Emissions from ships at berth play an important role regarding the exposure
of high density human populations to atmospheric pollutants in port areas;
however, these emissions are not well understood. In this study, volatile
organic compounds (VOCs) and particle emissions from 20 container ships at
berth were sampled and analyzed during the “fuel switch” period at Jingtang
Port in Hebei Province, China. VOCs and particles were analyzed using a gas
chromatography-mass spectrometer (GC-MS) and a single particle aerosol mass
spectrometer (SPAMS), respectively. VOC analysis showed that alkanes and
aromatics, especially benzene, toluene and heavier compounds e.g.,
The emission of numerous pollutants and greenhouse gases such as
Emission characteristics, including the size distribution of particles, the chemical
compositions of particles and the presence of volatile organic compounds (VOCs) are of
particular importance to understanding the climate and health impacts of
shipping. Numerous studies on emission factors and chemical characteristics
have been conducted throughout the world. In terms of research focused on gaseous pollutants and PM
emission factors in China, CO, HC,
Based on the abovementioned studies, it can be concluded that one of the
major gaps in exploring emission characteristics is understanding specific
shipping emissions when ships are in their “at-berth operating mode”. The significance of
ship emissions at berth is attributed to the fact that the contribution of emissions
in this operating mode is mainly dominated by the auxiliary engine
emissions. This contribution rapidly increases as the target areas is further defined from a global scale to
just port regions. For global shipping emissions, the share of emissions from
auxiliary engines in at-berth mode was between 2 and 6 % (Corbett and
Koehler, 2003). When the domain was further limited to a regional scale, e.g., East
Asia, the contribution of
Apart from the significance of at-berth emissions, another critical issue
is the impact of different fuels on shipping emission chemical
characteristics. The main fuel types applied in ship main engines or
auxiliary engines are residential oil, heavy fuel or intermediate
fuel oil (Hays et al., 2008) as well as marine diesel oil. Low-grade
heavy fuel oil (HFO), known as bunker oil or residual oil, which usually has
a sulfur content over 0.5 % and contains metallic elements such as
vanadium, nickel and copper, has been commonly used in marine engines and is
responsible for high levels of PM and gaseous pollutants such as
In order to explore the chemical composition of VOCs and particulate matter (PM) from ship auxiliary engines, this study took place in a key port area and was designed to cover the primary period of new policy implementation. The study period ran from 27 December 2016 to 15 January 2017 in Jingtang Port, one of the key pilot ports for the new fuel switch regulation that came into effect on 1 January 2017. Twenty container ships were sampled and measured for VOC and PM emissions from auxiliary engines in at-berth mode. Via the application of gas chromatography-mass spectrometry (GC-MS) and single particle aerosol mass spectrometry (SPAMS), this research conducted a comprehensive exploration of VOC profiles, PM size distribution and typical ion mass spectra, which could be fundamental to impact assessments of shipping emissions and source apportionment in key port regions.
The ambient sampling site was located in Jingtang Port, Tangshan City, Hebei
Province, China. Jingtang Port is located in Bohai Bay and belongs to the Port
of Tangshan, which is one of the core ports in the domestic emission control
area. According to the China Port Yearbook 2015, the annual ship traffic
in the Port of Tangshan reached 15 084 and the total throughput exceeded 500 million tons, ranking it 5th among global port throughputs.
Jingtang Port area is surrounded by the “Port Economic Development Area”, which has a population
of 78 300. Tangshan is a typical industrial city with an average PM
As is shown in Fig. 1a, the population center is mainly concentrated in a residential area, located to the north, about 2 km away from the port. Approximately 2.5 km to the west of the port area there is a thermal power plant with after-treatment facilities, which operates according to the strict emission control standards of power plants in China. Between the port and the other zones are two main roads that trucks use to carry containers in and out of the port; the roads are about 1 km away from the sampling site. Besides trucks and the power plants, there are no further emission sources near the port area.
The site where ambient particle collection and instrumental analysis was carried out is surrounded by four pools and a channel, and is located on an open, flat corner close to the Number 26 and Number 27 berths as well as the container yard inside the port (Fig. 1b). No tall buildings exist around the sampling instrument. The distribution of the berths, the pools and the sampling site guarantees that plumes from ships at berth are prone to reach the sampling instrument.
The information for the 20 container ships included in this study was collected via on-board inquiry and is listed in Table 2.
Abbreviations used in this text.
Brief information regarding the 20 ships sampled.
VOCs from ships at berth were sampled using Entech SUMMA canisters with a standard volume of 3.2 L. When auxiliary engines were in operation, 1 m Teflon tubing was stretched into the exhaust pipe and the other end was linked to the SUMMA canister. The flow rate was kept constant by an Entech CS1200ES passive canister sampler, which also filtered impurities like particles and ashes. The first batch of samples was regarded as preliminary tests in order to determine proper dilution factors, sampling time and flow rate. Sample dilution was conducted using an Entech 4600 dynamic diluter with various dilution factors ranging from 10 to 80, depending on the original sample concentration. An Agilent 5975C-7890A GC-MS was calibrated with standard gas and used for analyzing diluted VOC samples. A total of 93 VOC species were detected and the mass percentage of single compounds could be calculated according to the sample inlet volume, the dilution factor and the calibration curve. Owing to the limit of the chromatographic column property equipped on the GC-MS, alkanes and olefins with a carbon number smaller than four were not detected. VOCs with a carbon number larger than six are more relevant to the yield of secondary organic aerosols (Gentner et al., 2012).
In order to complement the quantification of lighter hydrocarbon compounds with two to three carbons, selected ion flow tube mass spectrometry (SIFT-MS) was applied, which has been recognized as a real-time analytical technique for combustion gases and components in exhaled breath (Smith and Spanel, 2010). This part of the analysis was done without pretreatments and took place immediately after the gaseous samples were taken from auxiliary engines. VOC samples were transferred from the canister to a Teflon bag using an Agilent headspace syringe with a standard volume of 10 mL and then diluted with nitrogen. The species that SIFT-MS quantified were not totally consistent with the GC-MS. According to the Photochemical Assessment Monitoring Stations (PAMS) and Toxic Organics-15 (TO-15) standard gas applied in GC-MS calibration, 51 species were selected and normalized by mass in SIFT-MS data. Among the 51 species, acetylene, ethane, ethene, propane and propene were the most significant low-carbon compounds that were not quantified by GC-MS. The mass proportion of these species was calculated according to the quantification results obtained by SIFT-MS analysis, and the impact on ozone forming potentials could then be evaluated.
Ship exhaust particles were collected directly from the exhaust pipes of the auxiliary engines on ships utilizing Tedlar bags and metal tubing designed specifically for particle sampling. The whole sampling process was achieved using a non-contact sampling box and air pump. Samples were then sent to the single particle aerosol mass spectrometer (Hexin Analytical CO., Ltd) (Li et al., 2011) as soon as possible to be analyzed. SPAMS shared a common principle and mechanism with the aerosol time-of-flight mass spectrometer (ATOFMS), in that it is frequently applied in the online measurement and analysis of single particle aerosol from heavy diesel vehicle exhausts (Shields et al., 2007) and biomass burning (Bi et al., 2011; Xu et al., 2017).
Ambient particle sampling was conducted from 27 December 2016 to 15 January 2017, spanning about 20 days. Ambient particles were sampled and analyzed by SPAMS, with the inlet fixed at a height of 3.6 m from the ground level.
Ion mass spectra with positive and negative ion information were derived
from SPAMS output, which were fundamental for further analysis. Both positive and
negative ions were in an
To identify each type of particle, ions with certain
Exhaust emissions from a total of 20 ships were sampled onboard and all samples
were diluted to a concentration of approximately 3–4 ppm in order to
guarantee the validity and accuracy of the GC-MS analysis. Ninety-three species were
detected for each ship sample. The sum of the mass concentration of the
93 identified species was defined as 1, which normalized the mass concentration of
single species. Four samples were excluded due to their irregularity after a 3-sigma
test for all data. The remaining 16 samples were then averaged by the percentage
of the mass concentration, and the VOCs speciation profile was subsequently obtained for all
16 ships; the ships had similar species mass concentration
distributions. A histogram of the VOC profile by mass percentage in shown in
Fig. 2 and the top 32 species are listed in Table 3. Alkanes and aromatics
dominated the total identified VOCs from ship auxiliary engine exhaust.
VOC source profile from ship auxiliary engine exhausts analyzed by GC-MS; made up of 93 species including PAMS and TO-15. VOC species are classified into alkanes, alkenes, aromatics, oxygenated compounds and haloalkanes, respectively. The top 32 species by weight percentage are listed in this figure.
Mass percentage of the top 32 VOC species from 16 container ships.
Based on VOC profiles, secondary organic aerosol (SOA) yields and ozone forming potentials (OFPs) for ships were
calculated. SOA yield values for individual precursors and for the definition of
non-precursors were referenced from Gentner et al. (2012).
It should be noted here that intermediate VOCs (IVOCs) were not identified
or quantified in this study, which may cause an underestimation of the actual
SOA yields. Furthermore, the VOC source profiles of three types of diesel trucks (light-,
middle- and heavy-duty trucks) from Yao et al. (2015) and the profiles of
heavy-duty diesel trucks from Huang et al. (2015) were
referenced to use in calculations and to compare with VOC profiles from this research.
The average VOC source profile for light-duty
gasoline passenger vehicles was also taken from previous research in China
(Cao et al., 2015) to make further comparisons. These comparisons have the
potential to provide insights into emission control strategies and fuel quality for
different sectors within the same country; therefore, comparisons among studies undertaken in
China make the most sense. The results are presented in Fig. 3, and the
average SOA yield for the 16 ships measured was 0.017 g SOA g
Comparison of SOA yields and OFP based on VOC source profiles with calculated results from previous studies (Yao et al., 2015; Cao et al., 2015; Huang et al., 2015). The purple dots represent ships tested in this study. Diesel trucks are divided into three types: light-duty diesel trucks (LDDTs), medium-duty diesel trucks (MDDTs) and heavy-duty diesel trucks (HDDTs).
The ozone forming potential showed a different trend to SOA generation, with
VOCs from ship exhaust displaying an approximately equal OFP to diesel and
gasoline vehicles. According to the maximum incremental
reactivity (MIR) (Carter, 1994b), lighter VOCs have a higher MIR scale,
which means that they contribute more to ozone formation. VOCs from ship exhaust
had a relatively low light hydrocarbon content, which lowered the
overall ozone forming potential. The average OFP of ship emitted VOCs was
2.63 g
Considering the impact of low carbon hydrocarbons on ozone forming potential, the quantification results by SIFT-MS were used to evaluate the underestimation caused by the absence of C2 and C3 hydrocarbons in GC-MS quantification. A total of 13 auxiliary engine exhaust samples were analyzed by SIFT-MS and the mass fraction of low carbon VOCs as well as their ozone forming potentials are presented in Fig. 4.
Figure 4 shows that besides Sample 1 and Sample 3, the total mass fractions of
the low-carbon compounds were below 0.05. Given the fact that SIFT-MS was not
able to analyze all 93 VOC species as the GC-MS did, the actual fractions could
be even lower. When using the maximum incremental reactivity scale from Carter (1994a),
the calculated OFPs of the low-carbon compounds in
Sample 1 to Sample 13 were between
0.02 and 0.47 g
Low carbon VOC mass fractions and corresponding ozone forming potentials.
In conclusion, VOCs from ship exhausts might play a more important role in secondary organic aerosol formation in port areas than diesel trucks and gasoline vehicles.
Samples collected by Tedlar bags and glass bottles from 20 ships at berth were analyzed using SPAMS. Excluding some invalid samples with very few particles, the average ion mass spectra of both positive and negative ions expressed in relative area were obtained after SPAMS analysis and classification (Fig. 5).
Average ion mass spectra derived from SPAMS analysis of 20 samples of ship exhaust. Standard deviations are given in the figure and typical ion peaks are also marked in both the positive and negative ion mass spectra.
The average ion mass spectra presented the overall characteristics of the total 20
ships. For positive ion spectrum, the
For the negative ion spectrum, the components were not as complex as for the positive ion spectrum.
The highest peak was
Based on single particle ion spectrum similarity and the ART-2a neural
network algorithm, particles from ship exhaust were further grouped into
clusters of 175, among which the top 86 clusters covered
95 % of total particle numbers, with the rest defined as
others. Clusters were set as
Following this, a composition analysis was conducted, the result of which are shown in Fig. 6.
It was shown that EC and ECOC particles dominated with respective proportions of
35.74 and 33.95 %, while
Manual classification of 10 types of particles from 20 samples and their proportion by numbers.
Particles with an aerodynamic diameter ranging from 0.2 to 2.5
In order to further analyze the size distribution information of different
types of grouped particles, Fig. 7 was developed to reveal the
variation in the proportion of different particles by size range. As OC particles only
accounted for less than 5 % of overall particles, they show no apparent trends
and were dispersedly distributed in each section. It can be observed that in
sections larger than 1.30
Specific size distribution of 10 classes of particles in the
0.2–2.5
Owing to the fact that all samples were collected after 1 January 2017,
the regulated date for the fuel switch, it could not be determined by date
whether ships had changed to diesel fuel. However, it was observed from the ion mass spectra
of individual ships that some ships had a high V
intensity while others did not (Ni is also an element mostly found in heavy
fuel, but SPAMS seemed to be less sensitive to Ni
detection (Agrawal et al., 2009)). There was also a considerable
difference in the
Vanadium and sulfate relative intensity correlation obtained from the ion mass spectra of 20 ships.
The two ships with vanadium signals higher than the others included a total
particle number of 30 009, 2633 of which were measured with ion mass
spectrometry. In the ion mass spectra of these ships, higher
Previous studies have illustrated that, owing to the vanadium contents in
heavy fuel, vanadium in the ambient atmosphere could be used as a tracer
indicating the heavy fuel combustion related to ship
exhausts (Celo and Dabek-Zlotorzynska, 2010). Field measurements
in Shanghai Port in 2011 revealed that the V
Owing to the working principle of SPAMS, the mass fraction of the vanadium element to other symbolic elements could not be derived, and Ni and La showed no apparent signals in SPAMS spectra. The main methodology was to make a comparison between the vanadium intensity of particles from ships to that of particles sampled from the ambient atmosphere. If the vanadium intensity in ship exhaust is commonly lower than that of the corresponding ambient data, then it is no longer a proper tracer to be used to identify ship exhaust sources. The calculation was performed using a similar method to the one previously mentioned. Due to the fact that samples and particle numbers detected by SPAMS differ quite apparently among ships, we defined the ratio of total intensity to SPAMS detected particle number as vanadium-related intensity. The overall results for the 20 ships sampled from 4 to 16 January as well as the ambient vanadium intensity are shown in Fig. 9.
Comparison of vanadium intensity of 20 individual ship particle samples to ambient particle samples from 1 to 16 January. The green line is the average vanadium intensity of ambient particles over time. The colored dots represent the average vanadium intensity of different ships distributed by sampling time.
Noticing that the ordinate was logarithmic, considerable distinction existed
among ships regarding vanadium intensity. The intensities in the exhausts from
17 ships were well below or comparable to the corresponding ambient intensity,
while Ship 2, Ship 13 and Ship 17 were observed to have apparently high vanadium
intensity. This phenomenon might revealed the fact that after fuel
substitution, vanadium from ship exhausts at berth would not appear to be
remarkably higher than that of the ambient atmosphere in most cases. Another
crucial piece of evidence is the elemental analysis of three diesel samples collected during field
measurements (shown in Table 4). Vanadium was not detected in any of the three diesel samples,
which was significantly different from the data from previous studies (38.0–133.8 mg kg
Elemental analysis of three fuel samples and comparison with previous studies.
IFO: intermediate fuel oil; MDO: marine diesel oil. Two of the fuel samples were from Ship 9 and Ship 19. The sample marked “unplanned ship” indicates that this ship was not among the 20 ships included in this study.
The primary goal of domestic ship emission control areas is to control
The first step was to identify and extract ship-source particles from the
total particles sampled and analyzed by SPAMS. This operation was based on
information obtained from the analyzed ion mass spectra after onboard
sampling. Then using algorithm calculation via MATLAB, certain particles were
identified as being from ship exhaust. The next step was to extract sulfate
particles from the identified particles from ships and ambient sources,
respectively. This step was accomplished by defining sulphates as particles
with
A total of 439 sets of data were included in the source apportionment analysis. By linear fitting for ship-source and ambient sulfate particle numbers, the Pearson correlation coefficient was 0.91 as shown in Fig. 10c; this indicated that sulfate particles from ships and those from ambient sources had a strong correlation with each other. Meanwhile, it can also be seen from the change tendency that the change of sulfate particles in ambient air always remained synchronous with ship-source particles, proving that ship-source sulfate particles made a relatively stable contribution to the ambient sulfate particle concentration.
The analysis was divided into two sections, using 1 January 2017 as the division.
Before 1 January 2017, it can be seen that (around 29 December)
both ambient and ship-source sulfate particles formed several peaks as did the ship-source
While it generally seems that the impacts noted in this study stem from the effects of the fuel switch policy on lowering sulfate contribution from auxiliary engines. The results were in fact also determined by a series of meteorological factors, such as wind direction and atmospheric layer stability. These issues will be discussed further in the following.
To better focus on the shipping emissions, we took wind direction data into consideration. The wind directions for the whole sampling period is shown in Fig. 10b. The geographic positions of berths and the wind directions meant that the berths, which were mainly distributed northwest, north and east of the sampling site, had plumes driven to the sampling site by winds from these directions. Moreover, no obvious emission sources other than ships at berth could have interfered with the ambient sampling.
Ambient data with wind directions ranging from northwest to southeast (clockwise) were extracted and separated by the 1 January 2017 cutoff. A total of 10 h with 37 825 particles and a total of 133 h with 682 176 particles were calculated before and after 1 January, respectively. The updated results for the ratio of sulfates identified as shipping emissions to ambient were 35 and 27 % before and after 1 January 2017, respectively, indicating a decrease of the at-berth shipping emission contribution to ambient sulfates.
Apart from wind direction, atmospheric layer stability is also a factor that
can affect particle diffusion in exhaust plumes. However, owing to the
experimental conditions and the obscure sampling location during the
measurement period, the actual planetary boundary layer (PBL) height and cloud
cover data were not available in this study; these factors can directly reflect
the atmospheric stability. Nevertheless, atmospheric layer stability is able to
be identified based on indirect parameters. According to a study on the
correlation between heavy haze episodes and synoptic meteorological
conditions in Beijing, China (Zheng et al., 2015b), polluted
periods have been associated with a lower PBL height. This means that the atmospheric
layer was found to be more stable during polluted periods than during clean periods. Moreover, according
to a study by Petäjä et al. (2016),
high particulate concentration decreases the height of the boundary
layer via feedback mechanisms. Therefore, it can be inferred that the
variation of PM
Based on the discussions above, temporal profiles of PM
Temporal profiles of PM
While the contribution of sulfate from ships showed an obvious converse
trend to pollution levels (
The explanation for this phenomenon can be summarized according to two aspects.
Firstly, during the winter haze periods in China, stable
atmospheric conditions are not solely to blame for increasing the total concentration of
PM
According to the source apportionment of sulfate particles in the port area, the number concentration contribution of sulfates from shipping emission at berth was lowered from 35 to 27 % after the switching fuel policy implementation on 1 January 2017. The stricter fuel sulfur limit did reduce the contribution of shipping emissions, but these emissions continue to play an important role in atmospheric pollution; electricity from land will is required to ameliorate this situation.
In general, PM and
Sampling of VOCs and particles from 20 ships at berth was conducted in
winter at Jingtang Port from 27 December 2016 to
15 January 2017. The average VOC source profile of container ships was
obtained, and SOA yields as well as ozone forming potentials were calculated
based on this source profile. Comparisons were made to diesel and gasoline
vehicles according to the source profiles from previous research. Secondary
organic aerosol yields and ozone forming potential were 0.017
SPAMS sampling and analysis provided information regarding particle average ion
mass spectra, manual grouping, total and specified group size distribution
as well as impact of ship-source sulfate particles on ambient atmosphere.
EC, ECOC and Na-rich particles were predominant species of the total particles sampled,
accounting for over 90.7 %. Size distribution indicated that
most particles concentrated in the range between 0.2 and 1.4
The issue of vanadium as tracer element was examined and the conclusion was drawn that after the fuel substitution, fuel vanadium contents had been significantly lowered. Therefore, vanadium from ship auxiliary engine exhausts no longer showed an obvious excess compared to the corresponding ambient data in most cases.
After identifying and extracting ship-source sulfate particles from ambient
sulfate particles during the entire sampling period, a temporal profile with
a resolution of 1 h was obtained. Comparing post-January 1st 2017 data to
that of December 2016, the number contribution of sulfate particles from
ships remained unchanged at a level of 24 %. When considering the wind
direction, with berths at upwind, the sulfate contribution of ships at berth
could be observed dropping from 35 to 27 % before and after the
implementation of switching oil policy. The contribution of shipping
emissions at berth to the ambient sulfates was lowered by the stricter sulfur
limit in fuels. The impact of atmospheric layer stability, reflected by the
concentration of PM
Data are available upon request.
QX mainly participated in the measurements and wrote the article. ML provided instrumental and analytical support during sampling and data processing. HL conceived this study and provided guidance on the whole research process as well as manuscript revision. MF participated in designing experiments and was responsible for pilot preparations. FD helped conduct on-board sampling. ZL contributed to manuscript revision. HM, XJ and SL contributed to setting instruments. KH provided constructive comments on this research. QX and ML contributed equally to this article.
The authors declare that they have no conflict of interest.
This article is part of the special issue ”Shipping and the Environment – From Regional to Global Perspectives (ACP/OS inter-journal SI)”. It is a result of the Shipping and the Environment – From Regional to Global Perspectives, Gothenburg, Sweden, 23–24 October 2017.
This work was supported by the National Natural Science Foundation of China (91544110&41571447), the Beijing Nova Program (Z181100006218077), the National Research Program for Key Issues in Air Pollution Control (DQGG0201&0207), the Special Fund of State Key Joint Laboratory of Environment Simulation and Pollution Control (16Y02ESPCT), the National Key R&D Program (2016YFC0201504), and the National Program on Key Basic Research Project (2014CB441301). Mei Li was supported by the National Natural Science Foundation of China (21607056). Edited by: Jianzhong Ma Reviewed by: two anonymous referees