Monitoring compliance with fuel sulfur content regulations of sailing ships by unmanned aerial vehicle (UAV) measurements of ship emissions in open water

Due to technical and cost limitations, the monitoring of emissions from ships sailing in open water within the ship 10 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 SO2 and CO2 within the plumes of sailing ships. Our method aims to provide a low-cost, remote approach for estimating the fuel sulfur content (FSC) of sailing ships in open water, which overcomes the limitations of ground-based and small aircraft methods. The selected monitoring area was the Yangtze River estuary, a domestic ECA with an FSC limit of 0.5% (m/m) implemented by the Chinese government. A 15 total of 27 sailing ships were monitored, 14 of which were found to have an FSC of > 0.5% (m/m). Moreover, the FSCs of the sailing ships were found to be higher than those of berthing ships in the study area. Based upon the online monitoring results, four of the monitored ships were intercepted by the maritime law enforcement, and fuel samples were collected and analyzed in a laboratory; the results confirmed that all four FSCs were > 0.5% (m/m). Among them, one offending ship was tracked down on July 15, 2019, which was the first time that a sailing ship had been caught for having failed the FSC regulations in 20 China. Overall, the present study provides scientific support for evaluating the effectiveness of ECA policies, and recommends that emissions from sailing ships should be monitored more often in the open water in the future.


Introduction
With the rapid development of the shipping industry (UNCTAD, 2017) over the past 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 25 by ships not only affect the global climate (Huebert, 1999;Corbet, 2016), but also local air quality and can harm people's health. (Yang et al., 2016;Wang et al., 2019). Shipping accounted for 15%, 13%, and 3% of the annual global anthropogenic emissions of NOx, SOx, and CO2 from 2007 to 2012, respectively (Smith et al., 2014). In Europe, estimated ship emissions were responsible for 3.0 million tons of NOx, 1.2 million tons of SOx, and 0.2 million tons of fine particulate matter (PM2.5) 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 non-representative assessments for the implementation of policies. At the same 65 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 to understand the FSCs of sailing ships in open waters. Although the cost of using patrol boats is not negligible, it is convenient and lower cost for maritime authorities compared with small 70 aircraft.

Experimental methods
The research undertaken in the present study forms part of the project "Monitoring and inspecting ship exhaust emissions in the Shanghai free-trade zone" (MISEE). In this project, an unmanned aircraft system (UAS) was designed and developed, and mainly included a pod for measuring the exhaust gas from ships and a UAV to carry the pod. In previous research (Zhou et al.,75 2019), the plumes of 23 berthing ships were measured using the first-generation pod. The deviation of the estimated FSC obtained by the UAS was < 0.03% (m/m) for an FSC of between 0.035% (m/m) and 0.24% (m/m).
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 Section 2.1. We measured the plumes of 11 berthing ships to verify the accuracy of the second-generation pod, and the 80 plumes of 27 sailing ships to estimate the FSC.

Instrumentation
The UAS 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. The white box installed underneath the UAV in Fig. 1 is the aforementioned second-generation pod for measuring 85 the exhaust gas. When the UAV approaches a ship's plume, the gas pump in the pod draws air using the gas probe. The water vapor, particles, and soot in the gas are subsequently removed by a hose filter valve. The sensors detect the gas and measurement information is sent out by communication modules. The pod has dimensions of 20 cm × 12 cm × 9 cm and weighs 900 g.
The sensors used were able to measure both SO2 and CO2, and were purchased from Shenzhen Singoan Electronic Technology 90 Co., Ltd., China. The SO2 sensor is based on the electrochemical method, and has a measuring range of 0-10 ppm, an accuracy of ± 3% (0.3 ppm), and a response time (T90) of ≤ 30 s. The CO2 sensor is based on the non-dispersive infrared analyzer method, and has a measuring range of 0-10000 ppm, an accuracy of ± 3% (300 ppm), and a T90 of ≤ 30 s. The T90 represents the time taken to reach 90% of the stable response following a full range change in the sample concentration. These sensor characteristics were provided by the instrument manufacturer and were ensured to be within the tolerances by calibration. The 95 zero and full scales are usually calibrated by a standard mixed gas when the equipment is used on a daily basis. The major parameters of the UAS are listed in Table 1.

Monitoring region
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 Yangtse River is the first (third) longest river in China (the world). Shanghai is one of the most 100 prosperous cities in the world, and at the end of 2017 that city had a permanent resident population of approximately 24 million people (Shanghai Municipal Bureau of Statistics, 2017). The Waigaoqiao port area is only 20 km away from the city center, and the air pollution caused by ship emissions directly affects the urban air environment and the health of residents Feng et al., 2019). The experimental area of the MISEE project is mainly within the Waigaoqiao port and the Yangtze River estuary. 105

Measurement method
During the experiment, the operator took a patrol boat to the channel and then selected a target ship at random. After identifying the target ship for monitoring, the patrol boat would accelerate to a distance to the left or right ahead of the vessel. The patrol boat would then stop and the UAV was operated to takeoff from its deck, and would then fly towards the plume of the target ship and measure the concentrations of SO2 and CO2 in the plume (Fig. 3). The distance between the patrol boat and the target 110 ship was a few hundred meters.
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 SO2 and CO2 were made such that the pod could effectively detect the plume. Generally, it was necessary for the UAV to follow the ship's funnel mouth for approximately 5 minutes, as illustrated in Fig. 4. The target ship continued to move during the measurements; hence, it was followed by the patrol boat in order to avoid the UAV moving too far away 115 from the operator. When the operator was sure that valid data had been collected, the patrol boat stopped and the UAV returned and landed back on the deck of the patrol boat.

Calculation
The FSC in this study was obtained directly by sampling the gas concentrations in the ship plumes using the UAS. The enhancements of SO2 and CO2 in measurements that were affected by exhaust gases were calculated, and the ratio of these 120 SO2 and CO2 peaks was used to calculate the FSC (Eqs. 1 and 2). This method has been widely used to calculate the FSC in related studies (Alföldy et al., 2013;Pirjola et al., 2014;Balzani Lööv et al., 2014;Beecken et al., 2014;Beecken et al., 2015;Kattner et al., 2015;Zhou et al., 2019). In the calculation, the molecular weights of carbon and sulfur are 12 g mol -1 and 32 g mol -1 , respectively, and the carbon mass percent in the fuel is 87 ± 1.5% (Cooper et al., 2003). By assuming that 100% of the carbon content of the fuel is emitted as CO2, and sulfur is emitted as SO2 and other forms, the FSC mass percent can be 125 determined using Eq. (1): (1) where R represents the sulfur content that is emitted in forms other than SO2 because preliminary studies have shown that 1-19% of the sulfur in fuel is emitted in other forms, possibly SO3 or SO4 (Schlager et al., 2006;Alföldy et al., 2013;Balzani Lööv et al., 2014). EF is the emission factor and bkg is the abbreviation of background. In Eq. (1), if the sensors measuring 130 SO2 and CO2 have approximately the same response time and can be set to be synchronized, the peak concentrations of SO2 and CO2 can be used to calculate the FSC; otherwise, integrals need to be used. In our research, the sampling rate of the SO2 and CO2 sensors was 1 s, and integrals were used because the two sensors could not be completely synchronized.
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 of a "good" quality, whereas those for plume 2019-3-29A (Fig. 5c) were 135 considered to be of a "poor" quality. Data were determined to be of a good-quality when obvious, easily distinguished peak values were observed, whereas less obvious peaks that still corresponded to a result were considered as poor-quality data. 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 5d depict the average concentrations of the SO2 and CO2 measurements (in Fig. 5a and 5c, respectively) for 10 s periods. The peak value of each average concentration 140 was selected for the calculation. This process is equivalent to selecting the area ratio of SO2 to CO2 within 10 s for the calculation, as shown in Eq. (2).
[%] = 0.232 where AVG (·) is the calculated function for the average measurement value within 10 s; hence, the data in this study are the average values of measurements in 10 s. When the UAV took off from the patrol boat and flew high into the air, the SO2 and 145 CO2 concentrations were relatively low. The background values were obtained at this stage as the minimum SO2 and CO2 concentrations. As the UAV flew into the plume, the measured concentrations of SO2 and CO2 increased. The obvious, stable maximum values in the observations of the average measurement values should be selected as the peak values. It can be seen that using the average values of measurements within 10 s makes it easier to select the peak values, especially with respect to poor-quality data. However, as there can still be several options for peak values, the data treatment methods reported by Zhou 150 et al. (2019) were incorporated in this study to select the most appropriate peak values. In Fig. 5b, the time point of selected peak values is at 10:19:11. The measurement values from 10:19:57 to 10:20:15 were not used because the CO2 concentration covered the full range. In Fig. 5d, the time point of the selected peak values is at 10:38:27. The measurement values from 10:39:57 to 10:41:41 were not used because we ruled out data exhibiting either dramatic changes or errors in continuous observations. The details for selecting the peak values are listed in Table 2.

Uncertainties
In previous research , 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 data treatment methods were designed to reduce these uncertainties. However, some uncertainties remain, as discussed below. 160 The average gas concentration within 10 s was chosen for the FSC calculations; however, this does not mean that 9 s or 11 s could not have been selected. To demonstrate this, a comparison calculation was carried out using both 9 s and 11 s, which showed that these led to very little differences in the results. However, 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 time is too short, it is difficult to determine whether the measurements are 165 stable and undisturbed over time. Similarly, if the time 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 ~5 minutes. As both the ship and the UAV were moving at this time, it was virtually impossible to ensure that the UAV was flying consistently within the plume and obtaining stable measurements. Accordingly, 10 s is also a relatively appropriate value for the measurement process. 170 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 175 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, for instance, the measurements of plume The deviation of the estimated FSC value obtained by the first-generation pod was < 0.03% (m/m) for an FSC level ranging from 0.035% (m/m) to 0.24% (m/m) . The second-generation pod was also verified on berthing ships by 190 using this method at a similar FSC level and the accuracy was approximately the same (see Section 3.1). These verifications of the deviation were based on the FSC measurement of berthing ships, which did not exceed the Chinese DECA FSC limit of 0.5% (m/m). However, some of the sailing ships did exceed this limit. It should be noted that the deviations for different FSC levels were not the same. Based on previous studies, the deviation of the FSC obtained from high-sulfur plume should be greater, for example, Van Roy and Scheldeman (2016a, b) estimated relative uncertainties of 20% at a level of 1% (m/m) FSC 195 and 50-100% at 0.1% (m/m) FSC. Therefore, the deviation of sailing ships may > 0.03% (m/m) when the FSC exceeds 0.5% (m/m). Nonetheless, our UAS was still able to accurately detect an FSC that obviously exceeded 0.5% (m/m).

Berthing ships
Before monitoring the sailing ships, we first monitored 11 berthing ships between March and April 2019 in the Waigaoqiao 200 port to verify the accuracy of the second-generation pod. Whilst 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 did not generally exceed 0.03% (m/m) for an 205 FSC level of between 0.03% (m/m) and 0.22% (m/m) (except for plume 2019-4-3B). Additionally, when the FSC of a target ship was low, for example, when light diesel fuel was used, the measured SO2 concentrations were mostly zero. When this occurred, the FSC was generally < 0.02% (m/m), for example, as for plumes 2019-4-3A and 2019-4-12A.

Sailing ships and comparison with berthing ships
Between March and December 2019, effective monitoring of 27 sailing ships was undertaken using the UAV that took off 210 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% (m/m) and the FSC of 14 of these exceeded the Chinese DECA FCS limit of 215 FSC of sailing ships in open water that leads to prosecution by China's maritime authorities has not existed prior to the present study. 220 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 all exceeded 2% (m/m). The officers boarded these ships for inspection on July 15, August 14, August 20, and September 27, 2019, and took fuel samples, which were sent for chemical analysis in a laboratory. The FSC of all four fuels was also found to exceed 0.5% (m/m): 0.534% (m/m), 0.744% (m/m), 0.813% (m/m), and 1.991% (m/m) (in chronological order). The reason that all of these laboratory results did not 225 exceed 2% related to the fact that ships cannot stop immediately in the channel for inspection and have to sail to the anchorage point; when the officers boarded the ships to take samples they found the crew taking various measures to drain the high-sulfur fuel in the main engine fuel oil pipeline. This means that the chemical analysis results of the sampled fuels were obviously lower than those of the UAV monitoring. Nevertheless, the four inspections successfully confirmed that the FSC of the fuels exceeded the standard for sailing ships. The inspection on July 15, 2019, was the first time that a sailing ship's FSC failed to 230 meet Chinese regulations, and this aroused wide concern in the shipping community.

Conclusions
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, 14 were found to have an FSC that exceeded 0.5% (m/m) and 5 exceeded 2% (m/m). Based on the monitoring results, law enforcement officers of the Pudong maritime safety administration caught the 235 first case of excessive FSC for a sailing ship and confirmed three other cases. Additionally, the UAV monitoring results demonstrated that the FSC values of sailing ships in the surrounding waters of Waigaoqiao port were higher than those determined for berthing ships in the port. Although the sample size was relatively small, observation of Fig. 6 suggests that the data are still convincing.
Although a global cap on the FSC in marine fuel was set at 3.5% (m/m) in 2012 following the IMO regulation, this was reduced 240 to 0.5% (m/m) in 2020 and has already been implemented in China. According to our monitoring results, the current situation for meeting the 0.5% limit is not optimistic. Successful compliance with this regulation by ship owners involves many challenges. We conclude that there is a need for further monitoring data on sailing ships in open water to ascertain the degree of exceedance and work toward compliance.
In addition, there are still some improvements to be made to the UAS. 4G transmission is the communication method for 245 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 to replace the camera in Fig. 1 with an infrared camera and establish new data communication.      The background values of plume 2019-4-15B were 0 ppm and 310 ppm for SO2 and CO2, respectively. The background values of plume 2019-3-29A were 0 ppm and 329 ppm for SO2 and CO2, respectively. The remarks indicate the reason for choosing or not choosing the peak. It can be seen that the peak value of plume 2019-4-15B was more obvious and that the results obtained by multiple alternative peaks were similar. The peak of plume 2019-3-29A was less obvious and there were fewer alternative peaks. This was also the basis for distinguishing data as being of a 385 "good"/"poor" quality. The FSC result of selected peak values are marked as "√".