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Article

Spatial–Temporal Distribution Characteristics and Correlation Analysis of Air Pollutants from Ships in Inland Ports

by
Liang Tong
,
Chunyu Zhang
*,
Zhongbo Peng
and
Lumeng Wang
School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing 400074, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(21), 14214; https://doi.org/10.3390/su142114214
Submission received: 9 September 2022 / Revised: 21 October 2022 / Accepted: 28 October 2022 / Published: 31 October 2022
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Abstract

:
The ship air pollution problem has become a global issue and poses a threat to the environment as well as the health of residents in the port area. This study used Automatic Identification System (AIS) data combined with a list of air pollutant emissions from ships. The total amount of air pollutant emissions was obtained. The spatial and temporal distribution characteristics of air pollutants on ships were analyzed, and the correlation of pollutant concentrations was studied on the three major ports. The three main intensive ports are Cuntan Port, Guoyuan Port, and Luoqi Port, and the Pearson correlation analysis is conducted based on the Statistical Product and Service Solutions (SPSS). The results showed that: (1) The air pollutant emissions from ships in China Chongqing port region during September to December 2021 were mainly dominated by CO2 and NOX. (2) The air pollutant emissions from ships in the Yangtze River Basin were highest in October 2021 and lowest in December. (3) The correlation between PM2.5 and CO2 showed the largest in Cuntan Port and Luoqi Port, and the correlation between CO and CO2 is the largest in Guoyuan Port. It is suggested to establish deceleration zones and emission control zones in Cuntan Port and Guoyuan Port, and to use shore power for berthing in Luoqi Port. We also suggest improving fuel quality and combustion quality, post-treating ship exhaust emissions, increasing port shore power facilities to reduce ship air pollutant emissions, and providing a reference for environmental protection in inland ports.

1. Introduction

The booming shipping industry has brought huge economic benefits to port areas; however, the problem of pollution from ship emissions cannot be underestimated [1,2,3]. Atmospheric pollutants are divided into primary pollutants and secondary pollutants. Direct pollution of the air is called primary pollutants, and is mainly sulfur dioxide, carbon monoxide, nitrogen oxides, nitrogen dioxide, particulate matter, etc. Primary pollutants place reactive substances into the atmosphere through physical and chemical changes, making new pollutants called secondary pollutants, mainly sulfuric acid and sulfate, nitric acid and nitrate, ozone, and photochemical smog. Carbon dioxide, nitrogen oxides, sulfur oxides, carbon monoxide, particulate matter 2.5, particulate matter 10, and other air pollutants in the ship emission inventory are serious hazards to human health and the environment [4,5,6]. Port area emissions have become an important factor affecting the ambient air quality of Chinese port cities, and port low emission zone planning is an important measure to control port pollution. Due to the high energy consumption and high emission characteristics of ports, the corresponding greenhouse gas emissions are also increasing, and the ecological and environmental problems of ports are becoming increasingly serious. In order to promote the low-carbon and green development of Chongqing ports it is necessary to study the spatial and temporal emission patterns and correlations of pollutants from port ships, construct a pollution gas emission inventory, and scientifically formulate emission reduction strategies for the long-term development of Chongqing ports. Construction of pollutant gas emission inventory and the scientifically formulated emission reduction strategies for the long-term development of Chongqing Ports should be implemented, contributing to the success of the Sustainable Development Goals-UN 2030 Agenda [7]. To achieve effective control of air pollutant emissions from ships, we first need to obtain accurate emission inventories and emission characteristics of ships [8,9]. In response to this situation, the calculation model of the emission inventory is being continuously improved [10,11,12]. Researchers used a ship traffic emission assessment model (STEAM) to calculate Baltic Sea ship exhaust emissions [13]. Based on this, the researchers further proposed the extended ship traffic emissions assessment model (STEAM2). They added the calculation of particulate matter (PM) and CO. Moreover they researched the analysis of ship load and fuel variation, and conceptualized the model based on the fuel consumption initially in 2017 by using the path regeneration algorithm and the data-assimilation method for the global ship air pollutant emission problem [14].
Based on the above research results, scholars have combined GIS to visualize and analyze the pollution situation, mostly focusing on coastal cities, such as the study of spatial distribution of air pollutants from ships in Tianjin port [15,16]. The scholars conducted actual emission tests on 12 inland vessels in the Jiangsu section of the Beijing–Hangzhou Canal using the Portable Emission Measurement System (PEMS) and analyzed the emissions of CO, NOX, and PM under different operating conditions [17]. In recent years, with the development of shipping trade and the increasingly serious problem of ship air pollution, more and more scholars began to pay attention to the problem of ship air pollution in inland areas. Through the analysis of the current situation and characteristics of ship air pollutant emissions in the Baiyangdian region of Hebei Province, it was found that the emission of hydrocarbon pollutants is the most serious [18]. The researchers used the Automatic Identification System (AIS) to obtain the ship activities and made a “bottom-up” emission inventory of Nanjing Longtan port area in 2014, and proposed emission reduction paths for ships in port based on the analysis of emission characteristics [19,20]. Since these research methods only analyze the distribution characteristics of air pollutant emissions from regional ships and ignore the correlation of pollutant concentrations, this limits the further development of targeted air pollution prevention and control programs in the region. The scholars analyzed the characteristics of ozone concentration changes in Chongqing atmosphere and made a correlation analysis between ozone precursors and meteorological factors, and the results indicated that there was a good negative correlation between ozone concentration and NOX concentration trends; through the correlation analysis of atmospheric pollutants and meteorological elements in Beibei area of Chongqing, the results showed that the concentrations of atmospheric pollutants PM10, SO2, and PM2.5 have a change pattern of decreasing and then increasing. Moreover, the cyclicality of the NO2 change pattern is not obvious [21,22,23]. However, the port area of Chongqing has complex hydrological characteristics and rich port trade features. The correlation analysis of air pollutant concentrations of regional ships only cannot accurately describe the spatial and temporal distribution characteristics of air pollutants of ships between different ports and the interaction effects between pollutant concentrations [24,25]. Therefore, integrating the distribution characteristics of ship air pollutant emissions in the inland port region and the development and limitations of correlation studies, this study selects Chongqing port region as the study area. In this paper, the total amount of air pollutant emissions from ships is obtained by using the list of air pollutant emissions from ships, it studies the differences of ship air pollution emissions in different time periods and spatial locations, and conducts correlation analysis for CO2, NOX, SOX, CO, and PM2.5 concentrations emitted by ships in Cuntan port, Guoyuan port, and Luoqi port. This paper provides reasonable emission reduction measures for air pollution caused by shipping trade in Chongqing ports.

2. Study Area and Subjects

The study area includes three major ports in the main city of Chongqing in China, Cuntan Port, Guoyuan Port, and Luoqi Port (106.25° E–107.06° E, 29.40° N–29.19° N), as shown in Figure 1. Among them, Cuntan Port has nine berths, mainly serving containers and commercial roll-on/roll-off vehicles; Guoyuan Port is connected with Yuhuai Railway and has 16 berths, with a total accumulated cargo throughput of 8.98 million tons; Luoqi Port has nine berths and is an important node of the industrial belt along the river. In order to promote “One Belt, One Road” and the Yangtze River Economic Belt, the foreign trade import container business of Cuntan Port has been transferred to Guoyuan Port. In this paper, the three most representative ports in Chongqing were selected for the study, among which Cuntan Port is the tourism window of Chongqing. Guoyuan Port is the largest port in Chongqing, and Luoqi Port is the port with the best implementation of low-carbon green measures. In this paper, the three representative ports and the ship emissions were selected and are summarized. The purpose is to speculate the ship air pollutant emission of other ports in Chongqing. Through the pilot study of the three ports, we evaluate and determine the adoption of stricter emission control requirements, expansion of emission control areas, and other further initiatives, such as extending the emission control requirements to other port areas in Chongqing.
The research period of this paper is from September to December 2021. According to statistics, the average monthly flow of Chongqing Yangtze River basin in December, January, February, and March is 4345 m3/s, 4280 m3/s, 4225 m3/s, and 4395 m3/s. The average monthly flow in April, May, October, and November is 7858 m3/s, 7733 m3/s, 7890 m3/s, and 8033 m3/s. The average monthly flow in June, July, August, and September is 19,252 m3/s, 21,111 m3/s, 15,864 m3/s, and 26,291 m3/s. The dry period of Yangtze River, when the water level is low and the navigation conditions are poor in Chongqing is January, February, March, and December. The mid-water period when the navigation conditions are the best is April, May, October, and November. The flood period, when the water level is high and the flow velocity is high, is June, July, August, and September. We selected the ship air pollutants in September during the flood period, October and November during the mid-water period, and December during the dry water period to study the changes in the Yangtze River in the three periods. Moreover, we can infer the ship pollutant emissions in other months of the year.
The ship types are classified into cargo ships, tankers, passenger ships, special ships, tugboats, container ships, law enforcement ships, and other types of ships. Moreover, the types of air pollutants involved include CO2, NOX, SOX, CO, PM2.5, and PM10. The ship activity states are classified into four different driving modes according to the sailing speed: cruising state (speed > 12 kn), slow state (8 kn < speed ≤ 12 kn), maneuvering state (1 kn < speed ≤ 8 kn), and moored state (speed ≤ 1 kn).

3. Calculation of Air Pollutant Emissions from Ships

3.1. Ship Air Pollutant Emission Inventory

The ship automatic identification system (AIS) has the role of identifying ships through the built-in the Global Positioning System (GPS). It can accurately obtain the position, heading, speed, and other information of the target ship. For the emission of air pollutants from ships, the publicly available AIS information is translated and decoded to obtain data containing basic ship information such as ship name, ship type, ship length, ship width, time stamp, latitude and longitude, speed, heading and destination, etc. A certain frequency is used to obtain the AIS data, and cleaning, automated identification of abnormal data, and supplementation of missing data are carried out (the website is https://coast.noaa.gov/htdata/CMSP/AISDataHandler/2021/ (accessed on 1 Sptember 2021)). According to the STEAM2 model, the emission inventory of ships in Chongqing port area is calculated, in which CO2 mainly depends on the carbon content of consumed fuel; NOx emission mainly depends on the temperature and duration of the combustion cycle of ship engines; SOX, PM2.5, and PM10 mainly depend on the quality and consumption of ship fuel; and CO emission depends on the engine load and engine power. Infrared radiation temperature measurement is a surface temperature measurement method that does not interfere with either the surface or the surrounding medium. Moreover, it has the advantages of high resolution, high sensitivity, high reliability, and short response time. Considering the complex and variable environment and ship characteristics in this region of Chongqing, a localized ship air pollution emission calculation model needs to be established. In this paper, the “bottom-up” power method is chosen to calculate the ship air pollutant emissions in the Chongqing port area [26], and the ship air pollution emission calculation model is shown in Equation (1) below:
E = M C R × L F × E F × F C F × D P × D U × T × 10 − 6
Among them, E represents the pollutant emission (t), MCR represents the power (kW), LF represents the load factor, EF represents the emission factor, FCF represents the fuel correction factor, DP represents the machine-pitch matching factor, DU represents the mainframe low load correction factor, and T represents the time (h).
The ship motor power (MCR) is proportional to the product of the torque and frequency. The load factor (LF) is the product of the load factor of the main auxiliary machine. In this study, diesel with a sulfur content of 2.7% is used as the basic emission factor (EF), and light diesel with a sulfur content of 0.1% is introduced as the fuel correction factor (FEF) for the emission factor. The mainframe low load correction factor (DU) is 1.25 [27].

3.2. Total Air Pollutant Emissions from Ships

The ship emissions of air pollutants CO, NOX, PM10, PM2.5, SOX and greenhouse gas CO2 from September to December 2021 in this study area are 95.03 t, 873.87 t, 25.04 t, 18.80 t, 43.44 t, and 61,002.97 t, respectively, totaling 62,059.16 t. The most emitted is CO2, followed by NOX, the third and fourth are CO and SOX, and the least emitted is PM2.5. The emission of air pollutants from the main and secondary engines under different sailing conditions is shown in Table 1 below.
As can be seen from Table 1, among the seven states of the main engine and auxiliary engine of the ship, the sum of the pollutant concentrations in the mooring state of the auxiliary mooring reaches the highest, followed by the cruising state of the main engine. The sum of the pollutant concentrations in the maneuvering state of the auxiliary aircraft is the smallest, and the mooring state is one of the most important and frequent activities of the ship to ensure the safe mooring of the ship. From the perspective of the discharge of the main engine and the auxiliary engine, the sum of the pollutant concentrations in the three states of the main engine is greater than the sum of the pollutant concentrations in the four states of the auxiliary engine. From the perspective of pollutant types, the total amount of greenhouse gas CO2 emissions is the largest, reaching 62,059.16 t, followed by NOX, CO, SOX, PM10, and PM2.5, mainly CO2 gas emissions.

4. Data Analysis

4.1. Temporal Distribution Characteristics

4.1.1. Monthly Distribution of Ship Air Pollutant Emissions by Major Ship Types

Different types of ships in the study area mainly include cargo ships, passenger ships, oil tankers, special ships, tugboats, and pilot boats. From September to December 2021, the regional cargo ships, passenger ships, oil tankers, special ships, tugboats, and pilot boats emitted air pollutants of 42,763.43 t, 9012.90 t, 4959.95 t, 514.82 t, 260.98 t, and 44.11 t respectively. The different functions of the ships themselves lead to large differences in total air pollutant emissions, so it is important to calculate their total pollution emissions from September to December based on the main ship types to understand the pollutant emission patterns of different ships in different months, as shown in Table 2 and Figure 2 for the month distribution of pollution emissions from the main ship types.
As can be seen from the monthly emission table of major ship type pollutants, in terms of ship types, the pollutant emissions of cargo ships reached a maximum of 11,768.71 t in October, followed by passenger ships, oil tankers, other types of ships, special ships, tugboats, and pilot vessels. In terms of months, in September, tugboats discharged the highest total amount of pollutants, and other types of ships had the least. In October, tugboat emissions were the largest, and cargo ships, oil tankers, and special ships discharged closely. In November, other types of ships emitted the most and tugboats the least. In December, passenger ships and pilot ships emitted similar amounts of pollutants, with tugboats and other types of vessels having the least.
Cargo ships and oil tankers both emit the most in October, with cargo ships emitting the least in December and oil tankers emitting the least in November; special ships and other types of ships both emit the most in November and the least in December; passenger ships and pilot ships both emit the most in December, with passenger ships emitting the least in November and pilot ships emitting the least in October. The tugboat emits the most in September and the least in December; because September is the transition period from flood to mid-water, the current is more turbulent and the water level is lower than that in mid-water, so the tugboat activity level increases; while December is the dry water period, the current is calm and the water level is higher, so the tugboat activity level decreases. The distribution of pollution emission month of the main vessel types above shows that the vessel activity level is related to the environment characteristics of the Yangtze River Three Gorges Reservoir Area.

4.1.2. Monthly Distribution of Ship Air Pollutant Emissions by Major Pollutant Type

The emissions of atmospheric pollutants CO2, NOX, CO, SOX, PM10, and PM2.5 from ships in the study area can be seen from Figure 3, with CO2 and NOX dominating and PM2.5 as the least, and the temporal distribution pattern of atmospheric pollutant emissions in the study area is basically consistent. The ship activity level in the Yangtze River Three Gorges Reservoir Area, the characteristics of the channel environment, the autumn–winter, and flooding periods and the dry period influence the poor navigation conditions during the dry and flooding periods, and the ship flow is relatively small, while the navigation conditions during the mid-water period are better and the ship flow is larger. In December, the water level is low, the flow is slow, and the navigation conditions are poor, so the flow of vessels in the region is reduced, which makes the discharge of pollutants low.

4.2. Spatial Distribution Characteristics

Spatial analysis is the quantitative study of phenomena, and its conventional ability is to manipulate spatial data to make it different forms and extract its potential information. Spatial analysis mainly uses the joint analysis of spatial data and spatial models to explore potential information about spatial targets. In this paper, the spatial analysis module is used to objectively and comprehensively evaluate the current situation of air pollutants in ships in the Yangtze River Basin of Chongqing so as to reflect the degree of pollution in the region and the spatial distribution.
In this paper, the geographic information system was used to analyze the spatial distribution of atmospheric pollutants on ships [28,29,30]. We first calculated pollution emissions from the acquired AIS data and recorded the corresponding latitude and longitude locations to generate emission files, with latitude and longitude locations as spatial data and emissions as attribute data [31,32,33,34]. Then, the spatial analysis of the emission file data was performed, and finally the spatial distribution map of various air pollutant emissions generated in the form of layout view was obtained using GIS, as shown in Figure 4.
From Figure 4, it can be seen that the distribution trends of CO2 and SOX emissions from ships in Chongqing port area are similar, showing a low trend in the upstream and downstream of the Yangtze River and a high trend in the midstream, especially concentrated in the area along the Cuntan Port to the Port of Guoyuan. For NOX, it is mainly concentrated in the Cuntan Port; PM10 and PM2.5 units of emissions are the lowest of all pollutants, with Guoyuan Port and Luoqi Port dominating the most. In summary, the spatial distribution of ship air pollutant emissions in Chongqing port area is mainly concentrated in Cuntan Port, Guoyuan Port, and Luoqi Port, among which Guoyuan Port is the most densely distributed, which is related to the distribution of Chongqing ports and the level of ship activities, the specific intrinsic correlation is studied in detail in the correlation section.

4.3. Correlation Analysis

Port greenhouse gas CO2 emissions exacerbate global climate change, and global sea level rise caused by climate change also poses a huge threat to the sustainable development of ports. The direct carbon emissions generated by port activities mainly include the emissions generated by port vessels through fuel consumption, and the corresponding carbon emissions are generated by cargo ships, passenger ships, oil tankers, special ships, tugboats, and pilot ships in Cuntan Port, Guoyuan Port, and Luoqi Port to varying degrees. Therefore, the CO2 correlation of the three ports was studied, combined with the ship’s own ship activity law, so as to formulate different emission reduction measures. The ship activities in these three ports are different: In Cuntan Port, which is focused on development of tourism and shipping, passenger ship activities are more frequent, and air particulate matter emissions are more. Guoyuan Port is a port for Chongqing’s trade in and out, and as such has many cargo ships and high carbon emissions. Luoqi Port is the port with the best implementation of current low-carbon green measures, so the emission of pollutants is better controlled.
By combining the above study and the characteristics of Chongqing’s port activities it can be seen that Cuntan Port is the core functional area of international consumption, international business, and international exchanges connecting ports and urban transportation, and is the main activity area of passenger ships. Guoyuan Port is a new window of logistics pattern in the western region, plays a demonstration role in promoting the green development of the Yangtze River Economic Belt, and is the main activity port for cargo ships and passenger ships. Located in the upper reaches of the Yangtze River in Chongqing, the Port of Loraine serves as a link between ship trade in the eastern region. The ship activities of the above three representative ports have their own characteristics and distribution laws, thus showing the different characteristics of the correlation of atmospheric pollutants of ships. Therefore, different ports have no way to implement regional emission reduction measures together. Moreover, it is necessary to formulate atmospheric environmental protection policies according to the characteristics of each port.
Firstly, the SPSS tool was used to test the normal distribution for the ports of the study area, Cuntan Port, Guoyuan Port, and Luoqi Port, and the available data of CO2 emissions from ships in the area were subjected to the quantile-quantile plot (Q-Q) test of natural analysis. Moreover, the results are shown in Figure 5. From Figure 5, the CO2 concentrations of Cuntan Port and Guoyuan Port in Chongqing in 2021 are approximately near a straight line on the Q-Q chart, indicating an approximate normal distribution, among which the fluctuation of Luoqi Port is the largest, while the fluctuation of Cuntan Port data is smaller. On the one hand, the Chongqing shipping hub is concentrated in Cuntan Port and Guoyuan Port, on the other hand, it may be related to factors such as flooding period and dry period in autumn and winter; the fluctuation of Guoyuan Port data is the smallest. On the other hand, this may be related to the flooding period and dry water period in autumn and winter. In general, the data obtained from this study based on the ship pollutant emission inventory are not significantly different from the theoretical normal distribution and satisfy the normal distribution, which are feasible data and can be further analyzed for correlation.
We can make an analysis from the information in Table 3. In the Pearson correlation analysis [35,36,37], CO2, NOX, SOX, CO, and PM2.5 were selected as the research objects, CO2 as the dependent variable, Y, NOX, SOX, CO, and PM2.5 as the independent variables, X1, X2, X3, and X4, and SPSS software was used to analyze the correlation of the mass concentration data of vessels in Cuntan Port and Guoyuan Port area of Chongqing main city from September to December 2021. The correlation analysis was conducted on the mass concentration data of five types of pollutants emitted in September to December of 2021, and the correlation and correlation characteristics criteria: 0.8–1.0 is highly correlated, 0.5–0.8 is significantly correlated, 0.3–0.5 is real correlation, and 0–0.3 is micro correlation [38]. As can be seen from Table 1, the CO2 concentrations emitted from ships in Cuntan Port, Guoyuan Port, and Luoqi Port area of Chongqing are positively correlated with NOX, SOX, CO, and PM2.5 concentrations, among which PM2.5 is the most correlated with CO2 at Cuntan Port and Luoqi Port, but CO is the most correlated with CO2 at Guoyuan Port, which is mainly related to the fact that ships are densely concentrated at Guoyuan Port; the correlation coefficients with CO2 that have significant positive correlation. The correlation coefficients of pollutants basically follow PM2.5 > SOX > NOX, so we can conclude that in the main port area of Chongqing, the closeness of the relationship between the air pollutants emitted by ships affecting the quality concentration is PM2.5, SOX, and NOX.
The correlation between PM2.5 and CO2 is the highest in Cuntan Port and Luoqi Port. It is known in the section of ship air pollutant emission inventory in section three, CO2 mainly depends on the carbon content of consumed fuel. PM2.5 mainly depends on the quality and consumption of ship fuel. We can take some measures to reduce carbon emission and control pollution. On one hand, we can increase the quality of ship fuel. Moreover, we can reduce the fuel consumption and improve the effectiveness of fuel use. We can improve the efficiency of the power engine. Moreover, we can reduce the fuel consumption rate of prime movers and boilers of main engines and generators, etc. We can save lighting, air conditioning, and other equipment and reduce the use of fuel. On the other hand, CO is most correlated with CO2 at Guoyuan Port, and CO emissions depend on engine load and engine power. The original ships with higher power can reduce the power and improve the propulsion efficiency. By taking appropriate measures in the three ports, PM2.5 and CO emissions are reduced, and thus CO2 emissions are reduced. These measures contribute to pollution control.

5. Study on Ship Pollution Control in Chongqing Port Area

By analyzing the spatial and temporal distribution of air pollutants from ships and the correlation situation, we can see that the air pollutants from ships are mainly concentrated in the area from Cuntan Port to Guoyuan Port. The air pollutants from ships near the port area spread to the surrounding areas and have an important impact on the air quality around the port. In order to ensure the air quality of Chongqing port area and the health of residents, it is recommended to adopt corresponding control techniques for ship air pollution emissions.
Recommendation one is to improve fuel quality and combustion quality. The most direct way to reduce air pollution emissions from ships is to improve fuel quality, such as imposing limits on fuel sulfur content. Another important way to improve fuel quality is to replace fuel with clean fuel energy, which includes wind energy, solar energy, oil–gas–electric hybrid, and liquid natural gas. The design of diesel engine internal structure can also be optimized, such as the use of variable compression ratio piston, the oil film evaporation combustion method can effectively improve the combustion quality.
Recommendation two is post-treatment of exhaust emissions from ships. Before the atmospheric pollutants emitted from ships enter the atmosphere, special equipment is added at the rear of diesel engines to further treat the exhaust gas so that the content of harmful components and pollutants in the exhaust gas is reduced. At present, the post-treatment technology of ship exhaust gas is mainly divided into two methods: physical treatment technology and chemical treatment technology [39]. The physical technology mainly includes electrostatic separation method, water washing method, and particle filtration method, etc.
Recommendation three is port shore power. The diesel engine stops working during the ship’s port call, and the daily power supply on board is supplied by the ship’s auxiliary engine. Although the pollution emission from the ship’s auxiliary engine is relatively small compared with that of the ship’s main engine, large amounts of pollutants are generated during the long stopping period, which, together with the large number of ships around the port and the large noise of the auxiliary engine operation, seriously affects the physical and mental health of residents around the port [40]. By adding port shore power facilities in the port or regional ship docking-concentrated places, the power supply from the port directly to the ship replaces the previous ship generator set burning fuel oil to generate electricity, which can reduce ship pollution emission to a large extent.
Chongqing has not formulated measures for the discharge of atmospheric pollutants from ships suitable for the port according to the characteristics of different ports, as it lacks corresponding basic research, has failed to form an effective and unified supervision and management system, and lacks technical standards and control technology for port and ship emissions. Combined with the above-mentioned spatial–temporal distribution map of ship atmospheric pollutant emissions and correlation research, ships have the most frequent activities in the watershed from Cuntan Port to Guoyuan Port, and the density of atmospheric pollutant emissions is the largest. According to the scale of the port and the characteristics of the port, the establishment of deceleration zones and emission control areas in Cuntan Port and Guoyuan Port, and the use of shore power as the first choice in Luoqi Port achieves better results.

6. Conclusions

In this paper, the emission inventory was used to study the atmospheric pollutants of ships in the Chongqing port area in combination with AIS data. On this basis, GIS technology is applied to systematically study the temporal and spatial distribution characteristics of atmospheric pollutants of ships. Pearson correlation analysis was used for the pollutant-dense areas of Cuntan Port, Guoyuan Port, and Luoqi Port. Moreover, the prevention and control of air pollution from ships in the Chongqing port area was analyzed. The conclusions reached are as follows:
(1)
In this paper, according to the ship’s atmospheric emission inventory, the atmospheric pollutant emissions of the main and auxiliary aircraft under different navigation conditions were obtained. Among them, the emissions of air pollutants such as CO2, NOX, SOX, CO, PM10, and PM2.5 were 61,002.97 t, 873.87 t, 43.44 t, 95.03 t, 25.04 t, and 18.80 t, respectively. Analysis was carried out from the condition of the vessel and the type of contaminant. The sum of the concentrations of each pollutant in the mooring state of the auxiliary aircraft is high. The sum of the concentrations of each pollutant in the auxiliary motor maneuvering state is small. The pollutants are mainly CO2 and NOX, of which the main ones are the emission of greenhouse gas CO2;
(2)
The temporal distribution characteristics of atmospheric pollutants on ships were analyzed. Because the level of ship activity is related to the hydrological environment characteristics of the Three Gorges Reservoir Area of the Yangtze River, as well as the different functions of the ship itself, there is a large difference in the total amount of air pollutant emissions. It is affected by factors such as the level of ship activity in the Three Gorges Reservoir Area of the Yangtze River, the hydrological characteristics of the waterway environment, autumn and winter, and flood periods and dry water periods. In October 2021, the air pollutants of ships in the Chongqing port area were higher than in other months, while December was low. On the other hand, the spatial distribution characteristics of air pollutants on ships were analyzed. This is related to the degree of distribution of Chongqing ports and the level of ship activity;
(3)
Correlation studies were conducted on the concentrations of atmospheric pollutants in ships at three ports. The ship activities of the three representative ports have their own characteristics and distribution laws. Therefore, it shows the different characteristics of the correlation of air pollutants on ships. Among them, PM2.5 is most correlated with CO2 in Cuntan Port and Luoqi Port, but CO is most correlated with CO2 in Guoyuan Port. This reflects the new situation of the transfer of foreign trade import container business in Chongqing Cuntan Port Area to Guoyuan Port Area. Moreover, Chongqing’s shipping hubs are concentrated in Cuntan Port and Guoyuan Port.
Different ports need to implement regional emission reduction measures according to their own characteristics. Cuntan Port and Guoyuan Port shall establish deceleration zones and emission control areas. The Luoqi Port uses shore power as its first choice. It is recommended to improve the quality of ship fuel by imposing limits on the sulfur content of fuel oil and replacing clean fuel energy, add special equipment to the tail of the diesel engine for post-treatment of exhaust gases, and add port shore power facilities to control pollution emissions where port or regional ship calls are concentrated.
Although the research in this paper can provide a reference for the prevention and control of air pollution from inland ships to a certain extent, there are some limitations to the study. On the one hand, there is uncertainty in the estimation process of ship emission inventories. To this end, identifying emission inventory uncertainties can effectively promote the improvement of future emission inventories. There is an urgent need to improve the completeness and accuracy of emission inventories. In addition, this paper only used the ship air pollutant data from September to December 2021 which covers dry periods, moderate water periods, and flood periods; however, no more datasets are included. Later, more comprehensive data sets can be combined to further study the characteristics of air pollutants emitted by ships. In addition, we will formulate more perfect measures for the protection of the atmospheric environment in ports.

Author Contributions

Conceptualization, methodology, writing—original draft preparation, L.T.; data curation, writing—review and editing, C.Z.; formal analysis, Z.P.; investigation, L.W. All authors have read and agreed to the published version of the manuscript.

Funding

It was sponsored by Chongqing Jiaotong University joint postgraduate training base.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to give special thanks to the editors and the anonymous reviewers of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kumar, J.; Ragumaran, S.; Nandagopal, G.; Ravichandran, V.; Mallavarapu, R.M.; Missimer, T.M. Green method of stemming the tide of invasive marine and freshwater organisms by natural filtration of shipping ballast water. Environ. Sci. Pollut. Res. Int. 2021, 28, 5116–5125. [Google Scholar] [CrossRef] [PubMed]
  2. Tai, H.H.; Wang, Y.M. Influence of vessel upsizing on pollution emissions along Far East-Europe trunk routes. Environ. Sci. Pollut. Res. Int. 2022, 29, 65322–65333. [Google Scholar] [CrossRef] [PubMed]
  3. Wu, Y.; Li, G.; Yang, Y.; An, T. Pollution evaluation and health risk assessment of airborne toxic metals in both indoors and outdoors of the Pearl River Delta, China. Environ. Res. 2019, 179, 108793. [Google Scholar] [CrossRef]
  4. Zhao, J.; Zhang, Y.; Wang, T.; Sun, L.; Yang, Z.; Lin, Y.; Chen, Y.; Mao, H. Characterization of PM2.5-bound polycyclic aromatic hydrocarbons and their derivatives (nitro-and oxy-PAHs) emissions from two ship engines under different operating conditions. Chemosphere 2019, 225, 43–52. [Google Scholar] [CrossRef] [PubMed]
  5. Zhao, J.; Zhang, Y.; Chang, J.; Peng, S.; Hong, N.; Hu, J.; Lv, J.; Wang, T.; Mao, H. Emission characteristics and temporal variation of PAHs and their derivatives from an ocean-going cargo vessel. Chemosphere 2020, 249, 126194. [Google Scholar] [CrossRef]
  6. Merico, E.; Conte, M.; Grasso, F.M.; Cesari, D.; Gambaro, A.; Morabito, E.; Gregoris, E.; Orlando, S.; Alebic-Juretic, A.; Zubak, V.; et al. Comparison of the impact of ships to size-segregated particle concentrations in two harbour cities of northern Adriatic Sea. Environ. Pollut. 2020, 266, 115175. [Google Scholar] [CrossRef]
  7. Varotsos, C.A.; Cracknell, A.P. Remote Sensing Letters contribution to the success of the Sustainable Development Goals-UN 2030 agenda. Remote Sens. Lett. 2020, 11, 715–719. [Google Scholar] [CrossRef]
  8. Liu, J.; Duru, O.; Law, A.W. Assessment of atmospheric pollutant emissions with maritime energy strategies using bayesian simulations and time series forecasting. Environ. Pollut. 2021, 270, 116068. [Google Scholar] [CrossRef]
  9. Zhou, M.; Jiang, W.; Gao, W.; Gao, X.; Ma, M.; Ma, X. Anthropogenic emission inventory of multiple air pollutants and their spatiotemporal variations in 2017 for the Shandong Province, China. Environ. Pollut. 2021, 288, 117666. [Google Scholar] [CrossRef] [PubMed]
  10. Fang, C.; Wang, L.; Gao, H.; Wang, J. Analysis of the PM2.5 emission inventory and source apportionment in Jilin City, Northeast of China. Environ. Sci. Pollut. Res. Int. 2020, 27, 37324–37332. [Google Scholar] [CrossRef]
  11. Fagbeja, M.A.; Hill, J.L.; Chatterton, T.J.; Longhurst, J.W.; Akpokodje, J.E.; Agbaje, G.I.; Halilu, S.A. Challenges and opportunities in the design and construction of a GIS-based emission inventory infrastructure for the Niger Delta region of Nigeria. Environ. Sci. Pollut. Res. Int. 2017, 24, 7788–7808. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. 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] [PubMed] [Green Version]
  13. Wang, P.; Mi, W.; Xie, Z.; Tang, J.; Apel, C.; Joerss, H.; Ebinghaus, R.; Zhang, Q. Overall comparison and source identification of PAHs in the sediments of European Baltic and North Seas, Chinese Bohai and Yellow Seas. Sci. Total Environ. 2020, 737, 139535. [Google Scholar] [CrossRef]
  14. 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] [Green Version]
  15. 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]
  16. 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]
  17. Cao, Y.-L.; Wang, X.; Yin, C.-Q.; Xu, W.-W.; Shi, W.; Qian, G.-R.; Xun, Z.-M. Inland Vessels Emission Inventory and the emission characteristics of the Beijing-Hangzhou Grand Canal in Jiangsu province. Process Saf. Environ. Prot. 2018, 113, 498–506. [Google Scholar] [CrossRef]
  18. Cerro-Galvez, E.; Sala, M.M.; Marrase, C.; Gasol, J.M.; Dachs, J.; Vila-Costa, M. Modulation of microbial growth and enzymatic activities in the marine environment due to exposure to organic contaminants of emerging concern and hydrocarbons. Sci. Total Environ. 2019, 678, 486–498. [Google Scholar] [CrossRef]
  19. Ronkko, T.J.; Jalava, P.I.; Happo, M.S.; Kasurinen, S.; Sippula, O.; Leskinen, A.; Koponen, H.; Kuuspalo, K.; Ruusunen, J.; Vaisanen, O.; et al. Emissions and atmospheric processes influence the chemical composition and toxicological properties of urban air particulate matter in Nanjing, China. Sci. Total Environ. 2018, 639, 1290–1310. [Google Scholar] [CrossRef]
  20. Ronkko, T.J.; Hirvonen, M.R.; Happo, M.S.; Leskinen, A.; Koponen, H.; Mikkonen, S.; Bauer, S.; Ihantola, T.; Hakkarainen, H.; Miettinen, M.; et al. Air quality intervention during the Nanjing youth olympic games altered PM sources, chemical composition, and toxicological responses. Environ. Res. 2020, 185, 109360. [Google Scholar] [CrossRef]
  21. Li, J.; Zhai, C.; Yu, J.; Liu, R.; Li, Y.; Zeng, L.; Xie, S. Spatiotemporal variations of ambient volatile organic compounds and their sources in Chongqing, a mountainous megacity in China. Sci. Total Environ. 2018, 627, 1442–1452. [Google Scholar] [CrossRef] [PubMed]
  22. Liao, T.; Gui, K.; Jiang, W.; Wang, S.; Wang, B.; Zeng, Z.; Che, H.; Wang, Y.; Sun, Y. Air stagnation and its impact on air quality during winter in Sichuan and Chongqing, southwestern China. Sci. Total Environ. 2018, 635, 576–585. [Google Scholar] [CrossRef] [PubMed]
  23. Maji, K.J.; Ye, W.F.; Arora, M.; Nagendra, S.M.S. Ozone pollution in Chinese cities: Assessment of seasonal variation, health effects and economic burden. Environ. Pollut. 2019, 247, 792–801. [Google Scholar] [CrossRef] [PubMed]
  24. Sanjay, P.; Tagolao, S.; Dirkzwager, I.; Bartlett, A. A survey of the accuracy of interpretation of intraoperative cholangiograms. HPB 2012, 14, 673–676. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Wang, Z.; Lv, J.; Tan, Y.; Guo, M.; Gu, Y.; Xu, S.; Zhou, Y. Temporospatial variations and Spearman correlation analysis of ozone concentrations to nitrogen dioxide, sulfur dioxide, particulate matters and carbon monoxide in ambient air, China. Atmos. Pollut. Res. 2019, 10, 1203–1210. [Google Scholar] [CrossRef]
  26. Zhang, F.; Chen, Y.; Su, P.; Cui, M.; Han, Y.; Matthias, V.; Wang, G. Variations and characteristics of carbonaceous substances emitted from a heavy fuel oil ship engine under different operating loads. Environ. Pollut. 2021, 284, 117388. [Google Scholar] [CrossRef]
  27. Peng, Z.B.; Wang, L.M.; Tong, L.; Zhang, C.Y.; Zou, H.; Tan, J.P. Establishment of Inland Ship Air Pollution Emission Inventory Based on Power Method Correction Model. Sustainability 2021, 14, 11188. [Google Scholar] [CrossRef]
  28. Dheenan, P.S.; Jha, D.K.; Das, A.K.; Vinithkumar, N.V.; Devi, M.P.; Kirubagaran, R. Geographic information systems and multivariate analysis to evaluate fecal bacterial pollution in coastal waters of Andaman, India. Environ. Pollut. 2016, 214, 45–53. [Google Scholar] [CrossRef]
  29. Andres-Costa, M.J.; Pascual-Aguilar, J.; Andreu, V.; Pico, Y. Assessing drugs of abuse distribution in Turia River based on geographic information system and liquid chromatography mass spectrometry. Sci. Total Environ. 2017, 609, 360–369. [Google Scholar] [CrossRef]
  30. Larsen, K.; Black, P.; Rydz, E.; Nicol, A.M.; Peters, C.E. Using geographic information systems to estimate potential pesticide exposure at the population level in Canada. Environ. Res. 2020, 191, 110100. [Google Scholar] [CrossRef] [PubMed]
  31. Golaki, M.; Azhdarpoor, A.; Mohamadpour, A.; Derakhshan, Z.; Conti, G.O. Health risk assessment and spatial distribution of nitrate, nitrite, fluoride, and coliform contaminants in drinking water resources of kazerun, Iran. Environ. Res. 2022, 203, 111850. [Google Scholar] [CrossRef] [PubMed]
  32. Nilkarnjanakul, W.; Watchalayann, P.; Chotpantarat, S. Spatial distribution and health risk assessment of As and Pb contamination in the groundwater of Rayong Province, Thailand. Environ. Res. 2022, 204, 111838. [Google Scholar] [CrossRef] [PubMed]
  33. Ajiboye, Y.; Isinkaye, M.O.; Khanderkar, M.U. Spatial distribution mapping and radiological hazard assessment of groundwater and soil gas radon in Ekiti State, Southwest Nigeria. Environ. Earth Sci. 2018, 77, 545. [Google Scholar] [CrossRef]
  34. Wu, Z.; Zhang, F.; Di, D.; Wang, H. Study of spatial distribution characteristics of river eco-environmental values based on emergy-GeoDa method. Sci. Total Environ. 2022, 802, 149679. [Google Scholar] [CrossRef]
  35. Filzmoser, P.; Hron, K.; Templ, M. Correlation Analysis. In Applied Compositional Data Analysis; Springer Series in Statistics; Springer: Cham, Switzerland, 2018; pp. 149–162. [Google Scholar]
  36. Wu, Q.; Wu, J.; Gao, M. Correlation analysis of earthquake impacts on a nuclear power plant cluster in Fujian province, China. Environ. Res. 2020, 187, 109689. [Google Scholar] [CrossRef]
  37. Li, L.; Liu, T.; Dong, H.; Wang, Y.; Yang, H.; Qiang, Z. Tracking spatio-temporal dynamics of fluorescence characteristics of Huangpu River, China by parallel factor analysis: Correlation with disinfection by-product precursor and pesticide level variations. Chemosphere 2021, 283, 131198. [Google Scholar] [CrossRef]
  38. Lv, Q.P. Research on Analysis and Visualization of Atmospheric Pollutant Data in Tianjin. Doctoral Dissertation, Tianjin University of Finance and Economics, Tianjin, China, 2019. [Google Scholar] [CrossRef]
  39. Zhao, J.; Wei, Q.; Wang, S.; Ren, X. Progress of ship exhaust gas control technology. Sci. Total Environ. 2021, 799, 149437. [Google Scholar] [CrossRef]
  40. Tan, Z.; Liu, Q.; Song, J.; Wang, H.; Meng, Q. Ship choice and shore-power service assessment for inland river container shipping networks. Transp. Res. Part D Transp. Environ. 2021, 94, 102805. [Google Scholar] [CrossRef]
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Figure 1. Study area.
Figure 1. Study area.
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Figure 2. Main ship type pollution emission month distribution.
Figure 2. Main ship type pollution emission month distribution.
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Figure 3. Major ship air pollutant emission month distribution.
Figure 3. Major ship air pollutant emission month distribution.
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Figure 4. Spatial distribution of air pollutant emissions from ships.
Figure 4. Spatial distribution of air pollutant emissions from ships.
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Figure 5. Normal Q−Q plots of CO2 concentration (a) Cuntan Port; (b) Guoyuan Port; (c) Luoqi Port.
Figure 5. Normal Q−Q plots of CO2 concentration (a) Cuntan Port; (b) Guoyuan Port; (c) Luoqi Port.
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Table
Table 1. Emissions of air pollutants from main engine and auxiliary under different vessel status (t).
Table 1. Emissions of air pollutants from main engine and auxiliary under different vessel status (t).
Vessel StatusCONOXPM10PM2.5SOXCO2
Main engine cruise27.52263.416.385.1011.5116,165.09
Main engine sailing35.68341.499.816.6214.9220,956.93
Main engine maneuvering1.398.300.230.180.37521.86
Auxiliary cruise0.685.810.190.150.37520.62
Auxiliary low flight1.7815.270.510.400.971368.67
Auxiliary maneuvering0.131.110.040.030.0799.52
Auxiliary mooring27.85238.497.896.3115.2221,370.28
Total95.03873.8725.0418.8043.4462,059.16
Table
Table 2. Major ship type pollutant month emissions (t).
Table 2. Major ship type pollutant month emissions (t).
TypeSeptemberOctoberNovemberDecember
Cargo ship10,754.8011,768.7111,717.908522.03
Passenger ship2183.891904.631752.683171.70
Oil Tanker1318.871751.62870.401019.07
Special ship140.92144.55172.4056.95
Tugboat119.18105.3525.5116.75
Pilot vessel7.425.7213.8317.98
Other types324.771332.262189.30235.55
Table
Table 3. Correlation analysis results of Cuntan Port, Guoyuan Port, and Luoqi Port.
Table 3. Correlation analysis results of Cuntan Port, Guoyuan Port, and Luoqi Port.
PortPollutantY (CO2)X1(NOX)X2(SOX)X3(CO)X4(PM2.5)
CuntanCO21.0000.7580.9420.5350.957
NOX 1.0000.7970.8490.764
SOX 1.0000.5820.957
CO 1.0000.538
PM2.5 1.000
GuoyuanCO21.0000.6620.8410.9780.943
NOX 1.0000.5510.6600.770
SOX 1.0000.8450.817
CO 1.0000.943
PM2.5 1.000
LuoqiCO21.0000.7630.8440.8710.905
NOX 1.0000.6880.7470.854
SOX 1.0000.9900.938
CO 1.0000.966
PM2.5 1.000
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Tong, L.; Zhang, C.; Peng, Z.; Wang, L. Spatial–Temporal Distribution Characteristics and Correlation Analysis of Air Pollutants from Ships in Inland Ports. Sustainability 2022, 14, 14214. https://doi.org/10.3390/su142114214

AMA Style

Tong L, Zhang C, Peng Z, Wang L. Spatial–Temporal Distribution Characteristics and Correlation Analysis of Air Pollutants from Ships in Inland Ports. Sustainability. 2022; 14(21):14214. https://doi.org/10.3390/su142114214

Chicago/Turabian Style

Tong, Liang, Chunyu Zhang, Zhongbo Peng, and Lumeng Wang. 2022. "Spatial–Temporal Distribution Characteristics and Correlation Analysis of Air Pollutants from Ships in Inland Ports" Sustainability 14, no. 21: 14214. https://doi.org/10.3390/su142114214

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