Air pollution from ship exhaust gas can be reduced by the
establishment of emission control areas (ECAs). Efficient supervision of
ship emissions is currently a major concern of maritime authorities. In this
study, a measurement system for exhaust gas from ships based on an unmanned aerial vehicle (UAV) was designed and developed. Sensors were mounted on the UAV
to measure the concentrations of SO2 and CO2 in order to calculate
the fuel sulfur content (FSC) of ships. The Waigaoqiao port in the Yangtze
River Delta, an ECA in China, was selected for monitoring compliance with
FSC regulations. Unlike in situ or airborne measurements, the proposed
measurement system could be used to determine the smoke plume at about 5 m
from the funnel mouth of ships, thus providing a means for estimating the
FSC of ships. In order to verify the accuracy of these measurements, fuel
samples were collected at the same time and sent to the laboratory for
chemical examination, and these two types of measurements were compared.
After 23 comparative experiments, the results showed that, in general, the
deviation of the estimated value for FSC was less than 0.03 % (m/m) at an
FSC level ranging from 0.035 % (m/m) to 0.24 % (m/m). Hence, UAV
measurements can be used for monitoring of ECAs for compliance with FSC
regulations.
Introduction
With the rapid development of international shipping in recent years, air
pollution caused by ship emissions has become serious. Estimations show that
ships contribute 4 %–9 % of global SO2 emissions and 15 % of NOx
(Eyring et al., 2010). According to the United Nations Conference on Trade
and Development (UNCTAD, 2017), the volume of the world's seaborne trade
grew by 66 % between 2000 and 2015. As global commerce expands,
ocean-going ships consume more fuel, generally low-quality residual fuel
containing high concentrations of sulfur and heavy metals (Lack et al.,
2011). From the viewpoint of spatial distribution, the highest emissions of
SO2 per unit area occur in the eastern and southern China seas, sea
areas in southeastern and southern Asia, the Red Sea, the Mediterranean Sea, North
Atlantic near the European coast, Gulf of Mexico and Caribbean Sea, and
along the western coast of North America (Johansson et al., 2017). Liu et
al. (2016) reported that East Asia accounted for 16 % of global shipping
CO2 emissions in 2013, which was an increase compared to only 4 %–7 %
in 2002–2005. In the research of Russo et al. (2018), who evaluated the
contribution of shipping to overall emissions over Europe, this sector was
found to represent on average 16 %, 11 %, and 5 % of the total
NOx, SOx, and PM10 emissions, respectively.
In order to limit hazards caused by ship emissions, the International
Maritime Organization (IMO) extended the MARPOL 73/78 International Convention for the Prevention of Pollution from Ships
(MARPOL, 1997). In 2005, some regulations went into effect after being
accepted by appropriate laws of the signatory states (at the European level
it was received with the directives 1999/32/EC, 1999, and 2005/33/EC, 2005)
and introduced limits to marine fuel sulfur content and engine performance
to reduce SOx and NOx emissions. Further amendments to Annex VI
were adopted in 2008 and entered into force in 2010. Fuel sulfur content
(FSC) is normally given in units of percent sulfur content by mass, in the
following written as % (m/m). Following the IMO regulation, the global
cap for FSC in marine fuel was set in 2012 at 3.5 % (m/m), and it will be
reduced to 0.5 % (m/m) by 2020. In addition, the IMO provides for the
establishment of emission control areas (ECAs) to control ship emissions,
where there are more stringent controls on ship emissions. At present, the
Baltic Sea, the North Sea, the North American area, and the United States
Caribbean Sea are designated as ECAs (IMO, 2017). The FSC limit was set to
0.1 % (m/m) in those areas beginning in 2015.
China is one of the world's busiest and fastest-growing shipping regions. In
2016, China accounted for 7 of the world's top 10 ports and 11 of the
top 20. In order to reduce the air pollution caused by ship emissions, the
Atmospheric Pollution Prevention and Control Law of the People's Republic of
China was promulgated in 2015 (Standing Committee of the National People's
Congress, 2015). Three domestic emission control areas (DECAs) were set up,
which include the Yangtze River Delta, the Pearl River Delta, and Bohai Rim
(Beijing–Tianjin–Hebei region). The current stage of the plan requires that
the FSC does not exceed 0.5 % (m/m).
With the above regulations in place, the main question of how to
efficiently verify compliance of ships in the ECAs with the regulation remains. At
present, the most accurate method for checking compliance is to collect fuel
samples from ships at berth by state port control authorities and then
analyze the samples at certified laboratories or by portable detectors.
However, it is time consuming and few ships are effectively controlled.
Another problem is that sailing ships within the ECAs are not checked.
Several studies have suggested inferring FSC by monitoring ship emissions
and then identifying ships with excessive FSC. According to the available
literature, these approaches include optical methods (lidar; Fan et al.,
2018; differential optical absorption spectroscopy (DOAS); Seyler et al.,
2017; UV camera; Prata, 2014) or “sniffing” methods (Balzani Lööv
et al., 2014; Beecken et al., 2014). Optical methods analyze the variation
of the light properties after interaction with the exhaust plume and allow,
if the local wind field is known, operators to determine the emission rate of
SO2. The simultaneous measurement of CO2 and SO2 emissions on
a routine basis with these systems is unrealistic at the moment (Balzani
Lööv et al., 2014). Thus, the amount of fuel burned at the time of
measurement is unknown and has to be estimated via modeling to calculate the
FSC. For instance, the model STEAM (Ship Traffic Emission Assessment Model),
developed by the Finnish Meteorological Institute (Jalkanen et al., 2009),
was used in research for estimating FSC by Balzani Lööv et al. (2014). In addition, using the ratio of SO2 and NO2 measured via
DOAS in the ship's plume can be used as an indicator of FSC (Johan et al.,
2017; Cheng et al., 2019). The advantage of the optical method is that it
can detect ship emissions at a long distance (thousands of meters away), but
it is limited in that it can only distinguish between a high FSC
(> 1 % (m/m)) and a low FSC (< 1 % (m/m)) (Johan et
al., 2017). The sniffing methods are based on simultaneous measurement
of elevated SO2 and CO2 concentrations in the exhaust plume from
the target ship and comparing them with the background. The measurement of
CO2 allows for relating the measurement of SO2 to the amount of
fuel burned at a given time, thus enabling the calculation of FSC directly.
The concentration of SO2 in plumes was generally measured using UV
fluorescence sensors, and CO2 was measured using a nondispersive
infrared analyzer (NDIR) or cavity ring-down spectrometer (CRDS). The
advantage of the sniffing method is that it offers more accurate
estimation for FSC. However, the instrument must be placed in the plume
exhausted by the target ship. In some studies (Van Roy and Scheldeman,
2016a, b), the sniffing method offers a measurement accuracy between
0.1 % and 0.2 % (m/m) FSC, which can be further increased up to 0.05 %–0.1 %
(m/m) FSC if combined with an additional NOx sensor. This is because
the response of SO2 analyzers (fluorescence) has a cross-sensitivity to
NO. Deviations are not the same at different FSC levels, with an estimated
relative uncertainty of 20 % (m/m) for ships with 1 % (m/m) FSC and a
relative uncertainty of 50 %–100 % at 0.1 % (m/m) FSC. Balzani
Lööv et al. (2014) obtained the following FSC measurements based on
the sniffer principle: 0.86±0.23 % (m/m) from land, 1.2±0.15 % (m/m) from an onboard stack, and 1.13±0.18 % (m/m) from a
mobile platform. There was a 6 % relative uncertainty for an FSC of 1 %
(m/m) but a 60 % relative uncertainty for an FSC of 0.1 % (m/m). It is
important to note that the accuracy of the results of monitoring is a
difficult issue to address, and the accuracy of estimates in the literature
may not always be comparable. For ideal comparison results, one would need
to board the ship to take fuel samples, which is particularly difficult for
sailing ships.
Ship emissions can be divided into land-based (Kattner et al.,
2015; Yang et al., 2016), airborne-based (Beecken et al., 2014; Aliabadi et
al., 2016), marine-based (Cappa et al., 2014), satellite-based (Ding et al.,
2018), and unmanned aerial vehicle (UAV)-based (Villa et al., 2019) measurements according
to different platforms. Land-based measurements provide continuous
observation but are greatly affected by wind speed, wind direction, and the
distance between the ship and equipment. Airborne-based measurements can
approach the ship's plume and collect exhaust gas from the target ship. However,
the cost of airborne platforms is high, and they require active sampling of
ship exhaust plumes at low altitude. The closer the detector is to the
ship's plume, the more accurate the results. However, safety risks are also
relatively high near the plume. Marine-based measurements are suitable for
studying the discharge from individual ships. The monitoring equipment is
generally installed and used by research institutions or ship owners. This
is not subjected to FSC inspection by government regulatory authorities.
Satellite-based measurements are suitable for large-scale observation and
mainly used to observe the NOx emissions of ships. UAV-based
measurements have gradually increased in the research regarding the
atmosphere (Malaver Rojas et al., 2015; Mori et al., 2016). However, to
date, there are relatively few applications of these measurements in ship
emissions. As such, the most suitable approach for monitoring compliance is
to employ sniffer measurements taken by aircraft. Optical measurements
and sniffer measurements of gases in the exhaust plume of ships and more
details on such measurements can be found in several related papers (Balzani
Lööv et al., 2014; Van Roy and Scheldeman, 2016a, b; Johan et
al., 2017).
Based on the experience from those studies, we established sensors mounted
on a UAV to simultaneously measure the concentrations of SO2 and
CO2 in order to calculate the FSC. The UAV can collect samples closer
to the exhaust gas than airborne-based measurements. Waigaoqiao port in the
Yangtze River Delta was selected as the study site. By using this
measurement system, we analyzed 23 ship plumes and compared the results with
the FSC of entering ships determined from fuel samples analyzed at certified
laboratories. Through these experiments, we investigated and analyzed the
emission process of SO2 and CO2 close to the funnel mouth of ships
and designed an accurate measurement of FSC.
MeasurementUAV
Image of the modified UAV platform. The black box installed under
the UAV is a pod which was designed and customized by us. It carries a gas
pump (to collect the ship's exhaust gas), gas circuit, a filter (to remove
water vapor), sensors for SO2 and CO2, a small motor (to provide
energy for pumping), a camera, and communication modules.
In the experiment, we used the Matrice 600 UAV (SZ DJI Technology Co., Ltd.)
with a few small modifications. We designed and customized a special pod,
which was installed underneath the UAV, to carry sensors, communication
circuit boards, gas circuit systems, and other modules, as shown in Fig. 1.
After the successful assembly of the UAV platform, we first carried out
preliminary experiments in the automatic engine room laboratory of Shanghai
Maritime University. Through the preliminary test, we verified the stability
and security of the whole UAV system. At the same time, it also allowed the
UAV operator to practice how to operate the UAV for sampling close to the
smoke stack. Figure 2 shows a photograph of the process of collecting exhaust
gas from near the smoke stack. The UAV can fly near the smoke for the
collection and detection of exhaust gas. The detection information can be
sent to the receiving end in real time. Table 1 presents the parameters of
the UAV. The weight of the pod is about 3 kg and the UAV can fly for about
25 min. Therefore, measurements can be taken from 1 to 2 ships using one set
of batteries.
UAV platform flying close to the smoke stack for collecting
exhaust gas in the automatic engine room laboratory of Shanghai Maritime
University.
Parameters of the UAV.
ParameterValueSymmetrical motor wheelbase1133 mmSize1668 mm × 1518 mm × 727 mmWeight9.5 kgRecommended maximum take-off weight15.5 kgHovering accuracy (P-GPS)vertical: ±0.5 m, horizontal: ±1.5 mMaximum rotational angular velocitypitch axis: 300∘ s-1, heading axis: 150∘ s-1Maximum pitch angle25∘Maximum rising speed5 m s-1Maximum rate of descent3 m s-1Maximum sustained wind speed8 m s-1Maximum horizontal flight speed65 km h-1 (no wind environment)Hover timenon-loaded: 32 min, load 6 kg: 16 minSensors
In the measurement process, the ship exhaust gas is pumped into the pod by
the gas pump. After the filter removes the water vapor, the sensors react
and the communication module sends the measurement results to the receiving
end. The sensors included instrumentation for both SO2 and CO2
measurements. These sensors were purchased from HANS HENNIG GmbH,
Germany.
For SO2, the sensor is based on the electrochemical method. An
electrochemical sensor determines the concentration of a gas via a redox
reaction, producing an electrical signal proportional to the concentration
of the gas. In previous measurements of ship exhaust gas, SO2 sensors
were mainly based on the UV fluorescence method (Balzani et al., 2014;
Beecken et al., 2014; Kattner et al., 2015; Johan et al., 2017), which is
not appropriate for the UAV due to weight limitations. The SO2
electrochemical sensor has the advantages of low power consumption, small
size, light weight, and high precision. In addition, this type of sensor is
capable of measuring SO2 in the low parts per billion (ppb) range (Hodgson et al., 1999).
Therefore, we used the electrochemical sensor to measure SO2
concentration. The measuring range of the sensor is 0–5 ppm, the resolution
level is 0.001 ppm, response time (t90) is less than 1 s, and the
accuracy is ±0.25 ppm. t90 is defined as the time it takes to
reach 90 % of the stable response after a step change in the sample
concentration.
For CO2, the sensor is based on the nondispersive infrared analyzer
method. This type of sensor is often used to measure the CO2
concentration of ship exhaust gas (Balzani Lööv et al., 2014; Beecken et al.,
2014; Kattner et al., 2015; Johan et al., 2017). An infrared beam passes
through the sampling chamber, and each gas component in the sample absorbs
infrared rays at a specific frequency. The concentration of the gas
component is determined by measuring the infrared absorption at the
corresponding frequency. The measuring range of the used sensor is 0–5000 ppm, resolution level is 1 ppm, response time (t90) is less than 1 s,
and its accuracy is ±50 ppm.
Sensor calibration is required when the equipment is used daily. The time
interval for sensor calibration is 3 months or when the accumulated
working time of the sensor exceeds 180 h. If either of these conditions is
met, calibration will be carried out. The zero and full scales are usually
calibrated by standard mixture gas. Before each mission, sensors are
activated and residual gas in the airway is discharged by the gas pump.
MethodsFlight procedures
Photographs showing the setup of the experiment. An infrared
camera is set up for locating the smoke plume (a, b). The target plume is
imaged by the infrared camera (c). The UAV takes off towards the smoke plume (d).
The preliminary positioning measurements of the ship smoke plume are as
shown in Fig. 3. The UAV platform with sensors flew close to the funnel of
a ship, hovered for collecting exhaust gas, and then detection information was
sent back. This procedure is not without risk and a well-considered flight
approach is recommendable. We summarize the experiment steps as follows:
Determine the position of the plume according to the wind speed, wind
direction, height gauge, infrared camera, and other factors.
Check the equipment to ensure that the power is sufficient, the GPS
signal is normal (it is recommended that the number of satellites is more
than 13), the electrochemical sensor is activated, and the residual gas is
discharged in the air path of the pod.
The UAV takes off vertically and rises to an altitude of 100 m (the first
measurement point) for 3 min to determine the background value of SO2
and CO2. The take-off position is usually on the dock and is more than
50 m away from the ship's smoke.
Fly the UAV towards the plume and hover to collect exhaust gas from about
10 m (the second measurement point) and 5 m (the third measurement point)
away from the funnel for 5 min each.
Lift the UAV and then return it to the starting point.
During the process, real-time observations of SO2 and CO2 were
sent to the receiving end. The operator adjusted the UAV's position according to
the observations to keep the sensors in the plume. Therefore, in general,
the UAV confirmed the approximate location of the plume at a distance of 10 m and then gradually approached the location of about 5 m for collection.
Calculation of FSC
When the UAV flew into the ship's plume, the peak areas of the SO2 and
CO2 measurements were determined, and the background was subtracted.
The background values of SO2 and CO2 are obtained when the UAV
hovers at the first measurement point. The peak values of SO2 and
CO2 are determined when the UAV hovers at the second measurement
point or the third measurement point (main observation point). In the
calculation, the molecular weights of carbon and sulfur are 12
and 32 g mol-1, respectively, and the carbon mass percent in the fuel
is 87±1.5 % (Cooper et al., 2003). With the assumption that 100 %
of the sulfur and carbon contents of the fuel are emitted as SO2 and
CO2, respectively, the FSC mass percent can be expressed as follows:
FSC%=Skgfuelkg=SO2ppm⋅A(S)CO2ppm⋅A(C)⋅87%=0.232∫SO2,peak-SO2,bkgdtppb∫CO2,peak-CO2,bkgdtppm%,
where A(S) is the atomic weight of sulfur and A(C) the atomic weight of carbon.
SO2,peak, SO2,bkg, CO2,peak, and CO2,bkg are the
peak and background values of SO2 and CO2, respectively. This
calculation method is consistent with that described in the MEPC guidelines
184(59) and previous studies (Beecken et al., 2014; Kattner et al., 2015;
Johan et al., 2017).
The response time of both sensors is less than 1s. Even if the sampling
rates of the two sensors are set to be consistent, the two sensors cannot be
completely synchronized. This makes it difficult to calculate the
instantaneous ratio of SO2 and CO2. Our approach is that the
sensor sends the average measurement value of the last 10 s to the receiver
at an interval of 10 s. Therefore, the interval of integration in Eq. (1) is
10 s. We found that taking the mean of measurements directly or at shorter
intervals leads to too many narrow peaks in one measurement process. This
makes it difficult to select the peak value, and the calculation results are
unstable. At the same time, the interval should not be set too long, which
will make the crest very inconspicuous or too flat. Therefore, we selected
10 s as the empirical parameter value after several experiments.
Uncertainties
Because measurements taken inside the ship plumes are analyzed relative to
the background, offset errors can be neglected. Nevertheless, there are
certain uncertainties in the estimation process of the FSC. They can be
summed up as sensor uncertainty, measurement uncertainty, calculation
uncertainty, exhaust uncertainty, and so on.
Regarding sensor uncertainty, the nonlinearity of the two sensors should be
no more than ±1 % and the linear error is negligible. It can be
corrected through frequent calibrations with standard gases and gradually
establishing a quality management system comprising sensor linearity,
sensitivity, repeatability, hysteresis, resolution, stability, drift, and
other attributes of the minimum requirements.
Measurement uncertainty is mainly attributable to inadequate sampling (the
UAV did not fly into the plume). Moreover, shipborne antennae, dock
facilities, and strong winds may cause interference in finding an
appropriate sampling point and even lead to sampling failure. This
uncertainty factor can lead to an incorrect estimation of the FSC.
Therefore, we formulated the flight procedures as described in Sect. 3.1.
Calculation uncertainty lies in selecting the background and peak values of
SO2 and CO2. According to the law of error propagation (widely
used in surveying, mapping, and statistics), the relationship between the
deviation in the measurement values and that in the FSC can be obtained. The
FSC calculation results are functions of independent observations of SO2,peak, SO2,bkg, CO2,peak, and CO2,bkg as in Eq. (1). The relationship
between the observation error (ΔSO2,peak, ΔSO2,bkg, ΔCO2,peak, and ΔCO2,bkg) and function
error (ΔFSC) can be approximated using the full differential of the
function as follows:
ΔFSC=∂f∂SO2,peakΔSO2,peak+∂f∂SO2,bkgΔSO2,bkg+∂f∂CO2,peakΔCO2,peak+∂f∂CO2,bkgΔCO2,bkg.
In our study, this deviation was generally on the order of hundreds of parts per million (ppm),
as explained in Sect. 4.
Exhaust uncertainty arises because not all the sulfur in the fuel is emitted
as SO2, which is a systematic uncertainty. Preliminary studies showed
that 1 %–19 % of the sulfur in the fuel is emitted in other forms, possibly
SO3 or SO4 (Schlager et al., 2006; Balzani Lööv et al.,
2014). Hence, the assumption that all sulfur is emitted as SO2
yields an underestimation of the true sulfur content in the fuel.
Accordingly, this factor needs to be considered when setting the alarm
threshold of the FSC.
In any case, these uncertainties will occur during the measurement process.
After the establishment of flight procedures as mentioned in Sect. 3.1 and
selection process as in Sect. 4, we observed that the deviation between
the estimated value of FSC and true value of FSC was generally not more than
300 ppm. In addition, none of the monitored ships were fitted with exhaust
cleaning equipment.
ResultsData treatment
Photographs showing the flight of the UAV during measurements. The
UAV platform was flown close to the funnel of ship for collecting exhaust
gas and detection at Waigaoqiao port.
Figure 4 shows the UAV platform with sensors flying close to the ship's plume.
It hovered to collect exhaust gas, and detection information was
subsequently sent back. Generally, changes in SO2 and CO2
observations can be divided into three stages. (1) The UAV took off and
approached the ship funnel for about 3 min. The SO2 and CO2
observations were relatively low, and the background value was obtained in
this stage. (2) The UAV was gradually flown to the plume center, and data
were collected. Rapid increases in SO2 and CO2 concentrations,
reaching their peaks, were observed, which took approximately 10–15 min.
The peak data were obtained in this stage. (3) The UAV completed the gas
collection and returned, which took about 5 min. Decreased SO2 and
CO2 concentrations relative to the observation when the UAV was in the
plume center were observed. Observed SO2 and CO2 values returned
to background levels, but they were not used as background values. Residual
gas in the airway needed to be discharged by the gas pump before the next
collection.
Numerous measurements have been made in the Waigaoqiao wharf since January
2018. After the adjustment of various technical parameters and the
accumulation of UAV flight experience, this method could provide accurate
results. From August 2018 to January 2019, 23 plumes exhausted by ships have
been detected. Fuel samples, which are considered the true value of FSC,
were taken and sent for laboratory chemical examination. Finally, the
results of the UAV method were compared with those of the laboratory tests.
According to Eq. (1), if the observations of SO2 and CO2 values
simultaneously reach their peaks, it is easier to select the background and peak values to calculate the FSC. However, the actual data collected are
sometimes not ideal, and there is calculation uncertainty when selecting the
background and peak values of SO2 and CO2. In previous studies,
procedures for selecting background and peak values were not discussed in
detail. As the number of experiments increased, we gradually developed a
selection process. In our experiment, observations of SO2 and CO2
in the receiving end were synchronized. Therefore, the background and peak
values for SO2 and CO2 that we selected to calculate the FSC were
observed at the same time point.
According to the flight record, the minimum values of SO2 and CO2 collected at the first measurement point are selected as the background
values. There is generally greater uncertainty in selecting the peak values.
The synchronous, stable, obvious, and maximal values in observations of
SO2 and CO2 are selected as the peak values. The selection method
is as follows:
The peak values in the observations of SO2 and CO2 are
determined at the second and third measurement points, respectively.
The peak values at the full range of the SO2 or CO2 sensors are
ruled out.
The peak values resulting from dramatic changes (for instance, if the
change in CO2 exceeded 500 ppm or if the change in SO2 exceeded
500 ppb) in continuous observations are ruled out, because these changes may
have been related to sensor uncertainty, exhaust uncertainty, or unstable
concentrations of SO2 or CO2 in the atmosphere.
The occurrence times of peak values in SO2 and CO2 are compared,
and then the simultaneous peaks and almost simultaneous peaks (no more 20 s
apart) are retained. If there is a small deviation between the time point of
the peak values for SO2 and CO2, we select the time point at peak
of SO2. This will make the FSC value relatively larger than that of
CO2. As in Eq. (1), a higher SO2 peak leads to a higher FSC
estimate, while a higher CO2 peak leads to a lower FSC estimate. As
discussed in Sect. 3.3, not all the sulfur in the fuel is emitted as
SO2, which will result in a lower estimate value. This selection allows
the estimate to be relatively close to the true value.
After the above filtration, approximately one to four time points will be left
as the selection points for peak values. The global maximum values are
selected as peak values to calculate the FSC. The maximum values are likely
to have been measured in the center of the ship's plume. At that location,
the measurement value is relatively stable, and the probability of
interference from other factors is lower.
FSC estimation
In our experience, using the above method can provide the FSC value that is
closest to the real value in most cases. In a few cases, it may be
suboptimal rather than optimal. However, the final deviation generally does
not exceed 0.03 % (m/m) at an FSC level of 0.035 % (m/m) to 0.24 %
(m/m). To illustrate this selection method, six typical sets of plume
measurement data for SO2 and CO2, marked as plumes 1–6, along
with the time and serial number, are shown in Fig. 5. In addition, we made a
distinction between good- and poor-quality data and rejected some plumes.
Good-quality data for a plume meant that the peak values were obvious and
easy to distinguish, whereas poor-quality data for a plume meant that the
peak values were less obvious but still able to produce a result. When
results could not be obtained, the plumes were rejected. An FSC of 0.1 %
(m/m) was used as the dividing line between plumes with high-sulfur and
low-sulfur content samples.
Six sets of plume measurement data for SO2 and CO2,
marked as plumes 1–6, along with the time and serial number. The background
and peak values of SO2 and CO2 were used to estimate the FSC. In
each plume, the time range of the first monitoring point is marked by two
vertical lines. The selected background and peak values of SO2 and
CO2 are written in red and alternative peak values are written in
black.
Comparison and verification of the estimated and true values
of FSC. We present the selected background (Bkg) and peak values of
SO2 and CO2 and alternative
peak values (mentioned in Fig. 5). The FSC results and deviations of these
different values are also listed for comparison purposes. They are
distinguished as follows in the column titled “Selected”: the selected peak
values are marked as “√” and indicate the selected peak values,
and “×” indicates alternative peak values (which are not selected
as the calculated values in the final result of FSC).
As shown in Fig. 5, the observations of plumes 1 and 3 simultaneously
reached the peak value. However, these were multiple SO2 and CO2
peak values, and the global maximum peak values of SO2 and CO2
were selected. In plume 2, there was a peak for SO2 at 10:32 LT (local time), but there
was none for CO2 at the same time. We used the data from the
simultaneous peaks of SO2 and CO2 for the calculations. The
observations of plumes 4 and 5 also simultaneously reached the peak value at
multiple time points. However, at 11:02 and 11:07 LT in plume 4 and 11:19 LT in
plume 5, the SO2 measurements reached the peak values, but the
CO2 measurements reached plateau levels above which they did not
increase any further. Therefore, the data in this period were not used as
peak values of the plumes. In plume 6, CO2 measurements did not
increase any further owing to the full range of the CO2 sensor at 10:02
and 10:04 LT. This happens in rare cases when the UAV is too close to the
funnel (less than 5 m), and these data cannot be used as peak values. After
the measurement of plume 5, the communication module was faulty when we
wanted to adjust sampling rate. We consequently replaced the HTTP communication protocol with the TCP/IP protocol. The main changes
involved adjusting the data sampling rate from 10 to 2 s to make it easier
to find the peak value (the sensors send the average measurement value of
the last 10 s to the receiver at an interval of 2 s), and the sensors were
consequently recalibrated by standard mixture gas. Therefore, the background
values of plumes 1–5 were different from those of plume 6. Nonetheless, Eq. (1) was used to calculate the ratio of sulfur dioxide difference to carbon
dioxide difference, and it therefore does not affect the final calculation
results. In addition, when the FSC of the target ship is low, for example,
when the fuel used is light diesel fuel, the SO2 observation values
were mostly 0. When this happened, according to our experience, the FSC was
generally lower than 200 ppm, and the ship was likely to meet the emission
requirements.
The background and peak values of SO2 and CO2 were selected from
plumes 1–6, and the FSC was calculated according to Eq. (1). The comparison
results of the estimated FSC values are presented in Table 2. The background
value of CO2 in plumes 1–4 exceeded 300 ppm, but the global background
CO2 was approximately 400 ppm. Meanwhile, the background value of
SO2 exceeded 400 ppb at some time. This was due to sensor calibration,
which did not affect the final result. This kind of situation did not happen
again after we recalibrated the sensors by standard mixture gas. In some
cases, background values seemed to fluctuate greatly. This was mainly
because the UAV took off from the dock, where multiple ships were berthed,
and wind speeds were high. In addition, the drift or cross-sensitivity in
the sensors also may have caused interference. Therefore, we used the flight
procedure given in Sect. 3.1 and the selection method of peak values to
minimize this impact. By comparing the results and deviations of the
different calculated values, it can be seen that appropriately selecting the
peak value is important. In general, the optimal value can be selected using
the selection method with the exception of plume 1. However, the deviation
is not large.
As shown in Fig. 6, the FSC in our experiments was mainly at a level of
0.035 % (m/m) to 0.24 % (m/m). There was one measurement of 0.37 %
(m/m), too. However, it is not enough to illustrate the deviation at
the level of 0.24 % (m/m) to 0.37 % (m/m), because deviations of FSC are
not the same at different FSC levels. Overall, the estimated FSC is smaller
than the true value in many cases. This could be due to the exhaust
uncertainty that not all the sulfur in the fuel is emitted as SO2. In
our experiments, this uncertainty factor led to low FSC estimation results,
and the deviation was generally not more than 200 ppm. This prediction is
based on the fact that several measurements of some plumes were taken at
particular times. Similar calculation results for FSC were obtained, but
they were all less than the real value of 100–200 ppm. This tendency of
underestimation has also been found in previous studies (Johan et al.,
2017).
Finally, the deviation of the estimated FSC value calculated using the
proposed method was within 300 ppm (0.03 % (m/m)), although there was some
uncertainty. Considering the uncertainties listed in Sect. 3.3, the
proposed method provides accurate results.
Comparison between the true values of FSC (x axis) against
the estimated values of FSC (y axis) of 23 measurements.
Conclusions
In this study, we performed close monitoring of ship smoke plumes using a UAV.
Observation data of SO2 and CO2 were collected at close range
(5–10 m) to ship funnel mouths. The estimated results were compared with
the FSC values determined at certified laboratories. In general, the
deviation of the estimated FSC value was within 0.03 % (m/m) at an FSC
level of 0.035 % (m/m) to 0.24 % (m/m). Because not all the sulfur in
the fuel is emitted as SO2, the estimated FSC is smaller than the true
value in many cases. Therefore, if the maritime department wants to take the
estimated value as the basis for the preliminary judgment regarding whether
the ship exceeds the emission standard, it needs to set an appropriate
threshold and a confidence interval.
At present, the FSC limit in China's emission control requirements is
0.5 % (m/m), and the limit for ECAs is 0.1 % (m/m). The proposed method can
be used for monitoring of ECAs for compliance with FSC standards. However,
after more than 1 year of testing and experiment, we found that there are
still many issues that remain to be resolved:
In about 10 % of the cases, the UAV did not measure the effective
background value and peak value. This is mainly caused by the UAV missing
the plume during its flight. Therefore, effective methods for finding and
navigating to plumes using real-time sensor feeds need to be explored.
In about 10 % of the cases, the absolute error was more than 0.03 %
(m/m), and even more than 0.05 % (m/m) in rare cases. Unstable
concentrations of SO2 or CO2 in the atmosphere just before the
measurement may cause such errors. Furthermore, uncertainties, such as
sensor uncertainty, measurement uncertainty, calculation uncertainty, and
exhaust uncertainty, may hinder accurate measurement. Poor-quality data or
rejected plumes may result from these situations, i.e., unstable
concentrations of SO2 or CO2 and uncertainties.
Currently, the pod can only carry two sensors. In subsequent tests, we
will modify the pod to carry more sensors. The use of different types of
UAVs also needs to be evaluated. In addition, our experiments mainly
involved the monitoring of berthing ships, and experiments on ships at sea
are needed in the future.
Data availability
Please address requests for data sets and materials to Fan Zhou
(fanzhou_cv@163.com).
Author contributions
FZ designed the study, analyzed the experimental data, and authored the
article. SP, WC, and XN contributed to the experiments. BA provided
constructive comments on this research.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Editage (https://www.editage.cn/, last access: 4 December 2018) for English language editing. We thank
J. Duyzer and one anonymous reviewer for reviewing this paper. We thank
Folkert Boersma for serving as editor.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41701523) and the Special Development Fund for China (Shanghai) Pilot Free-Trade Zone (Monitoring and inspecting the ship exhaust emissions in Shanghai Free-Trade Zone).
Review statement
This paper was edited by Folkert Boersma and reviewed by J. Duyzer and one anonymous referee.
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