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Article

Synergistic Effect of Atmospheric Boundary Layer and Regional Transport on Aggravating Air Pollution in the Twain-Hu Basin: A Case Study

by
Jie Xiong
1,
Yongqing Bai
1,*,
Tianliang Zhao
2,
Yue Zhou
1,
Xiaoyun Sun
2,
Jiaping Xu
3,
Wengang Zhang
1,
Liang Leng
1 and
Guirong Xu
1
1
Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
2
Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
3
Jiangsu Climate Center, Nanjing 210009, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(20), 5166; https://doi.org/10.3390/rs14205166
Submission received: 18 August 2022 / Revised: 5 October 2022 / Accepted: 12 October 2022 / Published: 15 October 2022
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Abstract

:
The impact of structural variations in the atmospheric boundary layer (ABL) during the regional transport of air pollutants on its local pollution changes deserves attention. Based on multi-source ABL detection and numerical simulation of air pollutants over the Twain-Hu Basin (THB) during 4–6 January 2019, the mechanism of the rapid growth of atmospheric pollutant concentrations in Xianning by the synergistic effect of regional transport and ABL evolution is explored, and the main conclusions are obtained as follows. The vertically stratified atmosphere is noticeable at nighttime, and the heavy humidity of near-surface fog within the stable boundary layer (SBL) promoted the generation and cumulative growth of secondary PM2.5 components during the pollution formation stage. The horizontal transport characteristics of atmospheric pollutant concentration peak were observed in the residual layer (RL) of 500–600 m. At the pollution maintenance stage, the convective boundary layer (CBL) developed during the daytime, and northerly wind transported high-concentration pollutants from the north to the THB. Under the combined action of horizontal transport and turbulent mixing, the high-concentration atmospheric pollutants in the mixing layer (ML) from the ground to the 500 m height were mixed uniformly and maintained accumulation growth. The next day, the strong vertical turbulent mixing caused the downward transport of high-concentration pollutants in the RL during nighttime due to the development of the CBL again, resulting in a doubling of near-surface pollutant concentration in a short time. With the development of ABL turbulence, local pollution dissipated rapidly without the continuous input of pollutants from external regions. This study emphasizes the importance of multi-scale processes impact on pollution variation, that is, regional transport of atmospheric pollutants at the CBL development stage for the rapid growth of PM2.5 concentration in the ML.

Graphical Abstract

1. Introduction

PM2.5 (fine particulate matter with aerodynamic diameters ≤2.5 μm) not only has deleterious effects on human health [1,2,3], but also alters land surface temperatures due to its absorption and scattering of solar radiation (IPCC,2014). Furthermore, the permanent effects of high PM2.5 concentration can influence climate change [4,5]. Therefore, air pollution with PM2.5 as the primary pollutant has always been a hotspot for research in the environmental field in China [6,7,8].
The PM2.5 concentration distribution is affected by emission, regional transport and atmospheric diffusion conditions [9]. Previous studies have confirmed that PM2.5 transport has a wide influential range and long transport distance, which not only shows an obvious transboundary transport effect [10,11,12,13], but can also remarkably affect the vertical distribution of air pollutants. Wang et al. (2020) [14] showed that air pollutants in the atmospheric boundary layer (ABL) were primarily derived from local and regional transport prior to the ABL development of morning in the Yangtze River Delta area. Xu et al. (2018) [15] also found that regional photochemically aged air masses from the Yangtze River Delta region influenced surface pollution concentration in the early morning by vertical mixing from the residual layer (RL) and contributed to daytime pollution buildup in the planetary boundary layer. The particles are mainly concentrated in the downstream areas in days with reasonable wind speed in the ABL, while they are uniformly dispersed in days with lighter wind speed [16]. However, there are still limited studies on the regional transport and vertical distribution characteristics of air pollutants for elucidating the mechanisms of regional haze evolution in the ABL [17]. Therefore, investigating the vertical distribution of air pollutants in ABL can reveal the synergistic effects of multi-scale physical and chemical processes on regional transport, which is imperative for a deeper understanding of its mechanisms and prevention.
Traditional vertical atmospheric structure observation methods include meteorological observation towers [18,19], mountain observations [20], tethered airships [21,22] and aircraft observations [23,24]. However, there are several drawbacks, such as detection height limitation, location restriction and high cost. Due to the small size and portability of unmanned aerial vehicles (UAVs), we can select different heights to use UAVs for observations [25,26], which break the limitations of traditional observation methods to a certain extent. Peng et al. (2015) [27], Bates et al. (2013) [28] and Brady et al. (2016) [29] conducted vertical observations of aerosol number concentration, mass concentration and the optical absorption coefficient using UAVs, and they confirmed the feasibility and effectiveness of UAVs in aerosol vertical observation. Illingworth et al. (2014) [30] and Li et al. (2017) [31] carried out air pollution observations by UAVs and discussed the spatio-temporal distribution characteristics of pollutant concentration, meteorological conditions, regional transport and other influencing factors. In addition, microwave radiometers can provide temperature, humidity and water vapor profiles up to 10 km height from the land surface [32,33], and tropospheric wind profiler radar can obtain multilevel horizontal wind direction and speed below 12 km height [34]. In order to illuminate the complicated mechanisms of vertical evolution, more extensive observations with ground-based remote sensing, such as UAVs, microwave radiometers and tropospheric wind profiler radars, are necessary.
The Twain-Hu Basin (THB) in the mid-Yangtze River is strategically placed in the downdraft direction of the East Asian winter monsoon within an area of serious haze transport in North, Central and East China. Additionally, the THB is an essential hub for the regional transport of atmospheric pollutants in Central and East China. Thus, the synergistic effects of the regional transport of atmospheric pollutants and the atmospheric boundary layer evolution on air pollution variations in the THB need to be further studied. In this study, the synchronous enhanced detection of the low-level atmospheric pollutants and vertical distribution of meteorological elements is carried out in the receptor area of pollution transport over the THB. Combining the multi-source detection data from the ground-based tropospheric wind profiler radars, microwave radiometers and eddy correlation systems, we comprehensively analyze three-dimensional wind fields, thermal fields and atmospheric stratification characteristics and their effects and mechanisms on air pollution processes, aiming to strengthen the understanding of the synergistic effect of pollution transport and ABL processes in the THB and improve the accuracy of the pollution weather forecast and early warning.

2. Data and Methods

2.1. Data Conditions

In this study, Xianning National Meteorological station (98.8 m a.s.l., Figure 1), located on a suburban mountain in the Heishan field test base in Xianning City, is minimally affected by the variation of surrounding emissions. Moreover, Xianning station is in the southeastern Jianghan Plain, including the key receptor region of the eastward transport route in the THB, which is a critical channel for the transport of serious polluted air mass originating from the north to the western part of Hunan and Hubei provinces [35]. A PM2.5 pollution episode in Xianning during 4–6 January 2019 was selected to investigate the cooperative influences of pollution transport and ABL structure development on local pollution variation.
The observation equipment, such as a meteorological sensor, particulate matter monitor and pollution gas detector on board a UAV, was used to carry out simultaneous enhanced detection of the low-level atmospheric pollutants and vertical distribution of meteorological elements at Xianning station during the daytime (07:00 LST, 09:00 LST, 12:00 LST, 15:00 LST and 18:00 LST) and nighttime (20:00 LST, 23:00 LST, 02:00 LST and 05:00 LST). The hardware and software of the UAV platform we used in this experiment is in accordance with the standard of Civil Aviation Administration of China (CAAC) and was registered in the online supervision and management system organized by CAAC (https://fsop.caac.gov.cn/indexNew.jsp, accessed on 8 April 2022). The flight missions were executed by specialized pilots who hold Civil unmanned aircraft system pilot certifications issued by aircraft owners and the pilots association of China (AOPA) and are employed by the local general aviation enterprise. Complete information of flight missions were filed at the local police office and regional Civil Aviation Administration, including take off and land location, flight trajectory, schedules, maximum flight height, and certifications mentioned above. We also had the following advantages: (1) We could easily adjust height limitation after obtaining official permission because our self-made UAV adopts open source flight control. (2) We launched our experiment with the weather bureau, which is in regular airspace for sounding balloons; thus, it was easier to achieve flight permission from the CAAC.
On the basis of the UAV platform, the PM2.5 and carbon monoxide (CO) concentrations as well as the vertical distribution of temperature, air pressure and relative humidity (RH) from 0–1 km altitude can be obtained 9 times per day. The temporal resolution of the UAV sounding equipment [36] used to detect the PM2.5 vertical distribution is 1 s, and it is widespread for detecting the concentration of aerosol particles [37,38]. A comparison of the PM2.5 and CO concentrations between the UAV observations at 10 m a.s.l. and the Xianning station observations suggests that the agreement of the PM2.5 and CO concentrations between the two is high, and the correlation coefficients are 0.86 (PM2.5) and 0.91 (CO) (Figure S1), demonstrating that the data from the UAV observations are available.
The meteorological elements from Xianning station include the air temperature and RH of 2 m, precipitation, visibility and 10 m wind velocity. The elements from the ground-based tropospheric wind profiler radar are the low-level wind direction and wind speed, and those from the microwave radiometer include the profiles of temperature, RH, water vapor density and liquid water content from the ground to the 10 km height. In addition, the turbulent kinetic energy (TKE) used in this research is from an eddy correlation system. Moreover, the chemical components of the PM2.5 from the Wuhan ground-based supersite, the aerosol lidar data from Jingzhou ground-based lidar station and the ERA5 reanalysis data with the spatio-temporal resolution of 0.25° × 0.25° and 1 h from the European Centre for Medium-Range Weather Forecasts (ECMWF, https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form, accessed on 8 April 2022) were used to analyze the surface wind fields and synoptic conditions during this episode.
The ground PM2.5 and CO datasets can be obtained from the Ministry of Ecology and Environment, the People’s Republic of China, with a temporal resolution of 1 h, derived from over 1600 air quality monitoring stations (http://www.mee.gov.cn/, accessed on 8 April 2022). The CO in the atmosphere is an inert gas tracer mainly affected by atmospheric transport and vertical mixing, while the variation of PM2.5 concentration is influenced by complex chemical processes in addition to the above-mentioned factors affecting CO. Therefore, the CO is a good tracer to study the contribution of meteorological conditions to PM2.5 concentration variation.

2.2. Methods

Potential temperature is generally used as the basis for determining atmospheric temperature inversion, which is the temperature when an air block is dry-adiabatically expanded or compressed to reach standard atmospheric pressure.
The potential temperature gradient method was used in this research to assess the atmospheric stability, whose advantage is the ability to determine the atmospheric stability at different heights. The calculating equation is as follows (Equation (1)).
∂ θ ∂ Z = θ T ( γ d − γ )
where θ is the potential temperature (K), Z the height (m), T the temperature (K), γ the reduction rate of temperature (K·m−1), and γ d the reduction rate of dry adiabatic (K·(100 m)−1) with the value of 0.98 K·(100 m)−1. The potential temperature gradient represents atmospheric stability, i.e., the atmosphere is in a stable state when the potential temperature gradient is greater than 0, and the atmosphere is unstable when the potential temperature gradient is less than 0 [39].
The ABL height is identified by the potential temperature gradient method used by Liu and Liang (2010) [40]; we examined the near-surface thermal gradient between the fifth and second levels to remove the raw data noises:
θ 5 − θ 2 { < − δ s       f o r   C B L             → a n   u n s t a b l e   r e g i m e > + δ s     f o r   S B L                   →     a   s t a b l e   r e g i m e e l s e             f o r   N R L           →     a   n e u t r a l   r e g i m e
where θ is potential temperature that was measured by the UAV observations in this study and its subscript number denotes for the data level index assuming surface air at l = 1 ; δ s is the θ increment for the minimum strength of the stable (inversion) layer above the C B L top or below the S B L top. The value of δ s can be set to zero for idealized cases but in practice is specified as small positive by this study depending on surface characteristics. Since buoyancy is the dominant mechanism driving turbulence in the CBL, we determined the ABL height as the height at which an air parcel rising adiabatically from the surface becomes neutrally buoyant [41]; see Liu and Liang (2010) [40] for more detail.
For better description of the variation of ABL heights, the heights were identified in this study as mixed layer (ML) heights during daytime and stable boundary layer (SBL) heights during nighttime.

2.3. FLEXPART-WRF Model

The FELXPART-WRF model consists of two parts, namely the meteorological driven field model (Weather Research and Forecasting model, WRF) and the flexible particle dispersion model (FLEXPART). The FLEXPART model [42,43] is a Lagrangian particle dispersion model developed by the Norwegian Institute for Air Research (NILU), including the processes of tracer transport, turbulent diffusion, wet and dry depositions, decay and linear chemistry in the atmosphere [44]. This model has been broadly applied to study the source–receptor relationships of environmental pollutants [45,46,47,48,49,50,51].
The meteorological field with high spatio-temporal resolution driving the FLEXPART model is derived from the WRF model. To trace the source of high-concentration PM2.5 in the ML during the development of the ABL, the 24 h backward trajectory simulations based on the FLEXPART-WRF model were carried out by releasing 50,000 particles at Xianning station from the ground to 500 m a.s.l. during 7:00–12:00 LST on the 5th and 6th according to the vertical profiles of the measured PM2.5 concentration. Afterward, the dwell time (not the lifetime) of all tracer particles, which were standardized by the sum of released particles, were identified on a homogeneous grid and regarded as trajectories (unit: s). The horizontal resolution of the results with the dwell times of the tracer particles was 0.1° × 0.1°. The emission sector source contributions and the spatial distribution of PM2.5 concentrations were then evaluated by integrating the gridded trajectories into a primary PM2.5 emission inventory derived from the China Multi-resolution Emission Inventory (MEIC, http://www.meicmodel.org/, accessed on 8 April 2022), a more detailed presentation of which can be found in other articles [45,52].

3. Results and Discussion

3.1. Overview of Air Pollution Cases

Figure S2 demonstrates the sea level air pressure, 10 m wind field by ERA5 and measured surface PM2.5 concentration patterns at 08:00 and 20:00 LST from 4 to 6 January 2019. During this period, the THB undergoes a phased cold air process. A Mongolian high pressure developed and moved eastward from 4–5 January. The central China region was governed by a northward flow at the bottom of the high-pressure system. An air mass with high surface PM2.5 concentration shifted remarkably from southern Hebei Province and Shandong Province to Henan Province and northern Anhui Province during the period from 08:00 on 4 January to 20:00 on 5 January, and then divided into two paths into THB in southern Henan Province with apparent surface northward winds. On 6 January, central China was governed by a homogeneously distributed air pressure field. A weak surface wind caused airborne contaminants to accumulate between 20:00 on 5 January and 08:00 on 6 January. Subsequently, the surface PM2.5 concentration increased slightly in central and western Hubei Province under the weak surface northerly winds, while the concentration in eastern Hubei Province decreased under the surface eastern winds.
During this regional heavy pollution process, the largest hourly surface PM2.5 concentration at the Xianning site exceeds 150 μg·m−3. According to the development of surface PM2.5 concentration and meteorological elements, this pollution process can be separated into three phases, namely formation stage (stage I), maintenance stage (stage II) and dissipation stage (stage III), as shown in Figure 2. At stage I (10:00 LST on 4 January to 08:00 LST on 5 January), the surface PM2.5 concentration gradually rises to 111 from 39 μg·m−3, while the surface CO concentration increased non-significantly. Simultaneously, the surface RH was maintained at 100%. The visibility was below 1000 m, and there was fog on the ground. At stage II (09:00 LST on 5 January to 11:00 LST on 6 January), the surface PM2.5 concentration increased to the first peak value of 152 μg·m−3, decreased slightly after 10 h of pollution maintenance and then increased again to the second peak value of 158 μg·m−3. The surface CO and PM2.5 concentrations varied synchronously, and the surface RH and temperature decreased with the surface northerly wind of 1–2 m·s−1. At stage III (12:00 LST on 6 January to 04:00 LST on 7 January), the surface PM2.5 concentration decreased rapidly and maintained a low value for a period. The surface CO and PM2.5 concentrations also varied synchronously. The surface RH and air pressure decreased rapidly, and the surface temperature and visibility increased during the surface PM2.5 dissipation process. At this time, the surface wind changed from a northerly wind to an easterly and southerly wind, with wind speed of 2–3 m·s−1. The reverse changes of PM2.5 and CO and the high humidity condition in the formation stage indicate that the increase in PM2.5 concentration in this stage may be mainly affected by complex chemical processes. The synchronous change of PM2.5 and CO in the maintenance and dissipation stage indicates that PM2.5 pollution in those stages may be mainly affected by atmospheric transport or vertical mixing. Those are consistent with the conclusions of Zhang et al. (2015) [53].
From 18:00 LST on 4 January to 02:00 LST on 7 January, a total of 22 effective sounding detections of the ABL were conducted by a UAV. Combined with the horizontal wind detected by the wind profile radar within 0–800 m above the ground and the continuous temperature changes detected by the microwave radiometer, the influence of cold air and the influencing period can be judged. From Figure 3a, it can be found that the high values of the PM2.5 concentration in Xianning are mainly concentrated at a height below 300 m at stage I, and the height of 400–800 m is governed by the northerly wind detected by wind profiler radar. The PM2.5 mass concentration at stage II further increases and maintains at high values, and its high values are all below 600 m. In addition, the height of 400–800 m is affected by the northerly wind. The whole layer of the PM2.5 mass concentration at stage III decreases rapidly, and the wind direction at 400–800 m varies from northerly to easterly and southerly, which is consistent with the surface wind variation. Figure 3b presents the 24 h temperature variation at 0–6 km height detected by microwave radiometers from 18:00 LST on 4 January to 02:00 LST on 7 January 2019. The results show that before 18:00 LST on January 5, the temperature at 1–3 km height decreases significantly, and the upper-level cold air moves southward and downward, corresponding to the cold air from the north that moved southward at stages I and II and may cause pollution.

3.2. Effect of the Atmospheric Vertical Stratification on Pollutant Transport and Accumulation under Heavy Fog and High Humidity Conditions

The formation of high-concentration aerosols is the result of the comprehensive influence of multi-scale physical and chemical processes, and the high humidity condition caused by heavy fog in the SBL is favorable for the generation of secondary PM2.5 components [54]. In order to further investigate the production of secondary PM2.5 components, CO concentration was used to normalize PM2.5 concentration to eliminate the impacts on primary burning emissions and meteorological elements [53]. The results suggest that the environmental condition at stage I is dominated by high humidity caused by heavy fog (Figure 2). Additionally, the RH on the surface and in the SBL reaches more than 99%, and the surface PM2.5 concentration increases cumulatively. Since the variation trend of the surface PM2.5 concentration is inverse to that of the CO concentration (Figure 4), the increase in PM2.5 concentration of the SBL is mainly due to the generation of the secondary PM2.5 components. Moreover, the chemical components of PM2.5 detected by nearby the Wuhan ground-based supersite indicate that NO3− and SO42− of the secondary PM2.5 components increase significantly under the high humidity condition, and the variation trends of the PM2.5 concentration and RH in Xianning and Wuhan station are consistent (Figure S3), which also demonstrates that the regional PM2.5 concentration growth under the high humidity condition caused by heavy fog was related to the generation of secondary PM2.5 components at stage I.
The effective period of UAV detections is from 18:00 LST on 4 January to 07:00 LST on 5 January at stage I. The profiles of the temperature ( T ), RH ( R ), potential temperature ( θ ) and potential temperature gradient ( Δ θ ) with height at 20:00 and 23:00 LST on 4 January (Figure 5) show that the atmosphere vertical stratification characteristics are obvious at stage I, i.e., there is near-surface temperature inversion at the height of 0–50 m at 20:00 LST, and the potential temperature increases with the height at 0–140 m, with the increasing potential temperature gradient. Meanwhile, the RH is more than 99% within the height of 140 m, showing a typical SBL feature. The height above 140 m is the RL, with wind speed of about 2 m·s−1 and wind direction of the northerly wind (Figure 3a).
The thickness of the SBL thinned from 20:00 LST (Figure 5a) to 23:00 LST (Figure 5b) on 4 January, and pollutants in the SBL were mixed vertically and uniformly. The profiles of PM2.5 and CO concentrations show the bimodal or multi-peaked distribution in the vertical direction, and the peaks mostly appear in the RL, where pollutants at 500–600 m are transported horizontally. Furthermore, the regional transport of atmospheric pollutants was driven by cold high pressure (Figure S2a,b) with obvious cold air transport over Xianning (Figure 3b). Simultaneously, the lidar (Figure S4) also detected a large-value band of particle extinction coefficient above the ABL in the Jingzhou area of the THB. All the above facts can demonstrate the horizontal transport of atmospheric pollutants in the RL of 500–600 m. At the same time, it can be concluded that pollutants are uniformly mixed in the SBL, and the peak value of pollutants above the SBL can be judged as the pollution transport zone.
In summary, the vertical stratification characteristics of the atmosphere are obvious at stage I, and the vertical mixing of pollutants is uniform in the SBL. The high humidity condition caused by heavy near-surface fog contributed to the production of secondary PM2.5 components and the cumulative increase in surface PM2.5 concentrations; this conclusion was consistent with the study by Huang et al. (2013) [54] in Beijing, while the horizontal transport of atmospheric pollutants exists in the RL of 500–600 m.

3.3. Rapid Growth of the Surface PM2.5 Concentration Due to the Horizontal and Downward Transport of Pollutants in Development of Convective Boundary Layer (CBL)

Figure 6 presents the PM2.5 and CO concentration profiles and the ABL height detected by UAV from 07:00 LST on 5 January to 09:00 LST on 6 January. As can be seen, in this stage, the variations of PM2.5 and CO concentrations are relatively consistent. During 07:00–15:00 LST on 5 January, the surface pollutant concentration increases rapidly with the growth of ABL height in the daytime (Figure 2). The vertical observation also shows that the pollutant concentration increases rapidly between the ground level and 500 m, and the pollutants are evenly mixed within the boundary layer. The height of the boundary layer decreases during 15:00–20:00 LST on 5 January, and the pollutant concentration remains high from the ground to the height of 500 m. Obvious vertical stratification appears in the boundary layer after sunset. The ABL height decreases to less than 100 m at night, and the pollutant concentration maintains as high in the RL. There is an obvious peak of high concentration at the height of 200–300 m at 05:00 LST (before sunrise) on 6 January. The ABL height increases during 07:00–09:00 LST (after sunrise) on 6 January, and the surface concentrations of PM2.5 and CO are suddenly doubled below 200 m (the surface PM2.5 concentration is close to 200 μg·m−3). The pollutant concentration in the RL decreases obviously, and the surface pollutant concentration increases rapidly for a short time (Figure 2).
Statistics demonstrate that there is a significantly negative correlation between the diurnal changes of ABL height and the pollutant concentration [55]. The diurnal changes of the surface PM2.5 concentration in Xianning from December 2018 to January 2019 (Figure S5) show that the surface PM2.5 concentration decreases significantly from 06:00 to 15:00 LST with the development of the ABL during the day. However, when the ABL height increased during 07:00–12:00 LST on 5 January and during 05:00–09:00 LST on 6 January, the surface pollutant concentration showed an increase instead of a decrease, and the pollutants in the ABL were uniformly mixed. Therefore, we speculate that the horizontal and vertical transport of pollutants may affect the vertical profiles of the PM2.5 concentration in the local ABL. The corresponding evidence is given in the following through observation analysis and simulations.
The analysis of the sea level pressure and 10 m wind field indicates that the THB is affected by the pollutants transported by the cold air from the north (Figure S2). In order to further investigate the regional PM2.5 transport between the upstream area and Xianning, we explored the hourly variation of the surface PM2.5 concentration and 10 m wind for the period from 2 to 7 January 2019 at five observation stations including Bozhou, Fuyang, Xinyang, Wuhan and Xianning stations (Figure 7). The results suggest that driven by the robust northerly wind on the surface (Figure 7), the location of the peak surface PM2.5 concentration advanced southward from Bozhou (18:00 LST on 3 January) to Fuyang (22:00 LST on 3 January), Xinyang (12:00 LST on 4 January), Wuhan (06:00 LST on 5 January) and Xianning (09:00 LST on 5 January), indicating that the PM2.5 regional transport has a substantial influence on the increase in PM2.5 concentration that caused air pollution in Xianning during 09:00–18:00 LST on 5 January.
Furthermore, the contribution of surrounding atmospheric pollution sources to surface PM2.5 concentration in Xianning was analyzed by 24 h backward trajectory simulations by the FLEXPART-WRF model. Figure 8 presents the trajectory of pollutants transported from northwestern Anhui through southeastern Henan to Wuhan and Xianning in Hubei Province on 5 January, which is consistent with the observed pollutant transport track from Bozhou, Fuyang and Xinyang stations to Wuhan and Xianning stations. Thus, the increase in the surface pollutant concentration is closely related to horizontal transport. However, on 6 January, the Xianning pollution episode was mainly contributed by local pollution sources, without long-range pollution transport. Hence, the increase in surface pollutant concentration may come from the vertical downward transport of pollutants in the RL [56], which can be demonstrated by the observation of turbulence variation.
In terms of the surface turbulence intensity variation in Xianning (Figure 9) station, the turbulent kinetic energy (TKE) increases from 0.0162 to 0.2067 m2·s−2 during 07:00–12:00 LST on 5 January, and the TKE value remains relatively high from 12:00 to 15:00 LST, corresponding to the rapid increase in the concentration of surface PM2.5. At this stage, the horizontal transport of pollutants in the ABL is sufficient, and the vertical mixing is uniform under the action of strong turbulence, resulting in the high-concentration PM2.5 pollution within the ML for the CBL development. The TKE increases from 0.1232 to 0.4286 m2·s−2 during 07:00–12:00 LST on 6 January, and the longitude wind profile of free tropospheric level shows that wind shear at 800 hPa and downdraft below 900 hPa over Xianning (Figure S6) were conducive to downward transport of pollution [57], with the synchronous increase in the surface PM2.5 concentration, indicating that strong vertical turbulent mixing caused the downward transport of upper-level pollutants in the RL at nighttime, thereby resulting in the rapid increase in the surface PM2.5 concentration for a short time.
The above comprehensive analysis reveals the synergistic mechanism between the regional transport of atmospheric pollutants with rapid pollutant concentration growth and the evolution of the ABL (Figure 10). That is, the CBL develops during the daytime, and then, under advantageous circulation conditions for the regional transport of pollutants, numerous pollutants from external areas are transported horizontally within the ABL channel. The turbulence effect can lead to the vertical mixing of pollutants within the ABL during transport, resulting in the rapid increase in the pollutant concentration in the ML with the development of CBL. At nighttime, the pollutant concentration in the upper RL still remains as high as during daytime. At sunrise the next day, with the development of the ABL, the high-concentration pollutants in the upper RL are transported to the ABL by strong turbulent mixing, promoting the rapid growth of the surface pollutant concentration. In the absence of continuous pollutant transport from external regions, the pollution dissipates quickly with the development of boundary layer turbulence.
During a serious pollution incident in Beijing, it was also observed that regional transport of atmospheric pollutants mixed with the turbulence at sunrise and aggravated the pollution near the surface [56]. This phenomenon of vertical turbulent mixing causing the downward transport frequently occurred during ozone pollution events [15]. The synergistic mechanism between the regional transport of atmospheric pollutants and the evolution of the ABL provides a new idea for air quality prediction in regions where regional transport of pollutants has great influence. Under the background of climate warming and frequent extreme weather events, our work will focus on how the synergistic interaction between pollution transport and boundary layer affects surface pollution in different circulation patterns and different geographical locations.

4. Conclusions

Based on the observation base of Xianning station, an exploratory investigation of the ABL and vertical atmospheric pollutant distribution was carried out for a PM2.5 pollution episode on 4–6 January 2019. Combining the data from the detection equipment such as ground-based aerosol lidar, wind profiler radar, microwave radiometer and eddy correlation system, we comprehensively analyzed the multi-scale process of local PM2.5 pollution variation based on the multi-source observations and numerical simulations. In addition, we revealed the synergistic mechanism between the regional transport of atmospheric pollutants with rapid pollutant concentration growth and the evolution of the ABL. The primary findings are listed below.
During the pollution formation process, the vertical stratification of the atmosphere was obvious, and the vertical mixing of pollutants in the SBL was uniform. The high humidity condition caused by near-surface heavy fog promoted the generation and cumulative growth of secondary PM2.5 components. Simultaneously, the northerly wind transported high-concentration pollutants from the north to the THB under the influence of upper-level cold air activities, and the horizontal transport characteristics of atmospheric pollutant concentration peak were observed in the RL of 500–600 m.
During the daytime, the ML developed, and the ABL height increased. At this time, both observations and simulations show that numerous pollutants from external regions were horizontally transported in the ABL channel. Because of the turbulent mixing, the CBL developed while the atmospheric pollutants were uniformly mixed and rapidly accumulated from the surface to the 500 m height. The next day, the CBL developed again, and the strong vertical turbulent mixing caused the downward transport of high-concentration pollutants in the RL during the previous nighttime, resulting in the doubling of the near-surface pollutant concentration in a short time. Then, without the continuous horizontal transport of pollutants from external regions, the pollution process dissipated rapidly with the development of ABL turbulence.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14205166/s1, Figure S1: Relationship of PM2.5 and CO concentrations measured by the UAV device at 10 m a.g.l. and those measured from the nearby environmental monitoring station in Xianning, Hubei province; Figure S2: Distributions of the sea level pressure (shaded area), 10 m wind field by ERA5 and surface PM2.5 concentration (μg·m−3) at (a) 08:00 LST and (b) 20:00 LST on 4 January, at (c) 08:00 LST and (d) 20:00 LST on 5 January and at (e) 08:00 LST and (f) 20:00 LST on 6 January. The red box denotes the study area, namely the THB; Figure S3: Hourly changes of surface PM2.5, relative humidity at Wuhan and Xianning station during 10:00–20:00 on 4 January 2019; Figure S4: Temporal variations of particle extinction coefficient detected by the laser radar at Jingzhou station from 18:00 LST on 3 January to 18:00 LST on 5 January 2019; Figure S5: Daily variation of surface PM2.5 in Xianning during December, 2018–January 2019; Figure S6: Longitude profile of vertical velocity along 114.25°E, longitude vertical circulation form 07:00 (a), 08:00 (b), 09:00 (c) LST on 6 January. The shadow indicates the vertical velocity (positive value is sinking velocity, negative value is rising velocity), the arrow is the zonal vertical circulation synthesized by V and -ω, and the black solid point on the X coordinate represents xianning’s position.

Author Contributions

Conceptualization, J.X. (Jie Xiong) and Y.B.; methodology, J.X. (Jie Xiong), Y.B. and X.S.; formal analysis, J.X. (Jie Xiong).; investigation, J.X. (Jie Xiong), Y.B. and X.S.; resources, Y.B., T.Z. and Y.Z.; data curation, J.X. (Jiaping Xu), W.Z., L.L. and G.X.; writing—original draft preparation, J.X. (Jie Xiong); writing—review and editing, Y.B., T.Z., J.X. (Jiaping Xu) and Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (grant nos. 42075186 and 41830965), and the Innovation and Development Project of China Meteorological Administration (CXFZ2022J010).

Data Availability Statement

The data used in this paper can be provided by the author Jie Xiong ([email protected]) upon request.

Conflicts of Interest

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

References

  1. Leikauf, G.D.; Kim, S.H.; Jang, A.-S. Mechanisms of ultrafine particle-induced respiratory health effects. Exp. Molec. Med. 2020, 52, 329–337. [Google Scholar] [CrossRef] [PubMed]
  2. Yao, L.; Zhan, B.; Xian, A.; Sun, W.; Li, Q.; Chen, J. Contribution of transregional transport to particle pollution and health effects in Shanghai during 2013–2017. Sci. Total Environ. 2019, 677, 564–570. [Google Scholar] [CrossRef] [PubMed]
  3. Orru, H.; Ebi, K.L.; Forsberg, B. The interplay of climate change and air pollution on health. Curr. Environ. Health Rep. 2017, 4, 504–513. [Google Scholar] [CrossRef] [PubMed]
  4. Kan, H.; Chen, R.; Tong, S. Ambient air pollution, climate change, and population health in China. Environ. Int. 2012, 42, 10–19. [Google Scholar] [CrossRef] [PubMed]
  5. Li, J.; Du, H.; Wang, Z.; Sun, Y.; Yang, W.; Li, J.; Tang, X.; Fu, P. Rapid formation of a severe regional winter haze episode over a mega-city cluster on the North China Plain. Environ. Pollut. 2017, 223, 605–615. [Google Scholar] [CrossRef] [PubMed]
  6. Xu, Y.; Xue, W.; Lei, Y.; Zhao, Y.; Cheng, S.; Ren, Z.; Huang, Q. Impact of meteorological conditions on PM2.5 Pollution in China during winter. Atmosphere 2018, 9, 429. [Google Scholar] [CrossRef] [Green Version]
  7. Liu, J.; Wu, D.; Fan, S.; Mao, X.; Chen, H. A one-year, on-line, multi-site observational study on water-soluble inorganic ions in PM2.5 over the Pearl River Delta region, China. Sci. Total Environ. 2017, 601, 1720–1732. [Google Scholar] [CrossRef]
  8. Xie, Z.; Fan, C.; Lu, R.; Liu, P.; Wang, B.; Du, S.; Jin, C.; Deng, S.; Li, Y. Characteristics of ambient bioearosols during haze episodes in China: A review. Environ. Pollut. 2018, 243, 1930–1942. [Google Scholar] [CrossRef] [PubMed]
  9. Ramanathan, V.; Feng, Y. Air pollution, greenhouse gases and climate change: Global and regional perspectives. Atmos. Environ. 2009, 43, 37–50. [Google Scholar] [CrossRef]
  10. Kim, C.-H.; Park, S.-Y.; Kim, Y.-J.; Chang, L.-S.; Song, S.-K.; Moon, Y.-S.; Song, C.-K. A numerical study on indicators of long-range transport potential for anthropogenic particulate matters over Northeast Asia. Atmos. Environ. 2012, 58, 35–44. [Google Scholar] [CrossRef]
  11. Khuzestani, R.B.; Schauer, J.J.; Wei, Y.; Zhang, L.; Cai, T.; Zhang, Y.; Zhang, Y. Quantification of the sources of long-range transport of PM2.5 pollution in the Ordos region, Inner Mongolia, China. Environ. Pollut. 2017, 229, 1019–1031. [Google Scholar] [CrossRef]
  12. Bai, Y.; Zhao, T.; Hu, W.; Zhou, Y.; Xiong, J.; Wang, Y.; Liu, L.; Shen, L.; Kong, S.; Meng, K.; et al. Meteorological mechanism of regional PM2.5 transport building a receptor region for heavy air pollution over Central China. Sci. Total Environ. 2022, 808, 151951. [Google Scholar] [CrossRef] [PubMed]
  13. Yuan, C.; Lau, W.K.M.; Li, Z.; Cribb, M. Relationship between Asian monsoon strength and transport of surface aerosols to the Asian tropopause aerosol layer (ATAL): Interannual variability and decadal changes. Atmos. Chem. Phys. 2019, 19, 1901–1913. [Google Scholar] [CrossRef] [Green Version]
  14. Wang, D.; Huo, J.; Duan, Y.; Zhang, K.; Ding, A.; Fu, Q.; Luo, J.; Fei, D.; Xiu, G.; Huang, K. Vertical distribution and transport of air pollutants during a regional haze event in eastern China: A tethered mega-balloon observation study. Atmos. Environ. 2021, 246, 118039. [Google Scholar] [CrossRef]
  15. Xu, Z.; Huang, X.; Nie, W.; Shen, Y.; Zheng, L.; Xie, Y.; Wang, T.; Ding, K.; Liu, L.; Zhou, D.; et al. Impact of biomass burning and vertical mixing of residual-layer aged plumes on ozone in the Yangtze River Delta, China: A tethered-balloon measurement and modeling Study of a multiday ozone episode. J. Geophys. Res. Atmos. 2018, 123, 11786–11803. [Google Scholar] [CrossRef]
  16. Xu, X.; Xie, J.; Li, Y.; Miao, S.; Fan, S. Measurement report: Vehicle-based multi-lidar observational study of the effect of meteorological elements on the three-dimensional distribution of particles in the western Guangdong-Hong Kong-Macao Greater Bay Area. Atmos. Chem. Phys. 2022, 22, 139–153. [Google Scholar] [CrossRef]
  17. Sun, Y.; Wang, Z.; Wild, O.; Xu, W.; Chen, C.; Fu, P.; Du, W.; Zhou, L.; Zhang, Q.; Han, T.; et al. “APEC blue”: Secondary Aerosol Reductions from Emission Controls in beijing. Sci. Rep. 2016, 6, 20668. [Google Scholar] [CrossRef] [Green Version]
  18. Han, S.; Bian, H.; Tie, X.; Xie, Y.; Sun, M.; Liu, A. Impact of nocturnal planetary boundary layer on urban air pollutants: Measurements from a 250-m tower over Tianjin, China. J. Hazard. Mater. 2009, 162, 264–269. [Google Scholar] [CrossRef]
  19. Sun, Y.; Wang, Y.; Zhang, C. Measurement of the vertical profile of atmospheric SO2 during the heating period in Beijing on days of high air pollution. Atmos. Environ. 2009, 43, 468–472. [Google Scholar] [CrossRef]
  20. Ou-Yang, C.-F.; Lin, N.-H.; Lin, C.-C.; Wang, S.-H.; Sheu, G.-R.; Lee, C.-T.; Schnell, R.-C.; Lang, P.-M.; Kawasato, T.; Wang, J.-L. Characteristics of atmospheric carbon monoxide at a high-mountain background station in East Asia. Atmos. Environ. 2014, 89, 613–622. [Google Scholar] [CrossRef]
  21. Li, J.; Fu, Q.; Huo, J.; Wang, D.; Yang, W.; Bian, Q.; Duan, Y.; Zhang, Y.; Pan, J.; Lin, Y.; et al. Tethered balloon-based black carbon profiles within the lower troposphere of Shanghai in the 2013 East China smog. Atmos. Environ. 2015, 123, 327–338. [Google Scholar] [CrossRef]
  22. Zhu, J.; Zhu, B.; Huang, Y.; An, J.; Xu, J. PM2.5 vertical variation during a fog episode in a rural area of the Yangtze River Delta, China. Sci. Total Environ. 2019, 685, 555–563. [Google Scholar] [CrossRef] [PubMed]
  23. Geng, F.; Zhang, Q.; Tie, X.; Huang, M.; Ma, X.; Deng, Z.; Yu, Q.; Quan, J.; Zhao, C. Aircraft measurements of O3, NOx, CO, VOCs, and SO2 in the Yangtze River Delta region. Atmos. Environ. 2009, 43, 584–593. [Google Scholar] [CrossRef]
  24. Peterson, D.A.; Hyer, E.J.; Han, S.-O.; Crawford, J.H.; Park, R.J.; Holz, R.; Kuehn, R.E.; Eloranta, E.; Knote, C.; Jordan, C.E.; et al. Meteorology influencing springtime air quality, pollution transport, and visibility in Korea. Elem. Sci. Anth. 2019, 7, 57. [Google Scholar] [CrossRef] [Green Version]
  25. Chen, Q.; Wang, D.; Li, X.; Li, B.; Song, R.; He, H.; Peng, Z. Vertical Characteristics of Winter Ozone Distribution within the Boundary Layer in Shanghai Based on Hexacopter Unmanned Aerial Vehicle Platform. Sustainability 2019, 11, 7026. [Google Scholar] [CrossRef] [Green Version]
  26. Vo, T.-D.-H.; Lin, C.; Weng, C.-E.; Yuan, C.-S.; Lee, C.-W.; Huang, C.-H.; Bui, X.-T.; Lo, K.-C.; Lin, J.-X. Vertical stratification of volatile organic compounds and their photochemical product formation potential in an industrial urban area. J. Environ. Manag. 2018, 217, 327–336. [Google Scholar] [CrossRef]
  27. Peng, Z.-R.; Wang, D.; Wang, Z.; Gao, Y.; Lu, S. A Study of vertical distribution patterns of PM2.5 concentrations based on ambient monitoring with unmanned aerial vehicles: A case in Hangzhou, China. Atmos. Environ. 2015, 123, 357–369. [Google Scholar] [CrossRef]
  28. Bates, T.S.; Quinn, P.K.; Johnson, J.E.; Corless, A.; Brechtel, F.J.; Stalin, S.E.; Meinig, C.; Burkhart, J.F. Measurements of atmospheric aerosol vertical distributions above Svalbard, Norway using unmanned aerial systems (UAS). Atmos. Meas. Tech. 2013, 6, 2115–2120. [Google Scholar] [CrossRef] [Green Version]
  29. Brady, J.M.; Stokes, M.D.; Bonnardel, J.; Bertram, T.H. Characterization of a Quadrotor Unmanned Aircraft System for Aerosol-Particle-Concentration Measurements. Environ. Sci. Technol. 2016, 50, 1376–1383. [Google Scholar] [CrossRef]
  30. Illingworth, S.; Allen, G.; Percival, C.; Hollingsworth, P.; Gallagher, M.; Ricketts, H.; Hayes, H.; Ładosz, P.; Crawley, D.; Roberts, G. Measurement of boundary layer ozone concentrations on-board a Skywalker unmanned aerial vehicle. Atmos. Sci. Let. 2014, 15, 252–258. [Google Scholar] [CrossRef]
  31. Li, X.-B.; Wang, D.-S.; Lu, Q.-C.; Peng, Z.-R.; Lu, S.-J.; Li, B.; Li, C. Three-dimensional investigation of ozone pollution in the lower troposphere using an unmanned aerial vehicle platform. Environ. Pollut. 2017, 224, 107–116. [Google Scholar] [CrossRef] [PubMed]
  32. Zhang, W.; Xu, G.; Xi, B.; Ren, J.; Wan, X.; Zhou, L.; Cui, C.; Wu, D. Comparative study of cloud liquid water and rain liquid water obtained from microwave radiometer and micro rain radar observations over central China during the monsoon. J. Geophys. Res. Atmos. 2020, 125, e2020JD032456. [Google Scholar] [CrossRef]
  33. Zhou, L.; Xu, G.; Wu, D.; Wang, B.; Zhang, W. Performance comparison of laser ceilometer and infrared thermometer in cloud bottom height detection. Torrential Rain Disasters 2018, 37, 470–478. (In Chinese) [Google Scholar] [CrossRef]
  34. Wan, R.; Zhou, Z.; Cui, C.; Li, W.; Xu, G.; He, W.; Wang, F.; Wang, D. Comparing Wind Profiler Data with Radiosonde Data and Analyzing. Torrential Rain Disasters 2011, 30, 130–136. (In Chinese) [Google Scholar]
  35. Shen, L.; Hu, W.; Zhao, T.; Bai, Y.; Wang, H.; Kong, S.; Zhu, Y. Changes in the Distribution Pattern of PM2.5 Pollution over Central China. Remote Sens. 2021, 13, 4855. [Google Scholar] [CrossRef]
  36. Shen, L.; Cheng, Y.; Bai, X.; Dai, H.; Wei, X.; Sun, L.; Yang, Y.; Zhang, J.; Feng, Y.; Li, Y.J.; et al. Vertical profile of aerosol number size distribution during a haze pollution episode in Hefei, China. Sci. Total Environ. 2022, 814, 152693. [Google Scholar] [CrossRef]
  37. Liu, C.; Huang, J.; Wang, Y.; Tao, X.; Hu, C.; Deng, L.; Xu, J.; Xiao, H.-W.; Luo, L.; Xiao, H.-Y.; et al. Vertical distribution of PM2.5 and interactions with the atmospheric boundary layer during the development stage of a heavy haze pollution event. Sci. Total Environ. 2019, 704, 135329. [Google Scholar] [CrossRef] [PubMed]
  38. Tang, L.; Shang, D.; Fang, X.; Wu, Z.; Qiu, Y.; Chen, S.; Li, X.; Zeng, L.; Guo, S.; Hu, M. More Significant Impacts From New Particle Formation on Haze Formation During COVID-19 Lockdown. Geophys. Res. Lett. 2021, 48, e2020GL091591. [Google Scholar] [CrossRef] [PubMed]
  39. Sheng, P.; Mao, J.; Li, J.; Zhang, A.; Sang, J.; Pan, N. Atmospheric Physics; Peking University Press: Beijing, China, 2003; pp. 135–136. [Google Scholar]
  40. Liu, S.; Liang, X.-Z. Observed Diurnal Cycle Climatology of Planetary Boundary Layer Height. J. Clim. 2010, 23, 5790–5809. [Google Scholar] [CrossRef]
  41. Stull, R.B. An Introduction to Boundary Layer Meteorology; Kluwer Academic Press: Norwell, MA, USA, 1988; 666p. [Google Scholar]
  42. Stohl, A.; Forster, C.; Eckhardt, S.; Spichtinger, N.; Huntrieser, H.; Heland, J.; Schlager, H.; Wilhelm, S.; Arnold, F.; Cooper, O. A backward modeling study of intercontinental pollution transport using aircraft measurements. J. Geophys. Res. Atmos. 2003, 108, 4370. [Google Scholar] [CrossRef]
  43. Stohl, A.; Forster, C.; Frank, A.; Seibert, P.; Wotawa, G. Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2. Atmos. Chem. Phys. 2005, 5, 2461–2474. [Google Scholar] [CrossRef]
  44. Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, D.; Seibert, P.; Angevine, W.; Evan, S.; Dingwell, A.; Fast, J.D.; et al. The Lagrangian particle dispersion model FLEXPART-WRF version 3.1. Geosci. Model. Dev. 2013, 6, 1889–1904. [Google Scholar] [CrossRef] [Green Version]
  45. Yu, C.; Zhao, T.; Bai, Y.; Zhang, L.; Kong, S.; Yu, X.; He, J.; Cui, C.; Yang, J.; You, Y.; et al. Heavy air pollution with a unique “non-stagnant” atmospheric boundary layer in the Yangtze River middle basin aggravated by regional transport of PM2.5 over China. Atmos. Chem. Phys. 2020, 20, 7217–7230. [Google Scholar] [CrossRef]
  46. Chen, B.; Xu, X.-D.; Zhao, T. Quantifying oceanic moisture exports to mainland China in association with summer precipitation. Clim. Dynam. 2017, 51, 4271–4286. [Google Scholar] [CrossRef]
  47. Zhai, S.; An, X.; Liu, Z.; Sun, Z.; Hou, Q. Model assessment of atmospheric pollution control schemes for critical emission regions. Atmos. Environ. 2016, 124, 367–377. [Google Scholar] [CrossRef]
  48. Gadhavi, H.S.; Renuka, K.; Ravi Kiran, V.; Jayaraman, A.; Stohl, A.; Klimont, Z.; Beig, G. Evaluation of black carbon emission inventories using a Lagrangian dispersion model–a case study over southern India. Atmos. Chem. Phys. 2015, 15, 1447–1461. [Google Scholar] [CrossRef] [Green Version]
  49. Sauvage, B.; Fontaine, A.; Eckhardt, S.; Auby, A.; Boulanger, D.; Petetin, H.; Paugam, R.; Athier, G.; Cousin, J.-M.; Darras, S.; et al. Source attribution using FLEXPART and carbon monoxide emission inventories: SOFT-IO version 1.0. Atmos. Chem. Phys. 2017, 17, 15271–15292. [Google Scholar] [CrossRef] [Green Version]
  50. Zhu, Q.; Liu, Y.; Jia, R.; Hua, S.; Shao, T.; Wang, B. A numerical simulation study on the impact of smoke aerosols from Russian forest fres on the air pollution over Asia. Atmos. Environ. 2018, 182, 263–274. [Google Scholar] [CrossRef]
  51. Shen, L.; Zhao, T.; Liu, J.; Wang, H.; Bai, Y.; Kong, S.; Shu, Z. Regional transport patterns for heavy PM2.5 pollution driven by strong cold airflows in Twain-Hu Basin, Central China. Atmos. Environ. 2022, 269, 118847. [Google Scholar] [CrossRef]
  52. Zhu, C.; Kanaya, Y.; Takigawa, M.; Ikeda, K.; Tanimoto, H.; Taketani, F.; Miyakawa, T.; Kobayashi, H.; Pisso, I. FLEXPART v10.1 simulation of source contributions to Arctic black carbon. Atmos. Chem. Phys. 2020, 20, 1641–1656. [Google Scholar] [CrossRef] [Green Version]
  53. Zhang, Y.-L.; Cao, F. Fine particulate matter (PM2.5) in China at a city level. Sci. Rep. 2015, 5, 14884. [Google Scholar] [CrossRef] [PubMed]
  54. Huang, X.; Liu, Z.; Zhang, J.; Wen, T.; Ji, D.; Wang, Y. Seasonal variation and secondary formation of size-segregated aerosol water-soluble inorganic ions during pollution episodes in Beijing. Atmos. Res. 2016, 168, 70–79. [Google Scholar] [CrossRef]
  55. Liu, L.; Guo, J.; Miao, Y.; Liu, L.; Li, J.; Chen, D.; He, J.; Cui, C. Elucidating the relationship between aerosol concentration and summertime boundary layer structure in central China. Environ. Pollu. 2018, 241, 646–653. [Google Scholar] [CrossRef] [PubMed]
  56. Li, J.; Sun, J.; Zhou, M.; Cheng, Z.; Li, Q.; Cao, X.; Zhang, J. Observational analyses of dramatic developments of a severe air pollution event in the Beijing area. Atmos. Chem. Phys. 2018, 18, 3919–3935. [Google Scholar] [CrossRef] [Green Version]
  57. Zhang, Y.; Guo, J.; Yang, Y.; Wang, Y.; Yim, S.H.L. Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China. Remote Sens. 2020, 12, 546. [Google Scholar] [CrossRef]
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Figure 1. Study area and locations of Xianning (XN), Wuhan (WH) and Jingzhou (JZ) stations.
Figure 1. Study area and locations of Xianning (XN), Wuhan (WH) and Jingzhou (JZ) stations.
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Figure 2. Hourly variations of surface PM2.5, CO concentrations and meteorological element at Xianning station during 4 to 7 January 2019.
Figure 2. Hourly variations of surface PM2.5, CO concentrations and meteorological element at Xianning station during 4 to 7 January 2019.
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Figure 3. (a) The time–height profile of PM2.5 concentration detected by the unmanned aerial vehicle (UAV), the near boundary horizontal wind field detected by a wind profiler radar, and the (b) 24 h temperature variations at 0–6 km height detected by microwave radiometers at Xianning station from 18:00 LST on 4 January to 02:00 LST on 7 January 2019.
Figure 3. (a) The time–height profile of PM2.5 concentration detected by the unmanned aerial vehicle (UAV), the near boundary horizontal wind field detected by a wind profiler radar, and the (b) 24 h temperature variations at 0–6 km height detected by microwave radiometers at Xianning station from 18:00 LST on 4 January to 02:00 LST on 7 January 2019.
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Figure 4. Hourly variations of the surface PM2.5 and CO concentrations, RH at Xianning station and surface PM2.5 chemical components at Wuhan ground-based supersite during 10:00–20:00 on 4 January 2019.
Figure 4. Hourly variations of the surface PM2.5 and CO concentrations, RH at Xianning station and surface PM2.5 chemical components at Wuhan ground-based supersite during 10:00–20:00 on 4 January 2019.
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Figure 5. Profiles of the PM2.5 concentration, CO concentration, T , R , θ and Δ θ with height at (a) 20:00 LST and (b) 23:00 LST on 4 January 2019. The R profile was detected by microwave radiometers, and the other element profiles were obtained by the UAV. The red dashed line represents the boundary layer height, and the red circle denotes the peak value of high-concentration atmospheric pollutants in the upper layers.
Figure 5. Profiles of the PM2.5 concentration, CO concentration, T , R , θ and Δ θ with height at (a) 20:00 LST and (b) 23:00 LST on 4 January 2019. The R profile was detected by microwave radiometers, and the other element profiles were obtained by the UAV. The red dashed line represents the boundary layer height, and the red circle denotes the peak value of high-concentration atmospheric pollutants in the upper layers.
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Figure 6. The profiles of the PM2.5 and CO concentrations detected by the UAV and the boundary layer height (red solid line) from 07:00 LST on 5 January to 12:00 LST on 6 January 2019. The red circle denotes the period of pollutants maintaining high concentration in the upper RL.
Figure 6. The profiles of the PM2.5 and CO concentrations detected by the UAV and the boundary layer height (red solid line) from 07:00 LST on 5 January to 12:00 LST on 6 January 2019. The red circle denotes the period of pollutants maintaining high concentration in the upper RL.
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Figure 7. Hourly variations of the surface PM2.5 concentration (μg·m−3) and 10 m wind (m·s−1) observed at Bozhou, Fuyang and Nanyang stations in the upwind direction and at Wuhan and Xianning stations in the downwind direction during 2–7 January 2019. The green arrows represent 10 m wind at five observation stations.
Figure 7. Hourly variations of the surface PM2.5 concentration (μg·m−3) and 10 m wind (m·s−1) observed at Bozhou, Fuyang and Nanyang stations in the upwind direction and at Wuhan and Xianning stations in the downwind direction during 2–7 January 2019. The green arrows represent 10 m wind at five observation stations.
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Figure 8. Spatial distribution of the contribution rates of surrounding atmospheric pollution sources to the surface PM2.5 concentration at Xianning station on (a) 5 and (b) 6 January 2019 by the FLEXPART-WRF model.
Figure 8. Spatial distribution of the contribution rates of surrounding atmospheric pollution sources to the surface PM2.5 concentration at Xianning station on (a) 5 and (b) 6 January 2019 by the FLEXPART-WRF model.
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Figure 9. Variations of the turbulence intensity by eddy correlation systems and surface PM2.5 concentration at Xianning station from 04:00 LST on 5 January to 12:00 LST on 6 January. The red circle indicates the rapid increase period of the surface PM2.5 concentration on 5 and 6 January.
Figure 9. Variations of the turbulence intensity by eddy correlation systems and surface PM2.5 concentration at Xianning station from 04:00 LST on 5 January to 12:00 LST on 6 January. The red circle indicates the rapid increase period of the surface PM2.5 concentration on 5 and 6 January.
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Figure 10. Synergistic mechanism between the regional transport of atmospheric pollutants and the evolution of the ABL. The yellow arrow represents the pollutant transport direction, and circles with arrows represent the vertical mixing effect.
Figure 10. Synergistic mechanism between the regional transport of atmospheric pollutants and the evolution of the ABL. The yellow arrow represents the pollutant transport direction, and circles with arrows represent the vertical mixing effect.
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Xiong, J.; Bai, Y.; Zhao, T.; Zhou, Y.; Sun, X.; Xu, J.; Zhang, W.; Leng, L.; Xu, G. Synergistic Effect of Atmospheric Boundary Layer and Regional Transport on Aggravating Air Pollution in the Twain-Hu Basin: A Case Study. Remote Sens. 2022, 14, 5166. https://doi.org/10.3390/rs14205166

AMA Style

Xiong J, Bai Y, Zhao T, Zhou Y, Sun X, Xu J, Zhang W, Leng L, Xu G. Synergistic Effect of Atmospheric Boundary Layer and Regional Transport on Aggravating Air Pollution in the Twain-Hu Basin: A Case Study. Remote Sensing. 2022; 14(20):5166. https://doi.org/10.3390/rs14205166

Chicago/Turabian Style

Xiong, Jie, Yongqing Bai, Tianliang Zhao, Yue Zhou, Xiaoyun Sun, Jiaping Xu, Wengang Zhang, Liang Leng, and Guirong Xu. 2022. "Synergistic Effect of Atmospheric Boundary Layer and Regional Transport on Aggravating Air Pollution in the Twain-Hu Basin: A Case Study" Remote Sensing 14, no. 20: 5166. https://doi.org/10.3390/rs14205166

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