Introduction
The marine transport sector is regarded as an important source of air
pollutants, emitting carbon monoxide (CO), sulfur oxides
(SOx), nitrogen oxides (NOx), particulate
matter (PM), volatile organic compounds (VOCs) and greenhouse gas (Corbett
and Fischbeck, 1997). The pollutants emitted from ships can be carried in the
atmosphere over several hundreds of kilometers inland by the onshore flow and
significantly affect the inland air quality, especially with higher aerosol
concentrations. In recent years, shipping emissions have become one of the
fast-growing sources due to the increase in global shipping business in the
long term. It is expected to contribute to 17 % of global CO2
emissions in 2050 (Cames et al., 2015). Liu et al. (2016) found that shipping
emissions in East Asia caused 14 500–37 500 premature deaths in 2013, the
amount of which had doubled since 2005. In China, the severe haze pollution
remains a significant concern because of its high frequency of occurrence,
especially in megacities, where ships can contribute 20 %–30 % of
the total PM2.5 during ship-plume-influenced periods (Fu and Chen, 2017;
Liu et al., 2017b). Therefore, it is necessary to quantify the effects of
shipping emissions on the air quality at local and regional scale.
The influence of ship traffic on air quality varies in different areas, due
to differences in many complicated factors, such as meteorological conditions
and emission intensities from ships and land-based sources. In Europe,
although the increase in PM2.5 concentrations caused by emissions from
ships is quite small, their relative contribution is large because of the low
background PM2.5 concentrations in these regions (Viana et al., 2009;
Aksoyoglu et al., 2016; Marelle et al., 2016). In China, although high
concentrations of reactive air pollutants probably cause higher secondary PM
(e.g., sulfate, nitrate) concentrations from shipping emissions, their
relative contributions are lower due to larger emissions of land-based
sources (Lang et al., 2017). However, the studies in China only focus on the
impacts of shipping emissions on a small scale, typically located in
Bohai Rim area, YRD (Yangtze River Delta) and PRD (Pearl River Delta) regions
including several ports and limited surrounding areas, which are not
available to comprehensively determine the characteristics of PM2.5
pollution caused by shipping emissions in the entire eastern coastal areas of
China (Fan et al., 2016; Chen et al., 2017; Tao et al., 2017; Liu et al.,
2017b, 2018a, b)
While reducing emissions from land-based sources, such as on-road vehicles
and power plants, requires only provincial and national legislation and
regulations, legislation to effectively control shipping emissions is a big
challenge due to international maritime trade. The International Maritime
Organization (IMO) is devoted to protecting the marine environment through
prevention of sea pollution caused by ships. It has published the
International Convention for the Prevention of Marine Pollution from Ships
(MARPOL) Annex VI (IMO, 2017), in which four typical maritime
regions are designated as Emission Control Areas (ECAs). The North and Baltic
seas in Europe are defined as Sulfur Emission Control Areas (SECAs), where
only low-sulfur-content fuel (<0.1 %) has been allowed to be used since
1 January 2015. Additionally, both SO2 and NOx emissions from vessels
should be seriously controlled in areas within 200 nautical miles (Nm) of
the coastline of North America and within 50 Nm of the included islands in
the United States Caribbean Sea. In China, the Domestic Emission Control
Area (DECA) is approved as a 12 Nm zone along the coastline in Bohai Rim,
YRD and PRD regions, and the sulfur content of any oil used on-board vessels
entering the DECA should not exceed 0.5 % after 2019. Whether reducing
shipping emissions in the 12 Nm DECA alone is enough to prevent ship-related
air pollution or not becomes an important issue. A study reported that even
if the DECA along the coast of PRD were expanded to 200 Nm, it would not
obviously reduce the air pollution from shipping emissions compared with the
effects of a 12 Nm DECA (Liu et al., 2018a). However, for other
coastal regions or cities in China, the rationality of the current DECA
policy is unknown.
Study area and
the contributions of different maritime areas for the total shipping
emissions. The yellow, red, gray and blue columns represent the amount of
shipping emissions in the areas within 12, 12–50, 50–100 and 100–200 Nm
of the Chinese coastline, respectively.
In this study, based on the shipping emission inventory with a high spatial
and temporal resolution within 200 Nm of the Chinese coast, the annual and
seasonal impacts of shipping emissions on PM2.5 concentrations in
eastern areas and some key regions and cities of China were investigated in
detail. The impacts of metrological conditions on PM2.5 pollution
induced by shipping emissions were further analyzed. In addition, a
source-oriented chemical transport model was applied to estimate
contributions of shipping emissions emitted from different maritime areas,
including within 12, 12–50, 50–100 and 100–200 Nm of the
coastline, to the inland PM2.5 concentrations. The results of this work
provided several suggestions for the development of DECA and related
policies.
Methodology
The models used in this study were the Weather Research and Forecasting Model
(WRF) with version 3.8.1 and the Community Multiscale Air Quality (CMAQ)
model with version 5.2, which were developed by US NCAR (National Center for
Atmospheric Research) and US EPA (Environmental Protection Agency),
respectively. To assess the influences of shipping emissions on air quality,
the WRF–CMAQ system was applied to simulate the PM2.5 with (base case)
and without shipping emissions (no ship case) during January, April, July
and October 2015, which represented winter, spring, summer and fall,
respectively, with 3 days of spin-up time for each run. As shown in Fig. 1,
the modeling domain covered all of China and some parts of East Asia with a
horizontal resolution of 36 km × 36 km, including three highly
developed city clusters (BTH, YRD, PRD). A total of 13 highly populated coastal
cities were selected to further discuss their air quality impacts from
shipping emissions, including Dalian, Tangshan (in BTH), Tianjin (in BTH),
Yantai, Qingdao, Lianyungang, Hangzhou (in YRD), Shanghai (in YRD), Ningbo
(in YRD), Fuzhou, Shenzhen (in PRD), Guangzhou (in PRD) and Zhuhai (in PRD),
and most of them had core ports. The first guess field and boundary
conditions for WRF were generated from the 6 h NCEP FNL Operational Model
Global Tropospheric Analyses dataset. The four-dimensional data assimilation
(FDDA) was enabled using the NCEP ADP global surface and upper air
observational weather data (http://rda.ucar.edu, last
access: 25 October 2018). WRF and CMAQ used 32
vertical layers up to 100 hPa, and the lowest layer had a thickness of
approximately 37 m. The major physical options in WRF included a Morrison
two-moment microphysics scheme (Morrison et al., 2009), a Kain–Fritsch cumulus
cloud parameterization (Kain, 2004), the Rapid Radiative Transfer Model
(RRTM) longwave and shortwave radiation scheme (Iacono et al., 2008), the
Pleim–Xiu Land Surface Model (Xiu and Pleim, 2001), and the Asymmetric
Convective Model version 3.0 for the PBL parameterization (Pleim, 2007). In
the latest version 5.2 of CMAQ the aerosol module version 6 (AERO6) was
updated to reflect the recent advances in the formation of PM especially for
secondary organic compounds. In this study, the CMAQ model was configured to
use the CB05 gas-phase mechanism and the AERO6 aerosol module with
aqueous chemistry.
A source-oriented CMAQ based on version 5.0.1 was also used in this study to
determine source contributions to inland PM2.5 concentrations from
shipping emissions of different maritime areas within 0–12, 12–50, 50–100
and 100–200 Nm to the coastline. This model tracked primary PM (PPM) and
sulfate–nitrate–ammonium (SNA) ions and their precursors from different
sources or source regions using tagged model species. Details of the
source-oriented approach for PPM and SNA have been documented in Ying et
al. (2008) and Ying and Kleeman (2006), respectively. The source-oriented
CMAQ models have been applied successfully to study source and source region
contributions in China (Zhang et al., 2012; Ying et al., 2014b; Hu et al.,
2015, 2017; Shi et al., 2017; Qiao et al., 2018). In this study, the
source-oriented CMAQ was configured to use SAPRC-07 as the gas phase
mechanism and AERO6 as the aerosol module. Updates were made to the aerosol
model to improve predictions of sulfate and secondary organic aerosol (SOA)
(Ying et al., 2014a, 2015; Li et al., 2015). Shipping emissions from
different distance ranges to the coastline, as determined using a geographic
information system (GIS), were tracked with different tagged species to
quantify their contributions. Emissions from other anthropogenic and biogenic
sources were represented by non-tagged model species. The same emission
inventories used to generate the base case simulations in CMAQv5.2 were also
applied for the emissions in the source-oriented CMAQ model so emission
totals at each grid cell remained the same.
A bottom-up shipping emission inventory within 200 Nm of China was used with
a high horizontal resolution of 0.01∘×0.01∘ for the
base year of 2013, including four main reactive species: SO2,
NOx, PM and hydrocarbon (HC) (Liu et al., 2016). The
ocean-going vessels considered in this study were classified by 10
classification schemes, and lumped into four main types by cargo types,
including cargo ship, container, tanker and others, as described in Table S1
in the Supplement. The distinct spatial characteristics of shipping emissions
in this inventory can be accurately depicted in both regional
and port scales (Liu et al., 2016; Fu et al., 2017). To capture the seasonal
variation of shipping emissions more accurately, based on the timestamp of
each Automatic Identification System (AIS) data and the shipping emission
inventory model (SEIM) introduced in Liu et al. (2016), the monthly emissions
from ships were recalculated, then used in the air quality models. Moreover,
the HC emissions from ships were assigned to specific VOC species based on
the measurement of VOC source profiles in our previous work (Xiao et al.,
2018), then mapped into CB05 and SAPRC-07 lumped species, respectively, shown
in Tables S2–S3. For PM speciation, the composition of the PM at 2.7 %
sulfur in fuel was used reported by Buhaug et al. (2009), and the black
carbon (BC), organic carbon (OC) and primary sulfate (PSO42-)
fractions were set to 5.1 %, 10.0 % and 45.1 %, which was
consistent with the work of Eyring (2005).
The land-based anthropogenic emission inventory for mainland China was from
the Multi-resolution Emission Inventory for China (MEIC) data at a resolution
of 0.25∘×0.25∘ for the base year of 2015 (MEIC,
http://www.meicmodel.org/, last access: 25 October 2018), but the vehicular evaporation emissions were not included,
which were responsible for 39.20 % of total VOC emissions from the on-road
sector (Liu et al., 2017a). Therefore, in this study, the vehicular
evaporation emissions at a provincial level in China calculated in Liu et
al. (2017a) were allocated to gridded emissions based on the spatial
distribution of the road network. The MIX inventory was selected to characterize
the emissions of anthropogenic sources from other countries in our domain (Li
et al., 2017). For emissions from natural sources, the Model of Emissions of
Gases and Aerosols from Nature (MEGAN) version 2.04 was used (Guenther et
al., 2006). Open burning emissions used in this work were developed by Cai et
al. (2017). Emissions from windblown dust and sea salt were calculated inline
during the CMAQ run.
Model evaluation
In this work, the meteorological data at every 1 or 3 h (most at 3 h) from
14 meteorological stations, which were located in or near the core coastal
cities mentioned above (Fig. S1), were obtained from the National Climate
Data Center (NCDC, ftp://ftp.ncdc.noaa.gov/pub/data/noaa/, last access:
25 October 2018) integrated surface database. The
model performance of four major meteorological parameters was evaluated:
temperature at 2 m (T2), wind speed at 10 m (WS10), wind direction at 10 m
(WD10) and relative humidity (RH). The criteria proposed by Emery et
al. (2001) was used to judge the meteorological performance (the mean bias
(MB) ≤±0.5 K for T2, MB ≤±0.5 m s-1 for WS10 and
MB ≤±10∘ for WD10). High correlation coefficients (R,
0.5–0.9) and low normalized mean errors (NMEs, 6 %–38 %) proved that
the model performances were acceptable, although the MB of T2 and WS10 were a
litter higher than the suggested goal (Table S4).
We also estimated the model performance of CMAQ v5.2 in predicting the
PM2.5 concentrations by comparing the modeled results with observations
at 280 monitoring sites of 32 provincial capital cities in China, as
described in Table S5. The real-time hourly observations were from the China
National Environmental Monitoring Center (CNEMC,
http://106.37.208.233:20035/, last access: 25 October 2018), which began to be released since January 2013. The
simulated hourly PM2.5 agreed well with observations, with the
overall model performance within the performance criteria suggested by Boylan
and Russell (2006) (mean fractional bias (MFB) ≤±60 % and mean
fractional error (MFE) ≤±75 %), while the model
overestimated PM2.5 a little, mainly due to uncertainties in emission inventory
and unavoidable deficiencies during meteorological and air quality
simulation. In order to verify the spatial accuracy of the simulation, the
observed annual mean concentrations of PM2.5 at all sites in the domain
were compared with modeling results, as shown in Fig. S2. Model performance
was better in coastal areas of eastern China where the economy was more
developed and the air quality suffered more serious impacts from shipping
emissions, compared to the less developed regions such as western China, due
to more accurate emission inventories in the more developed regions.
Furthermore, the differences in predicted concentrations of PM2.5 and
its components from CMAQ v5.2 and the source-oriented CMAQ were investigated
(Fig. S3). In general, the simulated PM2.5 were very similar, but slightly higher concentrations in CMAQ v5.2 were found compared with that in the
source-oriented CMAQ. The differences were mostly caused by the difference in
the SOA predictions as CMAQ v5.2 included additional SOA formation pathways
that were not included in CMAQ 5.0.1 (Woody et al., 2016; Murphy et al.,
2017). The predicted secondary inorganic aerosol concentrations showed
excellent agreement between the two models, which provided confidence in the
predicted source region contributions as described in the results section.
Spatial distributions of SO2 emissions from ships at a
resolution of 3 km × 3 km (unit, tonnes per grid) in (a) winter,
(b) spring, (c) summer and (d) fall.
Result and discussions
Shipping emission inventory with high resolution
The annual SO2, PM, NOx and HC emissions from
ships within 200 Nm of the coastline of China in 2013 were 918.4, 119.3,
1380.9 and 49.3 kt. The emissions were slightly lower than those reported by
Li et al. (2018), probably due to the differences in emission factors, AIS
data and ship registration databases. The increase in shipping emissions near
the coast of China was probably small from 2013 to 2015, because the global
CO2 emissions from ships only increased by 2.5 % over that
period (Olmer et al., 2017). SO2 and NOx emissions
from ships accounted for 20.0 % and 13.5 % of the inland emissions
from all sectors in coastal provinces of the MEIC inventory. The cargo ships
were the most important contributor to the total shipping emissions,
accounting for 43.7 %, 43.4 %, 41.9 % and 40.5 % of
SO2, PM, NOx and HC emissions. The container and
tanker also contributed 24.7 %–28.4 % and 17.5 %–19.7 % of
the total shipping emissions. However, emissions from fishing boats were
probably underestimated in this study (approximately 1.0 % of the totals)
since most of them had no AIS data, which could affect the air quality
significantly (Zhang et al., 2018). The emissions from ships within 12,
12–50, 50–100 and 100–200 Nm were further calculated to identify their
contributions (Fig. 1). The emissions within 12 Nm of the shore were the
dominant contributors of all pollutants, accounting for
51.2 %–56.5 % of the total shipping emissions. Emissions within
50–100 Nm only accounted for 10.2 %–11.9 % of the totals, which
was the least among the four regions. The areal emission rates of
SO2, PM, NOx and HC within 12 Nm were 1494.4,
185.7, 2033.5 and 73.3 kg km-2, respectively, approximately 5.1–6.3
times higher than those within 100–200 Nm.
Contributions of shipping emissions to the annual mean
PM2.5 concentrations, including (a) absolute contributions (base case
– no ship case) and (b) relative differences (relative to the base case
concentrations).
The seasonal variation of shipping emissions was analyzed using the monthly
data from January (winter), April (spring), July (summer) and October (fall).
The highest shipping emissions of all pollutants were observed in winter,
which was on average 1.04, 1.06 and 1.16 times higher than those in spring,
summer and fall, respectively. Generally, the changes of total shipping
emissions quantities in different seasons were quite small. This pattern was
consistent with other studies with similar conclusions (Corbett et al.,
1999; Fan et al., 2016; Li et al., 2016). The season variations in the
spatial distribution of shipping emissions were also investigated as
presented in Fig. 2. Overall, in winter, the shipping emissions were more
concentrated along the major lanes between ports than those in other seasons.
In addition, the distinct changes were observed in three areas, including the
maritime area about 150 km away from the YRD harbors (A1), the southeast of
the Taiwan Strait (A2) and the vicinity of Fuzhou port (A3). These seasonal
changes were closely related to the activity variations of different ship
types (Figs. S4–S6). In spring and summer, mainly due to the increase in
long-distance cargo ships, significant emissions occurred in water traffic
lanes far from the YRD region (A1). The decrease of cargo ship activities in
Fuzhou port during summer and fall also resulted in the obviously reduced
shipping emissions in A2. The emissions in A3 were lower in summer and fall
because of the decreased activities of all ship types, including cargo ships,
containers and tankers. The variations in spatial distribution indicated
that monthly shipping emissions used in the air quality model of this work
would capture the seasonal impacts more accurately than annual emissions
without considering monthly variations.
Contributions of shipping emissions to the annual mean
concentrations of total PM2.5 and detailed species in core coastal
regions and cities (base case – no ship case).
MD for annual and seasonal impacts of shipping emissions
in kilometers (km).
MD
MD
MD
(ΔC>0.1 µg m-3)
(ΔC>0.5 µg m-3)
(ΔC>1 µg m-3)
Annual
960
510
350
Winter
880
410
210
Spring
850
440
360
Summer
1300
520
380
Fall
1080
840
520
Annual PM2.5 impact from shipping
emissions
Based on the results of the CMAQ model with (base case) and without shipping
emissions (no ship case) within 200 Nm, the contributions of shipping sector
to the inland PM2.5 concentrations were estimated. The annual averaged
contribution of the shipping emissions to the increased concentration of PM2.5
(ΔCannual) in 2015 was calculated by averaging the modeling results of
January, April, July and October, as presented in Fig. 3. The increased
PM2.5 concentration in eastern China caused by shipping emissions was
up to 5.2 µg m-3, and as the distance from the coastline increased,
ΔCannual decreased dramatically. The most serious impacts were
predicted in coastal areas of YRD region (more than 2.5 µg m-3),
where large shipping emissions contributed to 20 % of the total shipping
emissions in China (Fu et al., 2017). However, due to higher background
PM2.5 concentrations mostly induced by land-based anthropogenic sources
in China (Fig. S2), the annual mean relative contribution of shipping
emissions was less than 12 %, except for Taiwan, which was much smaller
than that in Europe (Aksoyoglu et al., 2016). For the same reason,
although ΔCannual was smaller in Fuzhou and its surrounding areas
than that in YRD, the relative contribution were approximately 2–4 times
higher. In addition, in order to quantify how far the shipping emissions can
be carried to inland areas, the maximum linear distance (MD) between
coastline and areas where the PM2.5 concentration induced by ships was
larger than a specific threshold was calculated by GIS (Table 1). The MDs
of ΔCannual >0.1,
0.5 and 1 µg m-3 were 960, 510 and 350 km,
respectively. The farthest areas affected by shipping emissions were
typically located in a similar latitude to YRD (approximately
32∘ N), probably since the low terrain heights in this region were
favorable for the long-range transport of air pollutants from sea to the
inland areas (Fig. S1). The results illustrated that the shipping emissions
influenced the air quality in not only coastal areas but also the inland
areas as far as hundreds of kilometers away from the sea.
The shipping emissions caused not only the increase in PPM (element carbon
(EC), primary organic aerosol (POA) and primary sulfate (PSO42-)),
but also secondary PM (secondary sulfate (SO42-), nitrate
(NO3-), ammonium ion (NH4+) and SOA) formed from primary
emitted precursors. The contributions of shipping emissions to the annual
mean concentrations of the total PM2.5 and individual components in core
coastal regions and cities were obtained by averaging the modeled
concentrations in all grids of each area (Fig. 4). The averaged increases in
PM2.5 concentration in the whole of China and all coastal provinces were
0.2 and 1.3 µg m-3, respectively, and the impacts of shipping
emissions in YRD (2.0 µg m-3) and PRD
(1.6 µg m-3) were much greater than those in BTH
(0.4 µg m-3), where the shipping emissions were more
concentrated at port level (Fu et al., 2017). The top four cities most
seriously affected by shipping emissions were Qingdao
(4.0 µg m-3), Shanghai (3.8 µg m-3), Ningbo
(3.8 µg m-3) and Dalian (3.2 µg m-3). In
addition to Qingdao, high contributions of the maritime sector were also
observed in other coastal cities outside the three core city clusters, such
as Lianyungang (2.0 µg m-3) and Fuzhou
(2.3 µg m-3), which indirectly indicated that the existing
DECA was not long enough in the north–south direction to prevent
ship-related air pollution, and it should extend to the entire coastal area
of eastern China in the future.
Contributions of shipping emissions to the seasonal mean PM2.5
concentrations (base case- no ship case): (a) winter, (b)
spring, (c) summer and (d) fall. Arrows represent the WRF
modeled seasonal mean surface wind field.
The most important components of PM2.5 contributed by shipping emissions
were SNA, accounting for 82.7 %–97.6 % of the total PM2.5
increase, and regional averaged contributions of SO42-,
NO3- and NH4+ were 49.2 %, 24.7 % and 15.8 %.
The changes of SNA fraction in different areas were both related to shipping
emissions and land-based emissions because the NOx and
SO2 emissions from ships and ammonia emissions from the land led to
the formation of secondary ammonium nitrate and ammonium sulfate. The concentrations of ship-emitted
EC and the ratio of PSO42- / EC in primary emissions (8.8 times)
from ships were used to calculate the concentrations of ship-induced primary
sulfate. The majority of the
ship-induced sulfate was formed secondarily from oxidation of SO2,
and only about one-third of it was from primary emission. The proportions of
EC and organic aerosol (OA, the sum of POA and SOA) were relatively small due
to a small amount of SOA formed from HC shipping emissions and the small
fractions of EC and POA in PM emissions from ships. Moreover, we determined
whether the concentrations of PPM or the secondary PM2.5 was affected
more by shipping emissions. In all regions and cities, the PM2.5
pollution caused by shipping emissions was dominated by secondary species,
with a mean contribution of 78.8 %. The mandatory fuel oil standard
(0.5 % sulfur limit) in DECA would reduce the ship-induced sulfate and
PPM concentrations, while our results illustrated that this control policy
alone was not enough to reduce the total PM2.5 concentrations,
particularly for nitrate and ammonium. After-treatment techniques should also
be applied to the marine diesel engines to reduce NOx
emissions, and more efforts should be made to reduce NH3 emissions
from land-based sources, which is beneficial in reducing the secondary
NO3- formation from shipping emissions.
Seasonal PM2.5 impact from shipping
emissions
The changes in the mean PM2.5 concentration caused by shipping emissions
in each season (ΔCseasonal) were analyzed separately (Fig. 5). Large
seasonal variations in peak concentrations and the extent of impacted areas
could be observed, and the changing patterns in northern and southern areas
were also quite different. The largest impact of shipping emissions in the
north of the Yangtze River was predicted in summer, with ΔCseasonal
more than 5 µg m-3 in most coastal areas of the Bohai Rim, the YRD
and the surrounding areas of Qingdao. The impact during winter was less
significant, with ΔCseasonal less than 2.5 µg m-3.
The seasonal variations in southern areas presented an opposite trend. Ship
emissions were predicted to increase the PM2.5 concentrations by more
than 2 µg m-3 in spring and fall but there are fewer effects in summer.
The longest MD (ΔCseasonal >0.1 µg m-3) was calculated in summer with the value of
1300 km, showing that the emissions of ships could almost affect the
northernmost areas of China (Table 1), while when the threshold of
ΔCseasonal increased to 0.5 and 1.0 µg m-3, there
was larger MD in fall compared with those in other seasons, which indicated
that central China, hundreds of kilometers away from the sea, was
critically influenced by ship-related air pollution.
Contributions of shipping emissions to the seasonal
PM2.5 in core coastal regions and cities (base case – no ship case).
The differences in emission quantities and spatial distributions of vessels
in the selected months alone could not explain the predicted seasonal
variations. For example, in the YRD region, the emissions were larger and more
concentrated in areas near the land in winter, but the contributions were
still the lowest in the whole year. Therefore, these seasonal variations
were probably related to the temporal changes of the meteorological
conditions, particularly the direction of the prevailing winds, which was
crucial for the diffusion and long-range transport of shipping emissions.
Eastern China lies in the perennial monsoon region, and the summer monsoons
could carry air pollutants related with shipping emissions to inland areas
(Fig. 5c), while in winter, few contributions were observed in the BTH region
and its surrounding areas, mainly due to the dominated wind from the
northwest (Fig. 5a).
Pollution rose maps in coastal cities: (a) Qingdao, (b)
Shanghai, (c) Ningbo and (d) Dalian. The colors represent the daily
contributions of shipping emissions to the total PM2.5 concentrations,
and the baseline means the annual average of daily contribution. Max is the
maximum daily increased PM2.5 concentrations by shipping emissions.
The onshore WD is the wind direction in which marine air could be transported over land.
The contributions of shipping emissions to seasonal PM2.5
concentrations in coastal core regions and cities were further quantified
(Fig. 6). The effects were most evident in summer for China and the entire
coastal areas, which were 2.5 and 3.0 times higher than those in winter,
respectively. In specific regions and cities, the great differences between
the seasonal impacts could not be ignored. In summer, shipping emissions
increased the PM2.5 concentrations in Qingdao, Dalian and
Yantai by 9.4, 6.9 and 5.4 µg m-3, which were
16.4, 26.6 and 11.9 times larger than the smallest seasonal effects in
winter, respectively, while for Zhuhai and Guangzhou, the seasonal impacts in
spring were around 2 times higher than those in summer. Additionally,
in almost all coastal regions and cities, the seasonal relative
contributions of the shipping emissions to inland PM2.5 concentrations
reached the peak in summer (2.2 %–18.8 %), indicating it played an
important role in the inland PM2.5 air pollution during this period. It
should be noted that although in the PRD region ΔCseasonal was the
lowest in summer, the relative contribution of shipping emissions still
remained at a high level. These were mainly because in eastern China, low
emissions of land-based sources (e.g., fossil fuel combustion, biomass
burning) and favorable meteorological conditions (e.g., large wet
depositions, higher atmospheric mixing layer) in summer led to cleaner
PM2.5 background level (Zhang and Cao, 2015).
Contributions of shipping emissions in different maritime
areas to the total ship-induced PM2.5 concentration: (a) within
0–12 Nm, (b) within 12–50 Nm, (c) within 50–100 Nm
and (d) within 100–200 Nm.
Influences of the onshore wind
Since the direction of the prevailing wind had an important role in the
eventual impacts of emissions from ships on the inland air quality,
relationships between their daily means in four cities most affected by
shipping emissions were quantified (Fig. 7). The daily mean surface wind
directions in the local area were calculated from WRF modeling results by
vector-averaging. Based on the geographical location of the selected cities,
the wind direction regarded as onshore was identified. To determine the
daily impacted levels from shipping emissions, the annual averaged daily
contribution of marine transport sectors to the PM2.5 concentrations
was defined as a baseline value in each individual city. The results showed
that coastal locations frequently experienced onshore winds that transported
marine air over land. In Qingdao, Shanghai, Ningbo and Dalian, 48.0 %,
63.4 %, 48.8 % and 61.0 % of the days were considered as onshore flow
days, respectively. On these days, higher relative contributions from the
shipping sector to the inland air quality were observed. This was because the
onshore wind could carry not only the ship-related air pollutants, but also
the clean air from sea to land, which could cause a low PM2.5 background
level in inland areas. During the onshore flow days, shipping emissions led
to a ∼4.7 %–17.1 % increase on average in the inland PM2.5,
about 1.8–2.7 times higher than that in rest of the days. The frequency of heavy
shipping polluted days (daily relative contributions 1.5 times larger than
the baseline) along with the onshore wind was 82.4 %–100.0 %, and
simultaneously the inland daily PM2.5 concentrations increased by up to
20.3 %–38.1 % (10.5–26.8 µg m-3) due to shipping emissions. This
revealed that in the period of frequent “onshore” winds, such as in summer
for YRD and BTH regions, shipping emissions probably became one of the most
important contributors to the air pollution. However, not all onshore wind
would cause high contributions of ships, and it also depended on the spatial
distribution of shipping emissions in surrounding marine areas. For example,
during the period of westerly and northwesterly onshore flows in Dalian, the daily
contributions were typically small, due to the relatively fewer emissions
emitted from ships in the northern part of the Bohai Sea.
Contributions of shipping emissions from different maritime
areas
The contributions of shipping emissions from maritime areas within 0–12,
12–50, 50–100 and 100–200 Nm to the total ship-related PM2.5 in
summer were identified, respectively, using the source-oriented CMAQ model
(Fig. 8). Only PPM and SNA formed from shipping emissions were tracked, not
including SOA, which was quite a small portion (discussed in Sect. 3.2),
and we assumed that the sum of them represented the concentration of the
total ship-related PM2.5. Overall, shipping emissions within 12 Nm were
the dominant contributor with the contributions of 30 %–90 % over the
mainland China and more than 80 % for coastal areas of three major city
clusters. As the distance away from the land increased, fewer air pollutants
from shipping emissions were transported to the inland areas, and effects of
emissions within 100–200 Nm only accounted for less than 20 % in most
inland areas.
Due to the differences in distributions of shipping emissions, the
surface structure and meteorological conditions, the contributions of
emissions from ships within each maritime area may be diverse for individual
inland areas. In terms of the areas located in the north of the YRD region and
south of Lianyungang, due to the major water traffic lanes far from land
around A4 and the increase in shipping emissions around A3 in summer
(Fig. 2), significant emissions from ships within 12–100 Nm led to a
relative higher contribution (40 %–60 %) in these areas, when the
definite seasonal contributions of shipping emissions to the local PM2.5
concentration occurred (approximately 7 %). Moreover, some middle parts
of China, including Henan province, Hubei province and Hunan province, were
also significantly affected by the long-range transport of shipping emissions
away from the coastline with the distance more than 12 Nm, probably due to
the benefits of low terrain heights. Our results implied that DECAs within
12 Nm were not large enough in the latitudinal direction to prevent the
PM2.5 pollution from shipping emissions, particularly for YRD region and
its surrounding areas, and should be expanded to at least 100 Nm to the
coastline.
Conclusions
The effects of shipping emissions on the PM2.5 concentrations for the
base year of 2015 in China were described in details, using air quality
models with an emission inventory of ocean-going vessels at a high spatial
and temporal resolution. The annual SO2, PM, NOx
and HC emissions from ships within 200 Nm of the coastline of China in 2013
were 918.4, 119.3, 1380.9 and 49.3 kt. These emissions led to the largest
increase of 5.2 µg m-3 in annual averaged PM2.5
concentrations in eastern China, and the impacts in YRD
(2.0 µg m-3) and PRD (1.6 µg m-3) were much
greater than those in BTH (0.4 µg m-3). Qingdao
(4.0 µg m-3), Shanghai (3.8 µg m-3), Ningbo
(3.8 µg m-3) and Dalian (3.2 µg m-3) suffered
the heaviest PM2.5 pollution caused by shipping emissions among all
selected coastal cities. The ship-emission-impacted areas ranged from ∼350 to 960 km based on the cut-off concentrations of 1 and
0.1 µg m-3 from ship contributions. SNA accounted for
82.7 %–97.6 % of the total PM2.5 increase, and the ship-induced
concentration of secondary species was much higher than that of PPM.
Although no significant differences were observed in the amount of shipping
emissions in different seasons, the obvious seasonal variation in
distributions of shipping emissions was detected in some marine areas.
Distinct seasonal variations of impacts from shipping emissions were also
observed, mainly due to the changes of the direction of the prevailing winds,
and patterns in the north and south of the Yangtze River were also quite
different. In summer, the seasonal impacts were more than
5 µg m-3 in coastal areas of BTH, YRD and its northern
region, and relative contributions reached 2.2 %–18.8 % in all coastal
areas, while little impact occurred during winter. Furthermore, the
relationships between the daily contributions of shipping emissions and the
dominant wind directions were discussed. A close association between onshore
flow and significant increase in shipping contributions to PM2.5 was
predicted. During the onshore flow days, shipping emissions led to
a ∼4.7 %–17.1 % increase on average in the inland
PM2.5, about 1.8–2.7 times higher than that in the rest of the days.
The contributions of emissions within 12, 12–50, 50–100 and 100–200 Nm
accounted for 51.2 %–56.5 %, 15.8 %–17.4 %,
10.2 %–11.9 % and 17.5 %–19.7 % of the total shipping
emissions within 200 Nm, respectively, while their contributions to the
total PM2.5 impacts from ships in summer were quite different from their
shares in emissions. Shipping emissions within 12 Nm were the dominant
contributor, with contributions up to 30 %–90 % and more than
80 % for coastal areas of three major city clusters. Since there were
major water traffic lanes far from land in the north of the YRD region and south
of Lianyungang, a relatively high contribution (40 %–60 %) of
shipping emissions within 20–100 Nm was predicted.
Based on the analysis of our model results, some recommendations for
controlling shipping emissions in China can be made: (a) the area of DECA
should expand from three core city clusters to the entire coastal area of
eastern China; (b) in addition to using fuel with low sulfur content in
DECA, the after-treatment techniques should also be applied to the marine
diesel engines to reduce NOx emissions, and more efforts should be
made to reduce NH3 emissions from land-based sources; (c) to reduce the
major impacts of shipping emissions to PM2.5 concentrations in most
coastal areas of China, the DECA should be expanded to at least 100 Nm
to the coastline.