Due to technical and cost limitations, the monitoring of
emissions from ships sailing in open water within ship emission control
areas (ECAs) is relatively rare. The present study adopts a monitoring
method involving an unmanned aerial vehicle (UAV) that takes off from a
patrol boat to measure the concentrations of
With the rapid development of the shipping industry (UNCTAD, 2017) over the
past few decades, air pollution caused by ship emissions has received an
increasing amount of attention (Eyring et al., 2010; Wan et al., 2016). The
pollutant gases emitted by ships not only affect the global climate
(Huebert, 1999; Corbett, 2016), but also impact local air quality and
human health (Yang et al., 2016; Wang et al., 2019). Shipping accounted
for 15 %, 13 %, and 3 % of the annual global anthropogenic emissions
of
To reduce the negative impacts of ship emissions, the International Maritime
Organization (IMO) regulates emissions through the International Convention
for the Prevention of Pollution from Ships and its Annex VI (MARPOL, 1997).
The air pollution limits for shipping were adopted in 1997, but they only came
into force in 2005. The global cap for the fuel sulfur content (FSC) of
seagoing ships was set at 3.5 % (
The IMO has not yet set up ECAs in East Asia, which is a region that is home to the world's 10
largest container ports, including Shanghai, Ningbo-Zhoushan, and
Shenzhen ports. To limit the air pollution caused by ship emissions, the
Chinese government established three domestic emission control areas (DECAs)
in 2015: the Yangtze River Delta, the Pearl River Delta, and the Bohai Sea. The DECAs have been expanded to cover a wider area since 2020 and now include most of the
coastal ports, the Yangtze River main line, and the Xijiang River main line.
The FSC limit for sailing and berthing ships in the DECAs has been set at
0.5 % (
A key problem regarding the implementation of the policy of the ECAs is the question of how to enforce the FSC of ships. Several studies have suggested estimating the FSC by measuring ship plumes (Berg et al., 2012; Balzani Lööv et al., 2014). At present, the main method to monitor the emissions of surrounding ships is to place monitoring equipment either on the wharf, shore, port area, or bridge (i.e., ground-based methods) (Alföldy et al., 2013; Pirjola et al., 2014; Beecken et al., 2015; Kattner et al., 2015; Mellqvist et al., 2017a; Cheng et al., 2019; Zhang et al., 2019). Although ground-based methods can provide continuous monitoring, the results obtained depend on the wind speed, wind direction, and the position of a ship relative to the monitoring equipment. Additionally, the boundaries of the ECAs that are designated by the IMO are 200 nautical miles from the coast (Viana et al., 2015); hence, ground-based methods are not able to monitor the fuel that is used on the open sea in ECAs because sailing ships are too far from the shore or bridges.
Therefore, some researchers have used sensors that are carried by small aircraft to monitor navigating ships within ECAs (Berg et al., 2012; Beecken et al., 2014). However, as this kind of monitoring method is costly, the monitoring of navigating ships is relatively rare. Beecken et al. (2015) observed 434 plumes using ground-based measurements and 32 plumes from a helicopter. Balzani Lööv et al. (2014) took 475 measurements using “sniffing” instruments from ground-based measurements, whereas only 25 measurements were obtained using this method from mobile platforms. In the study undertaken by Mellqvist et al. (2017b), 114 individual ships were measured effectively during 27 flight hours at a cost of approximately EUR 470 per ship, although this amount only included the aircraft cost and did not consider the ferry, operator, or instrument rental costs. Therefore, the high cost of flying precludes extensive monitoring of ship emissions.
As a result of the aforementioned factors, there is less monitoring of ships on the open sea in ECAs. This is despite the fact that numerous studies (Pirjola et al., 2014; Kattner et al., 2015; Zhang et al., 2019) have shown that the FSC of ships has been significantly reduced by the implementation of the ECA policy. However, most of these studies did not involve the monitoring of ships on the open water, which could lead to nonrepresentative assessments regarding the implementation of policies. At the same time, the lack of open-sea monitoring results in a blind area for maritime enforcement and is not conducive to the implementation of ship ECA policy by maritime authority. The present study used an unmanned aerial vehicle (UAV) to monitor the FSC of sailing ships on the open sea in the Yangtze River estuary DECA. The method proposed in this study can be used to monitor ship emissions at a comparatively low cost in order to understand the FSCs of sailing ships in open waters. Although the cost of using patrol boats is not negligible, it is more convenient and cheaper for maritime authorities than using small aircraft.
The research undertaken in the present study forms part of the
“Monitoring and inspecting ship exhaust emissions in the Shanghai
free-trade zone” (MISEE) project. In this project, a UAV system was designed and
developed which mainly included a pod for measuring the exhaust gas from
ships and a UAV to carry the pod. In previous research (Zhou et al., 2019),
the plumes of 23 berthing ships were measured using the first-generation
pod, and the deviation of the estimated FSC was
In the present monitoring for sailing ships, we developed the second-generation pod by optimizing the structure and layout of the first-generation pod to achieve a lighter weight and smaller volume. A short overview of the instrumentation is provided in Sect. 2.1. We measured the plumes of 11 berthing ships to verify the accuracy of the second-generation pod, and we measured the plumes of 27 sailing ships to estimate the FSC.
The instrumentation that was used for monitoring the FSC of sailing ships is
shown in Fig. 1. The UAV was a MATRICE 600 PRO (SZ DJI Technology Co.,
Ltd., Shenzhen, China). This type of UAV cannot be used on rainy days or
when the wind speed is higher than 8 m s
Image of the UAV system. A gas probe, camera, and pod are
installed under the unmanned aerial vehicle (UAV). The gas probe is used to
collect the ship's exhaust gas, and the camera is used to assist in finding
the ship's funnel mouth during flight. The pod is used to carry a gas pump,
gas circuit, filter, small motor, sensors for
The sensors used were able to measure both
Parameters of the UAV system.
As illustrated in Fig. 2, the monitoring region was the channel of the Yangtze River estuary, near the Waigaoqiao port area to the north of Shanghai. The Yangtze River is the longest river in China and the third-longest river in the world. Shanghai is one of the most prosperous cities in the world and had a permanent resident population of approximately 24 million people at the end of 2017 (Shanghai Municipal Bureau of Statistics, 2017). The Waigaoqiao port area is only 20 km from the city center, and the air pollution caused by ship emissions directly affects the urban air environment and the health of residents (Wang et al., 2019; Feng et al., 2019). The experimental area of the MISEE project is mainly within the Waigaoqiao port and the Yangtze River estuary.
Monitoring regions in the channel of Yangtze River estuary, which
belong to the Chinese domestic emission control areas (DECAs). This area is to the north of Shanghai, on the
southwest side of Changxing Island. The distance between the two sides is
During the experiment, the operator took a patrol boat to the channel and selected a target ship at random. After identifying the target ship for
monitoring, the patrol boat would accelerate to a few hundred meters to the left or
right ahead of the vessel. The patrol boat would then stop and the UAV would
take off from its deck and fly towards the plume of
the target ship to measure the concentrations of
Operator controlling the takeoff of the UAV from a patrol boat.
During the measurements, the operator adjusted the position of the UAV to
ensure that it was in the ship's plume. Real-time measurements of
The UAV (marked by the red circle) monitoring a ship's emissions in the open sea. The enlarged UAV is shown in the top left corner. This picture was captured by another UAV.
The FSC in this study was obtained directly by sampling the gas
concentrations in the ship plumes using the UAV. The enhancements of
Typical measurement data for
The continuous measurement data for two typical plumes (2019-4-15B and
2019-3-29A) are exhibited in Fig. 5. The data for plume 2019-4-15B (Fig. 5a)
were considered to be “good” quality, whereas those for plume
2019-3-29A (Fig. 5c) were considered to be “poor” quality. Data were
determined to be good quality when obvious, easily distinguished peak
values were observed, whereas less obvious peaks that still corresponded to
a result were considered to be poor-quality data. Meanwhile, the correlation
between the
The selection of peak values leads to uncertainty because when the area
ratio is selected for the calculation, the starting and ending time points
of the area are still associated with substantial uncertainty. Figure 5b and
d depict the average concentrations of the
All peak values and their corresponding FSC results. The background
values of plume 2019-4-15B were 0 ppm and 310 ppm for
In previous research (Zhou et al., 2019), the main uncertainties of UAV measurements were summarized as sensor uncertainty, measurement uncertainty, calculation uncertainty, and exhaust uncertainty. The instrument calibration method, UAV flight procedures, and the data treatment methods were designed to reduce these uncertainties. However, some uncertainties remain, as discussed below.
To make the UAV lightweight and convenient, the second-generation pod was only
equipped with
The average gas concentration within 10 s was chosen for the FSC
calculations; however, this does not mean that 9 or 11 s could not have
been selected. To demonstrate this, a comparison calculation was carried out
using both 9 and 11 s, which showed that these led to very little
difference in the results. Nevertheless, it is necessary to ensure that the
gradient of the gas measurements is stable within the sampling time (the
interval length of the integral). Moreover, the interval length cannot be
too short (e.g., 2 s) or too long (e.g., 20 s). If the interval length is too short, it
is difficult to determine whether the measurements are stable and
undisturbed over time. Similarly, if the interval length is too long, it is also
difficult to ensure that all of the measurements in the integral interval
are stable and undisturbed. In addition, during the flight of the UAV in
this study, the time available for measuring the plume was
Nevertheless, there is also some uncertainty associated with choosing the peak values. After ruling out the peak values across the full range as well as those corresponding to dramatic changes, the global maximum values were selected as the peak values to calculate the FSC. The maximum values probably correspond to the measurements taken in the center of the ship's plume. At that location, the measurement values were relatively stable, and the probability of interference from other factors was lower. Furthermore, the higher the peak value is, the greater the proportion of exhaust gas is; hence, the impact from the incomplete mixing of the exhaust gas with clean air is relatively small.
In summary, the obvious and stable maximum values are selected as peak
values to calculate the FSC. There are, of course, situations where multiple
similar peaks can occur simultaneously. In this case, their calculated FSCs
may be very similar, and the results obtained by the calculation of the
highest peak should have high credibility, such as the measurements of
plume 2019-4-15B. Meanwhile, the occurrence times of the peak
Additional uncertainties were encountered during our monitoring of sailing ships because the UAV was usually hundreds of meters away from the operator. The location of a plume depended primarily on the following three aspects. (1) The position of most plumes with black smoke could be identified using the operator's visual judgment. (2) The real-time image shot by the camera could be used to assist in finding the ship's funnel mouth. (3) In the measurement process, the real-time measured concentration sent to the receiving equipment gradually increased, indicating that the UAV was approaching the center of the plume. However, the operator occasionally faced difficulties in accurately determining the ship's plume, which led to failed measurements. We attempted to measure more than 40 ship plumes in open water; however, only 27 of them resulted in good- or poor-quality data, i.e., usable data.
Comparison and verification of the estimated (UAV) and true
(sampled fuel) values of the FSC from 11 berthing ships. “N” means the deviation has no a specific value, but it is less than 0.020 % (
The deviation of the estimated FSC value obtained by the first-generation
pod was
Before monitoring the sailing ships, we first monitored 11 berthing ships
between March and April 2019 in the Waigaoqiao port to verify the accuracy
of the second-generation pod. While one person operated the UAV to monitor
one of the plumes, two maritime law enforcement officers boarded the
corresponding ship to collect a fuel sample. Both processes took
approximately 10–20 min. The fuel samples, which are considered to
represent the true FSC values, were then sent for chemical analysis in a
laboratory. The estimated (UAV) and true FSC values are listed in Table 3
along with the identification number of each plume and the time and serial
number. Table 3 shows that the deviation generally did not exceed 0.03 %
(
Between March and December 2019, effective monitoring of 27 sailing ships
was undertaken using the UAV that took off from the patrol boat (Table 4).
The FSC of 23 berthing ships measured by the first-generation monitoring
equipment and the FSC of 11 berthing ships (Table 3) measured by the
second-generation monitoring equipment in this study were taken as the FSC
monitoring results for berthing ships. We compared the distribution of the
FSCs of these 34 berthing ships with those of the 27 sailing ships. Figure 6
shows that the FSCs of the sailing ships were considerably higher than those
of the berthing ships; the FSC of all 27 sailing ships exceeded 0.1 %
(
Estimated (UAV) values of the FSC from 27 sailing ships. “
Comparison between the monitoring results of berthing ships and sailing ships.
According to the monitoring results, law enforcement officers of the Pudong
Maritime Safety Administration intercepted four sailing ships for which the
UAV FSC results were of a good quality and exceeded 1.5 % (
In this research, we used a UAV that took off from a patrol boat to monitor
emissions from sailing ships in open water. Of the 27 sailing ships that
were successfully monitored, 12 were found to have an FSC that exceeded
0.5 % (
Although a global cap on the FSC in marine fuel was set at 3.5 % (
In addition, there are still some improvements to be made to the UAV system. Currently, 4G transmission is the communication method for detecting information transmission; hence, in locations without a 4G signal (e.g., offshore), the receiving equipment cannot obtain real-time measurement results. Potential solutions include setting up small base stations on patrol boats or using satellite transmission. Although carrying an infrared camera on the UAV would make it easier to find the plume, this would require replacing the camera in Fig. 1 with an infrared camera and establishing new data communication.
Please address requests for data sets and materials to Fan Zhou (fanzhou_cv@163.com).
FZ designed the study and authored the article. FZ and LH analyzed the experimental data. RZ, WC, and XN contributed to the experiments. SP contributed to setting instruments. LH, MZ, and BA provided constructive comments on this research.
The authors declare that they have no conflict of interest.
We are grateful to Megan Anne for English language editing. We also thank Jan Duyzer and one anonymous reviewer for reviewing this paper and Andreas Richter for serving as editor.
This research has been supported by the National Natural Science Foundation of China (grant no. 41701523), the Special Development Fund for China (Shanghai) Pilot Free Trade Zone, the Special Foundation for Intelligent Manufacturing Industry of Shanghai Lin-Gang Area (grant no. ZN2017020325), and the Open Project Program of Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences.
This paper was edited by Andreas Richter and reviewed by Jan Duyzer and one anonymous referee.