Relative Contributions of Boundary-Layer Meteorological Factors to the Explosive Growth of PM2.5 during the Red-Alert Heavy Pollution Episodes in Beijing in December 2016

+ Author Affiliations + Find other works by these authors
Funds: 
Supported by the National Key Project of the Ministry of Science and Technology of China (2016YFC0203306) and Basic Scientific Research Project of the Chinese Academy of Meteorological Sciences (2016Z001)
DOI: 10.1007/s13351-017-7088-0

PDF

    Abstract

  • Abstract

    Based on observations of urban mass concentration of fine particulate matter smaller than 2.5 μm in diameter (PM2.5), ground meteorological data, vertical measurements of winds, temperature, and relative humidity (RH), and ECMWF reanalysis data, the major changes in the vertical structures of meteorological factors in the boundary layer (BL) during the heavy aerosol pollution episodes (HPEs) that occurred in winter 2016 in the urban Beijing area were analyzed. The HPEs are divided into two stages: the transport of pollutants under prevailing southerly winds, known as the transport stage (TS), and the PM2.5 explosive growth and pollution accumulation period characterized by a temperature inversion with low winds and high RH in the lower BL, known as the cumulative stage (CS). During the TS, a surface high lies south of Beijing, and pollutants are transported northwards. During the CS, a stable BL forms and is characterized by weak winds, temperature inversion, and moisture accumulation. Stable atmospheric stratification featured with light/calm winds and accumulated moisture (RH > 80%) below 250 m at the beginning of the CS is closely associated with the inversion, which is strengthened by the considerable decrease in near-surface air temperature due to the interaction between aerosols and radiation after the aerosol pollution occurs. A significant increase in the PLAM (Parameter Linking Aerosol Pollution and Meteorological Elements) index is found, which is linearly related to PM mass change. During the first 10 h of the CS, the more stable BL contributes approximately 84% of the explosive growth of PM2.5 mass. Additional accumulated near-surface moisture caused by the ground temperature decrease, weak turbulent diffusion, low BL height, and inhibited vertical mixing of water vapor is conducive to the secondary aerosol formation through chemical reactions, including liquid phase and heterogeneous reactions, which further increases the PM2.5 concentration levels. The contribution of these reaction mechanisms to the explosive growth of PM2.5 mass during the early CS and subsequent pollution accumulation requires further investigation.
    • FullText(HTML)

  • Fine particulate matter (PM) smaller than 2.5 μm in diameter (PM2.5) represents a major environmental threat that degrades visibility, negatively impacts human health (Chen et al., 2013), and directly/indirectly affects wea-ther and climate (Wei et al., 2011; Boucher et al., 2013; Zhang et al., 2013; Zhang R. Y. et al., 2015). As the world’s second largest economy, China has experienced heavy aerosol pollution episodes, with PM2.5 reaching unprecedented high levels in many cities, particularly Beijing and its vicinity, which is one of the nation’s most polluted regions (Zhang et al., 2012).

    A variety of explanations have been proposed for the causes of the heavy pollution episodes (Huang et al., 2014; Sun et al., 2014; Wang H. et al., 2014; Wang X. et al., 2014; Wang Z. et al., 2014). Previous studies found that atmospheric conditions were one of the most important parameters in regulating the cycles of pollution episodes in autumn 2013 in Beijing (Zhang et al., 2009; Guo et al., 2014) and in the North China Plain and other areas of China (Zhang X. Y. et al., 2015). In addition, meteorological factors and the secondary aerosols produced after the initiation of pollution facilitated the occurrence of severe haze in January 2013 (Zheng B. et al., 2015). An analysis of atmospheric background fields in January 2013 revealed that both the dynamic effects, including weak surface wind and vertical wind shear, and the thermodynamic effects, including stratification instability, temperature inversion, and dew-point deficit, substantially affected pollution formation (Zhang et al., 2014). By analyzing a typical autumnal haze episode during 21–26 October 2014, it was found that southerly transport of pollutants prevailed during the transition period and contributions of secondary inorganic species increased greatly in the polluted period (Zhu et al., 2016). Southerly/southwesterly surface winds were found more likely to contribute to severe air pollution in Beijing (Wang Z. B. et al., 2013). Atmospheric chemical reactions, including liquid phase reactions, in which aqueous SO2 is oxidized by NO2, H2O2, and O3 to form sulfate, and heterogeneous reactions, in which NO2 and N2O5 form nitrates with water, are conducive to secondary aerosol formation. High PM2.5 levels suspended in the boundary layer (BL) during hazy weather may remarkably influence local BL meteorology by reforming the regional solar and thermal radiative budgets (Wang et al., 2015), which results in further pollution accumulation.

    Despite these research outcomes, the formation mechanism and evolution of Beijing’s heavy pollution episodes have not been fully understood, particularly with respect to identifying the major contributors to the explosive growth of PM2.5 mass concentration, which rapidly increases by approximately 200 μg m–3, to highly elevated PM2.5 levels. Earlier studies attributed this dramatic increase to fast local chemical production (Wang Z. et al., 2014). This finding is contradicted by later studies, which proposed that the rapid changes are more likely to be caused by the regional transport of clean/polluted air masses based on surface meteorological factors, including wind, humidity, and the PM2.5 mass concentrations in various cities in the North China Plain (Zheng G. J. et al., 2015). However, surface meteorological factors do not suffice to persuasively explain the increase because the explosive growth of PM2.5 levels occasionally occurs with weak surface winds and low humidity. In addition, atmospheric vertical variations in the BL may significantly affect the PM2.5 mass concentration near the ground (Hua et al., 2016), but this has not been extensively investigated. Therefore, the present paper uses vertical measurements of meteorological variables to investigate the relative contributions of meteorological factors in the BL to the explosive growth of PM2.5 during the heavy pollution episodes in Beijing in December 2016; particularly, the red-alert heavy pollution episode will be focused on. The paper also attempts to quantify the effect of meteorological feedback on the explosive growth of PM2.5 levels at the beginning of the cumulative stages of heavy pollution.

    In this study, the following data are used.

    (1) ECMWF atmospheric reanalysis data collected 4 times daily at 0200, 0800, 1400, and 2000 Beijing time (BT) from 15 to 22 December 2016, on a horizontal resolution of 0.5° × 0.5°.

    (2) Hourly PM2.5 mass concentrations measured by national monitoring stations of the Ministry of Environmental Protection of China. The PM2.5 levels of two stations located in Haidian and Fangshan districts were used to represent the pollution conditions of northern and southern Beijing, respectively.

    (3) Hourly ground-level meteorological observations from automatic weather stations (AWSs) provided by the National Meteorological Information Center of the China Meteorological Administration.

    (4) Atmospheric vertical observations twice daily at 0800 and 2000 BT, including winds, temperature, and relative humidity (RH), measured by an L-band radiosonde radar at Observatory 54511 in southern Beijing from 1 December 2016 to 10 January 2017.

    A parameterized index, namely, PLAM (Parameter Linking Aerosol Pollution and Meteorological Ele-ments), is calculated with the observations at Observatory 54511 from 1 December 2016 to 10 January 2017, by use of the method presented in detail in previous studies (Zhang et al., 2009; Wang et al., 2012; Wang J. et al., 2013; Zhang X. Y. et al., 2015)

    3.1   Definitions of heavy pollution and explosive growth of PM2.5

    According to the PM2.5 daily mean mass concentration thresholds in China’s national environmental quality standards, a clean period is defined as a period during which the PM2.5 mass concentration is less than 35 μg m–3, while a pollution episode is defined as a period during which the PM2.5 mass concentration exceeds 80 μg m–3 for 3 consecutive days between two clean periods. Pollution episodes with peak PM2.5 values more than 400 μg m–3 are termed heavy pollution episodes (HPEs), while those with peak PM2.5 values less than 300 μg m–3 are termed light pollution episodes (LPEs).

    Figure 1 shows the temporal evolutions of PM2.5 mass concentration from 1 December 2016 to 10 January 2017 (this period represents typical winter time in Beijing). It is easily found that the measured PM2.5 levels exhibit a clear periodic cycle of 3–7 days, and at least three HPEs occur with each lasting for 5–7 days while multiple LPEs occur with each lasting for 3–4 days.

    Download

    Larger image

    PowerPoint slide

    Fig  1.  Temporal variations in PM2.5, PLAM, and vertical distributions of meteorological factors from 1 December 2016 to 10 January 2017. (a) PM2.5 mass concentration, (b) wind vector and wind velocity (shading; m s–1), (c) temperature (shading; °C), (d) RH (shading; %), and (e) PLAM. Red boxes correspond to original/transport growth processes, while green/white boxes correspond to subsequent/cumulative explosive growth processes.

    In Fig. 1, the LPE period and the early HPE period (red lines) are tentatively referred to as the pollution transport stages (TSs), during which the PM2.5 mass concentration varies more sensitively and rapidly in response to the wind shift from northerly to southerly in the BL (Fig. 1b), compared with the vertical variations of temperature and RH (Figs. 1c, d). The later periods of HPEs (green lines) are tentatively defined as pollution cumulative stages (CSs), when the stable atmospheric stratification characterized by anomalous temperature inversion occurs.

    During the TSs, periods of growth appear, during which the PM2.5 mass concentration rapidly increases from less than 30 to 150–300 μg m–3 in the initial several to 10 h or increases by over 100 μg m–3 rapidly afterwards. These growth periods of the TSs are termed original/transport growth processes. The growth periods of the CSs, when the PM2.5 mass concentration rapidly increases from 100–200 to approximately 400 μg m–3, are defined as subsequent/cumulative explosive growth processes.

    3.2   Meteorological causes of original growth

    3.2.1   Synoptic-scale weather conditions

    We now analyze the atmospheric background fields from 16 to 22 December 2016 during the red-alert aerosol HPEs in Beijing. Figure 2 depicts the surface pressure and 500-hPa geopotential height fields over East Asia. Before pollution formation, a surface high lay northwest of the North China Plain, over which northwesterly surface winds carrying unpolluted air masses from the less populated northern mountainous areas were dominant (Jia et al., 2008; Liu et al., 2013; Guo et al., 2014). The surface high subsequently moved to the south of the plain at 2000 BT 16 December, when southerly air masses from the more populated southern industrial regions dominated over the plain, corresponding to appreciably increased PM2.5 levels (Fig. 1a).

    Download

    Larger image

    PowerPoint slide

    Fig  2.  Distributions of surface pressure and 500-hPa geopotential height at (a) 0800 Beijing Time (BT) 15 December, (b) 2000 BT 16 December; (c) 2000 BT 19 December, and (d) 2000 BT 21 December 2016. Blue contours denote geopotential height, shadings represent surface pressure, and red points from west to east represent cities of Shijiazhuang, Gucheng, Baoding, Beijing, and Tianjin, respectively.

    The North China Plain was governed by uniform pressure that hampered the outward transport of pollutants at 2000 BT 19 December, when relatively stable atmospheric stratification characterized by negligible surface winds occurred and coincided with the beginning of the CS. On 21 December, at 2000 BT, an anomalous surface low over the plain was conducive to air convergence, which resulted in the maintenance and aggravation of aerosol pollution during the CS. A comparison of the 500-hPa contour lines at 0800 BT 15 December (clean) and 2000 BT 16 December (polluted) reveals that the midlatitude meridional circulations were weaker during HPEs, which indicates that the exchange between the northern and southern air masses was weakened, particularly during the CS.

    With the Taihang Mountains blocking the invasion of cold air from the rear of the East Asian trough into eastern China, the unique geographic features of the North China Plain facilitated pollutant accumulation over the plain.

    3.2.2   Pollutant transport from south of Beijing

    The ground-level PM2.5 mass concentration is affected by meteorological factors in the BL, including winds, temperature, RH, and BL height. Figure 1 shows the temporal variation of PM2.5 mass concentration (Fig. 1a) and temporal–vertical distributions of wind, temperature, and RH (Figs. 1b, c, d). Strong northwesterly winds, whose velocity increases with height, dominate in the BL during clean periods with PM2.5 levels less than 35 μg m–3. During the transition between clean and polluted periods, the wind shifts from northerly to southerly with dramatically decreased velocity. This wind shift coincides with the original growth of PM2.5 mass concentration, suggesting that the pollutant transport by southerly wind may be responsible for the growth, since southerly air masses originate from the more populated industrial regions in the south. For example, the PM2.5 mass concentration rapidly increases from 20 to approximately 140 μg m–3 within 12 h after the wind shifts from northerly to southerly at 2000 BT 23 December, and from 10 to over 300 μg m–3 at 1200 BT 30 December.

    The PM2.5 mass concentration varies sensitively and rapidly in response to a wind shift even during the TSs, which is indicative of the primary contribution of southerly pollutant transport to the original growth of PM2.5 levels. For example, during the period from 6 to 8 December, the occurrence of strong northwest winds broke the ascending trend of PM2.5 mass concentration, which quickly rebounded when southerly winds subsequently took over.

    The original growth is featured with a gentle (3.4–5.4 m s–1) or moderate (5.5–7.9 m s–1) wind speed rather than a light (0.3–1.5 m s–1) or calm (0–0.2 m s–1) airflow below 500 m during TSs, suggesting that local emissions under relatively stable atmospheric stratification with substantially lower wind speed are likely conducive but not dominant, with respect to pollution concentration growth. During the growth processes, the southerly air masses migrate more than 288 km day–1 below 500 m (estimated from the measured wind speed), which indicates a substantial impact from the southern PM2.5 transport. During the TSs, air temperature decreases with height without anomalous inversion, and moisture increases compared with clean periods. However, near-ground (< 250 m) RH varies substantially (40%–70%) among different TSs, which indicates that the original growth is unlikely to be primarily caused by vertical variations of temperature and RH. Nevertheless, weak inversion and near-ground moisture accumulation favor the growth of pollution.

    The contribution of southerly pollutant transport to original growth is further assessed by examining in detail the red-alert HPE of 16–22 December 2016. Four substantial decline processes (see Fig. 3, processes 1–4) appear, with clean northerly winds dominating southern and northern Beijing, while three growth processes (Fig. 3, processes A, B, D), before which the PM2.5 levels of southern cities (including Baoding and Shijiazhuang) were clearly higher than that of Beijing, concurrently occur with polluted southerly winds governing southern and northern Beijing. In the third growth process (Fig. 3, process C), relatively clean northern and relatively polluted southern air masses converge in the urban area of Beijing, resulting in cyclonic wind shear. Such a shear is favorable for pollutant accumulation, which is also aggravated by the occurrence of weak inversion and near-surface moisture accumulation (Figs. 1c, d).

    Download

    Larger image

    PowerPoint slide

    Fig  3.  Temporal variations in PM2.5 and wind direction in Beijing and its vicinity from 14 to 20 December 2016. (a) Wind direction in northern Beijing, (b) wind direction in southern Beijing, (c) wind direction in Gucheng, and (d) evolutions of urban mean PM2.5 mass concentration in Beijing (blue line), Baoding (orange line), and Shijiazhuang (green line). The red boxes mark the rapid growth of PM2.5 concentration while black boxes mark the substantial decline of PM2.5 concentration.

    Southerly pollutant transport primarily results in the original growth of PM2.5 levels, which is also facilitated by the occurrence of weak inversion and the near-surface moisture accumulation. Warm and humid airflows from the south transport more water vapor and pollutants to the North China Plain, creating the requisite moisture and pollution accumulation conditions for the subsequent explosive growth.

    3.3   Meteorological conditions for the subsequent explosive growth

    3.3.1   Pre-existing PM2.5 levels likely resulting in anomalous inversion

    The existing PM2.5 levels established by the original growth preceded the anomalous temperature inversion that occurred during the four CSs (see Fig. 1c). Common causes of the temperature inversion in Beijing include advection, topography, and radiation. Advection inversion occurs when a warm and less dense air mass moves over a cold and dense air mass. In the present case, the temperature inversion in the CSs occurred with light or calm winds, suggesting that the contribution of warm/cold advection is limited. Although the Taihang Mountains are situated to the north of Beijing and occasionally cold air climbs over the mountain peak down into the urban area of Beijing, in the case of this study, almost no striking northerly winds were observed during the CSs, indicating that topography contributes limitedly to the inversion. Definitely, at night, the ground would emit radiation that exceeds the amount of radiation received from the sun during daytime, which serves to create inversion, namely, radiation inversion. On the other hand, the radiative cooling effect of pre-existing aerosols would remarkably backscatter a large amount of radiation received by the ground to space to reinforce surface temperature reduction.

    With existing high PM2.5 levels, it is inferred that the radiation inversion is partly caused by the radiation cooling of aerosols. In addition, the radiative cooling effect of pre-existing aerosols (Wang J. et al., 2014), which are concentrated below 350 m (Fig. 4), would result in more noticeable decrease in temperature in the near-surface layer than in the higher layer. Previous studies reported that aerosol particle size increased by 20%–60% after moisture absorption in North China (Pan et al., 2009), and aerosol direct radiative forcing increased by approximately 50% (Che et al., 2015). These findings suggest that the feedback of pre-existing aerosols might play a major role in the formation of temperature inversion during the four CSs.

    Download

    Larger image

    PowerPoint slide

    Fig  4.  Vertical variations of PM number concentration in Beijing on 19 December 2016. The gray line marks the boundary layer height (approximately 340 m) at which the PM number concentration substantially decreases.
    3.3.2   Anomalous inversion resulting in additional accumulation of pollutants and moisture

    The subsequent explosive growth of PM2.5 levels in the four CSs coincides with the anomalous inversion as shown in Figs. 1a, c. This suggests that the inversion, with strong static stability trapping pollution-laden air beneath it, facilitates pollutant accumulation. For example, PM2.5 mass concentration rapidly increased from less than 300 μg m–3 on 31 December to approximately 500 μg m–3 on 1 January, in company with the occurrence of the anomalous inversion.

    Figures 1c and 1d show the temporal–vertical variations of temperature and RH in the BL from 1 December 2016 to 10 January 2017. A comparison of the four CSs and the seven TSs indicates that the near-ground moisture accumulation is not striking in the absence of temperature inversion, and the RH of the upper BL is occasionally higher than that of the near-ground surface. For example, the RH distributed evenly in the BL is less than 40% during clean periods, while the RH is approximately 60% at the near ground and over 80% above 750 m during the TSs from 11 to 13 December and from 24 to 26 December. When weak inversion occurs in the BL, the lower edge of the inversion layer is in approximate agreement with the RH contour of 60%. The RH of less than 40% (even 20%) over the inversion layer is distinctive from that below the layer. For example, during the TS from 16 to 20 December, the RH in the near-ground air below the inversion layer is maintained at 60%–80%, while the RH over the inversion layer is less than 20%. The sharp increase in near-surface moisture with the brief inversion on 8 December indicates that the vertical distribution of RH is likely under the control of temperature inversion. When an anomalous inversion layer occurs in the BL, the lower edge of the strong inversion layer coincides with the RH contour of 80%. The RH over the inversion layer differs substantially from that below the layer. Moisture accumulates anomalously below the inversion layer with RH over 80% in the near-ground atmosphere. For example, near-ground RH congregated increasingly with the stepped descending inversion from 1 to 2 January 2017.

    The previously noted relation between vertical temperature and RH indicates that anomalous inversion results in appreciable near-surface moisture accumulation by suppressing the vertical mixing of water vapor (Wallace and Hobbs, 2006). The vertical diffusion of the near-surface water vapor as the anomalous inversion disappeared on 5 January 2017 verifies the cited research outcome.

    3.3.3   Moisture accumulation facilitating aerosol hygro-scopic growth and additional secondary aerosol

    We found that PM2.5 mass concentration substantially increases with RH when RH exceeds 20% (Fig. 5). The PM2.5 concentration is approximately 50, 100, 200, and 300 μg m–3, respectively, when the range of RH stays between 20%–40%, 40%–60%, 60%–80%, and 80%–100%. This indicates that RH remarkably influences the increase of PM2.5 concentration levels. When additional water vapor appears in the air, strong absorbent aerosols such as sulfate, nitrate, ammonium, and soluble organic particles, absorb the moisture and grow (Che et al., 2015). Aerosol particle size increases 20%–60% after moisture absorption in North China (Pan et al., 2009).

    Download

    Larger image

    PowerPoint slide

    Fig  5.  Distribution of urban surface PM2.5 mass concentration in different ranges of RH from 1 December 2016 to 10 January 2017. The green nucleuses are the kernel density plots of PM2.5 concentration and the black columns are the box plots of PM2.5 concentration.

    Aerosol water is a key component of atmospheric aerosols and serves as a medium that enables aqueous-phase reactions (Pilinis et al., 1989; Ervens et al., 2011). For example, aerosol water serves as a reactor in which alkaline aerosol components trap the SO2 oxidized by NO2 to form sulfate, whereby high reaction rates are sustained by the high neutralizing capacity of the atmosphere in northern China (Cheng et al., 2016). The ratio of SO2 to SO42– ranges from less than 0.1 at RH < 20% to 1.1 at RH > 90%, exhibiting an exponential increase with RH (Wang et al., 2016). In addition, high RH is conducive to heterogeneous chemical processes, which accelerate the formation of air pollution (Zhu et al., 2011). For example, the net reaction probability of HNO3 uptake on CaCO3 particles was found to increase with increasing RH from about 0.003 at 10% to 0.21 at 80% (Liu et al., 2008). Under heavy pollution, humidity-related processes produced most atmospheric particles (Tang et al., 2016).

    3.3.4   Overall causes for subsequent explosive growth

    A comparison between the temopral variations in PM2.5 mass concentration and in the vertical distributions of wind, temperature, and RH in Beijing from 1 December 2016 to 10 January 2017 shows that stable atmospheric stratification primarily characterized by light/calm southerly winds, anomalous temperature inversion, and accumulated moisture (RH > 80%) below 250 m, occurs concurrently with the subsequent explosive growth of PM2.5 levels in the four CSs.

    The anomalous temperature inversion facilitates vertical accumulation of pollutants through suppressing convective activities, while light/calm southerly winds precluding the invasion of northerly cold winds facilitates local pollution accumulation by minimizing horizontal pollutant diffusion. Decreased near-ground solar radiation caused by the cooling effect of aerosols reduces the BL height (less than 350 m, Fig. 4) to condense pollutants. Further accumulated near-surface moisture (RH > 80%) caused by the previously noted ground temperature decrease and inhibited vertical mixing of water vapors is conducive to secondary aerosol formation to increase PM2.5 concentration levels. The joint effects of light/calm southerly winds, anomalous temperature inversion partly induced by radiative cooling of pre-existing aerosols, and accumulated moisture, result in the subsequent explosive growth of PM2.5 in the CSs.

    High PM2.5 concentration levels accumulated from the subsequent explosive growth further decrease the near-ground temperature by reflecting and scattering more so-lar radiation, thus strengthening the existing anomalous inversion to maintain or accumulate pollutants until the next synoptic process occurs (Fig. 1c). For example, the near-surface temperature ranged from 3°C at 2000 BT 19 December to –3°C at 0800 BT 20 December after elevated ground PM2.5 levels. The near-surface temperature remained at –1°C with PM2.5 of more than 400 μg m–3 over the next 2 days until northerly strong, clean winds blew the pollution away on 22 December.

    3.4   Quantification of meteorological feedback effect on the subsequent explosive growth

    The above analysis reveals that atmospheric factors have major impacts on the formation and maintenance of HPEs. To further examine the influence of meteorologi-cal conditions on PM, a comprehensive meteorological index, i.e., PLAM, is used. PLAM is derived from the relationship of PM2.5 with key meteorological parameters and primarily reflects the stability of the air mass and the condensation rate of water vapor on aerosol particles. The PLAM index was employed to evaluate the contribution of meteorological factors to changes in atmospheric composition and optical properties over Beijing during the 2008 Olympic Games (Zhang et al., 2009) and to identify the contribution of specific meteorological factors to a 10-day haze–fog event in 2013 (Zhang et al., 2013). The details have been described in Wang et al. (2012) and Wang Z. B. et al. (2013). Figures 1a and 1e show that the hourly variation of urban mean PM2.5 mass concentration is in close agreement with that of PLAM for Beijing. The squared correlation coefficient is up to 0.70, exceeding the 0.05 significance level (Fig. 6), which suggests that the appearance of the HPEs in December may primarily be caused by meteorological factors, which explain approximately 70% of the PM2.5 concentration variation.

    Download

    Larger image

    PowerPoint slide

    Fig  6.  Correlation between PM2.5 concentration and PLAM from 1 December 2016 to 10 January 2017.

    To further analyze the meteorological feedback effect of the more stable BL caused by the interaction between pre-existing aerosols and radiation on explosive growth of PM2.5 during the CSs, the correlation coefficient of PLAM and PM2.5 during the early CSs is calculated. The correlation coefficients are 0.73–0.93, exceeding the 0.05 significance level (Figs. 7a–d). This indicates that the meteorological feedback effect on PM2.5 accounts for approximately 84% on average, with a range of 73%–93%, in different stages of the explosive growth of PM2.5 mass concentration during the early CSs.

    Download

    Larger image

    PowerPoint slide

    Fig  7.  Correlation between PM2.5 concentration and PLAM in (a–d) four subsequent explosive growth stages.

    In this paper, the relative contributions of meteorological factors within the boundary layer to the explosive growth of PM2.5 mass concentration were elucidated for both the initial PM transport stages and the subsequent PM accumulation stages. Pollutants transported from the south of Beijing primarily result in the formation of PM2.5 pollution in Beijing. Typically, during this pollution transport stage, warm and humid airflow from the south transports more water vapor and pollutants to the North China Plain, creating the requisite moisture and pollution accumulation conditions for the subsequent explosive growth of PM2.5 and subsequent accumulation.

    When pollution occurs in Beijing with a certain amount of PM mass concentration within the BL, decreased surface solar radiation attributable to the radiative cooling effect of existing aerosols likely results in anomalous temperature inversion. This anomalous temperature inversion facilitates vertical pollutant accumulation by suppressing convective activities, while light/calm southerly winds that preclude the invasion of northerly cold winds facilitates local pollution accumulation by minimizing horizontal pollutant diffusion. Decreased surface solar radiation and weakened horizontal winds caused by the cooling effect of aerosols reduce the BL height, which is conducive to pollutant accumulation.

    These results are also confirmed by a significant increase in the PLAM index and PM2.5 mass during the early cumulative stages of PM2.5. Using the change in the PLAM index to indicate the increase in PM2.5 due to worsening weather conditions, we observed that approximately 84% of the total amount of explosive growth in PM2.5 mass concentration during the early cumulative stages of the HPEs in Beijing in December 2016 could be attributable to the feedback effect on PM2.5 of the deterioration of the meteorological conditions.

    The weak vertical mixing of moisture suppressed by anomalous temperature inversion and the ground temperature decrease caused by the cooling effects of PM result in appreciable near-surface moisture accumulation (RH > 80%). This moisture accumulation facilitates pollutant accumulation by enhancing hygroscopic growth and accelerating liquid phase and heterogeneous reactions. However, these specific reaction mechanisms have not been fully understood, particularly their contributions to explosive growth and maintenance of PM2.5 during the cumulative stages of pollution. This topic requires additional investigation.

    Exceedingly high PM2.5 concentration levels accumulated from the subsequent explosive growth further decreases the near-ground temperature by reflecting and scattering more solar radiation, and strengthening the existing anomalous temperature inversion to maintain or accumulate pollutants until the next synoptic process occurs.

  • Fig.  1.   Temporal variations in PM2.5, PLAM, and vertical distributions of meteorological factors from 1 December 2016 to 10 January 2017. (a) PM2.5 mass concentration, (b) wind vector and wind velocity (shading; m s–1), (c) temperature (shading; °C), (d) RH (shading; %), and (e) PLAM. Red boxes correspond to original/transport growth processes, while green/white boxes correspond to subsequent/cumulative explosive growth processes.

    Download: Larger image PowerPoint slide

    Fig.  2.   Distributions of surface pressure and 500-hPa geopotential height at (a) 0800 Beijing Time (BT) 15 December, (b) 2000 BT 16 December; (c) 2000 BT 19 December, and (d) 2000 BT 21 December 2016. Blue contours denote geopotential height, shadings represent surface pressure, and red points from west to east represent cities of Shijiazhuang, Gucheng, Baoding, Beijing, and Tianjin, respectively.

    Download: Larger image PowerPoint slide

    Fig.  3.   Temporal variations in PM2.5 and wind direction in Beijing and its vicinity from 14 to 20 December 2016. (a) Wind direction in northern Beijing, (b) wind direction in southern Beijing, (c) wind direction in Gucheng, and (d) evolutions of urban mean PM2.5 mass concentration in Beijing (blue line), Baoding (orange line), and Shijiazhuang (green line). The red boxes mark the rapid growth of PM2.5 concentration while black boxes mark the substantial decline of PM2.5 concentration.

    Download: Larger image PowerPoint slide

    Fig.  4.   Vertical variations of PM number concentration in Beijing on 19 December 2016. The gray line marks the boundary layer height (approximately 340 m) at which the PM number concentration substantially decreases.

    Download: Larger image PowerPoint slide

    Fig.  5.   Distribution of urban surface PM2.5 mass concentration in different ranges of RH from 1 December 2016 to 10 January 2017. The green nucleuses are the kernel density plots of PM2.5 concentration and the black columns are the box plots of PM2.5 concentration.

    Download: Larger image PowerPoint slide

    Fig.  6.   Correlation between PM2.5 concentration and PLAM from 1 December 2016 to 10 January 2017.

    Download: Larger image PowerPoint slide

    Fig.  7.   Correlation between PM2.5 concentration and PLAM in (a–d) four subsequent explosive growth stages.

    Download: Larger image PowerPoint slide
    • References (36)
  • Boucher, O., D. Randall, P. Artaxo, et al., 2013: Clouds and aerosols. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., D. Qin, G.-K. Plattner, et al., Eds. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 571–658. Online Link
    Che, H., X. Y. Zhang, X. Xia, et al., 2015: Ground-based aerosol climatology of China: Aerosol optical depths from the China Aerosol Remote Sensing Network (CARSNET) 2002–2013. Atmos. Chem. Phys. Disc., 15, 12715–12776. doi: 10.5194/acpd-15-12715-2015
    Chen, Y., A. Ebenstein, M. Greenstone, et al., 2013: Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy. Proc. Natl. Acad. Sci. USA, 110, 12936–12941. doi: 10.1073/pnas.1300018110
    Cheng, Y., G. Zheng, W. Chao, et al., 2016: Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Sci. Adv., 2 . doi: 10.1126/sciadv.1601530
    Ervens, B., B. J. Turpin, and R. J. Weber, 2011: Secondary orga-nic aerosol formation in cloud droplets and aqueous particles (aqSOA): A review of laboratory, field and model studies. Atmos. Chem. Phys., 11, 11069–11102. doi: 10.5194/acp-11-11069-2011
    Guo, S., M. Hu, M. L. Zamora, et al., 2014: Elucidating severe urban haze formation in China. Proc. Natl. Acad. Sci. USA, 111, 17373–17378. doi: 10.1073/pnas.1419604111
    Hua, Y., S. Wang, J. Wang, et al., 2016: Investigating the impact of regional transport on PM2.5 formation using vertical observation during APEC 2014 Summit in Beijing. Atmos. Chem. Phys., 16, 15451–15460. doi: 10.5194/acp-16-15451-2016
    Huang, R.-J., Y. Zhang, C. Bozzetti, et al., 2014: High secondary aerosol contribution to particulate pollution during haze events in China. Nature, 514, 218–222. doi: 10.1038/nature13774
    Jia, Y., K. A. Rahn, K. He, et al., 2008: A novel technique for quantifying the regional component of urban aerosol solely from its sawtooth cycles. J. Geophys. Res. Atmos., 113, 6089–6098. doi: 10.1029/2008JD01038
    Liu, Y., E. R. Gibson, J. P. Cain, et al., 2008: Kinetics of heterogeneous reaction of CaCO3 particles with gaseous HNO3 over a wide range of humidity. J. Phys. Chem. A, 112, 1561. doi: 10.1021/jp076169h
    Liu, X. G., J. Li, Y. Qu, et al., 2013: Formation and evolution mechanism of regional haze: A case study in the megacity Beijing, China. Atmos. Chem. Phys., 13, 4501–4514. doi: 10.5194/acp-13-4501-2013
    Pan, X. L., P. Yan, J. Tang, et al., 2009: Observational study of influence of aerosol hygroscopic growth on scattering coefficient over rural area near Beijing mega-city. Atmos. Chem. Phys., 9, 7519–7530. doi: 10.5194/acp-9-7519-2009
    Pilinis, C., J. H. Seinfeld, and D. Grosjean, 1989: Water content of atmospheric aerosols. Atmos. Environ., 23, 1601–1606. doi: 10.1016/0004-6981(89)90419-8
    Sun, Y., Q. Jiang, Z. Wang, et al., 2014: Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013. J. Geophys. Res. Atmos., 119, 4380–4398. doi: 10.1002/2014JD021641
    Tang, G., J. Zhang, X. 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
    Wallace, J. M., and P. V. Hobbs, 2006: Atmospheric Science: An Introductory Survey. Elsevier, 483 pp.
    Wang, J. Z., S. Gong, X. Y. Zhang, et al., 2012: A parameterized method for air-quality diagnosis and its applications. Adv. Meteor., 3181–3190. doi: 10.1155/2012/238589
    Wang, J., Y. Wang, H. Liu, et al., 2013: Diagnostic identification of the impact of meteorological conditions on PM2.5 concentrations in Beijing. Atmos. Environ., 81, 158–165. doi: 10.1016/j.atmosenv.2013.08.033
    Wang, Z. B., M. Hu, Z. J. Wu, et al., 2013: Long-term measurements of particle number size distributions and the relationships with air mass history and source apportionment in the summer of Beijing. Atmos. Chem. Phys., 13, 10159–10170. doi: 10.5194/acpd-13-5165-2013
    Wang, H., J. An, L. Shen, et al., 2014: Mechanism for the formation and microphysical characteristics of submicron aerosol during heavy haze pollution episode in the Yangtze River Delta, China. Sci. Total Environ., 490, 501–508. doi: 10.1016/j.scitotenv.2014.05.009
    Wang, J., S. Wang, J. Jiang, et al., 2014: Impact of aerosol–meteorology interactions on fine particle pollution during China’s severe haze episode in January 2013. Environ. Res. Lett., 9, 094002. doi: 10.1088/1748-9326/9/9/094002
    Wang, X., J. Chen, J. Sun, et al., 2014: Severe haze episodes and seriously polluted fog water in Ji’nan, China. Sci. Total Environ., 493, 133–137. doi: 10.1016/j.scitotenv.2014.05.135
    Wang, Z., J. Li, Z. Wang, et al., 2014: Modeling study of regional severe hazes over mid–eastern China in January 2013 and its implications on pollution prevention and control. Sci. China Earth Sci., 57, 3–13. doi: 10.1007/s11430-013-4793-0
    Wang, H., M. Xue, X. Y. Zhang, et al., 2015: Mesoscale modeling study of the interactions between aerosols and PBL meteorology during a haze episode in Jing–Jin–Ji (China) and its nearby surrounding region – Part 1: Aerosol distributions and meteorological features. Atmos. Chem. Phys., 15, 3257–3275. doi: 10.5194/acp-15-3257-2015
    Wang, G., R. Zhang, M. E. Gomez, et al., 2016: Persistent sulfate formation from London Fog to Chinese haze. Proc. Natl. Acad. Sci. USA, 113, 13630. doi: 10.1073/pnas.1616540113
    Wei, P., S. Y. Cheng, J. B. Li, et al., 2011: Impact of boundary-layer anticyclonic weather system on regional air quality. Atmos. Environ., 45, 2453–2463. doi: 10.1016/j.atmosenv.2011.01.045
    Zhang, X. Y., Y. Q. Wang, W. L. Lin, et al., 2009: Changes of atmospheric composition and optical properties over Beijing—2008 Olympic monitoring campaign. Bull. Amer. Meteor. Soc., 90, 1633. doi: 10.1175/2009BAMS2804.1
    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., 11, 26571–26615. doi: 10.5194/acp-12-779-2012
    Zhang, X., J. Sun, Y. Wang, et al., 2013: Factors contributing to haze and fog in China. Chinese Sci. Bull., 58, 1178. (in Chinese) doi: 10.1360/972013-150
    Zhang, R. H., Q. Li, and R. N. Zhang, 2014: Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013. Sci. China Earth Sci., 57, 26–35. doi: 10.1007/s11430-013-4774-3
    Zhang, R. Y., G. H. Wang, S. Guo, et al., 2015: Formation of ur-ban fine particulate matter. Chemical Reviews, 115, 3803–3855. doi: 10.1021/acs.chemrev.5b00067
    Zhang, X. Y., J. Z. Wang, Y. Q. Wang, et al., 2015: Changes in chemical components of aerosol particles in different haze regions in China from 2006 to 2013 and contribution of meteorological factors. Atmos. Chem. Phys., 15, 12935–12952. doi: 10.5194/acp-15-12935-2015
    Zheng, B., Q. Zhang, Y. Zhang, et al., 2015: Heterogeneous chemistry: A mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China. Atmos. Chem. Phys., 15, 2031–2049. doi: 10.5194/acp-15-2031-2015
    Zheng, G. J., F. K. Duan, H. Su, et al., 2015: Exploring the severe winter haze in Beijing: The impact of synoptic weather, regional transport and heterogeneous reactions. Atmos. Chem. Phys., 15, 2969–2983. doi: 10.5194/acp-15-2969-2015
    Zhu, T., J. Shang, and D. F. Zhao, 2011: The roles of heterogeneous chemical processes in the formation of an air pollution complex and gray haze. Sci. China Chem., 54, 145–153. doi: 10.1007/s11426-010-4181-y
    Zhu, X., G. Tang, B. Hu, et al., 2016: Regional pollution and its formation mechanism over North China Plain: A case study with ceilometer observations and model simulations. J. Geophys. Res. Atmos., 121, 14574–14588. doi: 10.1002/2016JD025730
    • Relative Articles

  • Related Articles

    Xi GONG, Rong ZHU, Lianshou CHEN. 2019: Characteristics of Near Surface Winds over Different Underlying Surfaces in China: Implications for Wind Power Development. Journal of Meteorological Research, 33(2): 349-362. DOI: 10.1007/s13351-019-8126-x
    Wanlin DONG, Hang YU, Lizhen ZHANG, Ruonan WANG, Qi WANG, Qingwu XUE, Zhihua PAN, Zhigang SUN, Xuebiao PAN. 2018: Asymmetric Ridge–Furrow and Film Cover Improves Plant Morphological Traits and Light Utilization in Rain-Fed Maize. Journal of Meteorological Research, 32(5): 829-838. DOI: 10.1007/s13351-018-8024-7
    Yangruixue CHEN, Yali LUO. 2018: Analysis of Paths and Sources of Moisture for the South China Rainfall during the Presummer Rainy Season of 1979–2014. Journal of Meteorological Research, 32(5): 744-757. DOI: 10.1007/s13351-018-8069-7
    Zhaobin SUN, Xiaoling ZHANG, Xiujuan ZHAO, Xiangao XIA, Shiguang MIAO, Ziming LI, Zhigang CHENG, Wei WEN, Yixi TANG. 2018: Oscillation of Surface PM2.5 Concentration Resulting from an Alternation of Easterly and Southerly Winds in Beijing: Mechanisms and Implications. Journal of Meteorological Research, 32(2): 288-301. DOI: 10.1007/s13351-018-7064-3
    Yan REN, Shuwen ZHENG, Wei WEI, Bingui WU, Hongsheng ZHANG, Xuhui CAI, Yu SONG. 2018: Characteristics of Turbulent Transfer during Episodes of Heavy Haze Pollution in Beijing in Winter 2016/17. Journal of Meteorological Research, 32(1): 69-80. DOI: 10.1007/s13351-018-7072-3
    Xiaojing SHEN, Junying SUN, Xiaoye ZHANG, Yangmei ZHANG, Yaqiang WANG, Kaiyan TAN, Peng WANG, Lu ZHANG, Xuefei QI, Haochi CHE, Zhouxiang ZHANG, Junting ZHONG, Huarong ZHAO, Sanxue REN. 2018: Comparison of Submicron Particles at a Rural and an Urban Site in the North China Plain during the December 2016 Heavy Pollution Episodes. Journal of Meteorological Research, 32(1): 26-37. DOI: 10.1007/s13351-018-7060-7
    Yangmei ZHANG, Yaqiang WANG, Xiaoye ZHANG, Xiaojing SHEN, Junying SUN, Lingyan WU, Zhouxiang ZHANG, Haochi CHE. 2018: Chemical Components, Variation, and Source Identification of PM1 during the Heavy Air Pollution Episodes in Beijing in December 2016. Journal of Meteorological Research, 32(1): 1-13. DOI: 10.1007/s13351-018-7051-8
    Ting WANG, Jie XIANG, Jianfang FEI, Yi WANG, Chunxia LIU, Yuanxiang LI. 2017: Evaluation of the Impact of Observations on Blended Sea Surface Winds in a Two-Dimensional Variational Scheme Using Degrees of Freedom. Journal of Meteorological Research, 31(6): 1123-1132. DOI: 10.1007/s13351-017-6798-7
    Boheng DUAN, Weimin ZHANG, Xiaoqun CAO, Yi YU, Haijin DAI. 2017: Assimilation of HY-2A Scatterometer Ambiguous Winds Based on Feature Thinning. Journal of Meteorological Research, 31(4): 720-730. DOI: 10.1007/s13351-017-6165-8
    Ting YOU, Renguang WU, Gang HUANG, Guangzhou FAN. 2017: Regional Meteorological Patterns for Heavy Pollution Events in Beijing. Journal of Meteorological Research, 31(3): 597-611. DOI: 10.1007/s13351-017-6143-1
    • Supplements (0)

  • Cited by

    Periodical cited type(120)

    1. Ruting Zhang, Chuanmin Chen, Songtao Liu, et al. Study on the emission reduction effect of emergency management and control measures in Tangshan city, Hebei province, China. Air Quality, Atmosphere & Health, 2025. DOI:10.1007/s11869-025-01772-x
    2. Lilai Song, Zhen Li, Jinqiu Zhang, et al. An hourly and localized optimization method for soil fugitive dust emission inventory based on machine learning. Journal of Environmental Sciences, 2025, 158: 1. DOI:10.1016/j.jes.2024.12.016
    3. Xinyan Wang, Linxiao Zhu, Yueting Hao, et al. Impacts of aerosol meteorological feedback on China's yield potential of soybean. Meteorological Applications, 2024, 31(2) DOI:10.1002/met.2198
    4. Xiuzhen Chen, Qingqing He, Tong Ye, et al. Decoding spatiotemporal dynamics in atmospheric CO2 in Chinese cities: Insights from satellite remote sensing and geographically and temporally weighted regression analysis. Science of The Total Environment, 2024, 908: 167917. DOI:10.1016/j.scitotenv.2023.167917
    5. Yuqing Zhang, Tianliang Zhao, Xiaoyun Sun, et al. Ozone pollution aggravated by mountain-valley breeze over the western Sichuan Basin, Southwest China. Chemosphere, 2024, 361: 142445. DOI:10.1016/j.chemosphere.2024.142445
    6. 王俊博 Wang Junbo, 王筱晔 Wang Xiaoye, 任超 Ren Chao, et al. 基于多普勒激光雷达的青岛地区大气物质边界层高度反演与分析. Acta Optica Sinica, 2024, 44(6): 0601005. DOI:10.3788/AOS230794
    7. Xun Zhang, Lijie Wu, Xiansheng Liu, et al. New particle formation event detection with convolutional neural networks. Atmospheric Environment, 2024, 327: 120487. DOI:10.1016/j.atmosenv.2024.120487
    8. Jung-Woo Yoo, Soon-Young Park, Wonbae Jeon, et al. Understanding the physical mechanisms of PM2.5 formation in Seoul, Korea: assessing the role of aerosol direct effects using the WRF-CMAQ model. Air Quality, Atmosphere & Health, 2024. DOI:10.1007/s11869-024-01538-x
    9. Xiaoyun Sun, Tianliang Zhao, Jun Hu, et al. Inverse effects of aerosol radiative forcing on heavy PM2.5 pollution of local accumulation and regional transport over Central China. Science of The Total Environment, 2024, 917: 170319. DOI:10.1016/j.scitotenv.2024.170319
    10. Jiayuan Lu, Xiaojing Shen, Qianli Ma, et al. Size-resolved effective density of ambient aerosols measured by an AAC–SMPS tandem system in Beijing. Atmospheric Environment, 2024, 318: 120226. DOI:10.1016/j.atmosenv.2023.120226
    11. Wenjie Zhang, Hong Wang, Xiaoye Zhang, et al. The impact of aerosol-cloud interaction on mesoscale numerical weather prediction when low-cloud and haze coexist in winter over major polluted regions of China. Atmospheric Environment, 2024, 319: 120270. DOI:10.1016/j.atmosenv.2023.120270
    12. Tian Zhang, Renhe Zhang, Junting Zhong, et al. The vertical pollution structure reflected by aerosol optical properties in Beijing and its relation with meteorological conditions in the autumn and winter of 2017–2020. Atmospheric Environment, 2023, 308: 119870. DOI:10.1016/j.atmosenv.2023.119870
    13. Lei Liu, Yu Shi, Zhe Zhang, et al. Variability of turbulence dispersion characteristics during heavy haze process: A case study in Beijing. Journal of Environmental Sciences, 2023, 124: 440. DOI:10.1016/j.jes.2021.10.034
    14. Jialing Wang, Lingyan Wu, Junying Sun, et al. Characteristics of PM2.5 and secondary inorganic pollution formation during the heating season of 2021 in Beijing. Frontiers in Environmental Science, 2023, 10 DOI:10.3389/fenvs.2022.1028468
    15. Zhanyong Wang, Ruhui Cao, Bai Li, et al. Characterizing nighttime vertical profiles of atmospheric particulate matter and ozone in a megacity of south China using unmanned aerial vehicle measurements. Environmental Research, 2023, 236: 116854. DOI:10.1016/j.envres.2023.116854
    16. Lin Shen, Jiaoshi Zhang, Yin Cheng, et al. Characterization of the vertical evolution of urban nocturnal boundary layer by UAV measurements: Insights into relations to cloud radiative effect. Environmental Research, 2023, 232: 116323. DOI:10.1016/j.envres.2023.116323
    17. S. P. Gautam, A. Silwal, B. Baral, et al. Influence of the rainfall and temperature oscillation on air quality in Kathmandu valley: The wavelet analysis. Environmental Engineering Research, 2023, 28(6): 220694. DOI:10.4491/eer.2022.694
    18. 倩惠 李, 宏昇 张, 小曳 张, et al. 华北平原霾污染天气大气边界层空间结构综合观测&mdash;&mdash;COATS实验. SCIENTIA SINICA Terrae, 2023, 53(5): 928. DOI:10.1360/SSTe-2022-0310
    19. Tian Zhang, Renhe Zhang, Junting Zhong, et al. Classification and estimation of unfavourable boundary-layer meteorological conditions in Beijing for PM2.5 concentration changes using vertical meteorological profiles. Atmospheric Research, 2023, 293: 106902. DOI:10.1016/j.atmosres.2023.106902
    20. Wei Wei, Hongsheng Zhang, Xiaoye Zhang, et al. Low-level jets and their implications on air pollution: A review. Frontiers in Environmental Science, 2023, 10 DOI:10.3389/fenvs.2022.1082623
    21. Min Shao, Xiaoying Xu, Yutong Lu, et al. Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China. Science of The Total Environment, 2023, 855: 158785. DOI:10.1016/j.scitotenv.2022.158785
    22. Yue Peng, Hong Wang, Xiaoye Zhang, et al. Superimposed effects of typical local circulations driven by mountainous topography and aerosol–radiation interaction on heavy haze in the Beijing–Tianjin–Hebei central and southern plains in winter. Atmospheric Chemistry and Physics, 2023, 23(14): 8325. DOI:10.5194/acp-23-8325-2023
    23. Qianhui Li, Hongsheng Zhang, Xiaoye Zhang, et al. COATS: Comprehensive observation on the atmospheric boundary layer three-dimensional structure during haze pollution in the North China Plain. Science China Earth Sciences, 2023, 66(5): 939. DOI:10.1007/s11430-022-1092-y
    24. Zhuozhi Shu, Tianliang Zhao, Yubao Liu, et al. Impact of deep basin terrain on PM2.5 distribution and its seasonality over the Sichuan Basin, Southwest China. Environmental Pollution, 2022, 300: 118944. DOI:10.1016/j.envpol.2022.118944
    25. Xuefang Wu, Tuan V. Vu, Roy M. Harrison, et al. Long-term characterization of roadside air pollutants in urban Beijing and associated public health implications. Environmental Research, 2022, 212: 113277. DOI:10.1016/j.envres.2022.113277
    26. Yuanxin Liang, Huizheng Che, Hong Wang, et al. Aerosols Direct Radiative Effects Combined Ground-Based Lidar and Sun-Photometer Observations: Cases Comparison between Haze and Dust Events in Beijing. Remote Sensing, 2022, 14(2): 266. DOI:10.3390/rs14020266
    27. Lihui Lv, Tianshu Zhang, Yan Xiang, et al. Distribution and transport characteristics of fine particulate matter in beijing with mobile lidar measurements from 2015 to 2018. Journal of Environmental Sciences, 2022, 115: 65. DOI:10.1016/j.jes.2021.06.013
    28. Deying Wang, Jizhi Wang, Yuanqin Yang, et al. Study on the Precursor Signal Capturing of Unfavorable Weather: Months/Years in Advance to Ultra-Early Forecast for Hourly Transient Weather Changes during the Beijing Winter Olympics. Advances in Meteorology, 2022, 2022: 1. DOI:10.1155/2022/1409229
    29. Jiejun Zhang, Pengfei Liu, Hongquan Song, et al. Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin. International Journal of Environmental Research and Public Health, 2022, 19(22): 15060. DOI:10.3390/ijerph192215060
    30. Heido Trofimov, Piia Post, Edward Gryspeerdt, et al. Meteorological Conditions Favorable for Strong Anthropogenic Aerosol Impacts on Clouds. Journal of Geophysical Research: Atmospheres, 2022, 127(4) DOI:10.1029/2021JD035871
    31. Wenjie Zhang, Hong Wang, Xiaoye Zhang, et al. The Different Impacts of Emissions and Meteorology on PM2.5 Changes in Various Regions in China: A Case Study. Atmosphere, 2022, 13(2): 222. DOI:10.3390/atmos13020222
    32. Ziyue Chen, Miaoqing Xu, Bingbo Gao, et al. Causation inference in complicated atmospheric environment. Environmental Pollution, 2022, 303: 119057. DOI:10.1016/j.envpol.2022.119057
    33. Meeta Cesler‐Maloney, William R. Simpson, Tate Miles, et al. Differences in Ozone and Particulate Matter Between Ground Level and 20 m Aloft are Frequent During Wintertime Surface‐Based Temperature Inversions in Fairbanks, Alaska. Journal of Geophysical Research: Atmospheres, 2022, 127(10) DOI:10.1029/2021JD036215
    34. Haichuan Long, Qiying Chen, Xi Gong, et al. Evaluation of the Planetary Boundary Layer Height in China Predicted by the CMA-GFS Global Model. Atmosphere, 2022, 13(5): 845. DOI:10.3390/atmos13050845
    35. Bijay Sharma, Shiguo Jia, Anurag J. Polana, et al. Seasonal variations in aerosol acidity and its driving factors in the eastern Indo-Gangetic Plain: A quantitative analysis. Chemosphere, 2022, 305: 135490. DOI:10.1016/j.chemosphere.2022.135490
    36. Yue Zhou, Jianping Guo, Tianliang Zhao, et al. Roles of Atmospheric Turbulence and Stratification in a Regional Pollution Transport Event in the Middle Reaches of the Yangtze River. Earth and Space Science, 2022, 9(1) DOI:10.1029/2021EA002062
    37. Wenjie Zhang, Xiaoye Zhang, Hong Wang. The Role of Aerosol‐Radiation Interaction in the Meteorology Prediction at the Weather Scale in the Numerical Weather Prediction Model. Geophysical Research Letters, 2022, 49(9) DOI:10.1029/2021GL097026
    38. Zhongcheng Zhang, Bo Xu, Weiman Xu, et al. Machine learning combined with the PMF model reveal the synergistic effects of sources and meteorological factors on PM2.5 pollution. Environmental Research, 2022, 212: 113322. DOI:10.1016/j.envres.2022.113322
    39. Junting Zhong, Xiaoye Zhang, Ke Gui, et al. Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China. Earth System Science Data, 2022, 14(7): 3197. DOI:10.5194/essd-14-3197-2022
    40. Mei Mei, Yihui Ding, Zunya Wang, et al. Effects of the East Asian subtropical westerly jet on winter persistent heavy pollution in the Beijing–Tianjin–Hebei region. International Journal of Climatology, 2022, 42(5): 2950. DOI:10.1002/joc.7400
    41. Lingyan Wu, Xiaoye Zhang, Junying Sun, et al. Intensified wintertime secondary inorganic aerosol formation during heavy haze pollution episodes (HPEs) in Beijing, China. Journal of Environmental Sciences, 2022, 114: 503. DOI:10.1016/j.jes.2022.01.008
    42. Qianqian Huang, Yiqi Chu, Qianhui Li. Climatology of low-level temperature inversions over China based on high-resolution radiosonde measurements. Theoretical and Applied Climatology, 2021, 144(1-2): 415. DOI:10.1007/s00704-021-03536-w
    43. Zhe Zhang, Yu Shi, Haijion Sun, et al. Positive and negative turbulent heat diffusivity observed on a 325-m meteorological tower in Beijing 基于北京325米气象塔观测的正, 负湍流热扩散系数. Atmospheric and Oceanic Science Letters, 2021, 14(1): 100009. DOI:10.1016/j.aosl.2020.100009
    44. Jianbo Yang, Min Shao. Impacts of Extreme Air Pollution Meteorology on Air Quality in China. Journal of Geophysical Research: Atmospheres, 2021, 126(7) DOI:10.1029/2020JD033210
    45. Tongqiang Liu, Qianshan He, Yonghang Chen, et al. Distinct impacts of humidity profiles on physical properties and secondary formation of aerosols in Shanghai. Atmospheric Environment, 2021, 267: 118756. DOI:10.1016/j.atmosenv.2021.118756
    46. Yushan Song, Ying Li, Haoxiang Xu, et al. A Multi‐Dimensional Decomposition Method of the Meteorology‐Driven and Emission‐Driven Effects on Year‐to‐Year Air Quality Variations. Earth and Space Science, 2021, 8(6) DOI:10.1029/2020EA001424
    47. Yan Xiang, Tianshu Zhang, Chaoqun Ma, et al. Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM2.5 concentrations over the North China Plain. Atmospheric Chemistry and Physics, 2021, 21(9): 7023. DOI:10.5194/acp-21-7023-2021
    48. A. Ibrahim Muntasir, Curci Gabriele, I. Habbani Farouk, et al. Association of Air Pollution Levels to Atmospheric Weather Regimes over Europe. Journal of Environmental Science and Pollution Research, 2021, 7(1): 442. DOI:10.30799/jespr.210.21070101
    49. Wenxing Jia, Xiaoye Zhang, Yaqiang Wang. Assessing the pollutant evolution mechanisms of heavy pollution episodes in the Yangtze-Huaihe valley: A multiscale perspective. Atmospheric Environment, 2021, 244: 117986. DOI:10.1016/j.atmosenv.2020.117986
    50. Sihui Fan, Chloe Y. Gao, Linlin Wang, et al. Elucidating roles of near-surface vertical layer structure in different stages of PM2.5 pollution episodes over urban Beijing during 2004–2016. Atmospheric Environment, 2021, 246: 118157. DOI:10.1016/j.atmosenv.2020.118157
    51. Junting Zhong, Xiaoye Zhang, Ke Gui, et al. Robust prediction of hourly PM2.5 from meteorological data using LightGBM. National Science Review, 2021, 8(10) DOI:10.1093/nsr/nwaa307
    52. Xiaojing Shen, Junying Sun, Xiaoye Zhang, et al. A novel method of retrieving low visibility during heavily polluted episodes in the North China plain. Atmospheric Environment: X, 2021, 9: 100101. DOI:10.1016/j.aeaoa.2021.100101
    53. Junting Zhong, Xiaoye Zhang, Yangmei Zhang, et al. Drivers of the rapid rise and daily-based accumulation in PM1. Science of The Total Environment, 2021, 760: 143394. DOI:10.1016/j.scitotenv.2020.143394
    54. Yang Liu, Tingzhen Ming, Chong Peng, et al. Mitigating air pollution strategies based on solar chimneys. Solar Energy, 2021, 218: 11. DOI:10.1016/j.solener.2021.02.021
    55. 玉强 宋. Long-Term Evolution and Characteristics of Extreme Air Pollution Meteorological Events in China. Climate Change Research Letters, 2021, 10(05): 538. DOI:10.12677/CCRL.2021.105064
    56. Jun Hu, Tianliang Zhao, Jane Liu, et al. Nocturnal surface radiation cooling modulated by cloud cover change reinforces PM2.5 accumulation: Observational study of heavy air pollution in the Sichuan Basin, Southwest China. Science of The Total Environment, 2021, 794: 148624. DOI:10.1016/j.scitotenv.2021.148624
    57. Longyi Shao, Jie Li, Mengyuan Zhang, et al. Morphology, composition and mixing state of individual airborne particles: Effects of the 2017 Action Plan in Beijing, China. Journal of Cleaner Production, 2021, 329: 129748. DOI:10.1016/j.jclepro.2021.129748
    58. Wenxing Jia, Xiaoye Zhang, Jizhi Wang, et al. The influence of stagnant and transport types weather on heavy pollution in the Yangtze-Huaihe valley, China. Science of The Total Environment, 2021, 792: 148393. DOI:10.1016/j.scitotenv.2021.148393
    59. Dandan Zhao, Jinyuan Xin, Chongshui Gong, et al. The impact threshold of the aerosol radiative forcing on the boundary layer structure in the pollution region. Atmospheric Chemistry and Physics, 2021, 21(7): 5739. DOI:10.5194/acp-21-5739-2021
    60. María Eugenia Fernández, Jorge Osvaldo Gentili, Alicia María Campo. Air Pollutants in an Intermediate City: Variability and Interactions with Weather and Anthropogenic Elements in Bahía Blanca, Argentina. Environmental Processes, 2021, 8(1): 349. DOI:10.1007/s40710-021-00502-6
    61. Junting Zhong, Xiaoye Zhang, Yaqiang Wang, et al. Attribution of the worse aerosol pollution in March 2018 in Beijing to meteorological variability. Atmospheric Research, 2021, 250: 105294. DOI:10.1016/j.atmosres.2020.105294
    62. Tian Zhang, Huizheng Che, Zhiqiang Gong, et al. The dominant mechanism of the explosive rise of PM2.5 after significant pollution emissions reduction in Beijing from 2017 to the COVID-19 pandemic in 2020. Atmospheric Pollution Research, 2021, 12(2): 272. DOI:10.1016/j.apr.2020.11.008
    63. Zhaoyang Meng, Lingyan Wu, Xiangde Xu, et al. Changes in ammonia and its effects on PM2.5 chemical property in three winter seasons in Beijing, China. Science of The Total Environment, 2020, 749: 142208. DOI:10.1016/j.scitotenv.2020.142208
    64. Wei Wei, Hongsheng Zhang, Xuhui Cai, et al. Influence of Intermittent Turbulence on Air Pollution and Its Dispersion in Winter 2016/2017 over Beijing, China. Journal of Meteorological Research, 2020, 34(1): 176. DOI:10.1007/s13351-020-9128-4
    65. Shiguang Miao, Weimei Jiang, Ping Liang, et al. Advances in Urban Meteorological Research in China. Journal of Meteorological Research, 2020, 34(2): 218. DOI:10.1007/s13351-020-9858-3
    66. Pengcheng Qi, Yan Cui, Haijun Zhang, et al. Evaluating Multivariable Statistical Methods for Downscaling Nighttime Land Surface Temperature in Urban Areas. IEEE Access, 2020, 8: 162085. DOI:10.1109/ACCESS.2020.3021034
    67. Wenqian Zhang, Shengrui Tong, Chenhui Jia, et al. Different HONO Sources for Three Layers at the Urban Area of Beijing. Environmental Science & Technology, 2020, 54(20): 12870. DOI:10.1021/acs.est.0c02146
    68. Wenjie Zhang, Hong Wang, Xiaoye Zhang, et al. Evaluating the contributions of changed meteorological conditions and emission to substantial reductions of PM2.5 concentration from winter 2016 to 2017 in Central and Eastern China. Science of The Total Environment, 2020, 716: 136892. DOI:10.1016/j.scitotenv.2020.136892
    69. Ju Li, Zhaobin Sun, Donald H. Lenschow, et al. A foehn-induced haze front in Beijing: observations and implications. Atmospheric Chemistry and Physics, 2020, 20(24): 15793. DOI:10.5194/acp-20-15793-2020
    70. Yang Chen, Guangming Shi, Jing Cai, et al. Simultaneous measurements of urban and rural particles in Beijing – Part 2: Case studies of haze events and regional transport. Atmospheric Chemistry and Physics, 2020, 20(15): 9249. DOI:10.5194/acp-20-9249-2020
    71. Xiaorui Chen, Haichao Wang, Keding Lu, et al. Field Determination of Nitrate Formation Pathway in Winter Beijing. Environmental Science & Technology, 2020, 54(15): 9243. DOI:10.1021/acs.est.0c00972
    72. Linlin Wang, Sihui Fan, Fei Hu, et al. Vertical Gradient Variations in Radiation Budget and Heat Fluxes in the Urban Boundary Layer: A Comparison Study Between Polluted and Clean Air Episodes in Beijing During Winter. Journal of Geophysical Research: Atmospheres, 2020, 125(14) DOI:10.1029/2020JD032478
    73. Yucong Miao, Huizheng Che, Xiaoye Zhang, et al. Integrated impacts of synoptic forcing and aerosol radiative effect on boundary layer and pollution in the Beijing–Tianjin–Hebei region, China. Atmospheric Chemistry and Physics, 2020, 20(10): 5899. DOI:10.5194/acp-20-5899-2020
    74. Dandan Zhao, Guangjing Liu, Jinyuan Xin, et al. Haze pollution under a high atmospheric oxidization capacity in summer in Beijing: insights into formation mechanism of atmospheric physicochemical processes. Atmospheric Chemistry and Physics, 2020, 20(8): 4575. DOI:10.5194/acp-20-4575-2020
    75. Hongsheng Zhang, Xiaoye Zhang, Qianhui Li, et al. Research Progress on Estimation of the Atmospheric Boundary Layer Height. Journal of Meteorological Research, 2020, 34(3): 482. DOI:10.1007/s13351-020-9910-3
    76. Wenxing Jia, Xiaoye Zhang. The role of the planetary boundary layer parameterization schemes on the meteorological and aerosol pollution simulations: A review. Atmospheric Research, 2020, 239: 104890. DOI:10.1016/j.atmosres.2020.104890
    77. Seung-Hee Eun, Sung-Min Park, Byung-Gon Kim, et al. Observational Analysis of Aerosol–Meteorology Interactions for the Severe Haze Episode in Korea. Atmosphere, 2020, 12(1): 33. DOI:10.3390/atmos12010033
    78. Tingyu Yang, Yu Wang, Yanan Wu, et al. Effect of the wetland environment on particulate matter and dry deposition. Environmental Technology, 2020, 41(8): 1054. DOI:10.1080/09593330.2018.1520307
    79. Wenjun Qu, Xiaoye Zhang, Yaqiang Wang, et al. Atmospheric visibility variation over global land surface during 1973–2012: Influence of meteorological factors and effect of aerosol, cloud on ABL evolution. Atmospheric Pollution Research, 2020, 11(4): 730. DOI:10.1016/j.apr.2020.01.002
    80. Yangmei Zhang, Tuan Van Vu, Junying Sun, et al. Significant Changes in Chemistry of Fine Particles in Wintertime Beijing from 2007 to 2017: Impact of Clean Air Actions. Environmental Science & Technology, 2020, 54(3): 1344. DOI:10.1021/acs.est.9b04678
    81. Lingyan Wu, Yu Wang, Lei Li, et al. Acidity and inorganic ion formation in PM2.5 based on continuous online observations in a South China megacity. Atmospheric Pollution Research, 2020, 11(8): 1339. DOI:10.1016/j.apr.2020.05.003
    82. T.F. Eck, B.N. Holben, J. Kim, et al. Influence of cloud, fog, and high relative humidity during pollution transport events in South Korea: Aerosol properties and PM2.5 variability. Atmospheric Environment, 2020, 232: 117530. DOI:10.1016/j.atmosenv.2020.117530
    83. Tian Zhang, Huizheng Che, Zhiqiang Gong, et al. The two-way feedback effect between aerosol pollution and planetary boundary layer structure on the explosive rise of PM2.5 after the “Ten Statements of Atmosphere” in Beijing. Science of The Total Environment, 2020, 709: 136259. DOI:10.1016/j.scitotenv.2019.136259
    84. Ziyue Chen, Danlu Chen, Chuanfeng Zhao, et al. Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism. Environment International, 2020, 139: 105558. DOI:10.1016/j.envint.2020.105558
    85. Wenjie Zhang, Xiaoye Zhang, Junting Zhong, et al. The effects of the “two-way feedback mechanism” on the maintenance of persistent heavy aerosol pollution over areas with relatively light aerosol pollution in northwest China. Science of The Total Environment, 2019, 688: 642. DOI:10.1016/j.scitotenv.2019.06.295
    86. Yan Ren, Hongsheng Zhang, Wei Wei, et al. A study on atmospheric turbulence structure and intermittency during heavy haze pollution in the Beijing area. Science China Earth Sciences, 2019, 62(12): 2058. DOI:10.1007/s11430-019-9451-0
    87. X.J. Shen, J.Y. Sun, X.Y. Zhang, et al. Variations in submicron aerosol liquid water content and the contribution of chemical components during heavy aerosol pollution episodes in winter in Beijing. Science of The Total Environment, 2019, 693: 133521. DOI:10.1016/j.scitotenv.2019.07.327
    88. Hong Wang, Jianghao Li, Yue Peng, et al. The impacts of the meteorology features on PM2.5 levels during a severe haze episode in central-east China. Atmospheric Environment, 2019, 197: 177. DOI:10.1016/j.atmosenv.2018.10.001
    89. Linlin Wang, Hong Wang, Junkai Liu, et al. Impacts of the near-surface urban boundary layer structure on PM2.5 concentrations in Beijing during winter. Science of The Total Environment, 2019, 669: 493. DOI:10.1016/j.scitotenv.2019.03.097
    90. Junting Zhong, Xiaoye Zhang, Yaqiang Wang, et al. The two-way feedback mechanism between unfavorable meteorological conditions and cumulative aerosol pollution in various haze regions of China. Atmospheric Chemistry and Physics, 2019, 19(5): 3287. DOI:10.5194/acp-19-3287-2019
    91. Liangke Liu, Xiaoye Zhang, Junting Zhong, et al. The ‘two-way feedback mechanism’ between unfavorable meteorological conditions and cumulative PM2.5 mass existing in polluted areas south of Beijing. Atmospheric Environment, 2019, 208: 1. DOI:10.1016/j.atmosenv.2019.02.050
    92. Ansheng Chen, Q. Xiaosheng, O. Amahmid. Analysis of meteorological factors on different stages of heavy haze pollution in Beijing, 2019. E3S Web of Conferences, 2019, 117: 00018. DOI:10.1051/e3sconf/201911700018
    93. Can Xia, Junying Sun, Xuefei Qi, et al. Observational study of aerosol hygroscopic growth on scattering coefficient in Beijing: A case study in March of 2018. Science of The Total Environment, 2019, 685: 239. DOI:10.1016/j.scitotenv.2019.05.283
    94. Junting Zhong, Xiaoye Zhang, Yaqiang Wang. Reflections on the threshold for PM<sub>2.5</sub> explosive growth in the cumulative stage of winter heavy aerosol pollution episodes (HPEs) in Beijing. Tellus B: Chemical and Physical Meteorology, 2019, 71(1): 1528134. DOI:10.1080/16000889.2018.1528134
    95. Dandan Zhao, Jinyuan Xin, Chongshui Gong, et al. The formation mechanism of air pollution episodes in Beijing city: Insights into the measured feedback between aerosol radiative forcing and the atmospheric boundary layer stability. Science of The Total Environment, 2019, 692: 371. DOI:10.1016/j.scitotenv.2019.07.255
    96. Linlin Wang, Junkai Liu, Zhiqiu Gao, et al. Vertical observations of the atmospheric boundary layer structure over Beijing urban area during air pollution episodes. Atmospheric Chemistry and Physics, 2019, 19(10): 6949. DOI:10.5194/acp-19-6949-2019
    97. Junting Zhong, Xiaoye Zhang, Yaqiang Wang. Relatively weak meteorological feedback effect on PM2.5 mass change in Winter 2017/18 in the Beijing area: Observational evidence and machine-learning estimations. Science of The Total Environment, 2019, 664: 140. DOI:10.1016/j.scitotenv.2019.01.420
    98. Lingyan Wu, Junying Sun, Xiaoye Zhang, et al. Aqueous-phase reactions occurred in the PM<sub>2.5</sub> cumulative explosive growth during the heavy pollution episode (HPE) in 2016 Beijing wintertime. Tellus B: Chemical and Physical Meteorology, 2019, 71(1): 1620079. DOI:10.1080/16000889.2019.1620079
    99. Yan Ren, Hongsheng Zhang, Wei Wei, et al. Effects of turbulence structure and urbanization on the heavy haze pollution process. Atmospheric Chemistry and Physics, 2019, 19(2): 1041. DOI:10.5194/acp-19-1041-2019
    100. Yucong Miao, Jing Li, Shiguang Miao, et al. Interaction Between Planetary Boundary Layer and PM2.5 Pollution in Megacities in China: a Review. Current Pollution Reports, 2019, 5(4): 261. DOI:10.1007/s40726-019-00124-5
    101. Yan Ren, Hongsheng Zhang, Wei Wei, et al. Comparison of the turbulence structure during light and heavy haze pollution episodes. Atmospheric Research, 2019, 230: 104645. DOI:10.1016/j.atmosres.2019.104645
    102. Weiqi Xu, Yele Sun, Qingqing Wang, et al. Changes in Aerosol Chemistry From 2014 to 2016 in Winter in Beijing: Insights From High‐Resolution Aerosol Mass Spectrometry. Journal of Geophysical Research: Atmospheres, 2019, 124(2): 1132. DOI:10.1029/2018JD029245
    103. Zirui Liu, Bo Hu, Dongsheng Ji, et al. Characteristics of fine particle explosive growth events in Beijing, China: Seasonal variation, chemical evolution pattern and formation mechanism. Science of The Total Environment, 2019, 687: 1073. DOI:10.1016/j.scitotenv.2019.06.068
    104. Renmin Yuan, Xiaoye Zhang, Hao Liu, et al. Aerosol vertical mass flux measurements during heavy aerosol pollution episodes at a rural site and an urban site in the Beijing area of the North China Plain. Atmospheric Chemistry and Physics, 2019, 19(20): 12857. DOI:10.5194/acp-19-12857-2019
    105. Xiaoye Zhang, Xiangde Xu, Yihui Ding, et al. The impact of meteorological changes from 2013 to 2017 on PM2.5 mass reduction in key regions in China. Science China Earth Sciences, 2019, 62(12): 1885. DOI:10.1007/s11430-019-9343-3
    106. Ziyue Chen, Danlu Chen, Wei Wen, et al. Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM2.5 concentrations, source apportionment, and the relative contribution of local emission and regional transport. Atmospheric Chemistry and Physics, 2019, 19(10): 6879. DOI:10.5194/acp-19-6879-2019
    107. Keding Lu, Hendrik Fuchs, Andreas Hofzumahaus, et al. Fast Photochemistry in Wintertime Haze: Consequences for Pollution Mitigation Strategies. Environmental Science & Technology, 2019, 53(18): 10676. DOI:10.1021/acs.est.9b02422
    108. Jizhi Wang, Xiaoye Zhang, Duo Li, et al. Interdecadal changes of summer aerosol pollution in the Yangtze River Basin of China, the relative influence of meteorological conditions and the relation to climate change. Science of The Total Environment, 2018, 630: 46. DOI:10.1016/j.scitotenv.2018.01.236
    109. Xiaoye Zhang, Junting Zhong, Jizhi Wang, et al. The interdecadal worsening of weather conditions affecting aerosol pollution in the Beijing area in relation to climate warming. Atmospheric Chemistry and Physics, 2018, 18(8): 5991. DOI:10.5194/acp-18-5991-2018
    110. Ju Li, Jielun Sun, Mingyu Zhou, et al. Observational analyses of dramatic developments of a severe air pollution event in the Beijing area. Atmospheric Chemistry and Physics, 2018, 18(6): 3919. DOI:10.5194/acp-18-3919-2018
    111. Wei Wei, Hongsheng Zhang, Bingui Wu, et al. Intermittent turbulence contributes to vertical dispersion of PM2.5 in the North China Plain: cases from Tianjin. Atmospheric Chemistry and Physics, 2018, 18(17): 12953. DOI:10.5194/acp-18-12953-2018
    112. Panyang Shao, Hezhong Tian, Yujiao Sun, et al. Characterizing remarkable changes of severe haze events and chemical compositions in multi-size airborne particles (PM1, PM2.5 and PM10) from January 2013 to 2016–2017 winter in Beijing, China. Atmospheric Environment, 2018, 189: 133. DOI:10.1016/j.atmosenv.2018.06.038
    113. Hong Wang, Yue Peng, Xiaoye Zhang, et al. Contributions to the explosive growth of PM2.5 mass due to aerosol–radiation feedback and decrease in turbulent diffusion during a red alert heavy haze in Beijing–Tianjin–Hebei, China. Atmospheric Chemistry and Physics, 2018, 18(23): 17717. DOI:10.5194/acp-18-17717-2018
    114. Junting Zhong, Xiaoye Zhang, Yaqiang Wang, et al. Heavy aerosol pollution episodes in winter Beijing enhanced by radiative cooling effects of aerosols. Atmospheric Research, 2018, 209: 59. DOI:10.1016/j.atmosres.2018.03.011
    115. Fenhua Ma, Zhaoyong Guan. Seasonal Variations of Aerosol Optical Depth over East China and India in Relationship to the Asian Monsoon Circulation. Journal of Meteorological Research, 2018, 32(4): 648. DOI:10.1007/s13351-018-7171-1
    116. Xiaojing Shen, Junying Sun, Xiaoye Zhang, et al. Comparison of Submicron Particles at a Rural and an Urban Site in the North China Plain during the December 2016 Heavy Pollution Episodes. Journal of Meteorological Research, 2018, 32(1): 26. DOI:10.1007/s13351-018-7060-7
    117. Chunhong Zhou, Xiaojing Shen, Zirui Liu, et al. Simulating Aerosol Size Distribution and Mass Concentration with Simultaneous Nucleation, Condensation/Coagulation, and Deposition with the GRAPES–CUACE. Journal of Meteorological Research, 2018, 32(2): 265. DOI:10.1007/s13351-018-7116-8
    118. X. Wang, X.J. Shen, J.Y. Sun, et al. Size-resolved hygroscopic behavior of atmospheric aerosols during heavy aerosol pollution episodes in Beijing in December 2016. Atmospheric Environment, 2018, 194: 188. DOI:10.1016/j.atmosenv.2018.09.041
    119. Haichao Wang, Keding Lu, Xiaorui Chen, et al. Fast particulate nitrate formation via N2O5 uptake aloft in winter in Beijing. Atmospheric Chemistry and Physics, 2018, 18(14): 10483. DOI:10.5194/acp-18-10483-2018
    120. Xiang Zheng, Jun Qin, Shengwen Liang, et al. The Development of Boundary Layer Structure Index (BLSI) and Its Relationship with Ground Air Quality. Atmosphere, 2018, 10(1): 3. DOI:10.3390/atmos10010003

    Other cited types(0)

Catalog

    • Abstract
    • 1.   Introduction
    • 2.   Data and methods
    • 3.   Results
    • 3.1   Definitions of heavy pollution and explosive growth of PM2.5
    • 3.2   Meteorological causes of original growth
    • 3.3   Meteorological conditions for the subsequent explosive growth
    • 3.4   Quantification of meteorological feedback effect on the subsequent explosive growth
    • 4.   Conclusions
    • References

    Authors

    About the Journal

    Links

    WeChat

    Domestic Purchase

    京ICP备09060741号-1   Copyright©Journal of Meteorological Research  Contact: jmr@cms1924.org; 86-10-68408571  Total visits: 881684

    Supported by Beijing Renhe Information Technology Co. Ltd.    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return