Despite the large number of domestic inland river vessels in
China, information on inland ship emissions is very limited, since
legislation for shipping emission control is limited and there is no
monitoring infrastructure. Taking the Yangtze River in the region of Nanjing
as a research area, we compiled a ship emission inventory based on real-time
information received from automatic identification system (AIS) signals
combined with ship-related data provided by the China Classification Society
(CCS) database. The total ship emissions we derived for the Jiangsu section
of the Yangtze River from September 2018 to August 2019 for NOx,
SO2, PM10 and PM2.5 were 83.5, 0.04, 0.006 and 0.005 kt yr-1, respectively. This ship emission inventory we constructed was compared with the Multi-resolution Emission Inventory for China (MEIC), the Shipping
Emission Inventory Model (SEIM) and the satellite-derived emissions using
the Daily Emissions Constrained by Satellite Observations (DECSO) algorithm.
The results show a consistent spatial distribution, with riverine cities
having high NOx pollution. With this comparison we analyzed the
relative impact of ship emissions on densely populated regions along the
river. Inland ship emissions of NOx are shown to contribute
significantly, accounting for at least 40 % of air pollution close
to the river.
National Natural Science Foundation of China42075176China Scholarship Council202109040001Introduction
Maritime transport plays an essential role in national trade, and as the
number of ships increases so does the quantity of emissions they emit into
the air. The main pollutants emitted by ships are sulfur dioxide (SO2),
nitrogen oxides (NOx= NO + NO2), particulate matter (PM) and
volatile organic compounds (VOCs)
(Endresen,
2003; Moldanová et al., 2009; Tzannatos, 2010), all of which can affect
regional air quality and human health
(Capaldo
et al., 1999; Dalsøren et al., 2009; Papanastasiou and Melas, 2009;
Eyring et al., 2010). In addition to direct pollution from ship emissions,
the secondary generation of fine particulate matter, sulfate and ozone
further contributes to atmospheric pollution (Corbett,
1997). These pollutants are also transported to large inland areas with sea
and land breezes, seriously endangering human health and ecosystems
(Dalsøren
et al., 2007; Endresen et al., 2007; Collins et al., 2009; Eyring et al.,
2010; Fan et al., 2016). Ship emissions have direct and indirect effects on
the radiation balance of the atmosphere. For example, O3 chemically
produced by NOx from ship emissions can have positive radiative
effects, while SO2 emissions have a negative radiative effect, and the
sulfate produced by its conversion also has a negative radiative effect at
the Earth's surface
(Endresen, 2003;
Eyring et al., 2007, 2010; Lauer et al., 2007).
According to the World Shipping Council (https://www.worldshipping.org/,
last access: 14 May 2023), China possesses seven of the world's top 10
biggest ports. The large number of ships in the ports has exacerbated air
pollution in the local and surrounding areas and increased traffic in the
connected rivers. Ship emissions in the Yangzte River Delta (YRD) are much higher than those in
the Bohai Bay and Pearl River Delta (PRD), reaching about 50 % of the
total emissions in these three regions
(Chen
et al., 2017; Wan et al., 2020). Shipping emissions affecting air quality in
the YRD region are mainly within 12 nmi of the coastline
(Li et al., 2018). They can
contribute between 30 % and 90 %: for example, over 75 % of
ship-related SO2 concentrations and 50 % of ship-related PM2.5
concentrations.
(Lv
et al., 2018; Feng et al., 2019). The data from the Ministry of Transport of
China show that by the end of 2021, the number of inland river transport
vessels was 11.36 million, which is higher than the sum of coastal
transport and ocean transport in China (Ministry of Transport; MOT, 2022). As one of the
most economically developed regions in the east of China, the YRD region is
the busiest inland river ship transportation corridor in China. Therefore,
we focus on inland river ships in the Jiangsu section of the Yangtze River
as an important area for China to investigate the contribution of inland
river ships to air pollution.
Methods of creating ship emission inventories have been developed and
improved over the last decades, from the early method of calculating
emissions based on ship fuel-consumption data
(Streets,
1997; Corbett et al., 1999; Streets et al., 2000; Endresen, 2003; Endresen
et al., 2005, 2007; Psaraftis and Kontovas, 2009; Trozzi, 2010) to the
method of calculating emissions based on high-temporal-resolution ship
navigation data provided by the automatic identification system (AIS).
Wang et al. (2008) pointed out that regional emissions
obtained from the fuel-consumption method are often underestimated. For
example, in North America, Europe and other regions, the values were only
20 %–70 % of the regional emissions established from the AIS method.
Because AIS data include detailed information of real-time ship speed,
direction, location and many other parameters, it allows for accurate
calculation of pollution emitted by ships.
Jalkanen et al. (2009,
2012) first developed the Ship Traffic Emission Assessment Model (STEAM)
using the AIS to study the effects of ship emissions on regional air
quality. Nowadays, the AIS method has been applied to many maritime regions,
such as major European seas, seas around Australia and other countries
(Jalkanen
et al., 2009, 2012, 2016; Kalli et al., 2013; Goldsworthy and Goldsworthy,
2015; Jonson et al., 2015; Johansson, 2017;
Dragović et al., 2018). Georgoulias et al. (2020)
first combined observations from the TROPOspheric Monitoring Instrument
on board the Sentinel 5 Precursor satellite (TROPOMI/S5P) with AIS data to
measure NO2 plumes that could be detected and attributed to individual
ships. Studies based on the method of combining satellite data with AIS data
have been carried out mostly over seas
(Kurchaba
et al., 2022; Riess et al., 2022) but seldom over rivers.
Studies have also been conducted on ship emissions in Chinese ports such as
the ports of Shanghai (Yang et al., 2007), Tianjin
(D. Chen et al., 2016), Qingdao
(Liu et al., 2011), Hong Kong
(Ng et al., 2013), Xiamen
(Wang et al., 2020) and Shenzhen (Yang et al., 2015).
These studies were mainly based on fuel-consumption methods. Currently,
studies based on AIS methods to establish ship inventories are gradually
being carried out in China because the accuracy of this approach is higher
than that for the fuel-based method. These studies are focusing on shipping
emissions at coastal regions and ports
(Song,
2014; Fan et al., 2016; Li et al., 2016; Huang et al., 2020) because the
Ministry of Transport in China started the control of ship emissions in 2016
(MOT, 2015). Domestic emission control areas (DECAs) were set up in the
waters of Bohai Bay, YRD and PRD to control, for example, the sulfur content
of the fuel. In addition, equipment with AIS is becoming a mandatory
requirement for ships of 100 gross tonnes and above. By the end of 2018, DECAs
were expanded from the major ports to 12 nmi outside the
coastline. The range of the Yangtze River covered by the DECAs was also
raised from the original offshore cities to stretch from Shuifu in Yunnan to
Liuhekou in Jiangsu (MOT, 2018). Wang et al. (2021) presented a detailed
timeline of the control policies, with the DECAs 1.0 policy starting in
2017 and DECAs 2.0 in 2019, while for river vessels, the fuel sulfur content
had to be gradually reduced to 10 ppm in the last half of 2017.
Wang et al. (2021) showed that the latest control policies have been effective in
reducing ship emissions of SO2 and PM, especially after 2018, when the
DECAs 2.0 control policy became effective. The scenario study of the DECAs
policies (Li et al., 2018)
showed that it will take several decades to reach a similar reduction for
NOx emissions compared to SO2 and PM, and this requires
significant technological changes for ships. However,
Li
et al. (2016) and Weng et al. (2020) concluded that studies based on AIS
methods over inland river ship emissions showed a higher uncertainty than
for coastal and ocean-going ships, especially for SO2 and PM10
emissions.
The total quantity of air pollutants emitted by inland river vessels in the
Yangtze River is higher than those emitted by seaport vessels in the Jiangsu
province (Xu et al., 2019; Zhu et al., 2019). In contrast to
the studies for ships on seas and in ports, studies on inland river vessel
emissions are still limited. Normally, those studies use estimations for the
engine power and the maximum design speed. For example, Zhu
et al. (2017) assumed that ship length is related to ship tonnage and power.
However, these methods require a large amount of initial ship data, which are
often difficult to obtain. Moreover, the ship emission factors for the
Jiangsu section of the Yangtze River used in the various studies show
significant differences. Their emission factors and the main engine fuel
adjustment factors are usually based on the international literature. Their
applicability in China, and in Jiangsu province, needs to be
validated. In addition, current studies of inland ship emissions have yet to
explore the relative contribution of ship emissions to air pollution.
The map of the study area. (a) Map of the Yangtze River. (b)
Schematic map of the Jiangsu section of the Yangtze River. (c) Ship
locations that were received by AIS in a 4 h interval shown on a grid
with a resolution of 0.1∘× 0.1∘ . The black
dots are the ship locations received by AIS, which demonstrates the range of
the AIS receiver. The blue box is the selected observation area with a
longitude between 118.65 and 118.95∘ E and latitude
between 32.05 and 32.25∘ N.
For the missing information on engine power in AIS data, our study proposes
a method to reduce the difficulty in deriving emissions based on parameters
directly provided by AIS. We set up an AIS receiver in Nanjing University of
Information Science and Technology (NUIST) to collect ship information
including ship name, ship type, position, length, speed and heading. Because
of the large number of inland river ships in China and the limited range of
the antenna detection, we propose a method based on the length of the river
per grid cell to extend the emission estimates to larger regions.
In this study, we compile a ship emission inventory of NOx, SO2,
PM10 and PM2.5 for the Jiangsu section of the Yangtze River using
a bottom-up approach based on 1 year of AIS data from September 2018 to
August 2019. We calculate the emission of each vessel at regular intervals
(usually a few seconds) and sum up all emissions of the 588 591 ships'
movement during 1 year. Emission characteristics such as ship-type dependency, monthly variation and spatial distribution will be discussed. A
comparison with the Multi-resolution Emission Inventory for China (MEIC)
model, Shipping Emission Inventory Model (SEIM) and the Daily Emissions
Constrained by Satellite Observations (DECSO) method is performed to check
the relative contribution of ship emissions to the total emissions. These
results are important for the policy-makers to formulate and evaluate
emission reduction policies and for ship companies in choosing the best
emission reduction measures.
Number of ships per day. The shaded gray area represents the
Spring Festival period. The red dots indicate days with strongly reduced
ship numbers.
MethodologyAIS observations
The automatic identification system (AIS) is transponder technology on board ships to enhance safety by broadcasting ship information via VHF (very
high-frequency) channels. It works in conjunction with the Global
Positioning System (GPS) to broadcast information such as ship position,
speed and heading, together with static ship information such as ship name,
ship length and ship type. Each AIS-equipped vessel is identified by a
unique Maritime Mobile Service Identity (MMSI) number, which is also part of
the AIS data.
We have set up an observation location in Nanjing, where an AIS receiver is
located on the roof of the meteorological building in NUIST. Ships transmit
AIS signals at intervals varying from every 3 s to a few minutes to
provide information on their position. The black dots in Fig. 1c are an
example of the locations of ships according to the AIS signals received in a
time interval of 4 h. We see that the antenna can receive signals within
at least a 50 km radius. A region with a longitude of less than
118.95∘ E and a latitude of more than 32.05∘ N was
selected as the area where all ships, including those with weak AIS
transmitters, can be tracked under all conditions. AIS data were collected for
365 d from September 2018 to August 2019. Based on the AIS information,
ships were classified into seven types: cargo ships, tankers, passenger
ships, tugboats, dredgers, patrol vessels and others. Cargo ships include
container ships, bulk carriers and ro-ro (roll-on/roll-off) vessels (excluding
passenger ships); tankers comprise liquid chemical tankers, liquefied gas
tankers and oil tankers; and passenger ships consist of ferries and ro-ro
passenger ships. Patrol vessels are classified separately because of their
small size in combination with high speed.
The AIS data show that approximately 2000 boats pass through the
observation area each day, with a drop in the number of ships on specific
days. Figure 2 shows the number of unique ships per day over a year. The
gray box in Fig. 2 shows the Chinese New Year period when the number of
vessels dropped sharply. The decrease in the number of ships on other days
is usually related to the weather conditions. On 26 November 2018, very
thick fog occurred, and the travel of both ships and vehicles was disrupted.
On 24 February 2019, the local meteorological bureau issued a fog warning,
so the number of passing vessels decreased. On 10 August 2019, typhoon
Lekima made landfall and brought catastrophic damage to southeastern China. The
number of ships coming from the typhoon region was greatly reduced, as was
the number of local vessels in Nanjing. On 5 January 2019, the Jiangsu
Maritime Bureau issued a navigational warning for construction work in the
Baguazhou branch of the Yangtze River in Nanjing.
Ship emission estimation
To estimate shipping emissions from the Yangtze River, we adopt the AIS
method to obtain high-resolution ship information and emission factors.
Equation (1) is used for calculating the emissions of various pollutants
such as NOx, SO2, PM2.5 and PM10 from a single ship
based on its main power
(Jalkanen et al., 2012). We only
considered the emissions of the main engine of the ship while ignoring the
relatively lower emissions from the auxiliary engines and boilers.
E=P⋅L⋅fEF⋅fLLAM⋅fF⋅fC⋅T,
where
E is the emission from the main ship engine,
P is the main engine power of the ship (See Sect. 2.2.1),
L is the main engine load factor (See Sect. 2.2.2),
fEF is the emission factor,
fLLAM is the low-load adjustment multiplier of the main engine,
fF is the fuel correction factor,
fC is the control factors for implementation of greener technology, and
T is the sailing time.
Below we will further discuss each element of this equation.
The emission factor (fEF) refers to the mass of pollutants emitted
per unit of work done by a single vessel in 1 h (g kW-1 h-1). It is an essential parameter for calculating ship emissions and
has not yet been studied for China. For example, the emission factors used
by Wan et al. (2020) for
Beijing–Tianjin–Hefei, the Yangtze River Delta and the Pearl River Delta were taken
from literature referring to Europe and the United States (USEPA, 2000;
Entec, 2002; EPA, 2009). Ship emission factors used by
Fan et al. (2016) for the Yangtze River Delta
and the East China Sea were based on an EPA report (2009) and on
Goldsworthy and Goldsworthy (2015), the latter
being a study for the Australian region. The emission factors used by
Fu et al. (2012) for the Shanghai port were taken from the air
pollutant emission inventory of the Port of Los Angeles (Archana et
al., 2013). Lacking studies of ships of the Chinese fleet, our study also
relies on emission factors for other regions, following the scheme in Table 1.
The low-load adjustment factor (fLLAM) is used when the main engine
load factor is less than 0.2. When the load factor is below 0.2, there is an
increase in emission intensity due to the inefficient engine use at low
speed. Hence, the emissions from low-load vessels were multiplied by the
low-load adjustment factor (EPA, 2009). The low-load adjustment
multipliers for the main engine are shown in Table S1.
The fuel correction factor (fF) corrects the emission factors
according to sulfur content of the fuel used by the ships. Ordinary inland
vessels typically use general diesel oil (GDO), while larger inland ships
and direct river and sea vessels use marine fuel oil (MFO). MFO includes
residual oil (RO) and marine distillates (MD). The sulfur content
requirements for various fuels are shown in Table S2. The emission factors
mentioned above are obtained based on the assumption that marine fuel is
heavy oil with a sulfur content of 2.7 % (EPA, 2009). In October 2018,
the DECAs began responding to a new policy that the sulfur content of inland
marine fuel cannot be higher than 0.001 % in 2019
(Wang et al. 2021). In our
study, the actual inland ship emissions are calculated based on the sulfur
content of 0.001 %, for which a fuel correction factor is needed. In
addition, we will calculate ship emissions for an average fuel sulfur
content of 1.5 % (Xu et al., 2019) for a scenario where the new policy
would be absent. The fuel correction factor fF for each air pollutant
is shown in Table 2.
The control factor fC is to potentially correct for higher-quality
marine diesel engines or the availability of emission reduction facilities
on the ships as a result of emission control policy. There is currently
limited legislation to control ship emissions in inland waterway zones in
China and insufficient monitoring infrastructure for enforcement; therefore
the control factor is set to 1.
Emission factor (fEF) in g (kWh)-1 of
the main engine reported in the literature. (SSD is slow-speed diesel, MSD is
medium-speed diesel, and ≤ 1999, 2000–2010 and 2011–2015 are the years when the
ship was built.)
Engine typeNOxSO2PM2.5PM10ReferenceSSD≤1999: 18.12000–2010: 17.010.50.961.05Entec (2002)2011–2015: 15.317.010.51.21.5Archana et al. (2013)18.110.31.221.378EPA (2009)MSD≤1999: 14.02000–2010: 13.011.51.021.11Entec (2002)2011–2015: 11.213.011.51.21.5Archana et al. (2013)14.011.311.221.193EPA (2009)HSD12.711.310.500.650EPA (2009)
Fuel correction factor (fF) used in this
study.
Fuel typeSulfurNOxSO2PM2.5PM10content (%)Unregulated1.5 %0.90.560.470.47marine fuelRegulated0.001 %0.90.00050.00070.0007marine fuelEngine power
Since engine power is missing in the AIS data, we develop a method to
relate the engine power to the ship type, length and speed. Those parameters
are available in the AIS data, unlike the engine power.
The supplied power by ship engines (Pship) for transport should be at
least equal to the dragging force (Fdrag) of the water resistance
multiplied by the speed (v) of the ship. According to fluid dynamics, the
resistance force is proportional to the area (As) experiencing the water
resistance multiplied by the speed squared. Hence, the supplied power of a
ship engine is proportional to the area times the cube of the speed:
Pship∼Asv3. This is known as the propeller law
(Theotokatos and
Tzelepis, 2015).
For each ship type, the length–width–height dimension ratios of the boats
are not expected to vary much, especially because ship design is focused on
efficiency, as fuel is a dominant cost in shipping. Logically, ship designs
are expected to be relatively similar. Hence, the ship area As (width
times height) is to the first order expected to be proportional to the
square of the ship length (As∼Ls2). Therefore, we
assume the relation of engine power of a ship with its length and its speed
to be P∼Ls2v3. The China Classification Society (CCS;
https://www.ccs.org.cn/ccswz/, last access: 14 May 2023) database of
Chinese domestic ships provides data such as ship type, main engine power,
maximum ship designed speed, ship length and construction year of the ship.
Using these ship parameters, we can derive the average regression
relationship for each category of ship by linear fitting of this proxy (P∼Ls2v3). The fitted linear relation between
Ls2v3 and the engine power P of the main engine is shown in
Table 3 for each ship category. Figure S1 shows the fitted linear
relationship between main engine power at full speed and the proxy
Ls2v3 at maximum speed vmax.
Regression analysis between main engine power and the square of the
vessel's length multiplied by the cube of the real-time speed. (The slope
refers to the ratio between power (Pmax) and
L2vmax3.) When the tug's length is more than 40 m, the
power of the main engine is assumed to be constant (1800 kW).
The load factor L reflects the actual output power of the main engine as a
percentage of the rated main engine power. The ship's main engine works
according to the characteristics of a propeller, and the engine load factor
is estimated using the propeller law, which means that the main engine load
varies as the third power of the ratio of the ship's actual speed to the
ship's designed speed. So the load factor is calculated as
L=vvmax3,
where v is the actual sailing speed of the ship and vmax is the maximum
speed of the ship using its full power.
For inland waterway ships, the maximum designed speed is often unknown and
cannot be obtained directly from AIS data. To obtain a maximum sailing speed
per ship type we used the median of all maximum speeds (per selected ship
type) registered in the CCS ship database (see Table 4).
Maximum speed per ship category. (The maximum speed value is based
on the statistics of the maximum speed of about 1900 different river
vessels.)
CargoTankerPassengerTugDredgerPatrolOthersvmax (km h-1)15.3312.0014.3013.3510.7619.8012.80Correction of ship velocity for river flow
From the previous equations for calculating ship emissions, we see that the
ship's real-time speed is critical. The speed related to the delivered power
by the ship is the speed relative to the water, rather than the ship speed
over ground, which is given in the AIS signal. Therefore, a correction for
the vessel speed is required.
Assuming that the ships use similar engine power going upriver or downriver,
the average speed difference between ships going downstream and upstream has
been used to derive the speed of the water flow. Then the actual speed of a
ship is obtained by correcting the speed with the actual river flow. When
the obtained speed for an individual ship is negative, this ship is assumed
to be at anchor, and the sailing speed of the ship is set to 0. Figure 3
shows the derived daily average speed of the river flow during a year. It is
clear from Fig. 3 that the water speed is higher in summer than in winter,
because of the difference in rain and melting water over the year. This is
confirmed by a hydrological study of J. Chen et al. (2016) concluding that monthly precipitation
and discharge in the Yangtze River basin are highest in July and August,
with January, February and December being the lowest months. The quick
change in river flow on 10 August 2019 happened during the landfall of
typhoon Leikma. The actual ship speed after correction for water speed tends
to be rather constant throughout the year. The actual engine power is
calculated using the corrected speed as discussed above.
It is worth noting that there are some limitations of our method. For
example, some ships would reduce their speed when going downstream, or some
increase their speed when going upstream to compensate for the actual speed.
In this case, our method may slightly underestimate the river's speed,
especially in July and August.
Average daily river speed (red line) and ship speed over a year.
Upriver speed and downriver speed of the ships come directly from
AIS data. The river speed is based on the average difference between
downstream speed and upstream speed.
Emission inventories for the Yangtze River Delta
To evaluate the emissions derived using our method, we compared them to
three other emission inventories in the YRD, including the satellite-derived
NOx inventory DECSO (Daily Emission derived Constrained by Satellite
Observations) and the following bottom-up inventories for East Asia: MEIC
(Multi-resolution Emission Inventory for China) and SEIM (Shipping Emission
Inventory Model).
MEIC
The Multi-resolution Emission Inventory for China (MEIC;
http://meicmodel.org/, last access: 14 May 2023), developed by Tsinghua
University, is an emission inventory of air pollutants from anthropogenic
sources in China with a spatial resolution of 0.25∘. Emissions of
NOx, SO2, PM10, PM2.5, CO2, OC and BC are
calculated for four sectors from 2008 to 2017: energy, industry, transport
and residential. For road motor vehicle emission sources, MEIC uses an
emission characterization model that includes parameters such as
temperature and humidity, and it combines meteorological fields with motor vehicle
emission factor models to construct a high-resolution dynamic motor vehicle
emission inventory. However, the transport sector of MEIC does not include
ship emissions (Zheng et al.,
2014). Li et al. (2017) and
Zheng et al. (2018) presented more details
of the latest version MEIC v1.3. Here, the MEIC v1.3 for 2017 is used.
SEIM
The Shipping Emission Inventory Model (SEIM) developed by Tsinghua
University has been used to construct the East Asia ship emission inventory.
The model has been developed based on high-precision AIS information of
ocean-going ships and encompassing worldwide international fleet activity.
It provides gridded annual ship emission data with a 0.1∘ spatial
resolution for the seas in the East Asia region in 2017, covering SO2,
NOx, CO, VOC, PM2.5, OC and BC, a total of seven species
(Liu et al., 2016, 2019). However, emissions over
inland rivers, except for the delta region, are not included. Emissions from
ocean-going vessels, coastal vessels and river vessels were calculated in
the latest version of SEIM (SEIM v2.0)
(Wang et al.,
2021). Here, we use SEIM v1.0, since only this version is publicly
available.
DECSO
Daily Emission estimates Constrained by Satellite Observations (DECSO) is an
inverse modeling method to update daily emissions of NOx based on an
extended Kalman filter (Mijling and van der A, 2012).
NOx emissions are constrained by combining simulated NO2 column
concentrations of a regional chemical transport model (CTM) with satellite
observations. The latest version is referred to as DECSO v6.1 and has a
spatial resolution of 0.1∘ using NO2 observations from
TROPOMI (TROPOspheric Monitoring Instrument). In addition, the algorithm
captures the seasonality of NOx emissions and reveals the trajectory of
ships near the Chinese coast (Ding et
al., 2018). DECSO provides monthly emissions with a maximum error of
approximately 20 % for each grid cell. We selected NOx emissions in
the Yangtze River Delta region for 2019, the earliest year that is available
from DECSO for this region.
Inland ship emissions
We calculated the emissions of NOx, SO2, PM10 and PM2.5
for the area considered (118.65–118.95∘ E and
32.05–32.25∘ N; the purple box in Fig. 1) from
September 2018 to August 2019 based on AIS data. The total ship emissions of
NOx, SO2, PM10 and PM2.5 in this area are 6679 and
2.98, 0.43 and 0.39 t yr-1, respectively.
Yearly emissions per ship category
We analyzed the collected AIS data from September 2018 to August 2019 and
concluded that the number of ships for each defined type was stable
throughout the time period. Detailed data on the number of vessels per month
are listed in Table S3. Figure 4 shows the contribution of NOx,
SO2, PM10 and PM2.5 from different ship types. Emissions from
cargo ships are higher than for other types of ships, followed by tankers
and tugs, with dredgers and patrol boats making the lowest contribution to
the emissions. Cargo ships contribute more than 58 % of the ship
emissions of all species in the Nanjing section of the Yangtze River. This
is because cargo ships are the dominant vessel type in this region and their
number accounts for about 81 % of the total.
Dividing the total emissions from each ship type by the number of ships of
that type, we obtained and analyzed average ship emissions per ship type. For
a single vessel, tugs emit more pollutants (Fig. S2). This is related to the
higher power needed for the engine of a tugboat, which was also concluded by
Xu and Bai (2017). Compared with cargo ships, tankers have a
relatively higher contribution to SO2. The sulfur content of tanker
fuel is slightly higher than the sulfur content of diesel fuel for cargo
ships (Zhu et al., 2019). Because SO2 emissions are directly
related to the sulfur content of fuel oil, tankers will emit more SO2.
Emissions from different ship categories.
Number of ships and NOx emissions for the various ship
categories.
Ship numberShareRankNOx emissionsShareRank(kt yr-1)Cargo571 01381.0 %14.364.4 %1Tanker96 88813.7 %21.015.5 %2Tug12 1281.7 %30.913.9 %3Others10 9321.5 %40.34.8 %4Passenger79711.1 %50.0270.41 %6Patrol56520.8 %60.0570.86 %5Dredger3460.05 %70.0010.16 %7Monthly variation of ship emissions in the observational area
We calculated the monthly emissions from the AIS data in the observational
area, as is shown in Table 6. Since the same calculation method is adopted
for all four pollutants, their spatial and temporal variations are
consistent. Figure 5 shows the monthly variations of NOx emissions from
ships from September 2018 to August 2019. We see that, except for the sharp
decrease in the number of ships in February, the number in other months has
basically stabilized at around 60 000. The monthly emissions are highly
related to the number of ships and the speed of ships.
Ship emissions peaked in July followed by June and August. In July, the
emissions are the highest due to the high river speed, and therefore more
engine power (proportional to v3) is needed for upstream ships. From
November to February, the emissions were lower, possibly because the
water flow of the Yangtze River is lower at this period and ship activities
during Spring Festival are reduced. In February, the number of ships dropped
sharply, but the emissions from ships in February were similar to that from
ships in January. This is due to the higher activity of tugs in February,
which have larger engines and emit higher levels of emissions (Fig. S3).
From February to March, we see that pollutant emissions increased due to the
increase in the number of ships. This is closely related to the resumption
of factory work and human activities after the holiday.
Ship emissions (t yr-1) in our study region around Nanjing
from September 2018 to August 2019.
Monthly changes in the emissions from inland river ships. The gray
bars show the number of ships per month.
Spatial distribution of NOx emissionsSpatial distribution of NOx emissions in the observational area
The spatial distributions of emissions from different air pollutants are the
same as they are based on the shape of the river. Here we choose NOx as
an example to present our results. Figure 6 shows the calculated ship
emissions with a spatial resolution of 0.01∘ in the observational
area, not only over the main channel but also over some very small branches
of the Yangtze River, such as the Chu River and Ma Cha River. The
observational area also includes a slightly wider branch of the Yangtze
River, known as the Jia Jiang River. On these branches, ship emissions are
much lower than from ships on the main Yangtze River. The Yangtze River is
around 2–3 km wide.
Spatial distribution of ship emissions in the observation area at a
resolution of 0.01∘× 0.01∘.
Spatial distribution of NOx emissions in the YRD region
Due to the limits of the observation area, we propose a new river-length-based method to estimate ship pollution in a larger region. Emissions
in inventories are often presented per grid cell. Figure 1 shows that the
river width is much smaller than the size of a grid cell, and emissions from
ships are more closely related to river length per grid cell than river
width. Therefore, inland river ship emissions can be seen as line sources,
assigning pollution to each kilometer of the river. First, we calculate the
emissions per kilometer length of the river. Once we have this quantity, we can
calculate the ship emissions per grid cell for any part of the river based
on the length of the river in that particular grid cell. For parts of the
Yangtze River that have parallel branches, the ship emissions are distributed
by the corresponding proportion of the number of ships per branch.
We allocated the total ship emissions Eall in the observational area to
the length Lriver of the mainstream river only:
Eall/Lriver. The length of the main channel of the observational area
can be inferred by analyzing the distance traveled by ships in downstream
and upstream directions (Fig. S4) using the location, speed and time of the
ships from the AIS data. We only calculated the distance for vessels that
have fully passed through the mainstream channel with a speed of more than 3 kn downstream and more than 1 kn upstream for the entire journey. The
downstream side of the river is approximately 30 km and the upstream side is
around 32 km. The average length of the main channel was calculated to be
30.9 km, weighted by ships navigating in both directions. We can now obtain
the ship emissions within each grid cell by multiplying the river's length
within the grid cell by the calculated emissions per kilometer of the river:
Eall/Lriver (kt yr-1 km-1). The Jia Jiang River has
been ignored in this calculation, since only 0.04 % of all ships pass this
side river.
According to the above method, we extrapolate our emissions per kilometer to the
river outside our study area assuming that the ship density and speed are
rather constant in this region. Figure 7 shows the spatial distribution of
ship emissions in the Jiangsu section of the Yangtze River with a spatial
resolution of 0.1∘. The spatial distribution of our ship emissions
depends only on the length of the river within the grid cell.
Spatial distribution of NOx emissions in the Jiangsu section
of the Yangtze River.
Uncertainty
In this section we will discuss the uncertainty in our emission inventory.
Our calculations have been based on the main engine only. However, during
the navigation of a ship, the main engine and auxiliary engine of the ship
are working at the same time. For a moored ship, the main engine stops
working. On average, 17 % of the ships in the observational area are in
dock every day, and this part of the ship emissions has been not taken into
account, but the auxiliary engines are still working. Based on the study of
Weng et al. (2020) in the Yangtze estuary, we estimate that our emissions
show an underestimation of about 12 % because of ignoring the auxiliary
engine and boiler emissions at berth and underway.
However, the locations of high ship emissions are consistent with previous
studies. Zhu et al. (2019) pointed out that the distribution of ship
emissions in the Jiangsu section of the Yangtze River in 2017 was uneven,
with the emission rates in the Nanjing section of the Yangtze River and the
Jiangyin section of the Yangtze River being relatively high. Xu
et al. (2019) noted that for ports along the river, Nanjing port had the
highest rate of ship emissions.
Spatial distribution of ship emissions for JSEI and SEIM.
As the Yangtze River becomes wider when getting closer to the sea, the speed
of the river will be reduced, and thus the emissions from ships can be lower.
In the extreme cases of stagnant water, ship emissions can be reduced by a
maximum of 3 %–33 % depending on the month. We estimate that this may lead
to an overestimation of about 10 % in the ship emissions outside our
study area around Nanjing.
Currently, the AIS-based approach is considered the best practice for
ship inventories. However, there is still a lack of reliable local emission
factors, auxiliary engine power ratings and fuel correction factor in the
YRD region, which contributes largely to the uncertainties in this study.
The selection of accurate emission factors is critical to the calculation of
the ship emission inventory and the uncertainty that comes with it. The
emission factors are closely related to the age and rotation of the ship's
engine and engine load, and the fuel correction factor depends on
the sulfur content of the marine fuel. Earlier heavy oil was a fuel of low
quality with a sulfur content of about 2.7 %. In contrast, the fuel
sulfur content in this study is only 0.001 %, while at the beginning of
time period the fuel sulfur content may be as high as 1.5 %. For the
scenario that the sulfur content is not regulated, we have calculated that
the SO2 and PM emissions would be about a factor of 700–1000 higher.
In conclusion, our derived emissions have an underestimation of 12 % due
to ignoring the auxiliary engines and boilers and an overestimation in some
regions of about 10 % due to the slower river flow. Adding this to the
uncertainties in emission factors, we estimate the total uncertainty to be
5 %–15 %.
The total emissions in the selected river grid cells.
YearResolutionNOxShare(∘)(kt yr-1)(JSEI / inventory)JSEI20180.125.4100 %MEIC20170.2591.327.8 %DECSO20190.159.242.9 %Contribution of inland ship emissions relative to emissions from other
sources
We constructed a Jiangsu Ship Emission Inventory (JSEI) over the Jiangsu
section of the Yangtze River (118.5–121∘ E and
31.5–32.5∘ N) at different resolutions to compare it
with other regional inventories. For the comparison and calculation of the
contribution of ship emissions to the total emissions of air pollutants
along the river, we only selected the emissions from the grid cells
containing the Yangtze River.
Spatial distribution of the contribution of the river emissions
(JSEI) to the total emissions (MEIC+JSEI) for NOx, SO2,
PM2.5 and PM10. Panels (a), (b), (c) and (d) are based on a scenario where
there is no sulfur content regulation. Panels (e), (f), (g) and (h) are based on the
actual situation with a sulfur content of 0.001 %.
Spatial distribution of NOx emissions for JSEI of
0.25∘(a) and 0.1∘(c) resolution, MEIC (b) and DECSO (d).
A comparison of the ship emission inventories
To verify the calculated emissions of JSEI, we compared them with the ship
emissions of SEIM for the overlapping region. Figure 8 shows the spatial
distribution of the ship emissions of NOx, SO2 and PM2.5 for
JSEI and SEIM and the ratio of JSEI to SEIM. JSEI and SEIM only calculate
ship emissions, and they overlap for less than half of the domain. When
comparing the overlapping grid cells, JSEI accounts for on average about 99 %, 0.05 % and 0.06 % of the SEIM emissions for NOx, SO2
and PM2.5, respectively. The average emissions from inland ships over
rivers (JSEI) compare well with average emissions of sea-going ships (SEIM)
for NOx. SEIM has higher values than JSEI for SO2 and PM2.5 because SEIM calculated the emissions for 2017, when only major ports
needed to strictly control the sulfur content of marine fuel. The sulfur
content of marine fuel was 0.001 % in our study. In comparison, the
sulfur content of ocean-going marine fuel in 2017 was about 2.7 %, much
higher than that of inland river ship fuel. Ship pollutants that are greatly
affected by the sulfur content of marine fuel, such as SO2 and PM, will
be reduced with the reduction of sulfur content. This shows that from 2017
to 2019, the policy was of great significance for ship emissions,
effectively reducing the emissions of SO2 and PM. For NOx
emissions, both inventories compare remarkably well.
Spatial distribution of the contribution of ship emissions (JSEI)
to the total emissions (DECSO) for NOx at a 0.1×0.1
resolution. The blue box is the AIS observation area. The red box represents
the section of the river with a broader width.
Monthly contribution of NOx ship emissions to the total
emissions (MEIC + JSEI, DECSO) for the grid cells including the river. The
resolution of JSEI / (MEIC + JSEI) is 0.25∘, and the resolution of
JSEI/(DECSO) is 0.1∘.
Contribution of ship emissions compared to MEIC and DECSO
Here JSEI is added to the MEIC to represent the value of total air
pollutants, as the MEIC does not include emissions from ships.
Figure 9 shows the spatial distribution of the contribution of the river
emissions (JSEI) over the total emissions (MEIC+JSEI) for NOx,
SO2, PM2.5 and PM10 along the river. SO2 ship emissions
along the river account for an average of 0.14 % of total SO2
emissions and only 0.82 % in the highest areas. PM emissions from ships
along the river contribute on average 0.01 % of the total PM emissions.
Even in a scenario where there is no policy regulation, PM emissions from
ships account for only a relatively low proportion of total PM, with a
maximum of 20 % and an average of 5 % in riverine areas, with
PM2.5 accounting for a slightly higher proportion than PM10. In
this scenario, SO2 emissions from ships contribute around 40 % of
the total SO2 pollution on average, while it can reach 83 % in some
areas. Under the regulation of the new policy, the SO2 and PM emitted
from ships are greatly reduced. Therefore, it is important to set up control
zones for sulfur emissions from ships.
We integrated JSEI to 0.1∘ resolution and 0.25∘
resolution to compare with DECSO and MEIC, respectively. Because DECSO only
covers NOx emissions, we focus on the contribution of NOx
emissions from ships to the total emissions. Figure 10 shows the spatial
distribution of NOx emissions derived from JSEI, MEIC and DECSO in the
Jiangsu section of the Yangtze River. MEIC does not include ship emissions
and has a coarser resolution than the other inventories.
When compared to DECSO, NOx emissions from ships account for 6.1 %–74.5 % of the total emissions (Fig. 11). From Fig. 11, we see that the width
of the Yangtze River varies from region to region. The width of the Yangtze
River increases significantly in Taizhou, Changzhou and the sections of the
river close to the estuary of the Yangtze River (red box), where the river
flow slows down. Because of the lower river speed, ship emissions in these
areas might be overestimated.
The JSEI NOx emissions contribute about 28 % of total NOx
emissions of MEIC in a region of 10–25 km around the river (Table 7).
Compared to DECSO, the JSEI accounts for 42.9 % of total NOx
emissions in the region of 5–10 km from the river. The MEIC grid cells are
larger than the DECSO grid cells and therefore include more emissions than
for DECSO, so the share of ship emissions for MEIC is nearly half of that
for DECSO. Even on the coarser grid of MEIC, the comparison shows that ship
emissions from riverine areas account for at least 25 % of the total
emissions, so ship emissions should not be ignored in NOx emission
inventories. When comparing the total ship emissions with the total
non-vessel anthropogenic emissions in Jiangsu province in 2017, NOx
emissions from ships still account for 5 % of the total non-vessel
anthropogenic NOx in Jiangsu province given by the MEIC
(Table S4).
Comparison of the monthly emissions in the selected river grid cells
Because SEIM is an annual emission inventory, we only compare the monthly
variation of JSEI with MEIC and DECSO. Since the ship emissions for all
species show similar monthly variability, we focus here only on NOx. We
see that the share of NOx emissions from ships in total NOx
regardless of the inventory is highest in the summer (Fig. 12). For the
MEIC, NOx emissions from ships can account for 17 %–28 % of the NOx total emissions, and for DECSO, NOx emissions from
ships account for 29 %–57 % of the NOx total emissions. The
JSEI / (MEIC + JSEI) ratio tends to coincide with the monthly variation in
ship emissions, as there is no significant monthly variation in emissions of
the MEIC. For DECSO, ship emissions accounted for more than 40 % of the total emissions in February, which shows that the pollution
caused by ship activities during the Spring Festival is quite significant in a
time period of lower emissions in general.
Conclusions
Ship emissions are calculated per ship based on the real-time information
reported by AIS. Since the AIS information is in general insufficient to
support emission calculations, we propose a method to link the engine power
and maximum speed to vessel type, length and speed to effectively supplement
the missing ship data. In addition, we have presented a method using the
river length per grid cell to extrapolate ship emissions along the river.
Based on this method, we have compiled a ship emission inventory with a
resolution of 0.1∘ in the Jiangsu section of the Yangtze River.
The results show that the total ship emissions of NOx, SO2,
PM10 and PM2.5 in the Jiangsu section of the Yangtze River from
September 2018 to August 2019 were 83.5, 0.04, 0.006 and 0.005 kt,
respectively. Cargo ships are the largest emitting ship type followed by
tankers and tugs. These three ship types account for 93 % of the total
emissions. Tugs have the highest single-ship emissions. The monthly ship
emissions are higher in the summer and lower in the winter, especially
during the Spring Festival. Ship emissions are mainly concentrated in the
main channel of the Yangtze River. Ship emissions in the side rivers are
minimal, accounting for only 0.1 % of the total emissions, which is
negligible.
Under the regulation of the sulfur content policy starting in 2019, the
SO2 and PM emitted from ships are strongly reduced. Atmospheric
emissions from ships are dominated by NOx. Vessel traffic in the
observation area is important because NOx emissions account for 46 %
of the total NOx emissions compared to the DECSO inventory (grid of
about 10×10 km). Our NOx results are similar to the SEIM
inventory, up to 99 %, which shows the credibility of the results. Even
if compared with the coarse resolution of the MEIC (grid of about
25×25 km), the emissions from vessels along the Yangtze River
(118.5–121∘ E and 31.5–32.5∘ N)
account for approximately 28 % of the total NOx emissions,
respectively. Compared with the DECSO inventory (grid of about 10×10 km) based on satellite data, NOx emissions from ships along the
Yangtze River account for 60 % of the total NOx emissions, which
indicates that NOx along the river mainly comes from ship emissions,
which are even higher than the emissions from power plants or
industries. Our study indicates that riverine NOx ship emissions
contribute significantly to air pollution. Riverine ship emissions can
adversely affect the health of people living along the Yangtze River and
have a negative impact on ecosystem, biodiversity and eutrophication.
In the future, the ship emission inventory can be applied in a chemical
transport model to explore in detail the impact of inland river ship
emissions on air pollution of cities along the Yangtze River. The
reliability of the air quality results can be verified with observations
from existing in situ stations or ground-based differential optical
absorption spectroscopy (DOAS) observations along the river (Cheng et al.,
2019; Krause et al., 2021). This can provide a monitoring tool for policies
such as the regulation of emission control standards for ships.
Data availability
The ship databases were obtained from https://www.ccs.org.cn/ccswz/ (CCS, 2014). The Multi-resolution Emission Inventory for China version 1.3 (MEIC v1.3) is available from http://meicmodel.org/ (MEIC Team, 2012). The Shipping Emission Inventory Model (SEIM) is from http://meicmodel.org.cn/?page_id=1770 (Liu et al., 2021). Daily Emission estimates Constrained by Satellite Observations version 6.1 (DECSO v6.1) are published on
https://www.temis.nl/emissions/data.php (Ding et al., 2020). The monthly Jiangsu Ship
Emission Inventory (JSEI) can be download from
https://www.temis.nl/emissions/region_asia/datapage.php (Zhang et al., 2023).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-5587-2023-supplement.
Author contributions
RvdA and YY planned the campaign; XZ and RvdA performed the
measurements; XZ provided the ship data; XZ, RvdA and JD
analyzed the data; XZ wrote the paper; and all authors provided input
on the paper for revision before submission.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank the China Classification Society and Nanjing
Maritime Bureau for their assistance. This research has received funding
from the National Natural Science Foundation of China (grant no. 42075176)
and the program of China Scholarship Council (no. 202109040001).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 42075176) and the China Scholarship Council (grant no. 202109040001).
Review statement
This paper was edited by Bryan N. Duncan and reviewed by Aristeidis Georgoulias and one anonymous referee.
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