1 

Emissions Processing and Sensitivity Air Quality Modeling of Category 3 

Commercial Marine Vessel Emissions 

Rich Mason and Pat Dolwick 

U. S. Environmental Protection Agency, 

Office of Air Quality Planning and Standards, Air Quality Analysis Division, 

Research Triangle Park, NC 27711 

Mail Drop C339-02 

mason.rich@epa.gov

Penny Carey 

U. S. Environmental Protection Agency, 

Office of Transportation and Air Quality Planning, 

Ann Arbor, MI 48105 

Ellen Kinnee and Melanie Wilson 

Computer Sciences Corporation, Research Triangle Park, NC, 27709 

The Environmental Protection Agency (EPA)’s Office of Air Quality Planning and Standards 

(OAQPS) and Office of Transportation and Air Quality (OTAQ) have developed a methodology 

to convert Category 3 (C3) commercial marine vessel emissions from a fine-grid ASCII raster 

dataset to a format that the Sparse Matrix Operator Kernel Emissions (SMOKE) processor can 

accept. This paper explains this process using a preliminary C3 vessel dataset consisting of a 

modified Waterway Network Ship Traffic, Energy, and Environmental Model (STEEM) 

inventory. The emission domain for this analysis encompasses much of the northern hemisphere 

between the central Pacific and western Atlantic and consists of commercial marine port and 

underway (inter-port) emissions from C3 vessels. Our analysis uses the point source One Record 

per Line (ORL) format with average stack parameters for all grid cells. The biggest challenge in 

developing this conversion methodology was converting the ASCII raster dataset into SMOKE 

ORL format, with accurate location coordinates and U.S. state-county FIPs where appropriate. 

This paper outlines several activities: 1) the pre-processing steps required to convert the 

modified STEEM inventory ASCII raster dataset to SMOKE ORL format; we will also discuss 

some of the methods we created using Geographic Information System (GIS) and other software 

that increased processing efficiency, 2) the SMOKE settings required to process the modified 

STEEM inventory, and the replacement of the existing C3 data in the (Version 3 of the) 2002 

National Emissions Inventory (NEI) modeling platform, 3) a comparison of different C3 

inventory methods by showing SMOKE gridded modified STEEM inventory in comparison to 

the current version of the 2002 NEI, and 4) a “zero-out” sensitivity model run that assessed the 

relative contribution of the coarsely-gridded preliminary modified STEEM inventory to overall 

air quality estimates from the Community Multiscale Air Quality (CMAQ) model. 
2 

INTRODUCTION 

EPA prepares a national database of air emissions information with input from numerous state 

and local air agencies, from tribes, and from industry. This NEI database contains information 

on stationary and mobile sources that emit criteria air pollutants and their precursors, as well as 

hazardous air pollutants (HAPs). The database includes estimates of annual emissions, by 

source, of air pollutants in each area of the country. The NEI includes emission estimates for all 

50 States, the District of Columbia, Puerto Rico, and the Virgin Islands. Emission estimates are 

created for individual point or major sources (facilities), as well as county level estimates for 

area, mobile and other sources. Data from the NEI are used for air dispersion modeling, regional 

strategy and regulation development, air toxics risk assessment, and tracking emissions trends 

over time. 

Category 3 (C3)1

 commercial marine vessel emissions in Version 3 of the 2002 NEI are derived 

from a spatial allocation of national estimates to individual counties (U.S. EPA, 2005) where 

port emissions were assigned to the 150 largest ports based on cargo handling activity data. This 

“top-down” method slightly over-estimates port-related emissions at the 150 ports because the 

national emissions are not assigned to the smaller ports. The NEI computes C3 emissions, which 

includes port and underway emissions from main and auxiliary engines, assigning 75% to ports 

and 25% as underway. However, for the NEI, several states provided their own C3 emissions, 

and these (unmodified) estimates replaced any EPA estimates. County-specific waterway 

activity data was used to allocate underway C3 national emissions to individual counties. GIS 

was used to determine the amount of waterway traffic in each shipping lane that spanned 

multiple counties. Emissions processing then allocated the port and underway C3 county 

estimates based on port and navigable water spatial surrogates. In the NEI, coastal county 

boundaries are extended into Federal waters in the waterway network beyond actual county 

boundaries. However, these emissions are then assigned by county surrogates that do not extend 

very far offshore into waters where shipping lanes actually exist. 

In contrast, the STEEM approach to modeling C3 emissions uses a “bottom-up” approach that 

uses detailed information about ship routes and destinations from actual ship positioning data, 

and detailed inventories from 117 ports. This improved spatial resolution and inventory 

technique for obtaining C3 emissions motivated the EPA to develop a method to incorporate the 

STEEM-based emissions into our air quality modeling platform. 

 

1

 For the purposes of this study, C3 emissions include the emissions from commercial marine vessels (CMV) with 

Category 3 propulsion engines (at or above 30 liters/cylinder), as well as the exhaust emissions from the smaller 

auxiliary engines onboard these vessels. The NEI database assigns these emissions as “residual fuel CMV” because 

the propulsion engines primarily use residual fuel; however, the auxiliary engines on these vessels can use distillate 

fuel, and future regulation on these vessels will also likely have more of these vessels using distillate fuel. These 

emissions are also often referred to as “C3 ocean-going vessels (OGV); however, the auxiliary engines are often C2 

and many of these vessels that operate in the Great Lakes remain there. In addition, there exists C2 tugs that are 

OGVs. Therefore, we simply refer to this inventory as “Category 3 commercial marine vessels”, or simply “C3” 

throughout this paper. 
3 

CREATING SMOKE-READY C3 STEEM-BASED EMISSIONS 

The development of the 2002 preliminary C3 STEEM-based emissions for port, near-port, and 

inter-port (underway) emissions is discussed in a U.S. EPA draft technical support document 

(U.S. EPA, 2007). These gridded modified-STEEM-based C3 inventories include emissions 

from both propulsion and auxiliary engines and are estimated for the following pollutants: NOx, 

PM10, PM2.5, VOC, CO, and SO2. The modified STEEM underway routing analysis 

(underway/inter-port) created emissions at a resolution of approximately 4km x 4km. This 

inventory was converted to ASCII raster dataset format, with one dataset per pollutant, for a grid 

that covered a very large area, from a southwest corner at approximately 171E, 0N spanning 

northeast through 54W, 72N, or, virtually the entire North Pacific and North Atlantic Ocean as 

far east as Greenland. Specifically, the emissions were provided with the following metadata 

(Corbett, et al., 2006): 

Projection: Equidistant_Cylindrical 

False_Easting: 0.0 – default ESRI parameter 

False_Northing: 0.0 – default ESRI parameter 

Central_Meridian: 180.0 degrees – UD defined 

Standard_Parallel_1: 0.0 – default ESRI parameter 

Linear Unit: User_Defined_Unit (1000 m) – UD defined 

Origin X,Y: -1000, 0 

Maximum X,Y: 14,000, 8,000 

Number of columns, rows: 3,750, 2,000 

Cell Size: 4km x 4km 

Emission Units: kg/year per cell (kg/yr-16km2) 

Each ASCII raster dataset contains 7,500,000 discrete cells with emission values, though many 

of these are zero because they are over land or are in grid cells without STEEM shipping 

segments over the oceans. The SMOKE processor requires One Record per Line (ORL) 

formatted emissions data. In the NEI, C3 emissions are provided at the county level and 

spatially allocated to model grid cells (typically 12-km or 36-km) using underway and port 

spatial surrogates. However, this was undesirable for the modified STEEM inventory because 

this inventory is so spatially resolved. To preserve the fine scale of the STEEM inventory, we 

needed to convert the ASCII raster data to latitude/longitude coordinates; we also required U.S. 

state-county FIPs for emission summaries, and possible emission growth by U.S. region for 

future years. The U.S. state-county FIPS also allows us to create county-level emission 

inventories in a format similar to the familiar NEI. 

Obtaining latitude/longitude Coordinates for all Grid Cells

Coordinates and U.S. FIPS assignments were accomplished using the ArcInfo GIS software. 

First, the modified STEEM C3 emissions ASCII data file was imported into ArcInfo as a “grid 

coverage”. Two built-in Grid coverages containing column and row values were then extracted 

for the entire domain and row values flipped to put the first row at the bottom of the grid. Next, 

the column and row grids were merged to create a single coverage containing unique 

column/row values for every grid cell in the domain starting with 1,1 in the lower left corner. 
4 

The ArcInfo SAMPLE command extracted latitude, longitude and FIPS code for each C3 

emissions grid cell into an ASCII file which was then read into Microsoft Access. This file 

assigns U.S. FIPS to the modified STEEM emissions for inland waterways and lakes, ports, and 

through the Exclusive Economic Zone (EEZ) up to 200 nautical miles offshore or until 

international water boundaries. The format of the FIPs-coordinates cross-reference file is 

provided here: 

OBJECTID Internal Shape file ID 

Shape Internal Shape file description 

GRID_CODE Internal ArcInfo® Grid value 

POINT_X Longitude of 4-km grid cell center in decimal degrees 

POINT_Y Latitude of 4-km grid cell center in decimal degrees 

FIPSSTCO U.S. State/County FIPS code (character) 

STFID U.S. State/County FIPS code (numeric) 

COLROW 4-km grid cell column/row 

CR 4-km grid cell column/row 

COL 4-km grid cell column 

ROW 4-km grid cell row 

We could have assigned state-county FIPs for Canada and Mexico; however, time constraints 

prevented us from merging these FIPs into the large cross-reference file. The assignment of 

(country)-state-county FIPs is not required for SMOKE modeling because we chose to process 

the modified STEEM inventory in SMOKE Point ORL format. SMOKE Point ORL format uses 

the latitude/longitude coordinates to process the modified STEEM inventory into SMOKE grid 

cells; for this paper, we used 36-km grid cell resolution. 

Similar to the NEI, for coastal counties, the county boundaries were extended into Federal waters 

to include those portions of the waterway network which are offshore. Because the coastal 

county boundaries were extended into Federal waters, this approach overestimates true county 

level emissions for coastal counties. This limitation is more noticeable for the STEEM inventory 

because it captures far more underway emissions than the NEI. 

Creating SMOKE-ready Modified STEEM C3 Emissions

Using SMOKE Point ORL format also allows for plume rise calculations; in contrast, NEI C3 

emissions, modeled as SMOKE area sources, are placed at ground level. To take advantage of 

the ability to calculate plume rise, we needed to agree on an overall, average set of “stack” 

parameters for C3 emissions. One of the limitations of the modified STEEM inventory is that 

while it provides highly resolved emissions spatially, it does not specify the type of C3 emission, 

e.g., port or underway, nor does the inventory indicate emissions mode. Stack parameters from 

auxiliary modes like hotelling and maneuvering are likely quite different from the stack 

parameters from main engines. In addition, information on the different ship sizes and loads due 

to cargo weight and relative speed also must be included; by relative speed, we assume that 

emissions are different for ships traveling at the same speed but in opposite directions against the 

current and/or wind. 
5 

Qualitative observations indicated that stack heights ranged from 30 to 100 feet, and several 

studies also provided ranges of stack diameters, exit velocity and exit temperatures. A 

compromise set of stack parameters summarized in a University of Delaware memo (Corbett, 

2007) assigned the following stack parameters for all modified STEEM emissions: 

Stack Height: 65.62 (feet), 20 (meters) 

Stack Diameter: 2.625 (feet), 0.8 (meters) 

Stack Velocity: 82.05 (feet/sec), 25 (meters/sec)

Stack Gas Exit Temperature 539.6 (oF), 282 (oC) 

Most of these stack parameters are mid-points between best available upper and lower bound 

stack parameter estimates (Corbett, 2007). These average stack parameters will create a larger 

than expected effective plume height for most near-port emissions, particularly with emissions 

from auxiliary engines and low speed/loads from main engines. Effective stack plume height is a 

function of the stack parameters and the meteorology, and the importance of each stack 

parameter depends on whether the plume rise algorithm is more buoyancy (temperature), or 

mechanically (exit velocity) driven. One concern was that the movement of the vessel is not 

included in the calculation of the effective plume height. For example, as wind speed increases, 

estimates for the effective plume height asymptotes to a specific effective plume height. It is 

therefore possible that modified STEEM emissions are being elevated in the SMOKE model to a 

higher effective plume height than expected when the meteorological winds are calm and vessel 

speed is significant. The opposite is also possible, where a vessel and the wind are moving at the 

same velocity (vector); in this case, the calculated effective plume rise could be lower than 

reality. However, this would require the rare, nearly parallel alignment of vessel and wind 

velocities and could be offset by vessels moving in the opposite direction in the same shipping 

lane segment. Overall, we had no method for adjusting the meteorological wind with the vessel 

velocities; this limitation was determined to be out of our control for our modeling needs. 

SMOKE reports indicated that a majority of the modified STEEM emissions –for SO2 and all 

other pollutants- elevated from the 20 foot stacks into the CMAQ model (Byun and Ching, 1999) 

layers 2 and 3. In contrast with the modeling of the NEI C3 emissions, where 100% of the 

emissions are in the lowest layer (layer 1), as seen in Figure 1, 5% or less of the modified 

STEEM emissions are in CMAQ layer 1. CMAQ layer 1 extends from the surface to 

approximately 125 feet; layer 2 extends up to approximately 250 feet, and layer 3 tops out at 

around 500 feet. Figure 1 also shows that plume rise is greater in the summer as more emissions 

mix up from layer 2 into layer 3 with the increased meteorological instability in the summer 

months. Locally, compared to the NEI, the elevated modified STEEM emissions should reduce 

near-source model estimates, which may be more important for PM than ozone; elevated 

emissions will also allow for greater transport potential. The vertical distribution of the other 

pollutants is almost identical to SO2. 

Temporal allocation of modified STEEM-based C3 emissions is identical to the NEI C3 

emissions by day-of-week and hour; in both cases, emissions rates are the same for every day 

and hour. NEI C3 emissions also applied uniform (no change) monthly temporal profiles. The 

modified STEEM emissions applied monthly temporal weightings based on the average monthly 

variation in emissions in the North American STEEM inventory; these variations were similar to 
6 

those obtained from global shipping activities (Corbett, et al., 2006). It is important to note that 

these monthly temporal weightings do not shift the locations of the shipping lane segments, but 

rather, are simply a uniform domain-wide adjustment. It was decided that a more regional 

approach would produce too many potential discontinuities where regions intersect; therefore the 

domain-wide monthly temporal weighting seen in Figure 2 were used. Budget and time 

constraints prevented the creation of monthly inventories from the start. One explanation for the 

late summer peak is the setup for production for peak holiday season consumerism beginning in 

the fall. 

Figure 1. SO2 Modified STEEM C3 Emission Allocation by Vertical Layer* and Month 

0%

10%

20%

30%

40%

50%

60%

70%

80%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Layer 6

* The top of layers 1, 2, and 3 are approximately 125 ft, 253 ft, and 505 ft, respectively. The top of layers 4, 5, and 

6 are approximately 1,017 ft, 1,539 ft, and 2,336 ft, respectively. 

Chemical speciation for the modified STEEM C3 emissions is the same as used for NEI C3 

emissions for all pollutants. Primary PM2.5 is estimated as 92% of the primary PM10. OTAQ 

provided emission factors to obtain certain hazardous air pollutants (HAPs) from VOC and PM. 
7 

Figure 2. Monthly Distribution of Modified STEEM C3 Emissions for all Pollutants 

7.0%

7.5%

8.0%

8.5%

9.0%

9.5%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Percent of Annual Emissions

We also had to assign an SCC that would cover both port and underway emissions: 2280003000 

("Mobile Sources; Marine Vessels, Commercial; Residual; Total, All Vessel Types"). Speciation 

and temporal allocations are identical for port (SCC=2280003100) and underway (2280003200) 

emissions; these profiles were simply copied into the more broad SCC we chose for processing 

the modified STEEM C3 inventory. We had to fix one erroneous FIP code carried over from the 

shipping lanes polygon shape file: FIPS=44055; this FIPS was changed to 44005 (Newport 

county, Rhode Island). All non-U.S. emissions were assigned a dummy FIPs=98001. The final 

steps involved converting emissions from kilograms to short tons and reducing the size of the 

inventory to include only non-zero emissions in and immediately around the U.S. 36-km domain. 

This greatly-reduced the size of the SMOKE Point source-formatted ORL inventory though it is 

still quite large: nearly 600MB for the criteria air pollutants (VOC, CO, NOx, SO2, PM10, and 

PM2.5) alone and another 2.75GB for the HAPs. A map showing the year 2002 actual (pregridded) preliminary modified STEEM C3 NOx emissions for the entire domain is provided in 

Figure 3. The 36-km CMAQ domain is superimposed. All subsequent maps showing C3 results 

are aggregated into the 36-km gridded CMAQ domain. 
8 

Figure 3. 2002 Preliminary Modified STEEM C3 NOx Emissions [tons/year] with 36-km 

Grid Overlay 

RESULTS 

We replaced the NEI C3 vessel inventory from our 2002 and 2020 base case emissions modeling 

platforms with the modified STEEM C3 inventory. Table 1 provides the contributions of the 

NEI-format C3 and modified STEEM-format C3 emissions, to overall anthropogenic emissions 

for 2002 and 2020. These numbers are provided for the lower-48 states (emissions through the 

EEZ, not the entire STEEM domain) and show significant growth in the C3 emissions between 

2002 and 2020 for both the NEI and modified STEEM-based C3 inventories. Also note that the 

relative contribution of the NEI and modified STEEM-based C3 emissions increases 

significantly as control programs in other sectors reduce emissions in 2020 from 2002 levels. . 
9 

Table 1. Contiguous U.S. Contribution of NEI and Preliminary Modified STEEM C3 

Emissions to 2002 and 2020 Anthropogenic Totals 

Inventory NOx SO2 PM2.5 

2002 C3 Preliminary Modified STEEM 596,658 371,550 47,760

2002 C3 NEI 244,924 150,497 12,617

2002 US Total (w/ Preliminary Modified STEEM C3) 21,190,253 14,865,120 12,847,241

2002 Preliminary Modified STEEM C3 % of Total 2.8% 2.5% 0.4%

 

2020 C3 Preliminary Modified STEEM 1,101,551 759,753 89,583

2020 C3 NEI 392,981 242,511 22,102

2020 US Total (w/ Preliminary Modified STEEM C3) 11,621,447 8,685,103 12,748,098

2020 Preliminary Modified STEEM C3 % of Total 9.5% 8.7% 0.7%

In the NEI, emissions are allocated to counties in a “top-down” methodology, from national 

totals of port and underway (or “inter-port”) activity data (U.S. EPA, 2008). 2002 NEI C3 

emissions, mapped to our 36-km air quality modeling domain (U.S. EPA, 2006) are shown in 

Figure 4. Spatial surrogates allocate the NEI C3 emissions to grid cells that intersect actual 

county boundaries, which extend a very limited distance offshore. This also makes spatial 

allocation of NEI underway shipping activity problematic, as emissions are essentially confined 

to the extent of the county boundaries, not expected shipping lanes. In many cases, these county 

boundaries are defined as the low-tide water. In addition, NEI C3 emissions are allocated to 

states and counties in a similar routine as smaller Class 2 (C2) vessels. As seen in Figure 4, this 

assumption allocated NEI C3 emissions to waterways such as the Missouri and Ohio Rivers 

where C3 vessels cannot access. In contrast to the NEI C3 inventory, the 2002 modified STEEM 

C3 emissions in Figure 5 allows for better allocation of C3 emissions for air quality modeling; 

modified STEEM C3 emissions are seen at ports and the shipping lanes between ports. With 

U.S. county boundaries extending outwards of up to 200 nautical miles, 

characterization/summarization of the U.S. portion of the modified STEEM C3 emissions seen in 

Table 1 is possible. The shaded areas in Figure 5 are the U.S. shipping lane polygon shape file, 

and indicate where modified STEEM C3 emissions are assigned to U.S. state-county FIPs. 
10 

Figure 4. 2002 36-km Gridded NEI (V3) C3 NOx Emissions [tons/year] 
11 

Figure 5. 2002 36-km Gridded Preliminary Modified STEEM C3 NOx Emissions 

[tons/year] 

The spatial pattern of the 2020 emissions is very similar to 2002 for both the preliminary 

modified STEEM and NEI-based C3 emissions because 2020 emissions were constructed from 

regional (preliminary gridded) or national (NEI) –based activity data. Increases in the modified 

STEEM C3 emissions in year 2020, as seen in summary in Table 1, show up in Figure 6; the 

largest increases in 2020 are in the same ports and shipping lanes with highest emissions in 2002. 
12 

Figure 6. Increase in 36-km Preliminary Modified STEEM C3 NOx Emissions [tons/year] 

from 2002 to 2020 

As part of our preliminary assessment, two CMAQ sensitivity modeling runs were completed to 

show the impacts of the modified STEEM C3 emissions on air quality over the continental U.S. 

One run contained the revised C3 sector emissions and the other simulation removed all 

emissions from this sector. The modified STEEM inventory was run through the CMAQ model 

to examine the impacts on estimations of the C3 contribution to 2020 PM2.5 air quality. The 

estimated results are shown in Figure 7. The complete zero-out of the modified STEEM C3 

emissions in 2020 resulted in estimated PM2.5 reductions of more than 0.5 ug/m3 over parts of 

California, Florida, the Gulf Coast, Southeast Coast, coastal New Jersey and New York City, as 

well as Seattle. Smaller but still significant estimated reductions of 0.25 to 0.50 ug/m3 may also 

be seen further downwind in the northwest and much of the deep south and coastal plain from 

South Carolina through the Mid Atlantic and New England. Ozone air quality in coastal regions 

is also strongly affected by the emissions from this sector. According to the zero out modeling, 

as much as 2-4 ppb of the 2020 projected 8-hour ozone design values in parts of the Northeast 

Corridor, southeast Texas, and southern California are due to emissions from the C3 sector. It 

should be noted that these air quality impacts are dependent on the year 2020 STEEM sensitivity 

modeling exercise and are based on eliminating all C3 emissions; they are not intended to 

illustrate any particular control case for these engines. 
13 

Figure 7. Change in Annual Average PM2.5 [ug/m3] when all 2020 Preliminary Modified 

STEEM C3 Emissions are Removed 

DISCLAIMER 

This paper has not been subject to EPA’s required peer and policy review, and therefore does not 

necessarily reflect the views of the Agency. No official endorsement should be inferred. 

REFERENCES 

Byun, D.W. and J. Ching, 1999, Science algorithms of the EPA Model-3 community Multiscale 

air quality (CMAQ) modeling system, Research Triangle Park (NC): EPA/600/R-99/030, 

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14 

Associate Professor, College of Marine and Earth Studies, College of Engineering, University of 

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28-08.pdf

KEY WORDS 

SMOKE 

Emissions Modeling 

C3 

STEEM