Impacts of Atmospheric Boundary Layer Structure on Haze Pollution Observed by Tethered Balloon and Lidar

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  • Corresponding author: Yu SHI, shiyu@mail.iap.ac.cn
  • Funds:

    Supported by the National Key Research and Development Program of China (2017YFC0209605), National Natural Science Foundation of China (41975108), and General Financial Grant from the China Postdoctoral Science Foundation (2020M670420)

  • doi: 10.1007/s13351-021-0076-4
  • Note: This paper will appear in the forthcoming issue. It is not the finalized version yet. Please use with caution.

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  • In this paper, the characteristics of the atmospheric boundary layer (ABL) over the North China Plain (NCP) during a comprehensive observation experiment conducted from 15 to 21 December 2018 were investigated. Observational data were obtained with a large tethered balloon, Doppler wind lidar, and ground-level instruments. The maximum concentration of PM2.5 exceeded 200 μg m−3, and the ratio of PM2.5/PM10 was approximately 0.4 (its maxi-mum was approximately 0.8) during the whole observation period, indicating the explosive growth of dominant fine-mode aerosols in the winter heating season. Elevated concentrations of pollutants decreased the solar irradiance received by the ground, thus resulting in lower temperature at ground level. The correlation coefficient between ground-level PM2.5 and CO concentrations was the highest (0.85) among the gas pollutants, and the relationship between O3 and PM2.5 was generally negative but was not a simple linear relationship. Our results illustrate three distinct types of vertical profiles: Type 1: convective state, the concentration of PM2.5 decreased nearly linearly as a function of the height below approximately 600 m; Type 2, stable state, the PM2.5 concentration sharply decreased from the ground to approximately 200 m; and Type 3, multilayer structure with some pollutants suspended aloft in the upper air layer. Diurnal evolution of the vertical profiles of PM2.5 and their relationship with the changes in meteorological factors was identified. From daytime to nighttime, the vertical profiles evolved from Type 1 to Type 2 or Type 3. All the 33 vertical PM2.5 profiles that we obtained showed a strong relationship with elements of the ABL structure, such as the distributions of winds, the inversion layer, and turbulence activities. A light-wind layer and weak turbulence activity, especially within the inversion layer, contributed greatly to the accumulation of pollutants. Vertical PM2.5 concentration patterns were also greatly affected by local ground-level emission sources and regional transport processes.
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  • Fig. 1.  (a) Map of the local topography of the North China Plain (NCP). The red circle represents the specific measurement position in Wangdu County. The map was retrieved from Google Maps©. (b) Large tethered-balloon detection platform utilized during the observation period in Wangdu County. The Doppler wind lidar system, meteorological station, and atmospheric environment monitoring vehicle are located near the tethered balloon (approximately 100 m).

    Fig. 2.  Synoptic weather circulations during the observation period. The clear high-pressure systems were figured out from the sea level pressures at (a) 0800 BT 16, (b) 0800 BT 19, and (c) 0800 BT 21 December. The blue lines represent the 850-hPa geopotential height and red lines represent the temperature at (d) 0800 BT 16, (e) 0800 BT 19, and (f) 0800 BT 21 December.

    Fig. 3.  Time series variations in the ground-level (a) concentrations of PM2.5 and PM10; (b) wind speed (WS) and wind direction (WD); (c) temperature (T), relative humidity (RH), and solar irradiance from 15 to 21 December 2018; and (d) the sampling altitude of the large tethered balloon. The shaded areas represent one clean period (clean) and three high-pollution periods (S1, S2, and S3) identified in this paper.

    Fig. 4.  Time series variations in the ground-level concentrations of (a) NO and NO2, (b) SO2 and CO, and (c) O3 and PM2.5 from 15 to 21 December 2018. The shaded symbols represent the one clean episode (clean) and three high-pollution episodes (S1, S2, and S3) identified in this paper. The detection altitude of the tethered balloon (d) was also plotted to clearly reveal the concentration variations of the ground-level pollutants during the tethered-balloon observation period.

    Fig. 5.  Scatter plots for PM2.5 and (a) CO, (b) O3, (c) NOx, and (d) SO2 observed at ground level during the whole observation period.

    Fig. 6.  (a) Differences in PM2.5 concentrations for all vertical profiles during the entire observation period. The dashed lines serve as separator lines to distinguish the various PM2.5 differences, and the larger d values indicate sharp variations in the PM2.5 concentrations. (b) Three distinctly different types of vertical profiles, e.g., Types 1, a convective state; Type 2, a stable state; and Type 3, a multilayer state.

    Fig. 7.  Vertical PM2.5, potential temperature (θ), water vapor mixing ratio (r), and WS and WD profiles for the three identified profile types. (a) Type 1: convective state, the PM2.5 concentration decreased almost linearly as a function of the height below approximately 600 m; (b) Type 2: stable state, a sharp decreasing trend of the PM2.5 concentrations below 250 m; and (c) Type 3: multilayer structure, some PM2.5 suspended aloft in the upper air.

    Fig. 8.  Vertical and temporal evolutions of (a) WS and (b) WD retrieved by Doppler wind lidar. Data from 2100 BT 18 to 0900 BT 19 November were not available due to the recalibration of the machine.

    Fig. 9.  Evolutions of the vertical profiles of (a) PM2.5, (b) T and RH, and (c) θ and r from 1200 BT 18 to 1600 BT 20 November. The ground-level PM2.5 for this period is also shown in Fig. 9a.

    Fig. 10.  The vertical profiles of PM2.5, RH, T, and WS from (a) 1156–1243 BT, (b) 1351–1419 BT, and (c) 1544–1558 BT 15 December 2018.

    Table 1.  The mean values of the seven air pollutant concentrations (CO, SO2, NO, NO2, O3, PM2.5, and PM10), WS, T, and RH during the entire study period, the one clean episode, and the three severe haze pollution episodes (S1, S2, and S3)

    Entire studyCleanS1S2S3
    0000 BT 15–
    2400 BT 20 December
    2300 BT 16–
    0900 BT 17 December
    1100 BT 15–
    1200 BT 16 December
    1600 BT 18–
    1100 BT 19 December
    1600 BT 19–
    1500 BT 20 December
    WS (m s−1)1.53.91.31.10.78
    T (°C)2.24.9−0.80.31.7
    RH (%)57.438.266.964.264.3
    CO (ppm)1.800.183.22.11.71
    SO2 (ppb)17.66.613.919.528.5
    NO (ppb)66.82.467.7113.5116.4
    NO2 (ppb)28.814.732.435.740.6
    O3 (ppb)10.717.06.707.68.29
    PM2.5 (µg m−3)112.814.8190.6153.1130.1
    PM10 (µg m−3)244.978.6369.1310.6273.2
    Download: Download as CSV

    Table 2.  The date and time for each vertical profile measured in this study, where “↑” indicates ascending profiles, and “↓” indicates descending profiles

    No.Height (m)DateWeather conditionp (hPa)Starting timeEnding time
    132–990 ↑12-15-2018Haze1023.411:5612:43
    2990–108↓12-15-2018Haze1021.713:5114:19
    3108–535↑12-15-2018Haze1020.914:1914:33
    4540–456 (456–103) ↓12-15-2018Haze1021.114:45 (15:31)14:50 (15:43)
    5103–562↑12-15-2018Haze1021.215:4415:58
    6563–28↓12-15-2018Haze1020.816:2316:45
    738–450↑12-16-2018Haze1017.510:3711:04
    8450–97↓12-16-2018Haze1017.111:3711:55
    997–395↑12-16-2018Clean1016.911:5512:11
    10395–40↓12-16-2018Clean1016.812:1412:26
    1130–974↑12-18-2018Clean1014.716:3617:07
    12974–136↓12-18-2018Clean1014.717:4018:07
    13136–490 (490–950) ↑12-18-2018Clean1014.718:07 (20:02)18:21 (20:17)
    14915–27↓12-18-2018Clean1015.020:1920:47
    1526–773↑12-19-2018Few clouds1017.710:4611:10
    16773–27↓12-19-2018Few clouds1017.711:5212:22
    1733–565 (565–942) ↑12-19-2018Few clouds1017.415:00 (15:46)15:22 (16:02)
    18940–135↓12-19-2018Few clouds1017.717:0617:31
    19135–943↑12-19-2018Few clouds1017.717:3117:55
    20943–32↓12-19-2018Few clouds1018.118:5319:30
    2118–937↑12-20-2018Stratus clouds1018.909:1309:41
    22934–142↓12-20-2018Stratus clouds1018.610:3010:54
    23142–940↑12-20-2018Stratus clouds1017.710:5511:19
    24940–127↓12-20-2018Stratus clouds1016.913:3714:02
    25127–947↑12-20-2018Stratus clouds1017.014:0214:28
    26947–136↓12-20-2018Stratus clouds1017.215:5116:15
    27136–946↑12-20-2018Stratus clouds1016.916:1516:41
    28944–148↓12-20-2018Stratus clouds1016.817:5518:20
    29148–392↑12-20-2018Stratus clouds1016.918:2018:28
    30395–146↓12-20-2018Stratus clouds1016.721:3221:40
    31146–800↑12-20-2018Stratus clouds1016.721:4022:00
    32800–500↓12-20-2018Stratus clouds1016.022:2922:39
    33500–12↓12-21-2018Few clouds1017.105:4105:56
    Download: Download as CSV
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Impacts of Atmospheric Boundary Layer Structure on Haze Pollution Observed by Tethered Balloon and Lidar

    Corresponding author: Yu SHI, shiyu@mail.iap.ac.cn
  • 1. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 2. University of Chinese Academy of Science, Beijing 100049
Funds: Supported by the National Key Research and Development Program of China (2017YFC0209605), National Natural Science Foundation of China (41975108), and General Financial Grant from the China Postdoctoral Science Foundation (2020M670420)

Abstract: In this paper, the characteristics of the atmospheric boundary layer (ABL) over the North China Plain (NCP) during a comprehensive observation experiment conducted from 15 to 21 December 2018 were investigated. Observational data were obtained with a large tethered balloon, Doppler wind lidar, and ground-level instruments. The maximum concentration of PM2.5 exceeded 200 μg m−3, and the ratio of PM2.5/PM10 was approximately 0.4 (its maxi-mum was approximately 0.8) during the whole observation period, indicating the explosive growth of dominant fine-mode aerosols in the winter heating season. Elevated concentrations of pollutants decreased the solar irradiance received by the ground, thus resulting in lower temperature at ground level. The correlation coefficient between ground-level PM2.5 and CO concentrations was the highest (0.85) among the gas pollutants, and the relationship between O3 and PM2.5 was generally negative but was not a simple linear relationship. Our results illustrate three distinct types of vertical profiles: Type 1: convective state, the concentration of PM2.5 decreased nearly linearly as a function of the height below approximately 600 m; Type 2, stable state, the PM2.5 concentration sharply decreased from the ground to approximately 200 m; and Type 3, multilayer structure with some pollutants suspended aloft in the upper air layer. Diurnal evolution of the vertical profiles of PM2.5 and their relationship with the changes in meteorological factors was identified. From daytime to nighttime, the vertical profiles evolved from Type 1 to Type 2 or Type 3. All the 33 vertical PM2.5 profiles that we obtained showed a strong relationship with elements of the ABL structure, such as the distributions of winds, the inversion layer, and turbulence activities. A light-wind layer and weak turbulence activity, especially within the inversion layer, contributed greatly to the accumulation of pollutants. Vertical PM2.5 concentration patterns were also greatly affected by local ground-level emission sources and regional transport processes.

    • Rapid industrial development and urbanization have caused severe air pollution in China, following a trajectory similar to what developed countries have previously experienced. A large number of anthropogenic pollutants have been emitted into the air, posing a significant threat to the living environment (Zhao et al., 2013; Sun et al., 2015). Environmental pollution impacts air quality, climate change, and human health. Deterioration in air quality is mainly caused by air pollution. Increased aerosols in the air could affect climate through their effects on the radiation budget. According to the World Health Organization, up to a third of lung cancer and heart disease deaths are attributed to air pollution (Mauderly and Chow, 2008; Jacob and Winner, 2009).

      As the largest developing country worldwide, severe haze pollution events in China caused by particulate matter have frequently occurred over the North China Plain (NCP) (Huang et al., 2014; Lyu et al., 2018; Shen et al., 2018; Zhang et al., 2018). According to the China Environmental Monitoring Station (http://www.cnemc.cn/), measurements in the NCP region conducted in the first half of 2017 revealed that the daily average concentration of PM2.5 (particulate matter with an aerodynamic diameter smaller than 2.5 μm) was 72 µg m−3. China has strengthened its efforts regarding air quality monitoring and regulation since 2013 (Zhang et al., 2016; Sun, 2018), but the NCP still experiences long-lasting particulate matter pollution episodes (approximately 3–5 days in duration), especially in winter, under unfavorable dispersion conditions such as the low wind speed (WS), high humidity or a low environmental diffusion capacity, and high anthropogenic emissions (Sun et al., 2006; Quan et al., 2011; Zhao et al., 2013). Haze pollution processes in the NCP area usually persist for 3–4 days at a time in autumn and winter (Wu et al., 2017).

      Haze pollution processes are closely related to the atmospheric boundary layer (ABL) structure, that is, the structure of the lowest part of the atmosphere directly influenced by the surface of earth (Li et al., 2019). Most of the pollutants or their precursors are directly emitted into the boundary layer (Gautam et al., 2016; Provençal et al., 2017). For example, NOx directly emitted at night from the surface is confined to at night from the surface is confined to at night from the surface is confined to (Han et al., 2009). Under stagnant weather circulation conditions, the lower ABL height limits the environmental capacity for pollutant dispersion, and the low WS also contributes to the persistence and increases in pollutants (Hu et al., 2014; Miao et al., 2017). In recent years, extensive studies have been conducted to characterize the sources, formation mechanisms, and evolution processes of severe haze pollution episodes (Zhang et al., 2013; Oozeer et al., 2016; Yang et al., 2017). The relationship between the ABL structure and pollution processes has also been reported. Han et al. (2009) analyzed the impact of the nocturnal boundary layer on urban air pollutants based on measurements from a 250-m tower.

      The vertical distribution of pollutants in the boundary layer is also very important for the study of pollutant evolution, accumulation, dispersion and transport, and the simultaneous measurement of pollutants and meteorological elements is relatively difficult (Han et al., 2009). A detailed description of the vertical and spatial distribution of pollutants can greatly improve air quality prediction models (Wang et al., 2014). Pollutant distribution is closely related to the ABL structure. The thermodynamic structure of the ABL affects vertical air pollutant mixing (Li et al., 2020). The occurrence of nocturnal low-level jets can also affect vertical pollutant profiles (Corsmeier et al., 1997; Salmond and McKendry, 2005; Hu et al., 2013). Largeron and Staquet (2016) reported that pollution episodes were primarily driven by persistent inversions. Many measurement techniques have been implemented to characterize the ABL structure and pollutants, e.g., involving ground-based lidar, aircrafts, unmanned aerial vehicles, and radiosondes (Sangiorgi et al., 2011; Li et al., 2017; Shi et al., 2019; Liu et al., 2020). There have been many studies based on ground observations but relatively fewer based on vertical observations, especially meteorological and pollution observations.

      Tethered balloons are a powerful platform for detecting the physicochemical features of the ABL (Sangiorgi et al., 2011; Li et al., 2015; Zhang et al., 2017), especially large tethered balloons with high payload capacities (approximately 200 kg). These can carry many instruments, allowing the direct measurement of the vertical distribution of pollution and meteorological elements simultaneously in the boundary layer with high degree of data reliability.

      In this paper, we investigate the correlation between ABL structure and haze pollution during an intensive observation experiment conducted from 10–25 December 2018 in the NCP. Based on all the profiles observed during the study period, typical vertical PM2.5 profiles were identified.

    2.   Description of the measurement site and instrumentation
    • An intensive observation experiment was conducted in Wangdu County (38.66°N, 115.25°E; shown in Fig. 1a) during 10–25 December 2018, which consists of city clusters on the NCP. The PM2.5 concentration in the city clusters on the NCP has generally been high (Yang et al., 2017). During the winters of 2013 and 2014, the monthly average concentration was as high as 151.2 μg m−3 (Chen et al., 2017). In December 2016, Beijing issued a red-alert for a haze episode when the ground-level PM2.5 concentration exceeded 450 μg m−3 (Shi et al., 2019). On the basis of data from sources such as a large tethered balloon, Doppler wind lidar, and ground-level measurements, our comprehensive observation experiments revealed the vertical distribution of both physical and chemical elements in the boundary layer. The measurement instruments at the observation site in Wangdu County and the data employed in this study are as follows:

      Figure 1.  (a) Map of the local topography of the North China Plain (NCP). The red circle represents the specific measurement position in Wangdu County. The map was retrieved from Google Maps©. (b) Large tethered-balloon detection platform utilized during the observation period in Wangdu County. The Doppler wind lidar system, meteorological station, and atmospheric environment monitoring vehicle are located near the tethered balloon (approximately 100 m).

      1) A tethered-balloon observation platform (shown in Fig. 1b) in Wangdu County simultaneously measured the vertical profiles of pollutants and meteorological parameters. The measurement instruments were mounted on a platform below the balloon (shown in Fig. 1b). The concentrations of PM2.5 and PM10 were measured in real time with a time resolution of 1 min by the instruments [MAS (Mini Air Station)-AF300, China] mounted on the tethered balloon. Additionally, a GPS [HC (Hemisphere Company)-12, China] instrument was deployed to track the balloon. Meteorological sensors (HC2-S, Rotronic, Switzerland) were also used to measure the air temperature (T), relative humidity (RH), and air pressure (p). The accuracy for particulate matter, T, RH, and p observed with the tethered balloon was 0.1 µg m−3, 0.1°C, 0.1%, and 0.1 hPa, respectively.

      The large tethered balloon was 1900 m3 in volume, and its maximum carrying weight was 200 kg with the highest observation altitude at approximately 1000 m. A custom-designed cable connected the balloon and a ground-level computer. This cable provided power to the devices suspended below the balloon, and transferred measurement data to the ground-level computer. An electric winch controlled the ascent and descent rate of the balloon at approximately 0.5 m s−1. As shown in Fig. 1b, there were no distinct emission sources around the observation site.

      2) A Doppler wind lidar system [FC (Fengcai)-II, Norinco Group, China] was installed at the same measurement location in Wangdu County, approximately 100 m away from the tethered balloon, to retrieve wind profiles with a spatial resolution of 50 m and a time resolution of 2–3 s. The accuracy of WS measurements was 0.4 m s−1 (WS ≤ 10 m s−1), 3% of WS (WS > 10 m s−1); and the wind direction (WD) accuracy was 3° (WS > 5 m s−1).

      3) An atmospheric environment monitoring vehicle simultaneously measured the ground-level concentrations of PM2.5 (TEMO1405, Thermo Fisher, USA), NO (42i, Thermo Fisher, USA), NO2 (42i, Thermo Fisher, USA), SO2 (43i, Thermo Fisher, USA), CO (48i, Thermo Fisher, USA), and O3 (49i, Thermo Fisher, USA) every minute during the observation period. The atmospheric environment monitoring vehicle was also installed approximately 100 m away from the tethered balloon (shown in Fig. 1b). The sampling accuracies for PM2.5, O3, and CO were 0.1 µg m−3, 1 ppb, and 0.01 ppm, respectively; and the accuracy for NO, NO2, and SO2 was 1 ppb.

      4) In addition, meteorological parameters such as WS, WD, T, and solar irradiance at 6 m above the ground were obtained from a meteorological station (FT-QXC7, Fengtu, China) at the observation site (shown in Fig. 1b). The resolutions for WS, WD, T, and solar irradiance were 0.1 m s−1, 1°, 0.1°C, and 1 W m−1, respectively.

      In addition, the indirectly observed parameters of potential temperature (θ) and the water vapor mixing ratio (r) analyzed in this paper were calculated as follows (Stull, 1988):

      $$\theta = T{\Bigg(\frac{{1000}}{p}\Bigg)^{0.286}},$$ (1)
      $$r = 622\frac{e}{{p - e}},$$ (2)
      $$e = \frac{{{\rm RH} \times {e_{\rm s}}}}{{100\text% }},$$ (3)
      $${e_{\rm s}} = 6.1078\exp \Bigg[\frac{{a (T - 273.15)}}{{T - b}}\Bigg],$$ (4)

      where e is water vapor pressure, es is saturated water vapor, and a (17.269) and b (35.86) are constants.

    3.   Results and discussion
    • Synoptic-scale pressure patterns during the entire observation period show that the observation site was mainly influenced by high-pressure systems and typical synoptic weather circulations in winter on the NCP (Miao et al., 2017). The small high-pressure system was maybe separated from the Mongolian high in the northwest (Figs. 2a–c). At 0800 BT (Beijing Time) 16, 0800 BT 19, and 0800 BT 21 December, the observation site was mainly controlled by cold advection at 850 hPa. Previous studies have proved that synoptic-scale pressure patterns can influence the evolution of the atmospheric pollution process (Miao et al., 2017; Wu et al., 2017).

      Figure 2.  Synoptic weather circulations during the observation period. The clear high-pressure systems were figured out from the sea level pressures at (a) 0800 BT 16, (b) 0800 BT 19, and (c) 0800 BT 21 December. The blue lines represent the 850-hPa geopotential height and red lines represent the temperature at (d) 0800 BT 16, (e) 0800 BT 19, and (f) 0800 BT 21 December.

      Figure 3 shows the concentration time series of PM2.5 and PM10 as well as the time series variations of ground-level meteorological parameters WS, WD, T, RH, and solar irradiance during the entire observation period. The shaded areas represent the three pollution episodes (S1, S2, and S3, respectively) and the one clean event.

      Figure 3.  Time series variations in the ground-level (a) concentrations of PM2.5 and PM10; (b) wind speed (WS) and wind direction (WD); (c) temperature (T), relative humidity (RH), and solar irradiance from 15 to 21 December 2018; and (d) the sampling altitude of the large tethered balloon. The shaded areas represent one clean period (clean) and three high-pollution periods (S1, S2, and S3) identified in this paper.

      The selection of the three pollution episodes (S1, S2, and S3) was based on the ground-level concentrations of PM2.5 greater than 100 µg m−3, and the clean period was identified with the PM2.5 concentrations lower than 35 µg m−3. In addition, the sampling altitude of the large tethered balloon is shown in Fig. 3d. Figure 3a shows that the observation experiment captured approximately five pollution periods based on ground-level PM2.5 (or PM10) concentrations. The maximum PM2.5 concentration exceeded 200 µg m−3, and the PM10 concentration peaked at approximately 500 µg m−3 during the whole observation period. The time series of the PM2.5 (or PM10) concentrations during the observation period was generally higher in the early morning and decreased to a certain extent at noon. Baumbach and Vogt (2003) indicated that pollutant emissions were mainly confined beneath the surface inversion layer, which developed in the late afternoon. The highest PM10 concentration observed in this study was 504.4 µg m−3 at 0200 BT 16 December due to the blocking effects of the inversion layer. In addition, the PM10 concentration dropped to 154.8 µg m−3 at 1200 BT 16 December.

      Previous studies have shown that northwest winds can effectively remove pollution around observation sites (Zhao et al., 2013; Shi et al., 2019). However, when northwest winds were observed at the ground during this observation experiment, the PM2.5 concentration was still high, which was likely due to the low WS at this time. In addition, the ratio of PM2.5 to PM10 is often used to measure the proportion of coarse-particulate concentrations (Liu et al., 2015). The major sources of PM10 are urban fugitive dust, crustal soil, biomass burning, coal combustion, and vehicle emissions; and PM2.5 mainly originates from industrial emissions, secondary aerosols, coal combustion, traffic exhaust, and biomass burning (Liu et al., 2015). Figure 3a shows that PM2.5 concentrations presented a pattern of variation very similar to PM10, indicating that the increase in PM2.5 was mainly responsible for the increase in PM10. During the whole observation episode, the averaged PM2.5/PM10 ratio was approximately 0.4, its minimum value was 0.19 (clean episode), and its maximum was 0.78 (S2 episode), indicating the enhanced role of fine-mode aerosols.

      Due to the abatement policies regarding coal quality and usage, the coarse particles have been greatly reduced. This is also reflected by the high PM2.5/PM10 ratios around the observation site. The variations of PM2.5/PM10 ratios were also due to the pollutant sources around the site. As shown in Fig. 1b, the observation site mainly consisted of a wheat field. According to the Baoding Yearbook 2019 (Zhang, 2019), there were no direct industrial sources near the observation site, and most of the pollutants mainly came from agricultural combustion, traffic emissions, and residential sources, but there were no direct industrial sources at the time of this study.

      There was a distinct inverse correlation between T and RH. Both RH and T had obvious diurnal variations. RH was nearly 80% in winter, which promoted increased moisture absorption in aerosol particles (Svenningsson et al., 1992). Solar irradiance also exhibited clear diurnal variations. At noon on each day, a clear peak value was recorded due to the large amounts of direct and scattered radiation from the sun. Moreover, compared with the peak value of solar irradiance during the high-pollution periods, the peak value was slightly higher on clean days, demonstrating that particles could reduce the solar irradiance received by the ground. In addition, the presence of the stratiform clouds on 19 and 20 December greatly decreased the solar irradiance received by the ground.

      Combined with the PM2.5 concentration variation and the observation records from the tethered balloon, Table 1 summarizes the mean values of the concentrations of the seven air pollutants (CO, SO2, NO, NO2, O3, PM2.5, and PM10), WS, T, and RH during the whole observation period, the one clean period, and the three severe haze pollution episodes. Table 1 indicates that the average PM2.5 concentration during the most severe haze episode was 190.6 µg m−3 (S1), which was 12 times higher than that during the clean episode (14.8 µg m−3). Concentrations of the various pollutants on the polluted days increased significantly; the lower WS and higher RH contributed to pollutant accumulation, and the contributions of regional transport can also not be ignored.

      Entire studyCleanS1S2S3
      0000 BT 15–
      2400 BT 20 December
      2300 BT 16–
      0900 BT 17 December
      1100 BT 15–
      1200 BT 16 December
      1600 BT 18–
      1100 BT 19 December
      1600 BT 19–
      1500 BT 20 December
      WS (m s−1)1.53.91.31.10.78
      T (°C)2.24.9−0.80.31.7
      RH (%)57.438.266.964.264.3
      CO (ppm)1.800.183.22.11.71
      SO2 (ppb)17.66.613.919.528.5
      NO (ppb)66.82.467.7113.5116.4
      NO2 (ppb)28.814.732.435.740.6
      O3 (ppb)10.717.06.707.68.29
      PM2.5 (µg m−3)112.814.8190.6153.1130.1
      PM10 (µg m−3)244.978.6369.1310.6273.2

      Table 1.  The mean values of the seven air pollutant concentrations (CO, SO2, NO, NO2, O3, PM2.5, and PM10), WS, T, and RH during the entire study period, the one clean episode, and the three severe haze pollution episodes (S1, S2, and S3)

      Figure 4 shows the time series of the ground-level concentrations of six pollutants (NO, NO2, SO2, CO, O3, and PM2.5) from 15 to 21 December 2018. The NO, NO2, and CO concentrations showed similar variations to the PM2.5 concentration, followed by the SO2 concentration. From 18 to 21 December 2018, the SO2 concentration varied sharply within a few hours. Kim et al. (2002) have reported certain effects of the NOx concentration on particle formation in air, whereby NOx notably influences the particle homogeneous nucleation and secondary aerosol nucleation of organic compounds. Figure 4c reveals that the O3 concentration was higher in the afternoon hours (1500–1600 BT) due to the strong solar radiation, corresponding to the lower PM2.5 and NO levels.

      Figure 4.  Time series variations in the ground-level concentrations of (a) NO and NO2, (b) SO2 and CO, and (c) O3 and PM2.5 from 15 to 21 December 2018. The shaded symbols represent the one clean episode (clean) and three high-pollution episodes (S1, S2, and S3) identified in this paper. The detection altitude of the tethered balloon (d) was also plotted to clearly reveal the concentration variations of the ground-level pollutants during the tethered-balloon observation period.

      Because of the significant pathway for the formation of particulates through heterogeneous reactions (Liggio et al., 2005; Wen et al., 2018), we considered the one-to-one scatter plots (shown in Fig. 5) showing different correlation coefficients between the ground-level CO, O3, NOx (NO + NO2), SO2, and PM2.5 concentrations.

      Figure 5.  Scatter plots for PM2.5 and (a) CO, (b) O3, (c) NOx, and (d) SO2 observed at ground level during the whole observation period.

      The correlation coefficient between PM2.5 and CO during the entire observation period was 0.85 (Fig 5a), indicating a strong relationship between these parameters. Higher PM2.5 concentrations are often observed with higher RH. O3 and PM2.5 concentrations showed an inverse correlation. When RH exceeded approximately 70%, O3 concentrations were low and became insensitive to RH variation. This implies that the relationship between O3 and PM2.5 is complex and does not show a simple linear correlation. The correlation between NOx and PM2.5 was somewhat weak, and NOx could be correlated with O3 since NO can deplete O3. The correlation between SO2 and PM2.5 was the poorest, and the SO2 concentrations were not high.

    • It is relatively difficult to obtain the vertical profile of pollutants. During the entire observation period of this experiment, a total of 33 vertical profiles of pollutant and meteorological variables were obtained. Table 2 lists the weather conditions, starting time, ending time and measurement height for all 33 profiles. These profiles were acquired during different periods, and the vertical profiles of physical and chemical elements could be simultaneously acquired during the planetary boundary layer evolution process. To better understand the vertical PM2.5 distribution, we calculated the differences (d) for each vertical profile; d was defined as follows:

      No.Height (m)DateWeather conditionp (hPa)Starting timeEnding time
      132–990 ↑12-15-2018Haze1023.411:5612:43
      2990–108↓12-15-2018Haze1021.713:5114:19
      3108–535↑12-15-2018Haze1020.914:1914:33
      4540–456 (456–103) ↓12-15-2018Haze1021.114:45 (15:31)14:50 (15:43)
      5103–562↑12-15-2018Haze1021.215:4415:58
      6563–28↓12-15-2018Haze1020.816:2316:45
      738–450↑12-16-2018Haze1017.510:3711:04
      8450–97↓12-16-2018Haze1017.111:3711:55
      997–395↑12-16-2018Clean1016.911:5512:11
      10395–40↓12-16-2018Clean1016.812:1412:26
      1130–974↑12-18-2018Clean1014.716:3617:07
      12974–136↓12-18-2018Clean1014.717:4018:07
      13136–490 (490–950) ↑12-18-2018Clean1014.718:07 (20:02)18:21 (20:17)
      14915–27↓12-18-2018Clean1015.020:1920:47
      1526–773↑12-19-2018Few clouds1017.710:4611:10
      16773–27↓12-19-2018Few clouds1017.711:5212:22
      1733–565 (565–942) ↑12-19-2018Few clouds1017.415:00 (15:46)15:22 (16:02)
      18940–135↓12-19-2018Few clouds1017.717:0617:31
      19135–943↑12-19-2018Few clouds1017.717:3117:55
      20943–32↓12-19-2018Few clouds1018.118:5319:30
      2118–937↑12-20-2018Stratus clouds1018.909:1309:41
      22934–142↓12-20-2018Stratus clouds1018.610:3010:54
      23142–940↑12-20-2018Stratus clouds1017.710:5511:19
      24940–127↓12-20-2018Stratus clouds1016.913:3714:02
      25127–947↑12-20-2018Stratus clouds1017.014:0214:28
      26947–136↓12-20-2018Stratus clouds1017.215:5116:15
      27136–946↑12-20-2018Stratus clouds1016.916:1516:41
      28944–148↓12-20-2018Stratus clouds1016.817:5518:20
      29148–392↑12-20-2018Stratus clouds1016.918:2018:28
      30395–146↓12-20-2018Stratus clouds1016.721:3221:40
      31146–800↑12-20-2018Stratus clouds1016.721:4022:00
      32800–500↓12-20-2018Stratus clouds1016.022:2922:39
      33500–12↓12-21-2018Few clouds1017.105:4105:56

      Table 2.  The date and time for each vertical profile measured in this study, where “↑” indicates ascending profiles, and “↓” indicates descending profiles

      $$d = (\max + \min)/(2 \cdot {\rm mean}),$$ (5)

      where “max” and “min” are the maximum and minimum PM2.5 concentrations, respectively, in each profile, and “mean” is the average PM2.5 concentration. Three distinctly different types of vertical profiles (Types 1, 2, and 3 in Fig. 6b) were identified.

      Figure 6.  (a) Differences in PM2.5 concentrations for all vertical profiles during the entire observation period. The dashed lines serve as separator lines to distinguish the various PM2.5 differences, and the larger d values indicate sharp variations in the PM2.5 concentrations. (b) Three distinctly different types of vertical profiles, e.g., Types 1, a convective state; Type 2, a stable state; and Type 3, a multilayer state.

      In Type 1, with d ranging from 0.9–1.2, the PM2.5 concentrations decreased nearly linearly as a function of the height below approximately 600 m, such as in profile No. 11. This kind of vertical profile of PM2.5 indicates a convective state. Type 2 shows a stable state, exhibiting a sharp decreasing trend from the ground to approximately 200 m, thus characterized by substantial vertical differences (d > 1.2). A typical case is represented by profile No. 22 (Fig. 6b). Type 3 shows increased particulates suspended aloft in the upper air, showing a multilayer structure, and a typical vertical profile No. 1 is shown in Fig. 6b. The vertical profiles of Type 3 were characterized by increasing PM2.5 as a function of height, and the slopes for this type approached a positive slope, although the d values were small.

    • As shown in Fig. 7a, the PM2.5 concentrations of Type 1 decreased almost linearly as a function of the height below approximately 600 m (Nos. 11, 17, and 24), and all differences were smaller than 1.2, where noticeable reductions in aerosol particles were observed above approximately 600 m. These vertical profiles accounted for about half of all the profiles that we obtained in this study, and most of the Type-1 vertical profiles were typically observed in the daytime with a relatively high air θ and strong turbulence activities. A well-mixed distribution of PM2.5 below 600 m in the daytime indicates that the estimated mixing layer height (MLH) at the observation site was approximately 600 m in winter. Figure 7a shows that θ of the three vertical profiles mainly increased with height. The water vapor mixing ratio r of No. 11 decreased with height uniformly and showed a high correlation (0.92) with the PM2.5 vertical profile.

      Figure 7.  Vertical PM2.5, potential temperature (θ), water vapor mixing ratio (r), and WS and WD profiles for the three identified profile types. (a) Type 1: convective state, the PM2.5 concentration decreased almost linearly as a function of the height below approximately 600 m; (b) Type 2: stable state, a sharp decreasing trend of the PM2.5 concentrations below 250 m; and (c) Type 3: multilayer structure, some PM2.5 suspended aloft in the upper air.

      Moreover, the WS values of Nos. 11 and 17 were very similar and decreased similarly between 500 and 800 m, exhibiting opposite vertical distributions compared with vertical profile No. 24. However, the common characteristic of all WS profiles is that a pronounced WS change was observed at approximately 600 m, reflecting notable wind shear. PM2.5 was also concentrated below this height due to high wind shear in the daytime.

      Furthermore, there was little difference in θ between Nos. 11 and 17, but r of No. 17 was basically higher than that of No. 11. Since the observation period of these two profiles varied little, the higher PM2.5 concentration of No. 17 may be the result of the increase in water vapor, which could promote the growth of particulates.

    • Type-2 vertical profiles were also frequently observed during the entire observation study. Figure 7b shows three typical Type-2 profiles with sharp decreases in PM2.5 concentrations above approximately 250 m in the lower layer. Type 2 often occurred during 0900–1100 BT and 1600–1800 BT. For the period of 1600–1800 BT, with the weakening of daytime mixing, the role of local emission sources became more prominent. In this period, the surface inversion layer was not as obvious in No. 20; perhaps the radiative cooling effects from the ground were not strong. The WS of both vertical profiles did not exceed 4 m s−1 below 500 m, and light winds also contributed to pollutant accumulation. In the winter morning, the surface inversion layer was very significant. For example, the surface inversion layer of No. 21 was quite distinct, and the inversion intensity was estimated to be approximately 0.02ºC m−1. The dispersion conditions in the nighttime stable boundary layer were unfavorable, so pollutants became more easily concentrated within the inversion layer during this period (Baumbach and Vogt, 2003; Largeron and Staquet, 2016).

    • Figure 7c shows three typical Type-3 vertical PM2.5 profiles characterized by pollutants suspended in the upper air of the profiles. The vertical profile of PM2.5 concentrations (No. 1) below 200 m was more closely related to the winter mixing conditions in the lower boundary. The PM2.5 concentrations decreased substantially above 150 m for vertical profile No. 1. The WD shows a clear change from an easterly wind to a westerly wind at approximately 450 m for No. 1. There was an obvious inversion from 600 to 900 m, and the vertical profile of PM2.5 shows a multilayer structure. Based on an examination of the solar irradiance received on the ground (Fig. 3c), there may also have been few clouds affecting this layer at the observation site. The WS values of the three vertical profiles were similar, and the WD measurements above 500 m were also similar. Although there were no PM2.5 data obtained by the tethered balloon at this time, PM2.5 below 600 m also had a clear multilayer distribution.

    • Figures 8 and 9 show the detailed diurnal evolution of PM2.5 vertical profiles and meteorological parameters from 1200 BT 18 to 1600 BT 20 December. Phase 1 lasted from 1636 BT 18 to 1602 BT 19 December and Phase 2 did from 1706 BT 19 to 1641 BT 20 December.

      Figure 8.  Vertical and temporal evolutions of (a) WS and (b) WD retrieved by Doppler wind lidar. Data from 2100 BT 18 to 0900 BT 19 November were not available due to the recalibration of the machine.

      Figure 9.  Evolutions of the vertical profiles of (a) PM2.5, (b) T and RH, and (c) θ and r from 1200 BT 18 to 1600 BT 20 November. The ground-level PM2.5 for this period is also shown in Fig. 9a.

      The vertical profiles mainly evolved from Type 1 in the daytime to Type 2 or Type 3 at nighttime. The stage of Phase 1 during 1636–2047 BT 18 December was characterized by a decreased WS from 5 to 2 m s−1. The PM2.5 vertical profiles evolved from Type 1 to Type 2, e.g., No. 14 (2019–2047 BT 18 December 2018). From 1046 to 1602 BT 19 December, this stage of Phase 1 was characterized by moderately higher r and increased T. Compared with the inversion layer of No. 15 at 300–600 m, T in No. 17 decreased nearly uniformly with height. The PM2.5 vertical profile of No. 15 showed a clear change at approximately 200 m, indicating the presence of a mixed layer below (Stull, 1988), consistent with the bottom of the inversion.

      Phase 2 was characterized by consistent northerly winds below 300 m and southerly winds above approximately 300 m (Fig. 8). From 1600 to 2000 BT 19 December, the vertical difference varied significantly, and the ground-level PM2.5 concentration changed from 99.7 to 169.5 µg m−3. No. 20 was a Type-2 profile measured during the nighttime.

      Figure 9c shows that the vertical profile of r gradually decreased between 150 to 600 m from 1000 to 1500 BT 20 December, and the values were relatively higher mainly due to advected water vapor. In addition, the vertical profiles of θ and r during 1030–1054 BT (No. 22) decreased below 200 m, indicating unstable stratification. The Type-2 (No. 22) vertical profile during the morning changed to Type 1 (convective state, No. 25) in the daytime. Profile No. 27 exhibited Type-3 characteristics with a multilayer aerosol structure, and an obvious feature was that WS increased and southerly winds prevailed in the upper portion of the profile.

    • December 15 was chosen as a high-haze day since the ground-level PM2.5 concentration exceeded 250 µg m−3. Figure 10 shows the PM2.5, T, RH, and WS vertical profiles observed on that day. We define a light-wind layer as WS lower than 3 m s−1. As shown in Fig. 10, the three vertical profiles were characterized by a light-wind layer below approximately 400 m. Clearly, particulates were mainly concentrated within the light-wind layer.

      Figure 10.  The vertical profiles of PM2.5, RH, T, and WS from (a) 1156–1243 BT, (b) 1351–1419 BT, and (c) 1544–1558 BT 15 December 2018.

      The profile during 1156–1243 BT 15 December 2018 (No. 1) was analyzed as typical of regional transport aloft, and it also had light-wind layer characteristics. Another significant feature is the effect of the inversion layer at 600–900 m on the accumulation of pollutants. The profile during 1351–1419 BT 15 December 2018 also exhibited typical light-wind layer features, and WS below 400 m did not exceed 2 m s−1, which was very favorable for pollutant accumulation. In addition, PM2.5 in the lower layer was relatively well mixed, partly because of the strong turbulence activity at noon. RH throughout the low-wind layer changed dramatically; namely, it decreased rapidly and had a high gradient. A dramatic decline in the PM2.5 concentration between 350 and 500 m was observed, where WS increased accordingly. The pollutants during this period were also trapped by the capping inversion layer, which appeared at approximately 300 m, and the capping inversion layer depth was approximately 400 m. There were also many pollutants trapped in the inversion layer. The PM2.5 concentration near 600 m was greater than 60 µg m−3, and RH and temperature at this height both increased (RH and temperature are generally inversely correlated). The high PM2.5 concentration near 600 m may have been caused by the transport of moist plumes with high pollutant concentrations.

      Another PM2.5 vertical profile was measured during the afternoon from 1544–1558 BT, as depicted in Fig. 10c. Although the sampling altitude was nearly 600 m above the ground at this time, the lower portion of the sampled layer still exhibited low-winds with a high particle concentration. It is worth noting that during this winter, the NCP area temperature vertical profile from 1544–1558 BT shows that the inversion layer had developed during this period, and the surface inversion layer depth exceeded 200 m. The pollutants in the lower layer were clearly trapped by the surface inversion layer, but there were still some pollutants suspended above the inversion layer when the developed inversion layer was not deep enough.

    4.   Conclusions
    • In this paper, the pollution characteristics in the North China Plain (NCP) area are analyzed, and the key factors influencing the vertical distribution of PM2.5 are clarified through observations from the tethered balloon, Doppler wind lidar, and ground-level instruments. Due to local control of the coal combustion industry, the PM2.5/PM10 ratio was generally approximately 0.4 (its maximum was approximately 0.8) during the whole observation period, revealing that fine-mode aerosols dominated in the winter heating season. Ground-level observations indicate that the correlation coefficient between the PM2.5 and CO concentrations was the highest (0.725) among the gas pollutants, and there was an inverse relationship between O3 and PM2.5 in general but not a simple linear relationship. Based on the 33 vertical profiles acquired with the tethered balloon, three types of vertical profiles were classified to illuminate the vertical evolution characteristics of PM2.5 during the three severe haze episodes. Type 1 was a convective state characterized by PM2.5 concentrations decreasing nearly linearly as a function of the height below approximately 600 m. This vertical profile was mainly observed in the daytime. Type 2 was a stable state, exhibiting a sharp decreasing trend from the ground to approximately 200 m, and this kind of vertical profile of the PM2.5 was closely associated with the surface inversion layer inhibiting the upward dispersion of pollutants. Type 3 demonstrated a multilayer structure with some pollutants remaining suspended aloft in the upper portions of the profile. The diurnal variation of boundary layer structure had great influences on the vertical profiles of PM2.5. Because of differences in ABL structures from daytime to nighttime, the vertical profiles evolved from Type 1 to Type 2 or Type 3. The vertical distributions of winds, inversion layers and turbulence activities notably influenced the PM2.5 vertical profiles. Both the light-wind layer, inversion layer, and weak turbulence activity contributed greatly to the accumulation of pollutants. Moreover, the PM2.5 vertical patterns were greatly affected by local surface emission sources and regional transport processes. These results will help to deepen the understanding of the relationships between the boundary layer structure and haze pollution. Further work will need to focus on the feedback between various pollutants and the ABL structure due to the effects of radiant energy balances.

      Acknowledgments. The authors gratefully acknowledge the staff of Shanghai Environmental Monitoring Center for the valuable data.

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