2. State Key Laboratory of Severe Weather/Institute of Atmospheric Composition of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing 100081;
3. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000;
4. Shijiazhuang Meteorological Bureau, Shijiazhuang 050081;
5. Laboratory for Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;
6. University of Chinese Academy of Sciences, Beijing 100049;
7. National Meteorological Center, China Meteorological Administration, Beijing 100081;
8. Institute of Meteorological Science of Jilin Province, Changchun 130062;
9. Key Laboratory for Earth System Modeling of Ministry of Education, Department of Earth System Science, and Joint Center for Global Change Studies, Tsinghua University, Beijing 100084
Aerosol particles can absorb and scatter solar radiation, resulting in direct radiative forcing (Hansen et al., 1997). Aerosol particles can also serve as cloud condensation nuclei, modifying the size distribution of cloud droplets and influencing their microphysical properties, and therefore altering the precipitation efficiency of clouds (Dubovik et al., 2002; Eck et al., 2012). Aerosol thus can not only affect global and regional climate change, but also cause environmental and public health problems.
China has experienced rapid economic growth over the past three decades, leading to expanding urbanization and industrialization and a rapid growth in the number of vehicles. As a result, high concentrations of aerosol particles are found in many megacities (Li et al., 2013; Zhang J. K. et al., 2013), resulting in a reduction in both visibility and the penetration of solar radiation (Watson, 2002; Che et al., 2005, 2007; Gui et al., 2016) and a deterioration in air quality (Wang et al., 2006; Zheng et al., 2016), especially in the North China Plain (He et al., 2009; Zhu et al., 2014; Yu et al., 2016).
Haze has frequently been observed in the North China Plain in recent years, especially during winter when emissions increase (e.g., from biomass burning, heating, traffic, and industry) and stable synoptic conditions are present (Sun et al., 2006; Quan et al., 2011). Numerous studies have investigated haze events in China based on ground observations, satellite remote sensing, and simulations, resulting in a comprehensive knowledge of haze pollution (Wang et al., 2006; Che et al., 2014; Quan et al., 2014; Tao et al., 2014). However, these studies have mainly focused on the chemical properties and formation mechanisms of aerosols during haze events and few published studies focused on the optical properties of aerosols.
We analyzed information from multiple sources—including ground-based and satellite data, meteorological observations, and atmospheric environmental monitoring data—obtained during a severe haze episode from 15 to 22 December 2016 over the North China Plain in an attempt to fully understand the optical and radiative properties of aerosols. These results will help to determine the formation mechanisms of haze events and will improve our knowledge of the optical properties of aerosols during haze events over the North China Plain.2 Sites, data, and methods 2.1 Site descriptions and instrument
Three urban observation sites in the cities of Beijing (Chinese Academy of Meteorological Sciences; CAMS), Shijiazhuang (Shijiazhuang Meteorological Bureau; SMB), and Jiaozuo (Henan Polytechnic University, HPU) on the North China Plain with available solar- and sky-scanning radiometer (Cimel Electronique CE-318) measurements were selected for study (Fig. 1). The observations at these three stations are representative of the aerosol properties of urban areas in the northern, central, and southern parts of North China Plain, respectively, and could present the overall characteristics of this haze pollution (Che et al., 2009).
The Cimel Electronique CE-318 sun photometer has 8 channels at 1640, 1020, 870, 670, 500, 440, 380, and 340 nm, and a 940-nm water vapor channel with a 1.2° field of view (Holben et al., 1998). The sun photometer at the Beijing site is operational within both CARSNET (the China Aerosol Remote Sensing Network) and AERONET (the Aerosol Robotic Network) (Che et al., 2008) and was calibrated by the PHOTONS (Photométrie pour le Traitement Opérationnel de Normalization Satellitaire) calibration (Holben et al., 1998) at Lille University (France). Calibration of the sun photometers at the Shijiazhuang and Jiaozuo sites followed the calibration protocol used by AERONET and inter-comparison and sphere calibrations were performed every year by CARSNET to ensure the accuracy of the measurements (Tao et al., 2014). This study analyzed the optical parameters of aerosols (using level-1.5 data), including the aerosol optical depth (AOD), the Ångström exponent (α), the single-scattering albedo (SSA), the absorption aerosol optical depth (AAOD), the absorption Ångström exponent (AAOD), the volume size distribution, and radiative forcing.
The meteorological data in this study were acquired from the China Meteorological Administration and the hourly particulate matter (PM) concentration data were obtained from the China National Environmental Monitoring Centre. ERA-Interim data (global reanalysis datasets, http://apps.ecmwf.int/datasets) were used to analyze the regional variation in the surface wind field and the height of the planetary boundary layer (PBLH). Version 4 of the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess, 1998) was used to track the transport paths of air masses arriving during the haze event.
True color images from MODIS (the Moderate Resolution Imaging Spectroradiometer) allow intuitive discrimination between the dust storms, heavy smog, and cirrus clouds (Tao et al., 2014). We used 1-km MODIS true color images during this haze event to present the general haze conditions over the North China Plain. Vertical profiles of the aerosol subtype data from the CALIPSO satellite were used to determine the overall characteristics of the atmospheric aerosols, so as to distinguish the aerosol types (Wang et al., 2014).
This study used potential source contribution function (PSCF) analysis, combining estimates of the motion of air backward in time with the concentrations measured at a receptor region. This technique has often been used to identify the probable locations of emission sources affecting pollutant loadings in this study region (Yan et al., 2015; Xin et al., 2016). The concentration-weighted trajectory (CWT) method, developed by Hsu et al. (2003), was used to distinguish between strong and weak sources, considering the limitation of the PSCF method in which grid cells can have the same PSCF value when the sample concentrations are either only slightly higher or much higher than the criterion. Detailed information about this method, including the algorithm and principle, has been reported previously (Stohl, 1996; Polissar et al., 2001) and is not presented here.3 Results 3.1 Meteorological conditions 3.1.1 Analysis of meteorological and particulate matter data
The temporal variation of the concentrations of PM and the meteorological conditions at these three sites were investigated before analyzing the optical properties of the aerosols. This was because the emission of pollutants and the meteorological conditions are the two main factors that make a substantial contribution to the haze pollution (Fu et al., 2014). Figure 2 shows the time series of PM concentrations in Beijing, Shijiazhuang, and Jiaozuo. Figure 3 illustrates the daily mean variation in the surface wind field and PBLH over the North China Plain. The wind direction contributes to the pollutant transportation and may dominate the distribution of atmospheric pollutants. The wind speed also has a substantial influence on the accumulation and diffusion of aerosol particles and thus is an important factor affecting the concentration of PM (Che et al., 2007).
In general, the concentration of PM showed an almost parabolic variation in each of the cities during this haze episode. Figures 2a, 2c, and 2e show that the PM2.5 and PM10 concentrations gradually increased in the beginning of the haze episode (15–18 December) and reached a peak value around 19–20 December. A sharp decrease was then seen and the haze episode ended. A strong northerly wind (> 6 m s–1) and relatively high PBLH (~550 m) in Beijing on 15 December favored the horizontal and vertical diffusion of air pollutants, resulting in a daily mean PM2.5 concentration < 75 μg m –3 and suggesting better air quality than in Shijiazhuang and Jiaozuo.
In contrast with the favorable diffusion conditions on 15 December, the dominant wind direction changed to southerly on 16 December and the wind speed decreased to ~2 m s–1. Pollutants originating from southern Hebei Province, where there are highly polluted industrial areas and high levels of anthropogenic activity, can be transported to Beijing by southerly winds and may lead to serious air pollution (Xia et al., 2007a). From 17 to 19 December, a large area with moderate southerly winds (< 2 m s–1) was present and the PBLH gradually decreased to ~200 m over the North China Plain. Compared with the conditions on a clean air day, the sustained weak winds and low PBLH during this haze event caused a significant reduction in the amount of diffusion space for pollutants, which contributed to the accumulation of both local and regional pollutants (Wang et al., 2014). Consequently, the concentration of PM2.5 gradually increased and reached a maximum on 19 and 20 December, with peak PM2.5 concentrations of 416, 676, and 677 μg m–3 for Beijing, Shijiazhuang, and Jiaozuo, respectively. The PM10 concentrations varied congruously with the PM2.5 concentrations, with maximum concentrations of 471, 934, and 806 μg m–3 for Beijing, Shijiazhuang, and Jiaozuo, respectively.
The wind speed over Jiaozuo region increased to 6 m s–1 on 21 December and the PBLH was higher (~400 m) than on the preceding days, indicating favorable diffusion conditions. The PM2.5 and PM10 concentrations began to decrease and reached relatively low levels of ~20 and ~50 μg m–3, respectively, on 22 December. However, the PBLH over Beijing and Shijiazhuang continued to lower (to ~100 m) from 20 to 21 December under the relatively calm wind conditions (< 1 m s–1), resulting in high PM concentrations on 21 December. The PBLH increased to 400 m on 22 December and the wind speed increased to 6 m s–1, which were conducive to the diffusion of air pollutants and led to a sharp decrease in the PM concentration in Beijing and Shijiazhuang. The PM2.5 concentrations in Beijing and Shijiazhuang were ~14 and 33 μg m–3, respectively, at the end of 22 December. Figures 2b, 2d, and 2f show the time series for the rations of PM2.5 to PM10 during this haze pollution. It is obvious that the rations of PM2.5 to PM10 were ~80% during the serious haze period (90% for Beijing), suggesting that the accumulation of fine-mode particles was responsible for the formation and development of this haze episode. When the strong northerly wind was dominant, the proportions of PM2.5 to PM10 became smaller, such as the relatively low level that occurred over Beijing on 15 and 22 December and over Shijiazhuang and Jiaozuo on 22 December. This may be a result of the high surface wind speed causing more fugitive dust in the atmosphere, with the consequence that the concentration of coarse particles increased (Che et al., 2014).
Figure 4 shows the temporal variation in meteorological parameters and pollutants during this haze episode. Table 1 shows the correlation coefficients for visibility with relative humidity (%), wind speed (m s–1), and PM2.5 concentrations (μg m–3). Figure 4 and Table 1 indicate that the visibility had a negative correlation with the relative humidity (about –0.72) and that a high relative humidity was a crucial factor in the formation of haze (Wang et al., 2006; Quan et al., 2014; Tao et al., 2014). The relative humidity affects the hygroscopic growth and scattering ability of aerosols and thus influences the visibility (Che et al., 2014; Fu et al., 2014). Even during the formation of the haze, the visibility decreased with increasing relative humidity (15 December, Beijing). During the days in this haze episode with serious pollution (18–20 December), the relative humidity was usually > 80% ( Figs. 4b, e, h) and the visibility was low at ~0.5 km.
Similar results were found for the PM2.5 concentrations, with a strong negative correlation (~0.60) between visibility and the PM2.5 concentration, showing that the visibility was strongly affected by the concentration of aerosol particles and their absorption and scattering abilities (Gui et al., 2016). The high humidity conditions strongly strengthened the conversion of secondary aerosols and secondary organic compounds (Blando and Turpin, 2000; Hennigan et al., 2008) in a similar manner to the processes occurring in clouds. The synergistic reactions of a variety of pollutants due to the high humidity were the key cause of the rapid increase in PM2.5 concentrations and, as a consequence, the visibility decreased sharply (Wang et al., 2014). Not only the high but also the low wind speeds contributed to the transport of atmospheric aerosols in this haze event and there was a positive correlation (~0.52) between visibility and wind speed.3.1.2 Analysis of atmospheric stratification of temperature
The continuous inversion of temperature near the surface in the vertical direction was conducive to the formation of haze. This is because a stable thermal structure restricts the vertical motion of air masses, preventing the convection and finally resulting in the accumulation of air pollution near the ground level (Fu et al., 2014; Wang et al., 2014).
We used the measurements at the Beijing station as an example to investigate the formation of this severe haze event. Figure 5 shows the thermal structure in urban Beijing in this study period. It could be found that the temperature inversions occurred continuously in the near ground and these were also sometimes present in the upper layer of the atmosphere. The air quality over Beijing was relatively good on 15 December, with no temperature inversion at 0800 LT (local time). However, a temperature inversion occurred near the ground at night (2000 LT), indicating adverse diffusion conditions. From 16 to 21 December, sustained temperature inversions dominated over Beijing and the top layer reached almost 800 m (at both 0800 and 2000 LT). Thus the whole boundary layer (Fig. 3) had a stable thermal structure, leading to the accumulation of pollutants. The surface temperature inversion disappeared on 22 December, improving the diffusion of anthropogenic pollutants and contributing to an improvement in air quality.3.2 Optical properties of aerosols 3.2.1 Aerosol optical depth, Ångström exponent, and water vapor
As can be seen from Figs. 6a, d, g, the variation in the AOD was consistent in all three areas during this haze event (the missing AOD data were due to the accumulation of cloud). The AODs in the haze formation period at three stations were < 0.60 and increased to > 1.00 during the serious pollution events. This suggests that serious air pollution is a regional phenomenon over the North China Plain. The AOD showed a gradually increasing trend at all three stations, indicating the accumulation of air pollutants. The air quality in Beijing was relatively good on 15 and 16 December, with daily AOD values of 0.09 and 0.19, respectively. The AOD then started to increase on 17 December and reached ~1.02 by the end of the day. The AOD varied consistently on 18 and 19 December, maintaining a high level of ~0.87. The AOD increased rapidly from 1.34 to 2.12 on 22 December and a peak value (~2.04) was observed in the afternoon of 23 December, reflecting the most serious air pollution. A strong northerly wind dominated in Beijing on 22 December and contributed to the spread of pollutants; the AOD sharply decreased to ~0.15 and the haze pollution ended. Similar temporal variations in the AOD were observed in Shijiazhuang and Jiaozuo. During this haze episode, the daily AOD varied by ~0.18–1.42 (minimum 0.14, maximum 2.01) at Shijiazhuang and 0.24–3.51 (minimum 0.14, maximum 3.85) at Jiaozuo. The variation in AOD at all three of sites was consistent with that for the PM 2.5 concentration (Figs. 2a, c, e). Li et al. (2013) showed that the correlation between the AOD at 500 nm and the PM2.5 concentration can reach 0.93 during haze events. In this study, the correlation between the AOD and the PM2.5 concentration was 0.98, 0.87, and 0.95 for Beijing, Shijiazhuang, and Jiaozuo, respectively, suggesting that the lower levels of the atmosphere were well mixed during this severe haze episode.
Figures 6b, 6e, and 6h show the spatial and temporal variation of α during this study period. The value of α was > 0.80 for all sites during the serious haze event, reflecting the fact that fine particles were dominant in this haze pollution event over the North China Plain. This result is similar to those reported previously by Eck et al. (2005) and Xia (2007b). From 15 to 17 December, an obvious increase of α from ~1.11 to ~1.49 occurred in Beijing. Similar observations were made in Shijiazhuang and Jiaozuo, with maximum variations of ~0.53 and ~0.39, respectively. This variation of α suggested that the atmospheric aerosols decreased in size as the haze process developed; this may be related to the synergistic reactions of different pollutants (Wang et al., 2014; Gui et al., 2016).
A significant reduction in α was seen over Beijing from 19 to 21 December. The value of α decreased to ~0.33 on 20 December and then slightly increased again to ~0.78 on 21 December, much lower than on the previous few days. This may be because, with the aggravation of haze pollution, a substantial portion of the fine particles grew hygroscopically under the high humidity conditions, or were activated to cloud and fog droplets. The value of α was relatively low over Shijiazhuang and Jiaozuo on 22 December compared with the value during the haze pollution event, indicating the increasing concentration of coarse particles in the atmosphere. This may result from the high speed surface winds increasing the amount of fugitive dust in the atmosphere (Che et al., 2014).
Fu et al. (2014) suggested that high aerosol loadings and the strong hygroscopic growth of aerosols are the two main reasons of the sever haze pollution. We also analyzed the water vapor content to investigate the formation of haze pollution. Figures 6c, 6f, and 6i demonstrate the temporal variation in the water vapor content, which shows a similar trend to the AOD. The AOD at the Jiaozuo site increased from 16 to 19 December and then reached a maximum value, while the water vapor content increased from ~0.35 to ~1.37 cm. The correlation coefficient between the AOD and the water vapor content was ~0.80 at Beijing, ~0.61 at Shijiazhuang, and ~0.89 at Jiaozuo, showing that the water vapor content had a vital role during haze formation (Gui et al., 2016).3.2.2 Single-scattering albedo and volume size distribution
The SSA presents the scattering proportion affected by aerosol particles in the total extinction and is one of the key variables in assessing the effects of aerosols on climate. Figures 7a, 7b, and 7c show the daily variations in the SSA in Beijing, Shijiazhuang, and Jiaozuo, respectively. These results suggest that scattering was enhanced by haze pollution.
In Beijing, the mean value of the SSA on 15 December was ~0.77, comparable with the value of 0.80 observed on a clean air day in Beijing (Jing et al., 2011). The mean SSA increased to ~0.86 in 16 December and showed a decreasing trend with wavelength. Based on the results shown in Figs. 2 and 6, the accumulation of air pollution could be used to explain this value. The daily SSA gradually increased over the following days and reached a maximum of 0.94 on 22 December. Over the same period, the SSA increased within 440–675 nm and then decreased within 675–1020 nm, suggesting that the accumulation of air pollutants caused higher absorption at lower wavelengths (Yu et al., 2011; Gui et al., 2016).
The spectral properties of the SSA are greatly influenced by the size of atmospheric aerosols. The relative humidity was > 80% and the water vapor content was > 1.0 cm on 21 December, which may have strongly affected the hygroscopic growth of water-soluble aerosols, resulting in the growth of fine mode particles and an increase of its scattering ability ( Kotchenruther and Hobbs, 1998). The rate at which the hygroscopic precursor particles (e.g., sulfur and nitrogen oxides) were converted into sulfates and nitrates was increased; these secondary aerosols usually have strong scattering properties (Watson et al., 1994; Tao et al., 2014). These factors could be valid reasons for the extremely high SSA on 21 December.
It can be clearly seen that the daily SSA over Shijiazhuang and Jiaozuo increased within 440–675 nm and then decreased within 675–1020 nm, showing higher absorption at shorter wavelengths. The mean SSA in Shijiazhuang varied from 0.80 to 0.87 and the maximum was ~0.88 on 17 December. The maximum SSA in Jiaozuo was observed on 18 December; this may be due to the hygroscopic growth of fine mode particles and thus the scattering increased (Che et al., 2014).
Figure 8 shows the daily variations of the size distribution of particles at the three sites. The volume size distributions showed a bimodal logarithmic normal structure during the haze formation period in Beijing. From 15 to 18 December, both the fine mode (radii < 0.60 μm) and coarse mode (radii > 0.60 μm) particles increased gradually and varied from 0.01 to 0.08 and 0.03 to 0.08 μm 3 μm–2, respectively. In contrast with the previous days with only two peaks at radii of ~0.10–0.20 and 2.50–3.50 μm, the daily volume size distribution had a trimodal pattern on 21 December, the day with the most intense pollution. There were three peaks at radii of ~0.15, 0.40–0.50, and 3.00 μm. This phenomenon probably reflected the hygroscopic characteristics of fine mode particles. Zhang R. et al. (2013) measured the mass concentration of non-refractory submicron particles in urban Beijing and pointed that the composition of these particles was about organic material (49.8%), sulfates (21.4%), nitrates (14.6%), ammonium (10.4%), and chlorides (3.8%). These hygroscopic compositions could be a major reason for the trimodal size distribution. The volume size distributions in Shijiazhuang and Jiaozuo showed a bimodal pattern with two peaks at radii of ~0.1–0.3 and 2.5–3.0 μm. In both Shijiazhuang and Jiaozuo, the radius corresponding to the peak value of the fine mode was larger on 18 December than on the other days. Combined with the high relative humidity (> 80%), we concluded that the hygroscopic growth of fine particles was responsible for this phenomenon.3.2.3 Absorption aerosol optical depth and absorption Ångström exponent
The AAOD reflects the proportion of aerosol particles absorbing sufficient radiation to result in total extinction. Figures 9a, 9c, and 9e demonstrate the daily variations in the AAOD at the observation sites and Figs. 9b, 9d, and 9f show the mean absorption Ångström exponent (AAE; 440–870 nm). The AAOD is usually related to the dust content and the black carbon and organic components (Russell et al., 2010; Giles et al., 2012) and the AAE can distinguish the different absorptive aerosol types. Bergstrom (1973) and Bohren and Huffman (2008) stated that AAE value for black carbon is close to 1.00. Higher AAE values between 1.00 and 2.00 indicate the organic aerosols in urban areas because these compounds are usually mixed with strongly absorbing components in the atmosphere. When the AAE increases to ~3.00, the more dust content is likely. Black carbon mixed with absorptive or non-absorptive materials may led to the AAE value substantially < 1.00 ( Gyawali et al., 2009).
The daily AAOD in Beijing decreased with wavelength during this haze episode. The AAOD varied congruously with the AAE from 15 to 18 December and both values showed a sustained increase. The AAOD at 440 nm increased from 0.01 to 0.15 and the AAE increased from 0.6 to 2.0, suggesting a significant increase in the organic aerosol content in the atmosphere. On the most severe haze day (22 December), the AAOD at 440 nm was > 0.21 and the AAE reached ~1.5, indicating that the more absorptive aerosols played an important part in the progress of the haze event. However, the AAE (~1.5) was slightly smaller than on previous days (17–19 December), showing that a good mix of pollutants contributed to this haze event ( Wang et al., 2014; Gui et al., 2016). A maximum AAOD at 440 nm was observed over Shijiazhuang on 18 December. During this haze episode, the daily AAOD and AAE varied from 0.01 to 0.13 and 1.0 to 1.5, respectively, suggesting that organic aerosols were dominant. However, the mean AAOD levels at Jiaozuo were lower than at the other two sites and varied between 0.01 and 0.04 with a maximum of 0.08 at 440 nm on 17 December. The higher AAOD values were observed at high relative humidity, which could be a result of the enhanced formation of secondary aerosol species and secondary organic compounds (Blando and Turpin, 2000; Hennigan et al., 2008).3.2.4 Aerosol radiative forcing
Che et al. (2014) reported that aerosol radiative forcing (ARF) provides the actual radiative effect of atmospheric aerosols. In order to make a comparison among the AFR, the ARF efficiency is more appropriate; this is defined as the rate at which the atmosphere is forced per unit of AOD.
Figures 10a and 10c show the daily variations in ARF at the bottom of the atmosphere (ARF-BOA) and at the top of the atmosphere (ARF-TOA). Figure 10e shows the mean heating effect of ARF on the atmosphere during the haze episode. As the pollution levels became more intense, the ARF-BOA values progressively increased (Fig. 10a). On the most polluted day (21 December) in Beijing, the daily ARF-BOA was greater than –225 W m–2, whereas the maximum ARF-BOA values in Shijiazhuang and Jiaozuo were observed on 18 December, with average values of about –199 and –191 W m–2, respectively. The value of the ARF-BOA varied from –23 to –227, –34 to –199, and –29 to –191 W m–2 in Beijing, Shijiazhuang and Jiaozuo, respectively, over the whole haze period, suggesting that the aerosol particles produced a cooling effect in this region.
The ARF-TOA showed the same trends as the ARF-BOA at all sites during this haze event. The values varied from –4 to –98, –10 to –51, and –21 to –143 W m–2 in Beijing, Shijiazhuang, and Jiaozuo, respectively, suggesting that the aerosol particles imposed a cooling effect at the TOA. However, the ARF-TOA values were smaller than the ARF-BOA values, which indicated that the higher absorption of atmospheric aerosol particles reduced the solar energy available to be backscattered to the TOA and a larger percentage of solar energy was retained in the atmosphere. Figure 10e shows that the daily heating effect of ARF remained positive during the haze event. The values varied between 25 and 128, 25 and 148, and 9 and 47 W m–2 in Beijing, Shijiazhuang, and Jiaozuo, respectively. This suggested the atmosphere was strongly heated by ARF during the haze episode.
Figures 10b, 10d, and 10f show the variations in ARF and heating efficiencies at all sites. Figure 10b shows that the forcing efficiency at the BOA decreased during the haze event; this could be due to the increase in the SSA. However, the daily forcing efficiency of the BOA varied from –107 to –311, –130 to –264, and –101 to –189 W m–2 over Beijing, Shijiazhuang, and Jiaozuo, respectively, and exceeded –100 W m–2 over the haze event. This may because a higher concentration of absorbing aerosols induces a larger ARF efficiency at the surface (Che et al., 2014). The ARF-TOA efficiencies at Jiaozuo were the highest among these stations, with values of –76 to –146 W m–2 during the haze event. This was probably due to the relatively high AOD and the increase in the ARF with the AOD (Xia et al., 2007b). Figure 10f shows that the heating efficiency of ARF progressively decreased during the haze event. The heating efficiency varied from 61 to 251, 83 to 190, and 25 to 78 W m–2 over Beijing, Shijiazhuang, and Jiaozuo, respectively.3.3 Satellite view of the haze over the North China Plain
Figure 11 shows the MODIS true images over the North China Plain during the haze event. The satellite observations clearly showed the progression of pollution across the North China Plain. During the haze formation period (15–19 December), the color of the polluted areas gradually deepened as the range expanded, in an identical manner to the variation in the daily concentrations of PM, and widespread pollution plumes were eventually seen. At the most severe haze stage (20–21 December), the North China Plain was entirely covered by both haze and cirrus cloud, which was the main reason for the missing data in our observations on these days. On 22 December, the haze cloud dispersed and the air quality rebounded.
The CALIPSO satellite can show the vertical and optical structure of atmospheric aerosols, which can be used to classify the aerosol subtype as dust, smoke, polluted dust, clean continental, polluted continental, and clean marine (Omar et al., 2009; Tao et al., 2014). The CALIPSO data in day time can be affected by strong solar radiation and therefore we used it as a reference and tried to analyze its influence on the haze event. Figure 12 shows that the aerosol vertical layer was much heavier on 17 December than on the day before and that the polluted dust and polluted continental aerosol subtypes were dominant over the North China Plain during the haze formation period (16–17 December). During the most polluted stage (20–21 December), a heavier aerosol layer composed of smoke, dust and polluted dust was seen, although the data were fragmentary due to noise from strong solar radiation and the accumulation of cloud. These well-mixed pollutants contributed greatly to this haze episode. The haze pollution ended on 22 December when the aerosols were mainly composed of the dust and polluted continental subtypes, which may be due to the strong northerly wind leading to an increase in fugitive dust (Wang Q. et al., 2009) and emissions of fly ash from burning coal (Yang et al., 2009).3.4 Potential source contribution function and concentration-weighted trajectory analysis
The PSCF analysis was conducted to explore the potential source regions of the air pollutants over these three cities by using TrajStat (Wang Q. et al., 2009). The results for the particle concentrations (PM2.5) are shown in Figs. 13a, c, e. Hourly measurement data from Beijing with the corresponding 72 h back-trajectories at an altitude of 500 m were used as the input for the PSCF model. The PSCF values were calculated by using the Class II Chinese standard (< 75 μg m–3) as the criterion. Figures 13b, 13d, and 13f show the CWT analysis, which provides information on the relative contribution of each potential source area to the high pollutant loadings at the receptor sites (Wang Y. Q. et al., 2009).
Figures 13a and 13b show that the pollutants transported from Northwest China by northerly winds could result in severe air pollution in Beijing. The high PSCF and CWT values were mainly located northwest of Beijing, including in the north of Shanxi Province and the central western region of Inner Mongolia. The high PSCF values derived from continental Northwest China suggested that particulate pollution in Beijing was partially caused by long-range transport from these upstream areas in winter (such as fine dust particles) (Wehner et al., 2008). The contribution of these potential source regions to the PM2.5 loadings in Beijing varied from 100 to 250 μg m–3 and even exceeded 280 μg m–3 in some areas. The area to the south of Beijing made a considerable contribution to the PM2.5 concentration of 190–220 μg m–3. The pollutants originating from Hebei Province, a highly polluted industrial area, were transported by southerly winds and arrived later in Beijing, resulting in serious air pollution (Xia et al., 2007a).
In contrast with Beijing, the air pollutants over Shijiazhuang were mainly derived from the western and surrounding areas (Fig. 13c). The high PSCF value was mainly located over the central and northern parts of Shanxi Province and central Hebei Province. It is clear from Fig. 13d that the surrounding regions strongly contributed to the deterioration in air quality in Shijiazhuang, characterized by the extremely high PM2.5 concentration of ~450 μg m–3. These results are comparable with those for Beijing because the central southern area of Hebei Province was responsible for the sudden increase in PM2.5 concentrations in both Beijing and Shijiazhuang.
In Jiaozuo (Fig. 13e), the air pollutants mainly originated from Northwest China, including southern Shanxi Province and central Shaanxi Province. The PM2.5 concentrations of these regions varied from 130 to 370 μg m–3. However, the pollutants emitted from the local area led to the most serious air pollution, which was similar to that in Shijiazhuang. Figure 13f shows that the areas surrounding Jiaozuo contributed to the high PM2.5 loading and the contribution from northern Henan Province was usually > 450 μg m –3. The pollutants transported from northern Jiangsu Province also increased the accumulation of PM in Jiaozuo by ~210–250 μg m–3.4 Conclusions
A comprehensive analysis of aerosol optical characters and radiative forcing was conducted during a severe haze episode over the North China Plain in December 2016 using ground-based and satellite data, meteorological observations, and atmospheric environmental monitoring data.
In addition to the strong emissions from natural sources, the meteorological conditions were a vital contributor to the most intense air pollution in this haze event. The high relative humidity and unfavorable diffusion conditions (weak winds, decreasing boundary layer height and surface inversion) contributed to the accumulation of aerosol pollutants (PM2.5) and the progress of the haze episode.
The optical properties of the aerosols based on ground measurements showed that the AOD varied consistently at Beijing, Shijiazhuang and Jiaozuo, reflecting the fact that serious haze episode is not only a local phenomenon, but also a regional problem in the North China Plain. During this haze episode, the AOD was increased at all three sites and the daily AOD varied by ~0.14–1.98 (minimum 0.07, maximum 2.31) in Beijing, ~0.18–1.42 (minimum 0.14, maximum 2.01) in Shijiazhuang and ~0.24–3.51 (minimum 0.14, maximum 3.85) in Jiaozuo. The value of α showed similar variations to the AOD at these three sites and was usually > 0.80, which suggested that small particles dominated the haze process. The water vapor content showed a gradually increasing trend and varied by about 0.21–1.05 in Beijing, 0.31–0.83 in Shijiazhuang, and 0.32–1.33 in Jiaozuo.
The SSA increased with the increase in haze pollution, especially under conditions of high relative humidity. The daily SSA values were all > 0.85 on the most polluted days and even exceeded 0.97, which could be the result of the hygroscopic growth of fine mode particles and thus increase its scatter ability. The size distribution showed an obvious bimodal pattern with two peaks at radii of 0.10–0.25 and 2.50–3.50 μm. The volumes of fine and coarse mode particles during the haze were 0.05–0.21 and ~0.01–0.43 μm 3 larger than on the days without haze, suggesting higher aerosol loading in the atmosphere during haze pollution events. The size distribution of aerosols in Beijing showed a trimodal pattern on the most polluted day, suggested the hygroscopic growth of fine mode particles. The daily AAOD increased with a decrease in the air quality and varied by 0.01–0.11 in Beijing, 0.01–0.13 in Shijiazhuang, and 0.01–0.04 in Jiaozuo. The AAE varied from 0.6 to 2.0, which indicated the presence of well-mixed absorptive aerosols over the North China Plain.
The ARF-BOA varied from –23 to –227, –34 to –199, and –29 to –191 W m–2 for the whole haze period in Beijing, Shijiazhuang, and Jiaozuo, respectively. The ARF-TOA showed the same trends as the ARF-BOA and varied from –4 to –98, –10 to –51, and –21 to –143 W m–2 in Beijing, Shijiazhuang, and Jiaozuo, respectively. The daily heating effect of ARF varied from 25 to 128, 25 to 148, and 9 to 47 W m–2 in Beijing, Shijiazhuang, and Jiaozuo, respectively, suggesting that the atmosphere was strongly heated by ARF during the haze episode.
Satellite observations showed the obvious aggravation of haze pollution. The vertical aerosol subtype structure showed that mixed pollution aerosols were responsible for the formation of haze, with smoke, polluted dust, and polluted continental components. The PSCF and CWT analysis showed that both local emissions and the pollutants transported from upstream areas contributed to the haze episode.
This study has shown the relationship between meteorological factors and aerosol optical properties during a haze episode on the North China Plain. Detailed information on the mechanisms still needs to be obtained by further observations or numerical simulations. Multiple measurements should be used in future research to obtain more complete characteristics of the aerosols, taking into consideration that the quality of the data can be influenced by the weather conditions.
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