ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-21-13835-2021Ship emissions around China under gradually promoted
control policies from 2016 to 2019Ship emissions around ChinaWangXiaotongYiWenLvZhaofengDengFanyuanZhengSongxinXuHailianZhaoJunchaoLiuHuanliu_env@tsinghua.edu.cnhttps://orcid.org/0000-0002-2217-0591HeKebinState Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, China
Ship emissions and coastal air pollution around China are expected to be
alleviated with the gradual implementation of ship domestic emission control area
(DECA) policies. However, a comprehensive post-assessment on the policy's
effectiveness is still lacking. This study developed a series of high-spatiotemporal ship emission inventories around China from 2016 to 2019 based
on an updated Ship Emission Inventory Model (SEIM v2.0) and analyzed the
interannual changes in emissions under the influence of both ship activity
increases and gradually promoted policies. In this model, NOx,
SO2, PM and HC emissions from ships in China's inland rivers and the
200 Nm (nautical miles) coastal zone were estimated every day with a spatial resolution
of 0.05∘×0.05∘ based on a combination of automatic
identification system (AIS) data and the Ship Technical Specifications
Database (STSD). The route restoration technology and classification of
ocean-going vessels (OGVs), coastal vessels (CVs) and river vessels (RVs) has
greatly improved our model in the spatial distribution of ship emissions. From
2016 to 2019, SO2 and PM emissions from ships decreased by
29.6 % and 26.4 %, respectively, while ship NOx
emissions increased by 13.0 %. Although the DECA 1.0 policy was implemented in 2017, it was not until 2019 when DECA 2.0 came into
effect that a significant emission reduction was achieved, e.g., a
year-on-year decrease of 33.3 %, regarding
SO2. Considering the potential emissions brought by the continuous
growth of maritime trade, however, an even larger SO2 emission
reduction effect of 39.8 % was achieved in these 4 years
compared with the scenario without switching to cleaner fuel. Containers and
bulk carriers are still the dominant contributors to ship emissions, and
newly built, large ships and ships using clean fuel oil account for an
increasingly large proportion of emission structures. A total of 4 years of consecutive
daily ship emissions were presented for major ports, which reflects the
influence of the step-by-step DECA policy on emissions in a timely manner and
may provide useful references for port observation experiments and local
policy making. In addition, the spatial distribution shows that a number of
ships detoured outside the scope of DECA 2.0 in 2019, perhaps to save costs on
more expensive low-sulfur oil, which would increase emissions in farther
maritime areas. The multiyear ship emission inventory provides high-quality
datasets for air-quality and dispersion modeling, as well as verifications
for in situ observation experiments, which may also guide further ship
emission control directions in China.
Introduction
Shipping is a significant anthropogenic source of air pollutants and greenhouse
gases and has come into the view of scientists and the public since the end of
the last century (Corbett and Fischbeck, 1997; Capaldo et al., 1999; Lawrence
and Crutzen, 1999). Air pollutants emitted from ships can be further
transported to inland areas by the onshore flow, along with atmospheric
chemical transformations, aggravating air pollution and endangering human
health (Endresen et al., 2003; Eyring et al., 2007, 2010;
Corbett et al., 2007). In recent decades, despite the improvement of global
fuel quality and engine posttreatment technology, shipping emissions have
continued to increase due to ever-growing maritime trade (IMO, 2020;
UNCTAD, 2019). Recent studies showed that global shipping emissions
constituted 3 % of anthropogenic CO2 emissions in 2017
(IMO, 2020) and much more proportions of reactive gases, e.g., 20 %
of NOx and 12 % of SO2 emissions (McDuffie et al.,
2020). China, as the world's largest maritime trading country and sitting on
seven of the world's top 10 ports with even more densely distributed coastal
ports, is meeting an even tougher challenge due to its lagging emission
control measurements compared to European and American countries (Mao and
Rutherford, 2018b).
In recent years, numerous researchers have attempted to quantify ship
emissions in China and evaluate their air-quality impacts. These studies
suggest that ship emissions of SO2 in China are nearly 5 times those
from road transportation (Chen et al., 2017a), and emissions within 12
nautical miles (Nm) account for ∼40% of the total
emissions from all ship emissions in coastal areas (Zhang et al., 2017; Li et
al., 2018). The influence of coastal ships on the annual average
PM2.5 concentration (>0.1µgm-3) can reach as far as 960 km inland
in China (Lv et al., 2018). Exhaust emissions from ships have contributed
significantly to air pollution in major port clusters, e.g., the Bohai Rim
Area (BRA), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD)
regions, and the maximum increase in annual PM2.5 concentrations has
reached ∼ 2–5 µgm-3, with the greatest impact on the YRD
region (Chen et al., 2018; Liu et al., 2018; Lv et al., 2018; Feng et al.,
2019). During ship plume-influenced periods, ships can even contribute to over
20 % of the total PM2.5 concentrations in port centers,
e.g., Shanghai Port and Qingdao Port (Fan et al., 2016; Chen et al.,
2017b). The adverse impact of ship emissions also places an enormous burden on
human health, causing ∼ 14 500–37 500 premature deaths in East Asia and
hundreds of those in the PRD of China (Liu et al., 2016; Chen et al., 2019).
These previous evaluations have made great efforts to support the formulation
of China's domestic emission control area (DECA) initially designed for BRA,
YRD and PRD and later expanded to the entire water area of 12 Nm from
the baseline of mainland China. Ships entering the DECA are required to switch to
clean fuel oil with a lower sulfur content. However, these assessments are
mostly so-called “prior assessments”, namely, evaluations of the cost and
benefits of environmental and health improvement by assuming control scenarios
based on earlier ship activities before implementing the policy. With the
increased shipping demand and the step-by-step implementation of control
measures, “post evaluation” is of equal importance to assess whether the
policies are effective and to provide powerful foundations for in situ
observation experiments (Wu et al., 2021). Although a number of studies have
demonstrated air-quality benefits due to ships switching to low-sulfur oil in
local port areas (Y. Zhang et al., 2019a; X. Zhang et al., 2019; Zou et al., 2020;
Zhang et al., 2018), there is so far a lack of a comprehensive national-scale
evaluation that reflects the benefits of gradually promoted DECA policy, which
is vital to guide further ship emission control direction in China.
With the advent of the big data era, the characterization of ship emissions
has evolved from the earlier “top-down” estimation based on global fuel
consumption (Corbett et al., 1999; Endresen et al., 2003) to the “bottom-up”
model based on big data from a ship's automatic identification system (AIS)
(Jalkanen et al., 2009; Winther et al., 2014; Liu et al., 2016; Johansson
et al., 2017; Nunes et al., 2017). AIS-based ship emission inventories have
great advantages in improving the spatiotemporal resolution for numerical
simulations, as well as providing possibilities for near-real-time emission
estimations to meet regulatory needs (Miola and Ciuffo, 2011; Nunes et al.,
2017; Huang et al., 2020). However, emission calculation methods based on big
data greatly depend on the data quality, thus demanding complicated steps for
data cleaning. As the loss of AIS signal occurs in many cases, dealing with
long-term missing AIS signals has been one of the key technical problems for
both scientific research and supervision (Y. Zhang et al., 2019b; Peng et al.,
2020; Zhang et al., 2020). Without targeted measures, the estimated ship
emissions would be spatially and temporally misallocated, thus further raising
uncertainties in environmental impact assessments.
In this study, we developed a series of ship emission inventories
(0.05∘×0.05∘, daily) for the inland rivers and the
200 Nm coastal zone of China from 2016 to 2019 based on global AIS
data and the updated version of the Ship Emission Inventory Model (SEIM
v2.0). The global AIS database with ∼30 billion signals annually was
combined with the Ship Technical Specifications Database (STSD) covering over
350 000 individual vessels, creating the fundamental data for emission
calculation. The technical details of upgrading the previous SEIM v1.0 to SEIM
v2.0 are introduced in the Methods section. Based on the multiyear ship
inventory data, the 4-year consecutive daily ship emissions and emission
structure were analyzed from the national to port level to track variations at
a fine timescale. The interannual spatial changes in emissions from
ocean-going vessels (OGVs), coastal vessels (CVs) and river vessels (RVs) were
presented and compared. In addition, a scenario without the DECA policy was
performed in order to evaluate the effect of China's gradually implemented
DECA policy, considering the actual change in interannual ship activities. The
results of this study provide high-quality emission inventory data for the
further numerical simulation of air quality and health benefits of ship
emission reduction.
MethodsShip Emission Inventory Model (SEIM v2.0)
The SEIM v1.0 model was established in our previous work to develop a
multiscale ship emission inventory with a high spatial and temporal
resolution, which is driven by high-frequency ship AIS data (Liu et al., 2016,
2018; Fu et al., 2017). In this model, emissions were calculated
based on the instantaneous operating status and power changes for each
individual ship between two successive AIS signals, usually ranging from a few
seconds to a few minutes. Each active ship in the AIS data was dynamically
matched with its technical profiles for identification and emission
calculation. With a high-frequency AIS signal transmission time and geographic
locations, the emissions could ultimately be aggregated by taking those from
all ships at all time intervals in the entire year, resulting in an inventory
with a high temporal and spatial resolution. Technical details including the
data collection and cleaning, calculation formula, emission factor (EF)
adoption, and default parameter setting of the SEIM model were introduced in
our previous studies (Liu et al., 2016; Fu et al., 2017). The general
calculation formula of the SEIM model is summarized in Sect. S1. Currently, the SEIM considers ship emissions for both air pollutants
(e.g., SO2, PM, NOx, CO and HC) and greenhouse gases (e.g.,
CO2, CH4 and N2O) from the main engines,
auxiliary engines and boilers.
To reduce the uncertainties in emission calculations, we have previously
introduced several techniques in SEIM v1.0 (Liu et al., 2016): (1) a
double-nested research domain was applied to reduce the boundary effects
(i.e., sharp increase/decrease on the boundary when calculating the emission
inventory in a defined region); (2) the gradient boosting regression tree
(GBRT) method was adopted to predict missing values of the ship properties;
(3) the propeller law was used to calculate the instantaneous engine loads; and
(4) the 10 min linear interval interpolation method was used to fill
long-distance AIS signal gaps. These factors all contributed to improving the
reliability of the ship emission inventories. Here, we introduce an upgraded
version of SEIM (SEIM v2.0). The major improvements include (1) developing a
route restoration module to restore the most likely trajectory for missing AIS
signals, (2) distinguishing river vessels from AIS data based on spatial
frequency distribution of ship trajectories, and (3) incorporating a
step-by-step Chinese emission control policy with a daily scale to reflect the
actual emission level in a timely manner. These improvements contributed to
the consistency of the model in the real world and, to some extent, alleviated
the uncertainties in our model. However, several uncertainties inevitably
still exist in this model, including AIS data gaps and anomalies (influenced
by methodological conditions, equipment maintenance, etc.), accuracy and
coverage of STSD information, accuracy of RV, CV and OGV classification, route
restoration algorithm, obedience of ships to DECA policy, etc.
Structure and flow chart of the SEIM v2.0. The STSD stands for the
ship technical specification database. The GBRT stands for the gradient
boosting regression tree. The LLAF stands for the low load adjustment factor.
Figure 1 shows the flow chart of SEIM v2.0, composed of several key modules:
data pre-processing, route restoration, emission calculation, policy-compliant modification and post-processing. First, the originally collected raw AIS
data and ship profile data from multiple sources are combined to form a ship
activity database and STSD, and the RVs are identified based on the ship
trajectories. Second, a route restoration module is applied for cross-land
trajectory with a long distance in the AIS data, in which the 10 min
linear interpolation will be applied on the shorted paths instead. Third, the
instantaneous emission along with the movement of the ship's trajectory will
be calculated based on the ship's static technical parameters, dynamic load
changes, and extra parameters and factors. Then, the policy-compliant modification will be applied for vessels entering the DECAs to switch to low-sulfur fuels (LSFs). Finally, ship emission inventory datasets will be established
and used for visualization and analyses from multiple perspectives. As most of
the technical methods have been described in our previous work, such as GBRT
methods, emission calculation algorithms and extra parameter preparations, we
focus on the study area definition, the latest data evaluations and the
improvements in SEIM v2.0 to introduce the technical details for developing
ship emission inventories around China.
Study area
Ships have a strong spatial mobility, unlike on-road mobile sources, which
mostly have a fixed geographical range of activities. Due to the complexity
brought by the inconsistency of a ship's flag state, operating country and
activity location, it is difficult to determine the attribution country of
ship emissions. In this study, the target area for developing a ship emission
inventory is navigable inland rivers and the coastal waters approximately
within 200 Nm away from the Chinese mainland's territorial sea
baseline (hereinafter referred to as the 200 Nm zone), as shown in
Fig. 2. We defined the target area for the following reasons. First, the
200 Nm zone is the water region with the most intensive ship traffic
and complex routes. Ship emissions occurring in this region have been proven
to have a significant impact on the air pollution and human health in China (Lv
et al., 2018). Second, as the current DECA is limited to 12 Nm to the
baseline of the territorial sea, which is far less than the proposed area of
the international emission control area (ECA; 200 Nm), it is possible to provide a scientific
reference by investigating the emission variation in the 200 Nm zone
for China's future policy design. In addition, the study area is also
generally consistent with the research scope of other AIS-based ship emission
inventories of China for comparison with other studies' results.
Definition of the study area for ship emission estimation around
China. The double-nested domain is used to filter global AIS data and reduce
the boundary effect. The distances on the map all refer to the distance from
the baseline of the Chinese mainland's territorial waters. The 200 Nm zone
is the coastal area approximately within 200 Nm away from the baseline,
which is further divided into different geographic regions according to the
distance lines.
A double-nested domain is set to calculate the ship emissions and reduce the
boundary effect. As illustrated in Fig. 2, the outer domain (D1) is
0–90∘ N and 90–140∘ E, and the inner domain (D2) is
14–43∘ N and 104–130∘ E. The spatial distribution of
emissions will be retained and presented with D2 as the boundary, and the
statistical results for China will finally be made for the inland rivers and
the 200 Nm zone. Figure 2 also shows the scope of DECA 1.0, which
includes three areas, namely, BRA, YRD and PRD, and the scope of DECA 2.0,
which is approximately equal to the area from the coastline to 12 Nm
from the Chinese mainland's territorial sea baseline (hereinafter referred to
as baseline). Meanwhile, ship emissions within different coastal areas, i.e.,
from the coastline to 12, 12–50, 50–100 and 100–200 Nm from the
baseline, illustrated in Fig. 2, are also decomposed and compared.
Data pre-processing and evaluations
The global dynamic AIS data for the entire years of 2016–2019 (from 1 January
to 31 December) with on average 30 billion signals per year, integrating both
satellite-based signals and terrestrial-based signals, were collected to build
a ship activity database. This database provides high-frequency information
including signal time, coordinate location, navigational speed, operating
status, etc. As the AIS data are composed of satellite AIS signals and
terrestrial-based AIS signals, the same messages received from multiple base
stations may lead to large quantities of duplicates, especially when ships are
berthing. To deal with the redundant information, only one record was kept
every 10 min if the continuous AIS signals met the condition that
their instantaneous speeds equalled 0 with displacements less than 0.01∘. In this way, on the premise of keeping the total operation time unchanged, the
volume of the raw AIS data was reduced. After reduction, the AIS homogeneity
in our study area was examined in terms of time and space (see Sect. S2 and Fig. S1 in the Supplement). Short period drops were probably the
result of missing or abnormal AIS signals for many reasons, such as disruption
to satellites, equipment maintenance, data transmission faults, ships sailing
beyond the terrestrial station receiving range, etc. AIS signals missing or
being anomalous is a common phenomenon that has been noted by previous studies
(Goldworthy et al., 2019; Johansson et al., 2017; IMO, 2020). To ensure the
reliability of total emissions, it is important to have data from the entire
year instead of using several weeks and then scaling them to the annual total.
The STSD describes ship properties such as the vessel type, dead weight
tonnage (DWT), engine power, designed speed, flag state, etc., which has also
been updated to 2019. The extended STSD currently contains over 350 000
vessels, of which 101 638 are OGVs, which is consistent with the statistics of
the United Nations (UNCTAD, 2019). In addition to the ship data collected from
Lloyd's Register and the classification societies of various countries, we
have also incorporated fishing ships and smaller ships that do not have
International Maritime Organization (IMO)
numbers from Global Fishing Watch (GFW) (Kroodsma et al., 2018). These ships
were observed to be quite active along China's coast. A further introduction
to the updated STSD is provided in the Sect. S3.
Statistics of AIS messages and active ships in 2016–2019.
Statistical items 2016201720182019GlobalArchived AIS messages (109)26353145Active ships with unique MMSI (103)523635754824China (river and 200 Nm zone)Number of identified ships (103)96928885Total operating hours (106 h)196197195202
Statistics of vessels' dynamic and static information for
2016–2019. (a) Daily average operating hours. (b) Vessel fleet compositions
from different aspects.
During the emission calculation method, vessels in AIS data need to match
their technical profiles in STSD. Detailed processing methods of data
collection, cleaning and matching are described in our previous work (Liu et
al., 2016). Table 1 shows the statistical results of the AIS messages and
active ships for different years in this study. From 2016 to 2019, an annual
average of approximately 90 000 vessels were observed in inland rivers and
the 200 Nm zone of China, where the number of vessels showed a
downward trend year by year. Figure 3 presents the statistics for dynamic
activities and static technical specifications for different ships in the
target region of China after matching AIS and STSD. As shown in Fig. 3a, the
average daily operating time of all vessels within the study area is
approximately 5.3×105hd-1. Among all the vessel
types, bulk carriers operate for an especially long time, followed by fishing
ships and containers. Most vessels show constant daily operating hours but a
slight decrease in the Spring Festival. However, fishing ships drop
significantly in summer due to the fishing off-season. Figure 3b shows the
cargo fleet structure from the perspectives of the vessel number, total DWT
and total installed power of the main engines. In terms of the vessel numbers,
the fishing ship accounts for the largest proportion of 42.5 %,
while general cargo also accounts for 29.8 %. For the total DWT, the
proportion of bulk carriers reaches 49.5 %, and the oil tanker also
occupies a considerable proportion (23.4 %). For the total power of
the main engines, the proportion of containers (35.4 %) exceeds
that of the bulk carrier (28.0 %), indicating a higher engine power
demand per unit volume for containers. Owning to the distinct technical
specifications of different ship types, the total DWT, power or navigation
time, as well as emissions, would not be linear with the number of vessels of
each type.
Model improvementsRoute restoration
Even if the AIS data have a high frequency of reporting ship activities, there
are sometimes long periods of signal loss due to equipment failure or a manual
shutdown. This type of signal only accounts for a minority of AIS data but may
lead to a large deviation in terms of the amount and distribution of ship
emissions, especially in the case of long operating hours. To solve this
problem, a route restoration module was developed in SEIM v2.0 to predict the
most likely navigation trajectories of the lost signals and spatially
reallocate ship emissions. Similar methods but with featured details have been
previously experimented with by Aulinger et al. (2016) on a regional scale and
Johansson et al. (2017) on a global scale. Here, we refer to their method and
apply it to China with a more refined resolution.
The ship route restoration method is based on the Dijkstra algorithm
(Cherkassky et al., 1996) which interpolates the lost signals evenly on the
shortest shipping route connecting two endpoints, namely, the experiential
routes. Thus, a comprehensive ship route network needs to be established
before applying the route restoration algorithm. As the global AIS data
provide massive signals of ship locations, the historical navigation
trajectories for all in-service vessels are clearly visible on the map. Based
on the aggregated ship traffic distribution and the geographic domain of D1 in
this study, the shipping route map was drawn and split into 870 arcs connected
by 656 nodes, as depicted in Fig. S2. Regarding the shipping route map as an
undirected graph, by applying the Dijkstra shortest-path algorithm, the
shortest route path between each node pair can be calculated, as well as the
geodesic distance aggregated by all arcs. In this way, the ship route network
connected with nodes and arcs was established ahead of time, and the shortest
geodesic paths for all the node pairs were pre-stored as a database to improve
the operation efficiency.
Diagrammatic sketch of the ship route restoration algorithm. (a)
Sketch map of the route restoration algorithm with an example of route AB.
(b) Algorithm flow chart of the example of route AB.
Figure 4 illustrates the ship route restoration algorithm, taking a segment of
the AIS positions as an example. The method can be summarized using the
following steps: (1) for each two consecutive AIS points A and B, judge
the geographical relationship between line AB and the continent; (2) if line
AB intersects the continent and is not contained in the continent, apply the
route restoration algorithm by first finding the nearest start node A′ and
end node B′ by traversing the pre-stored node library; (3) look up the
shortest path connecting nodes A′ and B′ (e.g.,
A′O1O2OiOj…B′) from the pre-stored ship route network
database and calculate the average speed resulting from the geodesic distance
of DA′O1O2OiOj…B′ and time internal TAB; (4) for
each segment OiOj in route A′B′, interpolate points p1,p2,pm,pn⋯ with a time span of 600 s along the
OiOj if TOiOj>600s; (5) for each arc pmpn,
calculate the ship emissions based on the average speed, instantaneous power
and emission factors; and (6) calculate emissions ∑E summed from each time
span along the restored route. However, as it was rather time consuming to
judge the geographical relationship between the trajectory line and the
continent polygon, an additional distance threshold of 50 km was
finally added in the model; i.e., the restoration method would only be applied
for “cross-land trajectory with a long distance”. This setting would skip
some cases in which ships were sailing in the estuaries, their trajectories
crossing the coastlines.
Classification of OGV, CV and RV
In SEIM v2.0, vessels are classified as OGVs, CVs and RVs for emission
estimation. In China, the number of inland vessels with the AIS equipment
installed has been increasing in recent years. As the fuel standard for RVs is more
stringent than that for OGVs, it is necessary to distinguish them from the AIS
data in order to calculate emissions accurately. In the methodology, since
OGVs are mostly engaged in international trade following the management of the
IMO, they are identified by both valid IMO numbers and the Maritime Mobile
Service Identity (MMSI) numbers. CVs and RVs are both domestic vessels
designed to operate in coastal and river areas, respectively. However, in
some cases, they do cross one another's navigational waters when the inland
waterway system borders the coastline (Mao and Rutherford, 2018a). Thus, we
identified RVs with a frequency distribution method based on the navigation
trajectories for each vessel. By defining the geographic domain of D2 in
Fig. 2, vessels with more than 50 % of the AIS signals throughout
the entire year occurring on inland rivers are considered as RVs
(Fig. 5a). This method allows the possibilities for CVs and OGVs to sometimes
travel into estuaries. Finally, vessels that are not identified as OGVs or
RVs are regarded as CVs.
Identification test and results of OGVs, CVs and RVs. (a) Frequency
test of ships in inland waterways. (b) Spatial distribution results of AIS
signals of OGVs, CVs and RVs. The sample year is 2016.
Figure 5b shows the identification results of OGVs, CVs and RVs, taking 2016
as an example. It is clear that the OGVs navigate between the major coastal
ports of China and other countries, with a few entering the Yangtze River.
CVs operate around the coastal seas of China, seldom contacting other
countries. RVs mostly sail on the Yangtze River and Pearl River systems, with
a small proportion wandering in coastal seas. The spatial distribution of the
AIS signals of OGVs, CVs and RVs was essentially consistent with experience,
with OGVs mainly at seas, CVs near the coast and RVs in inland waters.
Ship emission control policy
In recent years, a series of policy documents have been issued to control air
pollution from ships, among which the most effective measure is the
establishment and implementation of DECA (MOT, 2015, 2018). China's DECA
policy was put into effect step by step from 2016 to 2019. Figure 6 summarizes
the evolution of DECA, including the control area and fuel standards, as well
as their comparison with the international ECA. Before the global sulfur cap
taking effect in 2020, heavy fuel oil (HFO) with a sulfur content as high as
3.5 % had long been used in ships worldwide. In 2017, China
initially established three DECAs along the coastline (DECA 1.0), covering the
most busy port clusters in the world, with gradual mandates for ships to use
LSF with sulfur content <0.5% m/m (mass by mass). Later, DECA
1.0 evolved from regulating ships in core ports to the whole port clusters and
ships berthing to all operating modes. In 2019, an upgraded DECA 2.0 was
proposed to expand the region to cover the entire coastline (within
12 Nm from the Chinese mainland's territorial sea baseline; Fig. 2) in
which ships are required to use LSF regardless of the operating status. In
addition to fuel requirements, the DECA 2.0 policy also defined the control
requirement of NOx emissions from ships, in which diesel engines above
130 kW built or modified on or after 1 March 2015 must meet the Tier
II NOx emission limits of revised MARPOL Annex VI rules, which is in line
with international ships under the control requirement of the IMO.
Evolution of sulfur content requirements for fuels in DECAs and
inland rivers in China. The percentages refer to the sulfur content of the
fuel. The italics refer to the operating mode constrained by DECA policy.
The y axis is unevenly distributed to show the standard of fuel sulfur
content.
Despite the mandatory implementation time of DECA, some developed regions were
encouraged to experiment in advance. To provide a timely feedback on the
effect of policies, a broad investigation of the actual performance of DECA
was conducted, including both coastal seas and inland rivers in 2016–2019
(Table S1 in the Supplement). Before the mandatory date of 1 January 2017,
core ports in the YRD and Shenzhen port pioneered the DECA 1.0 policy 9 months and 3 months earlier, respectively. Core ports in YRD were supposed
to implement the DECA 2.0 policy 3 months before it fully came into effect
on 1 January 2019. Meanwhile, RVs are required to use the general diesel fuel
(GDO) with a much lower sulfur content, gradually iterating from 350 to
10 ppm and finally keeping pace with the China V standard of on-road
diesel fuel in 2018.
To be consistent with the DECA policy, a policy-compliant modification module
was developed in SEIM 2.0. Firstly, each AIS signal point will be dynamically
judged as to whether the vessel is located inside the scope of DECAs. Combined with
the signal transmission time and the vessel's operating mode, the module will
then determine whether the vessel needs to switch fuels or not. Finally, for
vessels demanding fuel switching, a fuel correction factor, which is the
quotient of the emission factors of the switched fuel and original fuel, will
be further applied to correct the emissions. Details about the emission
factors regarding different fuel types are introduced in Sect. S4. It is worth noting that, as far as we know, there has not been
sufficient evidence showing that all vessels stick to DECAs or the violation
rate each year. However, there have been studies indicating the effectiveness
of DECAs in recent years (Liu et al., 2018; X. Zhang et al., 2019; Y. Zhang et al., 2019a; Zou et al., 2020). Not only have fuels been found to be cleaner (X. Zhang
et al., 2019; Y. Zhang
et al., 2019a), but air pollution caused by shipping activities has also been
less significant in busy ports alongside the Chinese coast (Zou et al.,
2020). Guaranteed by the authority of the Chinese government, we assume that the
DECA policy should mostly be effective, but there is a lack of evidence about
violations of DECAs, which would add to uncertainties in this model.
Simulation scenario setting
To comprehensively investigate the effects of gradually implemented DECA
policies under the condition of a growing waterway transport demand, we
designed a scenario (No-DECA scenario) in SEIM v2.0, with its details listed
in Table 2. Compared to the base condition embedded with the actual DECA
policy described in Sect. 2.4.3, the No-DECA scenario was designed to simulate
the ship emissions around China by assuming vessels do not implement the DECA
policy, namely, keep using fuels with sulfur contents at pre-DECA levels. By
comparing the emission results from the base condition and the No-DECA
scenario, the absolute emission reduction effect of gradually implemented DECA
policies could be vividly illustrated.
Simulation scenario setting in this study.
AIS dataCoastal sea Inland river Policy settingFuel settingPolicy settingFuel settingBase condition2016–2019Actual implementation of DECA 1.0 andDECA 2.0Inside DECAs: LSF(S <0.5% m/m) Outside DECAs: no requirementAs required350, 50 and 10 ppmchronologicallyNo-DECA scenario2016–2019No DECA policyPre-DECA level (norequirement)Assumed fuel350 ppmResults and discussionOverall
With the development of China's waterway transport, seaborne trade increased
through 2016–2019. As illustrated in Fig. 7a, Chinese ports' total passenger
turnover, cargo turnover and cargo throughput increased by 10.9 %,
6.8 % and 17.4 % in 2019 compared to 2016,
respectively. A growing demand for water transport has stimulated ship
activities and fleet loading capacity improvements, coinciding with gradually
implemented DECA policies and upgraded vessel engine standards, resulting in
different interannual trends in ship emissions for different
pollutants. Figure 7b and c show the annual ship emissions of SO2
and NOx in China's inland waters and the 200 Nm zone from 2013 to
2019. Before the enforcement of DECA policy, ship emissions of SO2,
NOx, PM and HC in 2016 were estimated to be 1.8×106,
2.5×106, 2.3×105 and 1.1×105Mgyr-1, respectively. The emission results are generally
higher than other AIS-based ship emission inventories of China in recent years
(Table S2 in the Supplement) (Chen et al., 2017a; Li et al., 2018; Fu et al.,
2017; Huang et al., 2019). The primary reason might be that our study
established a larger ship activity database based on global AIS data (∼30 billion signals per year) and that the incorporation of the GFW database
also improved the recognition of ships, especially CVs and RVs in China. In
addition, the annual increase in ship activity driven by maritime trade could
also contribute to ship emission growth.
Annual changes in (a) seaborne trade and ship emissions of (b)SO2 and (c) NOx from 2016 to 2019. Data in (a) are collected from the
Chinese Statistical Yearbook (NBS, 2020). Emissions in 2013 are derived from
our previous work for comparison (Fu et al., 2017).
Among all vessels, OGVs composed the largest part of ship emissions, with a
proportion of 70.4 % regarding SO2 and 59.7 %
regarding NOx in 2016. Compared to a recent estimation of global ship
emissions (IMO, 2020), it is striking that OGVs in the 200 Nm zone of
China contributed to ∼ 9.7–14.3 % of global OGV emissions
(Table S3 in the Supplement) despite only being <1% of the
world's sea area. Such a result suggests a substantially high emission
intensity around China generated from the activities of the global fleet. CVs
are ranked after OGVs with a 29.4 % contribution to SO2
emissions and 27.1 % to NOx emissions, while the RV
composition was relatively small, accounting for 13.2 % for
NOx and <1% for SO2. The emission shares of RVs
may differ from those by Li et al. (2018), considering two major reasons. On
one hand, we identified RVs based on the spatial frequency distribution of
ship trajectories, which allows vessels to sometimes operate in coastal
waters. Given that CVs and even OGVs sometimes sail in inland waters, it is possible that some CVs and OGVs are mistakenly identified as
RVs. Thus, the identified vessels of RVs might be higher than that in Li
et al. (2018). On the other hand, since we applied GDOs with sulfur content
up to the national standard to RVs, for which the emission factors of
SO2 would also be much lower, the emission share of SO2
appeared to be lower than that in Li et al. (2018), but it was opposite for
NOx and other pollutants.
From 2016 to 2019, ship emissions of SO2 and PM have decreased by
29.6 % and 26.4 %, respectively (Table S2). During the
DECA 1.0 period, the annual ship emissions of SO2 around China
increased by 1.6 % and 3.8 % year-on-year in 2017 and
2018, respectively. After the implementation of DECA 2.0, however, ship
SO2 emissions in 2019 dropped significantly by 33.3 % in
2019 compared to 2018, even 2.8 % lower than those in 2013 (Fu
et al., 2017), showing great benefits with the extended control area and more
stringent requirements. In terms of NOx, however, emissions continuously
increased year by year, with a total increase of 13.0 % from 2016
to 2019, while emissions of other pollutants also showed a gradually
increasing trend (Table S2). Therefore, the ship DECA policy has a significant
impact on reducing SO2 and PM emissions, but the current vessel
engine emission standard only has a limited influence on controlling NOx
emissions.
The 4-year consecutive daily emissionsEmission composition variation
On a more refined timescale, we investigated the 5 d moving average
ship SO2 and NOx emissions on a daily basis for inland rivers
and the 200 Nm zone of China from 2016 to 2019, as shown in Fig. 8. It
is evident that ship emissions of SO2 seasonally grew in 2016–2018
until a sharp drop on 1 January 2019 due to
the implementation of the stringent DECA 2.0 control policy. The maximum daily
ship emission of SO2 reached 6.4×103Mgd-1
on 22 September 2018, which was 2.9 times that of the lowest point, 2.2×103Mgd-1 on 1 January 2019, while the daily discrepancy of
ship NOx emission intensity also reached 3.0 times throughout the 4 years. The monthly variation in ship emissions for most vessel types was
generally constant, except for a temporary decrease during the Spring Festival
in February (Fig. 8a). However, fishing ships showed significant seasonal
variations, which declined annually in the summer and returned in autumn due
to the fishing ban in China. This has also been demonstrated by other studies
(Chen et al., 2017a; Fu et al., 2017).
The 5 d moving average of SO2 and NOx emissions from
ships around China from 2016 to 2019. Ship SO2 emission composition by
(a) vessel type and (b) fuel type and ship NOx emission composition by
(c) vessel build period and (d) dead weight tonnage (DWT).
The 5 d moving average of SO2 emissions from ships in major
ports of China from 2016 to 2019: (a) Bohai Sea area (BRA), (b) Yangtze
River Delta (YRD) and (c) Pearl River Delta (PRD). The blue and red arrows
mark the actual implementation dates of DECA 1.0 and DECA 2.0 policies,
respectively.
Figure 8 also exhibits the emission structure of SO2 according to
vessel type and fuel type and NOx according to building year and DWT. The
full composition of the emission contribution for all pollutants from
different aspects is summarized in Table S4 in the Supplement. Containers
accounted for the largest part, and the contribution increased over the 4
years, e.g., from 31.7 % in 2016 to 42.9 % in 2019 for
SO2 (Fig. 8a). Although containers accounted for only about
3.5 % of the vessel numbers and 4.6 % of the operating
hours in Chinese waters (Fig. 3), their relatively higher engine power
contributed to significant emission intensities compared to other ships of the
same size, such as bulk carriers. The HFO contributed to the majority of ship
SO2 emissions due to its high content of sulfur, part of which,
however, was gradually being substituted by marine gas oil (MGO) with the
implementation of the DECA policy (Fig. 8b). In 2019, the MGO had accounted
for 15.4 % of the ship SO2 emissions and
38.9 % of the NOx emissions (Table S4). In terms of vessel
build year, ships built after 2016 made an increasing contribution to annual
NOx emissions, reaching 10.6 % in 2019 (Fig. 8c). Even though
the Tier II engine standard had been applied to domestic ships built after
2016, ship NOx emissions were not found to decrease as the emission
standard of Tier II only has minor improvements compared to Tier I. In
addition, we also found that ships with larger DWTs make up a growing proportion
of vessel fleets, as well as emission contributions (Fig. 8d), indicating the
developing trend of ship upsizing in the past few years. However, even though
the newly built, large-scale ships and ships using clean fuel oil all play an
increasingly large part in emission structure, the rising trend of NOx
emissions has not yet reversed.
Emission variation in major ports
As the DECA policy was implemented step by step in different ports
in China, we extracted the 5 d moving average ship SO2
emissions of the major ports in the BRA, YRD and PRD to track the consecutive
emission changes throughout the 4 years, as shown in Fig. 9. In the
initial stage, restriction on fuels with no more than a 0.5 %
sulfur content was only imposed on ships at berth for core ports in these
three crucial port clusters (Fig. 2 and Table S1). Before the mandatory date
of DECA 1.0, core ports in the YRD and Shenzhen port pioneered the
implementation 9 months and 3 months earlier, respectively, which
showed a significant decrease in ship SO2 emissions beginning on
1 April and 1 October 2016, respectively. For other core ports in BRA and
PRD, a noticeable decline could be observed on schedule on 1 January 2017.
However, the emission of ships at berth accounted for a relatively smaller
percentage (∼ 7.5 %–13.7 %) in the 200 Nm
zone according to our results (Table S4); thus, the emission reduction was
rather conservative inside the DECA 1.0 region in 2018 even though the
requirement was extended to all ports. In contrast, due to intensified ship
activities, ship SO2 emissions for some ports even largely
increased, such as Ningbo-Zhoushan Port and Shenzhen Port, which increased by
19.4 % and 11.4 % in 2018 compared to 2017. Fortunately,
in 2019 when the more rigorous DECA 2.0 policy was implemented, it is clearly
illustrated in Fig. 9 that all ports' SO2 emissions were sharply
reduced. Core ports in the YRD were supposed to implement the DECA 2.0 policy
3 months before fully coming into effect. Notably, those pilots witnessed
an earlier decline in SO2 emissions, which also proved the timely
and flexible response of the SEIM 2.0 model to the changeable DECA policy.
In addition to policy-driven emission changes, different ports showed distinct
monthly emission variations that were highly related to their geographical
location and ocean resources. For example, ship emissions in the YRD region
had a low point in July as their activities were influenced by typhoons,
particularly in the YRD (Weng et al., 2020), while ship emissions in
Ningbo-Zhoushan Port, Tianjin Port and Shenzhen appeared to be larger in
spring and autumn, probably owing to large-scale fishing ship operations (Chen
et al., 2016; Yin et al., 2017). In addition, steep short-term increases in
SO2 emissions were observed for the Tianjin, Ningbo Zhoushan and
Shenzhen ports in September 2019. These peaks were speculated to be due to the
inaccurate vessel dynamic information in AIS signals caused by the
interference of adverse weather, i.e., Super Typhoon Mangkhut. However,
more evidence is needed to verify the influence of extreme meteorological
conditions on AIS signals. The above port-based emissions fully presented the
daily ship emission variations for a long period from 2016 to 2019, which may
also provide useful data references for port observation experiments.
Spatial distribution changeEvaluation of the route restoration
Since the shipping route restoration module was developed in SEIM v2.0 to
solve the problem of AIS discontinuity, the spatial distribution of ship
emissions after route restoration was evaluated, as shown in Fig. 10. Direct
interpolations for AIS signals along the loxodrome would lead to part of the
emissions being distributed on unrealistic routes, e.g., crossing the land
areas, which could even be as long as connecting the South China Sea and the
Bohai Sea (Fig. 10a). By using the route restoration method, the ship's
navigation trajectory and emissions can be restored to more realistic shipping
routes, thus reducing the deviation in the spatial distribution of emissions
(Fig. 10b). Statistically, 15.3 % of NOx emissions and
7.5 % of SO2 emissions were spatially corrected in the
study area. More improvements were obtained around Taiwan Island, the Korean
Peninsula and the Philippine islands, probably due to the worse accessibility
of high-quality shore-based AIS signals. The misallocation of emissions in
China's land areas resulted in NOx underestimates of up to ∼ 2–4 Mg per grid downstream of the Yangtze River and Pearl
River, and the misallocation of emissions in water regions is more notable on
shipping routes farther from the coast. This spatial improvement of ship
emissions with the route restoration method is expected to improve the results
of any air-quality model applications.
Evaluation of estimating ship NOx emissions in China after
route restoration. (a) Emissions without route restoration. (b) Emissions
with route restoration. (c) Spatial difference of emissions (before-after). (d)
Spatial change rate of emissions (before - after / before × 100). The selected year is
2016.
Spatial change in ship emissions
Figure 11 presents the spatial changes in SO2 and NOx emissions
from ships in different coastal regions defined in Fig. 2 from 2016 to
2019. Remarkably, within 12 Nm, which approximately equates to the
scope of DECA 2.0 in 2019, SO2 emissions decreased by
78.8 % (7.2×105Mgyr-1) compared to
2016. Despite the year-by-year growth of seaborne trade, the DECA policy
effectively reduced ship-emitted SO2 overall and was especially
beneficial to coastal cities. However, we discovered that SO2
emissions increased by 41.5 % (1.3×105Mgyr-1) from 2016 to 2019 in areas between 12 and 50 Nm from the baseline, especially along the 12 Nm
boundary. The proportion of ship SO2 emissions from 12 to 50 Nm rose from 17.5 % in 2016 to 35.3 % in
2019, becoming the major spatial contributor in 2019. The emission of PM
exhibited a similar pattern (Fig. S3a in the Supplement). This peculiar
phenomenon implies the fact that some ships possibly made a detour to evade
switching to clean fuel oil, which could also be demonstrated by the larger
growth rate in the cargo turnover than throughput (Fig. 6a).
Spatial distribution changes in SO2 and NOx emissions
from ships in China in 2019 compared to 2016. The stacked bar plots
indicate the annual emissions occurred at different distances off the
coastline from 2016 to 2019. The “C-12 Nm” in the legend refers to the
area from the coastline to 12 Nm from the baseline of the territorial sea
(the same below), which is approximately equal to the scope of DECA 2.0.
Interannual spatial changes in NOx and SO2 emissions
from ships over China from 2016 to 2019. Annual average spatial distribution
comparison of NOx emission for (a) OGVs, (b) CVs and (c) RVs.
Interannual variations in NOx and SO2 emissions in different
geographic regions for (d) OGVs, (e) CVs and (f) RVs.
Figure 12b shows that NOx emissions from ships that occurred within
12 Nm of the baseline continuously increased from 2016 to 2018, until
it declined by 5.0 % (6.4×104Mgyr-1) in
2019 compared to the last year. Meanwhile, NOx emissions occurring in
areas between 12 and 50 Nm also showed a higher annual increase rate in
2019 (21.4 %) than in the previous 2 years (∼ 7.4 %–8.2 %). Such a phenomenon once again proves the possibility of
ship detours. Other species generally showed emission patterns similar to those
of NOx (e.g., HC in Fig. S3b in the Supplement). In summary, the DECA 2.0
policy has a positive effect on ships' SO2 and PM emission control
as a whole, especially for coastal areas. However, several ships detoured
outside the scope of DECA 2.0, perhaps to save on the cost of more expensive
clean fuel oil, which further elongated the sailing distance and thus
increased emissions in farther maritime areas.
Spatial changes in OGV, CV and RV emissions
Interannual spatial changes in OGVs, CVs and RVs were further compared for the
ship emissions of NOx and SO2, as shown in Fig. 12. The
emission intensity of identified OGVs was apparently higher than that of CVs
and RVs, demonstrating certain routes. The most intensive near-sea routes
included China–Korean Peninsula, mainland-China–Taiwan, the North Pacific Route, routes
from Chinese ports to the Malacca Strait and routes between busy ports of
China, such as the main ports in the BRA, YRD and PRD (Fig. 12a). Since the main
shipping routes are rather close to the land, OGVs within 12 Nm of the
baseline make up approximately 38 % and 32 % of the
total OGV emissions for NOx and SO2, respectively. From 2016 to
2019, OGV emissions generally increased in all regions, except SO2
emissions at 0–12 Nm, which showed a significant drop due to the DECA
2.0 policy.
For CVs, approximately 80 % of NOx emissions and
70 % of SO2 emissions were annually distributed mainly
within 12 Nm of the baseline, and the proportions that occurred
outside 12 Nm were greatly reduced compared to the OGVs. Despite
intensive emission routes between coastal ports, notable emissions from CVs
were more evenly distributed off the major routes (Fig. 12b), which was
attributed to large quantities of fishing ships operating (Kroodsma et al.,
2018). In the region of 0–12 Nm to the baseline, the annual
SO2 emission reduction ratio of CVs (81.0 %) in 2019 was
even higher than that of OGVs (76.9 %), indicating that CVs were
more affected by the DECA 2.0 policy.
Compared to OGVs and CVs, RVs have specific routes that were constrained by
inland waterways, with the most intensive emissions located on the Yangtze
River and the Pearl River (Fig. 12c). Meanwhile, RVs also operate along the
Chinese coast and produce a considerable proportion of emissions within
12 Nm of the baseline. With the increasingly stringent national fuel
oil standards for RVs (MEE, 2018), i.e., a sulfur content from 350 ppm
before 30 June 2017 to the current 10 ppm beginning on 1 January
2018, SO2 emissions from RVs had been reduced to a rather low level,
both for inland rivers and coastal areas. However, other pollutants, such as
NOx emissions, were still increasing. In addition, although China has
required certain categories of ships to install AIS equipment since 2010, a
large part of small RVs in China have not been equipped with AIS (Zhang
et al., 2017). The lack of ship activity data and highly reliable local
emission factors all bring uncertainties to the emission estimation of
RVs. However, the air quality and human health of inland cities near waterways
could be severely impacted by RV emissions (Wang et al., 2018). Therefore, RV
emissions need to be stressed and are worth further investigation.
Emission reduction effect of the DECA policyMonthly effect evaluation
Since the shipping activity increase and emission control policy collectively
influenced ship emissions, we designed a No-DECA scenario to evaluate the real
emission reduction effect of DECA policy. Figure 13 illustrates the monthly
ship emissions of SO2 for the base (real) condition and the No-DECA
scenario, which are aggregated from inland rivers and the 200 Nm zone
of China. Without the DECA policy, ship emissions of SO2 were
estimated to increase from 1.8×106Mgyr-1 in 2016 to
2.1×106Mgyr-1 in 2019, with an annual increase rate
of 4.5 %. Beginning in April 2016, the prior implementation of DECA
1.0 led by core ports of the YRD began to see the emission reduction benefit.
Since DECA 1.0, ship SO2 emissions were reduced by 4.6×104, 1.1×105 and 1.4×105Mgyr-1 in
2016, 2017 and 2018, respectively, compared with the No-DECA scenario.
Emissions were reduced even more remarkably in 2019 owning to the expansion of
DECA 2.0, with an 8.4×105 Mg SO2 reduction compared to the
No-DECA scenario. In retrospect, although ship SO2 emissions were
reduced by 29.6 % in 2019 compared to 2016 under base condition,
the DECA policy actually achieved a larger benefit with a reduction of
39.8 % compared to the same year considering the actual seaborne
trade growth and ship activity increase.
Monthly variation in ship SO2 emissions in inland rivers and
the 200 Nm zone of China under the base condition and the No-DECA scenario
in 2016–2019. The base condition refer to the real condition. The No-DECA
scenario reflects the emissions based on the real ship activities without
DECA policies.
Regional contributions to annual reduction in SO2 emissions
from ships within 12 Nm of the baseline of China's territorial sea. The
figures inside the blue bars refer to the annual emissions. The percentages
refer to the relative change in emissions due to total ship activity change
in the C-12 Nm region or the DECA policies in each region.
Annual regional contribution
To date, the implementation of the DECA policy and the effect of ship emission
reduction have been focused within 12 Nm of the baseline of China's
territorial sea. To further investigate the regional contribution of emission
changes in different regions, we finally summarized the ship activity and
emissions in the BRA, YRD and PRD from 2016 to 2019. As shown in Fig. 14,
although the annual change in SO2 emissions in 2017 and 2018 was not
significant, i.e., it decreased by 3.9 % and increased by
1.3 %, respectively, during the implementation of DECA 1.0, it is
undeniable that the policy indeed effectively reduced emissions as the growth
of ship activities would lead to 7.9 % and 17.1 %
increases in emissions without the DECA 1.0 policy. Moreover, the YRD and BRA
played a leading role in reducing ship SO2 emissions in 2017 and
2018, respectively. However, the further tightened DECA 2.0 policy implemented
in 2019 more effectively reduced SO2 emissions by 78.2 %,
in which the YRD, BRA and PRD contributed 30.1 %, 20.2 % and
16.2 %, respectively, while other waters contributed the remaining
26.7 %. Therefore, even though the controlling area of DECA 2.0 was
enlarged to 2.5 times that of DECA 1.0, the dominant regions of emission
reduction were still the three major port clusters. The primary factor driving
DECA 2.0 to achieve a larger emission reduction is the fuel switching
regulation for all operating statuses of ships sailing in the region rather
than only limiting the berthing status in DECA 1.0.
Conclusions and policy implicationsConclusions
The DECA policy effectively reduced SO2 and PM emissions from ships
in sea areas around China from 2016 to 2019. Although the preliminary DECA 1.0
policy targeting berthing ships only had limited effects on ship-emitted
SO2 and PM, the DECA 2.0 policy, tightening its limitation by
putting ships in all operating statuses under its control and expanding the
control areas from major ports to 12 Nm from the Chinese mainland's
territorial sea baseline, resulted in a significant emission reduction. As a
result, SO2 and PM emissions from ships decreased by
29.6 % and 26.4 %, respectively, in the 200 Nm
zone of China in 2019 compared to 2016. Considering the potential emissions
brought about by the continuous growth of maritime trade, a more substantial
benefit was even achieved, e.g., an SO2 emission reduction of
39.8 % in 2019 compared with the scenario without any emission
control policy. However, NOx emissions from ships increased by
13.0 % throughout the 4 years, indicating the limited effect of
the current control standard.
Based on a 4-year consecutive daily emission analysis, it is noticeable
that the ship emission structure had been gradually changing, i.e., newly
built, large ships and ships using clean fuel oil were making up an increasingly
large proportion in the emission structure. Containers and bulk carriers were
still the dominant vessel type in ship emission composition. On a local
scale, ship emissions in various ports exhibited different patterns in terms
of daily variation. For example, ports in the YRD were likely to encounter
typhoons in July, and fishing ships were particularly abundant in the
BRA. Relevant findings may help provide useful data references for port
observation experiments and local policy making.
The interannual spatial change in ship emissions also showed particular
characteristics. By contrasting ship emissions within different distances from
the Chinese coastal baseline, we discovered that in 2019, a number of ships
detoured outside the scope of DECA 2.0. However, this elongated the sailing
distance and resulted in more air pollutant emissions. This reminds us to pay
attention to additional environmental effects brought by detoring ships
during the continuous implementation of the DECA 2.0 policy. In addition, the
route restoration method developed in SEIM v2.0 effectively restored the
ship's navigation trajectory and emissions to more realistic shipping routes,
thus reducing the deviation of the spatial distribution of emissions which could
be expected to reduce uncertainties in the air-quality model.
Policy implications
Compared to the increasingly strict emission control policies of land-based
sources and improving the air quality in China, policies and regulations for
the prevention and control of ship emissions could be more urgent to
facilitate China's air quality to achieve the annual PM2.5
concentration standard of the World Health Organization (WHO) Air Quality
Guidelines (Wang et al., 2020; Q. Zhang et al., 2019). Although the current
emission policy has achieved a significant control effect on SO2 and
PM emissions, under the global low-sulfur oil demand, China still needs to
further apply for the international ECA to enlarge the control area and strengthen
the requirements for fuel quality. To make a comprehensive evaluation of and
in-depth improvement to the policy, attention is also needed during the design
process of the ECA scheme, such as the corresponding impact of ship detours
and further expansion of DECA 2.0 so as to enlarge the reduction effects
within the 200 Nm zone. Meanwhile, international cooperation is
also urgently needed to jointly control ship emissions due to ships' strong
spatial mobility and the intricate relations between the state of
registration, the ship owner and the actual operator. With the gradual
cleaning of marine fuel and the obsolescence of HFO, ship emissions of
SO2 and PM will be effectively mitigated in the near
future. However, ship NOx emissions are still expected to increase until
the gradual elimination of old ships and the iteration of the more stringent
Tier III standard for newly built ships. Other related factors, such as the
engine type, NOx post-treatment technology, etc., should be taken into
consideration in the future. For local decision makers, it is also important
to clarify the local ship emission structure and meteorological conditions to
conduct effective measures.
Code availability
Python codes used during the current study are available from the
corresponding author on reasonable request.
Data availability
The AIS data and STSD are not available to third parties and are used under license for the current study.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-13835-2021-supplement.
Author contributions
XW and WY contributed equally. XW extended the SEIM model and did the model
runs. WY did the data analysis. XW and WY are responsible for writing the
manuscript and preparing the figures and tables presented in this paper. ZL and FD
provided valuable ideas on data analysis for this research. SZ and HX helped
collect and clean the ship data. JZ assisted in the model development work.
HL and KH provided guidance on the research and revised the paper. All
authors contributed to the discussion and revision.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work is supported by the National Natural Science Foundation of China
(grant nos. 42061130213 and 41822505). Huan Liu is supported by the Royal
Society of the UK through the Newton Advanced Fellowship (NAF\R1\201166).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 42061130213 and 41822505). Huan Liu is supported by the Royal Society of the UK through the Newton Advanced Fellowship (grant no. NAF\R1\201166).
Review statement
This paper was edited by James Allan and reviewed by two anonymous referees.
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