Next Article in Journal
Hydroelastic Response to Oblique Wave Incidence on a Floating Plate with a Submerged Perforated Base
Next Article in Special Issue
Automatic Identification System (AIS) Data Supported Ship Trajectory Prediction and Analysis via a Deep Learning Model
Previous Article in Journal
A Novel Underwater Image Enhancement Algorithm and an Improved Underwater Biological Detection Pipeline
Previous Article in Special Issue
Integrated Carbon Emission Estimation Method and Energy Conservation Analysis: The Port of Los Angles Case Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JMSE
Volume 10
Issue 9
10.3390/jmse10091206
jmse-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation of Ship Pollutant Emissions in the Ports of Los Angeles and Long Beach

by
Guangnian Xiao
1,
Tian Wang
1,
Xinqiang Chen
2,* and
Lizhen Zhou
1
1
School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China
2
Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2022, 10(9), 1206; https://doi.org/10.3390/jmse10091206
Submission received: 30 June 2022 / Revised: 21 August 2022 / Accepted: 25 August 2022 / Published: 28 August 2022
(This article belongs to the Special Issue Marine Intelligent Transportation Systems: Data Mining and Control Optimization)
Browse Figures
Versions Notes

Abstract

:
The role of the shipping industry in international logistics has been highlighted with the development of the global economy and the increase in international trade. Simultaneously, some of the environmental problems caused by shipping activities have gradually surfaced. The development of modern communication technology and marine communication equipment increased the feasibility of real-time ship dynamic data, as an information source for monitoring ship sailing states, and provided a data basis for the control of ship pollutant emissions. Based on the Automatic Identification System (AIS) data and ship-related data obtained from the waters of the ports of Los Angeles and Long Beach in 2020, the dynamic method is combined with the ship traffic emissions model STEAM2 to calculate the ship pollutant emissions in the two ports, and the relevant analysis work is conducted to evaluate the control effect of the Emission Control Area (ECA) policies on pollutant emissions. Results show that the ship pollutant emissions for CO, CXHX, NOX, SO2, PM10, and PM2.5 were 1230, 510, 11,700, 6670, 248, and 232 tons, respectively. These results also indicate the possible presence of a large gap in the distribution trend of ship pollutant emissions, according to different ship types and sailing states. Moreover, the control effect of various ECA policies on pollutant emissions is not the same, that is, the impact of ECA policies on SO2 and particulate matter is the largest, and that on NOX is minimal.

1. Introduction

Ship transportation, a low-cost transportation mode, has a place in international logistics due to its large volume [1]. The shipping industry is the basic service industry for the development of the world economy. Shipping was previously a low-carbon and environmentally friendly transportation method, and its environmental pollution problems were undiscovered and lacked attention. A series of problems induced by shipping continued to surface, gradually, until various monitoring technologies are upgraded. People realized that the increasingly frequent shipping activities have become the main driving force for the destruction of the marine environment.
The huge consumption of fuel oil in the shipping industry has resulted in far more ship pollutant emissions than people predicted. The shipping market consumed approximately 3.8 million barrels of crude oil per day in 2017, making it a giant in the global fuel consumption market [2]. With the continuous development of communication technology and the popularization of various marine positioning equipment, several technologies that can monitor the sailing states and position information of ships in real time, such as Automatic Identification System (AIS), have gradually become an important means to support and maintain the development of the shipping industry. Ship pollutant emissions have shown the impact of shipping activities on the water environment and the health of residents near the port.
The International Maritime Organization (IMO), a specialized agency established by the United Nations, is responsible for the safety of maritime navigation and the prevention of marine pollution due to shipping [1]. IMO has established a series of conventions to control ship pollutant emissions, and it has continuously updated them to meet the latest needs. Among them, the 2020 “Sulfur Limit Order” for ships is the concern of major shipping companies and relevant departments of various countries. This order stipulates that, from 1 January 2020, the sulfur content in the fuel used by ships cannot exceed 0.5% m/m [1], which proposes high requirements for the marine equipment of shipping companies and increases the difficulty of shipping activities to a certain extent. In addition to using low-sulfur oil, equipping ships with marine scrubbers and using clean energy instead of fossil fuels are also other options for shipping companies.
In 2020, the COVID-19 pandemic hindered the implementation of the “Sulfur Limit Order”, and the detection of ship fuel oil became increasingly difficult. Many countries have also announced the implementation of the temporary “Sulfur Limit Order”. The British Maritime and Coast Guard Agency (MCGA) announced, in March, the suspension of ship inspections for compliance with the “Sulfur Limit Order” to ensure the mobility of goods and reduce the staying time of ships in ports [3]. In addition to the “Sulfur Limit Order”, IMO has also formulated a “Preliminary Strategy for Ship Greenhouse Gas Emissions Reduction” [4].
The objectives of this paper are summarized as follows. First, the ports of Los Angeles and Long Beach are taken as the object, and dynamic method, combined with STEAM2, is used to evaluate the ship pollutant emissions, according to the AIS data and ship-related data obtained from the water area of the two ports in 2020. Second, the influence and reasons of ship types, ports, sailing states, and different Emission Control Area (ECA) policies on the distribution of pollutant emissions are analyzed. Third, the effect of ECA policies on ship pollutant emissions is discussed and recommendations are presented to relevant managers.

2. Conceptual Remarks

The IMO carbon emission reduction measures can be divided into three stages. The details are shown in Table 1. In addition, IMO has established ECAs internationally. Furthermore, the European Union, the United States, and China have successively established ECAs in major waters. Among them, the United States is one of the early countries that implemented the ECA policies to detect and control ship pollutants and established the California ECA [5], and the two busiest ports in the United States, the ports of Los Angeles and Long Beach, are in the California ECA. ECAs generally limit SO2 or NOX, and restrictions also exist on both. The details of the major ECAs in the world are shown in Table 2 [5,6].
The Port of Los Angeles is in California, which is in the southwest of the United States and on the eastern side of the Pacific Ocean. It faces the sea on one side and is surrounded by mountains on three sides, which endows the Port of Los Angeles with inherent advantages of flat terrain, wide area, and long coastline. This port is near the south of Santa Monica Bay and the north of San Pedro Bay. It is the busiest port in the United States, with container throughput shown in Table 3 [7]. The Port of Long Beach, a port in California, is the second busiest container port in the United States and is adjacent to the Port of Los Angeles. It is one of the main ports on the trade route between the United States and Asia. As one of the important ports on the west coast of the United States, the container throughput of the Port of Long Beach is shown in Table 3 [7]. The container throughput in 2020 will not decrease, but increase, compared to that in 2019, due to the impact of the epidemic.

3. Literature Review

With the continuous strengthening of environmental protection and the development of scientific and technological means, the investigations on ship pollutant emissions have continued to deepen and provide a theoretical basis for the future green development of the shipping market. Jalkanen et al. [8] conducted a long-term study on the ship pollutant emissions in the Baltic Sea, based on the information provided by the AIS, and established the Baltic Sea ship traffic exhaust emissions inventory from 2006 to 2009. Tokuslu [9] calculated gas emissions from ships during cruising, maneuvering, and hoteling, in the Port of Samsun, based on ship activity-based methods. Lee et al. [10] adopted a bottom-up approach to generate the 2019 local ship emissions inventory of the Port of Incheon, and the proposed green emissions policies were applicable in practice.
Shipping is the main source of particulate matter emissions caused by human activities in most parts of the world. Kuittinen et al. [11] determined the particulate matter emission coefficients of different fuels by measuring the exhaust gas emitted by ship engines, and they used the ship emissions model STEAM to estimate the global shipping particulate matter emissions. Lehtoranta et al. [12] also investigated the types of fuels used and the impact of marine scrubbers on particulate matter emissions, and they found that the reduction in sulfur content in marine fuels can effectively decrease particulate matter emissions. However, as acknowledged in the discussion, this comparison is invalid because burning natural gas (NG) makes almost all emitted particles smaller than 23 nm. Corbin et al. [13] indicated that the source of black carbon (BC) and sub-10-nm particles were diesel pilot fuel and lubrication-oil metals. Alanen et al. [14] suggested that particle number (PN) emissions from marine engines may remain high regardless of sulfur content, mainly due to nanoparticle emissions.
In addition to technological changes and the control effect of international policies on ship pollutant emissions, other external factors will also have an impact on ship pollutant emissions to a certain extent. Xu et al. [15] evaluated the impact of the epidemic, government prevention, and control policies, as well as the economy on shipping trade in 2020. The growth of the shipping trade will be promoted, to a certain extent, with the control of the epidemic. Luigia and Franco [16] estimated the generated and saved NOX, SO2, and CO2 emissions by taking the cruise ship during the epidemic, as an example, and analyzed the cruise ship flow during the blockade and in the following months.
Many researchers investigated ship pollutant emissions in China. Numerous studies rely on ports due to a large number of broad-scale ports in China, relatively mature development, and the high availability of various data. According to the trade data of the Port of Qingdao, Liu et al. [17] established the ship emissions inventory of the Port of Qingdao, from 2005 to 2017, using the trade method and the grey prediction model GM (1,1). An upward trend of CO, NOX, PM10, and SO2 was observed, and the results showed that the four types of pollutant emissions would show a doubling increase in 2030. Yang et al. [18] established a basic information database of ship emissions in the Port of Tianjin, and they adopted a bottom-up method, on the basis of AIS data, to develop a high-temporal and spatial emissions inventory of ships. Chen et al. [19] proposed a method for estimating ship emissions inventory based on operating modes, classifying the operating modes of ships with AIS data, and considering three operating modes (berthing, unberthing, and maneuvering) for tugboats. Taking the Port of Dalian as a case study, the model uses different methods to estimate emissions.
China established three ECAs in 2016. Feng et al. [20] conducted a study on the characteristics of ship pollutant emissions in the Jiangsu section of the Yangtze River waters by using AIS data of the ECA in the Yangtze River Delta waters, using the STEAM2 model to establish a ship pollutant emissions inventory of four major river-crossing bridges in 2017, and analyzing the distribution characteristics of ship emissions. Li et al. [21] developed a high-resolution ship emissions inventory for the Pearl River Delta region of China, using refined data from the AIS. Ocean-going ships are the largest contributors to total emissions. The spatial distribution of emissions shows that the key emissions hotspots are all concentrated in the newly built ECA.
The nationwide research on pollutant emissions can be mainly divided into two aspects: the pollutant emissions list of inland ships and the pollutant emissions of coastal and ocean-going ships [22]. Zhang et al. [23] took Nanjing Longtan Container Port as an example and established a bottom-up activity-based ship emissions inventory by combining AIS data, ship databases, and on-site survey results. Xu et al. [24] discussed the decision-making behavior of upstream and downstream governments, as well as shipping companies in inland waterway shipping pollution control, based on evolutionary game and prospect theories. Their results showed that the optimal strategy set is active supervision, active supervision, and use of clean energy.
Some studies began exploring the reduction in ship pollutant emissions. Ma et al. [25] proposed an improved cell-based method, considering ECA regulations and weather conditions. Their findings suggest that this method is effective in reducing costs and ship emissions within the control area, but it may increase the overall emissions for the entire shipping event. Liu et al. [26] suggested that increasing the level of autonomous ships in the fleet will reduce ship pollutant emissions, and adopting autonomous shipping on major routes can achieve environmental benefits. Ma et al. [27] also established a multi-objective optimization model for ship route and speed that can simultaneously reduce ship emissions and total costs, considering ECA regulations. Ma et al. [28] developed a nonlinear mixed integer programming model to address the challenge of simultaneously optimizing the three variables of speed, routing, and refueling strategy to address the constraints for ships, regarding the use of low-sulfur fuel, due to the establishment of ECA. The management of ports and the development of maritime traffic have also attracted the attention of researchers, in recent years, to stay updated with real-time traffic conditions at sea, predict traffic flow to estimate energy consumption, and promote the development of the era of intelligent navigation, which can provide effective information for port ship management and help realize rational management decisions [29,30,31].
According to the existing research, the compilation of international ship emission inventories consists of top-down and bottom-up methods. The top-down methods include the fuel oil method and the trade method, while the bottom-up methods include the statistical method and the dynamic method [32]. The fuel oil method is suitable for global inventory calculation but not for regional inventory. The trade method has lower requirements for basic data, but it has considerable uncertainty. The statistical method mainly relies on the static data obtained at the early stage, and the dynamic data adopts the estimated value, which has a large uncertainty in the calculation and is suitable for the scope of ports. The dynamic method has the highest accuracy, but it also has higher requirements for basic data.
The largest feature of the dynamic method lies in its mastery of real-time information of the ship’s navigation, as well as its high standards and requirements for the acquisition of the ship’s dynamic information. The dynamic method compensates for the shortcomings of the statistical method to a certain extent. The most significant point is the determination and use of the emission factors of various pollutants. Compared with the statistical method, it reduces the error induced using a unified ship emission factor, to a certain extent, to ignore changes in dynamic factors such as ship sailing states.
The required systems of the dynamic method for the acquisition of ship dynamic data mainly depend on the following: Automated Mutual-Assistance Vessel Rescue System (AMVER), International Comprehensive Ocean Atmosphere Data Set (ICOADS), Long Range Identification and Tracking (LRIT), and the AIS. The establishment time of the first three is earlier than the AIS, and the coverage of Atlantic ships is high, but the penetration rate of ships in Asia is low. The AIS, which was introduced in the 1990s, markedly improved the coverage of ships and enhanced the quality of the acquired data. AIS is a new type of digital navigation aid system equipment, integrating communication and electronic information display technologies. Therefore, the dynamic method in the bottom-up calculation method of ship pollutant emissions will be used in this paper, in combination with the ship traffic emissions model STEAM2, to evaluate the ship pollutant emissions through AIS data and ship-related data.
Our academic contributions are summarized as follows. First, the study analyzes the differences in the distribution trends of ship pollutants in ship types, ports, and sailing states, and it presents corresponding suggestions for relevant managers. Second, this study discusses the impact of California ECA policies on the ports of Los Angeles and Long Beach. Third, the study provides the data basis for relevant departments and international organizations to evaluate the effect of the current ECA policies on the suppression of ship pollutant emissions.

4. Materials and Methods

4.1. AIS Data Sources and Meaning

The acquisition methods and channels of various AIS data sets are identified by collecting and reviewing information. At present, nine main ways to obtain AIS data are available [33], as shown in Table 4. The AIS data source that only provides real-time data cannot meet the requirement of calculating pollution emissions in the foregoing period. Historical AIS data can provide a data basis for the compilation of pollutant discharge inventory. The AIS data used in this study is from AIS E., which is historical and non-commercial. It is collected by the United States Coast Guard through onboard navigation safety equipment, and it transmits the location and characteristics of vessels in the United States and international waters.
The information contained in AIS data can generally be divided into two categories: static and dynamic information. The static information mainly includes Maritime Mobile Service Identity (MMSI), IMO number, the ship name, the ship size, call numbers, the ship type, cargo information, and the AIS equipment type; meanwhile, dynamic information mainly includes time information, the real-time ship position (latitude and longitude position), Speed Over Ground (SOG), Course Over Ground (COG), Heading (HDG), draft, and the sailing state [34].

4.2. AIS Data Descriptive Statistics

4.2.1. Geographical Range

The statistical time range of the data used in this study is from 1 January 2020 to 31 December 2020. The selected areas for the study are the ports of Los Angeles and Long Beach located in the California ECA. The selected geographic range is 33.685° to 33.780° north latitude and 118.1849° to 118.2875° west longitude. Figure 1. shows that the left side of the dotted line is the terminal and water area of Los Angeles, and the right side is Long Beach [35].

4.2.2. Ship Type

The ships are divided into six categories: container ships, cargo ships, passenger ships, oil tankers, tugboats, and other types (including military and other ships, whose equipment conditions cannot be obtained, and the ships with missing ship types in the AIS data records) based on “Vessel Type Code 2018” [36]. This study extracted a total of approximately 5.71 million AIS records from the water records of the two ports, based on port locations in the 2020 AIS data set provided by MarineCadastre.gov (accessed on 1 June 2022). The monthly distribution of AIS records of different ship types is shown in Figure 2.

4.2.3. Ship Sailing State

The sailing state of the ship can be divided into five situations according to the speed and main engine load [37]. The specific classification of the ship’s sailing states and its basis are shown in Table 5 [22]. In the acquired AIS data, AIS data records in the anchoring situation are few; thus, these records are divided into the berthing state, which is convenient for later observation and comparison.

4.2.4. Engine Classification and Power

The engines of ships can be roughly divided into three categories according to their different uses and performances: main engines, auxiliary engines, and boilers. The rated power of each engine and its load factor must be determined when calculating pollutant emissions. The former can be matched by the already determined data. If the rated power is missing or the identity of the ship cannot be identified, an assumed value is set for subsequent calculations. The default value of 1700 kW was used to replace the missing data items of the ship’s main engine [22].
Simultaneously, if the power of the auxiliary engine is missing, such power can be determined by using the ratio of the auxiliary engine to the main engine, according to the ship type, as shown in Table 6. At this time, the load ratio coefficient of the auxiliary engine can be determined in accordance with the ship type and sailing state, as shown in Table 7 [38].
The pollutant emission calculation of the boiler is different from that of the main and auxiliary engines. Owing to its special purpose, the boiler is closed in the normal and low-speed cruising state and can be turned on only in the maneuvering and berthing state. At this time, the load power of the boiler can be determined in accordance with the ship type and sailing state [22], and the details are shown in Table 8.

4.2.5. Sailing Time

This study determines the sailing time of the ship according to the interval between sending and receiving data by the category of AIS equipment. In the AIS data of the ports of Los Angeles and Long Beach, AIS equipment is roughly divided into: classes A and B. Combined with specific data records, the interval between sending and receiving data for class A equipment is about six minutes, while that for class B is three minutes [39].

4.2.6. Emission Factor

The policies of the California ECA have undergone three stages of changes [5], as shown in Figure 3. The key problem for using the dynamic method lies in the setting of the emission factors of various pollutants for various ship and engine types under different sailing conditions. This study is based on the emission factor data used in the relevant literature reviewed, and it is based on the conditions of the ports of Los Angeles and Long Beach, as well as the specific regulations of the ECAs. The emission factor data is subject to corresponding load and fuel corrections shown in Table 9 [22,40].
The emissions of SO2 and particulate matter are related to the sulfur content in the fuel, and the emission factor settings of the two are different from those of other pollutants. The calculation formula of the emission factor of SO2 is as follows [22]:
E F S O 2 = E O i × 2 × 0.9775 × S
where E F S O 2 refers to the emission factor of SO2 and the unit is g/kwh; E O i denotes the consumption of fuel oil and the unit is g/kw; S represents the sulfur content in the fuel oil because the ports of Los Angeles and the Long Beach are both within the regulated area of the California ECA, and the limit value of S is 0.1%.
The formula for calculating the emissions coefficient of particulate matter is [22]:
E F P M 10 = 0.22 + E O i × 7 × 0.0225 × S
E F P M 2.5 = 0.22 + E O i × 7 × 0.0224 × S
where E F P M 10 and E F P M 2.5 refer to the emission factor of particulate matter and the unit is g/kwh.

4.3. Evaluation of Ship Pollutant Emissions

We used AIS data collected in 2020, which was in the third phase of the California ECA policies. Figure 4 shows the idea of the dynamic method for calculating ship pollutant emissions. The ECA policies, at different stages, were used to adjust the emission factor to observe the impact of the ECA policies on the ship pollutant emissions in the waters of the two ports effectively. The ECA policies are used as the benchmark policies to compare and analyze the impact of the ECA policy changes in Phase II and Phase III on the emissions of various ships.
The dynamic information is mainly provided by the AIS data, while the static information includes the ship, engine, and the main pollutant types. The dynamic method calculation process involves engine rated power (P), engine load (LF), sailing time (T), and emission factor (EF). The engine rated power is determined by the engine type of the ship, the engine load is determined by the sailing state and engine type, the sailing time is divided by the AIS data acquisition time and sailing state, and the emission factor is determined by the ship, engine, and the pollution type.
The formula of the transportation emissions model STEAM2 is [41]:
E a , b , c , d = P b × L F b , d × T b , c , d × E F a , b , c
where a refers to the type of pollutants emitted by the ship, b denotes the type of the ship’s engine, c represents the type of fuel used by the ship, and d is the sailing state of the ship. E a , b , c , d refers to the emissions of a-type pollutants of a b-type engine using c-type fuel when the sailing state is d and the unit is g; P b signifies the rated power of the b-type engine and the unit is kW; L F b , d denotes the load of the b-type engine when the sailing state is d, which is a proportion; T b , c , d refers to the use time of the c-type fuel when the sailing state is d and the unit is h; E F a , b , c indicates the emission factor of a-type pollutants of the b-type engine using c-type fuel and the unit is g/kwh.

5. Results

5.1. Comparison of Pollutant Emissions from Different Ship Types

According to the AIS records of ships in the waters of the ports of Los Angeles and Long Beach in 2020, the total emissions of ships calculated for CO, CXHX, NOX, SO2, PM10, and PM2.5 were 1230, 510, 1170, 6670, 248, and 232 tons, respectively. The comparison of different ship types and their emissions is shown in Figure 5. Container ships, cargo ships, and oil tankers are the three most important sources among various types of pollutant emissions. This finding may be attributed to the large proportion of container ships, cargo ships, and oil tankers. The detailed proportions are shown in Figure 6.
Figure 7 shows the results of re-statistically grouping the overall emissions of ship pollutants, in the waters of the two ports, by ship type. The result indicates that the effects of various ship types on the emissions of CO, CXHX, and NOX demonstrate the same trend: cargo ships are the largest source of the three pollutants, accounting for 40%, followed by container ships and oil tankers, accounting for more than 30% and 25%, respectively; meanwhile, passenger ships, tugboats, and other ship types accounted for the ratio of less than 1%. The reorganization results of the three pollutants, according to the ship type, have the same trend as those in Figure 6.
The pollutant emissions of cargo ships, container ships and oil tankers are summarized as follows.
(1) The CO emissions from cargo ships, container ships, and oil tankers are 525.3, 372.4, and 326.7 tons, accounting for 42.60%, 30.20%, and 26.50%, respectively.
(2) The CXHX emissions from cargo ships, container ships, and oil tankers are 207.6, 165.8, and 130.6 tons, accounting for 40.70%, 32.50%, and 25.60%, respectively.
(3) The NOX emissions from cargo ships, container ships, and oil tankers are 4717.1, 3684.5, and 3226.9 tons, accounting for 40.20%, 31.40%, and 27.50%, respectively.
This result may be due to the large gap in the size of the ships. Containers are generally manufactured under the implementation of international, national, regional, and company standards [42]. The size of container ships can be determined in accordance with the maximum carrying capacity. Container ships have different sizes and the largest circulation, but the most frequently used container ships in the world are of a fixed size. Oil tankers also have different classification standards and size specifications. The size of oil tankers has certain standards, which are basically related to the load of the ship itself. Oil tankers have become the largest source of PM10, PM2.5, and SO2 emissions, which are 41%, 40%, and 57%, respectively.
As the first developed maritime transport mode of goods among the three, cargo ships have remarkable inconsistency and spanning in the size of ships. The main reason lies in the inconsistent relationship between ship sailing power and load, due to the differences between national standards. Furthermore, the convenience and economy provided by small-scale cargo ships are one of the reasons why cargo ships are close to container ships and oil tankers when considering pollutant emissions [43].

5.2. Comparison of Pollutant Emissions in Different Ports

According to longitude and latitude recorded by AIS data, the pollutant emissions may be characterized by spatial distribution, and the comparison of pollutant emissions of the two ports can be obtained, as shown in Figure 8. The specific situation of the comparison of pollutant emissions between the two ports is shown in Figure 9. The distribution of pollutant emissions from various ship types in the two ports is highly consistent. The pollutant emissions values of ship types are close to each other. This similarity may be due to the geographical proximity of the two ports, making them all part of the California ECA. Moreover, the closeness allows the two ports to share information resources to a certain extent, apply to the same set of ship emissions regulations, and have the same ship emissions self-control standards and regulatory mechanisms.
Table 10 and Table 11 show the details of pollutant emissions in the two ports. The pollutant emissions of all ship types in the Port of Long Beach are higher than that of Los Angeles. The reason may lie in the possible effect of the impact of the epidemic in 2020 on global shipping; thus, the production and operation of port ships and other industries will undergo major changes. In the second half of the year, the cargo throughput of the two ports began to rise due to the increase in demand on the Asia–Europe route. However, a serious congestion problem in the Port of Los Angeles simultaneously emerged, and many ships were stranded in the port [44], thus increasing the incurred expenses of ships at the port and the time cost induced by the inability to deliver on time. Some cargo owners choose air transportation instead to reduce the transportation cost, which decreases the cargo throughput of the port of Log Angeles [45,46].

5.3. Comparison of Pollutant Emissions in Different Sailing States

The ratio of pollutant emissions of various ships under different sailing states are obtained as shown in the Figure 10, Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15. The various pollutant emissions from the container ship in the maneuvering state accounts for the largest proportion; CO, CXHX, NOX, PM10, PM2.5, and SO2 provided 42.17%, 41.71%, 40.02%, 45.31%, 44.80%, and 35.05%, respectively. This finding is related to the development trend of containers, as shown in Figure 16. At the mature stage, from the end of the 1980s to the present, the transportation mode of container ships no longer pursues simple high-speed development due to various factors. On the one hand, container shipping companies have reduced the speed of ships in the process of sailing to minimize oil consumption because of the soaring oil price. On the other hand, in coastal areas, especially in areas with intensive human activities, the speed of container ships will be reduced to less than 10 nautical miles per hour to pursue green development [47]. These reasons contribute to the speed reduction in container ships in port waters, resulting in the largest proportion of pollutant emissions in the maneuvering state.
The pollutant emissions of cargo ships in normal cruising are remarkably higher than in other states. The proportions of each pollutant emissions for CO, CXHX, NOX, PM10, PM2.5, and SO2 are 47.03%, 49.84%, 48.05%, 51.10%, 50.97%, and 42.37%, respectively. This finding may be attributed to the limited import and export of goods in the United States in the first half of 2020, due to the dual impact of the epidemic and the China–US trade war.
Simultaneously, the proportion of pollutant emissions of passenger ships in the berthing state is the largest. The proportions of each pollutant emissions for CO, CXHX, NOX, PM10, PM2.5, and SO2 are 63.08%, 67.12%, 70.80%, 82.16%, 81.37%, and 85.21%, respectively. The key measure to cause this phenomenon lies in termination of the related operations of passenger ships in the two ports in mid-March 2020 and the spending of additional time of most passenger ships at berths [45,46].
The proportion of pollutant emissions of oil tankers in the berthing state is the largest. The proportions of each pollutant emissions for CO, CXHX, NOX, PM10, PM2.5, and SO2 are 56.73%, 57.12%, 55.20%, 69.32%, 68.55%, and 86.14%, respectively. The operating staff at the ports became insufficient in 2020 due to various reasons (such as the epidemic), resulting in a decrease in the efficiency of cargo loading and unloading.
Tugboats are port operation ships that assist other ship types to complete port berthing and departure activities. Thus, their pollutant emissions status is generally related to the activities of other ships and changes with the demand of the external environment. Among them, the proportion of each pollutant emissions in the berthing state is the highest. However, emission factors cannot be determined for other ships. Thus, the cargo ship emission factor is used for calculation in this study to avoid introducing excessive bias to the results due to the omission. Therefore, cargo ships have similar results.

5.4. Comparison of Pollutant Emissions under Different Policies

The emission factors of the Phase II and Phase III are shown in Table 12 [22,40], and the emission factors of SO2, PM10, and PM2.5 are calculated. The formula is the same as before, except that the sulfur content in the fuel used in the formula will be changed from 0.1% to 1% [5]. According to the different requirements of each pollutant emissions limit, at different stages of the California ECA, and the adjustment results of the relevant pollutant emission factors, the changes in ship pollutant emissions are shown in Figure 17.
Comparing the changes in the emissions of the four major pollutants caused by the two-stage policies, the change in SO2 is the most evident. Compared with Phase III, the specific changes in SO2 emissions of various ship types in Phase II are as follows: container and cargo ships, respectively, increased by 27.12% and 17.36%; passenger ships increased less with 2.78%; oil tankers increased the most with 47.13%; the upward trend of tugboats is the smallest, demonstrating only 1.73%. These changes are caused by the variations in the sulfur content of marine fuels, due to the two-stage ECA policies [5], thus leading to a different subsequent determination of the SO2 emission factor. The ECA policies are effective in suppressing SO2 emissions. However, the NOX emissions of various ship types did not demonstrate substantial change. Only the emissions of oil tankers in Phase II increased by 1.71% compared with Phase III. The NOX emissions of other ship types changed slightly and did not increase by more than 1%. The changes in the emissions of PM10 and PM2.5 are relatively evident.
The ECA policies can effectively reduce the emissions of ship pollutants, especially for SO2, but the effect on NOX is not observed. The comparison of pollutant emissions in the state of ship sailing reveals that passenger ships, oil tankers, and tugboats have the highest proportion of pollutants in the berthing state. During the epidemic period, ships stay at ports for a long time due to the requirements of epidemic prevention and control policies, resulting in increased pollutant emissions. Ships must use expensive low-sulfur fuel within the ECA range. However, ships usually choose to use high-sulfur fuel oil outside the ECA and switch to low-sulfur fuel when approaching the ECA. Ships choose to sail for a long time outside the ECA to control costs [48]. Chang et al. [49] analyzed the impact of the implementation of ECA policies on the efficiency of European ports, and they indicated that the implementation of ECA policies reduced productivity of ports due to strict regulation. In addition, NOX accounts for a high proportion of the overall pollution. However, the ECA policies have not played a satisfactory role in suppressing NOX emissions. Thus, measures to reduce NOx emissions should also be considered.

6. Discussions

This paper has some limitations. The impact of ship power systems on ship pollutant emissions is not reflected. Some errors in AIS data may also be observed. AIS equipment is divided into classes A and B when determining the sailing time. Many class B ships are observed, affecting the reception of class A ships [50]. AIS data is generally poor in places where the number of ships is dense, which will have a certain impact on data collection and accuracy.
The results of our study are compared with other studies on the specific impact of ECA policy implementation on pollutant emissions in China. Shi et al. [40] compared the changes in pollutant emissions from ships of various types, after the implementation of the ECA policies at different stages, in the waters of Shanghai Port using the AIS data in 2017. At stage V, all ships in the Shanghai Port were required to use fuel with a sulfur content of 0.1%. Compared with the benchmark policies, NOX, SO2, and PM2.5 emissions from cargo ships decreased by 4.3%, 91.48%, and 73.93%, respectively. Zhang et al. [51] focused on the performance of the ECA policies in Shanghai, considering SO2 concentration. The SO2 concentration in Shanghai decreased by at least 0.229 μg/m3 daily on average. Similar to the current findings, ECA policy can effectively inhibit SO2 emission, and the inhibition effect on SO2 is significantly larger than that of NOX. NOX and SO2 are the largest pollutants in our research. The research of Yang et al. [18] used the bottom-up research method, considering the proportion of various pollutants to the total, to establish a pollutant discharge inventory of Tianjin Port based on AIS data. The total ship pollutant emissions for CO, NOX, SO2, PM10, and PM2.5 were 2210, 28,610, 14,530, 2040, and 1820 tons, respectively. As with the obtained findings of the current study, NOX is the main pollutant, accounting for 56.9%, followed by SO2, which accounts for 28.9%. They also considered the impact of the engines, wherein the main engines were the largest sources. The impact of the engine on pollutant emissions was disregarded in the current study. Lee et al. [52] used a bottom-up activity-based approach, combined with real-time vessel activity data generated by Vessel Traffic Services (VTS), and found that NOX and SO2 accounted for the largest proportions of overall emissions.
The distribution of pollutant emissions from ships is affected by the development of the epidemic, the ECA policies, and the speed of ships. The speed is influenced by the weather and it affects the emissions of pollutants. In 2020, the AIS records of the two ports accounted for the highest proportion of cargo ships, with 62.39%. Figure 2 shows that the concentrated distribution is relatively dense in May, June, and July, and the ship traffic volume is high in summer [53]. Encountering wind and waves will have a certain impact on the speed of the ship, thereby affecting its activities and pollutant emissions. However, this effect is not evident in the port area.
SO2 and NOX account for a relatively high proportion of the total ship pollution emissions. SO2 and NOX account for 32.0% and 58.2% in the Port of Los Angeles, while SO2 and NOX contribute 32.6% and 55.7% in the Port of Long Beach. The pollution to the atmospheric environment mainly comes from the use of engine fuel. Before the ship emissions control, the high sulfur content in the heavy oil used by ships is one of the main driving forces to increase the SO2 content in the atmospheric environment. Sulfur oxides produced by shipping form strong acids and seriously affect biodiversity [54]. Most ships will be equipped with high-power engines and use fossil fuels, accelerating energy consumption. Ships also pollute the water environment. On the one hand, the wastewater is poured into the waters where the ship sails. On the other hand, the domestic sewage and garbage generated by the crew in their daily lives contribute to pollution [55]. These reasons deplete the availability of seawater as a resource and destroy the living environment of marine organisms, which is not conducive to ecological balance. The NOX generated by ship transportation produces eutrophic water [56]. Additionally, the decline in water quality affects the survival of marine plankton, resulting in the loss of biodiversity, which leads to changes in the food web in the waters and hinders the development of local fisheries [57].
These pollutions to the environment will eventually affect people’s daily life and health. The discharged sulfur from ships is a factor that can form acid rain, destroy the soil, and increase acidity [58], which seriously affects the development of agriculture, forestry, and fishery in related areas. Relevant studies have shown that the particulate matter emitted by shipping activities causes approximately 60,000 deaths yearly due to cardiopulmonary diseases and lung cancer [59]. The use of low-sulfur oil can reduce deaths by 34% and morbidity by 54% due to ship pollutant emissions. Low-sulfur oil has markedly reduced the side effects of shipping activities on human health; however, pollutants, such as emitted particulate matter, are still responsible for 250,000 deaths and approximately 6.4 million childhood asthma cases yearly [60].
Our study evaluated ship pollutant emissions in the ports of Los Angeles and Long Beach, the two busiest ports in the United States, so the impact of ECA policies on ship emissions from these two ports is relatively representative. The California state gradually has a multi-level legislative system, ranging from international conventions to the federal and state levels, in terms of ports and implements strict emission control regulations to promote the green development of the port. In 2006, the two ports jointly implemented the San Pedro Bay Port Clean Air Action Plan, including fuel switching and ship speed reduction [35].
Our results show that NOX accounts for a large proportion of ship pollution emissions, but the ECA policy has not played a good role in suppressing NOX emissions. It is necessary for relevant organizations, in various countries, to focus on how to effectively suppress NOX emissions through policy control worldwide in the future. In addition, the sailing states also markedly affect the ship pollutant emissions. The staying time in the ports needs to be minimized. In addition to optimizing the port loading and unloading processes, governments should also take other measures to ease port congestion, such as slowing down the speed to delay the arrival time and avoid many stranded ships at the ports, causing additional pollution to the port waters.
Although the control effect of low-sulfur oil on the emissions of pollutants from ships has been reflected in the research, the high price of low-sulfur fuel makes many ships choose to use high-sulfur fuel outside the ECA area to reduce costs, which may not have a remarkable inhibitory effect on worldwide pollutant emissions [48]. The implementation of ECA policies around the world should also consider the cost of ship operation, combining environmental and economic sustainability. In this case, other measures may also be used to offset the impact of the sulfur content in the fuel, such as utilizing marine scrubbers. Simultaneously, different emission limits and fuel standards may be formulated in accordance with the severity of ship pollutant emissions from various ships. Marine cleaning equipment should also become one of the important means to reduce pollutant emissions.

7. Conclusions

This paper investigates the ship pollutant emissions in the waters of the ports of Los Angeles and Long Beach in 2020, based on the AIS data and related ship data, and it evaluates the change in the ship pollutant emissions under the two-stage ECA policies. The main results are as follows. (1) The emissions of each pollutant for CO, CXHX, NOX, SO2, PM10, and PM2.5 were 1230, 510, 11,700, 6670, 248, and 232 tons, respectively. (2) In the emissions of various pollutants, container ships, cargo ships, and oil tankers are the three most important sources, and the total emissions of ship pollutants generated by the three account for almost 90% of the total pollutant emissions. (3) The ports of Los Angeles and Long Beach have the same trend in ship pollutant emissions. The specific pollutant emissions in the Port of Long Beach are slightly higher than in the Port of Los Angeles. (4) The distribution of pollutant emissions, of different ship types in each sailing state, is different. Container ships have the highest proportion of pollutant emissions in maneuvering state, while cargo ships in normal cruising state have a higher proportion than other ship types. Passenger ships have the largest proportion in berthing state. The proportion of tugboats in each sailing state does not show a clear bias. (5) Considering the change in the ECA policies in the California ECA, the changes in the emission factor are used to evaluate the detailed variations of the two-stage ECA policies. The results show that the ECA policies have the strongest inhibitory effect on the emissions of SO2, especially for the control of SO2 emissions from oil tankers. Compared with Phase III, the relaxed ECA policies in Phase II increased the SO2 emissions of oil tankers by 47.13%.
Regarding the future research in the ship pollutant emissions calculation, different ship power systems, as well as the differences in ship pollutant emissions of various engine types, can be highlighted to provide new development directions for the construction of ship power systems and the selection of fuels. Simultaneously, the scope of the research is expanded to obtain the realistic results, which can provide real-time data for the monitoring of pollutant emissions in the ports and present effective information for the management and control of ships in the ports.

Author Contributions

Conceptualization, G.X., T.W. and X.C.; methodology, G.X., T.W., X.C. and L.Z.; writing—original draft preparation, G.X., T.W. and X.C.; writing—review and editing, G.X., X.C. and L.Z.; funding acquisition, X.C. and G.X. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China Project 52002243, the Shanghai Science and Technology Innovation Action Plan Project 20511101800, the National Key R&D Project 2021YFB3901505, the National Natural Science Foundation of China 52102397, and the China Postdoctoral Science Foundation 2021M700790.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Readers can access our data by sending an email to the corresponding author Xinqiang Chen.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhi, Q. The impact of maritime energy transportation on the global shipping market. China Storage Transp. 2021, 10, 196–197. [Google Scholar]
  2. Mackenzie, W. IMO 2020: Mayhem or Opportunity for the Refining and Marine Sectors? 4 September 2018. Available online: https://www.woodmac.com/news/opinion/will-imo-2020-introduce-mayhem-or-opportunity-to-the-refining-and-marine-sectors/ (accessed on 1 June 2022).
  3. China Aviation Weekly. UK Announces Suspension of Sulfur Cap Inspections. 30 March 2020. Available online: https://www.sohu.com/a/384342027_265147 (accessed on 1 June 2022).
  4. Xu, J.; Gao, H. European and American Carbon Emissions Reduction Systems and Their Impact on the Port and Shipping Industry. China Ports 2021, 11, 12–16. [Google Scholar]
  5. Wang, H. Brief introduction on ship emission control area of foreign countries. China Marit. 2017, 9, 16–17. [Google Scholar]
  6. Sindh Maritime. IMO Adds Two New Nitrogen Oxide (NOx) Emission Control Areas. 11 November 2016. Available online: http://info.jctrans.com/news/hyxw/201611112295352.shtml (accessed on 1 June 2022).
  7. Shanghai International Shipping Research Center. Review of the Production Situation of the World’s Top 20 Ports in 2020. 3 March 2021. Available online: https://zhuanlan.zhihu.com/p/481694023 (accessed on 1 June 2022).
  8. Jalkanen, J.P.; Johansson, L.; Kukkonen, J. A comprehensive inventory of the ship traffic exhaust emissions in the Baltic Sea from 2006 to 2009. Ambio 2014, 43, 311–324. [Google Scholar] [CrossRef]
  9. Tokuslu, A. Assessment of environment costs of ship emissions: Case study on the Samsun Port. Environ. Eng. Manag. J. 2021, 20, 739–747. [Google Scholar]
  10. Lee, H.; Pham, H.T.; Chen, M.; Choo, S. Bottom-up approach ship emissions inventory in Port of Incheon based on VTS data. J. Adv. Transp. 2021, 2021, 5568777. [Google Scholar] [CrossRef]
  11. Kuittinen, N.; Jalkanen, J.P.; Alanen, J.; Ntziachristos, L.; Hannuniemi, H.; Johansson, L.; Karjalainen, P.; Saukko, E.; Isotalo, M.; Aakko-Saksa, P.; et al. Shipping remains a globally significant source of anthropogenic PN emissions even after 2020 sulfur regulation. Environ. Sci. Technol. 2021, 55, 129–138. [Google Scholar] [CrossRef]
  12. Lehtoranta, K.; Aakko-Saksa, P.; Murtonen, T.; Vesala, H.; Ntziachristos, L.; Rönkkö, T.; Karjalainen, P.; Kuittinen, N.; Timonen, H. Particulate mass and nonvolatile particle number emissions from marine engines using low-sulfur fuels, natural gas, or scrubbers. Environ. Sci. Technol. 2019, 53, 3315–3322. [Google Scholar] [CrossRef]
  13. Corbin, J.C.; Peng, W.; Yang, J.; Sommer, D.E.; Trivanovic, U.; Kirchen, P.; Miller, J.W.; Rogak, S.; Cocker, D.R.; Smallwood, G.J.; et al. Characterization of particulate matter emitted by a marine engine operated with liquefied natural gas and diesel fuels. Atmos. Environ. 2020, 220, 117030. [Google Scholar] [CrossRef]
  14. Alanen, J.; Isotalo, M.; Kuittinen, N.; Simonen, P.; Martikainen, S.; Kuuluvainen, H.; Honkanen, M.; Lehtoranta, K.; Nyyssönen, S.; Vesala, H.; et al. Physical characteristics of particle emissions from a medium speed ship engine fueled with natural gas and low-sulfur liquid fuels. Environ. Sci. Technol. 2020, 54, 5376–5384. [Google Scholar] [CrossRef]
  15. Xu, L.; Shi, J.; Chen, J.; Li, L. Estimating the effect of COVID-19 epidemic on shipping trade: An empirical analysis using panel data. Mar. Policies 2021, 133, 104768. [Google Scholar]
  16. Mocerino, L.; Quaranta, F. How emissions from cruise ships in the Port of Naples changed in the COVID-19 lock down period. Proceedings of the Institution of Mechanical Engineers. Part M J. Eng. Marit. Environ. 2022, 236, 125–130. [Google Scholar]
  17. Liu, L. Research on the Negative Externality Cost of Air Pollutant Emissions from Qingdao Port and Its Emissions Prediction; Qingdao University: Qingdao, China, 2020; p. 56. [Google Scholar]
  18. Yang, L.; Zhang, Q.; Zhang, Y.; Lv, Z.; Wang, Y.; Wu, L.; Feng, X.; Mao, H. An AIS-based emissions inventory and the impact on air quality in Tianjin port based on localized emissions factors. Sci. Total Environ. 2021, 783, 146869. [Google Scholar] [CrossRef] [PubMed]
  19. Chen, S.; Meng, Q.; Jia, P.; Kuang, H. An operational-mode-based method for estimating ship emissions in port waters. Transp. Res. Part D Transp. Environ. 2021, 101, 103080. [Google Scholar]
  20. Feng, X.; Yuan, S.; Zhang, Y.; Chen, B. Research on the time and space distribution characteristics of ship air pollutant emissions inventory in the Jiangsu section of the Yangtze River. J. Saf. Environ. 2018, 18, 1609–1614. [Google Scholar]
  21. Li, C.; Yuan, Z.; Ou, J.; Fan, X.; Ye, S.; Xiao, T.; Shi, Y.; Huang, Z.; Ng, S.K.; Zhong, Z.; et al. An AIS-based high-resolution ship emissions inventory and its uncertainty in Pearl River Delta region, China. Sci. Total Environ. 2016, 573, 1–10. [Google Scholar] [PubMed]
  22. Motor Vehicle Emissions Monitoring Center, Ministry of Environmental Protection. Report on China’s Ship Air Pollutant Emissions Inventory. 28 September 2016. Available online: https://max.book118.com/html/2018/0727/7041155050001141.shtm (accessed on 1 June 2022).
  23. Zhang, Y.; Gu, J.; Wang, W.; Peng, Y.; Wu, X.; Feng, X. Inland port vessel emissions inventory based on ship traffic emissions assessment model–automatic identification system. Adv. Mech. Eng. 2017, 9, 168781401771287. [Google Scholar] [CrossRef]
  24. Xu, L.; Di, Z.; Chen, J. Evolutionary game of inland shipping pollution control under government co-supervision. Mar. Pollut. Bull. 2021, 171, 112730. [Google Scholar]
  25. Ma, D.; Ma, W.; Jin, S.; Ma, X. Method for simultaneously optimizing ship route and speed with emission control areas. Ocean. Eng. 2020, 202, 107170. [Google Scholar]
  26. Liu, J.; Law, A.W.K.; Duru, O. Reducing emissions of atmospheric pollutants along major dry bulk and tanker routes through autonomous shipping. J. Environ. Manag. 2022, 302, 114080. [Google Scholar] [CrossRef]
  27. Ma, W.; Ma, D.; Ma, Y.; Zhang, J.; Wang, D. Green maritime: A routing and speed multi-objective optimization strategy. J. Clean. Prod. 2021, 305, 127179. [Google Scholar]
  28. Ma, D.; Ma, W.; Hao, S.; Jin, S.; Qu, F. Ship’s response to low-sulfur regulations: From the perspective of route, speed and refueling strategy. Comput. Ind. Eng. 2021, 155, 107140. [Google Scholar]
  29. Chen, X.; Ling, J.; Wang, S.; Yang, Y.; Luo, L.; Yan, Y. Ship detection from coastal surveillance videos via an ensemble Canny-Gaussian-morphology framework. J. Navig. 2021, 74, 1252–1266. [Google Scholar]
  30. Chen, X.; Wang, Z.; Hua, Q.; Shang, W.L.; Luo, Q.; Yu, K. AI-empowered speed extraction via port-like videos for vehicular trajectory analysis. IEEE Trans. Intell. Transp. Syst. 2022, 1–12. [Google Scholar] [CrossRef]
  31. Chen, X.; Wu, S.; Shi, C.; Huang, Y.; Yang, Y.; Ke, R.; Zhao, J. Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison. IEEE Sens. J. 2020, 20, 14317–14328. [Google Scholar] [CrossRef]
  32. Liu, H.; Shang, Y.; Jin, X.; Fu, M. Research methods and progress of ship emissions inventory. J. Environ. Sci. 2018, 38, 1–12. [Google Scholar] [CrossRef]
  33. Zhao, L. Anomaly Detection of Ship Trajectory Based on AIS Data and Recurrent Neural Network; Dalian Maritime University: Dalian, China, 2019; p. 125. [Google Scholar]
  34. AIS (Automatic Identification System for Ships) Data Analysis. 11 December 2020. Available online: https://blog.csdn.net/zrg_hzr_1/article/details/111029286 (accessed on 1 June 2022).
  35. Reporter of This Magazine. Overview of port green development in California, USA. Port Technol. 2018, 6, 40–42. [Google Scholar]
  36. AIS Vessel Type and Group Codes Used by the Marine Cadaster Project. 27 October 2021. Available online: https://coast.noaa.gov/data/marinecadastre/ais/VesselTypeCodes2018.pdf (accessed on 1 June 2022).
  37. Mujal-Colilles, A.; Guarasa, J.N.; Fonollosa, J.; Llull, T.; Castells-Sanabra, M. COVID-19 impact on maritime traffic and corresponding pollutant emissions. The case of the Port of Barcelona. J. Environ. Manag. 2022, 310, 114787. [Google Scholar] [CrossRef]
  38. Zhang, Z.; Huang, Z.; Xu, Y.; Chen, W.; Huang, L.; Bai, L.; Haung, J.; Zheng, J.; Yan, M. Improved method, and application of ship emissions spatial representation based on AIS trajectory repair. Chin. J. Environ. Sci. 2020, 40, 1931–1942. [Google Scholar]
  39. Ma, H. Research on ship inspection based on AIS. China Water Transp. 2019, 19, 23–24. [Google Scholar]
  40. Shi, K.; Weng, J.; Li, G. Exploring the effectiveness of ECA policies in reducing pollutant emissions from merchant ships in Shanghai port waters. Mar. Pollut. Bull. 2020, 155, 111164. [Google Scholar] [PubMed]
  41. Chen, J.; Wan, Z.; Zhang, H.; Liu, X.; Zhu, Y.; Zheng, A. Governance of shipping emissions of SOx in China’s coastal waters: The SECA policies, challenges, and directions. Coast. Manag. 2018, 46, 191–209. [Google Scholar] [CrossRef]
  42. Shi, J. The development status, and problems of the container ship market. Tianjin Sail. 2004, 6, 40–41. [Google Scholar]
  43. Cao, Y. Inventory and Characteristics of Air Pollutants Emissions from Ships and Port Areas in Typical Regions; Shanghai University: Shanghai, China, 2020; p. 243. [Google Scholar]
  44. Zhu, J.; Ma, H.; Tang, T. Analysis of port congestion in Los Angeles and Long Beach and enlightenment to China’s port development. Compr. Transp. 2022, 44, 119–125. [Google Scholar]
  45. Inventory of Air Emissions for Calendar Year 2020. Port of Los Angeles, 21 October 2021. Available online: https://www.portoflosangeles.org/environment/air-quality/air-emissions-inventory(accessed on 1 June 2022).
  46. Port of Long Beach 2020 Air Emissions Inventory. Port of Long Beach, 21 October 2021. Available online: http://www.polb.com/environment/air/emissions.asp(accessed on 1 June 2022).
  47. Gao, X. On the development of world container transport and its significant impact on the global economy. Logist. Technol. 2010, 29, 70–73. [Google Scholar]
  48. Ye, G.; Zhou, J.; Yin, W.; Feng, X. Are shore power and emission control area policies always effective together for pollutant emissions reduction? An analysis of their joint impacts at the post-pandemic era. Ocean. Coast. Manag. 2022, 224, 106182. [Google Scholar]
  49. Chang, Y.T.; Park, H.K.; Lee, S.; Kim, E. Have emission control areas (ECAs) harmed port efficiency in Europe? Transp. Res. Part D Transp. Environ. 2018, 58, 39–53. [Google Scholar]
  50. Xu, K. Port and Shipping Big Data Laboratory, Shanghai International Shipping Research Center, Description of the limitations of AIS Ship Monitoring Data Quality. 13 March 2017. Available online: https://weibo.com/ttarticle/p/show?id=2309404084901421948661 (accessed on 1 June 2022).
  51. Zhang, Q.; Zheng, Z.; Wan, Z.; Zheng, S. Does emission control area policies reduce sulfur dioxides concentration in Shanghai? Transp. Res. Part D Transp. Environ. 2020, 81, 102289. [Google Scholar] [CrossRef]
  52. Lee, H.; Park, D.; Choo, S.; Pham, H.T. Estimation of the Non-Greenhouse Gas Emissions Inventory from Ships in the Port of Incheon. Sustainability 2020, 12, 8231. [Google Scholar] [CrossRef]
  53. Toscano, D.; Murena, F.; Quaranta, F.; Mocerino, L. Assessment of the impact of ship emissions on air quality based on a complete annual emissions inventory using AIS data for the port of Naples. Ocean. Eng. 2021, 232, 109166. [Google Scholar]
  54. Turner, D.R.; Edman, M.; Gallego-Urrea, J.A.; Claremar, B.; Hassellöv, I.M.; Omstedt, A.; Rutgersson, A. The potential future contribution of shipping to acidification of the Baltic Sea. Ambio 2018, 47, 368–378. [Google Scholar] [PubMed]
  55. Rui, R.; Cao, L.; Wang, H.; Guo, J.; Wu, Z.; Tain, H. Research on the discharge law of domestic sewage pollutants from ships. Water Conserv. Hydropower Technol. 2022, 53 (Suppl. S1), 68–72, (In Chinese and English). [Google Scholar]
  56. Repka, S.; Erkkilä-Välimäki, A.; Jonson, J.E.; Posch, M.; Törrönen, J.; Jalkanen, J.P. Assessing the costs and environmental benefits of IMO regulations of ship-originated SOx and NOx emissions in the Baltic Sea. Ambio 2021, 50, 1718–1730. [Google Scholar] [PubMed]
  57. Rabalais, N.N. Nitrogen in aquatic ecosystems. Ambio 2002, 31, 102–112. [Google Scholar]
  58. Zhang, Q.; Zhu, J.; Wang, Q.; Xu, L.; Li, M.; Dai, G.; Mulder, J.; Xi, Y.; He, N. Soil acidification of forests in China caused by atmospheric deposition from 1980 to 2050. Sci. Bull. 2022, 67, 914–917. (In English) [Google Scholar]
  59. 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]
  60. Sofiev, M.; Winebrake, J.J.; Johansson, L.; Carr, E.W.; Prank, M.; Soares, J.; Vira, J.; Kouznetsov, R.; Jalkanen, J.P.; Corbett, J.J. Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nat. Commun. 2018, 9, 406–412. [Google Scholar]
Jmse 10 01206 g001 550
Figure 1. The ports of Los Angeles and Long Beach and related sea areas.
Figure 1. The ports of Los Angeles and Long Beach and related sea areas.
Jmse 10 01206 g001
Jmse 10 01206 g002 550
Figure 2. Monthly distribution of AIS data records for each ship type.
Figure 2. Monthly distribution of AIS data records for each ship type.
Jmse 10 01206 g002
Jmse 10 01206 g003 550
Figure 3. Policies changes of California ECA.
Figure 3. Policies changes of California ECA.
Jmse 10 01206 g003
Jmse 10 01206 g004 550
Figure 4. Calculation idea of ship pollutant emissions by the dynamic method.
Figure 4. Calculation idea of ship pollutant emissions by the dynamic method.
Jmse 10 01206 g004
Jmse 10 01206 g005 550
Figure 5. Comparison of total pollutant emissions from different types of ship (unit: ton).
Figure 5. Comparison of total pollutant emissions from different types of ship (unit: ton).
Jmse 10 01206 g005
Jmse 10 01206 g006 550
Figure 6. Ship type proportions in the AIS records.
Figure 6. Ship type proportions in the AIS records.
Jmse 10 01206 g006
Jmse 10 01206 g007 550
Figure 7. Pollutant emission proportions of various ship types.
Figure 7. Pollutant emission proportions of various ship types.
Jmse 10 01206 g007
Jmse 10 01206 g008 550
Figure 8. Comparison of various pollutant emissions in different ports (unit: ton).
Figure 8. Comparison of various pollutant emissions in different ports (unit: ton).
Jmse 10 01206 g008
Jmse 10 01206 g009 550
Figure 9. Comparison of pollutant emissions from different ship types in different ports (unit: ton).
Figure 9. Comparison of pollutant emissions from different ship types in different ports (unit: ton).
Jmse 10 01206 g009
Jmse 10 01206 g010 550
Figure 10. Comparison of CO emissions in different sailing states.
Figure 10. Comparison of CO emissions in different sailing states.
Jmse 10 01206 g010
Jmse 10 01206 g011 550
Figure 11. Comparison of CXHX emissions in different sailing states.
Figure 11. Comparison of CXHX emissions in different sailing states.
Jmse 10 01206 g011
Jmse 10 01206 g012 550
Figure 12. Comparison of NOX emissions in different sailing states.
Figure 12. Comparison of NOX emissions in different sailing states.
Jmse 10 01206 g012
Jmse 10 01206 g013 550
Figure 13. Comparison of PM10 emissions in different sailing states.
Figure 13. Comparison of PM10 emissions in different sailing states.
Jmse 10 01206 g013
Jmse 10 01206 g014 550
Figure 14. Comparison of PM2.5 emissions in different sailing states.
Figure 14. Comparison of PM2.5 emissions in different sailing states.
Jmse 10 01206 g014
Jmse 10 01206 g015 550
Figure 15. Comparison of SO2 emissions in different sailing states.
Figure 15. Comparison of SO2 emissions in different sailing states.
Jmse 10 01206 g015
Jmse 10 01206 g016 550
Figure 16. Changes in the development trend of container ships.
Figure 16. Changes in the development trend of container ships.
Jmse 10 01206 g016
Jmse 10 01206 g017 550
Figure 17. Changes in the proportion of ship pollutant emissions under different policies.
Figure 17. Changes in the proportion of ship pollutant emissions under different policies.
Jmse 10 01206 g017
Table
Table 1. Three stages of IMO carbon emissions reduction and its measures.
Table 1. Three stages of IMO carbon emissions reduction and its measures.
StageMeasures
2018–2023
(Short-term)		Improve the technology of existing ships and new ships, improve the energy efficiency of ship operations, and develop technologies such as alternative fuels and clean energy.
2023–2030
(Mid-term)		Introduce low-carbon alternative fuels, strengthen technical cooperation, and implement research and application of zero-carbon fuels.
After 2030
(Long-term)		Introduce zero-carbon fuels to achieve carbon emissions reduction in the whole process of shipping, and promote and encourage shipping companies to follow new carbon emissions reduction models.
Table
Table 2. ECA information.
Table 2. ECA information.
ECASet Up TimeFounderCategory
Baltic ECA1 July 2010IMOSO2
North Sea ECA1 July 2010IMOSO2
North American ECA1 August 2012IMOSO2
American Caribbean ECA1 January 2013IMOSO2
North American NECA1 January 2016IMONOx
American Caribbean NECA1 January2016IMONOx
European Sea ECAs1 January 2010European UnionSO2 and NOx
California ECA1 August 2012The United StatesSO2 and NOx
Pearl River Delta Sea ECA1 January 2016ChinaSO2 and NOx
Yangtze River Delta Sea ECA1 January 2016ChinaSO2 and NOx
Bohai Rim ECA1 January 2016ChinaSO2 and NOx
Table
Table 3. The ports of Los Angeles and Long Beach container throughput.
Table 3. The ports of Los Angeles and Long Beach container throughput.
PortContainer Throughput in 2020 (Million TEU)Year-On-Year Growth Rate (%)Container Throughput in 2019 (Million TEU)Year-On-Year Growth Rate (%)
Los Angeles9.21−1.39.34−1.3
Long Beach8.116.37.635.7
Table
Table 4. AIS data sources and their properties.
Table 4. AIS data sources and their properties.
Data SourceData TypeData Source CharacterData Address
Marine T.Historical, real-timeCommercialwww.marinetraffic.com (accessed on 1 June 2022).
exactEarthHistoricalCommercialwww.exactearth.com (accessed on 1 June 2022).
VT E.HistoricalCommercialwww.vtexplorer.com (accessed on 1 June 2022).
FleetMonHistorical, real-timeCommercialwww.fleetmon.com (accessed on 1 June 2022).
VesselfinderHistorical, real-timeCommercialwww.vesselfinder.com (accessed on 1 June 2022).
Treasure Boat NetReal-timeCommercialwww.myships.com (accessed on 1 June 2022).
Ship News NetworkHistorical, real-timeCommercialwww.shipxy.com (accessed on 1 June 2022).
AIS E.Historical, real-timeNon-commercialais.exploratorium.edu (accessed on 1 June 2022).
Marine C.HistoricalNon-commercialmarinecadastre.gov (accessed on 1 June 2022).
Table
Table 5. Division of sailing state.
Table 5. Division of sailing state.
Sailing StateMatched Ship State JudgmentUnmatched Ship State Judgment
BerthingSpeed < 1 knotSpeed < 1 knot
Anchoring1 knot ≤ speed < 3 knots1 knot ≤ speed < 3 knots
ManeuveringSpeed ≥ 3knots and host load < 20%3 knots ≤ speed < 8 knots
Low-speed cruiseSpeed ≥ 3 knots and 20% ≤ host load < 65%8 knots ≤ speed < 12 knots
Normal cruiseSpeed ≥ 3 knots and host load ≥ 65%Speed ≥ 12 knots
Table
Table 6. Power ratio of auxiliary engine and main engine for each ship type.
Table 6. Power ratio of auxiliary engine and main engine for each ship type.
Ship TypeRatio of Auxiliary Engine to Main Engine Power
Container ship0.220
Cargo ship0.222
Passenger ship0.278
Oil tanker0.221
Tugboat0.222
Others0.222
Table
Table 7. Load ratio coefficient of auxiliary engines of various ship types.
Table 7. Load ratio coefficient of auxiliary engines of various ship types.
Ship TypeNormal CruiseLow-Speed CruiseManeuveringBerthing
Container ship0.130.250.480.19
Cargo ship0.170.270.450.22
Passenger ship0.800.800.800.64
Oil tanker0.240.280.330.26
Tugboat0.170.270.450.22
Others0.170.270.450.22
Table
Table 8. Boiler load power for each ship type (unit: kw).
Table 8. Boiler load power for each ship type (unit: kw).
Ship TypeManeuveringBerthing
Container ship505505
Cargo ship105105
Passenger ship10001000
Oil tanker3703000
Tugboat00
Others370370
Table
Table 9. Pollutant emission factors in line with ECA policies (unit: g/kwh).
Table 9. Pollutant emission factors in line with ECA policies (unit: g/kwh).
Engine TypeOilCOCXHXNOX
Main engine1841.40.617.0
Auxiliary engine2161.10.413.9
Boiler2890.20.12.1
Table
Table 10. Pollutant emissions by ship types in the Port of Los Angeles (unit: ton).
Table 10. Pollutant emissions by ship types in the Port of Los Angeles (unit: ton).
Ship TypeCOCXHXNOXPM10PM2.5SO2
Container ship162.867.91825.624.322.6409.6
Cargo ship229.685.12337.245.743.9809.6
Passenger ship2.21.934.91.51.3150.4
Oil tanker142.853.51598.949.045.91808.0
Tugboat1.10.411.60.40.216.0
Others0.50.25.80.10.16.4
Table
Table 11. Pollutant emissions by ship types in the Port of Long Beach (unit: ton).
Table 11. Pollutant emissions by ship types in the Port of Long Beach (unit: ton).
Ship TypeCOCXHXNOXPM10PM2.5SO2
Container ship209.697.81858.925.523.4444.2
Cargo ship295.6122.52379.848.045.4877.9
Passenger ship2.82.735.51.51.3163.1
Oil tanker183.977.11628.051.447.61960.6
Tugboat1.40.611.80.40.217.4
Others0.70.35.90.10.16.9
Table
Table 12. Comparison of emission factors for different ECA policies (unit: g/kwh).
Table 12. Comparison of emission factors for different ECA policies (unit: g/kwh).
StageFuel ConsumptionCOCXHXNOX
Phase II (main engine)1951.40.618.2
Phase III (main engine)1841.40.617.0
Phase II (auxiliary engine)2281.10.414.5
Phase III (auxiliary engine)2161.10.413.9
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Xiao, G.; Wang, T.; Chen, X.; Zhou, L. Evaluation of Ship Pollutant Emissions in the Ports of Los Angeles and Long Beach. J. Mar. Sci. Eng. 2022, 10, 1206. https://doi.org/10.3390/jmse10091206

AMA Style

Xiao G, Wang T, Chen X, Zhou L. Evaluation of Ship Pollutant Emissions in the Ports of Los Angeles and Long Beach. Journal of Marine Science and Engineering. 2022; 10(9):1206. https://doi.org/10.3390/jmse10091206

Chicago/Turabian Style

Xiao, Guangnian, Tian Wang, Xinqiang Chen, and Lizhen Zhou. 2022. "Evaluation of Ship Pollutant Emissions in the Ports of Los Angeles and Long Beach" Journal of Marine Science and Engineering 10, no. 9: 1206. https://doi.org/10.3390/jmse10091206

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop