**Development of a 3D Real-Time Atmospheric Monitoring System (3DREAMS) Using Doppler LiDARs and Applications for Long-Term Analysis and Hot-and-Polluted Episodes**

#### **Steve Hung Lam YIM 1,2,3**


Received: 6 February 2020; Accepted: 18 March 2020; Published: 24 March 2020

**Abstract:** Heatwaves and air pollution are serious environmental problems that adversely affect human health. While related studies have typically employed ground-level data, the long-term and episodic characteristics of meteorology and air quality at higher altitudes have yet to be fully understood. This study developed a 3-Dimensional Real-timE Atmospheric Monitoring System (3DREAMS) to measure and analyze the vertical profiles of horizontal wind speed and direction, vertical wind velocity as well as aerosol backscatter. The system was applied to Hong Kong, a highly dense city with complex topography, during each season and including hot-and-polluted episodes (HPEs) in 2019. The results reveal that the high spatial wind variability and wind characteristics in the lower atmosphere in Hong Kong can extend upwards by up to 0.66 km, thus highlighting the importance of mountains for the wind environment in the city. Both upslope and downslope winds were observed at one site, whereas downward air motions predominated at another site. The high temperature and high concentration of fine particulate matter during HPEs were caused by a significant reduction in both horizontal and vertical wind speeds that established conditions favorable for heat and air pollutant accumulation, and by the prevailing westerly wind promoting transboundary air pollution. The findings of this study are anticipated to provide valuable insight for weather forecasting and air quality studies. The 3DREAMS will be further developed to monitor upper atmosphere wind and air quality over the Greater Bay Area of China.

**Keywords:** Doppler LiDAR; spatial wind variability; air quality

#### **1. Introduction**

Heatwaves and air pollution are major environmental problems [1–4] that adversely affect human health [5–19]. Despite the substantial burdens on human health, the present understanding of such weather and air quality problems remains limited by insufficient data. In particular, data concerning the wind and air quality in the upper atmosphere are required for data analyses and modeling. This absence of data limits research into heatwaves and air pollution.

Extremely high temperatures and air pollution may occur simultaneously because of their shared atmospheric driving conditions. For this study, an event featuring extremely high temperatures and air pollution was defined as a hot-and-polluted episode (HPE). The synergy between high temperatures and air pollution can result in serious public health burdens. Lee et al. [20] investigated a period of abnormally high temperatures and air pollution in the United Kingdom. They determined that

the regional entrainment of air from the upper atmosphere caused early morning increases in ozone during the episode and increased biogenic emissions under high temperatures. Stedman [21] reported that this increase in ozone and particulates caused more than 400 and 700 additional mortalities in England and Wales, respectively. Therefore, a more comprehensive understanding of the formation mechanism of such episodes is required.

In addition to local emissions, transboundary air pollution (TAP) is a major contributor to serious air pollution [14,22]. For example, studies have reported the severe TAP in the Greater Bay Area of China [23–27] and the resultant health impacts [10,28]. However, the understanding of upper atmosphere TAP remains limited. To mitigate this limitation and provide forecasting of TAP and HPEs, a monitoring system is required to collect extensive data of atmospheric variables from the upper atmosphere.

Conventional measurements have focused on ground-level air quality. Since 2013, China has been releasing hourly ground-level air pollutant data covering the entire country. Previous studies have intensively investigated ground-level air quality by using surface monitoring networks [29–32] or satellite-retrieval approaches [33]. One previous study attempted to retrieve vertical structures of aerosol from various satellite observations [34]. Nevertheless, air quality at higher altitudes and the meteorological driving conditions have yet to be fully understood. Some studies have employed upper air sounding data to investigate the meteorology at various altitudes [35,36]. However, such data are typically measured only two to three times per day because of the high cost of data collection. In addition, upper air sounding data are collected using a helium balloon that carries devices for meteorological measurement. Therefore, the measurement locations are dependent on the horizontal and vertical wind velocities at various altitudes. Although sounding data provide a valuable data set for upper atmosphere meteorology, methodological characteristics prevent the colocation and high time resolution required for conducting air quality studies.

To overcome data availability problems, Light Detection and Ranging (LiDAR) can be used for remote atmospheric sensing [37]. For example, an intercomparison study took place at Mace Head, Ireland [38] where one LiDAR and two co-located ceilometers were validated against the boundary-layer height derived from radiosoundings. As preparation for the European Space Agency ADM Mission [39], an intercomparison campaign that held in southern France showed the feasibility of the direct detection Dopple wind LiDAR technique to retrieve the horizontal wind speed atmospheric profile. A single Doppler LiDAR unit can perform wind profiling and air quality monitoring [40–42]. For example, Hong Kong International Airport applied Doppler Lidar to monitor wind shear near the airport [43–45]. The laser beam of their lidar points toward the departure and approach runways. Their application in Dec 2005 captured ~76% of reported wind shear events [46]. Another study applied Doppler LiDAR for evaluating offshore wind characteristics for wind energy [47]. A previous study assessed internal boundary layer structure in Hong Kong under sea-breeze conditions. The authors compared their simulated internal boundary layer with that derived from Doppler LiDAR data. This shows the importance of LiDAR for model developments [48]. Doppler LiDAR was also used for air quality research. A study applied Doppler LiDAR to investigate the characteristics of heavy particular matter pollution [49]. These studies have shown the capabilities of Doppler LiDAR for meteorological and air quality studies.

For cities with considerable spatial and temporal variations in wind, a single LiDAR unit may be insufficient to provide a complete description of complex atmospheric conditions. Previous studies have reported substantial spatial wind variability [50] and TAP [25,26] in Hong Kong. To improve understanding of the wind environment and its influence on air quality in this highly dense city, a 3-Dimensional (3D) Real-timE Atmospheric Monitoring System (3DREAMS) was developed in the present study. This system can be used for air pollution studies with high spatial wind variability or TAP, especially those in Hong Kong as well as in the Greater Bay Area to investigate the interactions between cities in the region.

The aim of this study was to develop the 3DREAMS using more than one Doppler LiDAR unit. The method is described in Section 2. Section 3 presents the results of comparison between the LiDAR data and upper air sounding data as well as those of the analyses for annual and episodic wind profiles. Conclusions are provided in Section 4.

#### **2. Materials and Methods**

Two 1.5-μm Doppler LiDAR units (Halo Photonics Stream Line Scanning Doppler LiDAR system) were employed for the development of the first stage of the 3DREAMS. The One LiDAR unit was installed at the Physical Geography Experimental Station of the Chinese University of Hong Kong (CUHK), and the other unit was installed at the Hong Kong Observatory weather station: King's Park (KP). The locations of the two LiDAR units are shown in Figure 1. The CUHK site is located in northeastern Hong Kong and has an elevation of 5 m above sea level. This site is surrounded by mountains, including Ma On Shan (≈700 m), Lion Rock (≈500 m), Kam Shan (≈370 m), and Tai Mo Shan (≈957 m), as shown in Figure 1. The KP site is located in the downtown area of Hong Kong and has an elevation of 65 m above sea level.

**Figure 1.** Locations of the two LiDAR sites (CUHK and KP), topography near the CUHK site, and schematic of the upslope and downslope winds. The arrows refer to wind direction. The arrow colors refer to altitude: orange: lower altitude; purple: higher altitude. This CUHK site is surrounded by mountains, including Ma On Shan (≈700 m), Lion Rock (≈500 m), Kam Shan (≈370 m), and Tai Mo Shan (≈957 m). The figure was built on a map obtained from Google Earth, earth.google.com/web/.

The LiDAR units were configured to retrieve aerosol backscatter and wind profiles in the boundary layer up to approximately 3 km above ground level. The principle of the Doppler LiDARs is that laser pulses are emitted by a transmitter and the reflected signals scattered by particles are received by a receiver which is built with the transmitter in the same unit. Particles, which are transported by horizontal wind, induce a Doppler shift as reflected in backscattered light signals. Through measuring the line-of-sight Doppler wind values, the optical heterodyning in the receiver determines the horizontal wind vector.

This study employed the both stare and velocity-azimuth display (VAD) scanning methods to measure horizontal wind information. Figure 2 depicts the schematic diagram of the stare and VAD scanning methods. The stare scan refers to a scanning using a continuous vertically pointing laser beam with Φ = 0◦ and θ = 90◦. The stare scan was configured to operate at height and temporal resolutions of 30 m and 1 s, respectively. For the VAD scan, the LiDAR units were set to have six azimuthal positions at a constant interval of an azimuth angle α = 60◦ with a constant evaluation angle of θ = 75◦ (Φ = 15◦ with respect to the zenith) at a 10-min interval. For quality control, the data with a signal-to-noise ratio less than −20 dB were removed.

**Figure 2.** The schematic diagram of both stare and velocity-azimuth display (VAD) scanning methods. The number of bean directions for a VAD scan was 6. The Doppler LiDAR laser beam pointed upward with a constant elevation angle θ and a constant angle Φ with respect to the vertical Z. The laser beam rotated around the vertical Z with a constant interval of an azimuth angle α.

Before their operations, the two LiDAR units were calibrated by the LiDAR manufacturer and validated using a colocation comparison test. The colocation comparison results confirmed consistency between the LiDAR units with a 95% confidence interval, thus indicating an acceptable calibration. Regular maintenance checks were conducted during operation. The LiDAR windows were cleaned hourly through automatic wiping and weekly with an optical cleaning solution. In addition, horizontal leveling checks were conducted weekly to correct for any settling, which can partly affect beam-pointing accuracy. The precision of the leveling was within ±1◦. The literature has reported the limitation of LiDAR performance in complex terrain [51–53]. The LiDARs' were configured and checked to make sure surrounding mountains would not significantly affect the LiDARs' performance, which could be further confirmed by a comparison between LiDAR data and available upper air sounding data at the same site. The comparison results are shown in Section 3.1.

Analyses in this study were conducted using data collected during the spring, summer, fall, and winter of 2019, which were defined as February–April, May–August, September–October, and November–January, respectively. The annual data availability for the CUHK and KP sites was 94% and 82%, respectively.

The annual mean vertical profiles of horizontal wind speed and direction and vertical wind velocity were investigated, and the vertical profiles during HPEs were analyzed. HPEs were defined by a temperature of ≥28.2 ◦C [54] and more than 50% of air quality stations reporting a concentration of fine particulate matter with an aerodynamic diameter ≤ 2.5 μm (PM2.5) higher than or equal to the median of the 90th percentile of daily PM2.5 of all stations for the year (33.2 μg/m3). The previous study [54] reported a statistically significant increase in premature mortality risk for an average 1 ◦C increase in daily average temperature above 28.2 ◦C. Overall, nine HPEs were identified during 2019. Information concerning these HPEs is provided in Table 1. The PM2.5 concentration data at 16 air quality monitoring stations were obtained from the Hong Kong Environmental Protection Department (http://www.aqhi.gov.hk/en.html). The temperature and surface wind, and the upper air sounding data at the KP weather station (latitude: 22◦18 43; longitude: 114◦10 22) were obtained from the Hong Kong Observatory (https://www.hko.gov.hk/en/cis/stn.htm).

**Table 1.** Nine identified HPEs during 2019 with the mean daily PM2.5 concentration (μg/m3) at both general and roadside stations and daily mean temperature (◦C).


#### **3. Results**

#### *3.1. Comparison with Upper Air Sounding Data*

LiDAR data were compared with the available upper air sounding data located at the same site. Figure 3 shows that the LiDAR units captured the general vertical profiles of horizontal wind speed and direction. Differences at heights less than 0.50 km were negligible, whereas those for heights greater than 0.50 km were more substantial. As discussed in the methods section, the measurement locations of the upper air sounding data varied with the wind velocities at the various measurement altitudes, possibly resulting in different measurement locations for the two data sources. The percentage difference between the averaged horizontal wind speeds of the two data sources for heights less than 1.00 km was less than 10%, which indicates sufficient agreement between LiDAR and upper air sounding data.

**Figure 3.** (**a**) Comparison of horizontal wind speed (m/s) according to KP LiDAR data (red cross) and upper air sounding data (blue circles) over a 4-day period (1 June 2019, 00:00 UTC to 4 June 2019, 12:00 UTC). (**b**) Comparison of horizontal wind direction according to LiDAR data (red cross) and upper air sounding data (blue circles) over a 4-day period (1 June 2019, 00:00 UTC to 4 June 2019, 12:00 UTC).

Figure 4c depicts the seasonal vertical profiles of horizontal wind speed based on the LiDAR measurements at the KP site, whereas Figure 4d shows the seasonal vertical profiles of horizontal wind speed averaged from the upper air sounding data throughout the entire year. The results show a high agreement between the profiles, despite the fact that the time resolution of the two data was different.

**Figure 4.** (**a**) Annual (black) and HPE (red) vertical profiles of horizontal wind speed (m/s) at CUHK (solid) and KP (dashed) sites. Seasonal vertical profiles at (**b**) CUHK and (**c**) KP derived from LiDAR data. (**d**) Seasonal and (**e**) annual vertical profiles of horizontal wind speed (m/s) based on the upper air sounding data collected at 08:00 (HKT) and 20:00 (HKT) every day. For (**b**–**d**): spring (black), summer (red), fall (blue), and winter (magenta). For (**e**): 08:00 (HKT) (blue) and 20:00 (HKT) (red).

#### *3.2. Horizontal Wind Speed*

#### 3.2.1. Annual and Seasonal Vertical Profiles

Figure 4a presents the annual vertical profiles of horizontal wind speed at the CUHK and KP sites. The results reveal the horizontal wind speed was higher at KP than at CUHK. The KP site is located in

an urban area, whereas the CUHK site is located in a suburban area. The lower horizontal wind speed at the CUHK site may be attributable to the complex topography near the site [50].

Figure 4b,c present the seasonal vertical profiles of horizontal wind speed. At the CUHK site, the mean wind speed for heights less than 1.00 km was higher in summer (6.4 m/s) than in other seasons (5.4, 4.6, and 4.6 m/s for spring, fall, and winter, respectively). For heights less than 0.42 km, the wind speeds in spring and fall were similar, whereas, for height between 0.42 km to 1.00 km, the wind speed in spring was clearly higher than that in fall. For heights of 1.60 to 2.00 km, the wind speeds in spring and winter were higher than those in summer and fall.

Similarly, at the KP site, the mean wind speed for heights less than 1.00 km was the highest in summer (7.6 m/s), followed by spring (6.8 m/s), winter (6.2 m/s) and fall (6.1 m/s). For heights less than 0.48 km, the wind speed was the highest in spring; for heights between 0.48 km and 1.74 km, the wind speed in summer was clearly higher than those of other seasons. For heights of 1.74 to 2.00 km, the wind speed was the highest in winter; nevertheless the seasonal difference at that level at KP was not significant as that at CUHK.

To understand the seasonal variation, the seasonal vertical profiles of wind speed measured by upper air sounding as well as the wind speed at 10 m above ground (wsd10m) at three automatic weather stations were analyzed. The similar seasonal trend was also shown in the upper air sounding data, see Figure 4d. Despite the fact that the upper air sounding only provided two data points (08:00 and 20:00 HKT) in a day, the sounding vertical profiles confirm the LiDAR seasonal profiles. Table 2 lists seasonal wsd10m at various Hong Kong Observatory (HKO) stations. The wsd10m at the Waglan Island station shows an obvious seasonal wind variation with the highest wind speed in winter, followed by summer, spring and fall. Nevertheless, the wsd10m at Sha Tin station (near to the CUHK site) and the KP site was the highest in summer and spring, followed by fall and winter. It is noted that the Waglan Island station is located at southeast Hong Kong, which is not affected by any mountains and buildings, and thus serves as a background weather station for Hong Kong. The different seasonal wind speed clearly demonstrates the influence of topography on wind environment in Hong Kong and supports to the findings of vertical wind profiles.


**Table 2.** The seasonal surface horizontal wind speed (unit: m/s) at the Hong Kong Observatory automatic weather stations: Sha Tin, King's Park and Waglan Island. The data was extracted from https://www.hko.gov.hk/en/cis/climat.htm on 7 Mar. 2020.

#### 3.2.2. Diurnal Vertical Profiles

Figure 5 shows the diurnal vertical profiles of horizontal wind speed. Similar to the annual vertical profiles, the diurnal horizontal wind was stronger at the KP site than at the CUHK site. The diurnal vertical profiles exhibited clear peak wind speeds for heights of 0.84 to 1.98 km at CUHK and 0.81 to 1.98 km at KP. The results reveal a clear vertical gradient of horizontal wind speed at the two LiDAR sites from 21:00 to 12:00 HKT. The maximum hourly mean wind speed was 10.6 m/s, which occurred at 20:00 HKT and 15:00 HKT at CUHK and KP, respectively. The minimum hourly mean wind speeds were 6.6 m/s and 7.8 m/s, which occurred at 17:00 HKT and 19:00 HKT at CUHK and KP, respectively.

**Figure 5.** Diurnal vertical profiles of horizontal wind speed at the CUHK (blue) and KP (red) sites.

The vertical gradient was shown clearly in the averaged upper air sounding profiles, see Figure 4e. Similar to the LiDAR profiles at 08:00 HKT and 20:00 HKT, the averaged mean vertical profile at 08:00 HKT has a clear vertical gradient with a peak at around 0.80 km, whereas it was not shown in the 20:00 HKT upper air sounding profile. Figure 6 shows the diurnal vertical profiles of prevailing wind direction and the corresponding mean wind speed at the two LiDAR stations. The weaker vertical gradient of horizontal wind speed at the two LiDAR sites between 13:00 HKT and 20:00 HKT was due to the relatively weak northerly wind between 0.8 km and 1.4 km. It is noted that the northerly wind should be blocked by the topography at the north of the two sites.

#### *3.3. Horizontal Wind Direction*

Figure 7 shows the annual vertical profiles of horizontal wind direction frequency at CUHK and KP. At CUHK, the prevailing horizontal wind direction was north for heights less than 0.20 km. The prevailing horizontal wind direction clearly shifted to northeast for heights of 0.20 to 1.80 km and to north and northeast for heights of 1.80 to 2.00 km. At KP, the prevailing horizontal wind direction was northeast for heights less than 1.70 km. Within this range, easterly wind was observed for heights less than 1.10 km and northerly wind was observed for heights from 1.10 to 1.70 km. For heights from 1.70 to 2.00 km, the prevailing horizontal wind directions were north and northeast. These results demonstrated the high spatial wind variability for heights less than 0.30 km and consistent prevailing horizontal wind direction for heights greater than 0.39 km. The high spatial wind variability was mainly due to the complex topography of Hong Kong [50], as discussed in Section 3.2.1.

Figure 8 shows the seasonal vertical profiles of horizontal wind direction frequency at the CUHK and KP sites. In spring, the prevailing horizontal wind directions at CUHK were north and northeast for heights less than 0.60 km, whereas those at KP were northeast and east. For heights from 0.60 to 1.70 km, the prevailing horizontal wind directions at these sites were more consistent (northeast and east). For heights of 1.70 to 2.00 km, the prevailing horizontal wind directions shifted west and southwest at these two sites.

**(c) (d)** 

**Figure 6.** The diurnal vertical profiles of prevailing wind direction (**a**,**c**) and the mean horizontal wind speed (m/s) of the prevailing horizontal wind direction (**b**,**d**) at the two LiDAR stations, CUHK (upper panel) and KP (lower panel).

**Figure 7.** Annual vertical profiles of horizontal wind direction frequency at CUHK (**left**) and KP (**right**).

**(c) (d)** 

**Figure 8.** Seasonal vertical profiles of horizontal wind direction frequency in spring (**a**,**e**), summer (**b**,**f**), fall (**c**,**g**), and winter (**d**,**h**) at CUHK and KP, respectively. The upper panel (**a**–**d**) is CUHK, whereas the lower panel (**e**–**h**) is KP.

In summer, the prevailing horizontal wind directions at ground level were similar to those in spring except that southerly and southwesterly winds were more frequently noted for heights less than 1.00 km. At heights from 1.00 to 2.00 km, the prevailing horizontal wind directions at the two sites consistently shifted to north and northeast. In fall and winter, the prevailing horizontal wind directions at the two LiDAR sites were relatively stable, mainly northeast, followed by north and east.

#### *3.4. Vertical Wind Velocity*

Figure 9 displays the annual and seasonal vertical profiles of vertical wind speed (cm/s) at the two LiDAR sites. At CUHK, the average vertical wind velocity for heights less than 1.00 km was −0.26 cm/s. At this site, upward and downward air motions were observed. The annual profile indicates that upward air motions dominated for heights less than 0.60 km, whereas downward air motions dominated for heights from 0.60 to 2.00 km. The upward air motions at heights less than 0.60 km may be induced by the surrounding topography. As shown in Figure 7, the prevailing horizontal wind directions at CUHK were north and northeast. The northerly and northeasterly air flows were lifted up by the surrounding topography, thus inducing upward air motions. Seasonal variations were also observed. Positive mean vertical velocities were observed in spring, fall, and winter (2.56, 1.34, and 0.59 cm/s, respectively), whereas the mean vertical velocity observed in summer (−1.34 cm/s) was consistent with the annual mean.

**Figure 9.** (**a**) Annual vertical profiles of vertical wind velocity (cm/s) at CUHK (blue) and KP (red) and seasonal vertical profiles at CUHK (**b**) and KP (**c**) during spring (black), summer (red), fall (blue), and winter (magenta). Positive values refer to upward motions; whereas negative values refer to download motions.

At KP, downward air motions dominated for heights less than 2.00 km. The annual mean vertical wind velocity for heights less than 1.00 km was −14.92 cm/s, highlighting that downward motions at KP were stronger than those at CUHK. Negative vertical wind velocities were consistent for all seasons. The strongest downward air motion was observed during spring (−20.07 cm/s), followed by summer (−18.42 cm/s), winter (−14.18 cm/s), and fall (10.90 cm/s).

Notably, when the prevailing northerly or northeasterly wind approaches Hong Kong, CUHK is located upwind, whereas KP is located downwind. For heights less than 1.00 km, the upslope wind at CUHK was clearly weaker than the downslope wind at KP. However, this difference was less apparent in summer (Figure 9b). This may be because of the unstable atmosphere that occurs during summer as a result of stronger solar radiation in that season. Relatively strong buoyancy force could be generated from the warmer ground surface. The unstable atmosphere is favorable for wind going over mountains, enhancing upslope flow [55]. On the other hand, the induced vertical rising air motion may suppress

downslope flow. These two effects may reduce the difference between upslope and downslope winds in summer.

#### *3.5. Wind Profiles in HPEs*

By examining the temperature and PM2.5 data for the thresholds described in the methods section, nine HPEs were identified. Detailed information regarding each HPE is provided in Table 1 This section details wind and backscatter analyses for the HPEs.

#### 3.5.1. Horizontal Wind Speed and Direction

Figure 4 displays the annual and HPE vertical profiles of horizontal wind speed at the two LiDAR sites, and Figure 10 depicts the vertical profiles of horizontal wind direction frequency during HPEs. Compared with annual means, the horizontal wind speeds were clearly lower during HPEs. At CUHK, the horizontal wind speed for heights less than 1.00 km during HPEs was 61.4% lower than the annual mean; the horizontal wind speed over these heights was 51.7% lower at KP. The relatively low horizontal wind speed was unfavorable for air pollutant dispersion.

**(a) (b) Figure 10.** Vertical profiles of horizontal wind direction frequency during HPEs at CUHK (**a**) and KP (**b**).

The prevailing horizontal wind directions for heights less than 0.60 km were south and southwest at the CUHK site and west and southwest at the KP site. In addition to lower horizontal wind speed, the prevailing westerly wind during HPEs introduced regional TAP from the Greater Bay Area to Hong Kong. Figure 11 depicts the weather chart of each HPE. The weather charts show that, in each HPE, a typhoon was located in South China sea (east of Hong Kong). The westerly wind was induced by the typhoon-associated counterclockwise wind flow, providing a wind environment for transboundary air pollution within the region [25] and thus, the formation of a HPE. For heights from 0.60 to 1.20 km, northerly and northwesterly wind predominated. For heights from 1.20 to 1.90 km, the prevailing horizontal wind directions was north and northeast. For heights greater than 1.90 km, the easterly winds predominated.

**(a) (b)** 

**(c) (d)** 

**(e) (f)** 

**Figure 11.** *Cont.*

**(k) (l)** 

**(m)** 

**Figure 11.** The weather charts (08:00 HKT) of each HPE (**a**–**i**) and of a cold front case occurred on 18 Nov. 2019: (**j**) 02:00 HKT; (**k**) 08:00 HKT; (**l**) 14:00 HKT and (**m**) 20:00 HKT. The weather charts were obtained from the Hong Kong Observatory web site (https://www.hko.gov.hk/en/wxinfo/currwx/wxcht.htm) on 2 Mar. 2020.

#### 3.5.2. Vertical Wind Velocity

Figure 12 shows the vertical profiles of vertical wind velocity at the two LiDAR sites. At CUHK, the annual vertical wind velocity for heights less than 0.66 km exhibited upward air motion (positive), whereas the HPE profiles exhibited downward air motion. Although upward air motions were observed for heights from 0.66 to 1.00 km, these motions were weak. For heights less than 1.00 km at KP, the strong downward air motions in the annual profile weakened during HPEs. The overall vertical air motions caused the accumulation of heat energy and air pollutants close to ground level.

**Figure 12.** Vertical profiles of annual (solid) and HPE (dashed) vertical wind velocity at CUHK (**left**) and KP (**right**). Positive values refer to upward air motions, whereas negative values represent downward air motions.

Figure 13 shows the mean aerosol backscatter profiles at the two LiDAR sites during the nine identified HPEs. Higher aerosol backscatter was observed for heights less than 1.00 km at nighttime and heights less than 1.20 km in the afternoon. The increased height of the top of the aerosol layer in the afternoon may be attributable to the higher mixing height at this time due to the peak in solar radiation. The aerosol backscatter profiles also reveal that that aerosol backscatter was typically the highest for heights less than 0.60 km. This demonstrates an association with the vertical wind velocity profiles in Figure 12. For heights less than 0.66 km, downward air motions were observed at the CUHK site; the air motions for heights from 0.66 to 1.00 km were directed upward. At KP, the magnitude of downward wind velocity decreased with heights up to approximately 0.60 km and then increased up to 0.90 km. Compared with CUHK, KP had stronger downward air motions that resulted in higher aerosol backscatter.

**Figure 13.** Mean aerosol backscatter log10(β) at CUHK (upper) and KP (lower) during the nine identified HPEs.

#### *3.6. A demonstration Case: Cold Front*

To demonstrate the real-time monitoring capability of 3DREAMS, a cold front case occurred on 18 Nov. 2019 was discussed. Figure 11j–m depicts the weather charts of the cold front case at 02:00, 08:00, 14:00 and 20:00 HKT on 18 Nov. 2019, respectively. The cold front arrived at HK in the afternoon on 18 Nov. 2019. Figure 14 shows the high-temporal-resolution vertical profiles of horizontal wind speed at the two sites on 18 and 19 Nov. 2019. The results show that 3DREAMS captured the first arrival of the cold front at the CUHK LiDAR site at between 16:30 and 17:00 HKT. The earlier arrival of the cold front at the CUHK LiDAR site was due to the fact that the site is located at the northeastern Hong Kong. When approaching from northwest, the cold front first hit the CUHK site. In addition, the CUHK LiDAR shows a clear increase in wind speed at the altitudes between 0.50 km and 1.00 km, which lasted until 08:00 HKT on the next day. It is noted that the increase in wind speed near the ground level at KP LiDAR site was not as high as that at the CUHK LiDAR site. This difference was due to the topographical effect that blocked the northerly flow to the KP LiDAR site.

pg p y

**Figure 14.** The high-temporal-resolution (10 minutes) vertical profiles of horizontal wind speed (m/s) at the (**a**) CUHK and (**b**) KP LiDAR sites.

#### **4. Discussion**

This study developed the 3DREAMS using two Doppler LiDAR units for instantaneously measuring wind and aerosol backscatter profiles, thus providing valuable datasets for wind and aerosol backscatter that can be employed in weather and air quality studies. Previous studies have relied heavily on data measured at the ground level. However, Tong et al. [24] identified significant influences of upper atmosphere meteorology on air quality. Although some studies have measured the vertical profiles of wind and air quality, the typically short measurement periods have resulted in limited understanding of the long-term characteristics of and relationships between weather and air quality. Other studies have relied on upper air sounding data that can be measured only two to three times per day, whereas the present study developed the 3DREAMS to collect real-time upper

atmosphere wind and aerosol backscatter information critical for long-term meteorological and air quality studies. It should be highlighted that, while upper air sounding measurements are useful, LiDARs can fill the missing data gaps, providing higher time-resolution measurements.

Spatial wind variability at various altitudes is critical for weather forecasting and air quality studies. For example, local air pollutant emissions may be mixed or transported by lower-level wind; whereas transboundary air pollution may be transported by a higher-level wind. Significant spatial wind variability may affect local weather and thus, air quality at different altitudes and locations. Yim et al. [50] identified high spatial wind variability in Hong Kong due to its complex topography. Nevertheless, their study was limited to ground-level spatial wind variability. The current study reveals that this high spatial wind variability extends to heights of 0.30 km for the horizontal wind direction and 0.60 km for the vertical wind velocity. These findings suggest that further studies should investigate wind shear at various altitudes in Hong Kong. Improved understanding of wind shear is particularly critical for aviation safety at Hong Kong International Airport, which is located in an area with complex topography. The demonstrated cold front case provided a useful example of how the 3DREAMS can be used to study spatial and vertical variations of various horizontal wind speed in weather events.

Previous studies have mainly focused on the effects of extremely hot weather or air pollution episodes. The present study introduced and investigated HPEs, which can adversely affect human health as a result of the synergistic effects of high temperature and high PM2.5 concentration. The results reveal that a prevailing horizontal wind direction introducing TAP from the Greater Bay Area and significant reductions in horizontal wind speed at all altitudes and vertical wind velocity for heights less than 0.66 km enhanced the accumulation of heat and air pollutants in the lower atmosphere.

#### **5. Conclusions**

This study developed the 3DREAMS to measure the long-term vertical profiles of horizontal wind speed and direction and vertical wind velocity and aerosol backscatter in a highly dense city with complex topography. In addition, the vertical profiles of nine identified HPEs were analyzed to assess the influences of horizontal wind speed and direction and vertical wind velocity on heat and aerosol accumulation. The results reveal high spatial wind variability for heights less than approximately 0.60 km in Hong Kong, highlighting the influence of mountainous topography on the wind environment in the city. Both upslope and downslope winds were observed at CUHK site, whereas downward air motions predominated at KP site. The different air vertical motions resulted in different vertical profiles of aerosol backscatter at the two sites during HPEs. Combining the analyses of horizontal wind speed and direction and vertical wind velocity reveal that high temperatures and PM2.5 concentrations were due to a prevailing westerly wind that introduced TAP from the Greater Bay Area. Moreover, a substantial reduction in horizontal wind speed and vertical wind velocity resulted in heat and air pollutant accumulation during HPEs. The findings of this study can provide critical insight for weather forecast and future air quality research. The 3DREAMS will be further developed to integrate new and existing LiDARs into the system and to include more sites to monitor wind and air quality for the Greater Bay Area.

**Author Contributions:** The research was solely done by S.H.L.Y. including conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, funding acquisition. The author has read and agreed to the published version of the manuscript.

**Funding:** This work was jointly funded by The Vice-Chancellor's Discretionary Fund of The Chinese University of Hong Kong (grant no. 4930744) and Dr. Stanley Ho Medicine Development Foundation (grant no. 8305509).

**Acknowledgments:** The author would like to thank the Hong Kong Observatory (HKO) for providing not only weather data but also the measurement site at King's Park weather station. Without the HKO full support, the 3DREAMS may not be able to be built smoothly. In addition, the author also appreciates the Hong Kong Environmental Protection Department for providing air quality data.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **The Determination of Aerosol Distribution by a No-Blind-Zone Scanning Lidar**

**Jie Wang 1,2, Wenqing Liu 1, Cheng Liu 3, Tianshu Zhang 1,4,\*, Jianguo Liu 1, Zhenyi Chen 1, Yan Xiang 1,4 and Xiaoyan Meng <sup>5</sup>**


Received: 1 January 2020; Accepted: 11 February 2020; Published: 13 February 2020

**Abstract:** A homemade portable no-blind zone laser detection and ranging (lidar) system was designed to map the three-dimensional (3D) distribution of aerosols based on a dual-field-of-view (FOV) receiver system. This innovative lidar prototype has a space resolution of 7.5 m and a time resolution of 30 s. A blind zone of zero meters, and a transition zone of approximately 60 m were realized with careful optical alignments, and were rather meaningful to the lower atmosphere observation. With a scanning platform, the lidar system was used to locate the industrial pollution sources at ground level. The primary parameters of the transmitter, receivers, and detectors are described in this paper. Acquiring a whole return signal of this lidar system represents the key step to the retrieval of aerosol distribution with applying a linear joining method to the two FOV signals. The vertical profiles of aerosols were retrieved by the traditional Fernald method and verified by real-time observations. To effectively and reliably retrieve the horizontal distributions of aerosols, a composition of the Fernald method and the slope method were applied. In this way, a priori assumptions of even atmospheric conditions and the already-known reference point in the lidar equation were avoided. No-blind-zone vertical in-situ observation of aerosol illustrated a detailed evolution from almost 0 m to higher altitudes. No-blind-zone detection provided tiny structures of pollution distribution in lower atmosphere, which is closely related to human health. Horizontal field scanning experiments were also conducted in the Shandong Province. The results showed a high accuracy of aerosol mass movement by this lidar system. An effective quantitative way to locate pollution sources distribution was paved with the portable lidar system after validation by the mass concentration of suspended particulate matter from a ground air quality station.

**Keywords:** lidar; dual-field-of-view (FOV); geometric overlap factor (GOF); blind zone; transition zone; aerosol; mass concentration; stereo-monitoring networks

#### **1. Introduction**

The gradient of energy distribution affects the evolution of the atmospheric boundary layer (ABL), and mainly induces the uneven distribution of aerosol and the formation and deterioration of air quality [1–5]. The transport of dust storms and smoke has caused severe regional air pollution on several occasions [6–8]. Laser detection and ranging (lidar), as an active remote-sensing technique,

is often utilized to detect and identify the physical properties, shapes and particle-size distribution of aerosols with a high spatial and temporal resolution from ground stationary lidar networks [9,10], mobile platforms [11], aircrafts [12] or satellites [13]. However, due to a monostatic receiving field and "geometric overlap factor (GOF)", the hundreds of meters of blind zone and the transition zone in traditional Mie-lidars [14–20] always lead to a difficulty in probing aerosols, especially in the lower troposphere [21–23]. A few groups have developed side-scattering imaging technologies [24–27] or multi-field-of-view (mFOV) techniques [28,29] to overcome such difficulties. However, the former methods are limited as the detection range ignores important information from the distant atmosphere, and the latter methods are laboratory instruments that are difficult to move. In this study, a compact portable dual-field-of-view (FOV) lidar prototype aimed at narrowing the transition zone is demonstrated. A reliable inversion method for determining the vertical and horizontal distributions of aerosols within 5 km is provided. Field experiments were also conducted to verify the applicability of the dual-FOV lidar for studying lower-atmosphere air pollution and quantitatively assessing the distribution of emission sources. With this scanning lidar, the pollution distribution near ground can be identified with no-blind-zone and quantitatively. The established lidar network with this kind of lidars can be used to study the pollution transport pathways.

#### **2. Materials and Methods**

#### *2.1. Dual-FOV Lidar System*

The system framework of the proposed dual-FOV lidar is shown in Figure 1. A homemade laser source provided a center wavelength of 532 nm and a fluctuation of ±0.3 nm. The single-pulse energy was 400 μJ. The repetition rate was 2–7 kHz. The linear-polarized laser beam has a divergence of 0.1 mrad collimated by a 10-times beam expander and shot into the air. The laser source with a green wavelength of 532 nm and a higher single-pulse energy from sophisticated commercial lasers was chosen to produce a higher signal-to-noise of observations and a less error of the extinction coefficients of aerosol.

**Figure 1.** Systematic framework of the dual-field-of-view (FOV) laser detection and ranging (lidar).

The primary FOV's Mie backscattering signals were collected by a Cassegrain-type telescope, and the secondary FOV's signals were collected by a Newtonian-type telescope at the same time. The primary telescope was designed with a diameter of 150 mm, a focal length of 700 mm, and an FOV of 1 mrad. The secondary telescope was designed with a diameter of 30 mm, a focal length of 118 mm, and an FOV of 8 mrad. A spacing of 25 mm was maintained between the optical axes of the two telescopes. The distances of the primary FOV optical axis and secondary FOV optical axis to the laser emitting axis were 121, and 58 mm, respectively. The intersection angle of the two optical axes was 0.2–0.4◦. This intersection angle was adjustable and rather important to the performance of the lidar's blind zone and transition zone. A typical GOF of the traditional dual-axis Mie-lidar is shown in Figure 2a and the calculated GOFs of the two FOVs of this lidar system are illustrated in

Figure 2b. The GOF η of the lidar in Figure 2a depends on the parameters of the laser divergence angle, receiving FOV, and the distance between laser (transmitter) and receiving telescope. Within a very short distance from the lidar, the laser beam emitted by the laser does not receive in the telescope's field of view. At this time, η = 0, and the receiving telescope cannot receive the atmospheric backscatter light. This area is called the blind zone of the lidar. Far away from the blind area, the emitted laser beam gradually enters the receiving field of view. Currently, η gradually increases from 0, but less than 1. The atmospheric backscattered light is only partially received. This area is called the transition zone of lidar. Far away from the transition region, the emitted laser beam is completely contained in the receiving field of view. At this time, there is always η = 1, and all the backscattered light in the atmosphere is received. From Figure 2b, it is demonstrated that a larger blind zone of about 75 m in primary FOV comparted with less than the 15 m of blind zone in the secondary FOV. It also illustrates that the transition zones of primary and secondary FOVs were 115.5, and 12.4 m, respectively. Here, we ignored the effects of the intersection angle of the secondary FOV in the GOF calculation.

**Figure 2.** (**a**) The typical geometric overlap factor (GOF) of the traditional dual-axis Mie-lidar and (**b**) the calculated GOFs of primary and secondary FOVs. The effects of the intersection angle were ignored in the calculation of the secondary FOV.

The polarized backscattered signals, collected by the primary FOV, traveled through an iris, a collimator, and a polarized beam splitter (PBS). After passing through the PBS, parallel and perpendicular signals went through two narrow-band filters with a bandwidth of 1.6 nm and an optical density of 5 to suppress the scattered light. The depolarization ratio was retrieved followed by the reference [14]. The signal in the secondary FOV passed through an iris and a narrow-band interference filter. All these three signals were detected by three photo multiplier tubes (PMTs). An amplifier and a 16-bit analog-to-digital converter were used to amplify and digitize the three-channel electrical signals. The data-acquisition rate was 20 MHz, and the transit time of PMTs was 2.7 ns. An industrial computer was embedded to save the raw data files. An outside view of the lidar, with detailed module identifications, is shown in Figure 3. The total weight of the lidar, including a scanning platform, which supports the three-dimensional (3-D) scanning of aerosols, was 65 kg. This portable lidar system is flexible for mobile applications and station observations.

**Figure 3.** Outside-view of dual-FOV lidar. Front view of dual FOV lidar (**left**) and field-experiment view of dual FOV lidar (**right**).

#### *2.2. Evaluation and Joining of the Signals from the Two FOVs*

The backscattering range corrected signals (PRR), received by the two FOVs, are shown in Figure 4. The primary FOV's signal (indicated by the black-square line in Figure 4) illustrated a higher signal-to-noise ratio (SNR) over 10 above 2 km. The strong intensity at 4.5 km referred to clouds. Many tiny peaks in the range of 0.75 km to approximately 4.5 km suggested more than 5 layers of aerosol distribution at the lower atmosphere. For the returned secondary FOV signal (red-dot line in Figure 4), a much higher SNR over 8 was shown below 2 km. Compared with the primary signal, sophisticated structures were clearly shown under 1 km.

**Figure 4.** Intensities of backscattering range-corrected signals (PRR) from primary FOV (black square line) and secondary FOV (red dot line). A case of the primary (black square line) and secondary FOV PRR signal (red dot line) at the combining region of 0.6~0.75 km is shown in the sub-chart.

To acquire a complete signal of dual FOV lidar, a careful joining evaluation of primary and secondary FOV signals was conducted thoroughly along the laser-beam path. An optimum combination interval with the most relevant linearity between the signals of the two fields was determined by the following Equation (1)

$$O(r) = \sum\_{i=m}^{m+\Delta n} \left\{ \left[ aP\_s(r) + b \right] - P\_P(r) \right\}^2 \tag{1}$$

where *O*(*r*) is the square loss function of {*Ps*(*r*), *Pp*(*r*)} at the range of r. *m* is the joining start-point of the combination interval, Δ*n* is the combination length, *Ps*(*r*) is the intensity of the PRR signal of the secondary FOV, and *Pp*(*r*) is the intensity of the PRR signal of the primary FOV. The intensity ratio of *a* and the slop of *b* will be determined by conducting a non-linear least square method.

A case is shown in Figure 4. Here, the combination interval was determined at 0.6~0.75 km (sub-chart in Figure 4), with a mean intensity ratio of 1212.7 and a minimum standard deviation of 29.2. The signal from the primary field over 0.75 km and the signal under 0.75 km from the secondary field corrected by the intensity ratio were composed to a complete return backscattering signal of the dual-FOV lidar. The original primary-field signal, the corrected secondary-field signal (multiplied by the intensity ratio), and the combined signal are illustrated in Figure 5. It is obvious that the combined signal retained excellent characteristics of the primary-field and secondary-field signals. In this way, the SNR of the entire signal exceeded 8, which was advantageous to inverse the extinction coefficients of aerosol. Although, different combination intervals will be different for every raw lidar signal, the combination length of 150 m was fixed in inversion procedure with a resolution of 7.5 m. In this lidar system, the signals' joining start-points are always located below 1 km.

**Figure 5.** Intensities of combined PRR signal (blue triangle line), primary FOV PRR signal (black square line), and secondary FOV PRR signal (red dot line) of dual-FOV lidar.

#### *2.3. Evaluation of the Blind Zone and the Transition Zone of Dual-FOV Lidar*

The blind zone and transition zone of this dual-FOV lidar system can be checked from the PRR of the secondary FOV. We aligned the laser beam parallel to the ground surface to measure the PRR signal, which is shown in Figure 6. This figure shows that the blind zone decreased to 0 m, and the transition zone was 60 m. This measured no-blind-zone was mainly due to the alignment of the intersection angle of the secondary FOV axis and the transmitter axis.

**Figure 6.** Evaluation of the blind zone and transition zone in dual FOV lidar with the parallel returned PRR signal of secondary FOV.

#### *2.4. Retrieval of the Vertical Profile of Aerosols*

The Fernald method [30,31], using the lidar equation given in Equation (2), was used to retrieve the vertical profile of aerosol extinction coefficients from the returned signals, which had been created by joining together the signals from the primary and secondary FOVs:

$$P(z) = \frac{\mathbb{C} \cdot E \cdot [\beta\_m(z) + \beta\_a(z)] \cdot \exp\left\{-2 \int\_0^r [\alpha\_m(z') + \alpha\_a(z')] dz' > \right\}}{z^2}. \tag{2}$$

Here, *P*(*z*) is the received power from altitude *z*. *C* is lidar system constant, *E* is the laser emitting pulse energy, and β*m*(*x*) and β*a*(*z*) are the backscattering coefficients of the atmosphere and aerosol at the range *z*, respectively. While, α*m*(*z*) and α*a*(*z*) are the atmospheric extinction coefficient and aerosol extinction coefficient at the range *z*, respectively.

#### *2.5. Retrieval of Horizontal Distribution of Aerosols*

To implement the mapping of the near-surface aerosol level density distribution, a combination retrieval algorithm of the Fernald method and the slope method [32] was applied in the lidar Equation (2). The inversion procedure was as follows and the flow chart is illustrated in Figure 7.


Here, the step of the interval Δ*z* is closely related to the spatial resolution δ*z* of the lidar system. In this study, an interval step of 75 m, which is 10 times δ*z*, was chosen in the inversion procedure. The

SNR threshold was chosen to be 5. The extinction-to-backscatter lidar ratio is an important factor for solving lidar Equation (2). A lidar ratio of 50, which is the representative value for continental aerosol detection at 532 nm, especially in eastern Asia [33,34], was applied in the lidar Equation (2).

**Figure 7.** Flow chart of inversion procedure of dual-FOV lidar. *Zi* is the initial position, whose SNR exceeds the threshold. Δ*z* is the step of the interval and δ*z* is the minimum spatial resolution of this lidar system. {α*n,* γ*n*} are the aerosol extinction coefficients inversed by the slope method and the Pearson coefficient at part n. {α*f,* γ*f*} are the optimum aerosol extinction coefficients with the maximum Pearson coefficient at part *f*. α*<sup>a</sup>* is the extinction coefficient of the aerosol profile inversed by the Fernald method.

#### **3. Results and Discussion**

#### *3.1. Vertical Distribution Observation of Aerosols*

The vertical observation of aerosols with this dual-FOV lidar was performed on 14 April 2019, at Wuxi, Jiangsu, China. In the data analysis, the time coordinate used was local standard time (LST), which equates to coordinated universal time (UTC) +8 h. The lidar was set on the roof of an office building, which was approximately 30 m above ground level. A typical diurnal variation of the aerosol extinction coefficients is shown in Figure 8a. Aerosols were mainly distributed below 2 km in the early morning and night. The maximum extinction coefficient was close to 2 km−<sup>1</sup> and was observed after 22:00. During the daytime, the atmospheric boundary layer (ABL) increased from 1 km at 8:00 to 2.3 km at 13:00, and the ABL decreased to 800 m at 20:00. From 7:00. Transporting aerosol layers at 1.5~2.5 km were also detected, and these layers mixed with the boundary layer at noon, which increased the extinction coefficient to 1 km<sup>−</sup>1. The maximum extinction coefficient reached 1.5 km−<sup>1</sup> at approximately 11:00. These tiny structures of aerosol distribution below 3 km are shown in Figure 8b. Cloud layers appeared from dawn to 18:00, with a spread range of 3~5 km, and the extinction coefficients are larger than 5 km−1. Then, the extinction coefficients of clouds at noon decreased to approximately 2 km−1. High-altitude clouds were also observed at 19:00 at 12 km. The depolarization observation from

this lidar system is shown in Figure 8c. The depolarization ratio is significant to identify the aerosol types that include haze, dust, smoke, volcanic emissions, and particles released through pollution (e.g., carbon-based) or by the surface of the ocean, and those created by gas-to-particle conversions [14]. The aerosol distribution below 2.5 km had a depolarization ratio less than 0.15 mainly dominated by fine particles, and a depolarization ratio of more than 0.6 referred to the cloud distributions at 3~5 km and 12 km.

**Figure 8.** Vertical profile of aerosol detected by dual-FOV lidar. (**a**) Aerosol extinction coefficients in 15 km. (**b**) Aerosol extinction coefficients in 3 km. (**c**) The depolarization coefficients at 532 nm on 14 April 2019.

#### *3.2. Horizontal Distribution Mapping of Aerosols*

Field horizontal scanning experiments were conducted in the Shandong Province, China. The lidar station (LS) was set up on the roof of a primary school, which was 22 m above ground level in Hanzhuang Town, Jining City. Weishan Lake is located to the west of the LS. Some industrial plants are scattered on the south of the LS. Farmland is located at both, the north and the east of the LS. A national air quality station (AQS), Hanzhuang Station, is located 272 m northwest of the LS. This station provides in situ data of O3, NO2, SO2, CO, PM10 and PM2.5. Meteorological parameters, i.e., the wind speed and wind direction, were also measured. The mass concentration of particulate matters ρ(PM10) and ρ(PM2.5) at the AQS were measured by TEOM™ (Thermo Scientific, Waltham, MA USA). The layout of the LS and AQS is shown in Figure 9.

**Figure 9.** Horizontal extinction coefficient distribution of aerosols at 1:00, 31 March. Sub-chart illustrated the aerosol distribution in the region between lidar station (LS) and air quality station (AQS) on 31 March. (Δ, the position of LS. ×, the position of AQS).

The scanning parameters of the lidar system were set as follows. The elevation angle was fixed at 5◦, and the azimuth angle was 0~360◦. The repetition rate of the laser pulses was selected to be 7 kHz. The scanning angle resolution was 2◦. Each returned signal was acquired with 150,000 times of the accumulation of the laser pulses. A total of 180 profiles were obtained for each cycle-scanning every 1.5 h. The scanned extinction coefficients of the aerosols were overlaid on the maps using a geo-information system (GIS).

One sample of aerosol distribution scanning on the early morning of 31 March is shown in Figure 9. It is noted that the aerosols were steadily suspended in the range of 2 km to the southwest, south and southeast of the LS. These areas are occupied by many large steel and iron factories and chemical plants. These pollution sources strongly contribute to the ρ(PM) at the AQS with a southeast wind direction. In Figure 9, a pollution mass with an approximate length of 5 km was observed 2~2.5 km north of the LS. The right-wing of this belt significantly influenced the ρ(PM) at the AQS. The corresponding ρ(PM10) increased to 135 μg/m<sup>3</sup> (Figure 10). The overhead extinction coefficient from the lidar system was 0.53 km<sup>−</sup>1. This pollution mass led to air quality deterioration at AQS in the next few hours.

Lidar scanning began at 17:53, 29 March and ended at 07:11, 31 March 2018. In total, 30 cycle-scanning maps were acquired from this field experiment. Time series of ρ(PM) at the AQS and four lidar scanning results of aerosol distribution are shown in Figure 10. ρ(PM10) and ρ(PM2.5) decreased from 128 μg/m3, and 27 μg/m3, respectively, at the beginning of the observation (upper panel of Figure 10). The corresponding lidar scanning illustrated few particulate matters around the AQS, and the overhead extinction coefficient of the AQS was 0.12 km−<sup>1</sup> (lower panel Figure 10a). The ρ(PM10) and ρ(PM2.5) climbed up to 114 μg/m3 and 47.4 μg/m3 at 08:00, 30 March (upper panel of Figure 10). The lidar observation at 07:23–08:50 confirmed the accumulation of aerosols over the AQS, with an overhead extinction coefficient of 0.21 km−<sup>1</sup> (lower panel Figure 10b). From 23:00, 30 March, the ρ(PM10) and ρ(PM2.5) climbed steadily from 122 μg/m3 and 41 μg/m3 to 160 μg/m3, and 109 μg/m3, respectively, at 07:00, 31 March, the end of lidar scanning (upper panel of Figure 10). At this stage, the lidar maps show that the aerosols were enriched toward the AQS and increased the PM mass concentrations. The corresponding overhead extinction coefficients were 0.43 km−<sup>1</sup> at 23:00, 30 March and 0.63 km−<sup>1</sup> at 06:00, 31 March (lower panel Figure 10c,d).

**Figure 10.** Time series of ρ(PM10), ρ(PM2.5) of the air quality station (AQS), and the overhead extinction coefficients of AQS from lidar (upper panel) and the aerosol mappings from lidar at Hanzhuang at 17:53–19:20, 29 March (lower pane (**a**)), 07:23–08:50, 30 March (lower pane (**b**)), 22:58, 30 March–00:25, 31 March (lower pane (**c**)), and 05:43–07:11, 31 March (lower pane (**d**)) (Δ, the position of lidar, LS. ×, the position of AQS).

#### *3.3. Quantitative Evaluation of Aerosol Distribution*

Mass concentration mapping of aerosols from the lidar system is an ideal choice for supporting environmental management, especially for air quality modeling simulations that rely on the following three advantages: (1) A higher spatial and temporal resolution of mass concentrations; (2) a greater observation range in both the vertical and horizontal directions; and (3) easy calibration, validation by an array of standard point instruments at ground level, and the establishment of a data set with data quality assurance.

Here, a nonlinear relationship can be fitted with the ρ(PM10) mass concentration at the AQS and the overhead extinction coefficients α<sup>a</sup> from lidar scanning. This nonlinear relationship is shown as follows,

$$
\rho(PM\_{10}) = \kappa \cdot a\_d^\zeta + \mathbb{C} \tag{3}
$$

where κ, ζ, and C are the fitted parameters.

Using 30 sets of ρ(PM10) and α<sup>a</sup> values, the fitting relationship of Equation (3) is determined and is illustrated in Figure 11. The parameters are κ = 148.85 μg/m3, ζ = 0.4, and *C* = 11.62 μg/m3, and the Pearson coefficient is 0.91.

The lidar scanning results, the extinction coefficients of the aerosol distributions, can be transformed to aerosol mass concentration distributions with this fitted relationship. The early morning case on 31 March is shown again but with mass concentrations in Figure 12. The mass concentration of the point sources 2.5 km southeast of the LS was close to 250 μg/m3. The mass concentration of the "aerosol-belt" northwest of the LS exceeded 250 μg/m3. Figure 12 also ignored the low SNR regions of extinction coefficients, especially over the radius of 3 km. The calculated overhead mass concentrations of AQS were also illustrated in the lower panel in Figure 10. The overhead mass concentrations were 75 μg/m3 (99 μg/m3) at 19:00, 29 March, 91.4 μg/m<sup>3</sup> (103 μg/m3) at 08:00, 30 March, 117.8 μg/m<sup>3</sup> (129 μg/m3) at 00:00, 31 March, and 135 μg/m3 (147 μg/m3) at 19:00, 31 March, respectively. A difference from

the ground sampling measurement from AQS (values in brackets) may be because of water vapor or relative humidity in Weishan Lake.

**Figure 11.** Fitting result with extinction coefficients and ρ(PM10).

**Figure 12.** Level mass concentration distribution of aerosols at 1:00, 31 March, of dual-FOV lidar (at altitude of 22 m). (Δ, the position of lidar, LS. ×, the position of air quality station, AQS).

An accurate lidar ratio is important for the retrieval of extinction coefficients. In general, the lidar ratio varies with height and depends on the shape, size distribution and refractive index of the aerosol particles, as well as the lidar wavelength. Here, we applied a constant lidar ratio in the lidar equation for both the vertical and horizontal profile inversion. When we applied the "Fernald-slope" method to retrieve the horizontal distribution of aerosols, the SNR threshold was a critical definition. In the future, the effect of the SNR threshold will be discussed. It is noted that the lidar beam probed across the Weishan Lake surface, and a not negligible factor of water vapor was carefully considered when we converted the extinction coefficients to mass concentrations. The influence of the water vapor or the relative humidity on the retrieval of extinction coefficients has been studied by Zhao et al. [35], and the extinction coefficients here need to be carefully corrected in further studies. In addition, deploying a PM sampling array instead of a single lidar station and providing a few ρ(PM10) and extinction coefficient datasets at different positions would be another reasonable measure to minimize the error of retrieving the mass concentration distribution. If possible, the chemical compositions of aerosols can be extracted to make a reasonable evaluation of the extinction coefficients.

#### **4. Conclusions**

A dual-FOV lidar was designed without a blind zone and a transition zone of 60 m to observe the lower atmosphere. Dual FOV technology was applied to overcome the traditional obstacles of Mie-lidars with hundreds of meters of blind zone. The lidar system is maneuverable and has a weight of less than 70 kg, including the scanning platform. A complete combined returned signal was acquired by carefully checking for the linear combination interval with the primary FOV signal and the secondary FOV signal. Vertical profiles of the aerosol field were retrieved with the Fernald method, while the horizontal distribution of aerosol extinction coefficients was retrieved by combining the slope method and the Fernald method. Horizontal scanning observations clearly showed the spatial-temporal distribution of aerosols. The lidar system can provide valuable and accurate pollution information with the mass concentration mapping, when validated with local quantitative PM measurements. Furthermore, the corrected mass concentration mapping data can be assimilated into 3-D air quality modeling, in order to significantly improve the accuracy of regional air quality predictions. This compact portable lidar system is also an ideal choice for the 3-D atmospheric stereo-pollution monitoring networks construction in severe polluted regions in the future, i.e., New Delhi in India, Beijing–Tianjin–Hebei (JJJ) in China, Bangkok in Southeast Asia.

**Author Contributions:** J.W.: original draft preparation, formal analysis, input references and drafting acknowledgement. W.L. and J.L.: conceptualization of system, investigation and administration of this project and funding. C.L.: formal analysis, data curation and input Figure 9. T.Z.: leading conceptualization of system, validation, project administration and input Figure 2, Figure 7, Figure 10. Z.C.: methodology, especially for the draft section of 2.2, validation and polishing for the draft preparation. Y.X.: Software development and algorithm optimization. X.M.: validation and data curation. All authors have read and agree to the published version of the manuscript.

**Funding:** This research was supported by the National Key Research and Development Program of China (2016YFC0200400, 2016YFC0200402, 2017YFC0212800, 2017YFC0212801, 2018YFC0213100, 2018YFC0213101) and the National Research Program for Key Issues in Air Pollution Control (DQGG0102).

**Acknowledgments:** Great thanks are owed to Jinsheng Zhu and Ling Li. It is not possible to conduct this research without their assistance in the field experiment, especially for the maintenance of lidar. The authors also gratefully acknowledge the China National Environmental Monitoring Centre (CNEMC) for providing the data set of air quality station in this research. We also thank Vivian Du for her editorial advice. We would like to thank three anonymous reviewers for their comments which greatly strengthened the manuscript. Special thanks are given to the editors of MDPI for their English editing works on this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
