Modeling Sulfur Oxides (SOX) Emissions
Transport from Ships at Sea
United States
Environmental Protection
Agency
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Modeling Sulfur Oxides (SOX) Emissions
Transport from Ships at Sea
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
Prepared for EPA by
Atmospheric & Environmental Research, Inc.
EPA Contract No. GS-10F-0615P
v>EPA
NOTICE
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments which may form the basis for a final EPA decision,
position, or regulatory action.
United States EPA420-R-07-009
Environmental Protection , , „„.,
Agency July 2007
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TABLE OF CONTENTS
1. Introduction 1-1
2. Modeling Approach 2-1
2.1 Air Quality Model (CALPUFF) 2-1
2.2 Meteorological Model (CALMET) 2-3
2.3 Approach 2-3
2.3.1 Receptors 2-4
2.3.2 Sources 2-4
2.3.3 Modeling domains 2-5
3. Model Inputs 3-1
3.1 Meteorology 3-1
3.1.1 Measurements 3-2
3.1.2MM5 outputs 3-7
3.1.3 CALMET winds 3-7
3.1.4 CALMET mixing heights 3-17
3.2 Emissions 3-17
3.2.1 Emission rates 3-26
3.2.2 Stack parameters 3-27
4. Results 4-1
4.1 Results for the Southern Pacific U.S. Coastline 4-2
4.2 Results for the Northern Pacific U.S. Coastline 4-16
4.3 Results for the Gulf of Mexico Coastline 4-32
4.4 Results for the Atlantic Ocean Coastline 4-43
5. Summary and Conclusions 5-1
6. References 6-1
Appendices
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea i
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LIST OF TABLES
Table 3-1 Stack characteristics 3-28
Table 5-1 Percentage of 862 concentrations below the design value as a function of
the distance from the coastline 5-2
Table 5-2 Percentage of sulfate concentrations below the design value as a function
of the distance from the coastline 5-2
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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LIST OF FIGURES
Figure 2-1 Modeling domain for the Southern Pacific Ocean U.S. coastline 2-6
Figure 2-2 Modeling domain for the Northern Pacific Ocean U.S. coastline 2-7
Figure 2-3 Modeling domain for the Gulf of Mexico coastline 2-9
Figure 2-4 Modeling domain for the Atlantic Ocean coastline 2-10
Figure 3-1 Land-based surface stations and over-water stations for the Southern
Pacific Ocean U.S. coastline 3-3
Figure 3-2 Land-based surface stations and over-water stations for the Northern
Pacific Ocean U.S. coastline 3-4
Figure 3-3 Land-based surface stations and over-water stations for the Gulf of
Mexico coastline 3-5
Figure 3-4 Land-based surface stations and over-water stations for the Atlantic Ocean
coastline 3-6
Figure 3-5 MM5 modeling domain 3-8
Figure 3-6a Wind roses based on CALMET outputs for the southern Pacific Ocean
during winter and spring 2002 3-9
Figure 3-6b Wind roses based on CALMET outputs for the southern Pacific Ocean
during summer and fall 2002 3-10
Figure 3-7a Wind roses based on CALMET outputs for the northern Pacific Ocean
during winter and spring 2002 3-11
Figure 3-7b Wind roses based on CALMET outputs for the northern Pacific Ocean
during summer and fall 2002 3-12
Figure 3-8a Wind roses based on CALMET outputs for the Gulf of Mexico during
winter and spring 2002 3-13
Figure 3-8b Wind roses based on CALMET outputs for the Gulf of Mexico during
summer and fall 2002 3-14
Figure 3-9a Wind roses based on CALMET outputs for the Atlantic Ocean during
winter and spring 2002 3-15
Figure 3-9b Wind roses based on CALMET outputs for the Atlantic Ocean during
summer and fall 2002 3-16
Figure 3-lOa Mixing heights for the southern Pacific Ocean during winter and spring
2002 3-18
Figure 3-10b Mixing heights for the southern Pacific Ocean during summer and fall
2002 3-19
Figure 3-1 la Mixing heights for the northern Pacific Ocean during winter and spring
2002 3-20
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea Hi
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Figure 3-1 Ib Mixing heights for the northern Pacific Ocean during summer and fall
2002 3-21
Figure 3-12a Mixing heights for the Gulf of Mexico during winter and spring 2002 3-22
Figure 3-12b Mixing heights for the Gulf of Mexico during summer and fall 2002 .. 3-23
Figure 3-13a Mixing heights for the Atlantic Ocean during winter and spring 2002. 3-24
Figure 3-13b Mixing heights for the Atlantic Ocean during summer and fall 2002... 3-25
Figure 4-1 Ratios of annual-average SO2 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-3
Figure 4-2 Cumulative frequency distribution of design ratios of SO2 concentrations
from ships at 125 km from the Southern Pacific U.S. coastline 4-4
Figure 4-3 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-5
Figure 4-4 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Southern Pacific U.S.
coastline 4-6
Figure 4-5 Ratios of annual-average SO2 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-8
Figure 4-6 Cumulative frequency distribution of design ratios of SO2 concentrations
from ships at 250 km from the Southern Pacific U.S. coastline 4-9
Figure 4-7 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-10
Figure 4-8 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Southern Pacific U.S.
coastline 4-11
Figure 4-9 Ratios of annual-average SO2 concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-12
Figure 4-10 Cumulative frequency distribution of design ratios of SO2 concentrations
from ships at 375 km from the Southern Pacific U.S. coastline 4-13
Figure 4-11 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Southern Pacific U.S.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea iv
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coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-14
Figure 4-12 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Southern Pacific U.S.
coastline 4-15
Figure 4-13 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-17
Figure 4-14 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 500 km from the Southern Pacific U.S.
coastline 4-18
Figure 4-15 Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-19
Figure 4-16 Cumulative frequency distribution of design ratios of SC>2 concentrations
from ships at 125 km from the Northern Pacific U.S. coastline 4-20
Figure 4-17 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-22
Figure 4-18 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Northern Pacific U.S.
coastline 4-23
Figure 4-19 Ratios of annual-average SC>2 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-24
Figure 4-20 Cumulative frequency distribution of design ratios of 862 concentrations
from ships at 250 km from the Northern Pacific U.S. coastline 4-25
Figure 4-21 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-26
Figure 4-22 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Northern Pacific U.S.
coastline 4-27
Figure 4-23 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Northern Pacific U.S.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea v
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coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-28
Figure 4-24 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Northern Pacific U.S.
coastline 4-29
Figure 4-25 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel 4-30
Figure 4-26 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 500 km from the Northern Pacific U.S.
coastline 4-31
Figure 4-27 Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-33
Figure 4-28 Cumulative frequency distribution of design ratios of SC>2 concentrations
from ships at 125 km from the Gulf of Mexico coastline 4-34
Figure 4-29 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-35
Figure 4-30 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Gulf of Mexico coastline 4-36
Figure 4-31 Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-37
Figure 4-32 Cumulative frequency distribution of design ratios of SO2 concentrations
from ships at 250 km from the Gulf of Mexico coastline 4-3 8
Figure 4-33 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-39
Figure 4-34 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Gulf of Mexico coastline 4-40
Figure 4-3 5 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-41
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea vi
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Figure 4-36 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Gulf of Mexico coastline 4-42
Figure 4-37 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-44
Figure 4-38 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 500 km from the Gulf of Mexico coastline 4-45
Figure 4-39 Ratios of annual-average SO2 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-46
Figure 4-40 Cumulative frequency distribution of design ratios of 862 concentrations
from ships at 125 km from the Atlantic Ocean coastline 4-47
Figure 4-41 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-48
Figure 4-42 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Atlantic Ocean coastline 4-49
Figure 4-43 Ratios of annual-average SO2 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-50
Figure 4-44 Cumulative frequency distribution of design ratios of SO2 concentrations
from ships at 250 km from the Atlantic Ocean coastline 4-51
Figure 4-45 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-52
Figure 4-46 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Atlantic Ocean coastline 4-53
Figure 4-47 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-55
Figure 4-48 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Atlantic Ocean coastline 4-56
Figure 4-49 Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Atlantic Ocean coastline to
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea vii
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the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel 4-57
Figure 4-50 Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Atlantic Ocean coastline 4-58
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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EXECUTIVE SUMMARY
A screening study was conducted to determine the air quality impacts (annual
average ground-level concentrations of SC>2 and sulfate) on land due to SOX emissions
from ships burning high-sulfur fuel at sea at various distances from the coastline. The
CALPUFF dispersion model was used for this screening study. Meteorological inputs
were prepared with the CALMET model using the outputs of a prognostic meteorological
model, MM5, in combination with surface measurements over water and on land. The
meteorology represents the year 2002 because it was the most recent year for which an
MM5 simulation covering the contiguous United States was available. CALPUFF tends
to overestimate the conversion of SO2 to sulfate in the gas phase and the results presented
here are likely to provide conservative estimates of the impacts of emissions from ships at
sea on inland air quality (because of the simplified treatment of aqueous-phase chemistry
in CALPUFF, this overestimation may not hold for cases where the interactions of the
ship plumes with fog dominate sulfate formation).
Four domains were studied: the southern Pacific coastline, the northern Pacific
coastline, the Gulf of Mexico coastline and the Atlantic coastline. The results were
compared with those calculated for ships burning low-sulfur fuel at the coastline to
determine upper bounds for Sulfur Emission Control Areas (SECAs), i.e., off-shore
distances at which the switch to high-sulfur fuel would not impair air quality. For each
offshore distance investigated, the percentage of receptors for which the air quality
impacts of ships at sea were lower than the impacts of ships at the coastline was
calculated.
Emission rates were estimated to be representative of ocean-going ships along U.S.
coastlines. The sulfur content of the fuel was assumed to be 15,000 ppm within the
SECA (i.e., here at the coastline) and 27,000 ppm outside the SECA (i.e., at the four off-
shore distances considered here, 125, 250, 375 and 500 km). The gas-phase 862 and
particulate-phase sulfate emissions per ship were estimated to be 100,320 g/h and 3.040
g/h, respectively, within the SECA and 180,640 g/h and 5,600 g/h, respectively, outside
the SECA. Based on an analysis of ship traffic off the Pacific coastline, a distance of 25
km between ships was used for all coastlines.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea E-l
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The results are summarized in Tables E-l and E-2 for concentration ratios of 862
and sulfate, i.e., the ratio of the concentration calculated for ships at sea to the
concentration calculated for ships at the coastline (the design value).
The results for SC>2 were different from those for sulfate, primarily due to
differences in the behavior of these two species downwind of a source. For all the
coastlines studied, the majority of the SO2 concentration ratios were less than one at
shorter off-shore distances than for sulfate. Thus, sulfate concentration ratios were the
limiting factor for defining the upper bounds of the SEC A for each coastline.
The results showed some differences in results among the various coastlines
studied. These differences are due to differences in the wind fields bringing the offshore
ship emissions and their secondary products to land as well as differences in
precipitation, which removes pollutants from the atmosphere.
The results from the two Pacific Ocean coastline simulations were qualitatively
similar. For both Pacific Ocean coastlines, over 90% of the receptors showed SO2
concentration ratios less than one for ships at 250 km from the coastline. For sulfate,
only about 49% and 56% of the receptors had concentrations less than one for ships at
500 km from the southern Pacific Ocean and northern Pacific Ocean coastlines,
respectively.
For the other two coastlines (Atlantic Ocean and Gulf of Mexico), the SO2 results
were qualitatively similar to those for the Pacific Ocean coastlines, i.e., over 90% of the
receptors showed SO2 concentration ratios less than one for ships at 250 km from the
coastline. However, there were some large differences for sulfate. For the Gulf of
Mexico coastline, over 70% of the receptors showed sulfate concentration ratios less than
one for ships at 250 km from the coastline. For the Atlantic Ocean coastline, nearly 60%
of the receptors showed sulfate concentration ratios less than one for ships at 250 km
from the coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea E-2
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Table E-l. Percentage of 862 concentrations below the design value as a function of
the distance from the coastline.
Distance from coastline
Southern Pacific
Northern Pacific
Gulf of Mexicoa
Atlantic
125km
40.7%
46.6%
84.4%
86.6%
250km
90.7%
97.9%
98.1%
100%
375km
100%
100%
100%
100%
500km
100%
100%
100%
100%
aNote that Florida values correspond to a shorter ship-coastline distance and the values
presented in the table should be seen as lower limits.
Table E-2. Percentage of sulfate concentrations below the design value as a function
of the distance from the coastline.
Distance from coastline
Southern Pacific
Northern Pacific
Gulf of Mexicoa
Atlantic
125km
4.4%
0.01%
40.4%
1.2%
250km
24.9%
3.6%
72.0%
57.9%
375km
41.9%
20.3%
80.5%
92.5%
500km
48.7%
55.7%
84.0%
100%
aNote that Florida values correspond to a shorter ship-coastline distance and the values
presented in the table should be seen as a lower limits.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
E-3
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These results suggest that an off-shore distance of 500 km should be sufficient
when conducting refined modeling of the potential impacts of ship emissions on air
quality inland, if a criterion of about 50% of inland receptors having sulfate
concentrations below the design value is acceptable to define the SECA.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea E-4
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1. INTRODUCTION
This document describes a screening study to model on-shore SO2 and sulfate
concentrations due to emissions of SOX from ships at sea. The objective of this screening
study is to obtain quantitative information on the shortest distance at which ships burning
high sulfur fuel (fuel content of 27,000 ppm) will have air quality impacts at land
receptors that are less than those anticipated from emissions from ships burning low
sulfur fuel (fuel content of 15,000 ppm) within coastal waters. This distance can
subsequently be used as the basis for defining the modeling domain for a more refined
Eulerian modeling study using the U.S. EPA Community Multiscale Air Quality model
(CMAQ). The results of the CMAQ modeling will yield the information required to
define the outer boundary of a Sulfur Emission Control Area (SECA) for various U.S.
coastlines. Because of differences in meteorology and other factors governing the
transport and transformation of ship emissions among the various coastlines, each
coastline is modeled separately in the screening study described here.
A review of available models and data was conducted prior to defining our
modeling approach (Seigneur et al., 2005a; see Appendix A). The modeling approach
was then formally documented in an analysis plan that was reviewed by EPA (Seigneur et
al., 2005b; see Appendix B).
This report is organized as follows. Section 2 describes the modeling approach,
including brief descriptions of the air quality model (CALPUFF) used for the screening
study, and the meteorological preprocessor for CALPUFF, referred to as CALMET.
CALPUFF is recommended by EPA for regulatory applications to assess the long-range
transport of pollutants. While CALPUFF has some limitations, as discussed in Section 2,
it is suitable for a screening study since it will tend to overestimate the oxidation of SCh
to sulfate in the gas phase (Karamchandani et al., 2006) and may thus provide a
conservative bound for the distance of interest for defining the SECA (because of the
simplistic treatment of aqueous-phase chemistry in CALPUFF, one cannot assess
whether sulfate concentrations would be overestimated if fog processes dominate sulfate
formation). Section 3 describes the development of meteorological, emissions and
geophysical data inputs for the CALPUFF simulations. Section 4 presents the results for
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 1-1
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the various U.S. coastlines that were simulated, and Section 5 provides a summary of the
study and presents some conclusions.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 1-2
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2. MODELING APPROACH
2.1 Air Quality Model (CALPUFF)
We used the EPA-recommended long-range transport model, CALPUFF, for this
screening study. CALPUFF is a multi-layer, multi-species non-steady-state puff
dispersion model that can simulate the effects of time- and space-varying meteorological
conditions on pollutant transport, transformation, and removal. It can accommodate
arbitrarily varying point source, area source, volume source, and line source emissions. It
is intended for use on scales from tens of meters to hundreds of kilometers from a source.
Detailed descriptions of the formulation and features of CALPUFF are provided in
the CALPUFF documentation (Scire et al., 2000a). Here, we briefly summarize some of
the features of CALPUFF that are relevant to our study and discuss the limitations of
CALPUFF in its treatment of atmospheric chemistry. CALPUFF includes algorithms for
near-source effects such as building downwash, transitional plume rise, partial plume
penetration, sub-grid scale terrain interactions as well as longer range effects such as
pollutant removal due to wet and dry deposition, simplified chemical transformations,
vertical wind shear, over-water transport and coastal interaction effects. Because the
latter features were relevant to simulating the transport and chemistry of SOX emissions
from ships, they were activated for our study.
CALPUFF offers several options to simulate the formation of secondary sulfate and
nitrate particles from the oxidation of the emitted primary gaseous pollutants, SO2 and
NOX respectively. Since the oxidation of SO2 to sulfate was of interest for this study, we
selected the more advanced chemistry module available in CALPUFF, which is based on
the RIVAD/ARM3 chemical mechanism (Morris et al., 1988). This option treats the NO
and NO2 conversion processes in addition to the NO2 to inorganic nitrate and SO2 to
sulfate conversions. The scheme assumes low background VOC concentrations and is
not suitable for urban regions. The NO-NO2-O3 chemical system is first solved to get
pseudo-steady-state concentrations of NO, NO2, and Os. During the day, this system
consists of the NO2 photolysis reaction to yield NO and O?, and the NO-Os titration
reaction to yield NO2. During the night, only the NO-Os titration reaction is considered.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 2-1
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In the implementation of the RIVAD/ARM3 scheme in CALPUFF, the background
63 concentration is used as the initial 63 concentration at each puff chemistry time step
(i.e., the plume O3 concentration does not evolve as a function of the downwind distance
but instead it is replenished at each time step). This may lead to errors if the sources that
are being simulated are large NOX emitters. For such sources, the high NO
concentrations in the plume deplete the Os concentrations near the source and, as a result,
OH concentrations are very low and the gas-phase rates of NO2 and SO2 oxidation to
HNOs and H2SO4, respectively, are negligible (Karamchandani et al., 1998;
Karamchandani and Seigneur, 1999). In CALPUFF, the lack of depletion of Os in the
plume leads to an overestimate of the steady-state daytime concentration of the hydroxyl
radical, OH, which is calculated from the final O3 concentration after the solution of the
NO-NO2-O3 system and is, therefore, also overestimated in the near field. Because the
OH concentrations are overestimated, CALPUFF overestimates the rates of formation of
HNO3 and H2SO4 in the near field.
CALPUFF uses dry deposition velocities to calculate the dry deposition of gaseous
and paniculate pollutants to the surface. These dry deposition velocities can either be
user-specified or calculated internally in CALPUFF using a resistance-based model. For
this study, we selected the latter option to calculate dry deposition velocities. For
gaseous pollutants, the resistances that are considered are the atmospheric resistance, the
deposition layer resistance, and the canopy resistance. For particles, a gravitational
settling term is included and the canopy resistance is assumed to be negligible. The
various resistances and particle settling rates are calculated as functions of atmospheric
variables (e.g., stability and wind speed), surface characteristics (e.g., surface roughness,
vegetation type, physiological state), and the properties of the depositing material (gas
diffusivity, solubility, and reactivity; particle size, shape, and density).
CALPUFF uses the scavenging coefficient approach to parameterize wet deposition
of gases and particles. The scavenging coefficient depends on pollutant characteristics
(e.g., solubility and reactivity), as well as the precipitation rate and type of precipitation.
The model provides default values for the scavenging coefficient for various species and
two types of precipitation (liquid and frozen).
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 2-2
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2.2 Meteorological Model (CALMET)
The recommended meteorological inputs for applying CALPUFF are the time-
dependent outputs of CALMET, a meteorological model that contains a diagnostic wind
field module and overwater and overland boundary layer modules (Scire et al., 2000b).
The outputs of CALMET are hourly gridded fields of micro-meteorological parameters
and three-dimensional wind and temperature fields. The wind field module in CALMET
combines an objective analysis procedure using wind observations with parameterized
treatments of slope flows, valley flows, terrain kinematic effects, terrain blocking effects,
and sea/lake breeze circulations. The boundary layer modules of CALMET produce
gridded fields of micrometeorological parameters, such as friction velocity, convective
velocity scale, and Monin-Obukhov lengths, as well as mixing heights and PGT stability
classes.
Inputs to CALMET include surface and upper air meteorological data. Optionally,
CALMET can also use the outputs of prognostic meteorological models, such as MM5
and CSUMM, to supplement observations and create the meteorological fields required
by CALPUFF. A processor (CALMM5) is available to convert MM5 data to the format
required for CALMET. For this study, we used the U.S. EPA's MM5 simulation outputs
for 2002. The MM5 domain contains the entire contiguous United States and portions of
Canada and Mexico and extends out to the Pacific Ocean in the west, the Gulf of Mexico
to the south and the Atlantic Ocean in the east. Thus, MM5 results for all the coastlines
relevant to our study were available from the EPA. Section 3 provides additional details
on the preparation of the meteorological data inputs for CALPUFF for this study.
2.3 Approach
For each coastline, a number of annual CALPUFF simulations were conducted. We
used 2002 as our reference year because it corresponds to the most recent year for which
an MM5 simulation covering all coastlines was available. The first simulation for each
coastline was to establish the target values of annual-average SO2 and sulfate
concentrations at an array of inland receptors (the placement of the receptors is described
in Section 2.3.1 below). These target values correspond to emissions from ships at
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 2-3
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dockside, i.e., those ships that are within the SECA and, therefore, will likely have to
burn low sulfur fuel (15,000 ppm fuel content). Then, we conducted annual CALPUFF
simulations for ships located at various distances from the coastline. For these
simulations, the ship emissions used were those based on ships burning high sulfur fuel
(i.e., 27,000 ppm fuel content). The comparisons of annual-average concentrations of
SC>2 and sulfate from these simulations with the target values determined previously
provide a basis for defining the SECA modeling domain for the CMAQ simulations to be
conducted by EPA.
In the following sections, we provide additional details on the placement of
receptors and sources for the simulations as well as the modeling domain for each
coastline.
2.3.1 Receptors
Ground-level receptors were located on land along the coastline and at various
distances from the coastline. The first line of receptors was located along the coastline,
with a distance of 10 km between adjacent receptors. This distance provides a finer
spatial resolution than that of the ship emissions (see Section 3). Nine additional lines of
receptors were then located inland parallel to the coastline receptors. The distances
between adjacent lines of receptors were variable, with higher resolutions near the
coastline and coarser resolutions further inland. The first line of inland receptors was
located at 10 km from the coastline receptors, while the last line of receptors was located
at 240 km (about 150 miles) from the coastline. Depending on the coastline being
simulated, the total number of receptors varied from about 1100 to 2800.
2.3.2 Sources
Ship emissions were represented by a set of stationary point sources. Each point
source represents one ship. The use of stationary sources to represent moving ships is an
appropriate approximation for this screening modeling study, because using stationary
sources will overestimate the downwind air quality impacts (emissions will be
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 2-4
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concentrated in specific locations rather than continuously distributed along the shipping
lane, thereby leading to greater ambient air concentrations).
For each simulation, the sources were located at a selected distance from shore
(along the coastline for the target value simulations, and at 125 km, 250 km, 375 km, and
500 km from the coastline for the SECA boundary simulations). The spacing between
adjacent ships for a given simulation was determined from ship traffic and estimated
shipping lane density (see Section 3). We maintained the same number of ships for the
at-sea emissions scenarios as the number of ships for the dockside emissions scenario.
This number varied from about 40 to 100 depending on the coastline being simulated.
2.3.3 Modeling domains
The following U.S. coastlines were simulated in this screening study:
• Southern Pacific Ocean coastline
• Northern Pacific Ocean coastline
• Gulf of Mexico coastline
• Atlantic Ocean coastline
All modeling domains were selected to allow an off-shore distance of at least 500
km from the coastline to include all ship-at-sea scenarios (see above) and an inland
distance of at least 240 km from the coastline to provide sufficient spatial coverage for
calculating air quality impacts.
The modeling domain for the Southern Pacific U.S. coastline extends from about 30
degrees North to 38 degrees North and includes Southern California, the Central
California coastline, and the southern portion of Northern California. Figure 2-1 shows
this domain as well as the locations of coastline ships for the target value simulation and
the locations of the receptors at the coastline and inland where SO2 and sulfate
concentrations were calculated.
Figure 2-2 shows the domain for the Northern Pacific U.S. coastline, which extends
from about 35 degrees North to about 52 degrees North and includes Northern California,
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 2-5
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o Receptors
• Ships
Figure 2-1. Modeling domain for the Southern Pacific Ocean U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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Receptors
Ships
Figure 2-2. Modeling domain for the Northern Pacific Ocean U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
2-7
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Oregon, and Washington in the U.S., and the southern half of the British Columbia
coastline in Canada.
In the east-west direction, the modeling domains for both the Southern Pacific and
Northern Pacific coastlines begin at the western boundary of the modeling domain for the
MM5 simulations that provided the hourly 3-D meteorological inputs for our study (see
Section 3). The eastern boundaries of the modeling domains for the Southern and
Northern Pacific coastline extend to about 115 degrees West and 112 degrees West,
respectively.
The modeling domain for the Gulf of Mexico coastline is shown in Figure 2-3. The
east-west extent of the modeling domain is from Western Texas (about 105 degrees
West) in the west to Florida and the Atlantic Ocean (about 75 degrees West). The
southern boundary of the modeling domain coincides with the southern boundary of the
MM5 domain while the northern boundary is at about 34 degrees North.
The southern boundary of the Atlantic Ocean modeling domain, shown in Figure 2-
4, also coincides with the southern boundary of the MM5 domain. The northern
boundary is at about 52 degrees North, just a few degrees lower than the northern
boundary of the MM5 domain. The western boundary of the Atlantic Ocean domain is at
about 85 degrees West, while the eastern boundary coincides with the eastern boundary
of the MM5 domain.
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o Receptors
• Ships
Figure 2-3. Modeling domain for the Gulf of Mexico coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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Figure 2-4. Modeling domain for the Atlantic Ocean coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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3. MODEL INPUTS
The following inputs were required for the CALPUFF simulations
• Meteorology
• Emissions
• Land use and terrain elevation data
• Coastline data
Terrain elevation data at 1 degree DEM (Digital Elevation Model) resolution were
downloaded from the U.S. Geological Survey (USGS). Land use data were also obtained
from the USGS at a 1:250,000 scale. These data were processed by the
CALPUFF/CALMET preprocessors: TERREL, CTGCOMP, CTGPROC, and
MAKEGEO.
Coastline data were obtained using the ZXPLOT package from the Center for the
Analysis and Prediction of Storms at the University of Oklahoma.
The preparation of meteorological and emission inputs for the CALPUFF
simulations is described below.
3.1 Meteorology
As described in Section 2.2, CALMET is the companion meteorological model that
is used to prepare the meteorological fields used by CALPUFF. CALMET is a
diagnostic meteorological model that can use standard surface and upper air
meteorological data, and also has an over-water option that allows the use of special
over-water measurements for grid cells that are over the ocean. In addition, CALMET
can use 3-D gridded meteorological fields from prognostic models, such as MM5, to
either supplement observations or to provide an initial guess field for the diagnostic
procedure.
For this study, we used a combination of land-based surface measurements, over-
water measurements, and MM5 outputs to create the CALPUFF meteorological fields.
The MM5 fields provide the vertical structure with sufficient temporal (hourly) and
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 3-1
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spatial (36 km) resolution to supplement the surface measurements. This approach
provides consistency with the subsequent grid-based modeling that will be conducted by
OAQPS to define SECAs using CMAQ, because CMAQ will be driven with the MM5
meteorology. It also addresses a weakness in the official release of CALMET (Scire et
al., 2005) in its calculation of mixing heights over-water surfaces. The mixing height
algorithm in CALMET underestimates over-water mixing heights, especially during light
wind conditions over warm water, since it only calculates mechanically-derived mixing
over water surfaces. This weakness has been corrected in a new version of CALMET
described by Scire et al. (2005). However, this new version was not available to us at the
time we performed the CALMET simulations for this study. Based on our discussions
with the CALMET developers (Scire, 2005), we used the MM5 mixing heights directly in
CALMET.
3.1.1 Measurements
The National Climatic Data Center (NCDC) Integrated Surface Hourly
Observations database provided the land-based hourly surface measurements. These
include wind speed, wind direction, temperature, and dew point temperature. Over-water
measurements were available for all the coastlines from the National Data Buoy Center
(NDBC) (http://www.ndbc.noaa.gov). These measurements are taken from buoys. The
buoys are at varying distances from the coast. Those near the coast are frequently near
harbors or bays. Most of the buoys are owned and operated by NDBC but there are also
several other agencies that submit their data to the NDBC database. The over-water
measurement coverage is sparse, as shown in Figures 3-1 through 3-4, which show the
locations of the land-based surface and over-water measurements for each of the
coastlines that were simulated in this study. We used 2002 observations for consistency
with the MM5 outputs (see below).
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» Stations
• Overwater Stations
Figure 3-1. Land-based surface stations and over-water stations for the Southern
Pacific Ocean U.S. coastline.
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Surface Stations
Overwater Stations
Figure 3-2. Land-based surface stations and over-water stations for the Northern
Pacific Ocean U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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Surface Stations
Overwater Stations
Figure 3-3. Land-based surface stations and over-water stations for the Gulf of
Mexico coastline.
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Overwater Stations
Figure 3-4. Land-based surface stations and over-water stations for the Atlantic Ocean
coastline.
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3.1.2 MM5 outputs
We used the outputs of the 2002 MM5 simulations sponsored by the U.S. EPA to
supplement the meteorological measurements. These outputs were provided to us by
ENVIRON Corporation. The MM5 modeling domain, shown in Figure 3-5, covers the
entire contiguous United States and extends significantly over the oceans. The horizontal
spatial resolution for the MM5 outputs is 36 km.
An interface program (CALMM5) converts the MM5 data into a form compatible
with CALMET. A beta version (not yet officially approved by the EPA) of CALMM5
processes MM5 Version 3 output data directly. This processor is available from the
CALPUFF-CALMET Download BETA-Test page.
3.1.3 CALMET Winds
As mentioned above, the wind fields were calculated with CALMET using the
outputs of MM5 in combination with available data. To illustrate the variability of wind
speed and direction with location and season, we present wind roses over the ocean based
on the calculated CALMET wind fields for the southern Pacific coastline, northern
Pacific coastline, Gulf of Mexico coastline and Atlantic coastline in Figures 3-6, 3-7, 3-8
and 3-9, respectively.
In the southern Pacific Ocean, winds are mostly from the southwest except during
winter in the northern part of the domain where the wind direction is more variable. In
the northern Pacific, the prevailing winds are from the west in the southern part of the
domain (i.e., off the coast of California and Oregon) during spring, summer and fall.
Wind direction is variable during winter in the southern part of the domain and for all
seasons in the northern part of the domain.
In the Gulf of Mexico, the wind direction varies with season and location. During
winter, in the western part of the domain, the prevailing winds are from the southwest
and the northeast; they are mostly from the west and north in the central part of the
domain and with more variable direction near the Florida coast. During spring and
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140 W 130 W 120 W 110 W 100 W 90 W 80 W 70 W 60 W
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160
Figure 3-5. MM5 modeling domain.
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Dataset. 2796292
Wind speed {mis,}
10-15 15-20 >2D
3.7%
5.0%
Dataset: 34&74D5
Wind speed (m>s)
0-2 2-5 5-1D 10-15 15-20 >2D
93%
3 Ota
Dataset. 355929D
Figure 3-6a. Wind roses based on CALMET outputs for the southern Pacific Ocean
during winter 2002 (top) and spring 2002 (bottom).
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
3-9
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Dataset: 267D393
Wind speed (rn/s)
0-2 2-5 5-10 10-15 15-20 >20
2.9%
4.1%
Dataset: 3559287
Wind speed (
0-2 2-5 5-10 10-15 15-20 >2D
4.7*
Dataset: 35206QQ
Figure 3-6b. Wind roses based on CALMET outputs for the southern Pacific Ocean
during summer 2002 (top) and fall 2002 (bottom).
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Figure 3-7'a. Wind roses based on CALMET outputs for the northern Pacific Ocean
during winter 2002 (top) and spring 2002 (bottom).
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Figure 3-7b. Wind roses based on CALMET outputs for the northern Pacific Ocean
during summer 2002 (top) and fall 2002 (bottom).
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Figure 3-8a. Wind roses based on CALMET outputs for the Gulf of Mexico during
winter 2002 (top) and spring 2002 (bottom).
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Figure 3-8b. Wind roses based on CALMET outputs for the Gulf of Mexico during
summer 2002 (top) and fall 2002 (bottom).
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Figure 3-9a. Wind roses based on CALMET outputs for the Atlantic Ocean during
winter 2002 (left) and spring 2002 (right).
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Figure 3-9b. Wind roses based on CALMET outputs for the Atlantic Ocean during
summer 2002 (left) and fall 2002 (right).
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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summer, the winds in the western and central parts of the domain are mostly from the
north, but they are variable in direction near the Florida coast (the prevailing wind
direction varies from north-north-east in the western part of the domain to north-north-
west in the eastern part of the domain). During fall, the winds are more variable with a
tendency to be from the west to north-east in the western part of the domain and from the
north to north-west in the central and eastern parts of the domain.
In the Atlantic Ocean, winds are mostly from the south to south-west in the
southern part of the domain. They are more variable in the northern part of the domain
with a prevailing northern trend that evolves from a northeastern direction during winter
to a northwestern direction during summer.
3.1.4 CALMET Mixing heights
As mentioned above, the CALMET mixing heights were obtained from the MM5
outputs. They vary spatially and temporally. We illustrate such variability in Figures 3-
10 through 3-13 where seasonally-averaged mixing heights are depicted for the southern
Pacific coast, northern Pacific coast, gulf of Mexico coast and Atlantic coast,
respectively.
Mixing heights are lowest in winter (December - February) and highest in summer
(June - August). They are lower over water than over land; they also tend to be greater
over the Gulf of Mexico than over the Pacific and Atlantic oceans. Ship emissions were
predominantly released after plume rise within the mixing layer.
3.2 Emissions
As described in Section 2.3.2, ship emissions were represented by a set of stationary
point sources. The point source emissions information required for the CALPUFF
simulations include stack locations, stack characteristics such as stack heights and stack
flow rates, and emission rates of SC>2, sulfate, NO and NO2.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 3-17
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100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
Mm = 399.6; Max = 990.5 ; Units = meter
100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
Figure 3-10a. Mixing heights for the southern Pacific Ocean during winter 2002 (top)
and spring 2002 (bottom).
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3-18
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Min = 383.4; Max = 1155.0 ; Units = meter
100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
Mm = 305.0; Max = 762.3 ; Units = meter
100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
Figure 3-1 Ob. Mixing heights for the southern Pacific Ocean during summer 2002 (top)
and fall 2002 (bottom).
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3-19
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too 100 760 340 470 fiOD 580 660 140 370 900 380 106D 114D 1770 1,100
100 ISO 760 340 470 f.OO SBO 660 ;40 070 9DO -MO lfH,Q 114D (770 1300
Figure 3-1 la. Mixing heights for the northern Pacific Ocean during winter 2002 (top)
and spring 2002 (bottom).
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too 130 760 MO 470 fiOO S80 660 ,40 S70 900
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Min = 277.4; Max = 858.8 ; Units = metei
100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
Figure 3-12a. Mixing heights for the Gulf of Mexico during winter 2002 (top) and spring
2002 (bottom).
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100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
100 180 260 340 420 500 580 660 740 820 900 980 1060114012201300
Figure 3-12b. Mixing heights for the Gulf of Mexico during summer 2002 (top) and fall
2002 (bottom).
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3-23
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100 180 260 340 420 500 580 660 740 820 900 9801060114012201300
100 180 260 340 420 500 580 660 740 820 900 9801060114012201300
Figure 3-13a. Mixing heights for the Atlantic Ocean during winter 2002 (left) and spring
2002 (right).
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3-24
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100 ISO 260 340 420 500 580 660 740 820 900 9801060114012201300
100 180 260 340 420 500 580 660 740 820 900 9801060114012201300
Figure 3-13b. Mixing heights for the Atlantic Ocean during summer 2002 (left) and fall
2002 (right).
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3-25
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3.2.1 Emission rates
Emission factors are needed to estimate the emissions of SOX (gas-phase 862 and
particulate-phase sulfate) associated with various ship activities. Based on the review of
available emission factors of Seigneur et al. (2005), the most recent EPA emission factors
were selected (EPA, 2002). Those emission factors pertain to ships with engines with
displacement exceeding 30 liters (so-called Category 3 engines). Emission factors are
reported for three different engine types (slow speed, medium speed and steam boiler) for
transit modes and hoteling modes. For this study of ships at sea, we are interested in
medium speeds for transit modes.
The SC>2 emission factor per unit of work is reported to be 9.56 g/hp-h for a 3%
sulfur fuel (i.e., 30,000 ppm) for a ship at slow or medium speed in transit mode. This is
equivalent to 12.8 g/kW-h. For a ship within the SECA, we assumed a fuel sulfur content
of 15,000 ppm, resulting in an emission factor of 6.4 g/kW-h. For ships at sea outside of
the SECA, a fuel sulfur content of 27,000 ppm was assumed. Therefore, the emission
factor for such ships was estimated to be 11.52 g/kW-h.
EPA assumes that 2% of sulfur is emitted as primary sulfate PM from Category 3
marine diesel engines. Therefore, we treated 2% of total sulfur emissions as sulfate
emissions and the 862 emission factor was adjusted down accordingly to maintain the
sulfur mass balance. (Note that for the same amount of S, the sulfate emission factor is
1.5 the SC>2 emission factor to account for the different molecular weights of 862 and
sulfate.)
Therefore, within the SECA, the gas-phase SC>2 and particulate-phase sulfate
emission factors are 6.27 g/kW-h and 0.19 g/kW-h, respectively. Outside of the SECA,
the gas-phase SC>2 and particulate-phase sulfate emission factors are 11.29 g/kW-h and
0.35 g/kW-h, respectively.
The sulfate emission rates calculated above are consistent with available data on the
sulfate fraction of paniculate matter (PM) emitted from ship diesel engines. Fleischer et
al. (1998) report that 20 to 30% of PM emissions from ship diesel engines are sulfate (for
a 3% sulfur fuel content). The EPA (2002) emission factor for PM is 1.3 g/hp-h, i.e.,
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 3-26
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1.74 g/kW-h. These values lead to an emission factor for sulfate in the range of 0.31 to
0.47 g/kW-h for a sulfur fuel content of 27,000 ppm. The emission factor of 0.35 g/kW-h
calculated above falls within this range.
Based on data from Corbett and Koehler (2003), the power of a typical ship was
estimated to be 16,000 kW (Corbett, 2005). It should be noted that there is a wide range
of power among various ships, with the largest container ships having power exceeding
65,000 kW.
The gas-phase SO2 and particulate-phase sulfate emissions per ship are then
calculated to be 100,320 g/h and 3,040 g/h, respectively, within the SECA and 180,640
g/h and 5,600 g/h, respectively, outside the SECA
A similar approach was used to calculate the NO and NO2 emission rates. The NOX
emission factor per unit of work is reported to be 12.38 g/hp-h (as NO2) for a ship at slow
or medium speed in transit mode (EPA, 2002). This is equivalent to 16.6 g/kW-h. For a
typical ship with a power of 16,000 kW, the resulting NOX emission rate is 266,000 g/h
(as NO2). Assuming that 5% of the NOX emissions are released as NO2 on a molar basis,
the NO and NO2 emission rates were calculated to be 164,800 g/h and 13,300 g/h,
respectively. These emission rates were used for ships within and outside the SECA, i.e.,
it was assumed that the switch to lower sulfur content fuel within the SECA did not affect
the NOX emission rates.
3.2.2 Stack parameters
These parameters include the locations of the sources and their stack characteristics.
As discussed in Section 2.3.2, the ships were placed along the coastline for the target
value calculations and at various distances from the coastline for the SECA boundary
estimation. In this section, we discuss the spatial density of the ships, i.e., the spacing
between each ship. This was determined based on analysis of ship activity data, as
described below.
We used the average number, N, of ships in transit along the coast per year and
average cruising speed, V (km/h), to calculate the average distance, D (km), between two
ships along a shipping lane.
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D = V * (24 h/day * 365 days/yr) / N
The annual number of ships transiting along the southern California coast was
estimated to be 13,000 (ICOADS, 2002). This number includes all ships transiting to and
from ports located on the southern Pacific coast as well as ships transiting
southward/northward from/to ports located on the northern Pacific coast. It is likely to be
an overestimate of the number of ships transiting along the coast because a fraction of
those ships will be transiting along shipping lanes that extend from the ports westward
into the Pacific Ocean. The cruising speed varies according to ship type. It is about 24
knots for container ships and about 16 knots for tankers. Here, the average ship cruising
speed was estimated to be about 20 knots, i.e., 36 km/h (ICOADS, 2002). Thus, the
average distance estimated for the southern Pacific coast was calculated as follows.
D = 36 * 24 * 365 / 13,000 = 24.3 km
Based on this analysis, we used a distance of 25 km between ships to calculate ship
emissions. The same distance was used for the other coastlines.
The other stack parameters required include stack characteristics such as stack
height, stack diameter, stack exhaust velocity, and stack exit temperature. These
parameters were obtained for typical container and tanker ship type categories from an
ARB report (ARB, 2000). For this study, we used the average values for these two
categories (see Table 3-1).
Table 3-1. Stack characteristics.
Stack height
Stack diameter
Exhaust velocity
Exhaust temperature
35.3m
1.9m
24.6 m/s
537 K
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4. RESULTS
The initial CALPUFF baseline (i.e., ships along the coastline) and SECA boundary
simulations for the Southern Pacific, Northern Pacific, and Gulf of Mexico coastlines
were conducted using the latest EPA-approved version of CALPUFF. However, after
discussions with the CALPUFF developers (Scire, 2006), it was decided that the final
simulations would be conducted using the latest BETA-Test version of the model. This
version addresses problems reported to the model developers by CALPUFF users. The
results presented here are all based on simulations conducted with the beta version of
CALPUFF.
Because the objective of the study is to identify upper limits for the off-shore
distances at which sea-going ships may switch from cleaner fuel to high-sulfur content
fuel, the results are presented in terms of the ratios of the ground-level SO2 and sulfate
concentrations at land-based receptors calculated from the off-shore source simulations to
those calculated from the coastline source simulations. This allows us to determine the
percentage of receptors at which the emissions from the sea-going ships will lead to air
quality impacts that are less than or equal to the target values, i.e., the ground-level SC>2
and sulfate concentrations calculated from the baseline simulation.
Before presenting the results, it is useful to discuss the expected differences
between 862 and sulfate in terms of the evolution of their downwind concentrations, and
how these differences affect the results obtained here. 862 and sulfate concentrations
will display different behaviors downwind of the ships. SO2 concentrations will decrease
continuously with distance from the source (due to dilution, removal, and conversion to
sulfate), whereas sulfate concentrations will first decrease (dilution and removal of
primary, i.e., directly emitted sulfate), then increase (formation of secondary sulfate from
the oxidation of 802) before finally decreasing (dilution and removal exceeding
formation).
This behavior of sulfate introduces an additional complication: the sulfate target
values at receptors near the coastline will be determined by the directly emitted sulfate,
while the target values at larger distances inland will be determined by some combination
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 4-1
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of primary and secondary sulfate, with the secondary sulfate component increasing and
the primary sulfate component decreasing. Even further inland, both components will
decrease as the rate of dilution and removal exceeds the formation of sulfate.
These differences between the behavior of SC>2 and sulfate suggest that the SC>2
concentrations will become smaller than the design values at a smaller distance than the
sulfate concentrations will. The SC>2 concentrations due to the higher SOX emissions
from the ships at sea burning higher sulfur fuel will be offset by the dilution and
conversion of 862 much sooner than the sulfate concentrations since the latter will
initially experience an increase from the SC>2 conversion.
In the discussion of the results that follows, we will refer to concentrations
calculated from the emissions of coastline sources (i.e., ships within the SECA burning
low-sulfur fuel) as the "target" values, and the concentrations due to emissions from ships
at sea (i.e., ships outside the SECA burning high-sulfur fuel) as the "design" values. The
ratios of the "design" concentrations to the "target" concentrations will be referred to as
the "design ratios".
4.1 Results for the Southern Pacific U.S. Coastline
Figure 4-1 shows the spatial patterns of the design ratios of the ground-level annual
average SC>2 concentrations for ships at 125 km from the coastline. While there are large
areas where the ratios are less than one, particularly near the southern part of the domain,
the ratios are larger than one for the majority of the receptors. This is depicted in Figure
4-2, which shows the cumulative frequency distribution of the design ratios. The design
ratios are less than one at about 41% of the receptors.
The spatial distribution of the sulfate design ratios for ships at 125 km from the
coastline is shown in Figure 4-3. In contrast to the SO2 results, the sulfate ratios are less
than one over a very small portion of the domain near the southern boundary. From
Figure 4-4, we see that the percentage of receptors for which the sulfate design ratios are
less than one is only about 4%. These differences between the SC>2 and sulfate results are
consistent with our expectations as discussed earlier.
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Figure 4-1. Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
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100 n
'80 -
JT
0)
f°
40
3
20 -
0
Annual SO2 Concentration Ratio
Figure 4-2. Cumulative frequency distribution of design ratios of SC>2 concentrations
from ships at 125 km from the Southern Pacific U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
-------
Min= 00185; Max = 23730
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 2.8 3.0 5.0
Figure 4-3. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
-------
100
C?80 --
o^
0
0
Annual SO4 Concentration Ratio
Figure 4-4.
Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Southern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
-------
The SC>2 results for ships at 250 km from the coastline are shown in Figures 4-5 and
4-6. From Figure 4-5, we see that, except for a small region in the Central Valley of
California (Kings county, most of Fresno county, and portions of Tulare and Kern
counties) and isolated locations along the coast in Santa Barbara county, most of the
receptors have ratios less than one. Figure 4-6 shows that the percentage of receptors that
have ratios less than one for ships at 250 km from the coastline is nearly 91%.
For sulfate, even when the ships are at a distance of 250 km, we see from Figure 4-7
that the sulfate design ratios are less than one only near the southern portion of the
modeling domain, in Orange and San Diego counties, southern Imperial county, western
Riverside county, and a small region of southern Los Angeles county. In the rest of the
domain, the design ratios are larger than one, suggesting that increases in downwind
sulfate concentrations from the conversion of SC>2 to sulfate are still the determining
factors for ship emissions at 250 km. Figure 4-8 shows that the percentage of receptors
for which the sulfate design ratios is less than one for ships at 250 km from the coastline
is only about 25%.
Figures 4-9 and 4-10 show the SC>2 results for ships at 375 km from the coastline.
As seen in Figure 4-9, except for one location along the coastline in Santa Barbara
county, all the receptors show design ratios less than one. The cumulative frequency
distribution, shown in Figure 4-10, confirms that 862 air quality impacts from the ships
burning high-sulfur fuel are less than those from coastline ships burning low-sulfur fuel
at over 99.99% of the land-based receptors.
Figures 4-11 and 4-12 show that the sulfate results for ships at 375 km from the
coastline still show larger air quality impacts than the coastline ships for a large majority
of the receptors. From Figure 4-11, a clear north-south gradient is evident. In the region
north of Los Angeles county, and including portions of northern Los Angeles county, the
sulfate ratios are larger than one. In the region south, all the sulfate ratios are less than
one. Figure 4-12 shows that about 42% of the receptors have sulfate ratios less than one.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 4- 7
-------
Mm = 0 0005; Max = 2 5602
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 2.8 3.0 5.0
Figure 4-5. Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
-------
100
90.7%
1—t
Figure 4-6.
Annual SO2 Concentration Ratio
Cumulative frequency distribution of design ratios of SC>2 concentrations
from ships at 250 km from the Southern Pacific U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
-------
Mm= 00139; Max = 23306
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 2.8 3.0 5.0
Figure 4-7. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-10
-------
100
'80 -
S,
0)
f°
40
3
20 -
0
24.9%
t
Annual SO4 Concentration Ratio
Figure 4-8. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Southern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-11
-------
Figure 4-9. Ratios of annual-average SC>2 concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-12
-------
100
C?80 --
o^
1 1
Annual SO2 Concentration Ratio
Figure 4-10. Cumulative frequency distribution of design ratios of SC>2 concentrations
from ships at 375 km from the Southern Pacific U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-13
-------
Mm = 00104; Max = 2.2243
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 2.8 3.0 5.0
Figure 4-11. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-14
-------
100
'80 -
S,
0)
f°
40
3
20 -
0
41.9%
t
Annual SO4 Concentration Ratio
Figure 4-12. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Southern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-15
-------
The sulfate results for ships at 500 km from the coastline are shown in Figures 4-13
and 4-14 (the corresponding SC>2 results are not shown here since the 375 km results
presented earlier show that a distance of 375 km is more than adequate for setting the
upper limit of the SECA for SCh impacts). The north-south gradient is still evident, as
shown in Figure 4-13, but the boundary between the two regions of ratios less than one in
the south to ratios larger than one in the north has shifted to the north (to Ventura county
in the west and to Kern and Tulare counties in the east). The two regions are
approximately equal in area, as confirmed by the cumulative frequency distribution in
Figure 4-14.
In these analyses, the Santa Barbara area tends to show higher concentrations of
SO2 and sulfate and, in some cases high concentration/target value ratios. One reason for
such high concentrations is that the Santa Barbara area extends westward into the Pacific
Ocean and, as a result, receptors near the coast have more ship emission sources in their
close vicinity than receptors located in other areas along the coast. All sources will not
impact the Santa Barbara receptors simultaneously because such impacts will depend on
the wind flow (see Figure 3-6). Nevertheless, the probability of impact from ship
emission sources should be higher for the Santa Barbara area than for other areas along
the southern Pacific coast because of the design of the source/receptor locations in this
screening study. This characteristic of the source/receptor relationship should be kept in
mind when interpreting the simulation results. Note that the air quality modeling to be
conducted later with the 3-D CMAQ model will locate ship emissions along shipping
lanes and, therefore, will provide a more realistic set of source/receptor relationships.
4.2 Results for the Northern Pacific U.S. Coastline
Figures 4-15 and 4-16 show the SO2 results for ships at 125 km from the Northern
Pacific coastline. We see from Figure 4-15 that the regions with ratios less than one are
approximately equal in area to the regions with ratios greater than one. As shown in
Figure 4-16, the ratios are less than one at about 47% of the receptors. The higher ratios
typically occur inland in areas of high elevation (e.g., the Cascade mountain range). The
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 4-16
-------
Min = 0 0078; Max = 2.2983
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 2.8 3.0 5.0
Figure 4-13. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Southern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-17
-------
100
'80 -
jf
§
40
E
O
20 --
0
48.7%
0
t
Annual SO4 Conceniration Raiio
Figure 4-14. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 500 km from the Southern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-18
-------
Min= 0.0032; Max = 2.9109
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0
1.4 1.6 2.0 2.6 3.0 7.0
Figure 4-15. Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-19
-------
100 -i
0
Annual SO2 Concentration Ratio
Figure 4-16. Cumulative frequency distribution of design ratios of SC>2 concentrations
from ships at 125 km from the Northern Pacific U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-20
-------
corresponding sulfate results are shown in Figures 4-17 and 4-18. The sulfate ratios are
larger than one over the entire domain except for a few isolated locations. Over a large
portion of the domain, the ratios range from 1.4 to 1.8. From Figure 4-18, we see that
over 99.99% of the receptors have ratios larger than one. The results from the San
Francisco Bay Area are similar to those that were obtained for the southern Pacific
domain, which suggests that most of the ships impacting this area are within the
modeling domains.
At 250 km from the coastline, the 862 ratios are less than one at nearly 100% of the
receptors, as shown in Figures 4-19 and 4-20. However, sulfate ratios are still larger than
one at a majority (nearly 96%) of the receptors, as shown in Figures 4-21 and 4-22.
The SC>2 ratios for ships at 375 km and 500 km from the North Pacific U.S.
coastline are less than one at all the receptors and are not shown here. Figure 4-23 shows
the spatial distribution of the sulfate ratios for ships at 375 km from the coastline, while
Figure 4-24 shows the cumulative frequency distribution of the ratios. From Figure 4-24,
we see that only about 20% of the receptors show ratios less than one. However, over a
very large part of the domain, the ratios larger than one are usually in the range of 1 to
1.4, as shown in Figure 4-23. The largest ratios, in the range of 1.4 to 1.8, are
concentrated in the western parts of southern Oregon and northern California, near the
boundary between the two states. This area is in the center of the domain and is,
therefore, exposed to the ship emissions located west and southwest from its coastline
(see wind roses in Figure 3-7).
The sulfate results for ships at 500 km from the North Pacific U.S. coastline are
shown in Figures 4-25 and 4-26. At a majority (56%) of the receptors, the ratios are less
than one for ships at this distance. The largest ratios are again near the boundary region
between California and Oregon. The results for the San Francisco Bay Area are
significantly lower than those obtained for the southern Pacific domain because, at that
distance, most of the ships that impact this area are located southwest of this area (see
windroses in Figures 3-6 and 3-7).
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 4-21
-------
Min = 0.0540; Max = 2.0844
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2
Figure 4-17. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-22
-------
100
C?80 --
o^
0)
f° +
140 +
3
20 -
0
0
1
Annual SO4 Concentration Ratio
Figure 4-18. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Northern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-23
-------
Min= 0.0013; Max = 1.4531
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2
Figure 4-19. Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-24
-------
100
0
97.9%
1—t
Annual SO2 Concentration Ratio
Figure 4-20. Cumulative frequency distribution of design ratios of SO2 concentrations
from ships at 250 km from the Northern Pacific U.S. coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-25
-------
Min = 0.0436; Max = 1.9272
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0
1.4 1.6 2.0 2.6 3.0 7.0
Figure 4-21. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-26
-------
100
C?80 --
o^
0
0
3.6%
1 1
Annual SO4 Concentration Ratio
Figure 4-22. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Northern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-27
-------
Min= 0.0371; Max = 1.8913
Figure 4-23. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-28
-------
100
'80 -
S,
0)
f°
40
3
E
3 20
0
20.3%
1 1
Annual SO4 Concentration Ratio
Figure 4-24. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Northern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-29
-------
Min= 0.0320; Max = 1.9360
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.4 1.6 2.0 2.6 3.0 7.0
Figure 4-25. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Northern Pacific U.S.
coastline to the concentrations (target values) due to dockside ships at the
coastline burning low-sulfur fuel. The red dots represent the locations of
the sea-going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-30
-------
100
'80 -
S,
0)
f°
40
3
20 -
0
55.7%
1 1
Annual SO4 Concentration Ratio
Figure 4-26. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 500 km from the Northern Pacific U.S.
coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-31
-------
4.3 Results for the Gulf of Mexico Coastline
The results for the Gulf of Mexico coastline are quite different from the two
coastlines on the West Coast. The relative air quality impacts at land-based receptors
from ships at sea are generally lower for the Gulf of Mexico than for the Pacific Ocean
even for ships located at 125 km from the coastline. The SO2 results for ships at 125 km
from the Gulf of Mexico coastline are shown in Figures 4-27 and 4-28. From Figure 4-
27, we see that the 862 ratios are less than one over most of the receptor network, except
in southern Florida. The ratios are larger than one at only about 16% of the receptors, as
shown in Figure 4-28. The larger values simulated in Florida result in part from the
design of the "shipping lane" that is located 125 km south of the coastline but, in the case
of Florida, closer from a coastline located directly east from the ships. Therefore, the
fraction of receptors that have ratios below one should be seen as a lower limit.
The sulfate results for ships at 125 km from the Gulf of Mexico coastline, shown in
Figures 4-29 and 4-30, are also different from the sulfate results for the Pacific Ocean
coastlines. Nearly 40% of the receptors have sulfate ratios less than one. Ratios larger
than one are seen in Florida, Georgia, Alabama, and portions of Mississippi and
Louisiana, as well as at the tip of southern Texas near the border with Mexico.
The SO2 results for ships at 250 km from the Gulf of Mexico coastline are shown in
Figures 4-31 and 4-32. We see from Figure 4-31 that, except for a small region in
southern Florida, the ratios at all the receptors are less than one. The percentage of
receptors with ratios less than one is over 98%, as shown in Figure 4-32. Figures 4-33
and 4-34 show the 250 km results for sulfate. We see from Figure 4-33 that the region
with sulfate ratios larger than one is confined to most of Florida and southern Georgia.
Figure 4-34 shows that only 28% of the receptors have sulfate ratios larger than one.
We only show the sulfate results for the 375 km and 500 km distances from the
Gulf of Mexico coastline, since all receptors satisfy the criterion of SO2 ratios less than
one at these distances. Figure 4-35 shows the spatial pattern of sulfate ratios for ships at
375 km from the coastline. Ratios larger than one are only seen in Florida and small
areas of southern Georgia. From Figure 4-36, we see that over 80% of the receptors
show sulfate ratios less than one.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 4-32
-------
Mm = 00365; Max = 40149
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 3.0 4.0 6.0
Figure 4-27. Ratios of annual-average SC>2 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-33
-------
100
80 -
0)
§
I40
E
d
20 -
0
Annual SO2 Conceniralion Ralio
Figure 4-28. Cumulative frequency distribution of design ratios of 862 concentrations
from ships at 125 km from the Gulf of Mexico coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-34
-------
Min = 0.3498; Max = 3.3762
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 3.0
H
4.0 6.0
Figure 4-29.
Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-35
-------
100
80
0)
§
I40
E
d
20 -
0
Annual SO4 Conceniralion Ralio
Figure 4-30. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Gulf of Mexico coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-36
-------
Min= 0.0063; Max = 3.1194
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 3.0 4.0 6.0
Figure 4-31. Ratios of annual-average 862 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-37
-------
100
'80 -
jf
0)
f°
§
I40
3
E
98.1%
0
0
t
Figure 4-32.
Annual SO2 Concentration Ratio
Cumulative frequency distribution of design ratios of SC>2 concentrations
from ships at 250 km from the Gulf of Mexico coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-38
-------
Mm= 01275; Max = 40711
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 3.0 4.0 6.0
Figure 4-33. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-39
-------
100
Annual SO4 Concentration Raiio
Figure 4-34. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Gulf of Mexico coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-40
-------
Mm = 0 0337; Max = 4 3880
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.4 1.6 1.8 2.0 2.4 3.0 4.0 6.0
Figure 4-3 5. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-41
-------
100
0
Annual SO4 Conceniraiion Ralio
Figure 4-36. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Gulf of Mexico coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-42
-------
The sulfate results for ships at 500 km from the Gulf of Mexico coastline are
qualitatively similar to the 375 km distance results, as shown in Figures 4-37 and 4-38.
For the 500 km scenario, sulfate ratios are larger than one only in Florida, as shown in
Figure 4-37. Figure 4-38 shows that the percentage of receptors with sulfate ratios less
than one increases only marginally (by about 3.5%) when the ships are placed at 500 km
instead of 375 km.
4.4 Results for the Atlantic Ocean Coastline
Figure 4-39 shows the spatial distribution of annual-average SO2 ratios for ships at
125 km from the Atlantic Ocean coastline, while Figure 4-40 shows the cumulative
frequency distribution of the ratios. As in the case of the Gulf of Mexico coastline, the
SO2 ratios are less than for a majority of the receptors at the 125 km distance. From
Figure 4-39, we see that the ratios are larger than one only in southern Georgia, most of
North Carolina, and portions of Connecticut and Massachusetts. The percentage of
receptors with SO2 ratios less than one is nearly 87%, as shown in Figure 4-40. In
contrast, the 125 km sulfate results for the Atlantic Ocean show that the ratios are larger
than one for almost the entire domain, as shown in Figures 4-41 and 4-42.
The SO2 results for ships at 250 km from the Atlantic Ocean coastline are shown in
Figures 4-43 and 4-44. At this distance, the SO2 ratios are less than one throughout the
domain. Figures 4-45 and 4-46 show the corresponding results for sulfate. We see from
Figure 4-45 that sulfate ratios are less than one in the southeastern U.S. (Florida, Georgia,
and South Carolina) and some of the New England states, such as Vermont, New
Hampshire and Maine. The ratios are larger than one in most of North Carolina, eastern
Virginia, eastern Pennsylvania, New Jersey, southern New York, Connecticut, Rhode
Island, and southern Massachusetts. Figure 4-46 shows that the sulfate ratios are less
than one at nearly 58% of the receptors.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 4-43
-------
Figure 4-37. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Gulf of Mexico coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-44
-------
100
80 -
0)
40
E
d
20 -
0
Figure 4-3 8.
Annual SO4 Concentration Raiio
Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 500 km from the Gulf of Mexico coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-45
-------
Min= 0.1032; Max = 1.5615
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 3.0
Figure 4-39. Ratios of annual-average SC>2 concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-46
-------
100
80
0)
§
I40
E
d
20 -
0
86.6%
i—r
t
Figure 4-40.
Annual SO2 Conceniralion Raiio
Cumulative frequency distribution of design ratios of 862 concentrations
from ships at 125 km from the Atlantic Ocean coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-47
-------
Min = 0.6869; Max = 2.0236
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 3.0
Figure 4-41. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 125 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-4
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100
80
0)
f°
§
40
20 -
0
1.2%
0
a
Annual SO4 Concentration Ratio
Figure 4-42. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 125 km from the Atlantic Ocean coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-49
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Min = 0.0466; Max = 0.7339
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 3.0
Figure 4-43. Ratios of annual-average SC>2 concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-50
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100
o
f°
§
I40
0
i—r
t
Annual SO2 Conceniralion Ralio
Figure 4-44.
Cumulative frequency distribution of design ratios of 862 concentrations
from ships at 250 km from the Atlantic Ocean coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-51
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Min= 0.5066; Max = 1.8310
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 3.0
Figure 4-45. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 250 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-52
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100
60 -
0
57.9%
0
1
Annual SO4 Conceniraiion Raiio
Figure 4-46.
Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 250 km from the Atlantic Ocean coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-53
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Figure 4-47 shows the spatial distribution of sulfate ratios for ships at 375 km from
the Atlantic Ocean coastline. We see that the sulfate ratios are less than one almost
everywhere, except in portions of eastern North Carolina and southern Massachusetts.
As shown in Figure 4-48, the sulfate ratios are less than one at over 92% of the receptors.
For ships at 500 km from the Atlantic Ocean coastline, the sulfate ratios are less
than one everywhere as shown in Figures 4-49 and 4-50.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 4-54
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Min= 0.3180; Max = 1.5239
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 3.0
Figure 4-47. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 375 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-55
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100
'80 -
jf
0)
f°
§
I40
92.5%
20 -
0
0
t
Annual SO4 Concentration Ratio
Figure 4-48. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 375 km from the Atlantic Ocean coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
4-56
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Min= 0.1813; Max = 1.1576
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 3.0
Figure 4-49. Ratios of annual-average sulfate concentrations due to sea-going ships
burning high-sulfur fuel at 500 km from the Atlantic Ocean coastline to
the concentrations (target values) due to dockside ships at the coastline
burning low-sulfur fuel. The red dots represent the locations of the sea-
going ships.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
4-57
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100
(D
f°
§
40
E
O
20 -
0
0
t
Annual SO4 Concentration Ratio
Figure 4-50. Cumulative frequency distribution of design ratios of sulfate
concentrations from ships at 500 km from the Atlantic Ocean coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
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5. SUMMARY AND CONCLUSIONS
A screening study with the CALPUFF dispersion model was conducted to
determine the air quality impacts (annual average ground-level concentrations of 862 and
sulfate) at an array of land-based receptors due to SOX emissions from ships burning
high-sulfur fuel at sea at various distances from the coastline. CALPUFF tends to
overestimate the conversion of SO2 to sulfate in the gas phase (Karamchandani et al.,
2006) and the results presented here are likely to provide conservative estimates of the
impacts of emissions from ships at sea on inland air quality. (Because of the simplified
treatment of aqueous-phase chemistry in CALPUFF, this assessment may be altered if the
interactions of the ship plumes with fog dominate sulfate formation.) The results were
compared with those calculated for ships burning low-sulfur fuel at the coastline to
determine upper bounds for Sulfur Emission Control Areas (SECAs), i.e., off-shore
distances at which the switch to high-sulfur fuel would not impair air quality. For each
offshore distance investigated, the percentage of receptors for which the air quality
impacts of ships at sea were lower than the impacts of ships at the coastline was
calculated.
The U.S. coastlines considered in this study include the Pacific Ocean coastline, the
Gulf of Mexico coastline, and the Atlantic Ocean coastline. The northern and southern
parts of the Pacific Ocean coastline were studied separately. The results are summarized
in Tables 5-1 and 5-2 for concentration ratios of SO2 and sulfate, i.e., the ratio of the
concentration calculated for ships at sea to the concentration calculated for ships at the
coastline.
The results for SO2 were different from those for sulfate, primarily due to
differences in the behavior of these two species downwind of a source. For all the
coastlines studied, the majority of the SO2 concentration ratios were less than one at
shorter off-shore distances than for sulfate. Thus, sulfate concentration ratios were the
limiting factor for defining the upper bounds of the SEC A for each coastline.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 5-1
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Table 5-1. Percentage of 862 concentrations below the design value as a function of
the distance from the coastline.
Distance from coastline
Southern Pacific
Northern Pacific
GulfofMexicoa
Atlantic
125km
40.7%
46.6%
84.4%
86.6%
250km
90.7%
97.9%
98.1%
100%
375km
100%
100%
100%
100%
500km
100%
100%
100%
100%
aNote that Florida values correspond to a shorter ship-coastline distance and the values
presented in the table should be seen as lower limits.
Table 5-2. Percentage of sulfate concentrations below the design value as a function
of the distance from the coastline.
Distance from coastline
Southern Pacific
Northern Pacific
Gulf of Mexicoa
Atlantic
125km
4.4%
0.01%
40.4%
1.2%
250km
24.9%
3.6%
72.0%
57.9%
375km
41.9%
20.3%
80.5%
92.5%
500km
48.7%
55.7%
84.0%
100%
aNote that Florida values correspond to a shorter ship-coastline distance and the values
presented in the table should be seen as lower limits.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea
5-2
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The results showed some differences in results among the various coastlines
studied. These differences are due to differences in the wind fields bringing the offshore
ship emissions and their secondary products to land as well as differences in
precipitation, which removes pollutants from the atmosphere.
The results from the two Pacific Ocean coastline simulations were qualitatively
similar. For both Pacific Ocean coastlines, over 90% of the receptors showed SO2
concentration ratios less than one for ships at 250 km from the coastline. For sulfate,
only about 49% and 56% of the receptors had concentrations less than one for ships at
500 km from the southern Pacific Ocean and northern Pacific Ocean coastlines,
respectively.
For the other two coastlines (Atlantic Ocean and Gulf of Mexico), the SO2 results
were qualitatively similar to those for the Pacific Ocean coastlines, i.e., over 90% of the
receptors showed SO2 concentration ratios less than one for ships at 250 km from the
coastline. However, there were some large differences for sulfate. For the Gulf of
Mexico coastline, over 70% of the receptors showed sulfate concentration ratios less than
one for ships at 250 km from the coastline. For the Atlantic Ocean coastline, nearly 60%
of the receptors showed sulfate concentration ratios less than one for ships at 250 km
from the coastline.
These results suggest that an off-shore distance of 500 km should be sufficient
when conducting refined modeling of the potential impacts of ship emissions on air
quality inland, if a criterion of about 50% of inland receptors having sulfate
concentrations below the design value is acceptable to define the SECA.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 5-3
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6. REFERENCES
ARB, 2000. Air Quality Impacts from NOX Emissions of Two Potential Marine Vessel
Control Strategies in the South Coast Air Basin, California Air Resources Board,
Sacramento, CA.
Corbett, JJ. and H.W. Koehler, 2003. Updated emissions from ocean shipping, J.
Geophys. Res., 108, doi:10.1029/2003JD003751.
Corbett, J.J., 2005. Private communication to Christian Seigneur, AER, July 2005.
EPA, 2002. Commercial Marine Emission Inventory, Final Report from PECHAN,
prepared by ENVIRON International Corporation, U.S. Environmental Protection
Agency, Office of Transportation and Air Quality, Ann Arbor, MI.
Fleischer, F., EJ. Ulrich, R. Krapp and W. Grundmann, 1998. Comments on particulate
emissions from diesel engines when burning heavy fuels, Proc. Of the 22nd
CIMAC Internal. Congress on Combustion Engines, Vol. 6, Copenhagen,
Denmark, May 18-21.
ICOADS, 2002. International Comprehensive Ocean Atmospheric Data Set, as
transmitted from ERG by Office of Transportation and Air Quality, U.S.
Environmental Protection Agency, Washington, D.C.
Karamchandani, P., A. Koo, and C. Seigneur, 1998. A reduced gas-phase kinetic
mechanism for atmospheric plume chemistry, Environ. Sci. Technol, 32, 1709-
1720.
Karamchandani, P., and C. Seigneur, 1999. Simulation of sulfate and nitrate chemistry in
power plant plumes, J. Air Waste Manage. Assoc., 49, PM-175-181.
Karamchandani, P., S.-Y. Chen, N. Kumar and M. Gupta, 2006. A comparative
evaluation of two reactive puff models using power plant plumes measurements,
AWMA Guideline on Air Quality Models Conference, Denver CO, 26-28 April.
Morris, R.E., R.C. Kessler, S.G. Douglas, K.R. Styles and GE. Moore, 1988. Rocky
Mountain Acid Deposition Model Assessment: Acid Rain Mountain Mesoscale
Model (ARMS), report prepared for the U.S. EPA, Research Triangle Park, NC.
Scire, J.S., D.G. Strimaitis and RJ. Yamartino, 2000a. A User's Guide for the CALPUFF
Dispersion Model (Version 5), Earth Tech, Inc. Report, Concord, MA, January
2000.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 6-1
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Scire, J.S., F.R. Robe, M.E. Fernau and RJ. Yamartino, 2000b. A User's Guide for the
CALMET Dispersion Model (Version 5), Earth Tech, Inc. Report, Concord, MA,
January 2000.
Scire, J.S., 2005. Communication via e-mail of Christian Seigneur, AER, with Joe Scire,
EarthTech, 18-19 March 2005.
Scire, J.S., D.G. Strimaitis and F.R. Robe, 2005. Evaluation of enhancements to the
CALPUFF model for offshore and coastal applications, Proceedings of the 10th
International Conference on Harmonisation with Atmospheric Dispersion
Modelling for Regulatory Purposes, Crete, Greece, 17-20 October 2005.
Scire, J.S., 2006. Private communication to Prakash Karamchandani, AER, February
2006.
Seigneur, C., K. Lohman and P. Karamchandani, 2005a. Review of Technical
Information relevant to Sulfur Oxides (SOX) Emissions Transport for Ships at Sea.,
Final Report to Office of Transportation and Air Quality, U.S. Environmental
Protection Agency, Washington, D.C.
Seigneur, C., P. Karamchandani and K. Lohman, 2005b. Analysis Plan - Modeling Sulfur
Oxides (SO%) Emissions Transport for Ships at Sea, Final Report to Office of
Transportation and Air Quality, U.S. Environmental Protection Agency,
Washington, D.C.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea 6-2
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APPENDIX A
REVIEW OF TECHNICAL INFORMATION RELEVANT TO
SULFUR OXIDES (SOX) EMISSIONS TRANSPORT
FOR SHIPS AT SEA
Prepared by
Christian Seigneur
Kristen Lohman
Prakash Karamchandani
Atmospheric & Environmental Research, Inc.
2682 Bishop Drive, Suite 120
San Ramon, CA 94583
Prepared for
U.S. Environmental Protection Agency
Office of Transportation and Air Quality
1200 Pennsylvania Avenue, NW
Washington, DC 20460
Document CP212-05-01b
June 2005
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-l
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INTRODUCTION
Marine shipping represents a major and growing source of uncontrolled air
pollution in coastal regions and inland areas downwind of coastal regions in many parts
of the world, particularly North America and Europe. This can be attributed to both
growth in global trade and port activity, as well as controls on land-based emissions. In
1973, an international conference of the International Maritime Organization (IMO)
adopted the International Convention for the Prevention of Marine Pollution from Ships
(MARPOL) designed to prevent pollution from ships. In 1997, the IMO agreed to
MARPOL Annex VI, a global treaty to reduce air emissions from ships. This treaty went
into effect on May 19, 2005. The treaty sets limits on emissions of sulfur oxides (SOX)
and nitrogen oxides (NOX) and prohibits the international emissions of ozone-depleting
substances, such as chlorofluorocarbons. One key element of Annex VI is the
establishment of "SOX Emission Control Areas" (SECAs) near coastal regions where
controls on SOX emissions from ships are more stringent (1.5% fuel content or 15,000
ppm) than in the open seas (4.5% or 45,000 ppm).
Countries wanting to obtain SECA designation for their coastal areas must submit
a formal application to the IMO. The U.S. Environmental Protection Agency (EPA) is
currently in the process of exploring the feasibility of a SECA for U.S. coastal areas and
plans to work with affected states to obtain the necessary data. As part of the application
process, emissions inventories will be developed and air quality modeling analyses will
be conducted.
EPA will conduct the air quality modeling analyses for the SECA application in-
house using three-dimensional (3-D) grid-based models such as CMAQ and/or CAMx.
One of the issues of interest for this modeling exercise is the determination of the "scales
of interest", i.e., a delineation of the extent and scope of the SECA for each coastal region
that will be considered in the analysis. This determination will be performed using a
"screening-level" modeling analysis, in which a methodology will be developed and
applied to estimate SOX emissions transport from ships at sea to areas off the U.S. coasts
(Pacific, Atlantic, and the Gulf of Mexico) using realistic ship emissions and
meteorology. SOX emissions transport from ships on the Great Lakes will be addressed
separately under the U.S.-Canada binational program.
This document describes the first component of the SOX emissions transport
methodology, which is a literature review of available tools and data to quantify the
transport and residence times of SOX over water. The fate and transport of pollutants over
water has long been of interest because of the potential impacts of off-shore platforms on
air quality over land and the potential impacts of ship emissions on global climate change
and air quality over land. Consequently, there is a significant body of information
available on the atmospheric transport, dispersion and chemistry of pollutants emitted
over water.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-l
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The Minerals Management Service (MMS) has conducted several studies to
investigate the meteorology and the fate and transport of oil platform emissions in the
Gulf of Mexico (e.g., Yocke et al., 1998). Those studies are not directly applicable to the
present study because the emission source is different; nevertheless, some valuable data
and useful experience were obtained in the MMS studies that are relevant to the present
study. The relevant aspects are discussed in this report. The U.S. Navy investigated the
potential of ship emissions reaching shore and that report provides useful information
regarding the different meteorological regimes along the U.S. coastline (Eddington and
Rosenthal, 2003). The California Air Resources Board (2000) also conducted a modeling
study of the transport and dispersion of NOX emissions from ships in the southern
California region. There have also been several academic investigations on the fate and
transport of pollutants emitted from ships. The most recent and relevant one (Song et al.,
2003) pertains to the simulation of sulfur chemistry in a ship plume released in the
marine boundary layer. The authors used a simple box model to simulate the plume.
They concluded that, in the presence of non-precipitating clouds, non-sea salt sulfate
could attain about 2 |ig/m3 after a few hours of plume travel time. The SO2/sulfate
chemistry was found to be linear (i.e., a change in 862 emissions would lead to a
proportional change in sulfate concentrations) except near the ship where SC>2
concentrations exceeded the hydrogen peroxide (H^C^) concentrations.
In this report, we first examine the fate and transport models available for
simulating the transport of pollutants over water. Then, available data sources are
discussed for some of the most important input data beginning with meteorology, then
emission factors, and finally ship activity data.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-2
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AIR QUALITY MODELS
Air quality models can be grouped in two major categories: grid-based Eulerian
models and Lagrangian plume (or puff) models. Eulerian models are well suited to
address air quality for urban and regional pollutants that are emitted from a large variety
of sources. However, their spatial resolution is limited by the grid size and they are not
well suited for addressing air quality impacts associated with individual sources or groups
of sources. Plume or puff models are better suited for such air quality impacts since their
formulation takes into account the dispersion of the emitted material from the source to
the downwind distances of interest.
For this screening study of the potential impacts of SOX emissions from ships at
sea, we are considering Lagrangian plume and puff models since they are the most
suitable. We are considering three models: OCD, CALPUFF and SCICHEM. We briefly
describe these models below and discuss their advantages and shortcomings before
making our recommendations for the air quality model to be used for this study.
OCD
OCD was developed under funding from the Minerals Management Service
(MMS) to simulate plume dispersion and transport from offshore sources to receptor
areas on land or water.
OCD is a steady-state Gaussian model that uses hourly inputs. The steady-state
assumption implies that the wind direction and speed are constant for an air parcel after it
leaves the source regardless of the time needed for an air parcel to travel between the
source and the receptor point. Its formulation includes enhancements that take into
account differences between overwater and overland dispersion characteristics, the sea-
land interface and off-shore platform aerodynamic effects.
OCD requires both overwater and overland meteorological data (i.e., including
wind speed and direction, water surface temperature, overwater air temperature, mixing
height and relative humidity). Missing overwater meteorological data such as turbulence
intensities are parameterized using bulk aerodynamic wind and temperature profile
relationships.
The effect of the source on plume dispersion (stack-tip downwash and building
downwash) can be taken into account. Corrections are made for the presence of complex
terrain. The evolution of the thermal boundary layer near the coast is simulated.
Transitional plume rise and the partial penetration of elevated temperature inversions are
simulated.
OCD can simulate the chemical decay of pollutants using first-order
transformation rates that are user-specified. However, the formation of secondary
pollutants from an emitted primary pollutant (e.g., formation of sulfate from emitted 802)
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-3
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cannot be simulated; this is a major deficiency for this study since it addresses the
possible impacts of sulfate concentrations on air quality. Removal processes (e.g., dry
deposition) are simulated using a first-order decay.
OCD is listed by EPA as a guideline model but only for primary pollutants
(Federal Register, 2003). Therefore, it is not recommended by EPA for secondary air
pollutants such as sulfate formed from SO2 oxidation in the atmosphere.
CALPUFF
CALPUFF was originally developed under funding from the California Air
Resources Board (ARE) along with its associated meteorological model, CALMET
(Scire et al., 2000a, 2000b).
CALPUFF is a non-steady-state puff dispersion model that can simulate the
effects of time- and space-varying meteorological conditions on pollutant transport,
transformation, and removal. It can accommodate arbitrarily varying point, area, volume,
and line source emissions.
The recommended meteorological inputs for applying CALPUFF are the time-
dependent outputs of CALMET, a meteorological model that contains a diagnostic wind
field module and overwater and overland boundary layer modules. Optionally,
CALMET can use the outputs of prognostic meteorological models, such as MM5 and
CSUMM, to create the meteorological fields required by CALPUFF.
CALPUFF includes algorithms for near-source effects such as building
downwash, transitional plume rise, partial plume penetration, sub-grid scale terrain
interactions as well as longer range effects such as pollutant removal due to wet and dry
deposition, simplified chemical transformations, vertical wind shear, overwater transport
and coastal interaction effects.
CALPUFF offers several options to simulate the formation of secondary sulfate
and nitrate particles from the oxidation of the emitted primary gaseous pollutants, 862
and NOX respectively. The oxidation of SO2 to sulfate is of interest for this study. The
more advanced chemistry module available in CALPUFF uses the RIVAD/ARM3
chemical mechanism (Morris et al., 1988). This simple mechanism treats the conversion
of NO to NO2 accompanied by its further transformation to total nitrate and conversion of
SO2 to sulfate. It is assumed that background concentrations of reactive hydrocarbons
(VOC) are low and, therefore, this mechanism is not considered suitable for urban
regions. It may be suitable for oversea situations where VOC concentrations are not too
high. However, in areas such as southern California, where urban coastal pollution may
be transported over the ocean via the land-sea breeze, the assumption of low background
VOC concentrations may sometimes be invalid.
In the RIVAD/ARM3 chemical mechanism, the NO-NO2-O3 chemical system is
first solved to generate pseudo-steady-state concentrations of NO, NO2, and Os. During
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-4
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the day, this system consists of the NC>2 photodissociation to yield NO and 63 and the
NO-Os titration reaction to yield NC>2. During the night, only the NO-Os titration
reaction is considered. The steady-state daytime concentration of the hydroxyl radical
(OH) is calculated from the O3 concentration after the solution of the NO-NO2-O3
system. Gupta et al. (2001) have noted that the 63 concentrations are incorrectly treated
in CALPUFF, resulting in the overestimation of OH concentrations, and thus
overestimations in the gas-phase oxidation rates of SO2 to sulfate and NOxto nitrate. The
RIVAD/ARM3 mechanism does not explicitly calculate the aqueous-phase oxidation of
SO2 to sulfate. Instead, a constant heterogeneous SO2 oxidation rate (0.2% per hour) is
added to the gas phase conversion rate. The partitioning of semi-volatile chemical
species (ammonium nitrate) between the gas phase and the particulate phase is simulated
with a simple thermodynamic model.
CALMET is the companion meteorological model that is used with CALPUFF.
A weakness of CALMET has recently been identified (Wheeler, 2005). CALMET does
not correctly handle cases of unstable convective atmospheric conditions over water
(when water temperature is warm and air temperature is cold, for example) because it
assumes near-neutral conditions over water. Consequently, the mixing height is
calculated based on a neutral mixing relationship and, under conditions of light wind
speeds when the mechanical mixing heights are small, CALMET underpredicts the actual
mixing height. This weakness can be an issue in areas such as the Gulf of Mexico where
warm water temperatures are possible. EarthTech, the developer of CALMET is
addressing this problem by adding a convective mixing height calculation in CALMET.
This new version of the model is currently being tested but it is not yet publicly available.
Based on our discussion with EarthTech (Scire, 2005), we will circumvent this potential
problem by inputting measured or modeled mixing heights directly into CALMET
(meteorological data are discussed in Section 3).
CALPUFF is listed by EPA as a preferred air quality model for assessing the
long-range transport of air pollutants and on a case-by-case basis for certain near-field
applications involving complex meteorological conditions (Federal Register, 2003).
CALPUFF is also recommended by the Federal Land Managers' Air Quality Values
Workgroup (FLAG) for assessing the effects of distant plumes on atmospheric visibility.
SCICHEM
SCICHEM is an extension of the Second-order Closure Integrated PUFF model
(SCIPUFF) that includes atmospheric chemical transformations. It has been developed
under funding from EPRI and the Defense Threat Reduction Agency (DTRA) ((Sykes et
al., 1988, 1993; Sykes and Henn, 1995; Karamchandani et al., 2000; EPRI, 2000).
SCICHEM is a non-steady-state multi-species model that incorporates a
comprehensive treatment for gas- and aqueous-phase chemistry, and PM formation.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-5
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SCIPUFF represents a plume with a multitude of three-dimensional puffs that are
advected and dispersed by the local micrometeorological conditions. Each puff has a
Gaussian representation of the concentrations of individual species. SCIPUFF simulates
the plume transport and dispersion using a second-order closure approach to solve the
turbulent diffusion equations, which provide a direct connection between measurable
velocity statistics and predicted dispersion rates.
SCIPUFF can assimilate observational data ranging from a single wind
measurement to multiple profiles. Alternatively, three-dimensional gridded wind and
temperature fields generated by a prognostic model or other analyses can be used as input
to the model. SCIPUFF can simulate the effect of wind shear since individual puffs
evolve according to their respective locations in an inhomogeneous velocity field. As
puffs grow larger, they may encompass a volume that cannot be considered homogenous
in terms of the meteorological variables. A puff splitting algorithm accounts for such
conditions by splitting puffs that have become too large into a number of smaller puffs.
Conversely, individual puffs that are affected by the same (or very similar)
micrometeorology may also merge to produce a larger single puff. Also, the effects of
buoyancy on plume rise and initial dispersion are simulated by solving the conservation
equations for mass, heat, and momentum.
For PM related regulatory applications, it is important that the underlying model
should account for processes responsible for phase-dependent chemical transformations
and PM characterization. We provide a brief description of chemical and PM
components of SCICHEM.
In SCICHEM, the gas-phase chemical reactions within the puffs are simulated
using a general framework that allows any chemical kinetic mechanism (e.g., CBM-IV,
SAPRC) to be treated. Therefore, SCICHEM can simulate atmospheric conditions
ranging from the clean atmosphere to polluted areas. To minimize the need for
computational resources needed to treat the typical chemical mechanisms, the gas-phase
puff chemistry can optionally be simulated using a three-staged chemical kinetic
mechanism where the number of reactions treated increases as the puff mixes with
background air (Karamchandani et al., 1998). This multistage approach offers reasonable
accuracy (within ±10%) with increased computational speed.
Chemical species concentrations in the puffs are treated as perturbations from the
background concentrations. This approach allows the treatment of overlapping puffs and,
therefore, provides great flexibility for simulating processes such as calm conditions,
wind shear and overlapping plumes for different sources. Optionally, SCICHEM can
explicitly simulate the effect of turbulence on chemical kinetics.
SCICHEM includes aqueous-phase chemistry. It is simulated using the RADM
chemical mechanism. When the aqueous-phase chemistry option is selected, the wet
deposition of pollutants is computed from the cloud water concentrations of pollutants
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-6
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and the precipitation rate. Otherwise, scavenging coefficients are used to calculate wet
deposition. The partitioning of semi-volatile chemical species between the gas phase and
the particulate phase is simulated with the thermodynamic model ISORROPIA.
EPA has added SCIPUFF to the list of alternate models (Appendix B of the EPA
Guideline on Air Quality Models, Federal Register, 2003) for the simulation of the long-
range transport and dispersion of air pollutants.
Recommendations
Table 2-1 presents a summary of the advantages and shortcomings of the three
models reviewed here. The major shortcomings of OCD are its use of the steady-state
assumption and its lack of treatment of chemical transformations. The steady-state
assumption implies that the wind direction and wind speed are assumed to be constant for
a puff released from the source, whereas the other two models allow for changes in wind
direction and wind speed. Chemical transformations in OCD are limited to a simple
decay of the emitted pollutants and do not allow the treatment of secondary pollutant
formation (such as the formation of sulfate from SO2). The major shortcoming of
CALPUFF is its simplified chemistry that tends to overestimate sulfate formation in the
gas phase and uses a simple parameterization for the cloud/fog aqueous phase.
CALPUFF offers the major advantage of being widely used and being an EPA preferred
guideline model for the long-range transport of SOX. SCICHEM offers a more
comprehensive formulation than CALPUFF. However, SCICFIEM is not yet an EPA
preferred guideline model. It is still considered a research-grade model and its
computational requirements are significantly greater than those of the other two models.
On the basis of this review, we recommend that CALPUFF be used to simulate
the transport, transformation and deposition of SOX emissions from ships, with the caveat
that one must bear its limitations in mind.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-7
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Table 2-1. Comparison of the advantages and shortcomings of three plume/puff
models for emissions from off-shore sources.
Characteristics
Steady-state vs.
transient
Spatial resolution
Wind-shear
Plume overlaps
Near-source effects
Chemical
transformations
Dry deposition
Wet deposition
Source types
Regulatory status
Computational
requirements
OCD
Steady-state
Gaussian plume
No
Yes
Yes
First-order decay
First-order decay
No
Point, line and area
EPA preferred
guideline model for
primary pollutants
released over water
Low
CALPUFF
Transient
Puffs
Yes
Yes
Yes
Simplified
chemistry
Yes
Yes
Point, line, area and
volume
EPA preferred
guideline model for
long-range transport
and visibility
impacts of air
pollutants
Moderate
SCICHEM
Transient
Puffs
Yes
Yes
Yes
Comprehensive
chemistry
Yes
Yes
Point
EPA alternate
guideline model
High
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METEOROLOGICAL DATA
Meteorological data are necessary to run an air dispersion model. For the
CALPUFF model, the meteorological input data must first be formatted by the CALMET
pre-processor. CALPUFF requires standard surface and upper air meteorological data.
CALMET also has an overwater option that allows the use of special overwater
measurements for grid cells that are over the ocean. The data required for the overwater
option are: air-sea temperature difference, air temperature, relative humidity, wind speed
and wind direction. Two optional measurements, overwater mixing height and overwater
temperature gradients, may be supplied if available. If the optional parameters are not
supplied, CALMET uses default values.
Land-based Measurements
Land-based meteorological measurements are required for both surface and upper
air observations above land portions of the domain. The data required are standard
format data from the National Climatic Data Center (NCDC) (Scire et al., 2000). The
upper air data required are standard NCDC format TD6201 radiosonde data including
pressure, elevation, temperature, wind direction, and wind speed for each sounding level.
The surface observations that are needed are provided in the NCDC Integrated Surface
Hourly Observations. These include wind speed, wind direction, temperature, and dew
point temperature.
Fixed Overwater Measurements
The required parameters are all available for the Pacific and Atlantic oceans near
the U.S. coastline and for the Gulf of Mexico and Great Lakes from the National Data
Buoy Center (NDBC) (NDBC, 2005). The measurements are taken from buoys. The
buoys are at varying distances from the coast. Those near the coast are frequently near
harbors or bays. Though the coverage is not uniform, the full length of the continental
U.S. coastline is covered by those data. Most of the buoys are owned and operated by
NDBC but there are also several other agencies that submit their data to the NDBC
database. Figures 3-1 through 3-6 show the locations of the NDBC buoys as well as
those that are run by other agencies and are included in the NDBC database.
The Minerals Management Service (MMS) has performed modeling for the
Breton National Wilderness Area which is on the Coast of the Gulf of Mexico. The
MMS has provided us additional overwater data for the Gulf of Mexico including both
surface and upper air data. The availability of upper air data will allow a more thorough
modeling of the unique conditions above the Gulf of Mexico. These data are available
for the years 1999-2001.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-9
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1464021-»
© NOS Stations
HEAR I Stations
• US Moored Buoys
Canadian Moored Buoys
i C-MAN Stations
© Drifting Buoys
3ft DART Buoys
Scripps Stations
* CORIE Stations
Figure 3-1. NDBC buoys along the Washington, Oregon, and northern California
coastline.
• NDBC Moored Buoys
A NDBC C-MAH Stations
Scripps Stations
® NOS Stations
Figure 3-2. NDBC buoys along the southern California coastline
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
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42043 42047
© LSU Stations
® NOS Stations
• NDBC Mooted Buoys
HDBC C-MAH Stations
LUMCON Stations
TABS Stations
TCOON Stations
Forest Oil Stations
Figure 3-3. NDBC buoys along the western Gulf of Mexico
• Moored Buoys
A C-MAH Stations
COMPS Stations
® NOS Stations
• \
Figure 3-4. NDBC buoys along the eastern Gulf of Mexico
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
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CBOS Stations
Stevens Institute
• NDBC Moored Buoys
A NDBC C-MAN Stations
Skidaway Stations
• Caro-COOPS Stations
• NC-COOS Stations
® NOS Stations
Figure 3-5. NDBC buoys along the southeastern U.S. coastline
Long Island Ferry Stations
NDBC Moored Buoys
NDBC C-MAN Stations
• MYSound Stations
GoMOQS Buoys
Stevens Institute
® NOS Stations
..
Figure 3-6. NDBC buoys along the northeastern U.S. coastline.
Modeling Sulfur Oxides (SO^ Emissions Transport From Ships At Sea
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Data from Ships
The CALMET preprocessor also allows for the location of meteorological
observations to vary so that measurements made from ships can be used for modeling as
well. NCDC as well as other agencies have shipboard measurements.
One available database is the International Comprehensive Ocean-Atmosphere
Data Set (ICOADS) from NOAA (NOAA, 2005). It provides data on the location of the
ship, as well as air temperature, sea temperature, wind speed and direction, pressure, and
dew point. These data are available from 1950 through 2002.
Model Outputs
The outputs of meteorological models can be used, particularly in cases where
there are insufficient meteorological observations. This may be the case for upper air
data in the Atlantic and Pacific Oceans. Examples of model outputs that could be used as
surrogates for upper air data include those from the 2001 or 2002 MM5 simulations
sponsored by EPA, those from the NCEP/NCAR reanalysis project and those from the
Advanced Climate Modeling and Environmental Simulations (ACMES) database.
Recommendations
There appears to be sufficient meteorological data to model the transport of SOX
emissions from ships. The availability of upper air data for the Gulf of Mexico will be
particularly valuable. In the absence of upper air data over water for the other areas,
either some default assumptions will need to be made regarding atmospheric stability or
the outputs of archived meteorological simulations will be used. We will discuss our
proposed technical approach in the Analysis Plan that will be prepared in Task 2.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-13
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4. EMISSION FACTORS
Emission factors are needed to estimate the emissions of SOX associated with
various ship activities. We reviewed available emission factors and provide our
recommendations below.
EPA (2000) Emission Factors
Emission factors for air pollutant emissions from ships are provided in the report
titled "Analysis of Commercial Marine Vessels Emissions and Fuel Consumption Data"
(EPA, 2000). Emission factors are provided for several air pollutants including SO2 and
PM. Those emission factors are provided for different oceangoing ship categories that
include bulk carriers and tankers, general cargo ships, container/RoRo/auto
carriers/refrigerated ships, and passenger ships. The emission factors are a function of
the operating mode of the engine; four modes were considered: normal cruise, slow
cruise, maneuvering and docking (hoteling).
For SC>2, the emission factor is a function of the fuel consumption rate and sulfur
content of the fuel. The fuel consumption rate is provided per unit of work (i.e., g/kW-h)
as a function of the fractional load. The engine work (kW-h) is a function of the ship
type (see above) and ship deadweight. The fractional load is the ratio of the actual engine
output and rated engine output; it is a function of the engine mode and ship type.
If one assumes a fuel sulfur content of 3%, the 862 emission factor per unit of
work is 16 g/kW-h for a cruising ship and 20 to 25 g/kW-h for a maneuvering ship. The
SC>2 emission factor per unit of fuel is 71 kg/metric ton.
EC Emission Factors
A recent report from the European Commission (EC) provides emission factors
for air pollutants from ships (EC, 2002). Emission factors are reported for pollutants
including 862 and PM. The emission factors are provided either by engine type and fuel
type (15 combinations) or by ship type (16 oceangoing ship types); different factors are
provided for at sea and in port activities (emission factors for PM are only provided for in
port activities).
The emission factors are reported in g/kW-h. Thus, the engine horsepower must
be estimated as a function of the ship type and activity.
The SC>2 emission factor per unit of work is in the range of 10 to 13 g/kW-h for a
ship at sea and 11 to 13 g/kW-h for a ship at port. The SC>2 emission factor per unit of
fuel is in the range of 46 to 54 kg/metric ton. These emission factors are slightly lower
(by 20 to 35%) than those reported in the EPA report cited above.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-14
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EPA (2002) Emission Factors
A recent EPA report (2002) presents a review of emission factors available from
several sources. The emission factors reviewed were for ships with engines with
displacement exceeding 30 liters (so-called Category 3 engines).
Emission factors are reported for three different engine types (slow speed,
medium speed and steam boiler) for transit modes and hoteling modes.
For slow and medium speed engines, the SC>2 emission factor per unit of work is
about 13 g/kW-h for a ship in transit mode, and 1.4 g/kW-h for a ship in hoteling mode.
For steam boilers, the SC>2 emission factor per unit of work is 20 g/kW-h for a ship in
both transit and hoteling modes. The 862 emission factor per unit of fuel is assumed to be
60 kg/metric ton in transit and 7 kg/metric ton when hoteling (steam boilers were
assumed to use the same fuel while hoteling as in transit, i.e., 30 kg/metric ton). These
emission factors appear to be consistent with those from the EC and lower than those
from the EPA 2000 report.
Recommendations
This brief review of available emission factors for SC>2 emissions from ships show
that there is some general consistency among the different sources of information. The
differences among the various references are well within the uncertainty ranges that one
would expect for emission factors of air pollutants. We propose to use the most recent
EPA emission factors (EPA, 2002) for this study because they represent the most recent
source of information. These emission factors combined with ship type and ship activity
data will provide emission rates of SC>2.
It should be noted that there are no emission factors for sulfate. 862 emission
factors are estimated as a function of the sulfur content of the fuel and the implicit
assumption is that all sulfur is emitted as gaseous SC>2. There is evidence that particulate
sulfate emissions are associated with diesel engines. For example, sulfate may account
for up to 12% of PM emissions from cars and trucks (Shi et al., 2000). PM emission
factors are available for ship emissions. By using the EPA (2002) PM emission factor for
diesel engines of a ship in transit and assuming that PM is 12% sulfate, we obtain an
emission factor of 0.2 g/kW-h. This value corresponds to 1.6% of the SC>2 emission
factor. Data from the Navy Pilot Emission Control Program (NPECP) on PM emissions
from marine diesel engines confirm these results, although the sulfate fraction of PM
depends on the fuel type and the engine regime, ranging from 2 to 19% of PM.
Furthermore, EPA assumes that 2% of sulfur is emitted as primary sulfate PM from
Category 3 marine diesel engines (i.e., those engines with displacement > 30 liters per
cylinder). Because the rate of oxidation of SC>2 to sulfate is slow in the absence of fog or
clouds (on the order of 1% per hour), emissions of sulfate from ships may contribute
significantly under such conditions to the sulfate concentrations over land that are due to
ship emissions. Therefore, we will treat 2% of total sulfur emissions as sulfate emissions
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-15
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and the 862 emission factor will be adjusted down accordingly to maintain the sulfur
mass balance.
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5. SHIP ACTIVITY DATA
Ship activity data must be determined so that the emission factors can be applied
to provide air pollutant emissions from ships. The activity data are typically calculated
based on four types of information: port locations, vessel descriptions, trip records, and
shipping lane definitions. Port locations are available from the United States Army Corps
of Engineers (USAGE) (USAGE, 2005). For efficiency, these data should be aggregated
so that all nearby ports are treated as one.
Once port data have been aggregated, trip data need to be added. Trip data are
necessary to track how many of each type of ship move between each port. The emission
factors will be applied according to ship type, therefore, it is important to characterize the
ship types per shipping lane per year. The USAGE provides data on entrances and
clearances (USAGE, 2005) for vessels traveling under foreign flags. This database lists
each entry and departure of a vessel bearing a foreign flag. Through these databases, a
ship can be traced through its travels through U.S. ports. Information on domestic ship
traffic is also compiled by the USAGE.
The entrances and clearances databases list a ship code that can then be matched
up to another USAGE database. This database provides data on each foreign ship that
has registered at a U.S. port providing information on type, size, and power.
Once all of these data have been gathered and processed, they can be combined to
provide a list of potential trips (e.g., Portland to San Francisco) by type of ship. The final
step is to provide a geographic location for the ship emissions. Since CALPUFF allows
the modeling of line sources, we need to determine the geographic definitions of the
shipping lanes that will be input into the model. These data are available from the
USAGE in the form of the Waterways Network (USAGE, 2005). It provides information
on the latitude and longitude of each node in the U.S. waterways.
Alternatively, the ICOADS database that provides meteorological measurements
from ships can be used to determine shipping lanes (NOAA, 2005). ICOADS provides
time- and space-resolved meteorological data. Because each record provides both the
ship code and a latitude and longitude, ships can be traced along their actual route. In
some places this approach may vary significantly from the theoretical ship lanes available
from the USAGE Waterways Network.
Recommendations
Information on ship activity data is not currently available in a format ready to use
for an air quality modeling study. For the Pacific coast, a moderate amount of work
would be required to complete the processing of the available data into a format suitable
for air quality modeling. For the other areas, a large amount of work would be required
based on the data that we identified. One may consider using hypothetical ship emissions
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-17
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for this air quality modeling study; however, those hypothetical emissions should be
representative of actual ship emissions in order to lead to realistic air quality predictions.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea A-18
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6. REFERENCES
California Air Resources Board. 2000. Air Quality Impacts from NOX Emissions of Two
Potential Marine Vessel Control Strategies in the South Coast Air Basin. Final
Report, prepared by the California Air Resources Board and the South Coast Air
Quality Management District in Consultation with the Deep Sea Vessel/Shipping
Channel Technical Working Group, Sacramento, CA.
EC, 2002. Quantification of emissions from ships associated with ship movements
between ports in the European Community, Final Report prepared by Entec UK
Limited.
Eddington, L. and J. Rosenthal, 2003. The Frequency of Offshore Emissions Reaching
the Continental United States Coast Based on Hourly Surface Winds from a 10
Year Mesoscale Model Simulation, Naval Air Systems Weapons Division, Point
Mugu, CA.
EPA, 2000. Analysis of Commercial Marine Vessels Emissions and Fuel Consumption
Data, EPA-420-R-00-002, U.S. Environmental Protection Agency, Office of
Transportation and Air Quality, Washington, DC
EPA, 2002. Commercial Marine Emission Inventory, Final Report from PECHAN,
prepared by ENVIRON International Corporation, U.S. Environmental Protection
Agency, Office of Transportation and Air Quality, Ann Arbor, MI.
EPRI, 2000. SCICHEM Version 1.2: Technical Documentation, EPRI Report 1000713,
EPRI, Palo Alto, CA.
Federal Register, 2000. Environmental Protection Agency, 40 CFR Part 51, Requirements
for Preparation, Adoption, and Submittal of State Implementation Plans
(Guideline on Air Quality Models); Proposed Rule, Vol. 65, No. 78, Friday, April
21,2000, pp. 21506-21542.
Federal Register, 2003. 40 CFR Part 51, Revision to the Guideline on air quality Models:
Adoption of a Preferred Long Range Transport Model and Other Revisions; Final
Rule, 18440-18482, April 15, 2003.
Gupta, M., N. Kumar, P. Karamchandani, and S.-Y. Wu, 2001. Intercomparison of
SCICHEM and CLAPUFF models using Cumberland plume data, Air & Waste
Management Association Conference on Guidelines on Air Quality Models: A
New Beginning, April 4-6, Newport, RI.
Hanna, S., L. Schulmann, R. Paine, and J. Pleim, 1984. The Offshore and Coastal
Dispersion (OCD) Model User's Guide Revised, MMS 84-0069, Minerals
Management Service.
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Hanna, S., L. Schulman, R. Paine, J. Pleim and M. Baer, 1985. Developoment and
evaluation of the offshore and coastal dispersion (OCD) model, J. Air Pollut.
Control Assoc., 35, 1039-1047.
Karamchandani, P.; A. Koo; C. Seigneur, 1998. Environ. Sci. Technol, 32, 1709-1720.
Karamchandani, P.; L. Santos; I. Sykes; Y. Zhang; C. Tonne; C. Seigneur, 2000. Environ.
Sci. Technol., 34, 870-880.
Morris, R.E., R.C. Kessler, S.G. Douglas, K.R. Styles and G.E. Moore, 1988. Rocky
Mountain Acid Deposition Model Assessment: Acid Rain Mountain Mesoscale
Model (ARMS), Report prepared for the U.S. EPA, Research Triangle Park, NC.
National Ocean and Atmospheric Administration (NOAA), 2005.
http://www.cdc.noaa.gov/coads/ "ICOADS" Last accessed April 20, 2005.
National Data Buoy Center (NDBC), 2005. http://www.ndbc.noaa.gov/index.shtml
"National Data Buoy Center" Last accessed April 20, 2005.
Scire, J.S., D.G. Strimaitis and RJ. Yamartino, 2000. A User's Guide for the CALPUFF
Dispersion Model (Version 5), Earth Tech, Inc. Report, Concord, MA, January
2000.
Scire, J.S., F.R. Robe, M.E. Fernau and RJ. Yamartino, 2000. A User's Guide for the
CALMET Dispersion Model (Version 5), Earth Tech, Inc. Report, Concord, MA,
January 2000.
Scire, J.S., 2005. Communication via e-mail of Christian Seigneur, AER, with Joe Scire,
EarthTech, 18-19 March 2005.
Shi, J.P., D. Mark and R.M. Harrison, 2000. Characterization of particles from a current
technology heavy-duty diesel engine, Environ. Sci. Technol., 34, 748-755.
Song, C.H., G. Chen and D. Davis, 2003. Chemical evolution and dispersion of ship
plumes in the remote marine boundary layer: investigation of sulfur chemistry,
Atmos. Environ., 37, 2663-2679.
Sykes, R. L, W. S. Lewellen, S. F. Parker and D. S. Henn, 1988. A Hierarchy of
Dynamic Plume Models Incorporating Uncertainty, Volume 4: Second-order
Closure Integrated Puff, EPRI, EPRIEA-6095 Volume 4, Project 1616-28.
Sykes, R. L; S. F. Parker; D. S. Henn; W. S. Lewellen, 1993 J. Appl. Met., 32, 929-947.
Sykes, R. I; D. S. Henn, 1995. J. Appl. Met., 34, 2715-2723.
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United States Army Corps of Engineers, 2005.
http://www.iwr.usace.army.mil/ndc/data/datal.htm "Navigation Data Center -
U.S. Waterway Data" Last accessed April 20, 2005.
Wheeler, N., 2005. Private communication from Neil Wheeler, Sonoma Technology,
Inc. to Christian Seigneur, AER, 14 March.
Yocke, M.A. et al., 1998. Meteorology of the northeastern Gulf of Mexico. ENVIRON
International Corp. U.S. DOI. OCS Study: final report, data from 1995 to 1997.
2000. 154 p. Available from GOM (with 3 CD's). MMS 2000-075.
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APPENDIX B
ANALYSIS PLAN
MODELING SULFUR OXIDES (SOX) EMISSIONS TRANSPORT
FOR SHIPS AT SEA
Prepared by
Christian Seigneur
Prakash Karamchandani
Kristen Lohman
Atmospheric & Environmental Research, Inc.
2682 Bishop Drive, Suite 120
San Ramon, CA 94583
Prepared for
U.S. Environmental Protection Agency
Office of Transportation and Air Quality
1200 Pennsylvania Avenue, NW
Washington, DC 20460
Document CP212-05-02a
July 2005
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea B-l
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INTRODUCTION
This document describes the analysis plan for modeling the SO2 and sulfate
concentrations due to emissions of SOX from ships at sea. The results of this screening
modeling study will provide quantitative information on the shortest distance at which
ships burning higher sulfur fuel (here, 27,000 ppm) will have air quality impacts at land
receptors that are less than those anticipated from emissions from ships burning low
sulfur fuel (here, 15,000 ppm) within coastal waters. This resulting distance can
subsequently be used as the basis for defining the modeling domain for sources to be
included in a subsequent modeling study using an Eulerian model (CMAQ). The results
of the CMAQ modeling will yield information to define the outer boundary of a Sulfur
Emission Control Area (SECA). We focus here on the southern Pacific coast. The
methodology presented here is consistent with an approach developed by the Office of
Transportation and Air Quality (OTAQ) of the U.S. Environmental Protection Agency
(EPA) which included input from EPA regional modelers, and staff from the U.S. Navy.
We first describe the overall modeling approach including the fate and transport
model, CALPUFF, that will be used to simulate the transport, transformation and
removal of pollutants over water and land. Then, we describe the selection of the model
input data including meteorological data, SOX emissions and ship activity data.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea B-l
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2. AIR QUALITY MODELING APPROACH
Air Quality Model
For this screening study of the potential impacts of SOX emissions from ships at
sea, we will use the CALPUFF model (Scire et al., 2000a, 2000b). CALPUFF is a non-
steady-state puff dispersion model that can simulate the effects of time- and space-
varying meteorological conditions on pollutant transport, transformation, and removal.
The rationale for selecting CALPUFF was described in the Task 1 report (Seigneur et al.,
2005).
The recommended meteorological inputs for applying CALPUFF are the time-
dependent outputs of CALMET, a meteorological model that contains a diagnostic wind
field module and overwater and overland boundary layer modules. Optionally,
CALMET can use the outputs of prognostic meteorological models, such as MM5 and
CSUMM, to create the meteorological fields required by CALPUFF. The preparation of
the meteorological data inputs for CALPUFF for this study is described in Section 3.
CALPUFF includes algorithms for near-source effects such as building
downwash, transitional plume rise, partial plume penetration, sub-grid scale terrain
interactions as well as longer range effects such as pollutant removal due to wet and dry
deposition, simplified chemical transformations, vertical wind shear, overwater transport
and coastal interaction effects. Because the latter features are relevant to simulating the
transport and chemistry of SOX emissions from ships, they will all be activated for our
study.
CALPUFF offers several options to simulate the formation of secondary sulfate
and nitrate particles from the oxidation of the emitted primary gaseous pollutants, SO2
and NOX respectively. Since the oxidation of SO2 to sulfate is of interest for this study,
we will select the more advanced chemistry module available in CALPUFF which is
based on the RIVAD/ARM3 chemical mechanism (Morris et al., 1988). The limitations
of this chemistry module were discussed in the Task 1 report (Seigneur et al., 2005).
Modeling Domain
The modeling domain for the southern Pacific coast will extend from about 32
degrees North to 36 degrees North and will, therefore, cover southern California.
(Northern California will be grouped with Oregon and Washington, i.e., from 36 degrees
North to 50 degrees North, to constitute the modeling domain for the northern Pacific
coast.) The modeling domain will extend 240 km (150 miles) inland to allow enough
distance to assess the potential air quality impacts of emissions from ships at sea. It will
extend over water at a distance from the coast that corresponds to air quality impacts
below the target concentration at all receptors.
Modeling Sulfur Oxides (SO.J Emissions Transport From Ships At Sea B-2
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Physiographic data (coastline and terrain elevation) will be obtained from the U.S.
Geological Survey.
Receptors
Receptors will be located on land as follows. A line of receptors will be located
at the coastline, 10 km apart. Such a distance provides a finer spatial resolution than that
of the ship emissions along the coast (see Section 5). Inland receptors will then be
located eastward at 10, 10, 20, 20, 30, 30, 40, 40 and 40 km apart from each other, i.e., up
to 240 km (150 miles) from the coastline; there will, therefore, be 10 lines of receptors
from the coast (included) up to 240 km inland. All receptors will be located at ground
level. The total number of receptors is, therefore, estimated to be on the order of 500.
Sources
Ship emissions will be represented by a set of stationary point sources. Each
point source will represent a ship. They will be located at a selected distance from shore
(see below) and apart at a distance to be defined based on ship traffic (see Section 5).
The use of stationary sources to represent moving ships is an appropriate approximation
for this screening modeling study, because using stationary sources will overestimate the
downwind air quality impacts (emissions will be concentrated in specific locations rather
than continuously distributed along the shipping lane, thereby leading to greater ambient
air concentrations).
We considered but rejected an alternative approach. The approach would treat
each ship as an individual source and simulate its impact on air quality inland. Target
concentrations would be calculated from individual ships at the coast (dockside mode)
with the highest concentration obtained at each receptor being selected as the target
concentration for that receptor. Then, the impacts of individual ships would be evaluated
against those target concentrations. This alternative approach offers the advantage of
providing more detailed information regarding the impacts from ships since it addresses
individual ships rather than a shipping lane; thus, different SECA distances could be
identified in different parts of the domain. Such an approach requires many more model
simulations than the approach proposed here, however, and, therefore, could not be
considered for this screening study. Also, comparing with the highest concentration
obtained for that receptor does not account for variability of concentrations at receptors,
and may result in an overestimation of the boundary distance. Nevertheless, we point out
below how the variability of the SECA distance within the study domain will be
addressed.
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Modeling Approach
Our modeling approach will consist of two phases. In the first phase, we will
calculate, at each inland receptor, the target values for the SCh and sulfate concentrations
that correspond to emission from ships at dockside; i.e., those ships that are within the
SECA and therefore must burn low sulfur fuel; i.e., 15,000 ppm). These will be annual
average concentrations. (It is not necessary to calculate the light extinction coefficient
because it will be proportional here to the sulfate concentration.) In the second phase, we
will calculate the annual average values of the SC>2 and sulfate concentrations
corresponding to emissions from ships burning high sulfur fuel (i.e., 27,000 ppm) at
various distances from the coast and will compare those values to the target values
obtained in the first modeling phase.
All simulations will be conducted for one year and we will calculate and use
annual average values in our analysis. We propose to use 2002 as our reference year
because it corresponds to the year that will be used for grid-based air quality modeling by
the EPA Office of Air Quality Planning and Standards (OAQPS).
For the first phase, we will locate the ships at the coastline (dockside mode).
They will be distributed spatially according to their estimated density in a shipping lane
(see Section 5). The SECA SOX emission rates will be used (see Section 4). We will
calculate the annual SO2 and, sulfate concentrations at each receptor. These values will
be defined as the target values that will be used as benchmarks for the Phase 2 modeling.
For the second phase, we will locate the ships at various distances from the
coastline. For a given modeling scenario, all ships will be at the same distance from the
coastline; they will be distributed spatially according to their estimated density in a
shipping lane (see Section 5), and for all modeling scenarios the number of dockside
ships will equal the number of off-shore ships. The SOX emission rates outside of the
SECA will be used (see Section 4). The objective is to determine a set of distances at
which those ship emissions will lead to air quality impacts that are less than or equal to
the target values calculated in Phase 1 for the following percents of onshore receptors:
50, 60, 70, 80, and 90. To that end, we will conduct CALPUFF annual simulations for
various distances from the coastline. We will start with a 100 km distance, and receptor
percentage of 50. If the modeling results show air quality impacts lower than the target
values, at 50 percent or more of the onshore receptors we will then use a shorter distance
(50 km). Conversely, if the modeling results show air quality impacts greater than the
target values at 50 percent or more of the onshore receptors, we will use a greater
distance (200 km). This process will be repeated until we identify the distance of interest
(i.e., the distance where air quality impacts are commensurate with the target values).
For example, if the modeling results conducted for a distance of 50 km show air quality
impacts lower than the target values, for at least 50 percent of the onshore receptors, we
will next use a shorter distance (20 or 30 km). If those modeling results show air quality
impacts greater than the target values for at least 50 percent of the onshore receptors, we
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will then use a greater distance (70 or 80 km). We will stop when we have identified a
distance that leads to air quality impacts commensurate with the target values. This
process will be repeated for the other percentages of onshore receptors (60, 70, 80 and
90). For all percentages, a tolerance of plus/minus 2 percent will be used. We propose to
use a resolution of 10 km (i.e., we will not refine those distances within less than 10 km
increments).
The criterion of percentages of onshore receptors is used as an initial
investigation. As we approach the distance of interest, some receptors will show values
greater than the target values whereas other receptors may show values lower than the
target values. The distribution of these receptors is significant. For example, by
definition, fewer receptors have concentrations in excess of target concentrations at the
60% level than 50%. But if the receptors in excess of the target concentrations at both
the 50 and 60% levels are located say, within 10 km of the coastline, then even at the
greater distances comparable levels of population may still be exposed to concentrations
greater than target levels. In this example, the distribution may indicate that a greater
distance should be considered. Therefore, evaluation of these various distances will be
conducted by the modeling review team as part of the Task 3 analysis.
Another reason for using the criterion of percentages of onshore receptors is that
SC>2 and sulfate concentrations will display different behaviors downwind of the ships.
SC>2 concentrations will decrease continuously with distance from the source (due to
dilution, removal, and conversion to sulfate), whereas sulfate concentrations will first
decrease (dilution and removal of primary, i.e., directly emitted sulfate), then increase
(formation of secondary sulfate from the oxidation of 802) before finally decreasing
(dilution and removal exceeding formation).
This behavior of sulfate introduces an additional complication: the sulfate target
values at receptors near the coastline will be determined by the directly emitted sulfate,
while the target values at larger distances inland will be determined by some combination
of primary and secondary sulfate, with the secondary sulfate component increasing and
the primary sulfate component decreasing. Even further inland, both components will
decrease as the rate of dilution and removal exceeds the formation of sulfate.
To understand how this complex behavior of sulfate may impact the analysis, let
us consider the extreme case of no primary sulfate, i.e., all the SOX is emitted as SC>2. In
this case, the target sulfate values next to the coastline will be negligible because there
will be minimal time for conversion of SC>2 to sulfate. However, there will be some
plume travel time for emissions from ships at sea that will allow some conversion of SC>2
to sulfate. Consequently, it may be impossible in this extreme case to meet target values
at the coastline receptors unless a very large SECA is defined.
Therefore, it is possible that all sulfate concentrations may not fall below the
target values as we approach the distance of interest for the SECA. Accordingly, we will
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need to report the results in terms of the fraction (or percentage) of receptors that exceed
the target values for each pollutant.
We will report the results for each distance in terms of maximum concentration,
average concentration and fraction of receptors above the target value for SC>2 and for
sulfate (all values will be for receptors over land). If significant differences appear for
different areas of the study domain (e.g., one area shows impacts above target
concentrations for at least 50 percent of the onshore receptors for a shorter distance than
another area), we will identify those differences and discuss whether they suggest the
need for some variability for the SECA distance within the study domain.
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3. METEOROLOGICAL DATA
CALMET is the companion meteorological model that is used to prepare the
meteorological fields used by CALPUFF.
A weakness of CALMET has recently been identified (Wheeler, 2005).
CALMET does not correctly handle cases of unstable convective atmospheric conditions
over water (when water temperature is warm and air temperature is cold, for example)
because it assumes near-neutral conditions over water. Consequently, the mixing height
is calculated based on a neutral mixing relationship and, under conditions of light wind
speeds when the mechanical mixing heights are small, CALMET underpredicts the actual
mixing height. This weakness can be an issue in areas where warm water temperatures
are possible, such as the Gulf of Mexico, the southern Pacific coast and the southern
Atlantic coast. Therefore, we address this potential issue here as it is important for this
area as well as for subsequent modeling areas. Based on our discussion with the
CALMET developer, EarthTech (Scire, 2005), we will circumvent this potential problem
by inputting measured or modeled mixing heights directly into CALMET. For the
southern Pacific coast, no upper air measurements are available over water and we will,
therefore, use modeled mixing heights, as described below.
Meteorological data are necessary to run an air dispersion model. For the
CALPUFF model, the meteorological input data must first be formatted by the CALMET
pre-processor. CALPUFF requires standard surface and upper air meteorological data.
CALMET also has an overwater option that allows the use of special overwater
measurements for grid cells that are over the ocean. The data required for the overwater
option are: air-sea temperature difference, air temperature, relative humidity, wind speed
and wind direction. Two optional measurements, overwater mixing height and overwater
temperature gradients, may be supplied if available. If the optional parameters are not
supplied, CALMET uses default values. We propose to supply temperature gradients
obtained from the outputs of a prognostic meteorological model (see below).
Land-based Measurements
Land-based meteorological measurements are required for both surface and upper
air observations above land portions of the domain. The data required are standard
format data from the National Climatic Data Center (NCDC) (Scire et al., 2000b).
The upper air data required are standard NCDC format TD6201 radiosonde data
including pressure, elevation, temperature, wind direction, and wind speed for each
sounding level. There are four upper air stations that are located within the modeling
domain:
• San Nicolas Island (33.25 degrees North, -199.45 degrees West)
• Miramar (32.87 degrees North, -117.15 degrees West)
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• Point Mugu (34.10 degrees North, -119.12 degrees West)
• Vandenberg (34.67 degrees North, -120.58 degrees West)
The surface observations that are needed are provided in the NCDC Integrated
Surface Hourly Observations. These include wind speed, wind direction, temperature,
and dew point temperature. There are many surface stations within the modeling domain
(255 for the state of California).
Overwater Measurements
The required CALMET parameters are all available for the Pacific Ocean near the
U.S. coastline from the National Data Buoy Center (NDBC) (NDBC, 2005). The
measurements are taken from buoys. The buoys are at varying distances from the coast.
Those near the coast are frequently near harbors or bays. Most of the buoys are owned
and operated by NDBC but there are also several other agencies that submit their data to
the NDBC database. Though the coverage is not uniform, there is a fairly comprehensive
coverage for the southern Pacific coast. Figure 3-1 shows the locations of the NDBC
buoys as well as those that are run by other agencies and are included in the NDBC
database.
Model Outputs
The outputs of meteorological models can be used, particularly in cases where
there are insufficient meteorological observations. This is the case for upper air data over
water in the Pacific Ocean. Examples of model outputs that could be used as surrogates
for upper air data include those from the 2001 or 2002 MM5 simulations sponsored by
EPA, those from the NCEP/NCAR reanalysis project and those from the Advanced
Climate Modeling and Environmental Simulations (ACMES) database.
CALMET can take as input the output of MM5. It can also combine MM5 output
with observations. An interface program (CALMM5) converts the MM5 data into a form
compatible with CALMET. A new version of this processor has been added to the
CALPUFF-CALMET Download BETA-Test page recently (May 25, 2005). This beta
version (not yet officially approved by the EPA) of CALMM5 processes MM5 Version 3
output data directly. Using the output of another meteorological model (e.g., ACMES)
would require the development of a new CALMET pre-processor that would be outside
the scope of this project. Therefore, we will use the MM5 output for this application.
Another advantage of using the MM5 outputs is that it will provide consistency with the
subsequent grid-based modeling that will be conducted by OAQPS using the Community
Multiscale Air Quality model (CMAQ), because CMAQ will be driven with the MM5
meteorology.
The MM5 modeling domain covers the entire contiguous United States and
extends significantly over the oceans. For the southern Pacific coast domain, it extends
at least 400 to 900 km westward from the coast. Therefore, it will cover the CALPUFF
modeling domain needed to address the SEC A.
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California
35 N
• HDBC Moored Buoys
A NDBC C-MAN Stations
Soripps Stations
® NOS Stations
Figure 3-1. NDBC buoys along the southern California coastline
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Summary
We will use a combination of MM5 model output, surface observations over
water from the NDBC database, surface observations over land from the NCDC database
and upper air observations over land from four stations from the NCDC database. These
data will be processed by CALMET to prepare a three-dimensional meteorological data
setforCALPUFF.
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4. SOX EMISSIONS
Emission factors are needed to estimate the emissions of SOX (gas-phase SO2 and
particulate-phase sulfate) associated with various ship activities. Based on the review of
available emission factors of Seigneur et al. (2005), the most recent EPA emission factors
were selected (EPA, 2002). Those emission factors pertain to ships with engines with
displacement exceeding 30 liters (so-called Category 3 engines).
Emission factors are reported for three different engine types (slow speed,
medium speed and steam boiler) for transit modes and hoteling modes. For this study of
ships at sea, we are interested in medium speeds for transit modes.
The SC>2 emission factor per unit of work is reported to be 9.56 g/hp-h for a 3%
sulfur fuel (i.e., 30,000 ppm) for a ship at slow or medium speed in transit mode. This is
equivalent to 12.8 g/kW-h.
For a ship within the SECA, a fuel sulfur content of 15,000 ppm will be assumed.
Therefore, the emission factor will be 6.4 g/kW-h.
For a ship at sea outside of the SECA, a fuel sulfur content of 27,000 ppm will be
assumed. Therefore, the emission factor will be 11.52 g/kW-h.
EPA assumes that 2% of sulfur is emitted as primary sulfate PM from Category 3
marine diesel engines. Therefore, we treat 2% of total sulfur emissions as sulfate
emissions and the SC>2 emission factor is adjusted down accordingly to maintain the
sulfur mass balance. (Note that for the same amount of S, the sulfate emission factor is
1.5 the SO2 emission factor to account for the different molecular weights.)
Therefore, within the SECA, the gas-phase SO2 and particulate-phase sulfate
emission factors will be 6.27 g/kW-h and 0.19 g/kW-h, respectively. Outside of the
SECA, the gas-phase SO2 and particulate-phase sulfate emission factors will be 11.29
g/kW-h and 0.35 g/kW-h, respectively.
The sulfate emission rates calculated above are consistent with available data on
the sulfate fraction of paniculate matter (PM) emitted from ship diesel engines. Fleischer
et al. report that 20 to 30% of PM emissions from ship diesel engines are sulfate (for a
3% sulfur fuel content). The EPA (2002) emission factor for PM is 1.3 g/hp-h, i.e., 1.74
g/kW-h. These values lead to an emission factor for sulfate in the range of 0.31 to 0.47
g/kW-h for a sulfur fuel content of 27,000 ppm. The emission factor of 0.35 g/kW-h
calculated above falls within this range.
Based on data from Corbett and Koehler (2003), the power of a typical ship was
estimated to be 16,000 kW (Corbett, 2005). It should be noted that there is a wide range
of power among various ships, with the largest container ships having power exceeding
65,000 kW.
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The gas-phase SC>2 and particulate-phase sulfate emissions per ship are then
calculated to be 100,320 g/h and 3,040 g/h, respectively, within the SECA and 180,640
g/h and 5,600 g/h, respectively, outside the SECA.
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5. SHIP ACTIVITY DATA
Ship activity data must be estimated so that the density of ships within the
modeling domain can be calculated. Knowing the average number, N, of ships in transit
along the southern Pacific coast per year and assuming an average cruising speed, V
(km/h), we can calculate the average distance, D (km), between two ships along a
shipping lane.
D = V * (24 h/day * 365 days/yr) / N
The annual number of ships transiting along the southern California coast was
estimated to be 13,000 (ICOADS, 2002). This number includes all ships transiting to and
from ports located on the southern Pacific coast as well as ships transiting
southward/northward from/to ports located on the northern Pacific coast. It is likely to be
an overestimate of the number of ships transiting along the coast because a fraction of
those ships will be transiting along shipping lanes that extend from the ports westward
into the Pacific Ocean. The cruising speed varies according to ship type. It is about 24
knots for container ships and about 16 knots for tankers. Here, the average ship cruising
speed was estimated to be about 20 knots, i.e., 36 km/h (ICOADS, 2002). Thus, the
average distance is estimated for the southern Pacific coast as follows.
D = 36 * 24 * 365 / 13,000 = 24.3 km
Based on this analysis, we propose to use a distance of 25 km between ships to
calculate ship emissions.
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6. REFERENCES
Corbett, JJ. and H.W. Koehler, 2003. Updated emissions from ocean shipping, J.
Geophys. Res., 108, doi:10.1029/2003JD003751.
Corbett, J.J., 2005. Private communication to Christian Seigneur, AER, July 2005.
EPA, 2002. Commercial Marine Emission Inventory, Final Report from PECHAN,
prepared by ENVIRON International Corporation, U.S. Environmental Protection
Agency, Office of Transportation and Air Quality, Ann Arbor, MI.
Fleischer, F., EJ. Ulrich, R. Krapp and W. Grundmann. Comments on particulate
emissions from diesel engines when burning heavy fuels.
ICOADS, 2002. International Comprehensive Ocean Atmospheric Data Set, as
transmitted from ERG by Office of Transportation and Air Quality, U.S.
Environmental Protection Agency, Washington, D.C.
Morris, RE., R.C. Kessler, S.G. Douglas, K.R. Styles and GE. Moore, 1988. Rocky
Mountain Acid Deposition Model Assessment: Acid Rain Mountain Mesoscale
Model (ARMS), Report prepared for the U.S. EPA, Research Triangle Park, NC.
Scire, J.S., D.G. Strimaitis and RJ. Yamartino, 2000a. A User's Guide for the CALPUFF
Dispersion Model (Version 5), Earth Tech, Inc. Report, Concord, MA, January
2000.
Scire, J.S., F.R. Robe, ME. Fernau and R.J. Yamartino, 2000b. A User's Guide for the
CALMET Dispersion Model (Version 5), Earth Tech, Inc. Report, Concord, MA,
January 2000.
Scire, J.S., 2005. Communication via e-mail of Christian Seigneur, AER, with Joe Scire,
EarthTech, 18-19 March 2005.
Seigneur, C. K. Lohman and P. Karamchandani, 2005. Review of Technical Information
relevant to Sulfur Oxides (SOX) Emissions Transport for Ships at Sea, Final
Report to Office of Transportation and Air Quality, U.S. Environmental
Protection Agency, Washington, D.C.
Wheeler, N., 2005. Private communication from Neil Wheeler, Sonoma Technology,
Inc. to Christian Seigneur, AER, 14 March.
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