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Comparison of Two Air Pollution Episodes over Northeast China in Winter 2016/17 Using Ground-Based Lidar

更新时间:2016-07-05

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 ex-periencing 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).

据报道,沙特阿拉伯政府与日本电信巨头软银集团计划联合投资建设全球最大的太阳能发电项目,可使沙特发电能力增加3倍。该项目计划到2030年实现200GW发电量目标,累计投资2 000亿美元,其中包括建设太阳能园区、整合电池技术、建造大型设施以及垂直整合太阳能设备的制造等。与此同时,该合资企业还计划建立研发中心和教育培训中心,可创造10万个工作岗位,帮助沙特国内生产总值增加120亿美元。与传统的石油发电相比,可节省400亿美元开支。

PDCA循环管理过程中领导高度重视,全员解决参加,职能发挥作用,将被动地等待问题发生变为主动地发现问题、解决问题,降低了医保投诉事件的发生,提高了医保服务管理水平,体现了医院以人为本的服务模式,方便患者就医、享受医保实惠,实现了患者满意度的全面提升,为构建和谐医患关系起到了积极的促进作用。

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 vertical 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 groundbased 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 vertical 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.

“果然是因为火把!”等到喘息甫定,李离才解释道,“一行大师司徒先生凿出来的牡丹花,不仅好看,还是一个绝妙的机关。我们举着火把,由山外一圈一圈走下来,来到洞底,又举着火把观看花瓣,热气累积上升,沿着山洞环绕,就会触发机关,这个有一点像孔明灯。

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

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

北京市高级人民法院在“百度诉奇虎案”中创设的非公益必要不干扰原则,是我国法院近年在处理新型竞争行为的司法实践中最重要的法律创新,尽管理论上对其合理性仍存在争议。这一原则在很大程度上得到了最高人民法院在该案再审程序中的支持,因此也被一些法院在特定案件中加以援引和适用。

(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.

3.身体和环境是认知的构成。传统认知心理学并不否认环境在认知过程中的作用,但具身认知理论认为身体和世界在认知加工中扮演了某种构成性的(constitutive)的角色,而不仅仅是因果作用的角色[2],即身体和环境不仅仅是认知的因果关系,更是认知的构成部分,其造就了某种认知的结果。

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.

(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/20120 3/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 groundbased lidar during E2.

Fig. 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.

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 PM2.5 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.

(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.

3.2 Variation in pollutant concentrations

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.

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

Fig. 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.

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.

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.

动态心电图不仅具有无创的优点,还能对ST波段的变化情况进行长时间、动态记录,能够实时反映出患者的ST波段异常[3],对无症状性心肌缺血的发作情况、缺血程度可予以有效的显示,从而降低漏诊率。

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

Fig. 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.

Table 1 presents the average values of PM and gaseous pollutant concentrations and multiple meteorological variables in E1 and E2. The average PM2.5 and PM10 concentrations were about 152.4 and 203.3 μg m-3 dur-ing 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.

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.

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.

在食品腐败菌和病原体中具有抑菌作用的化学成分,如醇类化合物和酮类化合物等也存在于花椒的精油中[21],它们会完全抑制金黄色葡萄球菌、炭疽杆菌、枯草杆菌等10 种革兰氏阳性菌以及变形杆菌、炭疽杆菌、霍乱弧菌等7种革兰氏阴性菌的活性。

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.

1.2.1 不同因素对甜菜苷类色素提取率的影响 色素提取工艺流程:新鲜原料→去除外层果皮→将果皮打碎→加入溶剂浸泡并搅拌→离心分纯→过滤→真空浓缩→色素溶液。

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.

Table 1. Variations in visibility, PM concentrations, and surface meteorological factors during the two pollution episodes

P, pressure; T, temperature; RH, relative humidity; WS, wind speed;VIS, visibility

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

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.

Fig. 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/).

Fig. 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/).

Fig. 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 Analysis data (https://rda.ucar.edu/datasets/ds083.2/).

Fig. 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 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.

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 Shenyang 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.

Fig. 8. Vertical profiles of the extinction coefficient on different days in December 2016, derived from the ground-based lidar in Shenyang,Northeast China.

In this study, the horizontal and vertical profiles of aerosol quantities and meteorological elements were ana-lyzed 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.

2.3.3 监护时间 患者在ICU滞留时间越长,与外界隔离时间越长,ICU环境及各种社会心理因素对患者的刺激就越大,患者由此而产生的不良情绪也越多,其发生ICU综合征的概率也就越大。熊涧秋等[21]对430例体外循环术后ICU患者分析表明,滞留时间≥5 d的289例患者中有76例(26.3%)发生ICU综合征,滞留时间<5 d的141例患者中有17例(12.06%)发生ICU综合征。提示患者滞留时间越长,其ICU综合征发生率越高。

REFERENCES

Ansmann, A., R. Engelmann, D. Althausen, et al., 2005: High aerosol load over the Pearl River Delta, China, observed with Raman lidar and Sun photometer. Geophys. Res. Lett., 32,L13815, doi: 10.1029/2005GL023094.

Chan, Y. C., R. W. Simpson, G. H. Mctainsh, et al., 1999: Source apportionment of visibility degradation problems in Brisbane(Australia) using the multiple linear regression techniques. Atmos. Environ., 33, 3237-3250, doi: 10.1016/S1352-2310(99)00091-6.

Che, H. Z., X. G. Xia, J. Zhu, et al., 2015: Aerosol optical properties under the condition of heavy haze over an urban site of Beijing, China. Environ. Sci. Pollut. Res., 22, 1043-1053,doi: 10.1007/s11356-014-3415-5.

Cheng, Z., S. X. Wang, J. K. Jiang, et al., 2013: Long-term trend of haze pollution and impact of particulate matter in the Yangtze River Delta, China. Environ. Pollut., 182, 101-110,doi: 10.1016/j.envpol.2013.06.043.

Cottle, P., K. Strawbridge, and I. McKendry, 2014: Long-range transport of Siberian wildfire smoke to British Columbia: Lidar observations and air quality impacts. Atmos. Environ., 90,71-77, doi: 10.1016/j.atmosenv.2014.03.005.

Deng, J. J., T. J. Wang, Z. Q. Jiang, et al., 2011: Characterization of visibility and its affecting factors over Nanjing, China. Atmos. Res., 101, 681-691, doi: 10.1016/j.atmosres.2011.04.016.

Elias, T., M. Haeffelin, P. Drobinski, et al., 2009: Particulate contribution to extinction of visible radiation: Pollution, haze,and fog. Atmos. Res., 92, 443-454, doi: 10.1016/j.atmosres.2009.01.006.

Emeis, S., R. Forkel, W. Junkermann, et al., 2011: Measurement and simulation of the 16/17 April 2010 Eyjafjallajökull volcanic ash layer dispersion in the northern Alpine region. Atmos. Chem. Phys., 11, 2689-2701, doi: 10.5194/acp-11-2689-2011.

Fernald, F. G., 1984: Analysis of atmospheric lidar observations:Some comments. Appl. Opt., 23, 652-653, doi: 10.1364/AO.23.000652.

Gao, Y., M. Zhang, Z. Liu, et al., 2015: Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog-haze event over the North China Plain. Atmos. Chem. Phys., 15, 4279-4295,doi: 10.5194/acp-15-4279-2015.

Guinot, B., J.-C. Roger, H. Cachier, et al., 2006: Impact of vertical atmospheric structure on Beijing aerosol distribution. Atmos. Environ., 40, 5167-5180, doi: 10.1016/j.atmosenv.2006.03.051.

Hänel, A., H. Baars, D. Althausen, et al., 2012: One-year aerosol profiling with EUCAARI Raman lidar at Shangdianzi GAW station: Beijing plume and seasonal variations. J. Geophys.Res., 117, D13201, doi: 10.1029/2012JD017577.

He, G. X., C. W. F. Yu, C. Lu, et al., 2013: The influence of synoptic pattern and atmospheric boundary layer on PM10 and urban heat island. Indoor Built Environ., 22, 796-807, doi:10.1177/1420326X13503576.

Hu, X. M., Z. Q. Ma, W. L. Lin, et al., 2014: Impact of the Loess Plateau on the atmospheric boundary layer structure and air quality in the North China Plain: A case study. Sci. Total Environ., 499, 228-237, doi: 10.1016/j.scitotenv.2014.08.053.

Ji, D. S., L. Li, Y. S. Wang, et al., 2014: The heaviest particulate air-pollution episodes that occurred in northern China in January 2013: Insights gained from observation. Atmos. Environ.,92, 546-556, doi: 10.1016/j.atmosenv.2014.04.048.

Jiang, C., H. Wang, T. Zhao, et al., 2015: Modeling study of PM2.5 pollutant transport across cities in China’s Jing-Jin-Ji region during a severe haze episode in December 2013. Atmos. Chem.Phys., 15, 5803-5814, doi: 10.5194/acpd-15-3745-2015.

Kim, Y. J., K. W. Kim, S. D. Kim, et al., 2006: Fine particulate matter characteristics and its impact on visibility impairment at two urban sites in Korea: Seoul and Incheon. Atmos. Environ., 40, 593-605, doi: 10.1016/j.atmosenv.2005.11.076.

Kompalli, S. K., S. S. Babu, K. K. Moorthy, et al., 2014: Aerosol black carbon characteristics over central India: Temporal variation and its dependence on mixed layer height. Atmos.Res., 147-148, 27-37, doi: 10.1016/j.atmosres.2014.04.015.

Li, M., G. Q. Tang, J. Huang, et al., 2015: Characteristics of winter atmospheric mixing layer height in Beijing-Tianjin-Hebei region and their relationship with the atmospheric pollution.Environ. Sci., 36, 1935-1943, doi: 10.13227/j.hjkx.2015.06.004. (in Chinese)

Liu, H. N., W. L. Ma, J. L. Qian, et al., 2015: Effect of urbanization on the urban meteorology and air pollution in Hangzhou.J. Meteor. Res., 29, 950-965, doi: 10.1007/s13351-015-5013-y.

Liu, Q., Y. Wang, Z. Y. Kuang, et al., 2016: Vertical distributions of aerosol optical properties during haze and floating dust weather in Shanghai. J. Meteor. Res., 30, 598-613, doi: 10.1007/s13351-016-5092-4.

Molnár, A., E. Mészáros, K. Imre, et al., 2008: Trends in visibility over Hungary between 1996 and 2002. Atmos. Environ., 42,2621-2629, doi: 10.1016/j.atmosenv.2007.05.012.

Oleniacz, R., M. Bogacki, A. Szulecka, et al., 2016: Assessing the impact of wind speed and mixing-layer height on air quality in Krakow (Poland) in the years 2014-2015. J. Civ. Eng. Environ. Arch., 63, 315-342, doi: 10.7862/rb.2016.168.

Qin, K., L. X. Wu, M. S. Wong, et al., 2016: Trans-boundary aerosol transport during a winter haze episode in China revealed by ground-based lidar and CALIPSO satellite. Atmos. Environ., 141, 20-29, doi: 10.1016/j.atmosenv.2016.06.042.

Quan, J. N., Y. Gao, Q. Zhang, et al., 2013: Evolution of planetary boundary layer under different weather conditions, and its impact on aerosol concentrations. Particuology, 11, 34-40, doi:10.1016/j.partic.2012.04.005.

Quan, J. N., X. X. Tie, Q. Zhang, et al., 2014: Characteristics of heavy aerosol pollution during the 2012-2013 winter in Beijing, China. Atmos. Environ., 88, 83-89, doi: 10.1016/j.atmosenv.2014.01.058.

Revuelta, M. A., M. Sastre, A. J. Fernández, et al., 2012: Characterization of the Eyjafjallajökull volcanic plume over the Iberian Peninsula by lidar remote sensing and ground-level data collection. Atmos. Environ., 48, 46-55, doi: 10.1016/j.atmosenv.2011.05.033.

Schichtel, B. A., R. B. Husar, S. R. Falke, et al., 2001: Haze trends over the United States, 1980-1995. Atmos. Environ., 35,5205-5210, doi: 10.1016/S1352-2310(01)00317-X.

Sugimoto, N., T. Nishizawa, A. Shimizu, et al., 2015: Detection of internally mixed Asian dust with air pollution aerosols using a polarization optical particle counter and a polarization-sensit-ive two-wavelength lidar. J. Quant. Spectros. Radiat. Transf.,150, 107-113, doi: 10.1016/j.jqsrt.2014.08.003.

Sun, Y., T. Song, G. Q. Tang, et al., 2013: The vertical distribution of PM2.5 and boundary-layer structure during summer haze in Beijing. Atmos. Environ., 74, 413-421, doi: 10.1016/j.atmosenv.2013.03.011.

Sun, Y. L., Q. Jiang, Z. F. Wang, et al., 2014: Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013. J. Geophys. Res., 119, 4380-4398,doi: 10.1002/2014JD021641.

Sun, Y. W., X. H. Zhou, K. M. Wai, et al., 2013: Simultaneous measurement of particulate and gaseous pollutants in an urban city in North China Plain during the heating period: Implication of source contribution. Atmos. Res., 134, 24-34, doi:10.1016/j.atmosres.2013.07.011.

Tang, G., X. Zhu, B. Hu, et al., 2015: Impact of emission controls on air quality in Beijing during APEC 2014: Lidar ceilometer observations. Atmos. Chem. Phys., 15, 12667-12680, doi: 10.5194/acp-15-12667-2015.

Tang, G. Q., J. Q. Zhang, X. W. Zhu, et al., 2016: Mixing layer height and its implications for air pollution over Beijing,China. Atmos. Chem. Phys., 16, 2459-2475, doi: 10.5194/acp-16-2459-2016.

Tesche, M., A. Ansmann, D. Müller, et al., 2007: Particle backscatter, extinction, and lidar ratio profiling with Raman lidar in South and North China. Appl. Opt., 46, 6302-6308, doi:10.1364/AO.46.006302.

Tie, X. X., and J. J. Cao, 2009: Aerosol pollution in China: Present and future impact on environment. Particuology, 7, 426-431,doi: 10.1016/j.partic.2009.09.003.

Uno, I., N. Sugimoto, A. Shimizu, et al., 2014: Record heavy PM2.5 air pollution over China in January 2013: Vertical and horizontal dimensions. SOLA, 10, 136-140, doi: 10.2151/sola.2014-028.

Wang, Y. S., L. Yao, L. L. Wang, et al., 2014: Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Sci. China Earth Sci., 57,14-25, doi: 10.1007/s11430-013-4773-4.

Wu, Y. H., L. Cordero, B. Gross, et al., 2012: Smoke plume optical properties and transport observed by a multi-wavelength lidar, sunphotometer and satellite. Atmos. Environ., 63, 32-42, doi: 10.1016/j.atmosenv.2012.09.016.

Yang, X., C. F. Zhao, L. J. Zhou, et al., 2016a: Distinct impact of different types of aerosols on surface solar radiation in China.J. Geophys. Res. Atmos., 121, 6459-6471, doi: 10.1002/2016 JD024938.

Yang, X., C. F. Zhao, J. P. Guo, et al., 2016b: Intensification of aerosol pollution associated with its feedback with surface solar radiation and winds in Beijing. J. Geophys. Res. Atmos.,121, 4093-4099, doi: 10.1002/2015JD024645.

Yue, D. L., M. Hu, Z. J. Wu, et al., 2010: Variation of particle number size distributions and chemical compositions at the urban and downwind regional sites in the Pearl River Delta during summertime pollution episodes. Atmos. Chem. Phys.,10, 9431-9439, doi: 10.5194/acp-10-9431-2010.

Zhang, J. K., Y. Sun, Z. R. Liu, et al., 2014: Characterization of submicron aerosols during a month of serious pollution in Beijing, 2013. Atmos. Chem. Phys., 14, 2887-2903, doi: 10.5194/acp-14-2887-2014.

Zhang, Q., X. C. Ma, X. X. Tie, et al., 2009: Vertical distributions of aerosols under different weather conditions: Analysis of insitu aircraft measurements in Beijing, China. Atmos. Environ.,43, 5526-5535, doi: 10.1016/j.atmosenv.2009.05.037.

Zhang, X. Y., Y. Q. Wang, T. Niu, et al., 2012: Atmospheric aerosol compositions in China: Spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmos. Chem. Phys., 12, 779-799, doi:10.5194/acp-12-779-2012.

Zhang, Y. W., Q. Zhang, C. P. Leng, et al., 2015: Evolution of aerosol vertical distribution during particulate pollution events in Shanghai. J. Meteor. Res., 29, 385-399, doi: 10.1007/s13351-014-4089-0.

Zhao, C. F., Y. Z. Wang, Q. Q. Wang, et al., 2014: A new cloud and aerosol layer detection method based on micropulse lidar measurements. J. Geophys. Res., 119, doi: 10.1002/2014 JD021760.

Zhao, H. J., H. Z. Che, X. Y. Zhang, et al., 2013: Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China. Atmos. Pollut. Res., 4, 427-434, doi:10.5094/APR.2013.049.

Zheng, C. W., C. F. Zhao, Y. N. Zhu, et al., 2017: Analysis of influential factors for the relationship between PM2.5 and AOD in Beijing. Atmos. Chem. Phys., 17, 13473-13489, doi:10.5194/acp-2016-1170.

YanjunMA,HujiaZHAO,YunshengDONG,HuizhengCHE,XiaoxiaoLI,YeHONG,XiaolanLI,HongbinYANG,YucheLIU,YangfengWANG,NingweiLIU,andCuiyanSUN
《Journal of Meteorological Research》2018年第2期文献

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