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Article

Insights into the Global Characteristics of Shipping Exhaust Emissions at Berth

by
Mei Sha
1,
Jiayu Zhai
1,
Zheng Wan
1,*,
Zhongdai Wu
2,
Yan Jin
1 and
Enyan Zhu
1,*
1
College of Transport & Communications, Shanghai Maritime University, Shanghai 201306, China
2
Shanghai Ship and Shipping Research Institute, Shanghai 200135, China
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(9), 1527; https://doi.org/10.3390/jmse12091527
Submission received: 24 July 2024 / Revised: 22 August 2024 / Accepted: 24 August 2024 / Published: 3 September 2024
(This article belongs to the Section Marine Pollution)
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Versions Notes

Abstract

:
The need for an accurate inventory of ship emissions is vital for atmospheric scientists assessing the environmental impact of shipping and for policymakers aiming to regulate and incentivize emission reduction. This study used data from 189 international ports, related to ship arrivals and departures, to develop emissions inventories. Emission characteristics were examined in detail, classifying emissions by factors like ship type, month, and region. The analysis identified oil tankers and container ships as main emitters among ship categories. A detailed analysis of the monthly distribution of emissions from vessels at berth worldwide was conducted based on precise arrival and departure times. Singapore, Rotterdam, and Antwerp were the ports with the highest emissions from ships at berth. Overall, this study presents the spatial and temporal emission characteristics of ships at berth in 3912 ports around the world, which can support the development of emission reduction strategies in port management.

1. Introduction

Greenhouse gas emissions from shipping account for 3% of total global emissions and are growing. As the linchpin of international trade and manufacturing supply chains, maritime transport facilitates over 80% of the global commodities trade [1]. The burgeoning global shipping industry has rendered ship exhaust emissions a predominant source of pollution on a global scale [2]. These emissions include particulate matter (PM), carbon monoxide (CO), and volatile organic compounds (VOCs), all of which impinge on human health; sulfur oxides (SOx) and nitrogen oxides (NOx), which contribute to acid rain; photochemically formed PM and tropospheric ozone; and carbon dioxide (CO2), black carbon, and methane (CH4), which are all associated with the greenhouse effect [3,4,5]. Projections indicate a 50% increase in CO2 emissions by 2050 compared to 2018 and a 90–130% increase compared to 2008 due to burgeoning maritime transport demand [6,7,8]. This underlines an urgent imperative to scrutinize ship emissions and promulgate efficacious strategies, in which the basis is a detailed pollutant emissions’ inventory including focused studies on ship emissions at berth, to help quantify significant air pollution sources present in ports, mitigate their threats to human health, and foster sustainable port development [1,9,10].
Research has demonstrated that air pollutants emanating from ships in port areas can permeate into adjacent port cities, impacting not only the urban environment but also potentially escalating health risks such as cardiopulmonary diseases and cancer [11,12,13]. Within the East China Sea coastline, over 60% and 85% of ship emissions occurred within distances of 100 km and 200 km, respectively [14]. In the Yangtze River Delta, the influence of ships on air quality is principally attributable to emissions within 12 nautical miles of the coast, although emissions from coastal areas extending from 24 to 96 nautical miles also substantially contribute to PM2.5 concentrations [15]. Typically, ships at berth must operate auxiliary engines to supply power for vital on-board functions [4]. These emissions are three to five times greater than those from other port-related activities, such as maneuvering and cruising, constituting the primary factor in port emission concentrations [16,17]. In Hong Kong, ocean-going vessels have been identified as the predominant contributors to emissions during berthing [18].
To gauge the impact of ship emissions on air quality, scholars have concentrated on ship emission inventories [18,19,20]. Defined as the aggregate quantity of exhaust emissions emanating from a specific source within a given time frame and region [21,22,23], ship emission inventories have been the subject of proliferating regional and global research in recent years [12,14,20,24,25,26,27,28,29,30,31].
In synthesis, extant studies have chiefly concentrated on two predominant domains: first, ship emissions in particular water bodies; and second, emissions in significant port locations. The study conducted by Dalsoren et al. [32] was seminal in considering ship emissions on a global scale, utilizing voyage data from more than 32,000 vessels in the global fleet. These data facilitated the creation of activity profiles across 15 disparate ship types and seven size categories, including a total of 105 ship segments. The computed activity profiles were amalgamated with data on ship type and size to calculate energy consumption and emissions. However, there has been a paucity of research concerning ship emissions at berth in international ports. Given the potentially grave consequences of ship emissions on both the port environment and human health, the development of a detailed inventory of emissions at port berths is imperative. This inventory would enable ports to formulate emission mitigation strategies and evaluate air quality within port vicinities. An accurate appraisal of emissions at port berths is particularly salient, as vessels spend more time at berth than in port waters, and the duration spent at berth significantly influences the ship’s emissions.
Methodologies for the assessment of ship emissions have evolved from fully top-down to fully bottom-up approaches [33]. In a fully top-down approach, total emissions are calculated at a larger scale (usually national), and then proxy variables are used to reduce the geographical scope to a smaller scale (regional or municipal), independent of the location of individual ships. Several studies have used fully top-down approaches [34,35]. Ju et al. estimated ship emissions in Tianjin port in 2006 based on fuel consumption [36]. They first selected an appropriate emission factor based on the ship power and then multiplied the emission factor by the ship fuel consumption. The fuel consumption was determined by the port cargo throughput and the distance of the cargo transport. It is generally accepted that top-down approaches can be used for initial estimates of local emissions, but results need to be confirmed by bottom-up studies [17,18,27,33,37,38].
A fully bottom-up approach estimates the air pollutants emitted by each ship at its particular location and for its particular activity and then aggregates the data in time and space. Bottom-up approaches are generally more accurate than top-down approaches because they rely on ship movements and attributes (including type, speed, and power) to directly estimate emissions. Bottom-up studies are now favored over top-down studies due to the increased availability of ship data, especially after the introduction of Automatic Identification Systems (AISs). Yang et al. used an activity and fuel-based approach to categorize ship trajectories based on ship visa data from Shenzhen ports [39]. Using ship profiles from Lloyd’s Register and ship trajectories from the Automatic Identification System (AIS), a ship emission inventory for Shenzhen was constructed for the year 2010. Previous studies have typically used annual ship visa data to obtain information on ship activities on major shipping routes. However, this approach limits the temporal and spatial characterization of ship emissions as detailed ship trajectories cannot be identified. In addition, ship visa data only cover ships entering the port and not ships transiting the port. Winther et al. presented a meticulous emission inventory of black carbon, NOx, and SO2 from ships in the Arctic in 2012 [40]. An AIS transmits dynamic information, such as ship position, speed, and heading, in addition to static information, such as ship name, call sign, and flag state, facilitating the construction of a comprehensive global database of shipping activity. This, in turn, enables a detailed examination of ship emissions across varied ports, countries, regions, and the world at large.
Several research initiatives, such as high-resolution estimates of ship emissions in the Yangtze estuary [31], monthly accurate ship emissions assessments based on June 2017 AIS data [41], and comprehensive emission inventories in the Pearl River Delta (PRD) [42], have utilized specific AIS data sets. Additional applications include detailed assessments of hourly emissions in Sydney’s vicinity [24] and global evaluations using the Ship Traffic Emissions Assessment Model (STEAM3) for 2015 [28]. Based on AIS data, further studies have presented intricate emission inventories, such as an appraisal of the port of Tianjin in 2014 [43] and a pioneering estimation of pollutants from cruise ships and ferries in the Port of Las Palmas in 2011 [2].
In summary, the method of building ship emission inventories based on AIS data has been applied in some ports in recent years, and the method has demonstrated many advantages in terms of high spatial and temporal resolution. However, given that AIS data are commercial in nature, limitations in temporal coverage exist in existing studies aimed at developing ship emission inventories. The objective of this study was to construct a global inventory of emissions from ships at berth in ports. This inventory enabled the assessment of the relative contributions of different categories of ships to the overall emissions when at berth. In addition, the characteristics of monthly emissions from ships at berth in ports around the globe were analyzed. The aim was to identify the factors influencing seasonal variations in emissions from ships. Finally, the spatial characteristics of emissions from ships at berth in ports around the globe were investigated. Hence, this paper offers an exhaustive annual inventory of ship emissions, including all vessels at berths from 3912 ports globally over an entire calendar year. The study was predicated on 2017 AIS data from ships at port berths worldwide, amalgamating information on pertinent vessels with various data inputs and emission factors from identified sources, constituting the foundation for estimating global emissions from ships at port berths, developing an exhaustive and large-scale emissions inventory.

2. Methodology

The structural framework for this paper is visually represented in Figure 1. This work used a bottom-up methodology predicated on ship AIS data, and this research estimated the emissions’ inventory for ships at berth in global ports throughout 2017. This approach facilitated a detailed analysis and estimation of ship emissions, categorized by ship type, month, and port location. The methodology deployed herein is conceptually analogous to approaches used in the Fourth IMO GHG Study 2020 [6] and the Port of Los Angeles Air Emissions Inventory 2021 [44]. Emissions were approximated as a function of ship energy demand, with energy demand, denominated in kW, being multiplied by an emission factor expressed in grams per kWh (g/kW·h) and the dwell time at berth in hours. The incinerator was excluded from emission estimates because it was inoperative within the study area. Dialogs with ship operators and industry representatives affirmed that vessels abstained from using incinerators at berth or proximate to coastal waters. Exhaust emissions from a single ship at berth were computed by multiplying the time at berth, auxiliary engine or boiler power, and the emission factor, with time at berth converted from seconds to hours. The formula was as follows:
E = P × T × E F
where E represents emissions (g); P represents the power output of the auxiliary engine or boiler (kW); T represents the dwell time at berth (h); and EF represents the emission factor (g/kW·h).
The data utilized herein included primarily static information (including IMO number, MMSI, ship type, deadweight tonnage), dynamic particulars (including time of arrival at berth, time of departure from berth), and voyage specifics (including port of destination). Technical specifications of the ship predominantly include construction year, auxiliary engine power, and boiler power. The array of exhaust gases accounted in this paper chiefly comprised air pollutants (NOx, PM10, PM2.5, SOx, CO, HC) and greenhouse gases (CH4, N2O, CO2).

2.1. Data Processing

The categorization of ship types is a salient consideration, as attributes of the ship, such as engine type and power, along with berthing and maneuvering time, are contingent on the type of vessel. The AIS records disclosed 195 distinct ship types, and these were further segregated by vessel function, referencing the 258 unique StatCode5 appellations for each vessel as cataloged in the IHS database [45]. Certain ship types, such as accommodation platform, jack-up and accommodation platform, semi-submersible, were not included in this tally. Additionally, some ship types that were absent from the StatCode5 nomenclature were disregarded, notwithstanding the negligible emissions attributed to these vessels. In adherence to prevailing studies [46], information on individual ship types was assembled and consolidated to constitute the ensuing 15 ship categories: bulk carrier, oil tanker, container, chemical tanker, cargo, liquefied gas tanker, offshore, ro-ro cargo, miscellaneous, passenger, fishing, tug, other liquid tanker, refrigerated bulk, and yacht [47]. The category designated as “Cargo” represented a consolidated concept including the 18 distinct ship types that are identified in the AIS data set. These included: barge carrier; deck cargo ship; general cargo ship; general cargo ship (with ro-ro facility); general cargo ship; self-discharging; general cargo; inland waterways; general cargo/passenger ship; general cargo/tanker; heavy-load carrier; heavy load carrier, semi-submersible; livestock carrier; nuclear fuel carrier; nuclear fuel carrier (with ro-ro facility); open-hatch cargo ship; palletized cargo ship; refrigerated cargo ship; stone carrier; yacht carrier, semi-submersible. The methodology employed for this aggregation is delineated in Table S1. Table 1 illustrates the 15 ship categories, arranged in descending order based on their proportion of the overall deadweight tonnage (DWT). The three preeminent vessel types comprised 83.06% of the cumulative DWT but constituted merely 39.23% of the total number of vessels, with the residual twelve categories accounting for the balance (Figure 2 and Figure 3). This distribution evidenced a degree of influence that the fleet structure wielded over the comprehensive precision of the emission estimations.
A comprehensive database comprising over 50,000 ships, which constitutes more than a third of the world’s fleet, along with AIS data records, furnished the requisite input values for this investigation. Each vessel was allocated a capacity bin predicated on the volume of cargo or passengers it transported. While these capacity bin categories bore resemblance to those employed in the Third IMO Greenhouse Gas Study of 2014 [48], they were not identical, primarily with respect to the number of ships encompassed within each category. The amalgamation of class and capacity bin categories culminated in 45 distinct ship groups. A detailed table elucidating the ship categories and capacities corresponding to various ship categories and capacity bins is delineated in Table S2. The principal motive for reclassifying each ship from its type to its class lay in the estimation of the power prerequisites for each ship’s auxiliary engines and boilers during berthing.
Ships generally have three engine varieties: main engine (ME), auxiliary engine (AE), and boiler. At berth, while engaged in loading and unloading cargo, ships ordinarily operate their engines, relying on the AE (unless shore power is accessible) and boiler, but not the ME [49]. The AE is principally employed to generate energy forms other than propulsion, while boilers furnish low-pressure steam for heating and subsistence functions [50]. Although some ports offer shore power, enabling vessels to deactivate the AE at berth, this analysis presupposed that the AE and boiler remained activated. This is underpinned by the fact that, with shore-side power accessible, ship AEs or boilers cease operation, shifting emission sources to shore-side power equipment. Hence, within this study, emissions at berth predominantly emanated from the AE and boiler. In the case of AEs and boilers, comprehensive specifications of installed power are often unattainable in the ship registration database. Correspondingly, AIS data offer minimal information on the individual power demands of AEs and boilers for specific vessels. Additionally, the current vessel power monitoring system yields an extremely limited representative sample of the entire fleet. To remedy this deficiency, the second IMO GHG study adopted assumptions regarding the number of ship classes and the operational loads of AEs, thereby estimating the power requirements for AEs and boilers based on the scarce data supplied by HIS [51]. The third IMO GHG study in 2014 utilized data from Starcrest’s Vessel Boarding Program [52], collected across various U.S. ports, to enhance the AE and boiler power requirements [48]. The power demand for all ship auxiliary engines and boilers by phase, ship category, and capacity bin in this study are described in Tables S3 and S4, respectively.

2.2. Emission Factor

Emission factors for ship pollutants depend on the engine type and the type of fuel oils used. Ships are typically outfitted with two kinds of AEs: medium-speed diesel engines—most prevalent, with a maximum engine speed surpassing 130 rpm (usually exceeding 400 rpm) but falling short of 2000 rpm—and high-speed diesel engines, with a maximum engine speed equal to or greater than 2000 rpm [44]. Owing to the absence of engine type data within the study, an assumption was made that all AEs were of medium speed [53]. Emission factors for all engine types used in this study were derived from equations or values delineated in the USEPA EI Guidance document. The proportion of ships utilized in the study for each IMO NOx classification is tabulated in Table 2. With contemporary marine engines being subject to increasingly stringent NOx emission standards, the construction year of a ship may impact its NOx emissions. MARPOL Annex VI, Regulation 13, codifies the stratification of NOx emission standards according to the ship’s construction year, as depicted in the two leftmost columns of Table 2. Concerning fuel types, as of January 2014, the AE was mandated to use 0.1% S fuel, in compliance with California Air Resources Board (CARB) and Emission Control Area (ECA) regulations, and ships operating at berth post-2015 are mandated to utilize fuel with a maximum sulfur content of 0.1%. The study presupposed that all ships utilized either distillate marine gas oil (MGO) or marine diesel oil (MDO) to align with fuel sulfur regulations. Table 3 elucidates the emission factors for different pollutants from auxiliary engines and boilers. Moreover, several assumptions had to be formulated, including specific requirements for auxiliary engine and boiler power, along with emission factors, which were in part derived from the IMO Fourth GHG Study 2020 [6] or updated based on contemporary research or maritime industry expert reviews.

3. Results and Discussion

3.1. Volume of Ship Emissions

Ship exhaust emissions were computed by ship categories. A profile of some of the sample calculations is presented in Figure S1. Figure 4 and Figure 5 showcase the inventory of emissions from the AE and boiler of ships at berth in global ports for the year 2017. Specific emissions data from these sources are detailed for various pollutants. As depicted in Figure 6, CO2 emerged as the predominant contributor to all pollutant emissions from AEs, comprising 98% of total emissions, followed by nitrogen and CO oxides at 1.7% and 0.15%, respectively. Despite constituting less than 2% of emissions, NOx and CO significantly outnumbered other pollutants. Figure 7 portrays CO2 as the principal contributor to all pollutant emissions from boilers, accounting for 99.7% of the total emissions, followed by NOx and sulfur oxides at 0.204% and 0.0608%, respectively. CO2 emissions overwhelmingly surpassed all other combined pollutants. The distribution of emissions from ships in port, as observed in the study by Dalsoren et al., revealed that the highest emissions were CO2, followed by NOx and SO2, then PM and CO, with other pollutants registering relatively low emissions [32]. In comparison to the study conducted by Johansson and Jalkanen [28], the results of this study were analogous in that CO2 emissions were markedly elevated in comparison to the other emissions, with NOx representing the second highest emitter. However, there was a discrepancy in the order of emission, with SOx emitted subsequent to CO2; and NOx in the aforementioned study, whereas in our study, there was a distinction between CO and SOx emissions. In their study, the oxides exhibited a similar pattern, whereas in ours, CO emissions exceeded SOx. This discrepancy can be attributed to the sulfur limits imposed on fuel used by vessels in port.
In the context of total emissions stemming from AEs across various ship classes, bulk carriers ranked as the most significant contributors among all ship types, constituting 16.29% of total emissions. They were closely trailed by container ships, which accounted for 14.95% of emissions. The emission percentage for cargo ships stood at 13.66%, whereas chemical tankers, passenger ships, oil tankers, ro-ro cargo ships, offshore ships, miscellaneous ships, liquefied gas tankers, fishing vessels, tug boats, yachts, other liquid tankers, and refrigerated bulk carriers corresponded to 12.00%, 10.30%, 10.07%, 7.90%, 5.77%, 2.45%, 2.41%, 2.12%, 1.22%, 0.67%, 0.12%, and 0.05% of the total emissions, respectively. These findings align with the results from existing studies [25,31,43,54].
Regarding total emissions from boilers across ship classes, oil tankers emerged as the most significant category, accounting for 43.88% of total emissions. They were succeeded by container ships, comprising 17.03% of emissions. Passenger ships were positioned third, with 9.19% of total emissions, while liquefied gas tankers, bulk carriers, chemical tankers, ro-ro cargo ships, cargo ships, other liquid tankers, and refrigerated bulk carriers contributed to 8.33%, 8.08%, 6.88%, 3.44%, 2.74%, 0.41%, and 0.02% of total emissions, respectively. Certain ship classes, such as fishing, miscellaneous, offshore, tug boats, and yachts, do not possess boilers. In the study conducted by Dalsoren et al., bulk carriers, container ships, and tankers were identified as the principal contributors to global emissions from ships in ports, together accounting for approximately half of the emissions [32].
In congruence with this, our study highlighted tankers and container ships as the main contributors to total auxiliary and boiler emissions, with bulk carriers playing a relatively minor role. An examination of the various ship categories within the AIS data revealed that bulk carriers constituted 43.50% of the total ship count, followed by oil tankers and containers, which accounted for approximately 25.75% and 13.81%, respectively. The remaining ship categories comprised a comparatively minor proportion of the total number of ships. These distinct categories exhibited different emission characteristics stemming from their respective technical specifications, fuel consumption patterns, and engine types. As illustrated in Figure 8, the contribution of each ship category to the emissions of individual gases mirrored the overall distribution depicted in Figure 9.

3.2. Emissions from Different Ship Categories

The findings concerning ship emissions ascertained in this study were juxtaposed with those from previous research. There was substantial variation in the outcomes relating to the contribution of emissions from diverse types of ships. These differences hinged on the specific use and emission characteristics of the individual ship categories. Figure 8 illustrates the distribution of total emissions in 2017 for each ship category, with the findings largely resonating with existing studies [25,31,54]. Within this study, oil tankers were found to have the highest total emissions from auxiliary engines and boilers, approximately 24.88%, which might be attributable to oil tankers being the predominant type of vessel, often equipped with larger engines, with both auxiliary engines and boilers being more powerful, and being longer at berth compared to other ships. Container ships, although numerically half as many as bulk carriers, tended to be more substantial in size and powered by more formidable engines.
Oil tankers were the predominant contributors to total emissions, accounting for 12.91%, 22.92%, 22.92%, 25.03%, 13.32%, 14.31%, 14.31%, 30.27%, and 25.02% of NOx, PM10, PM2.5, SOx, CO, HC, CH4, N2O, and CO2 emissions, respectively. Container ships also constituted a significant percentage of emissions, with 14.94%, 15.74%, 15.74%, 15.87%, 15.21%, 15.22%, 15.22%, 16.19%, and 15.87% of the same emissions, respectively. Moreover, emissions from bulk carriers exceeded those from passenger ships, chemical ships, and other ship categories. The elevated emissions from bulk carriers and containers, compared to other ship categories, can be attributed to their capacity to transport larger quantities of cargo.

3.3. Monthly Distribution Characteristics of Ship Emissions

This study computed emissions from ships at berth in global ports for the year 2017, as depicted in Figure 10. The emissions profile fluctuated monthly, with the minimum emissions recorded in February. This observation aligned with research focusing on the emissions profile of Chinese ports, which identified a similar pattern, likely linked to reduced cargo and passenger traffic during the Chinese New Year [20,23,31,43]. The emission spike observed from October to December 2017, peaking in December, was primarily due to four factors: (1) End-of-year holidays like Christmas and Thanksgiving drive increased consumer demand, leading to a surge in cargo transport, extended port stays, and congestion, all contributing to higher emissions. (2) Preparations for the Chinese Lunar New Year in January or February lead to increased production and transportation activities in preceding months, influencing global shipping patterns. (3) Businesses typically boost purchases before the fiscal year-end, adding to port congestion and emissions. (4) The peak in December emissions may also be linked to non-consumer sectors, such as oil and bulk goods, driven by seasonal factors like winter energy demand.
The AIS data employed in this paper possessed a high degree of temporal precision, with ship arrival and departure times accurate to the second level. This accuracy facilitated a more detailed description of the monthly distribution of emissions from docked ships globally. November 2017, a month characterized by relatively high emissions, was used to elaborate how emissions from ships at berth in global ports could be calculated for a full month based on high temporal accuracy data.
Initially, vessels were classified into three categories. Category 1 vessels had an arrival time at berth before 00:00:00 on 1 November 2017, and a departure time after 00:00:00 on the same date. Category 2 vessels were characterized by both an arrival and departure time between 00:00:00 on 1 November 2017, and 23:59:59 on 30 November 2017. Category 3 vessels arrived at berth before 23:59:59 on 30 November 2017 and departed after 00:00:00 on 1 December 2017.
The berth durations were then computed for the three vessel categories. For category 1 vessels, the time spent at berth was the difference between the departure time and 00:00:00 on 1 November 2017. For category 2 vessels, it was the difference between the times of departure and arrival at the berth. For category 3 vessels, it was the difference between the time of arrival at the berth and 23:59:59 on 30 November 2017 (see Figure 11).
Ultimately, the monthly emissions from ships at berth in global ports for November 2017 were determined based on the categorized berthing durations and consisted of three main components: partial emissions from category 1 vessels, full emissions from category 2 vessels, and partial emissions from category 3 vessels.

3.4. Emissions from Ports in Different Countries or Regions of the World

There were 678 records in the AIS data set where the port of destination for the ship was absent. The primary objective of this study was to quantify emissions from ships at berth in ports, providing a foundational basis for informed decision-making by port authorities. Ships lacking information on their destination ports could not be allocated to a specific port, although it remained possible to estimate their emissions while at berth. Therefore, this paper omitted the portion of the AIS data records where the ship’s destination port was unaccounted for. Following this processing, the residual AIS records contained 3912 ports globally. These ports from 189 countries and territories on all seven continents participating in this study are shown in Table 4. The geographical scope of this investigation aligns with the area depicted in Figure 12. The dwell time of an arriving vessel at the berth was determined by calculating the difference between the arrival time at the berth and the departure time from the berth. However, the AIS data that were accessible contained 57,548 instances of missing departure times. Since these omissions were correlated with the vessel’s historical route data, this absent segment was excluded during the data processing phase.
Examining the data from a port-centric perspective, the preeminent emissions from ships at berth in 2017 were generated in Singapore, followed by Rotterdam. Antwerp, Houston, and Ningbo occupied the third to fifth ranks, while Tianjin, Guangzhou, Kaohsiung, Shanghai, and Liouheng were also among the top ten ports with the highest emissions. Collectively, the emissions from these ten ports constituted 11.45% of the global total (Table 5).
As depicted in Figure 13, China was responsible for the largest aggregate emissions from ships at berth in its ports in 2017, followed by the United States. Japan, Singapore, and Italy held the third to fifth positions, with South Korea, the Netherlands, the United Kingdom, Russia, and Brazil also ranking among the top ten emitting countries or territories. In terms of proportion, the cumulative emissions from these top ten countries or territories accounted for 49.75% of the total global emissions, with nearly half of the world’s emissions originating from these nations.
This study included a total of 189 countries or territories that spanned these continents. In research conducted by Dalsoren et al., ports in Asia were predominant, accounting for 42% of the cumulative time ships spent across all ports, with Europe’s share standing at 31% [32]. From a global intercontinental perspective, our investigation revealed that Asian ports were the most significant emitters, constituting 45.52% of the overall emissions, followed by European ports at 27.47%. The remaining emissions were apportioned among North America, South America, Africa, Oceania, and Antarctica, as delineated in Table 6.

4. Conclusions

This paper employed a bottom-up approach to estimate emissions from ships at berth in global ports for 2017. The findings detailed the emissions of NOx, PM10, PM2.5, SOx, CO, HC, CH4, N2O, and CO2 from auxiliary engines and boilers. The results indicated that CO2 was the dominant contributor to emissions from both engines and boilers. Among ship categories, bulk carriers’ auxiliary engines and oil tankers’ boilers were the largest emission sources, followed closely by container ships. Emissions showed significant monthly variations, with the lowest in February and the highest in December. Singapore, Rotterdam, and Antwerp registered the highest emissions from ships at berth, while China, the US, and Japan led globally. Asian ports accounted for the highest regional emissions. The study highlights that emissions are influenced by emission reduction policies, ship categories, geographical factors, and regional practices. This work offers a foundation for further research, which should aim to enhance the accuracy of global ship emission inventories for berths, ideally validating estimates with on-site measurements to strengthen the findings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jmse12091527/s1, Table S1. Ship types represented. Table S2. Ship capacity bin by ship class. Table S3. Auxiliary engine power demand (kW) by phase, ship class, and capacity bin. Table S4. Boiler power demand (kW) by phase, ship class, and capacity bin. Figure S1. Profile of selected computational samples.

Author Contributions

Conceptualization, M.S., Z.W. (Zheng Wan), Z.W. (Zhongdai Wu), Y.J. and E.Z.; Methodology, J.Z.; Data curation, J.Z.; Writing—original draft, J.Z.; Writing—review & editing, M.S., Z.W. (Zheng Wan), Z.W. (Zhongdai Wu), Y.J. and E.Z.; Project administration, M.S., Z.W. (Zheng Wan) and Z.W. (Zhongdai Wu); Funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China under Grant 71972128, and the Fund from State Key Laboratory of Maritime Technology and Safety.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sirimanne, S.N.; Juan, W.; Asariotis, R.; Assaf, M.; Ayala, G.; Benamara, H.; Youssef, F. Review of Maritime Transport 2019; International Maritime Organization: London, UK, 2019. [Google Scholar]
  2. Tichavska, M.; Tovar, B. Port-city exhaust emission model: An application to cruise and ferry operations in Las Palmas Port. Transp. Res. Part A Policy Pract. 2015, 78, 347–360. [Google Scholar] [CrossRef]
  3. Christodoulou, A.; Gonzalez-Aregall, M.; Linde, T.; Vierth, I.; Cullinane, K. Targeting the reduction of shipping emissions to air. Marit. Bus. Rev. 2019, 4, 16–30. [Google Scholar] [CrossRef]
  4. Cullinane, K.; Tseng, P.H.; Wilmsmeier, G. Estimation of container ship emissions at berth in Taiwan. Int. J. Sustain. Transp. 2016, 10, 466–474. [Google Scholar] [CrossRef]
  5. Tai, H.-H.; Chang, Y.-H. Reducing pollutant emissions from vessel maneuvering in port areas. Marit. Econ. Logist. 2022, 24, 651–671. [Google Scholar] [CrossRef]
  6. Faber, J.; Hanayama, S.; Zhang, S.; Pereda, P.; Comer, B.; Hauerhof, E.; Yuan, H. Fourth IMO GHG Study; International Maritime Organization: London, UK, 2020. [Google Scholar]
  7. Kalli, J.; Jalkanen, J.-P.; Johansson, L.; Repka, S. Atmospheric emissions of European SECA shipping: Long-term projections. WMU J. Marit. Aff. 2013, 12, 129–145. [Google Scholar] [CrossRef]
  8. Serra, P.; Fancello, G. Toward the IMO’s GHG Goals: A Critical Overview of the Perspectives and Challenges of the Main Options for Decarbonizing International Shipping. Sustainability 2020, 12, 3220. [Google Scholar] [CrossRef]
  9. Budiyanto, M.; Huzaifi, M.; Sirait, S. Estimating CO2 Emissions in a Container Port based on Modality Movement in the Terminal Area. Int. J. Technol. 2019, 10, 1618. [Google Scholar] [CrossRef]
  10. Zheng, C.; Hoffmann, M.R.; Mauzerall, D.L.; Gan, J.; Song, L. Expanding our horizons on the Earth’s sustainable future. Sustain. Horiz. 2021, 1, 100001. [Google Scholar] [CrossRef]
  11. Corbett, J.J.; Winebrake, J.J.; Green, E.H.; Kasibhatla, P.; Eyring, V.; Lauer, A. Mortality from Ship Emissions: A Global Assessment. Environ. Sci. Technol. 2007, 41, 8512–8518. [Google Scholar] [CrossRef]
  12. Liu, H.; Fu, M.; Jin, X.; Shang, Y.; Shindell, D.; Faluvegi, G.; Shindell, C.; He, K. Health and climate impacts of ocean-going vessels in East Asia. Nat. Clim. Chang. 2016, 6, 1037–1041. [Google Scholar] [CrossRef]
  13. Liu, Z.; Lu, X.; Feng, J.; Fan, Q.; Zhang, Y.; Yang, X. Influence of Ship Emissions on Urban Air Quality: A Comprehensive Study Using Highly Time-Resolved Online Measurements and Numerical Simulation in Shanghai. Environ. Sci. Technol. 2017, 51, 202–211. [Google Scholar] [CrossRef] [PubMed]
  14. Fan, Q.; Zhang, Y.; Ma, W.; Ma, H.; Feng, J.; Yu, Q.; Yang, X.; Ng, S.K.W.; Fu, Q.; Chen, L. Spatial and Seasonal Dynamics of Ship Emissions over the Yangtze River Delta and East China Sea and Their Potential Environmental Influence. Environ. Sci. Technol. 2016, 50, 1322–1329. [Google Scholar] [CrossRef] [PubMed]
  15. Feng, J.; Zhang, Y.; Li, S.; Mao, J.; Patton, A.P.; Zhou, Y.; Ma, W.; Liu, C.; Kan, H.; Huang, C.; et al. The influence of spatiality on shipping emissions, air quality and potential human exposure in the Yangtze River Delta/Shanghai, China. Atmos. Chem. Phys. 2019, 19, 6167–6183. [Google Scholar] [CrossRef]
  16. Deniz, C.; Kilic, A.; Cıvkaroglu, G. Estimation of shipping emissions in Candarli Gulf, Turkey. Environ. Monit. Assess. 2010, 171, 219–228. [Google Scholar] [CrossRef]
  17. Tzannatos, E. Ship emissions and their externalities for the port of Piraeus—Greece. Atmos. Environ. 2010, 44, 400–407. [Google Scholar] [CrossRef]
  18. Ng, S.K.W.; Loh, C.; Lin, C.; Booth, V.; Chan, J.W.M.; Yip, A.C.K.; Li, Y.; Lau, A.K.H. Policy change driven by an AIS-assisted marine emission inventory in Hong Kong and the Pearl River Delta. Atmos. Environ. 2013, 76, 102–112. [Google Scholar] [CrossRef]
  19. Kiliç, A.; Deniz, C. Inventory of shipping emissions in Izmit Gulf, Turkey. Environ. Prog. Sustain. Energy 2010, 29, 221–232. [Google Scholar] [CrossRef]
  20. Song, S. Ship emissions inventory, social cost and eco-efficiency in Shanghai Yangshan port. Atmos. Environ. 2014, 82, 288–297. [Google Scholar] [CrossRef]
  21. Chen, D.; Wang, X.; Nelson, P.; Li, Y.; Zhao, N.; Zhao, Y.; Lang, J.; Zhou, Y.; Guo, X. Ship emission inventory and its impact on the PM2.5 air pollution in Qingdao Port, North China. Atmos. Environ. 2017, 166, 351–361. [Google Scholar] [CrossRef]
  22. Lee, H.; Pham, H.T.; Chen, M.W.; Choo, S. Bottom-Up Approach Ship Emission Inventory in Port of Incheon Based on VTS Data. J. Adv. Transp. 2021, 2021, 5568777. [Google Scholar] [CrossRef]
  23. Li, C.; Yuan, Z.; Ou, J.; Fan, X.; Ye, S.; Xiao, T.; Shi, Y.; Huang, Z.; Ng, S.K.W.; Zhong, Z.; et al. An AIS-based high-resolution ship emission inventory and its uncertainty in Pearl River Delta region, China. Sci. Total Environ. 2016, 573, 1–10. [Google Scholar] [CrossRef] [PubMed]
  24. Goldsworthy, B.; Enshaei, H.; Jayasinghe, S. Comparison of large-scale ship exhaust emissions across multiple resolutions: From annual to hourly data. Atmos. Environ. 2019, 214, 116829. [Google Scholar] [CrossRef]
  25. Goldsworthy, L.; Goldsworthy, B. Modeling of ship engine exhaust emissions in ports and extensive coastal waters based on terrestrial AIS data—An Australian case study. Environ. Model. Softw. 2015, 63, 45–60. [Google Scholar] [CrossRef]
  26. Jalkanen, J.P.; Brink, A.; Kalli, J.; Pettersson, H.; Kukkonen, J.; Stipa, T. A modeling system for the exhaust emissions of marine traffic and its application in the Baltic Sea area. Atmos. Chem. Phys. 2009, 9, 9209–9223. [Google Scholar] [CrossRef]
  27. Jalkanen, J.P.; Johansson, L.; Kukkonen, J.; Brink, A.; Kalli, J.; Stipa, T. Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide. Atmos. Chem. Phys. 2012, 12, 2641–2659. [Google Scholar] [CrossRef]
  28. Johansson, L.; Jalkanen, J.P.; Kukkonen, J. Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution. Atmos. Environ. 2017, 167, 403–415. [Google Scholar] [CrossRef]
  29. Peng, X.; Wen, Y.; Wu, L.; Xiao, C.; Zhou, C.; Han, D. A sampling method for calculating regional ship emission inventories. Transp. Res. Part D Transp. Environ. 2020, 89, 102617. [Google Scholar] [CrossRef]
  30. Wan, Z.; Zhang, Q.; Xu, Z.P.; Chen, J.H.; Wang, Q. Impact of emission control areas on atmospheric pollutant emissions from major ocean-going ships entering the Shanghai Port, China. Mar. Pollut. Bull. 2019, 142, 525–532. [Google Scholar] [CrossRef]
  31. Weng, J.X.; Shi, K.; Gan, X.F.; Li, G.R.; Huang, Z. Ship emission estimation with high spatial-temporal resolution in the Yangtze River estuary using AIS data. J. Clean. Prod. 2020, 248, 119297. [Google Scholar] [CrossRef]
  32. Dalsoren, S.B.; Eide, M.S.; Endresen, O.; Mjelde, A.; Gravir, G.; Isaksen, I.S.A. Update on emissions and environmental impacts from the international fleet of ships: The contribution from major ship types and ports. Atmos. Chem. Phys. 2009, 9, 2171–2194. [Google Scholar] [CrossRef]
  33. Miola, A.; Ciuffo, B. Estimating air emissions from ships: Meta-analysis of modelling approaches and available data sources. Atmos. Environ. 2011, 45, 2242–2251. [Google Scholar] [CrossRef]
  34. Corbett, J.J.; Fischbeck, P.S.; Pandis, S.N. Global nitrogen and sulfur inventories for oceangoing ships. J. Geophys. Res.-Atmos. 1999, 104, 3457–3470. [Google Scholar] [CrossRef]
  35. Endresen, O.; Sorgard, E.; Behrens, H.L.; Brett, P.O.; Isaksen, I.S.A. A historical reconstruction of ships’ fuel consumption and emissions. J. Geophys. Res.-Atmos. 2007, 112, D12301. [Google Scholar] [CrossRef]
  36. Jin, T.-S.; Yin, X.-G.; Xu, J.; Yang, L.; Ge, W.-H.; Ju, M.-T. Air pollutants emission inventory from commercial ships of Tianjin Harbor. Mar. Environ. Sci. 2009, 28. [Google Scholar] [CrossRef]
  37. Eyring, V.; Isaksen, I.S.A.; Berntsen, T.; Collins, W.J.; Corbett, J.J.; Endresen, O.; Grainger, R.G.; Moldanova, J.; Schlager, H.; Stevenson, D.S. Transport impacts on atmosphere and climate: Shipping. Atmos. Environ. 2010, 44, 4735–4771. [Google Scholar] [CrossRef]
  38. Yang, L.; Zhang, Q.; Zhang, Y.; Lv, Z.; Wang, Y.; Wu, L.; Feng, X.; Mao, H. An AIS-based emission inventory and the impact on air quality in Tianjin port based on localized emission factors. Sci. Total Environ. 2021, 783, 146869. [Google Scholar] [CrossRef] [PubMed]
  39. Yang, J.; Yin, P.L.; Ye, S.Q.; Wang, S.S.; Zheng, J.Y.; Ou, J.M. Marine Emission Inventory and Its Temporal and Spatial Characteristics in the City of Shenzhen. Huan Jing Ke Xue 2015, 36, 1217–1226. [Google Scholar]
  40. Winther, M.; Christensen, J.H.; Plejdrup, M.S.; Ravn, E.S.; Eriksson, Ó.F.; Kristensen, H.O. Emission inventories for ships in the arctic based on satellite sampled AIS data. Atmos. Environ. 2014, 91, 1–14. [Google Scholar] [CrossRef]
  41. Mao, J.; Zhang, Y.; Yu, F.; Chen, J.; Sun, J.; Wang, S.; Zou, Z.; Zhou, J.; Yu, Q.; Ma, W.; et al. Simulating the impacts of ship emissions on coastal air quality: Importance of a high-resolution emission inventory relative to cruise- and land-based observations. Sci. Total Environ. 2020, 728, 138454. [Google Scholar] [CrossRef]
  42. Zhang, Y.; Fung, J.C.H.; Chan, J.W.M.; Lau, A.K.H. The significance of incorporating unidentified vessels into AIS-based ship emission inventory. Atmos. Environ. 2019, 203, 102–113. [Google Scholar] [CrossRef]
  43. Chen, D.; Zhao, Y.; Nelson, P.; Li, Y.; Wang, X.; Zhou, Y.; Lang, J.; Guo, X. Estimating ship emissions based on AIS data for port of Tianjin, China. Atmos. Environ. 2016, 145, 10–18. [Google Scholar] [CrossRef]
  44. Starcrest Consulting Group, LLC. Port of Los Angeles Inventory of Air Emissions—2021. Available online: https://kentico.portoflosangeles.org/getmedia/f26839cd-54cd-4da9-92b7-a34094ee75a8/2021_air_emissions_inventory (accessed on 7 August 2024).
  45. IHS. StatCode 5 Shiptype Coding System. Available online: https://www.poseidonprinciples.org/finance/wp-content/uploads/2020/06/StatCode-5-Shiptype-Coding-System.pdf (accessed on 25 August 2024).
  46. EMSA. The 2021 World Merchant Fleet Statistics from Equasis; EMSA: Lisbon, Portugal, 2022. [Google Scholar]
  47. Schwarzkopf, D.; Petrik, R.; Matthias, V.; Quante, M.; Majamäki, E.; Jalkanen, J.-P. A ship emission modeling system with scenario capabilities. Atmos. Environ. X 2021, 12, 100132. [Google Scholar] [CrossRef]
  48. Smith, T.; Jalkanen, J.-P.; Anderson, B.A.; Corbett, J.J.; Faber, J.; Hanayama, S.; O’Keeffe, E.; Parker, S.; Johansson, L.; Aldous, L.; et al. Third IMO Greenhouse Gas Study 2014; International Maritime Organization: London, UK, 2015. [Google Scholar]
  49. Styhre, L.; Winnes, H.; Black, J.; Lee, J.; Le-Griffin, H. Greenhouse gas emissions from ships in ports Case studies in four continents. Transp. Res. Part D Transp. Environ. 2017, 54, 212–224. [Google Scholar] [CrossRef]
  50. Goldsworthy, B.; Goldsworthy, L. Assigning machinery power values for estimating ship exhaust emissions: Comparison of auxiliary power schemes. Sci. Total Environ. 2019, 657, 963–977. [Google Scholar] [CrossRef] [PubMed]
  51. Buhaug, Ø.; Corbett, J.J.; Endresen, Ø.; Eyring, V.; Faber, J.; Hanayama, S.; Lee, D.S.; Lee, D.; Lindstad, H.; Markowska, A.Z. Second Imo Ghg Study 2009; International Maritime Organization (IMO): London, UK, 2009; Volume 20. [Google Scholar]
  52. Starcrest Consulting Group, LLC. Port of Los Angeles Inventory of Air Emissions—2013. Available online: https://kentico.portoflosangeles.org/getmedia/9b47cda9-e282-458e-9a80-36b17e79c47c/2013_Air_Emissions_Inventory_Full_Report (accessed on 7 August 2024).
  53. USEPA. Ports Emissions Inventory Guidance: Methodologies for Estimating Port-Related and Goods Movement Mobile Source Emissions; USEPA: Washington, DC, USA, 2022. [Google Scholar]
  54. Winnes, H.; Styhre, L.; Fridell, E. Reducing GHG emissions from ships in port areas. Res. Transp. Bus. Manag. 2015, 17, 73–82. [Google Scholar] [CrossRef]
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Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Percentage of ships in different categories.
Figure 2. Percentage of ships in different categories.
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Figure 3. Percentage of deadweight tonnage of ships in different categories.
Figure 3. Percentage of deadweight tonnage of ships in different categories.
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Figure 4. Ship emission inventory from auxiliary engines (unit: t).
Figure 4. Ship emission inventory from auxiliary engines (unit: t).
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Figure 5. Ship emission inventory from boilers (unit: t).
Figure 5. Ship emission inventory from boilers (unit: t).
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Figure 6. Auxiliary exhaust emissions from ships at berth in ports worldwide in 2017.
Figure 6. Auxiliary exhaust emissions from ships at berth in ports worldwide in 2017.
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Figure 7. Boiler exhaust emissions from ships at berth in ports worldwide in 2017.
Figure 7. Boiler exhaust emissions from ships at berth in ports worldwide in 2017.
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Figure 8. Contribution of different ship classes to different emissions from ships at berth in global ports in 2017.
Figure 8. Contribution of different ship classes to different emissions from ships at berth in global ports in 2017.
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Figure 9. Proportion of exhaust emissions from different ship classes at berths in global ports in 2017.
Figure 9. Proportion of exhaust emissions from different ship classes at berths in global ports in 2017.
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Figure 10. Monthly emissions from ships at berth in global ports in 2017.
Figure 10. Monthly emissions from ships at berth in global ports in 2017.
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Figure 11. Breakdown of monthly emissions from ships at berth in global ports in November 2017.
Figure 11. Breakdown of monthly emissions from ships at berth in global ports in November 2017.
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Figure 12. Global port distribution.
Figure 12. Global port distribution.
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Figure 13. Country or regional distribution of total emissions from ships at berth in ports worldwide in 2017.
Figure 13. Country or regional distribution of total emissions from ships at berth in ports worldwide in 2017.
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Table 1. Overview of the number and deadweight tonnage of vessels in the 15 ship categories.
Table 1. Overview of the number and deadweight tonnage of vessels in the 15 ship categories.
Ship CategoryCount%DWT%
Bulk carrier10,23420.02%7.57 × 10843.50%
Oil tanker518810.15%4.48 × 10825.75%
Container46319.06%2.41 × 10813.81%
Chemical tanker44198.64%1.03 × 1085.92%
Cargo768515.03%6.75 × 1073.88%
Liquefied gas tanker16893.30%6.06 × 1073.48%
Offshore42488.31%2.93 × 1071.68%
Ro-ro cargo16323.19%1.87 × 1071.07%
Miscellaneous22434.39%6.47 × 1060.37%
Passenger24514.79%5.49 × 1060.32%
Fishing28555.58%2.09 × 1060.12%
Tug27605.40%7.79 × 1050.04%
Other liquid tankers400.08%3.45 × 1050.02%
Refrigerated bulk100.02%2.97 × 1050.02%
Yacht10842.12%1.79 × 1050.01%
Table 2. Classification of NOx emission standards by year of construction and percentage of ships.
Table 2. Classification of NOx emission standards by year of construction and percentage of ships.
TierYear of ConstructionDuring the Study Time Period
Vessel CountPercentage
Tier 0Pre-200015,54830.39%
Tier I2001–201019,70238.50%
Tier II2011–201512,23123.90%
Tier III2016–+36887.21%
TotalAll51,169100%
Table 3. Emission factor (unit: g/kW·h).
Table 3. Emission factor (unit: g/kW·h).
Emission FactorPollutantsTierAuxiliary EngineAuxiliary Boiler
Pollutant emission factorNOxTier013.81.97
Tier112.21.97
Tier210.51.97
Tier32.61.97
PM100.1890.202
PM2.50.1740.186
SOx0.4240.587
CO1.10.2
HC0.40.1
GHG emission factorCH40.0080.002
N2O0.0290.075
CO2696962
Table 4. Overview of global ports’ distribution.
Table 4. Overview of global ports’ distribution.
ContinentsThe Number of Nations or RegionsThe Number of Ports
Asia381095
Europe361424
North America39649
South America13285
Africa41266
Oceania21192
Antarctica11
Total1893912
Table 5. Top 10 ports in the world for emissions from ships at berth in 2017.
Table 5. Top 10 ports in the world for emissions from ships at berth in 2017.
PortEmissions (t)Percentage
Singapore1,586,5043.41%
Rotterdam949,3122.04%
Antwerp525,4571.13%
Houston451,9700.97%
Ningbo369,1040.79%
Tianjin335,4650.72%
Guangzhou323,2530.69%
Kaohsiung283,1090.61%
Shanghai273,9880.59%
Qingdao267,9740.58%
Table 6. Intercontinental distribution of total emissions from ships at berth in global ports in 2017.
Table 6. Intercontinental distribution of total emissions from ships at berth in global ports in 2017.
ContinentsNumber of CountriesTotal Emissions (t)Proportion
Asia3821,165,59745.52%
Europe3612,771,98627.47%
North American395,458,20011.74%
South American132,922,0056.28%
Africa412,838,8006.10%
Oceania211,345,4372.89%
Antarctica1180.00%
Total18946,502,043100.00%
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Sha, M.; Zhai, J.; Wan, Z.; Wu, Z.; Jin, Y.; Zhu, E. Insights into the Global Characteristics of Shipping Exhaust Emissions at Berth. J. Mar. Sci. Eng. 2024, 12, 1527. https://doi.org/10.3390/jmse12091527

AMA Style

Sha M, Zhai J, Wan Z, Wu Z, Jin Y, Zhu E. Insights into the Global Characteristics of Shipping Exhaust Emissions at Berth. Journal of Marine Science and Engineering. 2024; 12(9):1527. https://doi.org/10.3390/jmse12091527

Chicago/Turabian Style

Sha, Mei, Jiayu Zhai, Zheng Wan, Zhongdai Wu, Yan Jin, and Enyan Zhu. 2024. "Insights into the Global Characteristics of Shipping Exhaust Emissions at Berth" Journal of Marine Science and Engineering 12, no. 9: 1527. https://doi.org/10.3390/jmse12091527

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