Observed Interactions Among Haze, Fog and Atmospheric Boundary Layer during a Haze-fog Episode in the Yangtze River Delta Region, Eastern China

Received: June 28, 2020
Revised: October 10, 2020
Accepted: October 12, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Zou, J., Sun, J., Li, T., Han, X., Xiang, Z., Sha, J. (2021). Observed Interactions Among Haze, Fog and Atmospheric Boundary Layer during a Haze-fog Episode in the Yangtze River Delta Region, Eastern China. Aerosol Air Qual. Res. 21, 200354. https://doi.org/10.4209/aaqr.2020.06.0354

HIGHLIGHTS

• Potential temperature jump existed at fog-top suppresses PBLH growth.
• Wet deposition effect of fog on PM_2.5 is negligible without precipitation.
• Low PBLH is favorable for air moisture accumulation, thus good for fog formation.

Keywords: Haze-fog episode, Potential temperature jump, Planetary boundary layer height, PM2.5

1 INTRODUCTION

Because of the increasing energy consumption associated with rapid urbanization and industrialization, many regions of China, especially megacities and city clusters in coastal eastern China, have experienced high levels of air pollution (He et al., 2002; Chan and Yao, 2008; Tie and Cao, 2009; He et al., 2013; Ding et al., 2016a; Zhao et al., 2018; Barkjohn et al., 2020). In these regions, fine particular matter (PM_2.5; particles with an aerodynamic diameter of 2.5 microns or lower in ambient air) often attains extremely high concentrations during the winter haze period (Ding et al. 2013a; Huang et al., 2014; Guo et al., 2014; Zhang et al., 2014; Chen et al., 2017; Ma et al., 2017; Liu et al., 2019). High levels of air pollution can significantly modify the weather by changing air temperature, cloud conditions, and even precipitation (Banta et al., 1998, Fiore et al., 2002, Ding et al., 2013b, Huang et al., 2016, Karle et al., 2020). The increase in atmospheric aerosols over China has been shown to heat the atmosphere and generate a cyclonic circulation anomaly that weakens the East Asian winter monsoon (Niu et al., 2010a). The weakened East Asian winter monsoon circulation results in less cold and dry air being transported to the region and favors the formation of haze and fog over eastern China (Niu et al., 2010a; Zhang et al., 2014).

The high PM_2.5 levels reduce surface air temperature by blocking the solar radiation and increase air temperature in the upper convective planetary boundary layer (CBL) by the heating effect of absorbing aerosols, leading to a stabilized CBL. The enhanced CBL stability suppresses the vertical mixing and dispersion of air pollutants, resulting in higher PM_2.5 concentrations (Wang et al., 2014; Gao et al., 2015). The scattering and absorbing effects of aerosols can reduce the surface heat flux, leading to a decrease of CBL height (CBLH) and consequently an increase of PM_2.5 concentrations during the daytime (Zou et al., 2017). The aerosols might also decrease surface nocturnal cooling (similar to clouds), and it is possible that nighttime stability would not be as strong as under cleaner conditions (VanReken et al., 2017). Ding et al. (2016b) demonstrated that black carbon (BC) aerosols induce heating in the CBL, particularly in the upper CBL, and that the resulting stabilized stratification and decreased surface heat flux substantially depress the development of the CBL and consequently enhance the occurrences of extreme haze pollution episodes. They defined this process as “dome effect” of BC and suggested reducing BC emissions as an efficient way to mitigate the extreme haze pollution in megacities of China.

Due to heterogeneous nucleation, aerosols can promote fog formation (Sachweh and Koepke, 1995; Mohan and Payra, 2009) to such an extent that supersaturation or saturation is not needed for the fog to form (Kulmala et al., 1997). Therefore, in moist areas, high levels of air pollution often induce more fog events. Observations have shown that the frequencies of fog events in wintertime over eastern-central China have doubled over the past three decades (Niu et al., 2010a). In the Central Valley of California, observations have shown that the frequency of fog events increased by 85% from 1930 to 1970 because of increased air pollution (Gray et al., 2019).

The Yangtze River Delta (YRD), with Shanghai at its center, is a triangle-shaped megalopolis comprising 26 cities in Jiangsu, Zhejiang, and Anhui province. The YRD region is a flat topography. It is one of the most economically developed and densely populated areas in China. It is also one of the high fog occurrence regions in China (Niu et al., 2010b). Advection fog, which forms when warm, moist air from the East China Sea comes across the region, is common in the YRD (Shi et al., 2010; Lin et al., 2017). Ding et al. (2019) found that BC plays an important role in advection-radiation fog formation in the YRD region by inducing a mesoscale cyclonic wind anomaly that can transport moist air from the sea into this region and help form heavy fog at night.

Fog water can provide a medium for aqueous phase reactions and transform primary pollutants into secondary aerosols (Graedel et al., 1985; Dall'Osto et al., 2009). The wet scavenging effect of fog can also significantly reduce the aerosol concentration in the atmosphere (Niu et al., 2010b; Han et al., 2018). Clarifying the relationship between fog and aerosols is crucial for understanding the formation and evolution of both haze and fog episodes, which are associated with CBL processes (Ding et al., 2019). However, the interactions among haze, fog, and CBL have rarely been reported in the YRD. During 22–30 November, 2018, the YRD region suffered a severe haze-fog episode with a high PM_2.5 mass concentration and multi-day fog occurrences. In this study, we focused on the interactions among haze, fog, and CBL processes based on observations during this period.

 
2 OBSERVATION AND DATA PROCESSING

During the severe haze - fog event that occurred during 22–30 November, 2018, in the YRD region, PM_2.5 mass concentration, solar radiation, surface heat flux, and meteorological data (including wind, temperature, humidity, and precipitation) were collected at the Station for Observing Regional Processes of the Earth System (SORPES) site (32°7′14″N, 118°57′10″E) in Nanjing (Ding et al., 2013a, b; Zou et al., 2017). The SORPES station is a cross-disciplinary research and experimentation platform developed by Nanjing University in collaboration with the University of Helsinki. The observation site is located on the Xianlin Campus of Nanjing University, suburban area of Nanjing, approximately 20 km northeast of downtown Nanjing. The PM_2.5 mass concentration was measured by a mass analyzer (SHARP-5030, Thermo Fisher Scientific, Massachusetts, USA), which was housed on the top floor of a laboratory building. The precision of the mass analyzer is ±2 µg m^–3 below 80 µg m^–3 and ±5 µg m^–3 above 80 µg m^–3. The surface sensible and latent heat fluxes were measured by an eddy covariance system (CSAT 3A, Campbell Scientific, Utah, USA), which was mounted in the observation yard at 2.2 m above ground level (AGL). The signals for the eddy covariance calculations were sampled at 10 Hz, and the data were subjected to spike detection and rejection algorithms (Zou et al., 2017). The sensible heat flux (Hs) was computed at a half-hour interval, which is the widely accepted duration for calculating turbulent fluxes (Nordbo et al., 2013). Corrections were applied for sonic virtual temperature (Schotanus et al., 1983). Solar radiation was measured by a 4-component net radiometer (CNR4, Campbell, USA), which was mounted in the observation yard at 2.6 m AGL. Wind, temperature, and humidity data were measured by sensors installed at 9 m AGL on a 75 m tower. The measurements were conducted at six levels on this tower: 4, 9, 18, 36, 54, and 72 m. We use the data acquired at 9 m because this height was closest to the 10 m WMO standard. Wind speed (WS) and wind direction (WD) were measured by an anemometer, including a 010C wind speed sensor (Campbell, USA) and a 020C wind direction sensor (Campbell, USA). Temperature (T), relative humidity (RH) and specific humidity (q) were measured by a HMP155A temperature and relative humidity probe (Campbell, USA). Precipitation was measured by a TE525WS-L rain gage (Campbell, USA) in the observation yard.

To know the maximum CBLH of each day during this haze-fog event, radiosonde measurements were carried out at 1400 local standard time (LST) at the SORPES site. The iMet-4 radiosonde (International Met Systems, USA) was used to acquire the temperature, pressure, humidity and GPS information, and the sounding data were received by a 3050A receiver (International Met Systems, USA). The GPS data were used to calculate the wind speed and direction, assuming that the radiosonde moved with the wind. To obtain the vertical profiles of PM_2.5 concentration and the meteorological parameters, measurements were achieved by an unmanned aerial vehicle (UAV). The UAV was an M600 Pro hexacopter (DJI, China). The maximum flight height was 500 m AGL, and the flight time for descending with the payload was 16–18 min (airspeed was about 1 m s^–1). An iMet-4 radiosonde was mounted on the UAV to measure the air temperature and humidity, and an AM520 PM_2.5 sensor (TSI, USA) was used to monitor the PM_2.5 concentration. The AM520 PM_2.5 sensor was calibrated before the observation using the mass analyzer (SHARP-5030), the difference between these two measurements was less than 5 µg m^–3. The profiles measured in the ascending process of the UAV were used in this study. The UAV measurement times are listed in Table 1.

The CBLH can be derived from radiosonde data, as well as the UAV observation data (when the CBLH is below 500 m). The radiosonde-derived CBLH reveals a good agreement with other methods, for example, the CBLHs derived from radiosonde and lidar backscatter measurements coincide within ±200 m (Hennemuth and Lammert, 2006). In this study, when there was no fog, we determined the CBLH as the height at which the vertical gradient of potential temperature had its maximum value (Stull, 1988; Batchvarova and Gryning, 1991) and determined the nighttime planetary boundary layer height (PBLH) as the height of the top of the near-surface inversion layer (Stull, 1988). However, when fog existed, we define the CBLH as the height of the top of the fog layer, above which an elevated inversion or stable layer could be identified or where air moisture decreased significantly (Seibert et al., 2000).

 
3 RESULTS AND DISCUSSION

 
3.1 Overview of Observations

Time series of wind speed and wind direction (WS and WD), humidity (q and RH), temperature (T) and surface sensible heat flux (Hs), solar radiation (SR), CBLH, PM_2.5 mass concentration, and precipitation in the haze-fog event of 22–30 November, 2018, are shown in Fig. 1. The periods of fog occurrence are shaded. A relative humidity value exceeding 90% was used to distinguish light fog from haze (Schichtel et al., 2001; Doyle and Dorling, 2002). We used this criterion to separate fog from haze and determine the onset time of fog. In the nine days, the WS was often less than 2 m s^–1 and rarely reached 3 m s^–1, whereas the WD covered almost all orientations but was mostly northeasterly or southeasterly. Low wind speed is a favorable condition for the formation of haze-fog episodes. The near surface air temperature had diurnal variation between 6°C (279 K) and 20°C (293 K). The minimum temperature at night was not very low.

Fig. 1. Time series of (a) wind speed (WS) and wind direction (WD), (b) specific humidity (q) and relative humidity (RH), (c) temperature (T) and surface sensible heat flux (Hs), (d) solar radiation (SR) and reference solar radiation (ref_SR), (e) CBLH, and (f) PM_2.5 mass concentration and precipitation in the haze-fog event during 22–30 November, 2018. The periods of fog occurrence are shaded. 26–28 and 30 November are referred as the fog days, while 22–25 and 29 November are referred as the no-fog days.

The first pollution phase of the observation extended from 22 to 26 November (Period 1). The PM_2.5 mass concentration increased from 50 µg m^–3 to approximately 150 µg m^–3. There was no fog in Period 1, and the RH varied diurnally, having lower values in the daytime and higher values in the nighttime due to the diurnal change of temperature. However, q increased steadily from 4 g kg^–1 to approximately 7 g kg^–1 during these four days (an obvious increasing trend of RH was also observed), suggesting that air moisture accumulated during this period. This is a favorable condition for fog formation. The observed solar radiation (SR) and the reference solar radiation (Ref_SR) are shown in Fig. 1. The reference solar radiation was determined from the measured solar radiation on 9 November, when Nanjing was cloud-free and had relatively low PM_2.5 (< 50 µg m^–3). The SR curves were not smooth, indicating that there was intermittent cloud during the daytime during Period 1. At noon on 5 November, the maximum SR was lower than 600 W m^–2, which was lower than the values on 23 and 24 November. This may have been a result of the blocking effect of increased aerosols. The maximum CBLHs at 1400 LST decreased from about 1600 m (22 November) to about 1300 m (25 November), while the PM_2.5 mass concentration increased.

Period 2, during which fog occurred, extended from 26 November to 1 December. As the fog occurred during the night of 26 November, the RH remained high (> 90%), the SR was blocked by the fog, and the CBLH was very low. At noon on 27 November, q decreased to 4 g kg^–1 (a relatively low level of air moisture), and PM_2.5 mass concentration fell to about 50 µg m^–3. The likely reason for these changes is that precipitation occurred during the night before and removed some water vapor and aerosols (wet deposition) from the near-surface atmosphere, although the precipitation was light. From 28 November, pollution reappeared and PM_2.5 mass concentration began to increase. In the following days, the PM_2.5 mass concentration increased rapidly to more than 200 µg m^–3, and q increased to 8 g kg^–1. Fog continued to occur as the result of the accumulation of air moisture. The maximum solar radiation decreased to 400 W m^–2 on 30 November, which is about half the value of the Ref_SR, indicating that the “blocking effect” of the fog and pollution was strong. The maximum CBLHs at 1400 LST in the fog days were systematically lower than those in the no-fog days (Fig. 2). These results suggest that a lower CBLH is favorable for the accumulation of air pollutants, as well as air humidity, in the lower atmosphere.

Fig. 2. Maximum CBLHs at 1400 LST on fog days (black dots) and no-fog days (circles) during 22–30 November, 2018.

 
3.2 CBLHs on Fog and No-fog Days

On 29 November, there was no fog before sunrise. Therefore, the CBLH developed immediately after sunrise, which is similar to the situation in Period 1 (no-fog days). Here, we attribute 29 November as a no-fog day because there was no fog in the daytime and during the whole night before. However, on 26–28 and 30 November, fog persisted after sunrise (the fog began at sunrise on 28 November), and it is inevitable that the development of the CBLH was influenced by the fog in these days. We refer to these four days as fog days. Fig. 2 shows the maximum CBLH (at 1400 LST) on each day during 22–30 November, which is plotted against the daytime mean PM_2.5 mass concentration. The maximum CBLHs on the no-fog days were significantly higher than those in the fog days, suggesting that development of the CBL was suppressed by fog. As shown in Fig. 1, the fog lasted into the later morning and even the afternoon, which possibly delayed the development of the CBL and consequently reduced the maximum CBLH. A lower CBLH is favorable for the accumulation of air pollutants in the CBL, which can lead to heavy air pollution. From 27 to 30 November, the PM_2.5 mass concentration accumulated and increased from 50 µg m^–3 to more than 200 µg m^–3 at the end of this haze-fog episode (Fig. 1(f)). Fig. 2 also shows that the maximum CBLH decreased with PM_2.5 mass concentration at the surface on both no-fog and fog days. On the no-fog days, the shadowing effect of aerosols reduced the surface sensible heat flux and consequently deceased the CBLH (Barbaro et al., 2013; Zou et al., 2017). Our observational results of the maximum CBLH are consistent with the results of previous studies. However, the situation on the fog days seemed to be more complex. The development of the CBL forced by surface heating began after fog dissipation. On one hand, the maximum CBLH was influenced by the dissipation time of fog. When the fog dissipation was later in the morning, development of the CBL was later and thus the maximum CBLH was lower (if the fog did not last into the afternoon, as on 26 November, the CBL did not develop, and the CBLH was almost the same as the depth of the fog layer). On the other hand, it can be expected that the shadowing effect still functioned in the delayed development process of the CBL, which may have decreased the maximum CBLH further. Our observational results on the fog days revealed a decreasing trend of maximum CBLH with increasing PM_2.5 mass concentration, which seems to support this argument.

When neglecting large-scale synoptic forcing, daytime growth of the CBL is driven mainly by surface heating and upper-CBL entrainment (Stull, 1988; Sühring et al., 2014). In a simplified mixed-layer model, the CBLH can be predicted when the surface heat flux and the profile of potential temperature are known (Stull, 1988; van Zanten et al., 1999; Fedorovich et al., 2004; Sun, 2009). The growth rate of the CBLH can then be characterized by a simplified parameterization from Sun (2009), in which only the surface heat flux and the background stratification are needed. It is expressed as follows:

where z_i is the CBLH, A_e is the entrainment flux ratio (the ratio of the heat flux at the top of the CBL to that at the surface), which is a constant of approximately 0.2 (Deardorff et al., 1980),  is the surface kinematic heat flux, γ is the lapse rate of potential temperature in the free atmosphere above the CBL, and β is a parameter that is a constant of approximately 0.9 for a purely buoyancy-driven CBL (Deardorff et al., 1980; Fedorovich et al., 2004; Sun, 2009). Eq. (1) shows that the growth rate of the CBLH is inversely proportional to the lapse rate of potential temperature in the free atmosphere above the CBL. Therefore the profile of potential temperature plays an important role in the CBL development.

To understand the effects of haze and fog on the thermal structure of the CBL, we compared the profiles of potential temperature at 0800 LST and 1400 LST on each day and at the end of the fog period during 22–30 November, 2018. These profiles are plotted in Fig. 3. As shown in the upper panels, on the no-fog days at 0800 LST, the near-surface atmosphere became more stable when the PM_2.5 mean mass concentration increased to a high level, whereas the profiles of potential temperature at 1400 LST presented a decreasing trend of maximum CBLH. This situation was more evident on 29 November, a heavy air pollution day, during which the lapse rate of potential temperature in the near-surface layer at 0800 LST was significantly larger and the CBLH at 1400 was significantly lower than those of the previous no-fog days. A larger lapse rate of potential temperature in the near-surface layer at 0800 LST means that after sunrise, the CBL develops in a more stably stratified background atmosphere and the growth rate of the CBLH is suppressed. The result that the near-surface atmosphere at the end of night becomes more stable on a heavier air pollution day is consistent with the long-term observations presented in Zou et al. (2017). Therefore, air pollution can impact the CBLH by different processes. On one hand, a high level of aerosol loading in the PBL allows the formation of a more stable nocturnal PBL, which suppresses the growth rate of the PBLH in the morning. On the other hand, high PM_2.5 mass concentration increases the shadowing effect of aerosols on solar radiation and reduces the surface sensible heat flux, which leads to a lower CBLH. The heating effect of aerosols also influences the development of the CBL. The absorbing aerosols induce solar heating in the atmosphere, and the heating effect is stronger in the upper part of the CBL (Barbaro et al., 2013; Ding et al., 2016b). To the best of our knowledge, the stabilized upper part of the CBL can suppress the overshooting of thermals and weaken the entrainment process, which means that the entrainment flux ratio A_e is reduced. According to Eq. (1), a smaller A_e leads to a smaller growth rate of the CBLH.

Fig. 3. Profiles of potential temperature at 0800 LST (blue curves), 1400 LST (red curves) and the moment of fog dissipation (black curves) on the fog days (lower panels) and the no-fog days (upper panels) during 22–30 November. The fog dissipates at 1513 LST on 26 November, 1214 LST on 27 November, 1048 LST on 28 November, and 1027 LST on 30 November. The mean mass concentration of PM_2.5 on each day is given below each panel.

The lower panels in Fig. 3 show profiles of potential temperature at different times on the fog days. 26 November is somewhat special because the fog lasted into the afternoon and dissipated at about 1530 LST. When the fog dissipated, the potential temperature profile (the black line) showed that the CBLH was about 350 m. At 1400 LST, the potential temperature profile from radiosonde (the red line) indicates that the CBLH was 400 m (we choose the middle height of the lowest inversion layer as the CBLH, when the potential temperature profile revealed more than one inversion layer). Fig. 1 shows the time series of the CBLH on that day. The CBLH is actually simply the depth of the fog layer during the fog period. Because the fog dissipated in the afternoon, the CBLH decreased slightly over the rest of the day. Therefore, the depth of the fog layer determined the CBLH on that day. On 27 November, the profile at 0800 LST (the blue line) showed a large potential temperature jump at the height of about 150 m, the height of the top of the fog layer. At about 1200 LST, the fog dissipated, but the potential temperature jump at that height still existed. The fog layer depth was shallow and the potential temperature jump became lower at this moment. Additionally, the surface sensible heat flux increased rapidly to about 200 W m^–3 after fog dissipation (Fig. 1). Thus, in the following time, the surface heating was relatively strong, and the potential temperature jump could be consumed easily. The potential temperature profile (the red line) shows that at 1400 LST the CBLH reached a relatively high altitude of about 800 m. A similar situation occurred on 28 and 30 November. On these two days, at 0800 LST, there was a strong capping inversion layer above the fog layer, and at the time of fog dissipation, a step-like potential temperature jump still existed at the top of the fog layer. Because the fog layer depth (about 350 m) on these two days was thicker than that on 27 November, consumption of the potential temperature jump at the top of the fog layer was more difficult. The CBLH remained unchanged until the potential temperature jump was consumed. This situation is similar to that in many large-eddy simulation studies of the CBL, in which a large potential temperature jump is set at a chosen height so that the top of the mixed layer is approximately fixed during the simulation. To our knowledge, Eq. (1) is not suitable for this situation because the potential temperature jump cuts off the interaction between the mixed layer and the free atmosphere. Here, we distinguish the potential temperature jump from the potential temperature difference across the entrainment zone for a CBL that is developing in the equilibrium state. The former corresponds to a step-like shaped potential temperature profile, but the latter corresponds to a smoothly curved potential temperature profile. Therefore, Eq. (1) is only applicable when the CBL reaches an equilibrium state, in which the interaction between the mixed layer and the free atmosphere forms a stable interfacial layer, i.e., the entrainment zone, or the so-called capping inversion layer.

The potential temperature profile at 1400 LST on 28 November shows that the step-like shape of the profile disappeared but the CBLH changed very little, suggesting that the potential temperature jump was consumed just before this time. The potential temperature profile at 1400 LST on 30 November shows that the CBLH increased only about 150 m from the moment of fog dissipation, implying that most of the time between 1030 LST and 1400 LST was used to consume the potential temperature jump. Therefore, our observations indicate that the potential temperature jump formed at the top of the fog layer delays CBL development after fog dissipation because a certain amount of time is needed for surface heating to increase the air temperature in the whole mixed layer so that the potential temperature jump can be eliminated and then the CBL begins to develop. This raises the question of why a potential temperature jump can form at the top of the fog. The primary mechanism for the observed vertical temperature structure is likely cloud top cooling (Koračin et al., 2005; Guo et al., 2020; Zhang et al., 2020). When the fog becomes optically thick enough, there is greater radiative flux divergence near the top of the fog than the bottom. At this point, longwave radiative cooling occurring at the fog top generates cold thermals that sink and begin to convectively mix the fog layer. The fog becomes more uniform in the vertical with a well-defined top edge. And this sharp top concentrates the radiative divergence closer to that region, which reinforces vertical mixing in the fog layer (Stull, 1988). Therefore, the well-mixed fog appears uniform in the vertical direction and has a well-defined top edge, which forms a sharp potential temperature jump at the top of the fog layer. Our observations show that the step-like jump corresponds to 3–5 K temperature increase over a thin layer with a depth of only about 20 m. Such a large lapse rate cannot be resolved by weather and climate models, and the growth rate of the CBLH after fog dissipation may be overestimated.

To determine the exact strength of the potential temperature jump at the top of the fog layer, we calculated the lapse rate in the thin layer with a depth of 20 m from each temperature profile measured by UAV. The results are shown in Fig. 4 and are denoted by green stars. On 26 November, the potential temperature jump was relatively small (as shown in Fig. 3), but the value of the lapse rate at the top of the fog layer was larger than 50 K km^–1 and could reach 100 K km^–1. In the subsequent three fog days, the potential temperature jump was relatively strong, and the lapse rate at the top of the fog layer was typically larger than 100 K km^–1 and could reach 250 K km^–1. Observations showed that a very strong stable layer formed in a thin layer at the fog top, although the lapse rate in this layer varies with time and the mean strength was different in different cases. In high-resolution numerical models, the vertical grid distance is about 100 m. To determine the strength of stratification in a layer with a depth of 100 m at the fog layer top, we also calculated the mean lapse rate of potential temperature in this layer. The results are plotted as blue stars in Fig. 4. On 26 November, the lapse rate is about 25 K km^–1, but it is about 50 K km^–1 on the following three fog days. This large lapse rate is maintained until the end of the fog period. This means that the CBL develops in a much more stably stratified background atmosphere after fog dissipation and that the growth rate of the CBLH is significantly suppressed. In this situation, the overshooting of rising thermals from the mixed layer is inhibited by the strongly stabilized layer above the top of the mixed layer, which eliminates entrainment. Therefore, we can use the encroachment model (Boers et al. 1984) to estimate CBLH growth rate. The model can be described as , where Γ is the lapse rate of potential temperature immediately above the mixed layer. This model neglects entrainment, implying that the mixed layer top erodes into the strongly stable layer due to the increased air temperature in the mixed layer. Assuming that Γ = 50 K km^–1 (considering the situation in the later three fog days),  = 0.15 K m s^–1 (the corresponding surface heat flux is 180 W m^–2, which was estimated according to our measurements at noontime), and with an initial CBLH of 350 m at 1030 LST (according to observed fog layer depth), the calculation showed that about 3.7 h is required for the CBLH to increase to 450 m. This estimation agrees with the observations in the latter two fog days. Our observations on 28 and 30 November indicated that about 3.5 h is required for the mixed layer top to penetrate this stable layer. Therefore, fog can significantly suppress the CBL development by inducing a strongly stabilized layer at the top of the fog layer.

Fig. 4. Mean lapse rate of potential temperature at the fog layer top in each temperature profile measured by UAV. The shaded areas represent the daytime duration of fog. The green stars denote the lapse rate in the layer with a depth of 20 m, and the blue stars denote the lapse rate in the layer at a depth of 100 m.

 
3.3 Vertical Distribution of PM_2.5 during Fog Days

Fog droplets interact with aerosol particles and soluble gases in the atmosphere. Thus, fogs can affect pollutant formation, transformation, and removal. In this study, we observed the vertical distribution of PM_2.5 mass concentration in the lower atmosphere both during fog periods and after fog dissipation. Fig. 5 shows profiles of PM_2.5 mass concentration and relative humidity at different times during the fog days. On 26 November, the profiles of relative humidity at 0830 LST and 1000 LST revealed that the fog layer top reached about 300 m AGL. Meanwhile, the profiles of PM_2.5 mass concentration at the two times showed that in the upper part of the fog layer, the PM_2.5 mass concentration in the atmosphere was very small (only about 25 µg m^–3), although it increased with proximity to ground level. However, at 1430 LST, the profiles show that the PM_2.5 mass concentration in the fog layer increased to about 100 µg m^–3 while the relative humidity in this layer decreased to about 90%, suggesting that the fog still existed at this time but that the evaporation of fog droplets released a lot of PM_2.5 back to the atmosphere. At 1530 LST, the fog dissipated, evaporation ended, and the PM_2.5 mass concentration increased further to about 120 µg m^–3, which is almost the same as the level before fog formation, implying that almost all of the PM_2.5 in the fog droplets was returned to the atmosphere. Fig. 5 shows that the same process repeated in the following fog days. We note that on 27 November, at the time of fog dissipation, the PM_2.5 mass concentration was reduced to a lower level in comparison with the value before fog formation, which was evidently the result of the light precipitation that occurred during the fog period (Fig. 1). However, on the no-precipitation fog days, the PM_2.5 mass concentration at the time of fog dissipation returned to the level before fog formation. The scavenging effect of fog can significantly reduce the aerosol concentration in the atmosphere (Niu et al., 2010b; Han et al., 2018). Our observations suggest that the wet deposition effect of fog on PM_2.5 is negligible unless precipitation occurs during the fog period. This means that the wet scavenging effect of fog on aerosols only occurs during a fog period. Once the fog dissipates, the aerosols return to the atmosphere from the fog droplets. The fog droplets provide a liquid phase reaction environment for PM_2.5 with different chemical compositions and soluble gases. However, this problem is beyond the scope of this study. We will investigate this problem based on observations in a future research.

Fig. 5. Vertical profile of relative humidity (left) and PM_2.5 mass concentration (right) for the fog days. The different colors represent different measurement times. The fog dissipates at 1513 LST on 26 November, 1214 LST on 27 November, 1048 LST on 28 November, and 1027 LST on 30 November.

4 CONCLUSIONS

A severe haze-fog event occurred in the YRD region, eastern China, during 22–30 November, 2018. In this study, we focused on the interactions among haze, fog, and atmospheric boundary layer during the haze-fog episode based on observations at the SORPES site in Nanjing, which is located in the northeastern part of the YRD region. The observations showed that the PM_2.5 mass concentration increased from about 50 µg m^–3 to more than 200 µg m^–3 during this period, while the maximum CBLH decreased from about 1500 to 500 m or even lower. During Period 1 (22–26 November), i.e., on the no-fog days, PM_2.5 accumulated due to lower wind speed, and increased air humidity. During Period 2 (26–30 November), fog occurred almost every day, the PM_2.5 mass concentration increased, and the maximum CBLHs on the fog days were significantly lower than those on the no-fog days. These observational results suggest that the interactions among haze, fog and atmospheric boundary layer enhance air pollution. On one hand, the increase of aerosol loading reduces the CBLH by decreasing the daytime surface heating and stabilizing the atmospheric boundary layer, and the aerosol-induced reduction of the CBLH limits the aerosols and air moisture to a smaller space and results in higher PM_2.5 mass concentration and air humidity. This is a well-known interaction process on the no-fog days. On the other hand, increased aerosol loading and air humidity provide favorable conditions for fog formation, and the CBLH significantly reduces after fog dissipation, which can further increase PM_2.5 mass concentration and lead to heavy air pollution.

Observations showed that the maximum CBLHs on the fog days were much lower than those on the no-fog days, suggesting that the occurrence of fog significantly suppressed the development of the CBL. The CBL develops after sunrise on no-fog days but develops after fog dissipation on fog days. Therefore, fog delays the development of the CBL. However, the existence of fog results in a step-like potential temperature jump at the top of the fog layer because of longwave radiative cooling and maintains it until the time of fog dissipation. Thereafter, the CBL first increases the air temperature in the mixed layer by surface heating for a few hours to eliminate the step-like potential temperature jump. Then, it develops freely in the equilibrium state. This process further delays the development of the CBL, which leads to a much lower CBLH and consequently, a high level of PM_2.5 mass concentration on fog days.

Observations also showed that the wet deposition effect of fog on PM_2.5 is negligible if the fog cannot form precipitation. Previous studies pointed out that the scavenging effect of fog can significantly reduce aerosol concentration in the atmosphere. Our observations suggest that this effect only occurs during the fog period. When the fog dissipates, the aerosols are returned to the atmosphere from the fog droplets. Along with the effect of fog on the thermal structure of the CBL, which can significantly reduce the CBLH, fog plays an important role in the occurrence of heavy air pollution in moist areas, such as the YRD region, eastern China.

 
ACKNOWLEDGEMENTS

The work was supported by the Ministry of Science and Technology of China (2016YFC0200500) and National Natural Science Foundation of China (91544231).