J. Meteor. Res.  2018, Vol. 32 Issue (2): 313-323   PDF    
The Chinese Meteorological Society

Article Information

Ma, Y. J., H. J. Zhao, Y. S. Dong, et al., 2018.
Comparison of Two Air Pollution Episodes over Northeast China in Winter 2016/17 Using Ground-Based Lidar. 2018.
J. Meteor. Res., 32(2): 313-323

Article History

Received August 25, 2017
in final form December 7, 2017
Comparison of Two Air Pollution Episodes over Northeast China in Winter 2016/17 Using Ground-Based Lidar
Yanjun MA1, Hujia ZHAO1,3, Yunsheng DONG2, Huizheng CHE3, Xiaoxiao LI4, Ye HONG1, Xiaolan LI1, Hongbin YANG1, Yuche LIU1, Yangfeng WANG1, Ningwei LIU1, Cuiyan SUN5     
1. Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016;
2. Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031;
3. State Key Laboratory of Severe Weather/Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081;
4. Dalian Municipal Meteorological Observatory, Dalian 116001;
5. Jinan Center of Aviation Control, Jinan 250107
ABSTRACT: This study analyzes and compares aerosol properties and meteorological conditions during two air pollution epi-sodes in 19–22 (E1) and 25–26 (E2) December 2016 in Northeast China. The visibility, particulate matter (PM) mass concentration, and surface meteorological observations were examined, together with the planetary boundary layer (PBL) properties and vertical profiles of aerosol extinction coefficient and volume depolarization ratio that were measured by a ground-based lidar in Shenyang of Liaoning Province, China during December 2016–January 2017. Results suggest that the low PBL height led to poor pollution dilution in E1, while the high PBL accompanied by low visibility in E2 might have been due to cross-regional and vertical air transmission. The PM mass concentration decreased as the PBL height increased in E1 while these two variables were positively correlated in E2. The enhanced winds in E2 diffused the pollutants and contributed largely to the aerosol transport. Strong temperature inversion in E1 resulted in increased PM2.5 and PM10 concentrations, and the winds in E2 favoured the southwesterly transport of aerosols from the North China Plain into the region surrounding Shenyang. The large extinction coefficient was partially attributed to the local pollution under the low PBL with high ground-surface PM mass concentrations in E1, whereas the cross-regional transport of aerosols within a high PBL and the low PM mass concentration near the ground in E2 were associated with severe aerosol extinction at high altitudes. These results may facilitate better understanding of the vertical distribution of aerosol properties during winter pollution events in Northeast China.
Key words: aerosol pollution     ground-based lidar     Northeast China    
1 Introduction

To understand the degradation of air quality in relation to meteorological factors, heavy pollution episodes have been studied extensively (Chan et al., 1999; Schichtel et al., 2001; Kim et al., 2006; Molnár et al., 2008; Elias et al., 2009). In the last few decades, rapid increases in anthropogenic sources, coal combustion, and vehicle exhaust have been the primary drivers of atmospheric pollution, causing severe air pollution in China (Tie and Cao, 2009; Deng et al., 2011; Liu et al., 2015). The main haze regions in China are the Beijing‒Tianjin‒ Hebei megalopolis, the Pearl River Delta, the Yangtze River Delta, and the Sichuan basin, which have been experiencing major issues associated with air and visibility degradation (Yue et al., 2010; Zhang et al., 2012; Cheng et al., 2013; Sun Y. W. et al., 2013; Yang et al., 2016a).

High particulate matter (PM) concentrations, the physical, chemical, and optical properties of aerosol particles, and meteorological conditions have been studied to understand the causes of severe haze pollution in China (Ji et al., 2014; Quan et al., 2014; Sun et al., 2014; Zhang et al., 2014; Che et al., 2015; Jiang et al., 2015; Yang et al., 2016b; Zheng et al., 2017). Moreover, the aerosol verti-cal distribution has been studied to determine the dispersion of air pollutants from near the ground surface to the upper troposphere across the world (Guinot et al., 2006; Emeis et al., 2011; Kompalli et al., 2014; Oleniacz et al., 2016). In China, several studies have evaluated the aerosol vertical distribution and its implications for air pollution in several regions (Zhang et al., 2009; Quan et al., 2013; Sun Y. et al., 2013; Tang et al., 2015, 2016; Zhang et al., 2015; Liu et al., 2016). However, only a few studies (e.g., Zhao et al., 2013; Hu et al., 2014) have examined the vertical distribution of aerosols during pollution periods especially in the urban-industrial region of Northeast China. More such studies are needed. By using ground-based lidar together with ground station observations, this study intends to make a better analysis of the aerosol vertical distribution in the boundary layer in Northeast China.

Ground-based lidar is a direct remote sensing tool that can provide aerosol vertical profiles to study the air pollution (Tesche et al., 2007; Hänel et al., 2012; Revuelta et al., 2012; Wu et al., 2012; Cottle et al., 2014; Zhao et al., 2014). For example, Uno et al. (2014) used ground-based lidar to identify a shallow aerosol layer over Beijing during a PM2.5 air pollution event in January 2013. Sugimoto et al. (2015) used Mie lidar to detect internally mixed Asian dust with air pollution aerosols. Tang et al. (2015) obtained the height of the atmospheric mixing layer and vertical attenuated backscattering coefficient using a lidar ceilometer from 15 October to 30 November 2014. Ansmann et al. (2005) presented the height-resolved data of the vertical extent of the haze layer and the diurnal cycle of vertical mixing over the Pearl River Delta in southern China. Recently, Qin et al. (2016) identified similar episodes of external aerosols passing through and mixing in three cities in eastern China with ground-based lidar. The above studies investigated the characteristics of aerosol vertical distribution in northern, southern, and eastern China with higher aerosol loading. In this paper, we provide the aerosol verti-cal information of extinction coefficient and volume depolarization ratio in Shenyang, an industrial city of northeastern China, during two air pollution episodes in winter 2016/17, in comparison with the results from the above studies for other areas of China.

This study investigates the variations in visibility, PM mass concentration, and vertical profiles of aerosol extinction coefficient detected with ground-based lidar, as well as the meteorological conditions during the pollution episodes in Northeast China from December 2016 to January 2017. The aim of this study is to obtain a comprehensive look of the aerosol vertical profiles correlated with ground surface meteorological elements during air pollution in Northeast China. The study site, instruments, and data are introduced in Section 2. The PBL height, pollutants concentration, and meteorological conditions, together with aerosol vertical properties directly derived from the ground-based lidar, are examined in detail in Section 3. A summary is given in Section 4.

2 Study site, instruments, and data

Shenyang is the political, economic, and cultural center of Liaoning Province in Northeast China (41.77°N, 123.50°E; 60.0 m a.s.l.). Human activities and local industrial emissions affect the urban/industrial air quality in this area. The observation site is located on the roof of the Northeast Regional Meteorological Observation Center in Shenyang. The following data are used.

(1) The daily and monthly mean visibility and meteorological data (relative humidity, wind speed, temperature, and pressure) at the surface were calculated by using hourly data obtained at the Shenyang surface observation station from 1 December 2016 to 31 January 2017.

(2) The corresponding daily mass concentrations of ground-surface PM (PM10 and PM2.5) and gaseous pollutants (SO2, CO, NO2, and O3) were obtained from the China Air Quality Online Monitoring and Analysis Platform (https://www.aqistudy.cn/) for the period from 1 December 2016 to 31 January 2017.

(3) The twice daily (0000 and 1200 UTC, corresponding to 0800 and 2000 Beijing Time, respectively) verti-cal profiles of wind speed and temperature were obtained from the University of Wyoming website http://weather. uwyo.edu/ for Shenyang station

(4) We also used the 6-hourly NCEP FNL (Final) Operational Global Analysis data on 1° × 1° grids to analyze the regional variations in the 10-m wind and 850-hPa geopotential height fields (https://rda.ucar.edu/ datasets/ds083.2/).

(5) The planetary boundary layer (PBL) height and aerosol vertical profiles including the extinction coefficient and volume depolarization ratio were detected with a ground-based lidar (Lidar-D-2000; Wuxi CAS Photonics, China) installed at the Shenyang observation station. The lidar was deployed at this site to make experimental observations since December 2015. The Lidar-D-2000 provides backscattering at 532 and 355 nm with a temporal resolution of about 1 min and a vertical resolution of about 7.5 m.

In this study, two pollution episodes during winter 2016/17 were selected according to the observed daily PM2.5 concentration that exceeded China’s national ambient air quality standard (75 μg m−3) (GB3095-2012: http://kjs.mep.gov.cn/hjbhbz/bzwb/dqhjbh/dqhjzlbz/201203/t20120302_224165.htm). The two events covered two periods, referred to as Episode 1 (19–22 December 2016; E1) and Episode 2 (25–26 December 2016; E2). In addition, the kinetic and thermodynamic factors differed significantly between the two periods, which will be further illustrated by using the extinction coefficients based on the lidar data for the two selected episodes.

3 Results 3.1 Variation in visibility, PBL height, and surface meteorological elements

The deterioration in visibility during the haze events revealed poor air quality in Shenyang from 1 December 2016 to 31 January 2017 (Fig. 1a). During this period, observation days with visibility < 10 km lasted for 43 days from 20 December 2016, accounting for nearly 69% of the total observation days. Figure 1b shows the variation in the PBL height during the study period. The PBL height reached approximately 1898 m on 1 December 2016 before the pollution began, with visibility of nearly 19 km (Fig. 1a). During E1, the visibility decreased to < 10 km, while the PBL height declined sharply to a minimum value of 369.3 m on 21 December 2016 ( Fig. 1b). The lower PBL height led to poor pollution dilution, resulting in worsening of the air quality conditions during E1. Wang et al. (2014) pointed out that a lower PBL height is one of the main contributors to regional haze formation. By contrast, the PBL height during E2 exceeded 1000 m for some time (Fig. 1b). This relatively higher PBL height in winter coupled with the poor visibility (about 7 km; Fig. 1a) might be due to the intensive air transport with the increasing surface wind speeds as shown in Fig. 1d. These conclusions will be further discussed in Sections 3.4 and 3.5 according to the 10-m wind field and the aerosol extinction detected by ground-based lidar during E2.

Figure 1 Variations in (a) visibility, (b) PBL height, (c) relative humidity (RH), (d) wind speed, (e) pressure, and (f) temperature during 1 December 2016–31 January 2017. The PBL height was measured by the lidar at Shenyang station, and the other variables were from the surface meteorological observations at the same site.

Figures 1c–f show the relative humidity, wind speed, air pressure, and temperature during the study period to facilitate understanding of the effects of ground-surface meteorological conditions on air quality. The variation in the meteorological conditions did not change as significantly as the visibility during the period of pollution. Generally, the observation site was controlled by the same weather system during the study period. This result reflects the weak effects of meteorological elements compared with the other factors causing this pollution. Nevertheless, we could still see some minor changes in the meteorological conditions during the study. As the polluted period progressed, the temperature increased to 0.27℃ in E1 and 0.91℃ in E2 (Fig. 1f). There may be a certain degree of feedback between the aerosol and meteorological elements in the atmospheric boundary layer. Gao et al. (2015) reported that the temperature decreased by 0.8–2.8℃ at the surface and increased by 0.1–0.5℃ at 925 hPa during a fog–haze event in North China.

During the period of pollution, the relative humidity increased slightly to nearly 80% and the surface pressure decreased from 1023 to 1010 hPa. Meanwhile, the visibility continued to deteriorate as the wind speed increased (Fig. 1d). Compared with the lower average wind speed (1.24 m s−1) at the beginning of the pollution (1 December 2016), wind speed increased to 3.0 m s−1 in the later pollution period with poor visibility (less than 5 km). The variation of wind speed suggests that the strong wind in the later period in January 2017 may help to contribute to the local fugitive dust and help the aerosol transport.

3.2 Variation in pollutant concentrations

The concentrations of some main air pollutants exhibited different variations in E1 and E2 (Fig. 2). With the deterioration of visibility, the concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 increased in various degrees in E1. When the visibility decreased to < 10 km, the maximum PM 2.5 mass concentration reached 224.0 μg m−3 on 8 January 2017, more than 3.0 times the daily limit of China’s national ambient air quality standard (75 μg m−3) (GB3095-2012: http://kjs.mep.gov.cn/hjbhbz/ bzwb/dqhjbh/dqhjzlbz/201203/t20120302_224165.htm). The maximum daily average PM10 concentration was 271.8 μg m−3 when the visibility was < 10 km, which was more than 1.8 times the daily limit of China’s national ambient air quality standard (150 μg m −3). The maximum daily average SO2, CO, NO2, and O3 concentrations were 174.4, 2.6, 86.5, and 88.0 μg m−3, respectively when the visibility decreased to 10 km.

Figure 2 Variations in mass concentrations of (a) PM2.5, (b) PM10, (c) SO2, (d) CO, (e) NO2, and (f) O3 during the pollution period 1 December–31 January 2016. The PM mass concentrations and the gaseous pollutant concentrations were obtained from the Shenyang observation site and the China Air Quality Online Monitoring and Analysis Platform, respectively.

However, the concentrations of the above 6 air pollutants decreased during E2 (Fig. 2). On 25 December 2016, the PM2.5 and PM10 mass concentrations were 61.7 and 84.6 μg m−3, respectively, while the daily average mass concentrations of SO2, CO, NO2, and O3 were 108.6, 1.6, 54.8, and 63.0 μg m−3, respectively. The decrease in the ground-surface pollutant concentrations may have been due to the higher PBL height and stronger transport process during E2.

3.3 Relationship between pollutants concentration and PBL height

Figure 3 shows the vertical variations of PM1.0, PM2.5, PM10 mass concentrations under different PBL heights during the two pollution episodes E1 and E2. During E1, as the PBL height increased, the PM mass concentration began to decrease for both the fine mode and the coarse mode particles. The hourly values of PM1.0, PM2.5, and PM10 decreased from 494.1 to 7.1, 387.8 to 4.3, and 183.0 to 1.2 μg m−3, respectively, as the PBL height increased from the ground to about 800 m.

Figure 3 Relationship of PM1.0, PM2.5, and PM10 with PBL height during (a1, a2, a3) E1 and (b1, b2, b3) E2. The PM mass concentrations were obtained from the Shenyang observation site and the PBL data were detected by the lidar at the same station.

On the contrary, the PM mass concentration exhibited different trends during E2. The fine and coarse mode particles show a positive correlation with the increased PBL height. The hourly values of PM1.0, PM2.5, and PM10 increased from 9.0 to 280.0, 6.3 to 219.8, 2.2 to 92.6 μg m−3, respectively, as the PBL height increased from the ground to about 1200 m. The instantaneous value of PM mass concentration in the upper PBL could be increased to 30–40 times that of the ground concentration in E2.

Overall, these results suggest that the PBL height is associated with significant temporal variations in the particulate matter distribution. There are obviously different dynamic characteristics in the two periods.

3.4 Variation in PM concentrations and meteorological fields in E1 and E2

Table 1 presents the average values of PM and gas-eous pollutant concentrations and multiple meteorologi-cal variables in E1 and E2. The average PM2.5 and PM10 concentrations were about 152.4 and 203.3 μg m−3 during E1, and 44.3 and 69.4 μg m−3 during E2, respectively. The concentrations of PM2.5 and PM10 in E1 were nearly 3.4 and 2.9 times those in E2, indicative of more aerosol loading of local pollution near the ground in E1 than in E2.

Table 1 Variations in visibility, PM concentrations, and surface meteorological factors during the two pollution episodes
Episode E1 E2
PM2.5 (μg m−3) 152.4 ± 78.9 44.3 ± 24.7
PM10 (μg m−3) 203.3 ± 93.1 69.4 ± 21.5
SO2 (μg m−3) 78.6 ± 30.1 73.1 ± 50.3
CO (mg m−3) 1.8 ± 0.8 1.1 ± 0.7
NO2 (μg m−3) 58.8 ± 20.7 40.3 ± 20.5
O3 (μg m−3) 38.3 ± 8.8 63.5 ± 0.7
PBL (m) 427.7 ± 54.1 1436.7 ± 287.6
P (hPa) 1019.7 ± 0.3 1018.6 ± 0.2
T (°C) 0.3 ± 0.2 0.9 ± 0.2
RH (%) 61.6 ± 1.4 67.5 ± 0.1
WS (m s−1) 1.5 ± 0.02 1.5 ± 0.03
VIS (km) 9.4 ± 0.9 7.3 ± 0.05
PL of PM2.5 (mg m−2) 63.4 ± 33.4 60.0 ± 22.7
PL of PM10 (mg m−2) 84.8 ± 38.8 96.6 ± 10.9
P, pressure; T, temperature; RH, relative humidity; WS, wind speed; VIS, visibility

He et al. (2013) pointed out that the changes in the mixing layer height (MLH) could affect the pollutant concentration to a certain extent. Li et al. (2015) defined pollution loading (PL) as the PM mass concentration multiplied by the PBL height or the mixing layer height (MLH). The PL can be used to represent the aerosol holding capacity of unit air column in the atmospheric boundary layer. In this study, we also use this concept for both fine (PM2.5) and coarse (PM10) mode particles to measure the effect of aerosol transport from the upper troposphere and/or from local surface emissions without considering the influence of PBL height or MLH.

Table 1 shows that the PL of PM2.5 in PBL during E1 was about 63.4 mg m−2, with high PM2.5 matter concentrations of 150–200 μg m−3 near the surface. E2 had the same PL of about 60.0 mg m−2, but with a much lower PM2.5 concentration of 20–60 μg m−3 near the ground. This indicates that higher PM2.5 concentrations near the surface during E1 might have been accumulated with relatively weaker diffusion; conversely, the about the same PL but with lower surface PM mass concentrations during E2 could be attributed to pollutant transport out of the study region.

The PL of PM2.5 was similar (63.4 vs. 60.0 mg m−2) during E1 and E2 (Table 1). The PL of PM10 was 84.8 and 96.6 mg m−2, respectively, in E1 and E2. These results perhaps indicate the enhanced contribution of aerosol vertical diffusion in E2 (lower mass concentration of PM2.5 and PM10 in the ground, higher in the upper as indicated in Figs. 7a, 8) than in E1 (higher mass concentration of PM2.5 and PM10 in the ground, lower in the upper as indicated in Figs. 7a, 8). As seen in Table 1, the PBL height in E2 was almost 3.4 times that in E1. Therefore, the lower PBL height in E1 (427.7 m) accompanied by higher PM2.5 and PM10 mass concentrations was indicative of substantial ground-surface pollution. In E2, the PBL height rose to about 1436.7 m, enhancing the transport of pollutants to the upper boundary layer.

Figure 4 shows the vertical distributions of temperature from 19 to 26 December 2016. There were continuous temperature inversions in the near ground layer and at altitudes of 1.0–1.5 km. The temperature inversion was strong from 19 December, limiting the diffusion of pollutants near the ground, which corresponded to the increase in PM2.5 and PM10 concentrations (Table 1). The temperature inversion may also have contributed to preventing the diffusion of pollutants transported from the ground below 1.5 km to the upper boundary layer.

Figure 4 Vertical distributions of temperature at 0800 (black lines) and 2000 (red lines) Beijing Time during 19–26 December 2016, based on the data obtained from the University of Wyoming website (http://weather.uwyo.edu/).

Figures 5 and 6 show the variations in 850-hPa geopotential height and 10-m wind fields from 19 to 26 December 2016, respectively. Isobars were sparse on 19 December 2016, with a small pressure gradient, leading to weak wind conditions. However, isobars were dense on 25 December 2016, with a larger pressure gradient, leading to strong winds, which promoted horizontal pollutant diffusion. The wind speed at 10 m was low (about 1 m s−1) on 19 December 2016, advantageous for the local PM accumulation in the continuous presence of the temperature inversion layer (Fig. 6). In contrast, the 10-m wind speed increased substantially (approximately 9 m s−1) on 25 December 2016, favouring the transport of aerosol particles. Compared with those on 19 December, the 10-m wind speed and direction on 25 December 2016 were more conducive to spreading the pollutants transported from the North China Plain southwesterly into the study region surrounding Shenyang.

Figure 5 The geopotential height fields (contour and shading) at 850 hPa at 1400 Beijing Time during (a–h) 19–26 December 2016, based on the NCEP FNL Operational Global Analysis data (https://rda.ucar.edu/datasets/ds083.2/).
Figure 6 The wind fields at 10 m at 1400 Beijing Time during (a–h) 19–26 December 2016, based on the NCEP FNL Operational Global Analy-sis data (https://rda.ucar.edu/datasets/ds083.2/).
3.5 Extinction coefficient and volume depolarization ratio detected by ground-based lidar in E1 and E2

Figure 7a shows the time–height cross-section of the ground-based lidar derived extinction coefficient at 532 nm from December 2016 to January 2017 in Shenyang. To correct the signal, the aerosol extinction coefficient was retrieved by using the algorithm of Fernald (1984). The lidar signals revealed two major aerosol extinction events corresponding to the two episodes examined in this study.

During E1, the extinction coefficient increased markedly below 500 m; whereas during E2, the extinction coefficient increased to about 1000 m. Comparing the extinction coefficients at different heights, we found that the maximum extinction coefficient did not appear near the ground, but occurred at a height of approximately 225 m. We calculated the extinction coefficient below 2 km to assess quantitatively the contribution of aerosols to the pollution (Fig. 8). On 19 December 2016, the maximum extinction coefficient was 1.5 km−1 near the surface. Combined with the ground observation data, the PM2.5 and PM10 concentrations increased to as high as 227.5 and 289.5 μg m−3 on 19 December 2016, respectively. Over time, the aerosol pollution layer was increasing in height. On 25 December 2016, the maximum extinction coefficient of around 1.8 km−1 occurred at 0.8–0.9 km (Fig. 8), with lower ground-surface PM2.5 and PM10 concentrations of about 61.7 and 84.6 μg m−3, respectively. The extinction coefficient values during the two episodes indicate that aerosol extinction in E1 contributed to the local pollution under the lower boundary layer with higher ground-surface PM mass concentrations, while the aerosol extinction at high altitudes (see Fig. 8) during E2 was based on cross-regional transmission with a higher boundary layer and lower PM mass concentrations near the ground. In addition, the aerosol distribution detected with ground-based lidar (Fig. 7c) also showed greater ground-surface aerosol pollution in E1 and lower aerosol distribution near the surface in E2.

Figure 7b shows the time–height evolution of the volume depolarization ratio at 532 nm. The volume depolarization ratio during the high extinction coefficient episodes was not high, indicating that dust may not have been the main cause of the high extinction coefficient during this period.

Figure 7 Time–height evolutions of (a) aerosol extinction coefficient (km−1; shaded), (b) volume depolarization ratio (shaded), and (c) PM2.5 mass concentration (μg m−3; shaded) detected by the ground-based lidar during the study period. “BT” on the x-axis denotes Beijing Time.
Figure 8 Vertical profiles of the extinction coefficient on different days in December 2016, derived from the ground-based lidar in Shenyang, Northeast China.
4 Summary

The horizontal visibility, PM mass concentration, PBL height, and certain meteorological fields as well as vertical profiles of aerosol extinction coefficient and volume depolarization ratio were studied during a highly polluted period from 1 December 2016 to 31 January 2017 in Shenyang, China, based on surface meteorological observations and ground-based lidar detections. Two pollution episodes E1 and E2 were selected from the entire pollution period, and the aerosol vertical properties in association with the meteorological conditions over the Shenyang observation site during E1 and E2 were compared. The results are summarized as follows.

The PBL height plays different roles in the process of pollution. The lower PBL height with higher PM mass concentration near the surface led to poor pollution dilution during pollution episode E1, while the higher PBL height with lower surface PM mass concentrations was attributed to pollutant transport in the atmosphere during pollution episode E2. Analyses of corresponding meteorological data indicate that strong winds might have helped the pollutant dispersion at the ground surface and favoured aerosol cross-regional transmission.

There are obviously different dynamic characteristics in the two pollution episodes E1 and E2. The PM mass concentration decreased with the increasing PBL height in E1, while in E2, the PM mass concentration exhibited a positive correlation with the PBL height, which increased to about 1200 m.

The meteorological fields showed strong temperature inversion near the ground and in the upper boundary layer on 19 December, limiting the diffusion of pollutants from the ground to 1.5 km. Meanwhile, weak winds had a smaller influence on PM accumulation in E1, while the greater wind speed and appropriate wind direction at 10 m favoured aerosol transport from the North China Plain southwesterly into the region surrounding Shen-yang during E2.

The vertical distributions of extinction coefficient in the two episodes further indicate that this polluted period was driven not only by local pollution accumulation, but also by aerosol transport from other provinces in the North China Plain, according to the aerosol extinction events derived from the ground-based lidar data.

In this study, the horizontal and vertical profiles of aerosol quantities and meteorological elements were analyzed during winter pollution in 2016/17 in Shenyang, China. However, further investigation should be conducted to study the impacts of weather conditions and chemical compositions on air pollution in Northeast China.

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