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Mitigating the Health Impacts of Pollution from Oceangoing Shipping: An Assessment of Low-Sulfur Fuel Mandates

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Department of STS/Public Policy, 1356 Eastman Building, Rochester Institute of Technology, Rochester, New York 14623, College of Earth, Ocean, and Environment, 305 Robinson Hall, University of Delaware, Newark, Delaware 19716, International Pacific Research Center, University of Hawaii, 1680 East-West Road, Honolulu, Hawaii 96822, and Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Wessling, Germany
* Corresponding author phone: 585-475-4648; e-mail: [email protected]
†Rochester Institute of Technology.
‡University of Delaware.
§International Pacific Research Center.
∥Institut für Physik der Atmosphäre.
Cite this: Environ. Sci. Technol. 2009, 43, 13, 4776–4782
Publication Date (Web):June 3, 2009
https://doi.org/10.1021/es803224q

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Abstract

Concerns about health effects due to emissions from ships have magnified international policy debate regarding low-sulfur fuel mandates for marine fuel. Policy discussions center on setting sulfur content levels and the geographic specification of low-sulfur fuel use. We quantify changes in premature mortality due to emissions from ships under several sulfur emissions control scenarios. We compare a 2012 No Control scenario (assuming 2.7% or 27 000 ppm S) with three emissions control scenarios. Two control scenarios represent cases where marine fuel is limited to 0.5% S (5000 ppm) and 0.1% S (1000 ppm) content, respectively, within 200 nautical miles of coastal areas. The third control scenario represents a global limit of 0.5% S. We apply the global climate model ECHAM5/MESSy1-MADE to geospatial emissions inventories to determine worldwide concentrations of particular matter (PM2.5) from oceangoing vessels. Using those PM2.5 concentrations in cardiopulmonary and lung cancer concentration-risk functions and population models, we estimate annual premature mortality. Without control, our central estimate is approximately 87 000 premature deaths annually in 2012. Coastal area control scenarios reduce premature deaths by ∼33 500 for the 0.5% case and ∼43 500 for the 0.1% case. Where fuel sulfur content is reduced globally to 0.5% S, premature deaths are reduced by ∼41 200. These results provide important support that global health benefits are associated with low-sulfur marine fuels, and allow for relative comparison of the benefits of alternative control strategies.

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Synopsis

Regulatory proposals to reduce the sulfur content of fuel used in international shipping could prevent tens of thousands of premature deaths annually.

Introduction

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One of the more nefarious environmental impacts of goods movement is its effect on air quality and human health (1-6). Specifically, particulate matter with aerodynamic diameters of 2.5 μm or less (PM2.5) from international shipping poses special concerns. Ambient concentrations of PM2.5 have been associated with a wide range of health effects including asthma, heart attacks, and hospital admissions. Increases in atmospheric PM2.5 concentrations have also been closely associated with increases in premature cardiopulmonary and lung cancer mortalities in exposed populations (7, 8).
Particulate matter from ship emissions is related to the sulfur content of marine fuel. In the case of oceangoing vessels, fuel sulfur content averages around 2.7% (27 000 ppm) with upper limits as high as 4.5% S (45 000 ppm) (3). We have estimated in previous work that PM2.5 due to the burning of such fuel can lead to on-land health impacts on the order of 60 000 premature mortalities in 2002 (3).
Given these health impacts and other concerns related to pollution from high-sulfur fuels, policies aimed at reducing the sulfur content of marine fuel were adopted by the International Maritime Organisation (IMO) under ANNEX VI of MARPOL 73/78 (the International Convention for the Prevention of Pollution from Ships). In particular, the global fuel sulfur cap will be reduced to 3.5% S in 2012 and 0.5% S as early as 2020, which implies a movement from residual fuel oil to distillate fuels (i.e., marine diesel oil and marine gas oil). More stringent reductions will be required for ships operating in sulfur emission control areas (SECA). These areas (of which there are currently three, but are expected to increase in the coming years) will require sulfur content of no more than 1% S (10 000 ppm) by 2010 and 0.1% S (1000 ppm) by 2015 within 200 nautical miles (nm) of coastal areas.
We quantify premature mortality due to emissions from ships operating under several sulfur emissions control scenarios. We do this by applying the global climate model ECHAM5/MESSy1-MADE to a geospatial inventory of shipping emissions to determine worldwide concentrations of PM2.5 from oceangoing vessels assuming no emissions control regimes. We then use those PM2.5 concentrations in cardiopulmonary and lung cancer concentration-risk functions and population models to estimate annual premature mortality from these emissions. The paper compares a 2012 No Control scenario (assuming a global average of 2.7% or 27 000 ppm S) with three emissions control scenarios. The first two control scenarios represent cases where marine fuel is limited to 0.5% S (5000 ppm) and 0.1% S (1000 ppm) content, respectively, within 200 nm of coastal areas. The third control scenario represents a case where sulfur content is limited to 0.5% S globally. In this way, we provide a global estimate of some human health benefits associated with different control options. Other impacts, such as the effects of these policies on atmospheric aerosol burdens and the Earth’s radiation budget, are discussed in a related paper (9).
The next section of the paper discusses our analytical approach, including the process of generating geospatially resolved emissions inventories for ships, processing these inventories through ECHAM5/MESSy1-MADE to calculate PM2.5 concentrations, and applying these concentrations to risk functions to estimate health impacts. This is followed by a discussion of our results and the implications of this study.

Analytical Approach

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Emissions Inventories and Concentrations

We employ the approach taken in previously published work and described in more detail in related papers (3, 9). This approach involves (1) generating geospatial emissions inventories for ships under different regulatory mandates (e.g., fuel sulfur content controls); (2) processing these emissions inventories with global inventories from other sources through a global atmospheric aerosol/chemistry model to determine the geospatial concentrations of pollutants attributed to shipping; and (3) applying concentration-risk (C-R) functions and population models to determine health impacts from these concentrations.
We compare health impacts from a 2012 “No Control” scenario against three emissions control scenarios. Therefore, as mentioned above, we have four scenarios for our analysis: (1) No Control, representing a case where marine fuel is 2.7% S globally; (2) Coastal 0.5, representing a case where marine fuel is 0.5% S within 200 nm of coastal areas; (3) Coastal 0.1, representing a case where marine fuel is 0.1% S within 200 nm of coastal areas; and (4) Global 0.5, representing a case where marine fuel is 0.5% S globally.
We employ the same geospatial inventory (10), atmospheric model, uncertainty factors, and health risk functions as reported previously (3). A detailed discussion of uncertainty is provided in the Supporting Information (SI). Where earlier work demonstrated mortality results that were consistent with previous scientific studies at both a global and regional scale (11, 12), this paper compares control scenarios with our prior study to assess health benefits of fuel sulfur control.
PM2.5 emissions estimates were obtained based on a global representation of ship emissions inventories of sulfur, black carbon, and organic carbon. We use the previously derived 2002 emissions inventories based on the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) and the Automated Mutual-Assistance Vessel Rescue System (AMVER) (10). These data sets combine detailed information about vessel characteristics with vessel traffic densities to determine emissions geospatially. All oceangoing commercial ship types are included in these data sets, with some sampling bias based on over-reporting of some vessel types, depending on the data set. More details on both data sets and a comparison are given by Wang et al. (2008) and our companion paper (9).
Ship emissions for 2012 were estimated from current inventories using a uniform global annual compound growth rate of 4.1% to establish our No Control scenario (13). To create inventories for our Coastal 0.5, Coastal 0.1, and Global 0.5 cases, we reduced emissions of sulfur and particulate organic matter (POM) to correspond with properties for different marine fuel types, as shown in Table 1. Aside from the reductions in fuel sulfur content made plain by our scenario titles, we also reduced POM emissions by ∼68% for marine diesel oil at 0.5% S and by ∼80% for marine gas oil at 0.1% S to correspond to expected differences among residual and distillate fuel PM2.5 measurements (14).
Table 1. Relative Emissions by Pollutant for Each of Our 2012 Scenarios Compared to the No Control Casea
ship emission scenario fuel sulfur content NOx SO2 primary SO4 CO BC POM
2012 No Control 2.7% 100% 100% 100% 100% 100% 100%
2012 Coastal 0.5 0.5% 100% 18.5% 18.5% 100% 100% 32.1%
2012 Coastal 0.1 0.1% 100% 3.7% 3.7% 100% 100% 20.0%
2012 Global 0.5 0.5% 100% 18.5% 18.5% 100% 100% 32.1%
a

Emission reductions are applied within 200 nautical miles of coastal areas in the Coastal scenarios and globally in the Global scenario. Source: ref 9

These ship emissions inventories are overlaid on global emissions inventories without ship traffic to obtain the increased PM2.5 pollution attributed to vessel operations. Inventories for nonship sources are for the year 2000 and therefore do not capture changes in nonship emissions that may occur between 2000 and 2012. A sensitivity study with scaled-up nonship emissions showed that our approach may introduce an uncertainty of about 5−15% to the calculated number of premature deaths. This is similar to the uncertainty associated with the geographical distribution of the ship emissions (AMVER, ICOADS) investigated in this study. Additional details can be found in the SI.
The fate and transport of particles emitted or formed from gaseous ship emissions were then summed into PM2.5 concentration data (in μg/m3 dry weight) from atmospheric modeling using ECHAM5/MESSy1-MADE (referred to as E5/M1-MADE (15)), an aerosol microphysics module (MADE) coupled to a general circulation model (ECHAM5 (16)), within the framework of the Modular Earth Submodel System MESSy (17). Along with global PM2.5 concentrations attributed to nonship sources, the E5/M1-MADE model provides ambient concentrations of BC, POM, sulfates (SO4), nitrates (NO3), and ammonium ion (NH4) aerosols. This model was run under similar input assumptions as previously published (15), and described in greater detail in related papers (3, 9). Model output at 2.8 × 2.8° was interpolated to a 1 × 1° global resolution; therefore, we report our results at the global and continental scales.
Because health-risk studies have concentrated on long-term mortality impacts, we aggregated monthly PM2.5 data into annual averages. Monthly variations in total ship activity and near coastlines are small, and seasonal variation in open-ocean shipping lanes can be neglected at this model resolution, especially given that we are primarily interested in the change across scenarios (10). For example, although some variation does exist over open oceans (due to seasonal shifts in shipping routes and varying meteorological conditions), concentration variation on land is much smaller because nearly 70% of global ship emissions are within 400 km of land, and because ship activity and emissions near shore remain relatively constant throughout the year (18). We do not expect modest seasonal variations near coastlines represented in ICOADS or AMVER global shipping patterns to affect long-term mortality results at the model resolution used in this global study.
Comparing results of each scenario with and without ship inventories of PM2.5 components and precursor gases, we quantify changes in ambient concentrations of PM2.5 due to marine shipping. Figure 1 presents globally distributed PM2.5 concentrations due to ships on a 1 × 1° global grid for each of our scenarios using the ICOADS inventory data set (a similar map for AMVER may be found in the SI.

Figure 1

Figure 1. Concentrations of PM2.5 for four scenarios with ICOADS data in micrograms per cubic meter. Shows geospatial distribution of PM2.5 pollution due to oceangoing vessels in the 2012 No Control scenario and the three control scenarios explored here; values represent concentrations due to shipping (compared to an environment without shipping emissions). (a) No Control scenario; (b) Coastal 0.5% scenario; (c) Coastal 0.1% scenario; (d) Global 0.5% scenario.

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Health Impacts

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We calculate health impacts due to these PM2.5 concentrations by applying a health effects model that incorporates demographic data, background incidence data, and concentration-risk (C-R) functions for cardiopulmonary and lung cancer premature mortality. We use population forecasts obtained in a 1 × 1° format from the Socioeconomic Data and Applications Center at Columbia University (19). Acquiring values for 2010 and 2015, we linearly interpolate values for 2012. We also used U.S. Census Bureau International Database numbers to derive, by continent, the percent of population between 30 and 99 years of age (the age group of concern for the examined mortality impacts).
The incidence rates of these health impacts (necessary for calculating the increased risk due to ship emissions, as shown below) were estimated using World Health Organization (WHO) data. We use 2002 WHO estimates for causes of death by age to derive the incidence rates by WHO region for each type of mortality examined. United States cardiopulmonary incidence values obtained from Environmental Benefits Mapping and Analysis Program (BenMAP) technical documentation of the U.S. Environmental Protection Agency (EPA) were used for North American incidence estimates (20).
Mortality impacts are estimated using C-R functions from Pope et al. (2002), the same epidemiological study used to estimate mortality impacts in the U.S. EPA’s regulatory analysis of controlling emissions from nonroad diesel engines. Here, we assume that the global PM2.5 concentration-mortality association is equivalent to that of the United States. As noted in other work (21), epidemiological studies have found a relatively consistent association between short-term PM2.5 exposure and mortality across several countries—from South America to Western Europe. By analogy, we assume long-term PM2.5 exposures will be similarly consistent—an assumption made by other experts in air pollution health-risk assessments (22). Functional coefficient values were obtained for cardiopulmonary and lung cancer mortality from Ostro (2004).
We employed log-linear C-R functions to estimate changes in relative risk of mortality, as recommended in Ostro (2004). The relative risk is calculated considering the C-R function and change in PM2.5 concentration, and is given by where β = 0.1551 (95% confidence interval (CI) = 0.05624, 0.2541) for cardiopulmonary mortality, β = 0.232179 (95% CI = 0.08563, 0.37873) for lung cancer related mortality, X0 represents the concentration (μg/m3) of the base case, and X1 represents the concentration (μg/m3) of the case under evaluation (22). Ostro (2004) calls this the log-linear formulation because it relates risk to a logarithmic function of concentration.
We applied changes in relative risk, population, and existing incidence rates to calculate the change in mortality due to ship pollution for each grid cell. Ship pollution incrementally increases ambient PM2.5 conditions over a base pollution concentration attributed to nonshipping activities. Our formulation does not include a threshold effect for PM2.5 and there has been some discussion in the literature as to whether a threshold effect exists for PM2.5 at low concentrations (23-25). However, we decided to use the previously published nonthreshold models given that most impacts will be seen over populated regions where PM2.5 concentrations are relatively high already (i.e., above potential thresholds). Thus, the total effect (E) of additional PM2.5 concentration is given by where B represents the incidence of the given health effect (e.g., deaths/1000 people); P represents the relevant exposed population; and, AF is the relative risk due to the increase in pollution, and is given by In addition, for our control scenarios, we only calculate health impacts where differences between the climatological annual mean concentrations of PM2.5 in the control scenario compared to the No Control scenario were statistically significant at a 99% confidence level compared to their interannual variability. This trimming controls for potentially insignificant differences among the No Control and three control scenarios, a refinement to our previous study.

Results

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Figure 2 shows global distributions of premature mortality due to ship emissions for our No Control case using the ICOADS data set. Figure 3 shows premature mortality for each of our cases using a box plot, where the low, middle, and high estimates are presented. Lastly, Figure 4 presents geospatially the estimated number of avoided premature deaths as policy-driven controls move from the No Control case to each of our three control scenarios. Similar figures for AMVER can be found in the SI. We also include in Table 2 specific values for expected annual mortality due to ship emissions, and expected avoided premature mortalities under our three scenarios. Tables that show disaggregated values by region are included in the SI.

Figure 2

Figure 2. Annual premature mortality for the No Control scenario compared to a “no shipping” case using ICOADS data.

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Figure 3

Figure 3. Total mortality for four different scenarios showing low, mean, and high estimates for the ICOADS data set.

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Figure 4

Figure 4. Annual avoided premature mortality for the three control scenarios: (a) Coastal 0.5, (b) Coastal 0.1, and (c) Global 0.5 for the ICOADS data set. Reductions in estimated premature mortality use the 50th-percentile beta values in the C-R function and are relative to the No Control scenario.

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Table 2. Global Premature Mortality Due to Ship Emissions and the Avoided Premature Deaths Associated with Three Fuel Sulfur Control Strategies Using a Log-Linear Relative Risk Formulationa
  cardiopulmonary lung cancer
  mid range mid range
AMVER Inventory
2012 No Control case premature mortality 83 500 30 300−136 400 7100 2600−11 500
2012 Coastal 0.5 reduced mortality from No Control 33 800 12 200−55 200 2800 0−4500
2012 Coastal 0.1 reduced mortality from No Control 41 600 15 100−68 000 3400 1300−5500
2012 Global 0.5 reduced mortality from No Control 42 500 12,100−54,700 3500 1000−4300
ICOADS Inventory
2012 No Control Case premature mortality 76 700 27 800−125 400 7000 2600−11 400
2012 Coastal 0.5 reduced mortality from No Control 28 200 10 200−46 200 2500 900−4100
2012 Coastal 0.1 reduced mortality from No Control 38 800 14 100−63 500 3400 1300−5500
2012 Global 0.5 reduced mortality from No Control 33 500 12 200−54 800 3000 1100−4800
a

Notes: This table shows results from both the AMVER and ICOADS analyses. The 2012 No Control case represents annual premature mortality due to PM2.5 pollution from oceangoing vessels assuming fuel sulfur content of 2.7% S. The other cases represent reduced mortality associated with various sulfur control scenarios compared to the No Control case. For example, our analysis shows that a coastal 0.5% fuel sulfur limit will reduce annual cardiopulmonary premature mortality by 33 800 compared to the No Control case.

We show that PM2.5 impacts due to ships may cause 83 500 and 76 700 premature cardiopulmonary deaths each year for AMVER and ICOADS inventories, respectively (see Table 2); Likewise ship emissions may cause 7100 and 7000 lung cancer deaths annually, depending on underlying inventory pattern. These tables show 50th-percentile values setting β at its 50th percentile estimate, as well as ranges representing results based on the 95% confidence intervals for β from the literature as described above (22). All values are rounded to the nearest hundred.
By showing avoided deaths compared to the No Control scenario, we report relative health benefits (reduced premature mortality) due to each of the control scenarios. For example, based on AMVER results reported in Table 2, a 50th-percentile β value yields a 33 800 decrease in premature deaths due to cardiopulmonary illness in the Coastal 0.5 scenario, a 41 600 decrease in the Coastal 0.1 scenario, and a 42 500 decrease in the Global 0.5 scenario. Likewise, based on ICOADS data, these annual avoided mortalities are 28 200, 38 800, and 33 500, respectively. These values represent reductions in mortality of around 30−50% compared to the No Control case.
As may be expected, the Coastal 0.1 scenario shows the greatest benefits. Using the ICOADS spatial distribution, this scenario is clearly preferred from a health standpoint, although it is almost indistinguishable from the Global 0.5 scenario using the AMVER spatial distribution. We believe this is because ICOADS places more shipping (and ship emissions) along coastal routes frequented by containerships along coastal shipping particularly in the Europe and Mediterranean areas, whereas AMVER data places more shipping along bulk and tanker routes toward Southeast Asia and North America (10). In other words, less benefit may be expected for global control scenarios compared to coastal control scenarios under the ICOADS spatial distribution. Moreover, formation of aerosol nitrate is stronger in the ICOADS scenarios than the AMVER scenarios (9). This strong nitrate formation in the Global 0.5 scenario may be another reason why a global emission reduction is less beneficial than coastal action under the ICOADS spatial distribution.
These benefits are not linear with respect to sulfur content reductions. For example, moving from noncontrolled sulfur levels (2.7% S) to 0.5% S represents ∼80% decrease in sulfur content and provides ∼50% reduction in premature mortality; yet moving from a 0.5% sulfur level to a 0.1% sulfur level (also a decrease of 80%), only provides ∼30% incremental benefits in premature mortality. This nonlinear relationship may be partially due to nitrate substitution that exists when SOx emissions are reduced without concomitant reductions in nitrates. In such cases, ammonium in the atmosphere (which would preferentially combine with SOx to make ammonium-sulfate particles) instead combines with nitrate to make ammonium-nitrate particles. This effect could be further explored as potential cobenefits of reducing nitrate emissions along with sulfur emissions (9).
SI Tables S-1 and S-2 also show the global distribution of avoided mortality impacts. Europe and Asia see large benefits (measured in avoided deaths) in moving from the No Control scenario to any of the control scenarios, with Southeast Asia seeing some of the most significant benefits.
A comparison of the Global 0.5 and the Coastal 0.5 cases is also informative in terms of quantifying the marginal benefits of moving from a coastal (e.g., ECA) to a global control regulatory regime. Here we show that, at the 50th-percentile β value, the coastal scenario provides between ∼31 000 and ∼37 000 avoided deaths annually (ICOADS and AMVER, respectively), while the global scenario avoids ∼36 000−46 000 deaths annually (ICOADS and AMVER, respectively). This suggests a movement from a coastal regulatory regime to a global one may avoid additional 5000−9000 premature deaths annually.
These results confirm that meaningful benefits are achieved from either a 0.5% S or 0.1% S control strategy. Our findings demonstrate that upward of 45 000 premature mortalities could be prevented annually across the globe with a movement toward lower sulfur fuels in the future. Of course, reduced premature mortality is directly, but nonlinearly, related to the geographic use of clean fuels: lower sulfur fuels lead to larger health benefits, particularly when used in a near-coastal environment.
The premature mortality impacts demonstrated in this paper are only two of many impacts that are related to shipping emissions and fuel quality. Climate change, acidification, visibility, eutrophication, and other environmental effects are closely related to the type of fuel used in ships. This paper and complementary papers on this topic, offer important input to current science and policy discussions about the application of low sulfur fuel regulations for international shipping.

Supporting Information

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Additional figures corresponding to results using the AMVER dataset, additional mortality and avoided death data by world region, and presentation and discussion of uncertainty factors. This material is available free of charge via the Internet at http://pubs.acs.org.

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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Author
    • J. J. Winebrake - Department of STS/Public Policy, 1356 Eastman Building, Rochester Institute of Technology, Rochester, New York 14623, College of Earth, Ocean, and Environment, 305 Robinson Hall, University of Delaware, Newark, Delaware 19716, International Pacific Research Center, University of Hawaii, 1680 East-West Road, Honolulu, Hawaii 96822, and Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Wessling, Germany;  Email: [email protected]
  • Authors
    • J. J. Corbett - Department of STS/Public Policy, 1356 Eastman Building, Rochester Institute of Technology, Rochester, New York 14623, College of Earth, Ocean, and Environment, 305 Robinson Hall, University of Delaware, Newark, Delaware 19716, International Pacific Research Center, University of Hawaii, 1680 East-West Road, Honolulu, Hawaii 96822, and Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Wessling, Germany
    • E. H. Green - Department of STS/Public Policy, 1356 Eastman Building, Rochester Institute of Technology, Rochester, New York 14623, College of Earth, Ocean, and Environment, 305 Robinson Hall, University of Delaware, Newark, Delaware 19716, International Pacific Research Center, University of Hawaii, 1680 East-West Road, Honolulu, Hawaii 96822, and Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Wessling, Germany
    • A. Lauer - Department of STS/Public Policy, 1356 Eastman Building, Rochester Institute of Technology, Rochester, New York 14623, College of Earth, Ocean, and Environment, 305 Robinson Hall, University of Delaware, Newark, Delaware 19716, International Pacific Research Center, University of Hawaii, 1680 East-West Road, Honolulu, Hawaii 96822, and Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Wessling, Germany
    • V. Eyring - Department of STS/Public Policy, 1356 Eastman Building, Rochester Institute of Technology, Rochester, New York 14623, College of Earth, Ocean, and Environment, 305 Robinson Hall, University of Delaware, Newark, Delaware 19716, International Pacific Research Center, University of Hawaii, 1680 East-West Road, Honolulu, Hawaii 96822, and Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Wessling, Germany

Acknowledgment

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This work was partly supported by the Oak Foundation (J.J.W., J.J.C., E.H.G.), and by the German Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) and the German Aerospace Center (DLR) within the Young Investigators Group SeaKLIM (V.E., A.L.). We acknowledge Chengfeng Wang, currently with the California Air Resources Board, for his efforts in constructing some of the emissions inventory data. All global model simulations were performed on the High Performance Computing Facility (HPCF) at the European Centre for Medium-Range Weather Forecasts (ECMWF).

References

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  • Figure 1

    Figure 1. Concentrations of PM2.5 for four scenarios with ICOADS data in micrograms per cubic meter. Shows geospatial distribution of PM2.5 pollution due to oceangoing vessels in the 2012 No Control scenario and the three control scenarios explored here; values represent concentrations due to shipping (compared to an environment without shipping emissions). (a) No Control scenario; (b) Coastal 0.5% scenario; (c) Coastal 0.1% scenario; (d) Global 0.5% scenario.

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    Figure 2

    Figure 2. Annual premature mortality for the No Control scenario compared to a “no shipping” case using ICOADS data.

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    Figure 3

    Figure 3. Total mortality for four different scenarios showing low, mean, and high estimates for the ICOADS data set.

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    Figure 4

    Figure 4. Annual avoided premature mortality for the three control scenarios: (a) Coastal 0.5, (b) Coastal 0.1, and (c) Global 0.5 for the ICOADS data set. Reductions in estimated premature mortality use the 50th-percentile beta values in the C-R function and are relative to the No Control scenario.

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  • References

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    This article references 25 other publications.

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      Winebrake, J. J.; Corbett, J. J.; Meyer, P. E. Energy use and emissions from marine vessels: A total fuel cycle approach J. Air Waste Manage. Assoc. 2007, 57 (January) 102– 110
      5
      Energy use and emissions from marine vessels: a total fuel life cycle approach
      Winebrake, James J.; Corbett, James J.; Meyer, Patrick E.
      Journal of the Air & Waste Management Association (2007), 57 (1), 102-110CODEN: JAWAFC; ISSN:1096-2247. (Air & Waste Management Association)
      Regional and global air pollution from marine transportation is a growing concern. In discerning the sources of such pollution, researchers have become interested in tracking where along the total fuel life cycle these emissions occur. In addn., new efforts to introduce alternative fuels in marine vessels have raised questions about the energy use and environmental impacts of such fuels. To address these issues, this paper presents the Total Energy & Emissions Anal. for Marine Systems (TEAMS) model. TEAMS can be used to analyze total fuel life cycle emissions and energy use from marine vessels. TEAMS captures "well-to-hull" emissions, i.e., emissions along the entire fuel pathway, including extn., processing, distribution, and use in vessels. TEAMS conducts analyses for six fuel pathways: (1) petroleum to residual oil, (2) petroleum to conventional diesel, (3) petroleum to low-sulfur diesel, (4) natural gas to compressed natural gas, (5) natural gas to Fischer-Tropsch diesel, and (6) soybeans to biodiesel. TEAMS calcs. total fuel-cycle emissions of three greenhouse gases (carbon dioxide, nitrous oxide, and methane) and five criteria pollutants (volatile org. compds., carbon monoxide, nitrogen oxides, particulate matter with aerodynamic diams. of 10 μm or less, and sulfur oxides). TEAMS also calcs. total energy consumption, fossil fuel consumption, and petroleum consumption assocd. with each of its six fuel cycles. TEAMS can be used to study emissions from a variety of user-defined vessels. This paper presents TEAMS and provides example modeling results for three case studies using alternative fuels: a passenger ferry, a tanker vessel, and a container ship.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhsFanurY%253D&md5=9acfe9ce5c197a8865b59c736676eac3
    6. 6
      Winebrake; J. J.; Corbett; J. J.; Falzarano; A.; Hawker; J. S.; Korfmacher; K.; Ketha; S.; Zilora; S. Assessing energy, environmental, and economic tradeoffs in intermodal freight transportation. J. Air Waste Manage. Assoc.2008, 58 (8).
      There is no corresponding record for this reference.
    7. 7
      Pope, C. A.; Burnett, R. T.; Thun, M. J.; Calle, E. E.; Krewski, D.; Ito, K.; Thurston, G. D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution J. Air Waste Manage. Assoc. 2002, 287 (9) 1132– 1141
      7
      Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution
      Pope, C. Arden, III; Burnett, Richard T.; Thun, Michael J.; Calle, Eugenia E.; Krewski, Daniel; Ito, Kazuhiko; Thurston, George D.
      JAMA, the Journal of the American Medical Association (2002), 287 (9), 1132-1141CODEN: JAMAAP; ISSN:0098-7484. (American Medical Association)
      Context. Assocns. have been found between day-to-day particulate air pollution and increased risk of various adverse health outcomes, including cardiopulmonary mortality. However, studies of health effects of long-term particulate air pollution have been less conclusive. Objective. To assess the relationship between long-term exposure to fine particulate air pollution and all-cause, lung cancer, and cardiopulmonary mortality. Design, Setting, and Participants. Vital status and cause of death data were collected by the American Cancer Society as part of the Cancer Prevention II study, an on-going prospective mortality study, which enrolled ∼1.2 million adults in 1982. Participants completed a questionnaire detailing individual risk factor data (age, sex, race, wt., height, smoking history, education, marital status, diet, alc. consumption, and occupational exposures). The risk factor data for ∼500,000 adults were linked with air pollution data for metropolitan areas throughout the United States and combined with vital status and cause of death data through Dec. 31, 1998. Main Outcome Measure. All-cause, lung cancer, and cardiopulmonary mortality. Results. Fine particulate and sulfur oxide-related pollution were assocd. with all-cause, lung cancer, and cardiopulmonary mortality. Each 10-μg/m3 elevation in fine particulate air pollution was assocd. with approx. a 4%, 6%, and 8% increased risk of all-cause, cardiopulmonary, and lung cancer mortality, resp. Measures of coarse particle fraction and total suspended particles were not consistently assocd. with mortality. Conclusion. Long-term exposure to combustion-related fine particulate air pollution is an important environmental risk factor for cardiopulmonary and lung cancer mortality.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XhslGgtbo%253D&md5=d0a548184ba2167232f70d59400411c8
    8. 8
      Pope, C. A.; Burnett, R. T.; Thurston, G. D.; Thun, M. J.; Calle, E. E.; Krewski, D.; Godleski, J. J. Cardiovascular mortality and long-term exposure to particulate air pollution: Epidemiological evidence of general pathophysiological pathways of disease Circulation 2004, 109 (1) 71– 77
      8
      Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease
      Pope C Arden 3rd; Burnett Richard T; Thurston George D; Thun Michael J; Calle Eugenia E; Krewski Daniel; Godleski John J
      Circulation (2004), 109 (1), 71-7 ISSN:.
      BACKGROUND: Epidemiologic studies have linked long-term exposure to fine particulate matter air pollution (PM) to broad cause-of-death mortality. Associations with specific cardiopulmonary diseases might be useful in exploring potential mechanistic pathways linking exposure and mortality. METHODS AND RESULTS: General pathophysiological pathways linking long-term PM exposure with mortality and expected patterns of PM mortality with specific causes of death were proposed a priori. Vital status, risk factor, and cause-of-death data, collected by the American Cancer Society as part of the Cancer Prevention II study, were linked with air pollution data from United States metropolitan areas. Cox Proportional Hazard regression models were used to estimate PM-mortality associations with specific causes of death. Long-term PM exposures were most strongly associated with mortality attributable to ischemic heart disease, dysrhythmias, heart failure, and cardiac arrest. For these cardiovascular causes of death, a 10-microg/m3 elevation in fine PM was associated with 8% to 18% increases in mortality risk, with comparable or larger risks being observed for smokers relative to nonsmokers. Mortality attributable to respiratory disease had relatively weak associations. CONCLUSIONS: Fine particulate air pollution is a risk factor for cause-specific cardiovascular disease mortality via mechanisms that likely include pulmonary and systemic inflammation, accelerated atherosclerosis, and altered cardiac autonomic function. Although smoking is a much larger risk factor for cardiovascular disease mortality, exposure to fine PM imposes additional effects that seem to be at least additive to if not synergistic with smoking.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2c%252FgvFGitQ%253D%253D&md5=4b4ffdb5fb4dcbbc0dd82bb21c03cb55
    9. 9
      Lauer, A.; Eyring, V.; Corbett, J. J.; Wang, C.; Winebrake, J. J. An assessment of near-future policy instruments for oceangoing shipping: Impact on atmospheric aerosol burdens and the Earth’s radiation budget. Environ. Sci. Technol. 2009, in press.
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    10. 10
      Wang, C.; Corbett, J. J.; Firestone, J. Improving spatial representation of global ship emissions inventories Environ. Sci. Technol. 2008, 42 (1) 193– 199
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      Cohen, A; J.; Anderson, H. R.; Ostro, B.; Pandey, K. D.; Krzyzanowski, M.; Kunzli, N.; Gutschmidt, K.; Pope, C. A.; Romieu, I.; Samet, J. M.; Smith, K. R., Mortality impacts of urban air pollution. In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Due to Selected Major Risk Factors; Ezzati, M.; Lopez, A. D.; Rodgers, A.; Murray, C. U. J. L., Eds.; World Health Organization: Geneva, 2004; Vol. 2, pp 1353− 1394.
      There is no corresponding record for this reference.
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      Appendix A: Quantification of the Health Impacts and Economic Valuation of Air Pollution from Ports and Goods Movement in California; California Air Resources Board: 3/22/2006: Sacramento, 2006.
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      Corbett, J. J.; Wang, C.; Winebrake, J. J.; Green, E. Allocation and Forecasting of Global Ship Emissions. IMO Subcommittee on Bulk Liquids and Gases, 11th Session, Agenda Item 5, London, England January 12, 2007.
      There is no corresponding record for this reference.
    14. 14
      Lack, D. A.; Corbett, J. J.; Onasch, T.; Lerner, B.; Massoli, P.; Quinn, P. K.; Bates, T. S.; Covert, D. S.; Coffman, D.; Sierau, B.; Herndon, S.; Allan, J.; Baynard, T.; Lovejoy, E.; Ravishankara, A. R.; Williams, E. Particulate emissions from commercial shipping: Chemical, physical, and optical properties J. Geophys. Res. 2009, 114
      There is no corresponding record for this reference.
    15. 15
      Lauer, A.; Eyring, V.; Hendricks, J.; Jöckel, P.; Lohmann, U. Global model simulations of the impact of ocean-going ships on aerosols, clouds, and the radiation budget Atmos. Chem. Phys. 2007, 7, 5061– 5079
      15
      Global model simulations of the impact of ocean-going ships on aerosols, clouds, and the radiation budget
      Lauer, A.; Eyring, V.; Hendricks, J.; Joeckel, P.; Lohmann, U.
      Atmospheric Chemistry and Physics (2007), 7 (19), 5061-5079CODEN: ACPTCE; ISSN:1680-7316. (Copernicus Publications)
      International shipping contributes significantly to the fuel consumption of all transport related activities. Specific emissions of pollutants such as sulfur dioxide (SO2) per kg of fuel emitted are higher than for road transport or aviation. Besides gaseous pollutants, ships also emit various types of particulate matter. The aerosol impacts the Earth's radiation budget directly by scattering and absorbing the solar and thermal radiation and indirectly by changing cloud properties. Here we use ECHAM5/MESSy1-MADE, a global climate model with detailed aerosol and cloud microphysics to study the climate impacts of international shipping. The simulations show that emissions from ships significantly increase the cloud droplet no. concn. of low marine water clouds by up to 5% to 30% depending on the ship emission inventory and the geog. region. Whereas the cloud liq. water content remains nearly unchanged in these simulations, effective radii of cloud droplets decrease, leading to cloud optical thickness increase of up to 5-10%. The sensitivity of the results is estd. by using three different emission inventories for present-day conditions. The sensitivity anal. reveals that shipping contributes to 2.3% to 3.6% of the total sulfate burden and 0.4% to 1.4% to the total black carbon burden in the year 2000 on the global mean. In addn. to changes in aerosol chem. compn., shipping increases the aerosol no. concn., e.g. up to 25% in the size range of the accumulation mode (typically >0.1 μm) over the Atlantic. The total aerosol optical thickness over the Indian Ocean, the Gulf of Mexico and the Northeastern Pacific increases by up to 8-10% depending on the emission inventory. Changes in aerosol optical thickness caused by shipping induced modification of aerosol particle no. concn. and chem. compn. lead to a change in the shortwave radiation budget at the top of the atm. (ToA) under clear-sky condition of about -0.014 W/m2 to -0.038 W/m2 for a global annual av. The corresponding all-sky direct aerosol forcing ranges between -0.011 W/m2 and -0.013 W/m2. The indirect aerosol effect of ships on climate is found to be far larger than previously estd. An indirect radiative effect of -0.19 W/m2 to -0.60 W/m2 (a change in the atm. shortwave radiative flux at ToA) is calcd. here, contributing 17% to 39% of the total indirect effect of anthropogenic aerosols. This contribution is high because ship emissions are released in regions with frequent low marine clouds in an otherwise clean environment. In addn., the potential impact of particulate matter on the radiation budget is larger over the dark ocean surface than over polluted regions over land.
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    16. 16
      Roeckner, E.; Brokopf, R.; Esch, M.; Giorgetta, M.; Hagemann, S.; Kornblueh, L.; Manzini, E.; Schlese, U.; Schulzweida, U. Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atm model J. Clim. 2006, 19 (16) 3771– 3791
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      Jöckel, P.; Sander, R.; Kerkweg, A.; Tost, H.; Lelieveld, J. Technical Note: The Modular Earth Submodel System (MESSy)—A new approach towards earth system modeling Atmos. Chem. Phys. 2005, 5, 433– 444
      There is no corresponding record for this reference.
    18. 18
      Wang, C.; Corbett, J. J.; Firestone, J. AModeling energy use and emissions from north american shipping: application of the ship traffic, energy, and environment model. Environ. Sci. Technol.2007.
      There is no corresponding record for this reference.
    19. 19
      SEDAC Gridded Population of the World. (2008) http://sedac.ciesin.columbia.edu/gpw/.
      There is no corresponding record for this reference.
    20. 20
      Abt Associates. BenMap: Environmental Benefits Mapping and Analysis Program, Technical Appendices, May 2005; Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency: Research Triangle Park, NC, , 2005; p 275.
      There is no corresponding record for this reference.
    21. 21
      Davis, D. L.; Kjellstrom, T.; Sloof, R.; McGartland, A.; Atkinson, D.; Barbour, W.; Hohenstein, W.; Nalgelhout, P.; Woodruff, T.; Divita, F.; Wilson, J.; Deck, L.; Schwartz, J. Short term improvements in public health from global-climate policies on fossil-fuel combustion: an interim report Lancet 1997, 350, 1341– 1349
      There is no corresponding record for this reference.
    22. 22
      Ostro, B. Outdoor air pollution: Assessing the environmental burden of disease at national and local levels; World Health Organization: Geneva, 2004.
      There is no corresponding record for this reference.
    23. 23
      Zanobetti, A.; Schwartz, J. The effect of particulate air pollution on emergency admissions for myocardial infarction: A multicity case-crossover analysis Environ. Health Perspect. 2005, 113 (8) 978– 982
      There is no corresponding record for this reference.
    24. 24
      Samet, J. M.; Zeger, S. L.; Dominici, F.; Curriero, F.; Coursac, I.; Dockery, D. W. The national morbidity, mortality, and air pollution study. part ii: morbidity and mortality from air pollution in the united states Res. Rep. Health Eff. Inst. 2000, 94, 5– 70
      24
      The National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and mortality from air pollution in the United States
      Samet J M; Zeger S L; Dominici F; Curriero F; Coursac I; Dockery D W; Schwartz J; Zanobetti A
      Research report (Health Effects Institute) (2000), 94 (Pt 2), 5-70; discussion 71-9 ISSN:1041-5505.
      BACKGROUND: Epidemiologic time-series studies conducted in a number of cities have identified, in general, an association between daily changes in concentration of ambient particulate matter (PM) and daily number of deaths (mortality). Increased hospitalization (a measure of morbidity) among the elderly for specific causes has also been associated with PM. These studies have raised concerns about public health effects of particulate air pollution and have contributed to regulatory decisions in the United States. However, scientists have pointed out uncertainties that raise questions about the interpretation of these studies. One limitation to previous time-series studies of PM and adverse health effects is that the evidence for an association is derived from studies conducted in single locations using diverse analytic methods. Statistical procedures have been used to combine the results of these single location studies in order to produce a summary estimate of the health effects of PM. Difficulties with this approach include the process by which cities were selected to be studied, the different analytic methods applied to each single study, and the variety of methods used to measure or account for variables included in the analysis. These individual studies were also not able to account for the effects of gaseous air pollutants in a systematic manner.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD3M3lsFanug%253D%253D&md5=76c8e7dcda8dfdcb0168504a533c7da9
    25. 25
      Schwartz, J. Assessing confounding, effect modification, and thresholds in the association between ambient particles and daily deaths Environ. Health Perspect. 2000, 108 (6) 563– 8
      There is no corresponding record for this reference.

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