Present-Day and Future Global Bottom-Up Ship Emission Inventories

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Environ. Sci. Technol. 2010, 44, 1333–1339

Present-Day and Future Global Bottom-Up Ship Emission Inventories Including Polar Routes A N D R E A S P A X I A N , †,§ V E R O N I K A E Y R I N G , * ,† W I N F R I E D B E E R , † ROBERT SAUSEN,† AND CLAIRE WRIGHT‡ Deutsches Zentrum fu ¨ r Luft- und Raumfahrt (DLR), Institut fu ¨ r Physik der Atmospha¨re, Oberpfaffenhofen, Germany, and Lloyd’s Marine Intelligence Unit, London, U.K.

Received July 29, 2009. Revised manuscript received December 18, 2009. Accepted December 18, 2009.

We present a global bottom-up ship emission algorithm that calculates fuel consumption, emissions, and vessel traffic densities for present-day (2006) and two future scenarios (2050) considering the opening of Arctic polar routes due to projected sea ice decline. Ship movements and actual ship engine power per individual ship from Lloyd’s Marine Intelligence Unit (LMIU) ship statistics for six months in 2006 and further mean engine data from literature serve as input. The developed SeaKLIM algorithm automatically finds the most probable shipping route for each combination of start and destination port of a certain ship movement by calculating the shortest path on a predefined model grid while considering land masses, sea ice, shipping canal sizes, and climatological mean wave heights. The resulting present-day ship activity agrees well with observations. The global fuel consumption of 221 Mt in 2006 lies in the range of previously published inventories when undercounting of ship numbers in the LMIU movement database (40,055 vessels) is considered. Extrapolated to 2007 and ship numbers per ship type of the recent International Maritime Organization (IMO) estimate (100,214 vessels), a fuel consumption of 349 Mt is calculated which is in good agreement with the IMO total of 333 Mt. The future scenarios show Arctic polar routes with regional fuel consumption on the Northeast and Northwest Passage increasing by factors of up to 9 and 13 until 2050, respectively.

Introduction Ship emissions of particulate organic matter (PM) and exhaust gases like carbon dioxide (CO2), nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and sulfur dioxide (SO2) into the marine boundary layer contribute significantly to total emissions from the transportation sector (1). Emissions of NOx and other ozone precursors lead to tropospheric ozone (O3) formation and perturb hydroxyl radical (OH) concentrations, and hence the lifetime of methane (CH4). The dominant aerosol component resulting from ship emissions is sulfate (SO4) formed by the oxidation of SO2. Particle * Corresponding author phone: 0049 8153 28 - 2533; e-mail: [email protected]. † Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut fu ¨ r Physik der Atmospha¨re, Oberpfaffenhofen, Germany. ‡ Lloyd’s Marine Intelligence Unit, London, U.K. § Now at Universita¨t Wu ¨rzburg, Geographisches Institut, Wu ¨rzburg, Germany. 10.1021/es9022859

 2010 American Chemical Society

Published on Web 01/20/2010

emissions from ships change the physical properties of low clouds and impact on climate (2). Since it has been realized that such emissions give rise to direct impacts on human health (3), contribute toward regional acidification and eutrophication (4), and influence radiative forcing (5, 2), ship emissions have been recognized as a growing problem for environmental policy makers. The magnitude of present-day fuel consumption and emissions from shipping has been discussed in literature and values around 600-900 Tg CO2 in 2000 have been published (5). Following the nomenclature of Wang et al. (6), these estimates are all based on top-down approaches where emissions are calculated without respect to location by means of quantifying fuel consumption by power production first and then multiplying the consumption by emission factors. The resulting emission totals are distributed over the globe by using spatial proxies. The corresponding fuel consumption is calculated either by summing up worldwide sales of bunker fuel per country or by modeling fleet activity per ship/ segment based on ship engine power, number of hours at sea, bunker fuel consumed per power unit [kW], and average engine load (7-9, 1). In bottom-up approaches emissions are directly estimated within a spatial context resulting in spatially resolved emission inventories. Ship- and routespecific emissions are calculated based on ship movements, ship attributes (e.g., ship type, size, speed, and engine power), and ship emission factors. The locations of emissions are determined by the most probable navigation routes. A bottom-up inventory for Europe has been developed by Entec UK Limited (10). The Waterway Network Ship Traffic, Energy and Environment Model (STEEM) applies advanced geographic information system (GIS) technology and computes routes automatically, following actual shipping routes on a predefined shipping network for North America (6). The aim of this study is to develop the first global bottomup ship emission inventory that automatically finds the most probable navigation route for each combination of start and destination port of a certain ship movement while considering land masses, sea ice, sizes of shipping canals, and significant wave heights. The next two sections describe the input data and the developed SeaKLIM algorithm that calculates fuel consumption, emissions, and vessel traffic densities for 2006 and two future scenarios representative of 2050. Thereby, we consider the possible opening of Arctic polar routes, i.e., the Northwest and Northeast Passage, due to projected sea ice decrease resulting from climate warming. The corresponding results, a comparison to existing approaches, and implications of this study are presented in the subsequent sections.

Ship Activity and Emission Input Data Ship Movement Data. We derive spatially resolved ship movements from the following three databases of Lloyd’s Marine Intelligence Unit (LMIU) which are linked by unique LMIU ship IDs and LMIU port IDs: (1) The ship movement database available for this study contains movements of the international commercial fleet larger than 100 GT leaving the start port in February, April, June, August, October, or December 2006. Purchasing the full year of data from LMIU was not possible due to financial constraints. However, the six months of ship movements are considered representative for the whole year of 2006 and thus extrapolated. The data set includes the ship IDs, start and end ports, arrival and sailing dates [d] and partly also times [h] of 1,001,123 ship movements from 40,055 vessels. (2) The ship database contains information on ship name, size, main engine power, VOL. 44, NO. 4, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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average speed, flag, and type of 90,840 ships. (3) The port database includes names and locations in geographical coordinates of 8,541 ports. Besides main engine power no further engine data was available from LMIU. To estimate the possible range of future conditions for 2050 we use two extreme future scenarios from Eyring et al. (11). The BAU- (Business-as usual) and CLE-scenario (clean) follow the Special Report on Emissions Scenarios (SRES) scenario A1 and A2 of IPCC (12) with annual world economic growth rates of 3.6% and 2.3% resulting in different growth rates of world seaborne trade and ship numbers. Scaling the absolute growth from 2001 to 2050 from Eyring et al. ((11), Table 3) onto this study’s period 2006 to 2050, we obtain corresponding ship number growth of 36.2% and 81.5% for the CLE- and BAU-scenarios, respectively. Ship Characteristic and Engine Data. Since no further engine data are available from LMIU, we use mean ship characteristic for ship speed, auxiliary engine power, and main and auxiliary engine load factors from Wang et al. (13) grouped into nine different ship classes (Table S3). From Eyring et al. (1) we derive further information on specific fuel oil consumption and emission factors for main and auxiliary engines. Finally, Entec UK Limited (10) provides CO2 emission factors and main and auxiliary engine load factors, engine running hours, specific fuel oil consumption, and emission factors for harbor activities. CO emission factors in harbors are not included. In both future scenarios we assume main engine power rising by 33.0% per ship which results from scaling the corresponding value from Eyring et al. ((11), Table 3) to this study’s reference period. The auxiliary engine power and the specific fuel oil consumption are expected to remain constant. Following Eyring et al. (11) the CLE scenario applies low sulfur fuels with sulfur contents of 0.5%, aggressive NOx reduction, and a 25% reduction in fuel consumption due to alternative energies. The BAU scenario includes fuels with high sulfur contents of 2%, emission reductions not exceeding IMO regulations that were agreed in the year 2000, and a diesel fleet without application of alternative fuels or energies. For resulting emission reduction factors see Supporting Information (SI). Model Grid Data: Land Masses, Sea Ice, Shipping Canals and Sea State. Several input data sets describe the model grid characteristics. The distribution of land masses is derived from a 0.5° × 0.5° land-sea mask from the Data Collection of the International Satellite Land Surface Climatology Project Initiative II (14). The present-day extension of polar sea ice is taken from the sea ice concentration of the monthly ensemble mean of three simulation runs with the coupled atmosphere ocean model ECHAM5/MPI using the moderate scenario SRES A1B for 2001-2010 (15). Sea grid boxes with sea ice concentrations larger than 50% are identified as sea ice. For both future scenarios we select the time period 2051-2060 in which receding sea ice allows the Arctic polar shipping routes to open for the three summer months August, September, and October. This is in good agreement with recent estimates of future sea ice decline (16-18). Three grid boxes are defined representing the shipping canals Panama, Suez, and Kiel. These canals only allow certain ship sizes to pass and furthermore act as delay areas for passing ships due to reduced ship speeds compared to open sea voyages. Therefore, shipping canal data such as average canal ship speed and ship length, breadth, and draft restrictions is gathered from canal authorities taking into account future canal enlargements (SI and Table S1). Finally, significant wave heights [m] for present-day and future conditions are obtained from ECMWF ERA 40 data (19). A 2.5° sea state climatology for 1958-2001 is derived 1334

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and scaled to the 0.5° model grid (SI) to identify other delay areas for shipping routes.

Method for Emission Calculation and Allocation The SeaKLIM algorithm developed in this study calculates the shortest shipping route for each pair of LMIU start to end ports on the predefined model grid. The resulting distances a certain ship navigates on the model grid boxes serve as input for the calculation of fuel consumption and emissions of several species per grid box. Thus, a spatially resolved global bottom-up ship emission inventory is created. Several steps are needed to prepare the LMIU input database for the emission calculation and allocation of the SeaKLIM algorithm such as the derivation and handling of geographical port coordinates, shipping canals, or transit times. After preprocessing 969,662 ship movements for the present-day and two future scenarios remain. It is possible to limit the given LMIU ship movement database and thus the resulting emission inventory to some special selections, e.g., name, country, or world region of start and end ports, flag, type, or size of ships or transit time. For reasons of validation with observational data of the Automated Mutual-assistance Vessel Rescue system (AMVER), a ship reporting system used worldwide by search-and-rescue authorities, and comparison with the bottom-up emission inventory of the STEEM model from Wang et al. (13) two adequately adjusted present-day ship movement databases are built (SI). Fuel Consumption and Emission Calculation. The fuel consumption of every ship movement is calculated separately for main engines, auxiliary engines, and harbor activities, i.e., maneuvering and loading in harbors, which does not include shipping canals, from engine power P, load factor F, engine running time τ, and specific fuel oil consumption SFOC. For main and auxiliary engines the engine running time τ can be derived from route distance D and ship speed v. If attributes are missing, the LMIU transit time of a certain ship movement can serve as engine running time (SI). We obtain the fuel consumption for main engines (FCM,j), auxiliary engines (FCA,j), and harbor activities (FCH,j) for each model grid box j from eqs 1, 2, and 3 in summing up over all ship movements k. FCM,j )

∑ FC

M,j,k

∑P

)

k

FCA,j )

∑ FC

∑ FC

H,j,k

k

)

· FM,k ·

k

A,j,k

∑P

)

k

FCH,j )

M,k

k

∑ (P

M,k

A,k

· FA,k ·

( )

Dj,k · SFOCM,k vk

( )

Dj,k · SFOCA vk

(1) (2)

· FHM + PA,k · FHA) · τH,j,k · SFOCH,k

k

(3)

The main engine power is derived per individual ship and the engine running time per ship movement. All other engine input variables are gathered from other studies averaged by ship type (Table S3). In the future scenarios the corresponding growth in ship numbers and main engine power and reductions in fuel consumption due to alternative energies are considered. Finally, the total fuel consumption FC is calculated by adding the values of all model grid boxes j for main engines, auxiliary engines, and harbor activities. Additionally, NOx, CO2, SOx, CO, PM, and HC emissions of each ship movement are determined using the fuel consumption values FC and specific emission factors EI while considering emission reduction factors in the future scenarios. In adding the emissions of main engines (EM,x,j,k), auxiliary engines (EA,x,j,k), and harbor activities (EH,x,j,k) over all ship movements k following eq 4, we calculate the emission

FIGURE 1. Resulting distance field [km] of the SeaKLIM algorithm for an example shipping route (upper left), the 0.5 × 0.5° standardized and weighted ship density [millionth of global total] for present-day (2006, upper right) and for the CLE/ BAU future scenario in 2050 (lower left), and the difference [millionth of global total] between present-day and future scenarios (lower right). value Ex,j of a certain species x and grid box j, i.e., a global bottom-up 0.5° × 0.5° ship emission inventory of a certain species for each scenario: Ex,j )

∑E k

x,j,k

)

∑ (FC k

M,j,k

· EIM,x,k + FCA,j,k · EIA,x + FCH,j,k · EIH,x,k)

(4)

The specific emission factors for main engines and harbor activities are derived per ship type. For auxiliary engines there is no such specification (Table S3). CO emissions from harbor activities could not be calculated because corresponding emission factors were not available, but estimates show that this harbor share is much less than 10% of total CO emissions. The total emissions Ex of a certain species x are derived by summing up the emission values of all model grid boxes j. Ship Routing. The LMIU ship database gives start and end ports of every ship movement, but the navigation route is unknown. The SeaKLIM algorithm was built to find the appropriate shipping route, defined as the shortest path between start and end port of each movement on the model grid without passing land masses or sea ice. The route distance derived from this approach serves as input for emission calculation on each model grid box using eqs 1-4. The SeaKLIM algorithm emanates from the Dijkstra algorithm ((20), chapter 24) of the mathematical graph theory which finds the shortest path between two points using wave propagation, starting from the first point and spreading toward all directions until the final point is reached. The Maze algorithm is a modification on a uniform raster grid with impassable obstacles. The SeaKLIM algorithm works on a geographical raster grid with decreasing grid box distances toward the Poles. Land masses and sea ice act as obstacles for wave propagation. The wave propagates from the initial grid point and at every propagation step, the great circle distances of all wavefront grid boxes to all directly neighboring grid boxes are calculated (SI and Figure S2).

The efficiency of this algorithm lies in the fact that the model grid boxes are systematically treated beginning with those grid boxes with small distances to the start point and finally reaching those with large distances where impassable obstacle grid boxes are successfully surrounded. This significantly reduces the computing time. We obtain a distance field [km] for each ship movement which provides the distance a ship navigates in each model grid box between start and end port following the shortest path (Figure 1, upper left). Furthermore, a ship traffic density field for all ship movements is calculated that shows the number of ships navigating in all grid boxes in 2006. For validation with the International Comprehensive OceanAtmosphere Data Set (ICOADS, 2000-2002), a global marine surface observation data set including ship positions, and the AMVER (2005) observation data from Wang et al. (6), all ship traffic density fields are weighted with ship main engine power and standardized afterward resulting in units of millionth of global total. The model grid is based on a predefined 0.5° × 0.5° land water mask, further expanded by river systems, monthly sea ice, and shipping canals (Figure S1). The attributes canal type and travel month and the corresponding scenario of a certain ship movement define if shipping canals are passable or not and describe the extension of impassable sea ice (SI). Furthermore, in canal grid boxes and those with high sea state the wave propagation is delayed due to reduced ship speed. Concerning canal grid boxes the calculated great circle distances are increased using average canal speeds. In grid boxes with sea state values higher than 3 m the calculated great circle distance is enlarged by taking into consideration the actual sea state. Details for the delay methods for canals and sea state are explained in the SI. Observation data show only little ship activity south of 50-60° S due to high sea state and icebergs. To avoid great circle routes near Antarctica the sea state influence south of 45° S is emphasized and an Antarctic sea state swell is installed identifying all sea grid boxes south of 60° S as permanent impassable sea ice (SI). VOL. 44, NO. 4, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Total Fuel Consumption and Emissions for Present-Day (2006) and Two Future Scenarios (2050) Resulting from the SeaKLIM Algorithm in Comparison to Recent Global Top-Down Studies from Buhaug et al. (21), Eyring et al. (1), and Eyring et al. (11) Scaled to This Study’s Ship Number and Reference Years 2006 and 2050 present-day (2006)

future (2050)

this Buhaug Eyring this study study et al. (21) et al. (1) (CLE/ BAU) FC [Mt] NOx [Tg NO2] CO2 [Tg] SOx [Tg SO2] CO [Tg] PM [Tg PM10] HC [Tg]

221 15.5 695 9.9 1.0 1.3 1.4

210 --667 ---------

261 18.2 578 9.0 1.0 1.2 1.4

287-510 2.0-25.3 859-1525 2.7-18.9 1.0-2.2 1.0-2.7 1.1-3.0

Eyring et al. (11) 291-525 2.6-33.0 789-1424 2.7-19.4 1.1-2.5 1.1-2.8 2.6-4.5

Emission Allocation. To calculate the main and auxiliary engine fuel consumption and emissions of a certain ship movement in the route grid boxes found in the path-finding algorithm we use the derived grid box route distances and the eqs 1, 2, and 4. The corresponding fuel consumption and emissions of harbor activities are calculated using eqs 3 and 4 and allocated to the end port grid box of each shipping route. Finally, the fuel consumption and emission fields of all ship movements are added to receive the 0.5° × 0.5° fuel consumption and emission inventories for each species and scenario. Three scale factors are applied to the different emission inventories (SI). The first scale factor considers the ship movements with two missing or inland ports whose emissions could be calculated but not allocated; they are distributed according to the prevailing pattern of all allocated ship emissions. The second one treats those ship movements with missing route distances or travel times and ship speeds whose emission shares could not be calculated. In this case we scale the fuel consumption and emissions of the calculated 965,611 ship movements to all 969,662 active LMIU ship movements proportionally. Finally, the third factor scales all 1,001,123 ship movements of the available six months of 2006 to the total LMIU ship movement number for the entire year 2006 (2,029,532 movements). The polar routes’ share is defined as all grid boxes northward of 66° N, further subdivided into the North-West Passage (50° W - 170° W), the North-East Passage (50° E 170° W), and the Greenland Sea (50° W - 50° E) which is considered separately due to current high ship activity.

Results Present-Day Scenario. For the 969,662 active LMIU ship movements for six months of 2006 the SeaKLIM algorithm calculates a global 0.5° × 0.5° standardized and weighted ship density (Figure 1, upper right) and emission inventories for several species (Figure S4, left). High ship activity can be seen on the main shipping routes connecting world’s biggest ports in the North Atlantic, North Pacific, Indian Ocean, and in coastal areas of Europe, East Asia, and the Gulf of Mexico, whereas polar areas show little activity. After applying the three scale factors described above to the resulting global totals of the present-day scenario for all LMIU ship movements from six months in 2006 we obtain a total fuel consumption of the world-merchant fleet larger than 100 gross tonnage of 221 Mt in 2006 and total CO2, NOx, SOx, CO, HC, and PM emissions (Table 1). The auxiliary engine and harbor activity consumption shares yield 11% and 10% of main engine fuel consumption. As previously mentioned CO harbor emissions could not be calculated. In the Arctic, 1336

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the largest activity is calculated in the Greenland Sea with 1.60 Mt and only 0.09 Mt and 0.14 Mt on the North-West and North-East Passages. Future Scenarios. For each future scenario the SeaKLIM algorithm calculates a global 0.5° × 0.5° ship density (Figure 1, lower left) and several emission inventories (Figure S4, center, right) from the 969,662 active LMIU ship movements while taking into account reduced polar sea ice, enlarged shipping canals, increased ship numbers, and main engine powers as well as fuel consumption and emission reductions in the CLE scenario. The resulting standardized and weighted ship densities are identical for both future scenarios because intensity differences due to different future growth factors disappear in standardization. In comparison to the present-day result future Arctic polar routes are prominent: one main shipping route in the NorthEast Passage and two routes north and south of Baffin Island in the North-West Passage. The difference plot (Figure 1, lower right) visualizes this Arctic ship activity growth and the corresponding decrease on southern main shipping routes especially from Europe to East Asia. On the North Atlantic and Pacific shipping routes increase and decrease due to changing sea state affecting the distribution. A growth in shipping routes from Europe to the Panama Canal and a decrease around Cap Horn show a ship activity shift due to Panama Canal enlargement. The sum of all grid box deviations between the present-day and one future scenario determines the shift in ship activity distribution caused by polar routes and canal enlargements. For both future scenarios this results only in relatively low (3.9%) changes in the global total ship activity compared to present-day conditions. Figure 2 presents a comparison of the original present-day and future ship densities in units of real ship numbers. Without weighting more and more widely spread shipping routes of smaller vessels can be seen. Without standardization the different growth factors of ship activity from present-day to CLE and BAU future scenarios are prominent, especially west of Mexico and in the Red Sea. The polar-stereographic projection demonstrates the reduced Arctic sea ice coverage and reduced distance of future polar routes. Total fuel consumption results in 287 and 510 Mt for the CLE and BAU future scenarios in 2050 after application of the three scale factors. Table 1 shows the total CO2, NOx, SOx, CO, HC, and PM emissions. The polar routes’ fuel consumption adds up to 2.41 and 4.28 Mt in the Greenland Sea, 0.68 and 1.20 Mt in the North-West Passage, and 0.73 and 1.29 Mt in the North-East Passage. The comparison of the CLE and BAU future emission inventories to the present-day results shows an increase of 30-131% in fuel consumption and 24-119% in CO2 emissions. All other emissions double in the BAU scenario and are reduced in the CLE scenario. Similar to Eyring et al. (11), we conclude that alternative energy sources and efficient emission reduction can compensate for future growth in ship numbers and main engine powers. Fuel consumption on the North-West and North-East Passages grows by the factors 7.6-13.3 and 5.2-9.2, with the Greenland Sea value only increasing by factors 1.5-2.7 due to pre-existing high presentday ship activity. Thus, this study yields only slight future growth of polar shipping routes in comparison to global ship activity, but an enormous increase in comparison to current regional Arctic ship activity. The reason for this is that only 116 of the 1,001,123 LMIU ship movements directly navigate from Europe or North America to East Asia and are thus considered as possible future polar shipping routes by the SeaKLIM algorithm. As described in the SI, the SeaKLIM algorithm allows skipping intermediate voyage stops in Dover Strait, Gibraltar, Suez/Port Said, and Panama Canal, e.g., for voyages

FIGURE 2. Original global ship densities [ship number] for present-day (left), CLE (center), and BAU future scenario (right) in polar-stereographic projection with sea ice coverage in light gray.

FIGURE 3. Comparison of 1° × 1° standardized and weighted ship densities [millionth of global total]: present-day scenario 2006 (left) and ICOADS (right, 2000-2002). from Rotterdam to Tokyo, to extend possible polar routes. But stops at intermediate ports like Piraeus or Singapore are not skipped. In general, it is very difficult to develop a probable future ship movement pattern out of the given LMIU database showing present-day multistop conditions using the methodology described here. A possible solution might be to analyze the present-day flow of goods between different ports, project future changes, and divide future freight into ships which sail on the conventional shipping routes while stopping at intermediate ports and ships which sail directly on the shorter polar route.

Validation and Comparison to Other Studies Present-Day Scenario. To perform a validation with 1° × 1° ICOADS and AMVER ship reports (6) the present-day standardized and weighted ship density is calculated on a 1° × 1° model grid. For the comparison with AMVER the LMIU movement database is restricted to those 593,614 ship movements fulfilling the AMVER participation criteria (SI). Both comparisons show good agreement in general distribution of ship activity (Figures 3 and S5). Rather than punctual ship report data, the SeaKLIM algorithm results appear as linear shaped shipping routes. The calculated main shipping routes in the South Atlantic, Indian Ocean, and the Mediterranean agree well, but some ICOADS shipping routes in the Central Pacific and west of Greenland are not reproduced. This might be explained by missing records in the LMIU ship movement database of the available six months of 2006. Due to linear ship routing this study’s ship activity shows less spreading on North Atlantic and North Pacific in spite of the performed sea state parameterization. This study results

in higher ship activities in coastal areas of Europe and East Asia in comparison to ship report data which usually underestimates coastal activities. The sum of all grid box deviations between the SeaKLIM algorithm results and ICOADS and AMVER ship reports yields 1,019,618 and 860,767 millionths of global total and thus lie in the same order of magnitude as differences between the ICOADS and AMVER distributions themselves (700,377 millionths of global total). The present-day total fuel consumption and emissions are compared to recent global top-down emission inventories. A main uncertainty is caused by the number of active ships. Buhaug et al. (21), Corbett and Ko¨hler (7), and Eyring et al. (1) use ship numbers of over 90,000 inclusive military vessels, Endresen et al. (8) use only 45,000 passenger and freight vessels. The LMIU ship statistics registers 90,840 vessels but only contains movements of 40,055 vessels during the given six months and 44,351 during the entirety of 2006 because nontrading vessels such as tugs, pleasure craft, fishing, and small domestic coastal vessels are only registered but not monitored (Table S2). To facilitate an appropriate comparison, fuel consumption and emissions of Eyring et al. (1) and Buhaug et al. (21) are scaled to this study’s ship numbers separately by ship type (Table S2) and extrapolated from their reference years 2001 and 2007 to this study’s reference year 2006 by applying sea trade growth rates from Fearnleys (22). The resulting fuel consumption yields 210 Mt for Buhaug et al. (21) and 261 Mt for Eyring et al. (1). Thus, the corresponding SeaKLIM result of 221 Mt lies within the previously published uncertainty range. The SeaKLIM VOL. 44, NO. 4, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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emissions show good agreement with the CO2 emissions from Buhaug et al. (21) and with most of the emissions calculated by Eyring et al. (1) (Table 1). Scaling in the other direction, i.e., extrapolating this study’s reference year and ship number per ship type to Buhaug et al. (21), the SeaKLIM algorithm results in a total fuel consumption for the total shipping fleet of 349 Mt and 1097 Tg of CO2 emissions for 2007, which agrees well with the 333 Mt of fuel consumption and 1053 Tg CO2 from Buhaug et al. (21). Due to low numbers of recorded LMIU ship movements or an overestimation of transit times in the comparison studies, with average engine running hours of 2300-4500 h as accumulated transit times of all ship movements per ship this study lies up to 40% lower than those given elsewhere (1, 7, 8). To further validate our results, the present-day emission inventory is compared to the STEEM model from Wang et al. (13). This regional bottom-up approach uses a detailed route network of predefined shipping route segments for North America and finds the shortest path between two ports using GIS technology. The STEEM results for 102,261 given ship movements of 2002 are extrapolated to this study’s reference year (2006) and yield 60 Mt of fuel consumption. The SeaKLIM algorithm agrees closely with 57 Mt for 116,096 North American ship movements of the correspondingly restricted LMIU database (SI and Figure S6). Future Scenarios. With 287-510 Mt the SeaKLIM fuel consumption for the CLE and BAU future scenarios agrees well with 291-525 Mt from the future global top-down emission inventory from Eyring et al. (11) for 2050 (Table 1) from which this study’s future scenarios have been derived. The resulting polar routes can be compared to Dalsøren et al. (23) who estimate North-East Passage fuel consumption for 2015 to be 0.1 Mt following assessments of future freight transport volumes. Scaling the SeaKLIM North-East Passage growth factors from 2006 to 2050 to the period 2006 to 2015 and applying them to the corresponding present-day value we obtain a North-East Passage fuel consumption of 0.15-0.25 Mt for 2015 which is of the same order of magnitude as the Dalsøren et al. (23) results. Granier et al. (16) estimate future NOx and CO emissions on both polar routes in 2050 to 1.3 Tg N and 0.14 Tg CO due to the arbitrary assumptions that 11% and 6% of the Eyring et al. (11) global totals are emitted on polar routes. The corresponding SeaKLIM results are, with 0.01-0.12 Tg NOx and 0.005-0.012 Tg CO, significantly lower. Even if this study underestimates future polar routes due to a lack of an appropriate future ship movement pattern the arbitrary assumptions from Granier et al. (16) seem too high.

Discussion This study calculates global bottom-up ship emission inventories for a present-day and two future scenarios. An automatic path-finding algorithm between start and end port on a 0.5° × 0.5° model grid has been developed taking into account land masses, sea ice, and sea state. The results yield a better spatial resolution than global top-down approaches and represent the first global bottom-up approach. Thus, this study presents appropriate input data for atmospheric models calculating ship emission impacts on atmosphere, environment, and climate. The totals of fuel consumption and emissions lie in the range of recent global top-down and regional bottom-up approaches. Furthermore, this study shows low future ship activity growth on polar routes in comparison to the global total, but a high future increase in comparison to current regional Arctic ship activity. Even if implementation problems of the given present-day LMIU multistop ship movement pattern underestimate the future polar routes’ ship activity this study shows a higher accuracy at least in terms of 1338

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geographical distribution than recent studies. Finally, this highly flexible SeaKLIM algorithm allows the calculation of emission inventories under predefined allocation criteria, e.g., restriction to certain regions, ports, or ship types. Improvements to this study can be achieved by using engine information per individual ship and ship movements for a whole year to avoid averaged values per ship type and extrapolations. The path-finding algorithm could be improved by a model grid with higher resolution and an optimization following shipping routes’ costs in addition to distances. In general, the SeaKLIM algorithm finds the shortest path and thus always the lowest fuel consumption of a certain port combination. For a better estimation of future polar routes an adaptation of the present-day multistop ship movement pattern to future conditions should be done. Further improvements of future scenarios can be considered, such as changing fish and oil stocks, different growth rates of certain ship types and sizes,as well as altered trade routes due to population changes. Future studies of Arctic polar routes should include estimates of black and organic carbon emissions because these could influence the melting of polar sea ice. The methodology used in this study demonstrates the flexibility to integrate all these improvements.

Acknowledgments This work has been carried out within the HelmholtzUniversity Young Investigators Group SeaKLIM, which is funded by the Helmholtz Association of German Research Centres, and the Deutsches Zentrum fu ¨r Luft- und Raumfahrt e.V. (DLR). We thank Wolfram Mauser (Ludwig-MaximiliansUniversity, Munich, Germany) for supervising the related Master thesis and Axel Lauer (University of Hawaii) for helpful contributions. We thank the anonymous reviewers for their constructive comments.

Supporting Information Available Further details concerning the data preprocessing, the SeaKLIM algorithm, and additional results. This material is available free of charge via the Internet at http://pubs.acs.org.

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