Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China

Journal Pre-proof Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China

Ben Liu, Cheng Wu, Nan Ma, Qi Chen, Yaowei Li, Jianhuai Ye, Scot T. Martin, Yong Jie Li PII:

S0048-9697(19)36105-4

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136109

Reference:

STOTEN 136109

To appear in:

Science of the Total Environment

Received date:

22 August 2019

Revised date:

11 December 2019

Accepted date:

12 December 2019

Please cite this article as: B. Liu, C. Wu, N. Ma, et al., Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136109

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© 2019 Published by Elsevier.

Journal Pre-proof

Vertical Profiling of Fine Particulate Matter and Black Carbon by Using Unmanned Aerial Vehicle in Macau, China Ben Liu1, Cheng Wu2,3, Nan Ma4, Qi Chen5, Yaowei Li6, Jianhuai Ye7, Scot T. Martin7, Yong Jie Li1,* 1

Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China 2

Institute of Mass Spectrometry and Atmospheric Environment, Jinan University, Guangzhou 510632, China 3

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Guangdong Provincial Engineering Research Center for on-line source apportionment system of air pollution, Guangzhou 510632, China 4

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Center for Pollution and Climate Change Research (APCC), Institute for Environmental and Climate Research, Jinan University, Guangzhou, China 5

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State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Science and Engineering, Peking University, Beijing, China 6

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School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA 7

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School of Engineering and Applied Sciences & Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA

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To Whom Correspondence Should be Addressed

Yong Jie Li: E11-3017, Faculty of Science and Technology, University of Macau, E11, Avenida da Universidade, Taipa, Macau, China Tel: (853) 8822-4943; Fax: (853) 8822-2426 Email: [email protected]

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Journal Pre-proof Abstract: An unmanned aerial vehicle (UAV) equipped with miniature monitors was used to study the vertical profiles of PM2.5 (particulate matter with a ≤2.5-µm diameter) and black carbon (BC) in Macau, China, from the surface to 500 m above ground level (AGL). Twelveand 11-day measurements were conducted during February and March 2018, respectively. In total, 46 flights were conducted between 05:00 and 06:00 AM Local Time (LT). The average concentrations of PM2.5 and BC were significantly lower in March (40.1 ± 17.9 and 2.3 ± 2.0 µg m−3, respectively) when easterly winds prevailed, compared with those in February (69.8 ± 35.7 and 3.6 ± 2.0 µg m−3, respectively) when northerly winds dominated. In general, PM2.5 concentrations decreased with height, with a vertical decrement of 0.2 µg m−3 per 10 m. BC

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concentrations exhibited diverse vertical profiles with an overall vertical decrement of 0.1 µg

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m−3 per 10 m. Meteorological analyses including back-trajectory analysis and atmospheric stability categorization revealed that both advection and convection transports may have

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notable influences on the vertical profiles of PM pollutants. The concentration of PM pollutants above the boundary layer was lower than that within the layer, thus exhibiting a

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sigmoid profile in some cases. In addition, the lighting of firecrackers and fireworks on February 16 (first day of the Chinese New Year) resulted in the elevated concentrations of

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PM2.5 and BC within 150 m AGL. The takeoff of a civil flight on February 10 may have resulted in a substantial increase in the PM2.5 concentrations from 80.8 (±2.1) µg m−3 at the

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ground level to 119.2 (±9.3) µg m−3 at a height of 330 m. Although the results are confined to a height of 500 m AGL, the current study provides a useful dataset for PM vertical

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distributions, complementing the spatiotemporal variations by ground-based measurements. Key words: particulate matter, PM2.5, black carbon, unmanned aerial vehicle, vertical profile

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Journal Pre-proof 1. Introduction Particulate matter (PM) pollution has various influences on the environment (Gerharz et al., 2009; Li et al., 2015; Vallius et al., 2000). Fine particulate matter (PM2.5; i.e. PM with a diameter of ≤2.5 µm) can alter the atmospheric radiative budget and obscure light in the range of vision, thus affecting climate and visibility (Praveen et al., 2012; Schmid et al., 2005; Wang et al., 2015). Moreover, PM2.5 has strong influences on human health (Bond et al., 2013; Lei et al., 2016; Rao et al., 2013; Zhao et al., 2011). Black carbon (BC), a lightabsorbing carbonaceous component of PM2.5 (Janssen et al., 2012), has been suggested to be more closely associated with certain health effects than PM2.5 (Bell et al., 2009; Janssen et al.,

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2012; Li et al., 2016; Patel et al., 2009). The high potency in the toxicity of BC could be

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attributed to its larger specific surface area and its ability to penetrate into the deepest regions of the lung (Braniš et al., 2010; Janssen et al., 2012; Suglia et al., 2008).

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Ground-based PM measurements have been conducted in many studies worldwide (Jeong

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et al., 2004; Jinsart et al., 2002; Klompmaker et al., 2015; Liu et al., 2019a; Querol et al., 2008; Retama et al., 2015; Sandeep et al., 2013; Vallius et al., 2000). However, some studies

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showed that the mass concentration, size distribution, and chemical composition of particles can vary greatly along the vertical atmospheric column (Corrigan et al., 2007; Ferrero et al.,

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2010; Lu et al., 2016; Minguillón et al., 2015). Moreover, the dispersion, accumulation, and deposition of particles are influenced by various transport processes due to atmospheric

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dynamics, particularly the planetary boundary layer (PBL) dynamics (Harrison et al., 2012; Holmes and Morawska, 2006; Hondula et al., 2010; Tang et al., 2016; Vecchi et al., 2004). Therefore, studies on the vertical profiles of PM concentration are of great importance to further understand the spatial characteristics and temporal variance of particulate pollution in the atmosphere.

The vertical distributions of PM pollutants have been conventionally investigated by means of meteorological towers (Han et al., 2015), tethered balloons (Ran et al., 2016), remote sensing (Strawbridge and Snyder, 2004), and manned aircrafts (Ding et al., 2009). Meteorological tower monitoring has advantages in terms of the measurement duration and co-location of instruments, offering more mounting space. However, the disadvantages are low vertical resolution, monitoring height, and mobility. Measurements conducted using tethered balloons can provide PM concentrations as a function of altitude. However, conducting such experiments can be very costly and can provide low horizontal mobility. Remote sensing techniques such as light detection and ranging (LiDAR) can provide 3

Journal Pre-proof information on particle distribution throughout the atmosphere. However, this technique is greatly affected by other meteorological factors such as fog, rain, or other light-attenuating hydrometeors (Baars et al., 2008; Menut et al., 1999). Manned aircrafts may overcome some drawbacks of the aforementioned methods, but the cost is very high for frequent deployments. Unmanned aerial vehicles (UAVs) are a good alternative to manned aircrafts and provide high cost efficiency, flexibility, and mobility for vertical profile measurements of air pollutants (Schuyler and Guzman, 2017; Villa et al., 2016a). A number of measurements have been conducted to investigate the vertical distributions of PM2.5 and BC by using UAVs. Bates et al. (2013) and Zhu et al. (2019) demonstrated

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different distribution patterns of particle number concentration. Bates et al. (2013) found that

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aerosol layers aloft exhibited high particle number concentrations and enhanced light absorption. By contrast, a decreasing trend was observed in the number concentration of

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particles with size > 0.3 μm with increase in height in the evening measurements conducted by Zhu et al. (2019). Moreover, Ferrero et al. (2011) and Ran et al. (2016) indicated that the

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BC concentration above the PBL was significantly lower than that at ground level under

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clean conditions. Similarly, during the UAV flights conducted by Chilinski et al. (2016), a layer of very high BC concentration was found at elevations of <100 m above ground level

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(AGL). This result can be attributed to the PBL diurnal evolution and local emissions, as suggested by Lu et al. (2019) and Ran et al. (2016). Li et al. (2018) indicated that the lower concentrations of PM2.5 and BC in the troposphere might be mainly due to the local fossil-

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fuel combustion sources in the Yangtze River Dela region, rather than the long-range transport sources from the north and northwest China. Peng et al. (2015) mentioned that the diurnal variation of temperature significantly influenced the vertical profiles of PM2.5 between 300 and 1000 m AGL. However, most recent studies were conducted for a limited number of days or flights. There remains a lack of comprehensive investigations on the relationship between local emissions, trans-boundary pollution, and atmospheric stability. Therefore, there is still a great need for conducting vertical profile measurements for longer durations with diverse meteorological conditions to better understand the vertical distributions of PM2.5 and BC. In this study, we performed vertical profile measurements of PM2.5 and BC by using a hexacopter UAV for 23 days in the course of 2 months in 2018 in Macau, China. In total, 46 flights with a maximum height of 500 m AGL were conducted between 05:00 and 06:00 AM Local Time (LT), with 24 flights in February and 22 flights in March 2018. As the boundary 4

Journal Pre-proof layer are usually lower before sunrise, the period of 05:00–06:00 AM LT was selected so that it was more likely to reveal the characteristics of PM pollutants within the whole boundary layer (Fan et al., 2011; Quan et al., 2013). In these 2 months, the prevailing wind direction gradually shifted from northerly to easterly in Macau, thus providing an opportunity to investigate the effects of regional transport and atmospheric characteristics (e.g. temperature and relative humidity) on the local PM pollution. The vertical profiles of PM2.5 and BC were categorized using the Pasquill stability (PS) class to reveal how the boundary-layer heights (BLH) and atmospheric stability influence the vertical distributions of PM pollutants. The potential influence of other meteorological parameters, such as temperature (T), relative

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humidity (RH), and wind, together with air mass origins were also compared for different

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days and different months (February versus March). Moreover, case studies with influences of civil flights that took off from the neighboring airport (~5 km) and lighting of fireworks

2. Methods

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2.1 Site Description and Experiment Design

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and firecrackers on the Chinese New Year (CNY) were presented and discussed.

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Macau is located at the west of the Pearl River Estuary in China (see Figure 1) with the South China Sea to the east and south. The city comprises the Macau Peninsula, Taipa Island,

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Coloane Island, and reclaimed Cotai area. Measurements were conducted on the campus of the University of Macau (22°07'47.6"N, 113°32'48.4"E), which is separated from the Taipa

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Island by a waterway and has the Hengqin Mountain in the southwest (see Figure 1). The campus is approximately 7.9 km from the most densely populated Macau Peninsula, 3.2 km from the Taipa downtown area, and 3.8 km from the Hengqin business area that is under an ongoing development. By following the regulation and permission of the Civil Aviation Authority of Macau, two successive flights were conducted at the waterside grassplot between 05:00 and 06:00 AM LT from February 05 to 16 (12 days in February) and on March 10, 12, 13, 17, 20, 22, 23, 24, 26, and 27, 2018 (11 days in March). The flight site is approximately 5 m the above sea level. The round trip of each flight (0–500 m from the ground and back) took 17 min. A 5-min ground-based measurement was conducted before each flight. 2.2 UAV Platform and Instruments Vertical monitoring was conducted using a hexacopter (UAV Matrice 600, DJI, China). During the flights, the drone climbed vertically from the ground to 500 m AGL at a constant 5

Journal Pre-proof speed of 1 m s−1, hovered at the peak for 20 s, and then descended along the same path at the same speed. The payload module was developed and mounted below the drone for conducting PM2.5 and BC measurements, as well as CO, T, and RH measurements. All inlets of the instruments were placed 20 cm above the drone to minimize the influence of the downwash effect (Villa et al., 2016b; Zhou et al., 2018). Yet, visibly inconsistence of BC vertical profiles between the ascent and descent was observed in some flights. Detailed information is provided in the Supporting Information 3 (Figure S3, Figure S4 and Table S2). PM2.5 mass concentration was measured using a battery-operated, light-scattering laser photometer (DustTrak™ II Aerosol Monitor 8530, TSI, USA). As the optical measurements

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based on scattering are influenced by the hygroscopic behavior of particles, it is necessary to

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correct the raw data by an RH-dependent algorithm (Day and Malm, 2001; Lu et al., 2016; Peng et al., 2015). In this study, we conducted a 2-step correction of the Dusttrak data based

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on (1) the RH-dependent algorithm, and (2) collocated measurements with standard instruments. The post-processed data displays a much higher R2 (0.89) than using the

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uncorrected data (0.48) when compared to the background PM2.5 at TG station. Detailed

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information on the data correction is provided in section 1.1 of the Supporting Information (Figure S1a and Table S1). A portable microaethalometer (microAeth AE51, Aethlabs, USA)

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was used to measure the mass concentration of BC. The aethalometer samples particles on a filter strip. A beam of light was directed on the spot of the particle-laden filter, and the attenuation (ATN, which scales from 0 to 100) of the transmitted light (wavelength: 880 nm)

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was recorded continuously. Thus, the optical absorption measured continuously is proportional to the light-absorbing materials in the PM collected (Hansen et al., 1984). Filter strips were changed before ATN exceeding 30 to minimize the loading effect. The AE51 has been previously validated to be consistent with an AE33 aethalometer (Magee Scientific, USA). Detailed information is provided in section 1.2 of the Supporting Information (Figure S1b). Raw data of the AE51 were processed using the optimized noise-reduction algorithm program (Hagler et al., 2011). Moreover, an indoor air quality monitor (Q-TRAK™ Indoor Air Quality Monitor 7575, TSI, USA) was attached to the payload to measure CO, T, and RH during all flights. The CO concentration is used as an additional indicator of primary emissions. The time of all instruments was synchronized to the same computer each day before conducting measurements. Time resolutions for all instruments were set to 10 s. The flow rate was set to 3 L min−1 for DustTrak and 150 mL min−1 for the AE51. Zero-calibration was 6

Journal Pre-proof performed for DustTrak with a HEPA filter attached to the inlet before each measurement. Moreover, a 15-min warm-up round was conducted for the AE51 before each measurement. The data of the meteorological parameters, such as T, RH, ground wind speed (WS), and wind direction (WD), were obtained from the meteorological station at the University of Macau (UM). PM2.5 mass concentrations acquired from the Taipa Grande (TG) station were used as the background concentrations. T and RH measured at the measurement site are well correlated with those at TG station (see Figure S2 in the Supporting Information). The TG station is located at the top of Mount Taipa Grande with an elevation of 150 m above sea level and is 3.7 km from the measurement site. Information of the vertical wind from a wind

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radar (LAP-3000, Scintec) was also acquired from the TG station. Both stations are operated

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by the Meteorological and Geophysical Bureau (SMG) of Macau. The UM and TG stations are illustrated in Figure 1 with the flight location indicated.

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Backward trajectory and clusters were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler et al., 2016; Draxler and Hess,

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1998, 1997; Stein et al., 2015) that was developed by the Air Resources Laboratory of the

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National Oceanic and Atmospheric Administration (NOAA), United States. Pasquill stability (PS) classification was used to indicate atmospheric stability, in the categories of very

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unstable (A), unstable (B), slightly unstable (C), neutral (D), slightly stable (E), stable (F), or very stable (G) (Pasquill, 1961). The PS classifications adopted in our study were publicly available from the website of the Air Resources Laboratory of NOAA, United States

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(www.ready.noaa.gov/archives.php). Together with the PS class, the heights of the PBL at 05:00 AM LT for each measurement day. The data were acquired from the same NOAA resource. Both PS class and BLH were retrieved from the meteorological fields obtained from the Global Data Assimilation System (GDAS) 1° latitude–longitude grids. When applied locally in this study, the system used the location of Macao International Airport, which is is about 5 km away from the measurement site, to generate the output. The reliability and applicability of the PS class and BLH from the GDAS data have been evaluated in a few previous studies (Belegante et al., 2014; Kahl and Chapman, 2018). 3. Results and Discussion 3.1 General Characteristics Hourly background PM2.5 concentrations from the TG station are displayed in panels a1 and a2 of Figure 2 with the ground WS and WD, T, and RH (panels b1/b2 and c1/c2 in Figure 2) from the UM station. For comparison, the average PM2.5 and BC concentrations recorded 7

Journal Pre-proof at the measurement site at the ground level before the flights are also presented in Figure 2 (panels a1 and a2). Table 1 lists the 3-h average concentrations (i.e., covering the flight time) of the background PM2.5 concentrations at TG, measured vertical PM2.5 and BC concentrations, as well as BC/PM2.5 ratios during the flights for each measurement day. In the periods of UAV measurements (05:00–06:00 AM LT), the background PM2.5 concentration in February increased to the highest value on February 10 (60.0 µg m−3) and decreased afterwards to the lowest value on February 16 (12.3 µg m−3). In March, the background PM2.5 concentration varied only slightly at first, followed by an obvious increase from March 24. The highest concentration (35.3 µg m−3) in March was observed on March 27, and the lowest

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concentration (8.0 µg m−3) was on March 20. The near-flight-time 3-h averages and one standard deviation (σ) of the background PM2.5 concentration during the two months were

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34.6 ± 14.3 and 19.6 ± 8.8 μg m−3, respectively. Similarly, as shown in Table 1,

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concentrations (mean ± 1σ) of PM2.5 and BC measured during flights were significantly lower in March (25.4 ± 11.3 and 2.3 ± 2.0 µg m−3, respectively) than those in February (44.7 ± 22.9

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and 3.6 ± 2.0 µg m−3, respectively). The coefficients of variation (CV) for PM2.5 and BC concentrations during the flights were calculated by dividing σ with the corresponding mean

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value. The CV of PM2.5 and BC concentrations were respectively 0.51 and 0.56 in February and 0.44 and 0.87 in March. The larger CV value of BC in March implies a larger temporal

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variation of the particle concentration in that month. In general, a consistent trend of concentrations was observed between ground-level PM2.5 and background PM2.5 (panels a1

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and a2 in Figure 2). The trends of the ground-level BC concentration agreed well with those of the ground-level PM2.5 concentration, except for a larger variance. Moreover, the BC/PM2.5 ratios during the measurements were 8.0 ± 3.2% in February and 9.6 ± 7.5% in March. Specifically, higher ground-level BC concentrations and BC/PM2.5 ratios were observed in late March with a BC concentration of 5.7 ± 2.2 µg m−3 and BC/PM2.5 ratio of 12.3 ± 4.4%. During the last two measurement days (March 26 and 27), emissions from fishing vessels on the waterway near the flight site were more significant in these warmer days and can be clearly spotted. As the waterway is only about 15 m east to the flight site, with dominating easterly wind during measurements in March (see Figure S6 in Supporting Information), emissions from the fishing vessels would have a direct impact on the measurement results. The ground-level WS was 2.7 ± 1.9 m s−1 during the measurements in February and decreased to 2.0 ± 1.7 m s−1 in March. Moreover, the direction shifted from northerly in 8

Journal Pre-proof February to easterly in March, as shown in Figure 2 (panels b1 and b2). Ground-level T and RH increased throughout the campaign from 13.1 ± 2.7 °C and 64.6 ± 16.1% in February to 18.2 ± 2.0 °C and 82.0 ± 11.4% in March, respectively (panels c1 and c2 in Figure 2). Panels a and b in Figure 3 present the air mass origins from the 72-h backward trajectory analysis during the measurement periods in February and March, respectively. The change in the air mass origins from northern China in February to the coastal waters in southeast China in March (see Figure 2 b1 and b2) may explain the drastic difference between the PM2.5 and BC concentrations in these two months both from the background station (TG for PM2.5) and during our flights (for PM2.5 and BC).

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Vertical profiles of the monthly average concentrations of PM2.5 (Figure 4a) and BC

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(Figure 4b), the BC/PM2.5 ratio (Figure 4c), the concentrations of CO (Figure 4d), T (Figure 4e), and RH (Figure 4f) are illustrated in Figure 4. Measurements on February 10 and March

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17 were excluded because both measurements coincided with a civil flight departing from the nearby Macau International Airport (5 km northeasterly). These two cases are discussed

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separately in Section 3.3. PM2.5 concentrations distributed more uniformly along the height

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than the BC concentrations, especially in March (Figure 4). More specifically, the BC concentrations exhibited a marked reduction between 60 and 120 m, as well as >380 m in

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March. Although the average concentration of BC was lower in March than in February, a notable accumulation of BC near the ground in March suggests efficient near-surface accumulation of local emissions during that period. This assertion is supported by the

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observation that more fishing vessels, which can contribute substantially to PM pollution (Buffaloe et al., 2014; Tao et al., 2017), were witnessed in mid-March. These results are also in accordance with the study of Sajani et al. (2018), who indicated that more pronounced vertical gradients were usually associated with primary pollutants such as BC from ground emissions. The more “uniform” vertical distribution of PM2.5 than that of BC suggests that BC and PM2.5 come from different sources. At a high altitude, precursors and oxidants can react to form secondary PM, whereas the primary components of PM (such as BC) might not be transported vertically with high efficiency. The observed more “uniform” distribution of PM2.5 vertically might not necessarily imply true uniformity at the molecular level. Similar results were revealed by Zhou et al. (2019), who investigated vertical distributions of sizesegregated particles in Guangzhou, China, in 2015. They found that element carbon was present in a lower concentration at higher altitudes; however, the fraction of secondary inorganic ions in PM2.5 increased with height. However, direct evidence on the vertical 9

Journal Pre-proof distributions of species in PM2.5 is lacking without more detailed chemical measurements in this study. The sharp decline of BC above 450 m was observed in March only. It might be due to the different BLH in February and March. In this study, the average BLH were estimated to be 414 m and 342 m in February and March, respectively. Besides, as given in Table 2, the BLH in 8 days (out of 12 days) are close to and above 450 m in February, with only 3 days out of 11 days in March. As a result, BC was more likely trapped under the top of boundary layer around 450 m in March than in February. Moreover, T and RH were higher during the flights in March than those in February, with a vertically declining trend of T and

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generally stable RH in both months (see Figure 4c and 4d). 3.2 Atmospheric Stability and Vertical Distribution

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Atmospheric stability can strongly affect the dispersion and accumulation of local

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emissions and transport of regional pollutants. The turbulence of the atmosphere is one of the most important phenomena that governs the dilution of airborne particles (Essa et al., 2006).

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Vertical profiles of PM were usually categorized based on their distribution patterns along the altitude in previous studies. For example, Lu et al. (2019) categorized four profile types of

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BC that were clearly associated with the daily circle of the BLH. Based on their findings, we adopted the PS class to further investigate the influences of atmospheric stability and BLH on

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the vertical distributions of PM2.5 and BC. Moreover, as flights were carried out at a fixed time (05:00 to 06:00 AM LT) on the measurement days in February and March, the

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influences of both local emissions and regional transport could be studied. As shown in Table 2, during our measurement periods, the atmosphere exhibited either neutral (eight days out of 23 days) or stable conditions (E, F, and G, 12 days out of 23 days) for most cases with only three slightly unstable (C) cases. The BLH are also shown in Table 2, together with T and RH. Figure 5 displays the vertical profiles of both PM2.5 (panels a1 to a5) and BC (panels b1 to b5) under different atmospheric stability conditions (C, D, E, F, and G in this study). Moreover, the profiles of T (Figure 5, panels c1 to c5) and RH (Figure 5, panels d1 to d5) are presented. Convective activities are enhanced when the atmosphere is unstable (e.g., C in this study), thus making it more favorable for the dispersion of particulate pollutants within the boundary layer. The BLH exceeded the upper limit of the flight height (500 m) on February 5, 6, and 11 (C). As a result, PM2.5 distributed uniformly with a slightly decreasing trend along the altitude. By contrast, higher concentrations of both PM2.5 and BC were clearly observed near the ground in slightly stable (E), stable (F), and very stable (G) conditions (Figure 5,

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Journal Pre-proof panels a3–a5 and b3–b5, respectively). These meteorological conditions facilitated the accumulation of particulate pollutants near the ground when the vertical motion was inhibited, and the boundary layer contracted (see Table 2) due to weak convection and turbulence in stable atmospheric conditions. The vertical decrements of the PM2.5 concentration were 0.4 (under 150 m), 0.8 (under 290 m), and 1.7 (under 180 m) µg m−3 per 10 m for E, F, and, G, respectively. These decreasing rates are much higher than that of the campaign average (0.2 µg m−3 per 10 m), suggesting accumulation of PM2.5 near the ground. As shown in Figure 5 (a3–a5), the well-mixing tendency of the PM2.5 concentration was observed above the previous mentioned critical heights (150, 290, and 180 m) under those three PS classes (E, F,

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and G). February 16 is the only day that exhibited very stable conditions (G). The elevated

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PM2.5 concentration between 400 and 420 m may be attributed to the simultaneous effects of the fireworks and regional transport during the CNY (Section 3.3).

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The vertical distributions of BC under different PS classes were in broad accordance with PM2.5, exhibiting more stratified layers. This finding is consistent with the vertical profiles of

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the monthly average BC concentration discussed in Section 3.1. Interestingly, both uniform

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distribution (e.g., February 12 and March 10) and profiles with gradients (e.g., February 9 and March 24) for PM2.5 concentrations were observed under a neutral condition (D) (Figure

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6a). A neutral atmosphere neither enhances nor inhibits the mechanical turbulence. Comparing the two profiles corresponding to February 9 and 12, less-polluted air mass was transported from the southeast coastal regions of China on February 9 with an RH of 82.3%.

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On the contrary, more pollution was brought with drier air (RH = 63.9%) from central China on February 12 (Figure 3a and Table 2). With similar BLH (448 m on February 9 and 505 m on February 12), the difference in the air mass may cause the vertical inhomogeneity of secondary formation events that are more favorable in the cleaner and moister atmospheric conditions (Wehner et al., 2007; Zhu et al., 2019a). Conversely, the diverse profiles on March 10 and 24 are mostly due to the disparity of BLH (528 and 261 m, respectively). Therefore, this result emphasizes that not only atmospheric stability but also the effects of humidity and regional transport (Figure 3) have considerable influences on the vertical distributions of particles. Note that compared with the corresponding profiles of PM2.5 concentration, the more scattered BC concentration suggest a higher variability for BC. This result is in line with the findings of Weber et al. (2014), who indicated that BC concentrations and ultrafine particle concentrations varied strongly with altitude.

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Journal Pre-proof The vertical distributions of PM2.5 and BC in this study are limited to 500 m, which is within the boundary layer in some cases (Table 2). This limitation confines our discussion to a qualitative description of PM vertical profiles, instead of a quantitative analysis on the dynamics of PM vertical distributions. Nevertheless, our results supplement the current ground-based measurements, and provide direct measured results for simulation of PM exposure assessments in high-rise buildings (Lee et al., 2017; Zhang et al., 2015). The growing number of high-rise buildings in China and worldwide draws attention to the air quality at relevant heights.

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3.3 Special Cases We observed two special cases (EP1 and EP2) that were affected by the primary

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emissions of PM. The first case is related to the takeoff of civil flights from a nearby airport

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(5 km away from the measurement site) during the measurement periods (5:00 to 6:00 AM LT) of 2 days. These civil flights took off at 5:09 AM LT on February 10 (Flight No.:

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QFA7534) and 5:08 AM LT on March 17 (Flight No.: LKH7219). Accurately quantifying the influence of aviation emissions during its takeoff would be difficult at the measurement site

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that is 5 km away from the airport. However, being a potential source of PM and CO, the engine exhaust of the airplane displayed some influence on the vertical distributions of

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measured PM and CO at the site. The highest concentrations of PM2.5, BC, and CO (124.6 µg m−3, 8.7 µg m−3, and 1.2 ppm, respectively) were observed at 300 m on February 10; however,

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such increases were not observed on March 17 (panels a1 to a3 and b1 to b3 in Figure 7). On March 17, the PM2.5 concentration first decreased with increase in altitude up to 330 m and then increased up to the maximum flight height of 500 m. The trend of BC concentration was similar to that of PM2.5 concentration with a smaller fluctuation. The differences in the WS and WD at 350 m AGL on February 10 (3.1 m s−1, northeasterly) and March 17 (11.3 m s−1, easterly) could be part of the reasons for this drastic difference. This is because stronger winds were more favorable for the dispersion of air pollutants that were generated by the aircraft engines from the northeast, where the airport is situated. More importantly, different atmospheric stability on February 10 (E) and March 17 (D) may serve a key role in shaping the two profiles. The height of PBL on February 10 was 240 m and that on March 17 was 507 m (Table 2). On February 10, it was difficult for the aviation emission over 300 m to be transported downward into the shallow PBL (240 m). By contrast, the aircraft exhaust could have accumulated above the PBL (507 m) on March 17, which was above the UAV measurement altitude. As illustrated in Figure S3, the vertical wind inverted from downward 12

Journal Pre-proof to upward at an altitude between 400 and 500 m on March 17, thus potentially facilitating well mixing of particles below the PBL top (<507 m). However, the downward vertical wind through the whole measurements on February 10 might have trapped particles around the top of the shallow PBL (<240 m). Another case (EP3) is related to the lighting of firecrackers and fireworks on February 16 (first day of the CNY). This resulted in elevated concentrations of PM2.5 in the early morning of February 16 near the ground (below ~150 m), as shown in Figure 7 (panel c1). Firework and firecracker performance began at around midnight (12 PM LT) on the eve of the CNY and lasted for approximately 1 h. This increase in PM concentration is in good agreement

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with the drastically increased hourly average concentration of PM2.5 obtained from the

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ground-based measurements in nearby cities (Figure S2). Note that the concentration of CO on the same day exhibited marked peak values at approximately 400 m (panel c3 in Figure 7).

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Nonmetallic fuels such as charcoal, sulfur, and red phosphorus in fireworks and firecrackers can emit a large amount of CO and particles after burning (Pathak et al., 2013). The

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accumulation of CO and PM were in accordance with the findings of current studies (Saha et

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al., 2014; Wang et al., 2007). Han et al. (2014) noted that the lighting of fireworks and firecrackers on CNY eve influenced the atmosphere up to the altitude of 450 m, and such

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influence gradually faded away within 4 h. Han et al. (2014) also suggested that CO may have lingered aloft because of very stable atmosphere, whereas PM pollutants sank due to faster deposition. However, the reasons for the different heights of PM (PM2.5 and BC) and

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CO maxima is still not sufficiently clear. BC was observed to accumulate under 100 m on the CNY day. However, many studies revealed that the concentration of BC usually decreases around the CNY eve due to the limited traffic (Feng et al., 2016; Jiang et al., 2015; Wang et al., 2017). For instance, Feng et al. ( 2016) observed lower organic carbon and element carbon concentrations during the CNY despite the increase in PM2.5 concentration. Similarly, in our study, the BC/PM2.5 ratio was the lowest (6.3% ± 5.4%) on the CNY eve compared with the ratios on other measurement days. Therefore, it is most likely that the very stable atmosphere (G) facilitated the accumulation of PM near the ground on the CNY day (see Table 2). 4. Conclusions This study presents the vertical profiles of BC and PM2.5 by conducting UAV measurements at a fixed location in Macau. In total, 46 flights were conducted (24 in February and 22 in March) between 05:00 and 06:00 AM LT. Higher particle concentrations 13

Journal Pre-proof but less pronounced vertical variations were observed in February than those in March. In general, PM2.5 concentrations decreased with height. Moreover, a vertical decrement of 0.2 µg m−3 per 10 m was observed for PM2.5. BC concentrations showed diverse vertical profiles with an overall vertical decrement of 0.1 µg m−3 per 10 m. The results reveal that apart from local emissions, meteorological parameters such as RH, wind, atmospheric stability, and regional transport may play important roles in the vertical profiles of PM2.5 and BC. In particular, as Macau is located at the southernmost part of the Pearl River Delta region, pollutant concentration is more significantly influenced by the air masses with different origins than other inland cities. The influence of the regional transport was analyzed by back-

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trajectory analysis. Moreover, the influence of atmospheric stability was determined by the

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categorization of the PS class. A few special cases suggested the influences of primary emissions on the vertical profiles of PM2.5 and BC. First, PM pollutants at the ground level

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were enhanced when there were fishing vessels operating nearby. Second, two cases related to civil flights during two measurement days were obtained during the campaign, but these

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cases showed different results. As WS, vertical motion, atmospheric stability, and boundarylayer height were quite different for the two cases, totally different vertical profiles of

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particles were observed. Third, the lighting of firecrackers and fireworks during the CNY led to an increase in PM2.5 concentration in the lower troposphere (150 m). This study shows the

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feasibility of vertical profiling of PM pollutants by using UAV measurements. However, further investigations on other locations, other time of the day, extended altitudes, and other

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pollutants are warranted to better understand the vertical distribution of PM pollution as related to PBL dynamics. Acknowledgment

The work was supported by the Multi-Year Research grant (No. MYRG2017-00044-FST) from the UM and the Science and Technology Development Fund, Macau SAR (File no. 016/2017/A1). Cheng Wu acknowledges financial support from the National Natural Science Foundation of China (grant No. 41605002). The authors are grateful to the Macau Meteorological and Geophysical Bureau (SMG) for providing the meteorological data and background PM2.5 concentration data.

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Wang, Y.Q., Zhang, X.Y., Sun, J.Y., Zhang, X.C., Che, H.Z., Li, Y., 2015. Spatial and temporal variations of the concentrations of PM 10 , PM 2.5 and PM 1 in China. Atmos. Chem. Phys. Discuss. 15, 15319–15354. https://doi.org/10.5194/acpd-15-15319-2015

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Weber, K., Pohl, T., Fischer, C., Lange, M., 2014. Determination of a vertical profile of black carbon by a combined application of a light research aircraft and a quadcopter unmanned aerial vehicle – a case study using an airborne ultraportable microaethalometer for black carbon measurements at a rural s. Recent Res. Electr. Comput. Eng. 13–18. Wehner, B., Siebert, H., Stratmann, F., Tuch, T., Wiedensohler, A., Petäjä, T., Dal Maso, M., Kulmala, M., 2007. Horizontal homogeneity and vertical extent of new particle formation events, in: Tellus, Series B: Chemical and Physical Meteorology. https://doi.org/10.1111/j.1600-0889.2007.00260.x Zauli Sajani, S., Marchesi, S., Trentini, A., Bacco, D., Zigola, C., Rovelli, S., Ricciardelli, I., Maccone, C., Lauriola, P., Cavallo, D.M., Poluzzi, V., Cattaneo, A., Harrison, R.M., 2018. Vertical variation of PM2.5mass and chemical composition, particle size distribution, NO2, and BTEX at a high rise building. Environ. Pollut. 235, 339–349. https://doi.org/10.1016/j.envpol.2017.12.090 Zhang, Y., Kwok, K.C.S., Liu, X.P., Niu, J.L., 2015. Characteristics of air pollutant dispersion around a high-rise building. Environ. Pollut. https://doi.org/10.1016/j.envpol.2015.05.004 Zhao, P., Zhang, X., Xu, X., Zhao, X., 2011. Long-term visibility trends and characteristics in the region of Beijing, Tianjin, and Hebei, China. Atmos. Res. 101, 711–718. https://doi.org/10.1016/j.atmosres.2011.04.019 20

Journal Pre-proof Zhou, S., Peng, S., Wang, M., Shen, A., Liu, Z., 2018. The Characteristics and Contributing Factors of Air Pollution in Nanjing: A Case Study Based on an Unmanned Aerial Vehicle Experiment and Multiple Datasets. Atmosphere (Basel). 9, 343. https://doi.org/10.3390/atmos9090343 Zhou, S., Wu, L., Guo, J., Chen, W., Wang, X., Zhao, J., Cheng, Y., Huang, Z., Zhang, J., Sun, Y., Fu, P., Jia, S., Chen, Y., Kuang, J., 2019. Vertical distribution of atmospheric particulate matters within urban boundary layer in southern China: size-segregated chemical composition and secondary formation through cloud processing and heterogeneous reactions. Atmos. Chem. Phys. Discuss. https://doi.org/10.5194/acp2019-155

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Zhu, Y., Wu, Z., Park, Y., Fan, X., Bai, D., Zong, P., Qin, B., Cai, X., Ahn, K.-H., 2019a. Measurements of atmospheric aerosol vertical distribution above North China Plain using hexacopter. Sci. Total Environ. 665, 1095–1102. https://doi.org/10.1016/j.scitotenv.2019.02.100

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Zhu, Y., Wu, Z., Park, Y., Fan, X., Bai, D., Zong, P., Qin, B., Cai, X., Ahn, K.H., 2019b. Measurements of atmospheric aerosol vertical distribution above North China Plain using hexacopter. Sci. Total Environ. 665, 1095–1102. https://doi.org/10.1016/j.scitotenv.2019.02.100

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Journal Pre-proof Tables Table 1. Statistics of the average mass concentrations of background PM2.5 (at TG), PM2.5, and BC during the flights, as well as the average mass fraction of BC in PM2.5 during UAV measurements on a daily basis. Note that the concentration of background PM2.5 is the 3-h average (04:00–06:00 AM LT). Date

Background PM2.5 (µg m-3)

PM2.5

BC

-3

BC/PM2.5

(µg m )

-3

(µg m )

(%)

2/05

32.7 ± 2.1

49.3 ± 1.8

2.9 ± 0.3

5.9 ± 0.7

2/06

24.0 ± 2.2

36.3 ± 1.7

2.4 ± 0.3

6.7 ± 1.0

3/10

2/07

38.5 ± 2.5

21.1 ± 22.0

3.9 ± 1.4

9.9 ± 2.7

3/11

2/08

45.7 ± 2.5

58.9 ± 6.5

4.7 ± 0.8

8.1 ± 1.1

3/12

2/09

23.7 ± 2.9

23.8 ± 6.4

1.9 ± 0.4

8.5 ± 3.2

3/13

2/10

60.0 ± 0.8

79.5 ± 21.4

7.2 ± 1.4

9.4 ± 1.8

3/17

2/11

54.0 ± 2.9

69.3 ± 2.3

5.9 ± 1.2

2/12

45.3 ± 1.2

69.0 ± 1.9

4.5 ± 1.5

2/13

35.3 ± 2.4

42.5 ± 3.2

3.5 ± 0.4

2/14

18.0 ± 2.9

20.1 ± 5.4

2.0 ± 0.7

2/15

27.3 ± 1.7

27.5 ± 10.4

2/16

12.3 ± 0.5

29.1 ± 18.7

February

34.6 ± 14.3

44.7 ± 22.9

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BC

-3

BC/PM2.5 -3

(µg m )

(µg m )

(%)

-

-

-

11.0 ± 0.8

21.2 ± 2.4

1.5 ± 1.4

7.5 ± 8.0

15.7 ± 2.6

19.6 ± 2.3

1.6 ± 1.6

8.9 ± 10.2

18.0 ± 4.1

22.5 ± 10.8

2.0 ± 1.2

10.0 ± 9.5

19.0 ± 0.8

22.3 ± 7.3

1.4 ± 0.5

6.7 ± 2.1

22.3 ± 4.2

30.1 ± 9.4

1.3 ± 0.6

4.8 ± 3.5

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PM2.5

8.6 ± 1.9

3/20

8.0 ± 0.8

15.9 ± 6.5

2.3 ± 1.0

15.4 ± 7.2

6.6 ± 2.3

3/22

15.0 ± 0.8

23.3 ± 0.7

2.2 ± 1.1

9.5 ± 4.9

8.4 ± 0.7

3/23

12.0 ± 2.8

14.0 ± 2.5

1.6 ± 1.0

11.9 ± 9.1

10.0 ± 3.7

3/24

27.7 ± 1.7

33.6 ± 12.5

2.7 ± 1.8

8.0 ± 4.3

2.5 ± 1.1

9.7 ± 5.3

3/26

32.0 ± 1.4

32.9 ± 11.0

3.0 ± 1.7

9.0 ± 5.0

1.4 ± 1.2

6.3 ± 5.4

3/27

35.3 ± 3.3

43.1 ± 7.0

6.1 ± 2.8

13.9 ± 6.3

3.6 ± 2.0

8.0 ± 3.2

March

19.6 ± 8.8

25.4 ± 11.3

2.3 ± 2.0

9.6 ± 7.5

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Background PM2.5 (µg m-3)

Date

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Journal Pre-proof Table 2. Pasquill-Stability classes and boundary-layer heights (BLH) at 05:00 AM LT at each measurement day. Data were acquired from the National Oceanic and Atmospheric Administration of the United States (https://ready.arl.noaa.gov). The average temperature and relative humidity (RH) measured on the ground during the campaign was presented. 2/05 C 694 7.4 49.5

2/06 C 671 8.1 46.6

2/07 D 486 10.7 68.4

2/08 D 584 10.8 48.2

2/09 D 448 13.9 82.3

2/10 E 240 15.9 74.7

2/11 C 530 14.5 62.3

2/12 D 505 13.0 63.9

2/13 E 430 13.3 61.6

2/14 E 262 14.0 62.3

2/15 F 65 16.7 72.0

2/16 G 50 16.8 79.9

March PS Class BLH (m) T (°C) RH (%)

3/10 D 528 13.9 76.8

3/11 D 652 15.8 79.8

3/12 E 375 17.0 83.1

3/13 E 166 18.6 85.9

3/17 D 507 19.2 95.6

3/20 E 215 20.6 88.0

3/22 E 404 16.6 60.9

3/23 E 409 17.5 75.2

3/24 D 261 19.0 86.5

3/26 F 164 19.4 84.1

3/27 E 83 20.3 85.4

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February PS Class BLH (m) T (°C) RH (%)

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Figures

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Figure 1. Location of Macau, China. The University of Macau (UM) with the flight site and the UM meteorological station is denoted using a red dot in the small map at the lower left portion of the figure. The location of the Taipa Grande (TG) station is presented by a red dot in the middle portion of the small map.

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PM2.5 (g m-3)

a2

PM2.5 _ Flight Site PM2.5 _ TG Station BC_Flight Site

80

10 8

60

6

40

4

20

2

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Wind Direction

b1

b2

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10

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10

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2/17

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February

2/16

2/15

2/14

2/13

2/12

2/11

2/9

2/10

2/8

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2/6

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100

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a1

PM2.5 _ Flight Site PM2.5 _ TG Station BC_Flight Site

120

March

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Figure 2. Comparison between the ground-level PM2.5 (red dots) and BC concentrations (blue dots) measured before flights and the background PM2.5 concentration acquired from the Taipa Grande (TG) station in February (a1) and March (a2), respectively. Note that the scale of BC is the y-axis on the right in the upper panels, and the scale of the PM2.5 concentration is the y-axis on left. The wind direction is represented by arrows, whereas the wind speed (b1, b2) is marked by the position of arrowheads. Temperature and relative humidity (RH) are given in c1 (February) and c2 (March). All meteorological measurements were conducted on the ground in the campus of the University of Macau.

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b

2/6 1

2/7 1

2/8 1

2/9 2

2/10 2/11 2/12 2/13 2/14 2/15 2/16 Date 3/10 3/11 3/12 3/13 3/17 3/20 3/22 3/23 3/24 3/26 3/27 2 1 1 3 3 2 2 2 3 4 4 4 1 1 1 3 4 Cluster 1

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Figure 3. Clusters of the 72-h backward trajectory and the occurrence of the clusters in (a) February and (b) March.

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a

b

500

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PM2.5 (g m-3)

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c

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0

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500

of

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500

60

80

100

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Figure 4. Comparison between the vertical profiles of (a) BC, (b) PM2.5, (c) T, and (d) RH in February (in red) and March (in black). The dots in the figure represent the average value with error bar as the corresponding one standard deviation. Note that data of February 10 and March 17 were excluded due to aviation emission encountered.

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Height (m, AGL)

500

D

a1 500

a2

E

500

a3

F

500

a4

400

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400

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a5

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500

b1 500

400

400

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400

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b2

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c5

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c2

500

re

c1

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b5

500

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b4

500

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PM2.5 (g m )

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500

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d3

500

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60

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d4

500 400

400

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100

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0

100 20

0

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d5

500

400

na

d1

500

lP

T (°C)

0

40

60

80

100 20

40

60

80

100

RH (%)

Figure 5. Vertical profiles of PM2.5 and BC concentrations under different PS classes (C to G) during the measurements. Gray lines represent the individual measurements. Average values and the standard deviation are denoted by red lines and pink shadows for each parameter. Note that data on February 10 and March 17 are not included in the analysis due to the aviation emission encountered.

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a

2/09 2/12 3/10 3/24

b

Height (m, AGL)

400

400

300

300

200

200

100

100

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10

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30

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50

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70

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500

-p

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BC (g m-3)

ro

PM2.5 (g m-3)

of

0

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500

T (°C)

200

100

0

10 20

30 40

50 60

70 80

0 90 100

RH (%)

Figure 6. Vertical distribution of PM2.5, BC, T, and RH under neutral (PS Class-D) atmospheric stability. The dots represent two cases with uniform distributions on February 12 and March 10, and the circles represent two cases with stratified distribution on February 9 and March 24. Circles and dots in red are the two profiles of February and those in black correspond to March. The error bars denote one standard deviation for each profile.

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Journal Pre-proof a1

a4

500

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500

2/10 (EP1)

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500

0

14

16

18

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22 40 50 60 70 80 90 100

RH (%)

Figure 7. Comparison between the measurements that coincided with the takeoff of civil flights on February 10 (upper panels: a1–a5) and March 17 (middle panels: b1–b5) and the measurements on Chinese New Year (CNY, lower panels: c1–c5). The gray lines represent individual measurements. The average values and standard deviations are denoted by red lines and pink shadows, respectively. The blue dashed lines represent the concentration decline rates of PM2.5 and BC during the entire campaign.

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Journal Pre-proof Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Graphical abstract

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Journal Pre-proof Highlights

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In total 46 flights were conducted using unmanned aerial vehicle for fine particulate matter (PM2.5) and black carbon (BC) in Macau, China. Vertical distributions of PM2.5 and BC were analysed together with local emission, regional transport, and atmospheric stability. Special cases with influences from takeoff of civil flights and lighting of firecrackers and fireworks on vertical distributions of PM2.5 and BC are also discussed.

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