, on the other hand, may be less than or equal to one depending on the 

vessel type, since it may be harder to adjust production and/or store output if the price drops, or 

rapidly increase production if the price increases. Because of the nature of this industry, it would 

not be possible to easily switch production to other goods, or to stop or start production of new 

vessels. 

On the consumption side, we assume that the demand for engines is a function of the 

demand for vessels, which is a function of the demand for international shipping (demand for 

engines and vessels is derived from the demand for marine transportation services). This makes 

intuitive sense: Category 3 engine and ocean-going vessel manufacturers would not be expected 

to build an engine or vessel unless there is a purchaser, and purchasers will want a new 

vessel/engine only if there is a need for one to supply marine transportation services. Deriving 

the price elasticity of demand for the vessel and engine markets from the international shipping 

market is an important feature of this analysis because it provides a link between the product 

markets. 

Finally, we assume that the price elasticity of demand for marine transportation services 

is nearly perfectly inelastic. This stems from the fact that, for most goods, there are no 

reasonable alternatives to shipping by vessel for the vast majority of products transported by sea 

to the United States and Canada. It is impossible to ship goods between these countries and 

Asia, Africa, or Europe by rail or highway. Transportation of goods between these countries and 

7-13 
Regulatory Impact Analysis 

Central and South America by rail or highway would be inefficient due to the time and costs 

involved. While aviation may be an alternative for some goods, it is impossible for goods 

shipped in bulk or goods shipped in large quantities. There are also capacity constraints 

associated with trans-continental aviation transportation, and the costs are higher on a per tonne 

basis. As a result, approximately 90 percent of world trade by tonnage is moved by ship, and 

ships provide the most efficient method to transport these goods on a tonne-mile basis.8 Stopford 

notes that “shippers need the cargo and, until they have time to make alternative arrangements, 

must ship it regardless of cost ... The fact that freight generally accounts for only a small portion 

of material costs reinforces this argument.”9

 A nearly perfectly inelastic price elasticity of 

demand for marine transportation services means that virtually all of the compliance costs can be 

expected to be passed on to the consumers of marine transportation services, with no change in 

output for engine producers, ship builders, or owners and operators of ships engaged in 

international trade. 

7.2.1.2 Multi-Market, Partial-Equilibrium Approach 

This is also a multi-market, partial equilibrium approach. It is a multi-market approach 

in that more than one market is examined: the markets for marine engines, vessels, and 

international shipping transportation services. It is a partial-equilibrium approach in that rather 

than explicitly modeling all of the interactions in the global economy that are affected by 

international shipping, the individual markets that are directly affected by the ECA requirements 

are modeled in isolation. This technique has been referred to in the literature as “partial 

equilibrium analysis of multiple markets.”10 

This EIA does not examine the economic impact of the proposed ECA on finished goods 

that use ocean transportation services as inputs. This is because international shipping 

transportation services are only a small part of the total inputs of the final goods and services 

produced using the materials shipped. A change in the price of marine transportation services on 

the order anticipated by this program would not be expected to significantly affect the markets 

for the finished goods. So, for example, while we look at the impacts of the program on ocean 

transportation costs, we do not look at the impacts of the controls on gasoline produced using 

crude oil transported by ship, or on manufactured products that use petroleum products as inputs. 

It should also be noted that this EIA estimates the aggregate economic impacts of the 

control program at the market level. This is not intended to be a firm-level analysis; therefore 

compliance costs facing any particular ship operator may be different from the market average, 

and the impacts of the program on particular firms can vary significantly. The difference can be 

important, particularly where the rule affects different firms’ costs over different activity rates. 

7.2.1.3 Competitive Market Structure 

The methodology used in this EIA relies on the assumption that the relevant markets have 

a competitive market structure. This means that consumers and firms are price takers and do not 

have the ability to influence market prices. Competitive market structure is a widely accepted 

assumption for this type of analysis and only in rare cases are other approaches used.11 

Stopford’s description of the shipping market and how prices are set in this market supports this 

assumption.12 

7-14 
Chapter 7: Economic Impact Analysis 

In a competitive market at equilibrium with no externalities, the market price equals the 

value society (consumers) places on the marginal product, as well as the marginal cost to society 

(producers). Producers are price takers, in that they respond to the value that consumers put on 

the product. It should be noted that the assumption of competitive market structure is not 

primarily about the number of firms in a market. It is about how the market operates: whether or 

not individual firms have sufficient market power to influence the market price. Indicators that 

allow us to assume a competitive market structure include absence of barriers to entry, absence 

of strategic behavior among firms in the market, and product differentiation.C,13 Finally, 

according to contestable market theory, oligopolies and even monopolies will behave very much 

like firms in a competitive market if it is possible to enter particular markets costlessly (i.e., there 

are no sunk costs associated with market entry or exit). This would be the case, for example, 

when products are substantially similar (e.g., a recreational vessel and a commercial vessel). 

7.2.1.3.1 Competition in the Marine Transportation Services Market 

Ships that service the marine transportation services market are either tramp vessels or 

liner vessels. As explained below, both of these sectors can reasonably be modeled as 

competitive markets. 

Tramp vessels “carry bulk and general cargoes not catered for by the liner industry.”14 

These vessels have no fixed route or ports of call, or published freight rates. Instead, they 

arrange to carry loads on a per voyage basis and operate on the spot market. According to a 

survey performed for the World Trade Organization in 1998 (37 member countries, counting the 

EU as one, and representing nearly half of the tonnage of the world fleet), bulk traffic accounted 

for about 65 percent of the volume of trade in that sample.15 That report notes that “contracts are 

allocated on an extremely competitive basis; business is won on the basis of freight rates a few 

cents per ton lower than the competition. Stopford notes that this sector has expanded greatly 

since the 1970s, benefiting from advances in communication that facilitated information flows. 

The result, he notes, is “the highly efficient transport system for bulk cargoes we have today.”16 

Consequently, it is reasonable to assume a competitive market structure. 

Liner vessels, in contrast, operate on fixed routes and schedules with published rates. 

The liner sector is specifically exempt from antitrust legislation, and so-called conference 

agreements set rates and conditions of service for the scheduled routes. However, this sector can 

also be assumed to operate competitively. This is because the Ocean Shipping Reform Act of 

1998 allows the use of “service contracts,” which are private contracts that do not have to 

comply with the conference rates. These open conferences exist only on the U.S. routes. The 

amount of freight shipped in liners using service contracts instead of published rates has grown 

tremendously since 1998; it has been estimated that 80% of cargo transported on routes between 

U.S. ports and other countries carried by conference members uses service contracts.17 This 

availability of service contracts appears to have increased substantially the competitiveness of 

liner shipping in the U.S. In addition, the World Trade Organization survey suggests that “the 

share of traffic held by the conferences has been eroded as new state trading and South East 

C

 The number of firms in a market is not a necessary condition for a competitive market. See Robert H. Frank, 

Microeconomics and Behavior, 1991, McGraw-Hill, Inc., p 333. 

7-15 
Regulatory Impact Analysis 

Asian operators have emerged and become powerful enough to offer on their own services 

equivalent to those of the conferences.”18 

It should be noted that contracts for liner shipping typically include a bunker fuel 

adjustment factor (BAF) in addition to the shipping rate. The BAF is used to adjust shipping 

costs in response to unexpected fuel cost variation after shipping rates are set.19 The BAF is 

determined by individual line shipping company and allows ship owners to adjust their rates by 

these increases in operating costs, thereby passing increases in operating costs to the entities that 

purchase their transportation services. 

In sum, tramp shipping appears to be a competitive market. Liner shipping, although 

exempt from antitrust laws, nevertheless is much less able than in the past to enforce its 

noncompetitively set published rates, and it faces some degree of competition from tramp 

shipping and from liners that are not members of the established conferences. 

7.2.1.3.2 Competition in the Vessel Building Market 

With regard to the vessel building market, there are short- and medium-term barriers to 

trade that could impede competition. Specifically, the shipping industry is characterized by high 

fixed costs (building a vessel). High entry costs can serve as a temporary barrier to entry in an 

industry: since existing shippers in the industry have already incurred those costs, they can 

operate at costs low enough to deter entry. However, this condition is offset by the excess 

capacity that appears to exist at many shipyards.20 An industry with excess capacity faces strong 

competition, as shipyards are likely to compete with each other for any new business. As a 

result, shipyards are likely to operate with margins small enough that they must pass along any 

increases in costs. Any requirements that affect the costs of a new vessel are likely to be 

included in the cost of a new vessel. Finally, while vessel building is concentrated in a few 

countries (South Korea, China, Japan, Germany), the purchaser of a new vessel has many 

shipyards available since most countries maintain at least some vessel building capacity. 

7.2.1.3.3 Competition in the Engine Manufacturing Market 

In recent years, the Category 3 marine diesel engine industry has become more 

consolidated. In 1998, there were 19 Category 3 engine manufacturers, with four sharing 80 

percent of the market.21 Since that time, Wartsila purchased Sulzer, and Caterpillar purchased 

MaK. Currently, these companies along with MAN B&W and Mitsubishi account for the vast 

majority of the Category 3 engine market. 

We modeled this market as competitive. This is because the Category 3 engine market is 

a mature industry, and pricing power in mature markets is typically limited. Vessel owners and 

builders can chose among many engine models and sizes for a particular vessel. In addition, 

Category 3 engine manufacturers also compete in the market for land-based power plants and 

and other industrial applications. 

Because the market is dominated by a small number of engine manufacturers, however, 

an oligopolistic market structure may be more appropriate. In an oligopoly, it is not uncommon 

for manufacturers to exercise market power to set prices above their average cost, thereby 

7-16 
Chapter 7: Economic Impact Analysis 

securing greater profits.22 In such markets, the economic impacts of a regulatory program may 

exceed the compliance costs of the program. 

However, an oligopoly market structure does not necessarily mean that the firms 

necessarily act non-competitively. According to the Bertrand competition model, price 

competition in an oligopoly achieves the similar results as a perfectly competitive market.23 In 

this case, each firm chooses its price to compete with the other firms. Varian describes this as a 

model of competitive bidding: “Suppose that one firm ‘bids’ for the consumers’ business by 

quoting a price above marginal cost. Then the other firm can always make a profit by 

undercutting this price by a lower price.”24 In the Bertrand model, price competition under 

constant returns to scale yields a price equal to the constant marginal cost. In other words, the 

price bidding prices leads to perfectly competitive results. 

The theoretical conditions for Bertrand competition are: there are at least two firms 

producing homogeneous products; firms do not cooperate; firms have the same marginal cost 

(MC); marginal cost is constant; demand is linear; firms compete in price, and choose their 

respective prices simultaneously; there is strategic behavior by both firms; both firms compete 

solely on price and then supply the quantity demanded; and consumers buy everything from the 

cheaper firm or half at each, if the price is equal.25 In an oligopoly market, price competition 

may be softened when the manufacturers face sharply rising marginal costs, when they compete 

repeatedly, or when their products are differentiated. 

For the above reasons, we assume the Category 3 engine market is competitive, and firms 

in the market are involved in Bertrand competition in this analysis. 

7.2.1.4 Intermediate-Run Impacts 

This EIA explores economic impacts on affected markets in the intermediate run. In the 

intermediate run, some factors of production are fixed and some are variable. A short-run 

analysis, in contrast, imposes all compliance costs on producers, while a long-run analysis 

imposes all costs on consumers. The use of the intermediate run means that some factors of 

production are fixed and some are variable, and illustrates how costs will be shared between 

producers and consumers as the markets adjust to the new compliance program. The use of the 

intermediate time frame is consistent with economic practices for this type of analysis. 

7.2.1.4.1 Short-Run Analysis 

In the very short run, all factors of production are assumed to be fixed, leaving producers 

with no means to respond to the increased costs associated with the regulation (e.g., they cannot 

adjust labor or capital inputs). Within a very short time horizon, regulated producers are 

constrained in their ability to adjust inputs or outputs due to contractual, institutional, or other 

factors and can be represented by a vertical supply curve, as shown in Figure 7-2. Under this 

time horizon, the impacts of the regulation fall entirely on the regulated entity. Producers incur 

the entire regulatory burden as a one-to-one reduction in their profit. This is referred to as the 

“full-cost absorption” scenario and is equivalent to the engineering cost estimates. Although 

there is no hard and fast rule for determining what length of time constitutes the very short run, it 

is inappropriate to use this time horizon for this type of analysis because it assumes economic 

7-17 
Regulatory Impact Analysis 

entities have no flexibility to adjust factors of production. Note that the BAF is a way to avoid 

this scenario. Additionally, the fact that liner price schedules are renegotiated at least annually, 

and that individual service contracts may be negotiated more frequently, suggests that a very 

short-run analysis would not be suitable. 

Figure 7-2 Short-Run: All Costs Borne by Producers 

7.2.1.4.2 Long-Run Analysis 

In the long run, all factors of production are variable, and producers can be expected to 

adjust production plans in response to cost changes imposed by a regulation (e.g., using a 

different labor/capital mix). Figure 7-3 illustrates a typical, if somewhat simplified, long-run 

industry supply function. The supply function is horizontal, indicating that the marginal and 

average costs of production are constant with respect to output. This horizontal slope reflects 

the fact that, under long-run constant returns to scale, technology and input prices ultimately 

determine the market price, not the level of output in the market. 

7-18 
Chapter 7: Economic Impact Analysis 

}

$ 

Q1 Q0

d 

c b S1

: With Regulation P

Price 1 } Unit Cost Increase Increase a P0 S0

: Without Regulation 

D 

Output 

Figure 7-3 Long-Run: Full Cost Pass-Through 

Market demand is represented by the standard downward-sloping curve. The market is 

assumed here to be competitive; equilibrium is determined by the intersection of the supply and 

demand curves. In this case, the upward shift in the market supply curve represents the 

regulation’s effect on production costs and is illustrated in Figure 7-3. The shift causes the 

market price to increase by the full amount of the per-unit control cost (i.e., from P0 to P1). With 

the quantity demanded sensitive to price, the increase in market price leads to a reduction in 

output in the new with-regulation equilibrium (i.e., Q0 to Q1). As a result, consumers incur the 

entire regulatory burden as represented by the loss in consumer surplus (i.e., the area P0ac P1). In 

the nomenclature of EIAs, this long-run scenario is typically referred to as “full-cost passthrough.” 

Taken together, impacts modeled under the long-run/full-cost-pass-through scenario 

reveal an important point: under fairly general economic conditions, a regulation's impact on 

producers is transitory. Ultimately, the costs are passed on to consumers in the form of higher 

prices. However, this does not mean that the impacts of a regulation will have no impact on 

producers of goods and services affected by a regulation. For example, the long run may cover 

the time taken to retire today’s entire capital equipment, which could take decades. Therefore, 

transitory impacts could be protracted and could dominate long-run impacts in terms of present 

value. In addition, to evaluate impacts on current producers, the long-run approach is not 

appropriate. Consequently a time horizon that falls between the very short-run/full-costabsorption case and the long-run/full-cost-pass-through case is most appropriate for this EIA. 

7.2.1.4.3 Intermediate Run Analysis 

The intermediate run time frame allows examination of impacts of a regulatory program 

during the transition between the very short run and the long run. In the intermediate run, there 

is some resource immobility which may cause producers to suffer producer surplus losses. 

Specifically, producers may be able to adjust some, but not all, factors of production, and they 

7-19 
Regulatory Impact Analysis 

therefore will bear some portion of the costs of the regulatory program. The existence of fixed 

production factors generally leads to diminishing returns to those fixed factors. This typically 

manifests itself in the form of a marginal cost (supply) function that rises with the output rate, as 

shown in Figure 7-4. 

Figure 7-4 Intermediate-Run: Partial-Cost Pass-Through 

Again, the regulation causes an upward shift in the supply function. The lack of resource 

mobility may cause producers to suffer profit (producer surplus) losses in the face of regulation; 

however, producers are able to pass through some of the associated costs to consumers, to the 

extent the market will allow. As shown, in this case, the market-clearing process generates an 

increase in price (from P0 to P1) that is less than the per-unit increase in costs, so that the 

regulatory burden is shared by producers (net reduction in profits) and consumers (rise in price). 

In other words, there is a loss of both producer and consumer surplus. 

7.2.1.5 Economic Impacts of a Control Program – Single Market 

A graphical representation of a general economic competitive model of price formation, 

as shown in Figure 7-5(a), posits that market prices and quantities are determined by the 

intersection of the market supply and market demand curves. Under the baseline scenario, a 

market price and quantity (p,Q) are determined by the intersection of the downward-sloping 

market demand curve (DM) and the upward-sloping market supply curve (SM). The market 

supply curve reflects the sum of the domestic (Sd) and import (Sf) supply curves. 

7-20 
Chapter 7: Economic Impact Analysis 

Figure 7-5 Market Equilibrium Without and With Regulation 

With the regulation, the costs of production increase for suppliers. The imposition of 

these regulatory control costs is represented as an upward shift in the supply curve for domestic 

and import supply by the estimated compliance costs. As a result of the upward shift in the 

supply curve, the market supply curve will also shift upward as shown in Figure 7-5(b) to reflect 

the increased costs of production. 

At baseline without the new standards, the industry produces total output, Q, at price, p, 

with domestic producers supplying the amount qd and imports accounting for Q minus qd, or qf. 

With the regulation, the market price increases from p to p, and market output (as determined 

from the market demand curve) decreases from Q to Q. This reduction in market output is the 

net result of reductions in domestic and import supply. 

As indicated in Figure 7-5, when the new standards are applied the supply curve will shift 

upward by the amount of the estimated compliance costs. The demand curve, however, does not 

shift in this analysis. This is explained by the dynamics underlying the demand curve. The 

demand curve represents the relationship between prices and quantity demanded. Changes in 

prices lead to changes in the quantity demanded and are illustrated by movements along a 

7-21 
Regulatory Impact Analysis 

constant demand curve. In contrast, changes in consumer tastes, income, prices of related goods, 

or population would lead to change in demand and are illustrated as shifts in the position of the 

demand curve.D,26 For example, an increase in the number of consumers in a market would 

cause the demand curve to shift outward because there are more individuals willing to buy the 

good at every price. Similarly, an exogenous increase in average income would also lead the 

demand curve to shift outward or inward, depending on whether people choose to buy more or 

less of a good at a given price. 

7.2.1.6 Economic Impacts of a Control Program – Multiple Markets 

The above description is typical of the expected market effects for a single product 

market considered in isolation (for example, the ocean transportation service market). However, 

the markets considered in this EIA are more complicated because they are linked: the market for 

engines is affected by the market for vessels, which is affected by the market for international 

marine transportation services. In particular, it is reasonable to assume that the input-output 

relationship between the marine diesel engines and vessels is strictly fixed and that the demand 

for engines varies directly with the demand for vessels. Similarly, the demand for vessels varies 

directly with the demand for marine transportation services. A demand curve specified in terms 

of its downstream consumption is referred to as a derived demand curve. Figure 7-6 illustrates 

how a derived demand curve is identified. 

Figure 7-6 Derived-Demand Curve for Engines 

D An accessible detailed discussion of these concepts can be found in Chapters 5-7 of Nicholson’s (1998) 

intermediate microeconomics textbook. 

7-22 
Chapter 7: Economic Impact Analysis 

Consider an event in the engine market, such as a new technology requirement, that 

causes the price of an engine to increase by ΔPeng. This increase in the price of an engine will 

cause the supply curve in the engine market to shift up, leading to a decreased quantity (ΔQeng). 

The change in engine production leads to a decrease in the demand for equipment (ΔQE). The 

difference between the supply curves in the equipment market, S’E – SE, is the difference in price 

in the engine market, ΔPeng, at each quantity. Note that the supply and demand curves in the 

equipment market are needed to identify the derived demand in the engine market. 

In the market for vessels and engines, the derived demand curves are expected to be 

vertical. The full costs of the engines will be passed into the cost of vessels, and the cost of 

vessels will be passed into the cost of ocean transportation. 

7.2.1.7 Using Economic Theory to Estimate the Social Costs of a Control Program 

The economic welfare implications of the market price and output changes with the 

regulation can be examined by calculating consumer and producer net “surplus” changes 

associated with these adjustments. This is a measure of the negative impact of an environmental 

policy change and is commonly referred to as the “social cost” of a regulation. It is important to 

emphasize that this measure does not include the benefits that occur outside of the market, that 

is, the value of the reduced levels of air pollution with the regulation. Including this benefit will 

reduce the net cost of the regulation and even make it positive. 

The demand and supply curves that are used to project market price and quantity impacts 

can be used to estimate the change in consumer, producer, and total surplus or social cost of the 

regulation (see Figure 7-7). 

7-23 
Regulatory Impact Analysis 

P1 

P2 

S 

S

D 

$/Q 

A 

Q2 Q1 Q/t 

(a) Change in Consumer Surplus with 

Regulation 

P1 

P2 

S 

S

D 

$/Q 

B 

C 

Q2 Q1 Q/t 

(b) Change in Producer Surplus with 

Regulation 

P1 

P2 

S 

S

D 

$/Q 

D 

Q2 Q1 Q/t 

(c) Net Change in Economic Welfare with 

Regulation 

Figure 7-7 Economic Welfare Calculations: Changes in Consumer, Producer, and Total Surplus 

The difference between the maximum price consumers are willing to pay for a good and 

the price they actually pay is referred to as “consumer surplus.” Consumer surplus is measured 

as the area under the demand curve and above the price of the product. Similarly, the difference 

between the minimum price producers are willing to accept for a good and the price they actually 

receive is referred to as “producer surplus.” Producer surplus is measured as the area above the 

supply curve and below the price of the product. These areas can be thought of as consumers’ 

net benefits of consumption and producers’ net benefits of production, respectively. 

7-24 
Chapter 7: Economic Impact Analysis 

In Figure 7-7, baseline equilibrium occurs at the intersection of the demand curve, D, and 

supply curve, S. Price is Pl with quantity Ql. The increased cost of production with the 

regulation will cause the market supply curve to shift upward to S. The new equilibrium price 

of the product is P2. With a higher price for the product there is less consumer welfare, all else 

being unchanged. In Figure 7-7(a), area A represents the dollar value of the annual net loss in 

consumer welfare associated with the increased price. The rectangular portion represents the 

loss in consumer surplus on the quantity still consumed due to the price increase, Q2, while the 

triangular area represents the foregone surplus resulting from the reduced quantity consumed, Ql 

– Q2. 

In addition to the changes in consumers’ welfare, there are also changes in producers’ 

welfare with the regulatory action. With the increase in market price, producers receive higher 

revenues on the quantity still purchased, Q2. In Figure 7-7(b), area B represents the increase in 

revenues due to this increase in price. The difference in the area under the supply curve up to the 

original market price, area C, measures the loss in producer surplus, which includes the loss 

associated with the quantity no longer produced. The net change in producers’ welfare is 

represented by area B – C. 

The change in economic welfare attributable to the compliance costs of the regulations is 

the sum of consumer and producer surplus changes, that is, –(A) + (B–C). Figure 7-7(c) shows 

the net (negative) change in economic welfare associated with the regulation as area D. 

7.2.1.8 Fixed and Variable Costs in a Competitive Market 

The estimated engineering compliance costs, consisting of fixed costs (R&D 

capital/tooling, certification costs), variable costs, and operational costs, provide an initial 

measure of total annual compliance costs without accounting for behavioral responses. The 

starting point for assessing the social costs and market impacts of a regulatory action is to 

incorporate the regulatory compliance costs into the production decision of the firm. 

7-25 
Regulatory Impact Analysis 

Figure 7-8 Modeling Fixed Regulatory Costs 

In general, shifting the supply curve by the total cost per unit implies that both capital and 

operating costs vary with output levels. At least in the case of capital, this raises some questions. 

In the long run, all inputs (and their costs) can be expected to vary with output. But a short(er)-

run analysis typically holds some capital factors fixed. For instance, to the extent that a market 

supply function is tied to existing facilities, there is an element of fixed capital (or one-time 

R&D). As indicated above, the current market supply function might reflect these fixed factors 

with an upward slope. As shown in Figure 7-8, the marginal cost (MC) curve will only be 

affected, or shift upwards, by the per-unit variable compliance costs (c1=TVCC/q), while the 

average total cost (ATAC) curve will shift up by the per-unit total compliance costs (c2=TCC/q). 

Thus, the variable costs will directly affect the production decision (optimal output rate), and the 

fixed costs will affect the closure decision by establishing a new higher reservation price for the 

firm (i.e., pm). In other words, the fixed costs are important in determining whether the firm will 

stay in this line of business (i.e., produce anything at all), and the variable costs determine the 

level (quantity) of production. 

Depending on the industry type, fixed costs associated with complying with a new 

regulation are generally treated differently in an analysis of market impacts. In a competitive 

market, the industry supply curve is generally based on the market's marginal cost curve; fixed 

costs do not influence production decisions at the margin. Therefore, the market analysis for a 

competitive market is based on variable costs only. 

7-26 
Chapter 7: Economic Impact Analysis 

Implicit in this approach is the assumption that manufacturers do not recover their 

production fixed costs by passing all or part of them to consumers through new price increases. 

Yet, production fixed costs must be recovered; otherwise, manufacturers would go out of 

business. Manufacturers in any industry are likely to have ongoing product development 

programs the costs of which are included in the current market price structure. It is expected that 

the resources for those programs would be re-oriented toward compliance with the regulatory 

program until those costs are recovered for each manufacturer. If this is the case, then the rule 

would have the effect of shifting product development resources to regulatory compliance from 

other market-based investment decisions. Thus, fixed costs are a cost to society because they 

displace other product development activities that may improve the quality or performance of 

engines and equipment. In this EIA, fixed costs are included in the total social costs in the year 

in which they occur. 

7.2.2 How Is This Economic Theory Applied In This EIA? 

In the above explanation of how to estimate the market and social welfare impacts of a 

control action, the price elasticities of supply and demand were nonzero. This was reflected in 

the upward-slope of the supply curve and the downward slope of the demand curve. In the 

derived demand analysis, a nonzero price elasticity of demand in the vessel market yielded a 

nonzero price elasticity of demand in the engine market. 

However, the price elasticity of demand in the international shipping market is expected 

to be nearly perfectly inelastic (demand curve with near-infinite slope – a vertical demand 

curve). This is not to say that an increase in price has no impact on quantity demanded; rather, it 

means that the price increase would have to be very large before there is a noticeable change in 

quantity demanded. 

A nearly perfectly inelastic price elasticity of demand simplifies the analysis described 

above. Figure 7-9 reproduces the relationships in a multi-level market but this time with a nearly 

perfectly inelastic demand curve in the international shipping market. The relationships between 

this market and the markets for vessels and engines means that the derived demand curves for 

engines and vessels are also expected to be nearly perfectly inelastic. Specifically, if demand for 

transportation services is not expected to be affected by a change in price, then the demand for 

vessels will also remain constant, as will the demand for engines. 

7-27 
Regulatory Impact Analysis 

Figure 7-9 Market Impacts in Markets with Nearly Perfectly Inelastic Demand 

As indicated in Figure 7-9, a change in unit production costs due to compliance with the 

engine emission and fuel sulfur requirements in the proposed ECA shifts the supply curves for 

engines, vessels, and ocean transportation services. The cost increase causes the market price to 

increase by the full amount of per unit control cost (i.e. from P0 to P1) while the quantity 

demanded for engines, vessels, and transportation services remains constant. Thus, engine 

manufacturers are expected to be able to pass on the full cost of producing Tier III compliant 

engines to the vessel builders, who are expected to be able to pass the full cost of installing the 

engines and fuel switching equipment on to the vessel owners. The vessel owners, in turn, are 

expected to be able to pass on these cost increases, as well as the additional operating costs they 

incur for the use of SCR reductant (urea) and low sulfur fuel while operating in the ECA. 

Note that the fuel and urea costs affect the ocean transportation services market directly, 

but affect the vessel and engine markets only through the derived demand curves. That is, the 

7-28 
Chapter 7: Economic Impact Analysis 

equilibrium prices and quantities for vessels and engines will change only if the quantity of 

ocean transportation services demanded changes due to fuel and urea costs. Because the changes 

in fuel and urea prices are expected to be too small to affect the quantity of ocean transportation 

services demanded, the markets for vessels and engines are not expected to be affected by fuel 

changes. 

The sole exception for the assumption of nearly perfectly price elasticity of demand is the 

cruise market. Clearly, the consumers in that market, tourists and holiday-makers, have 

alternatives available for their recreational activities. If the cost of a cruise increases too much, 

they may decide to spend their vacation in other activities closer to home, or may elect to fly 

somewhere instead. As a result, the costs of compliance for the cruise industry are more likely to 

be shared among stakeholders. If the price elasticity of demand is larger (in absolute value) than 

the price elasticity of supply, ship owners will bear a larger share of the costs of the program; if 

the price elasticity of demand is smaller (in absolute value) than the price elasticity of supply, 

consumers will bear a larger share of the program. Similarly, the vessel builders and engine 

manufacturers will also bear a portion of the costs. If the quantity demanded for cruises 

decreases, the derived quantity demanded for vessels will decrease, as will the derived quantity 

demanded for engines. If the supply curves for these industries are not perfectly elastic (i.e., 

horizontal), then the downward-sloping derived demand curves will lead to shared impacts 

among the sectors. 

As described in Section 7.3 of this chapter, the impacts on the cruise market are expected 

to be small, with total engine and vessel costs increasing about one percent and operating costs 

increasing between 1.5 and 6 percent. These increases are within the range of historic variations 

in bunker fuel prices. The impact on the cruise market, then, may be similar in effect to the 

market’s response to those changes. 

Finally, it may be possible for cruise ships to offset some of these costs by advertising the 

environmental benefits of using engines and fuels that comply with the ECA requirements. 

Many cruise passengers enjoy this form of recreational because it allows them a personal-level 

experience with the marine environment, and they may be willing to pay an increased fee to 

protect that nature. If people prefer more environmentally friendly cruises, then the demand 

curve for these cruises will shift up. Consumers will be willing to bear more of the costs of the 

changes. If the demand shift for environmentally friendly cruises is large enough, both the 

equilibrium price and quantity of cruises might increase. 

7-29 
Regulatory Impact Analysis 

7.3 Estimating Market Impacts on the Marine Transportation Market 

To characterize the increase in vessel operating costs due to the coordinated strategy, 

calculations were performed for three types of ocean going vessels, container, bulk carrier, and 

cruise liner. Our estimates were developed using typical vessel characteristics, projected fuel 

and urea costs, and worst case sea-route data. This section presents the methodology used for 

these calculations. 

7.3.1 Container Vessel 

A typical container vessel was derived using data obtained from the Lloyd’s of London 

Sea-Web Database27. This data base includes information on actual vessel size (Dead Weight 

Tonnes (DWT)) and engine power (kilowatt – hour (kW-hr)) for a wide range of vessel types. 

Operating costs included those associated with switching from residual fuel to 0.1% 

sulfur distillate fuel and urea consumption for vessels equipped with selective catalytic reduction 

(SCR). The fuel and urea costs are based on projections that are presented in the ECA proposal. 

These fuel costs estimates are $322/tonne for residual fuel and $468/tonne for 0.1% sulfur 

distillate fuel. We use a urea consumption rate of 7.5% of fuel consumption, at $1.52/gallon. 

Singapore to Seattle: 7,100 nm 

Los Angeles to Singapore: 7,700 nm 

Distance in ECA 

1,700 nm 

Figure 7-10 Example Sea Route 

To develop a representative sea-route for our price estimations, we created a ‘circle route’ 

for a theoretical trip. Since the Port of Los Angeles28, one of the largest ports in the U.S., lists 

the majority of its cargo as traveling from South Asia, our route had a vessel hypothetically 

travel from Singapore to the Port of Seattle, then down the West Coast of the United States 

(U.S.) to the Port of Los Angeles, then back to Singapore. To map this route, we divided it into 

three “legs.” The first leg has the vessel traveling from Singapore to the Port of Seattle; the 

second part travels down the West Coast of the U.S. to the Port of Los Angeles/Long Beach 

7-30 
Chapter 7: Economic Impact Analysis 

(POLA/LB); the third leg continues from Los Angeles to Singapore. This trip is illustrated in 

Figure 7-10. The total distance for this route was determined from http://nauticaldistance.com/, 

and is described below. 

We understand that it will take some additional time and distance to switch vessel 

operations from one fuel to another. Additionally, we acknowledge that vessels may enter the 

ECA at an angle relative to the port in question, and would be operating in the ECA for a slightly 

longer distance than the 200 nautical miles of the ECA. Therefore, to make our fuel usage 

estimates as accurate as possible, we included some additional ECA traversing distances in our 

circle route calculations, adding 183 nm to the distance for reaching the Port of Seattle, and 35 

nm to the distance from POLA/LB. 

7.3.1.1 Baseline Operating Costs 

In order to begin our estimated fuel cost increases, we needed to establish the fuel usage 

and prices for our baseline route (i.e., the price of the route operating on residual fuel). We 

determined average operational values for our hypothetical vessel by selecting the mid-point of 

the operational ranges used today by OGV. Therefore, our baseline estimations for the fuel 

usage for the first leg were determined by multiplying the engine power for the average sized 

containership (in kilowatts (kW)) by the average estimated engine efficiency (80 percent) as well 

as the average residual fuel consumption (195 grams fuel per kilowatt hour (g/kW-hr)). 

(Equation 7-1) This value was then multiplied by the nautical miles (nm) for the first leg of the 

trip (the distance from Singapore to Seattle (7,064 nm)), and divided by the average engine speed 

(16 knots). To obtain the correct units for the calculation, a unit conversion was also included. 

(Equation 7-2) As average values are represented here, it is possible that these values could 

fluctuate slightly depending on the vessel’s speed, engine efficiency, and specific fuel 

consumption, but we believe that these estimates provide a reasonable forecast for the majority 

of container vessels in operation today. 

Equation 7-1 

36,540kW 0.8195 gresid 

kW  hr  5,700,240 gresid hr 

Equation 7-2 

5,700,240 gresid 

hr  7,064nm 



tonne  2,517tonneresid 16 knots

hr 1,000,000g 

The same determinations were conducted for the second leg of the trip (1,143 nm, 

Equation 7-3) and the third leg (7,669 nm, Equation 7-4). 

7-31 
Regulatory Impact Analysis 

Equation 7-3 

5,700,240 gresid 1,143nm hr tonne

  407tonne 

16 knots

hr 1,000,000g 

resid 

Equation 7-4 

5,700,240 gresid 

hr  7,669nm tonne 

16 knots

hr 



1,000,000g  2,732tonneresid 

Total fuel usage for each leg of the trip was multiplied by the price of the fuel (2006 U.S. 

dollars per tonne ($/tonne) which provided the baseline cost of fuel for each leg. These costs 

were then summed to produce an aggregate estimation of fuel cost for the entire circle trip 

(Equation 7-5). This calculation provides the baseline cost of about $1.8M for an average sized 

container ship to traverse the theoretical circle route. 

Equation 7-5 

(2,517tonneresid  407tonneresid  2,732tonneresid )  $322.48 / tonneresid  $1,823,947 

7.3.1.2 Operating Costs with an ECA 

Operating cost increases due to an ECA are due to increased fuel costs and urea 

consumption within the ECA. Operating costs are assumed to remain unchanged outside the 

ECA. In addition, the ECA is assumed to have no impact on the route travelled. 

7.3.1.2.1 Increased Fuel Costs 

To determine the fuel usage and price increase caused by the ECA on our vessel traveling 

our theoretical circle route, we conducted the same analysis as our baseline using the appropriate 

distillate fuel properties. Since the distillate fuel will most likely only be used in the ECA, the 

remainder of the trip will continue operating on residual fuel. Therefore, we adjusted our trip 

section distances accordingly, using residual fuel over the first leg for 6,679 nm and over 7,434 

nm for the third leg, while the remainder of the trip was determined using a distillate fuel. 

Equation 7-6 provides the approximation for engine power and fuel consumption using distillate 

fuel and Equation 7-7, 8, and 9 calculate the corresponding trip segment fuel usages. Due to the 

chemical properties of the two marine fuels, there is approximately a five percent (5%) increase 

in energy, on a mass basis, when operating on the distillate fuel instead of the residual fuel, and 

this increase is accounted for in Equation 7-6. 

7-32 
Chapter 7: Economic Impact Analysis 

Equation 7-6 

36,540kW 0.8

195 gdistil 

kW  hr  5,428,800 gdistil 

1 0.05 hr 

Equation 7-7a Residual Fuel Estimation 

5,700,240 gresid  6,679nm hr tonne

  2,379tonne

16 knots

hr 1,000,000g 

resid 

Equation 7-7b Distillate Fuel Estimation 

5,428,800 gdistil 

hr  385nm tonne 

16 knots

hr 



1,000,000g  131tonnedistil 

Equation 7-8 

5,428,800 gdistil 

hr 1,143nm 



tonne  388tonnedistil 16 knots

hr 1,000,000g 

Equation 7-9a Residual Fuel Estimation 

5,700,240 gresid 

hr 7,434knots 



tonne  2,648tonne 

16knots

hr 

1,000,000g 

resid 

Equation 7-9b Distillate Fuel Estimation 

5,428,800 gdistil 

hr  235nm tonne

  80tonnedistil 16 knots

hr 1,000,000g 

7-33 
Regulatory Impact Analysis 

7.3.1.2.2 Urea Costs 

Switching to a distillate marine fuel will achieve reductions only in sulfur and particulate 

emissions. In order to meet the required nitrogen oxides (NOX) emission reductions, vessel 

owners/operators would need to install a Selective Catalytic Reduction (SCR) device, or similar 

technologies, on new vessels built in 2016 and later. Using an SCR requires dosing exhaust 

gases with urea to aid with the emission reductions, which adds some additional costs to the 

operation of the vessel. In an SCR on a marine engine, the average dosage of urea is seven and a 

half percent (7.5%) per gallon of distillate fuel used. Subsequently, to estimate the volume of 

urea required for our circle route, we multiplied the distillate quantity determined above by this 

urea percentage (Equation 7-10). As we expect these costs to be incurred several years in the 

future, we used the analysis preformed for the EPA by EnSys29 which predicted that in 2020, 

33.2% of the fuel used in ECAs will be on vessels equipped SCR. The urea costs below are 

adjusted to reflect this prediction. 

Equation 7-10 

599tonnesdistil  kg 

m3 



264.17

3 

gal 0.075  14,185galurea 0.332  4,709galurea 0.001tonne 836.6kgdistil m 

To determine the additional price of our vessel’s operation through the ECA, we then 

multiplied the fuel and urea quantities by their corresponding prices ($322.48/tonne for residual, 

$467.92/tonne for distillate, and $1.52/gal for the urea). We then summed these values to 

determine the aggregate price for fuel and urea required for our container vessel to travel our 

circle route with the proposed ECA in place (Equation 7-11). 

Equation 7-11 

[(2,379tonneresid  2,648tonneresid ) $322.48/ tonneresid ] 

[(131tonnedistil  388tonnedistil  80tonnedistil ) $467.92/ tonnedistil ] 

(4,709galurea $1.52/ galurea )  $1,908,549ECA 

The total estimated price for an average sized containership traversing the circle with the 

ECA in place is just over $1.9M. The cost increase of this trip caused by the fuel and urea prices 

used in the ECA came from subtracting the baseline (residual fuel) trip price from the ECA price 

(Equation 7-12). The price differential between the baseline trip and the ECA trip is 

demonstrated in Equation 7-13 and takes into consideration the fuel cost portion of the 

operational cost for a vessel, which is typically around 60 percent of the total. As can be seen, 

by operating in the ECA for our theoretical circle route it is estimated that the operational costs 

due to the distillate fuel is approximately three percent (3%). 

7-34 
Chapter 7: Economic Impact Analysis 

Equation 7-12 

$1,908,549ECA  $1,823,947baseline  $84,602 

Equation 7-13 

$1,908,549ECA  $1,823,947baseline 0.60 100  2.8% 

$1,823,947baseline 

To put this price increase in some perspective, we assumed our average sized 

containership was hauling goods, such Twenty-foot Equivalent Units (TEU), and estimated the 

increase per each TEU. Estimating these prices required the cargo weight of the vessel. 

Literature shows that approximately 93-97% of a container vessel’s DWT is used for hauling 

cargo, with the remaining weight composing the crew, vessel engines and hull, and fuel.30 

Equation 7-14 shows the calculation used to convert the vessel’s DWT to cargo weight using the 

middle value of 95%. 

Equation 7-14 

50,814DWT  0.95  48,273c arg o _ tonnes 

Dividing the difference between the baseline fuel price and the ECA fuel price we 

calculated previously by the cargo tonnes as established in Equation 7-14 provided the price 

increase per tonne of good shipped for the entire route (Equation 7-15). 

Equation 7-15 

($1,908,549ECA  $1,823,947baseline )  $1.75/ c argo _ tonneincrease 48,273c argo _ tonnes 

Using this value and the weight of a full TEU (10 metric tonnes)31, we determined the 

cost increase for shipping a fully loaded TEU across our circle route (Equation 7-16). 

Equation 7-16 

$1.75 10tonnes

  $17.53/ full _ TEUincrease

c argo _ tonneincrease full _ TEU 

7.3.2 Bulk Carrier 

Since the majority of goods transported to the U.S. are brought by bulk carriers as well as 

container vessels, and bulk carriers are of a different construction than container vessels, we also 

conducted estimations as to what the price increase per tonne of bulk cargo would be due to the 

ECA. For a comparison, we calculated what the price increase would be for a tonne of bulk 

cargo carried on a vessel traversing the same theoretical circle route as the containership. 

7-35 
Regulatory Impact Analysis 

Equation 7-17 shows the same calculations as performed above for the containership 

using the average engine power for a bulk carrier (3,825 kW) and the total trip distance (15,876 

nm) 

Equation 7-17 

3,825kW 0.8195gresid 

kW  hr 15,876nm 



tonne  592tonneresid 16knots 1,000,000g hr 

This determination was also conducted for the ECA, using the appropriate values for the 

distillate part of the circle route (1,763 nm) and the residual fuel part of the route (14,113 nm) 

(Equation 7-18 and 19 respectively). Equation 7-20 determines the urea required for use in the 

ECA (as was established in Equation 7-10), and Equation 7-21 estimates the overall price 

increase for the bulk carrier if it was to operate on the theoretical circle route through the ECA. 

Equation 7-18 

3,825kW  0.8 

195 gresid 

kW  hr 1,763nm tonne 

1  0.05 



16 knots 

1,000,000g  62.6tonnedistil 

hr 

Equation 7-19 

3,825kW  0.8 195 gresid 14,113nm kW  hr 

tonne  526tonne 

16 knots

hr 1,000,000g 

resid 

Equation 7-20 

62.6tonnesdistil  kg 

m3 



264.17

3 

gal 0.075  1,483galurea 0.332  492galurea 0.001tonne 836.6kgdistil m 

Equation 7-21 

[(62.6tonnedistil  $467.92 / tonnedistil )  (526tonneresid  $322.48 / tonneresid )  (492galurea  $1.52 / galurea )] 

 [592tonneresid  $322.48 / tonneresid ]  $8,756increase 

To establish this price increase in terms of bulk cargo shipped, the value from Equation 

7-21 was divided by the available cargo weight for the bulk carrier which was determined from 

the actual vessel weight (16,600 tonnes) as was performed in Equation 7-14 (Equation 7-22). 

7-36 
Chapter 7: Economic Impact Analysis 

Equation 7-22 

$8,756increase  $0.56/ bulk _ c argo _ tonneincrease (16,600bulk _ c argo _ tonnes 0.95) 

As can be seen, for an average bulk carrier that would travel from Singapore to Seattle, 

POLA/LB, and then back out to Singapore, the price increase caused by operation in the ECA 

would be around $0.56 per tonne of good shipped. As with the other vessels, this price would 

fluctuate depending on the distance traveled within the ECA, the vessel’s speed, and the engine 

power used. 

7.3.3 Cruise Ship 

We also conducted an analysis on a typical Alaskan cruise liner. These vessels tend to 

operate close to shore and would be within the ECA for the majority of their routes. As such, 

this analysis presents worst case cost impacts for this type of vessel. 

To conduct this analysis, a series of average vessel characteristics were chosen along with 

a typical 7 day (168 hours) Alaskan cruise route. The characteristics used below are the main 

engine power (31,500 kW), auxiliary engine power (18,680 kW), base specific residual fuel 

consumption (178 gfuel/kW-hr for main engines, 188 gfuel/kW-hr for auxiliary engines), distance 

between voyage destinations (5 destinations with a distance ranging between 230 to 700 nm 

(shown in Table 7-8)), maximum vessel speed (21.5 knots), and the average number of 

passengers on-board the vessel (1,886 people). Additionally, the arrival and departure times at 

the various ports of call along the cruise route were used to calculate the average speed travelled 

between each destination (shown in Table 7-8). The required power for a given journey segment 

was calculated using the relationship shown in Equation 7-23. This relationship was developed 

for the “2005-2006 BC Ocean-Going Vessel Emissions Inventory,”32 which was conducted by 

the Chamber of Shipping of British Columbia, Canada. 

Equation 7-23 

Required engine power  0.8199 (avg speed/max speed)3 - 0.0191(avg speed/max speed)2 

+ 0.0297 (avg speed/max speed) + 0.1682 

This relationship was developed to approximate effective power given cruise ships’ 

diesel-electric operation. The auxiliary engines reported within the Lloyd’s of London ‘Seaweb’ 

database33, and are presumably operated independently of the vessels main diesel-electric power 

generation, as well as assumed to operate at an average of 50% power for the entire voyage. 

To demonstrate the price increase for the cruise liner that would operate within the ECA, 

calculations for one leg of the Alaskan voyage are shown in Equation 7-24 to 27, the entire trip 

operational cost increase per person in Equation 7-28, and with Table 7-8 depicting the total 

increases over the entire trip broken out by destination. 

7-37 
Regulatory Impact Analysis 

Equation 7-24 

178g fuel hr tonne 31,500kW  0.5683   704nm    134tonneresid kW  hr 16.76knots 1,000,000g 

Equation 7-25 

134tonneresid $322.48 

1,886 people 



tonneresid 

 $22.89 / personresid 

Equation 7-26 

178g fuel hr tonne 31,500kW  0.5683   704nm    127tonnedistil (1.05)kW  hr 16.76knots 1,000,000g 

Equation 7-27 

127tonnedistil 

$467.92  $31.62 / persondistil 1,886people tonnedistil 

Equation 7-28 

$31.62  $22.89  $8.73/ personmain _ increase 

Table 7-8 Alaskan Cruise Liner Destinations and the Corresponding Operational Price Increases 

Destination 

Origin 

Destination 

Conclusion 

Distance 

Between 

Locations (nm) 

Average Speed 

Traveled Between 

Ports (knots) 

Calculated 

Engine Load 

(Equation 7-23) 

Estimated Price 

Increase / Person ($) 

Vancouver Sitka 704 16.76 0.5683 $8.73 

Sitka Hubbard Glacier 253 16.32 0.5385 $3.06 

Hubbard Glacier Juneau 246 14.47 0.4295 $2.67 

Juneau Ketchilkan 237 13.17 0.3675 $2.42 

Ketchilkan Vancouver 534 15.48 0.4856 $6.13 

Total $23.02main_increase 

Additionally, the operational cost increases for the auxiliary engines were estimated. 

Equation 7-29 to 33), as well as the cost increases caused by dosing the engine exhaust with urea 

(Equation 7-34 & 35), and the total price increase for the cruise (Equation 7-36) divided by the 

length of the cruise (Equation 7-37). 

7-38 
Chapter 7: Economic Impact Analysis 

Equation 7-29 

188g fuel tonne 18,680kW  0.50  168hrs   295tonneresid kW  hr 1,000,000g 

Equation 7-30 

295tonneresid 

$322.48  $50.44 / personresid 1,886 people tonneresid 

Equation 7-31 

188g fuel tonne 18,680kW  0.50  168hrs   281tonnedistil (1.05)kW  hr 1,000,000g 

Equation 7-32 

281tonnedistil 

$467.92  $69.71/ persondistil 1,886 people tonnedistil 

Equation 7 33 

$69.71  $50.44  $19.27 / personaux _ increase 

Equation 7-34 

616.75tonnesdistil  kg 

m3 



264.17

3 

gal  0.075 14,606galurea  0.332  4,849galurea 0.001tonne 836.6kgdistil m 

Equation 7-35 

4,849galurea  $1.52 / galurea  $3.91urea _ increase 1,886 people 

Equation 7-36 

$23.02main _ increase  $19.27aux _ increase  $3.91urea _ increase  $46.20 / persontotal _ increase 

Equation 7-37 

$46.20 / persontotal _ increase  $6.60 / person / day 7dayscruise _ length 

To put this price increase in perspective of the additional cost for a typical seven-day 

Alaskan cruise, we also determined the % increase for the various stateroom types available on 

the vessel. These values were established as shown in Equation 7-38 and Table 7-9 lists the four 

main stateroom types used on a typical Alaskan cruise liner. It should be noted that these 

7-39 
Regulatory Impact Analysis 

estimates are provided for illustration only; cruise ship lines may choose some other method to 

allocate the increase in operating costs among passengers. 

Equation 7-38 

$46.20 

100  7.7%

Stateroom _ price($599) 

Table 7-9 Representative Alaskan Cruise Liner Stateroom Price Increases 

Stateroom Type Original Average Price Per 

Night ($) 

Percentage 

Increase 

Interior $100 6.6% 

Ocean View $200 3.3% 

Balcony $300 2.2% 

Suite $400 1.7% 

As can be seen from all the above price increase estimations, the additional costs of the 

distillate fuel and the urea required to operate in the proposed ECA will not be a significant 

monetary increase to the overall operation of the vessel, regardless of vessel type. 

7-40 
Chapter 7: Economic Impact Analysis 

Reference 

1

 U.S. EPA. (2000). EPA Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. A 

copy of this document can be found at 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpates/guidelines.html 

2

 Stopford, M. (2009). Maritime Economics, 3rd Edition. Routledge, Page 519. See also, World 

Trade Organization, World Trade Report 2004: Exploring the linkage between the domestic 

policy environment and international trade. Page 117, Taboel 11B.4 Sea freight rates on the 

three major liner trade routes, 2000-2004. 

3

 Census Bureau’s Foreign Trade Division, U.S. Waterborne Foreign Trade by U.S. Custom 

Districts, as reported by the Maritime Administration at 

http://www.marad.dot.gov/library_landing_page/data_and_statistics/Data_and_Statistics.htm, 

accessed April 9, 2009. 

4

 U.S. Census Bureau, Industry Statistics Sampler, NAICS 48311, Deep sea, coastal, and Great 

Lakes transportation, at http://www.census.gov/econ/census02/data/industry/E48311.HTM, 

assessed on April 9, 2009. 

5

 UNCTAD, Review of Marine Transportation (2003), as cited in World Trade Organization, 

World Trade Report 2004: Exploring the linkage between the domestic policy environment and 

international trade. http://www.wto.org/english/res_e/booksp_e/anrep_e/world_trade_report04_e.pdf assessed 

on April 9, 2009. 

6

 U.S. EPA. “OAQPS Economic Analysis Resource Document.” Research Triangle Park, NC: 

EPA 1999. A copy of this document can be found at 

http://www.epa.gov/ttn/ecas/econdata/6807-305.pdf; U.S. EPA “EPA Guidelines for Preparing 

Economic Analyses.” EPA 240-R-00-003. September 2000. A copy of this document can be 

found at http://yosemite.epa.gov/ee/epa/eed.nsf/webpates/guidelines.html 

7

 Bingham, T.H., and T.J. Fox. “Model Complexity and Scope for Policy Analysis.” Public 

Administration Quarterly, 23(3), 1999. 

8

 Harrould-Koleib, Ellycia. Shipping Impacts on Climate: A Source with Solutions. Oceana, 

July 2008. A copy of this report can be found at 

http://www.oceana.org/fileadmin/oceana/uploads/Climate_Change/Oceana_Shipping_Report.pdf 

9

 Stopford, Martin. Maritime Economics, 3rd Edition. Routledge, 2009. p. 163. 

10 Berck, P., and S. Hoffman. “Assessing the Employment Impacts.” Environmental and 

Resource Economics 22:133-156. 2002. 

11 U.S. EPA “EPA Guidelines for Preparing Economic Analyses.” EPA 240-R-00-003. 

September 2000, p. 113. A copy of this document can be found at 

http://yosemite.epa.gov/ee/epa/eed.nsf/webpates/guidelines.html 

12 Stopford, Martin. Maritime Economics, 3rd Edition. Routledge, 2009. See Chapter 4. 

13 Frank, Robert H. Microeconomics and Behavior, 1991, McGraw-Hill, Inc., p 333. 

14 Stopford, Martin, Maritime Economics, 3rd Edition. Routledge, 2009, page 32. 

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Regulatory Impact Analysis 

15 World Trade Organization, Council for Trade in Services. Maritime Transport Services: 

Background Note by the Secretariat. S/C/W/62, 16 November 1998, p. 2. This document can be 

found at www.wto.org/English/Tratop_E/serv_e/w62.doc 

16 Stopford, Martin, Maritime Economics, 3rd Edition. Routledge, 2009, p. 41. 

17 Sagers, Chris, “The Demise of Regulation in Ocean Shipping: A Study in the Evolution of 

Competition Policy and the Predictive Power of Microeconomics,” Vanderbilt Journal of 

Transnational Law 39 (May 2006), pp. 792-3. 

18 World Trade Organization, Council for Trade in Services. Maritime Transport Services: 

Background Note by the Secretariat. S/CW/62, 16 November 1998. This document can be 

found at: http://www.wto.org/English/Tratop_E/serv_e/w62.doc 

19 Stopford, Martin, Maritime Economics, 3rd Edition. Routledge, 2009. 

20 Shipbuilders Association of Japan. (2008). Global Shipbuilding Supply and Demand Outlook. 

Presented at Shipbuilding Workshop with non-OECD economies and industry, Paris, France, 

December 4-5, 2008; http://www.oecd.org/dataoecd/61/41/41812839.pdf , accessed March 5, 

2009. 

21 US EPA (2003) Final Regulatory Support Document: Control of Emissions from New 

Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder. EPA420-R-03-004, 

January 2003. 

22 Nicholson, Walter (1989). Microeconomic Theory: Basic Principles and Extension. Dryden 

Press. p. 563, 

23 Tirole, Jean. The Theory of Industrial Organization (1989). MIT Press. See pages 223-224. 

24 Varian, Hal. R. (1993). Intermediate Economics: A Modern Approach, 3rd Edition. Norton. 

P.462. 

25 http://en.wikipedia.org/wiki/Bertrand_competition 

26 Nicholson, W., Microeconomic Theory: Basic Principles and Extensions, 1998, The Dryden 

Press, Harcourt Brace College Publishers. 

27 Lloyd’s of London Sea-Web Database, Ocean going vessel statistical queries, Retrieved 

Autumn 2008, from http://www.sea-web.com/seaweb_welcome.aspx 

28 Port of Los Angeles Website, Facts and Figures, Retrieved Spring 2009, from: 

http://www.portoflosangeles.org/newsroom/press_kit/facts.asp 

29 EnSys Navigistics. (2007). Analysis of Impacts on Global Refining & CO2 Emissions of 

Potential MARPOL Regulations for International Marine Bunker Fuels. Final Report for the 

U.S. Environmental Protection Agency, 26 September 2007. 

30 Wellmer, F.W., Dalheimer, M., Wagner, M. 2008. Economic Evaluations in Exploration. New 

York, NY: Springer-Verlag Berlin Heidelberg 

31 International Maritime Organization (IMO), (2005), Interim Guidelines for Voluntary Ship 

CO2 Emission Index for use in Trials, MEPC/Circ. 471, 29, Retrieved Spring 2009, from: 

http://www.imo.org/includes/blastDataOnly.asp/data_id%3D12740/471.pdf 

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Chapter 7: Economic Impact Analysis 

32 The Chamber of Shipping of British Columbia, Canada, (2007, January 25), 2005-2006 BC 

Ocean-Going Vessel Emissions Inventory, Retrieved Spring 2009, from: 

http://www.cosbc.ca/index.php?option=com_docman&task=doc_view&gid=3&tmpl=component 

&format=raw&Itemid=53 

33 Lloyd’s of London Sea-Web Database, Ocean going vessel statistical queries, Retrieved 

Autumn 2008, from http://www.sea-web.com/seaweb_welcome.aspx 

7-43 
Chapter 8: Small Entity Impact Analysis 

CHAPTER 8: SMALL ENTITY IMPACT ANALYSIS 

8.1 Standards Under Consideration ..................................................................................... 8-2

8.2 Marine Diesel Engine Manufacturers ............................................................................ 8-2

8.3 Vessel Manufacturers ...................................................................................................... 8-3

8.4 Fuel Manufacturers and Distributors ............................................................................ 8-3

8-1 
Regulatory Impact Analysis 

CHAPTER 8:Small Entity Impact Analysis 

This chapter contains the results of our small entity screening analysis for the proposed 

rule regarding emissions from Category 3 marine diesel engines (i.e., those marine diesel engines 

with per cylinder displacement at or above 30 liters). This analysis is required under the 

provisions of the Regulatory Flexibility Act as amended by the Small Business Regulatory 

Enforcement Fairness Act (RFA/SBREFA). As described below, our analysis shows that there 

are no small entities that would be impacted by the proposed regulations. Therefore, consistent 

with EPA’s RFA/SBREFA guidelines, we plan to certify that this rule will not, if promulgated, 

have a significant economic impact on a substantial number of small entities. 

This chapter provides some background information on the proposed rule and describes 

the outcome of our screening analysis. Section 8.1 describes the engine and fuel standards we 

are considering. Sections 8.2 and 8.3 provide small business information for the diesel marine 

engine program. Section 8.4 provides small business information for the diesel fuel program. 

8.1 Standards Under Consideration 

In October 2008, negotiations were successfully concluded for amendments to Annex VI 

to the International Convention for the Prevention of Pollution from Ships (MARPOL Annex 

VI). These amendments, which are based on the proposal submitted to IMO by the United States 

Government in February 2007, set additional tiers of standards for marine diesel engine oxides of 

nitrogen (NOX) emissions and the sulfur content of fuel. 

Our Category 3 proposal would add the Annex VI NOX limits to our Clean Air Act 

marine diesel engine requirements for Category 3 engines, and create an allowance for the 

production of diesel fuel specifically for these engines. Specifically, we are proposing to adopt 

two additional tiers of NOX limits for Category 3 engines. The Tier 2 standards would result in a 

20 percent reduction in NOX in 2011 as compared to the existing Tier 1 standards, based largely 

on in-cylinder control technologies. The Tier 3 standards, taking effect in 2016, would rely upon 

high-efficiency exhaust aftertreatment technology such as selective catalytic reduction (SCR) 

and would result in an 80 percent reduction in NOX. We are also proposing to modify our diesel 

fuel program to allow the manufacture and sale of marine diesel fuel with a sulfur content up to 

1,000 parts per millions (ppm) for use in engines with a displacement of more than 30 liters per 

cylinder. 

As explained below, this proposal is not expected to have a significant impact on a 

substantial number of small businesses. 

8.2 Marine Diesel Engine Manufacturers 

The responsibility for meeting the new engine standards would fall on the engine 

manufacturers. Such manufacturers are those primarily engaged in manufacture of large diesel 

marine engines as defined by North American Industry Classification System (NAICS) code 

333618. There are no U.S. companies that manufacture Category 3 marine diesel engines in the 

U.S. 

8-2 
Chapter 8: Small Entity Impact Analysis 

While there is one U.S. company that is a parent company to a foreign Category 3 engine 

manufacturing company, this company is not a small business (using the Small Business 

Administration definition of companies with less than or equal to 1,000 employees), and the 

engine manufacturing does not occur in the U.S. We are unaware of any foreign manufacturers 

of such engines with a U.S–based facility that qualify as a small business. 

For these reasons, we conclude that the proposed engine regulations would not place a 

substantial burden on any small U.S. engine manufacturers. 

8.3 Vessel Manufacturers 

While the primary responsibility for meeting the new engine standards lies with the 

engine manufacturers, the vessel manufacturers are potentially affected as well in the case of the 

Tier 3 standards. Such manufacturers are those primarily engaged in the shipbuilding and 

repairing as defined by NAICS code 336611. Vessel manufacturers will have to accommodate 

the addition of exhaust aftertreatment hardware in their design and manufacturing processes. 

We have identified 6 shipyards in the U.S. capable of producing Category 3 vessels. Of 

those, most build primarily military vessels. One of these shipyards is owned by a foreign 

company, and none of these shipyards is a small business that would meet the Small Business 

Administration definition of 1,000 or fewer employees. 

For these reasons, we conclude that the proposed regulations would not place a 

substantial burden on any small U.S. vessel manufacturers. 

8.4 Fuel Manufacturers and Distributors 

We are proposing a revision to our diesel fuel program to allow the manufacture and sale 

of marine diesel fuel with a sulfur content up to 1,000 ppm for use in Category 3 engines. This 

would allow our regulations to be consistent with the new sulfur limits that will become 

applicable in 2015 under IMO regulations in Emission Control Areas. Our current diesel fuel 

program sets a sulfur limit of 15 ppm that is fully phased in by December 1, 2014 for the 

production of diesel fuel designated for use in Category 1 and Category 2 marine applications 

(DMX and DMA). Without this proposed change to our diesel fuel regulations, while fuel with a 

sulfur content of up to 1,000 ppm could be used on Category 3 applications, it would be unlawful 

to produce, distribute or sell it within the United States. 

This revision to our diesel fuel program will not require any person to manufacture, 

distribute or sell 1,000 ppm sulfur fuel. It simply allows for its production and sale, which is 

precluded under our current diesel fuel regulations. 

This allowance for 1,000 ppm sulfur fuel would be a benefit to those fuel producers, 

distributors or marketers who choose to produce or sell it, as it allows for higher sulfur content 

than diesel fuels allowed under current EPA regulations. Since we are not considering 

mandating production of this fuel, fuel manufacturers, distributors and marketers can opt out of 

producing, distributing or selling it. Thus, allowing this fuel would not require a mandatory 

change in any company’s business situation. Those companies that would find it beneficial to 

8-3 
Regulatory Impact Analysis 

produce, distribute or sell this fuel would do so. Conversely, those companies that would not 

find it beneficial would simply continue to operate the way they otherwise would in the absence 

of this new allowance. 

For the reasons just outlined, the allowance we are considering for 1,000 ppm sulfur 

marine diesel fuel would not place a substantial burden on any small U.S. refiners, pipeline 

operators, fuel terminal operators, or fuel marketers. 

8-4