Special Issue on 2019 Asian Aerosol Conference (AAC)

Can Meng1,2, Tianhai Cheng 1, Fangwen Bao3, Xingfa Gu1, Jian Wang4, Xin Zuo1,2, Shuaiyi Shi 1

State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
Department of Ocean Sciences and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Department of Geography, The Ohio State University, Columbus, OH 43210, USA

Received: October 24, 2019
Revised: February 19, 2020
Accepted: February 21, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.2019.10.0534 

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Cite this article:

Meng, C., Cheng, T., Bao, F., Gu, X., Wang, J., Zuo, X. and Shi, S. (2020). The Impact of Meteorological Factors on Fine Particulate Pollution in Northeast China. Aerosol Air Qual. Res. 20: 1618–1628. https://doi.org/10.4209/aaqr.2019.10.0534


  • Wind speed, HPBL and temperature inversion impact PM2.5 in NE China significantly.
  • Harbin: 91.6% pollution days occur when wind < 20 knots, HPBL < 1500 m, TI_T > 8°C.
  • Changchun: 61.7% pollution days occur when wind < 20 knots, HPBL < 600 m.
  • Shenyang: 70.3% pollution days occur when wind < 20 knots, TI_D > 775 m.


Due to biomass burning and coal combustion, heavy fine particulate matter (PM2.5) pollution frequently occurs in Northeast China, threatening the health of more than 117 million inhabitants. Although meteorological conditions have always been considered key factors in the accumulation and dilution of PM2.5 pollution, their exact contribution to particulate pollution in Northeast China is still highly uncertain. Applying multiple regression analysis to observational data, we identify the wind speed, temperature inversion, and height of the planetary boundary layer as the dominant meteorological factors affecting PM2.5 pollution (PM2.5 > 75 µg m–3) in the major cities of Northeast China, with the wind speed and the planetary boundary layer playing the primary roles in Harbin and Shenyang, and Changchun, respectively. Heavy pollution (PM2.5 > 150 µg m–3) in this region typically occurs when the wind speed is less than 20 knots, the planetary boundary layer is below 500 m, and the temperature inversion is greater than 6°C. These results suggest that reducing the PM2.5 pollution requires us to focus not only on anthropogenic emissions but also on special meteorological conditions that can affect the air pollution mechanisms in Northeast China.

Keywords: Fine particulate (PM2.5) pollution; Multiple regression method; Wind speed; Temperature inversion; Planetary boundary layer height.


Several extremely severe atmospheric haze events have occurred recently in Northeast China, a major crop straw burning region of China, with air pollution reaching up to nearly 2–3 times of the historical record (Chen et al., 2015; Wen et al., 2018). Fine particulate matter (PM2.5) has been proved harmful to human health and regarded as the main factor impairing visibility (He et al., 2001; Wang and Christopher, 2003; Jung et al., 2010; Heal et al., 2012; Kan et al., 2012). It exerts large influences on the development of planetary boundary layer (PBL) through the positive feedback loop between aerosol and PBL (Petäjä et al., 2016; Li et al., 2017b; Lou et al., 2019) which in turn deteriorates the air quality. Besides, aerosol tends to affect the formation and development of cloud and precipitation through directly altering the radiation reaching the ground surface (Lohmann and Feichter, 2001; Yang et al., 2016) or indirectly altering the structure of clouds by acting as cloud condensation nucleus (Twomey, 1977; Chen et al., 2016; Guo et al., 2016; Guo et al., 2019), albeit its inconclusive effect on weather and climate systems (Rosenfeld et al., 2014; Fan et al., 2016; Li et al., 2019). In view of its potential huge influence, fine particulate pollution has attracted great attention from the Chinese government. Many regulations and standards have been made to control the fine particulate pollution (Wen et al., 2018). By adopting these measures, a gradual decrease in the PM2.5 concentration over time has been witnessed recently (Wen et al., 2018). However, PM2.5 pollution in Northeast China is still non-negligible, especially around harvest time (Chen et al., 2017).

The pollution during the heating season in Northeast China is mainly caused by evening biomass burning and coal combustion (Fang et al., 2001; Zhang et al., 2011; Fang et al., 2017). In addition, meteorological factors are important to the variation of PM2.5 concentrations. Researches show that wind speed has a negative effect on PM2.5 concentration (Chen et al., 2015; Liang et al., 2015; Chen et al., 2017; Wen et al., 2018). Wind speed can facilitate plume spread and dilution, leading to lower PM2.5 concentrations. In such way, atmospheric horizontal mixing has been considered as one of the most efficient dilution mechanisms (Marcazzan et al., 2001). Studies show that lower height of the planetary boundary layer (HPBL) weakens the dispersion ability of the atmosphere (Seinfeld et al., 1998; Dickerson et al., 2007; Marcazzan et al., 2001; Zhang et al., 2015; Wen et al., 2018). HPBL, also known as the boundary layer top, refers to the thickness of the planetary boundary layer. The characteristic of the boundary layer height is that the turbulence coefficient at this height is zero and the free atmosphere is above it. The diffusion and transportation of pollutants in the lower atmosphere depends to a large extent on the boundary layer structure. Within the boundary layer, turbulence can fully mix particles and gases (Garratt, 1994). In addition, the inversion layer boosts the accumulation of pollutants in the atmosphere (Wang et al., 2014b; Fang et al., 2017). Temperature inversion is a reversal of the normal behavior of temperature in the troposphere, in which a layer of cool air at the surface is overlain by a layer of warmer air. When inversion occurs, upward movement of the lower atmosphere decreases and the pollutants are trapped in the lower atmosphere (Xu et al., 2019). In winter, the recurrent or continuous thermal inversions with fog at ground level are the major contributors to the massive amount of air pollutants accumulated in the lower layer of the atmosphere (Marcazzan et al., 2001). Moreover, haze weather can form in a faster way under high humidity conditions. Some researchers conclude that the relative humidity (RH) could well explain the daily variations of PM2.5 (Sun et al., 2013; Fang et al., 2017; Ma et al., 2017). Previous studies indicated that the synoptic scale meteorological conditions and multiple meteorological factors can have a complicated impact to the PM2.5 pollution (Chen et al., 2018; Liu et al., 2018; Miao et al., 2017; Wang et al., 2014a).

Three typical large cities of Northeast China (i.e., Harbin, Changchun, and Shenyang) were studied in this research, using statistical data and regression model analysis, to investigate the impact of different meteorological factors on major cities in Northeast China. The study result of this research is of great benefit to making suitable and effective strategies for PM2.5 control in Northeast China.


Study Area

As shown in Fig. 1, Northeast China is covered mostly by plain. The Northeast Plain is surrounded by mountains, alluvial plains and terraces, with an elevation of about 200 m. The Northeast Plain is located in a temperate and warm temperate zone with continental monsoon climate characteristics. Significant seasonal differences exist in this area. The summer is short, warm and rainy. Meanwhile the winter is long, cold, and snowy. And the monsoon alternates between winter and summer. 

Fig. 1. Map of the study area: (a) the topographic map of China and the location of Northeast China; (b) the topographic map of Northeast China and the location of Shenyang, Changchun, and Harbin; and the location of PM2.5 studying sites in (c) Shenyang, (d) Changchun, and (e) Harbin.Fig. 1. Map of the study area: (a) the topographic map of China and the location of Northeast China; (b) the topographic map of Northeast China and the location of Shenyang, Changchun, and Harbin; and the location of PM2.5 studying sites in (c) Shenyang, (d) Changchun, and (e) Harbin.

Considering their meteorological conditions and urban development status, three typical large cities in Northeast China were selected in this study to reflect the major characteristics of PM2.5 pollution in Northeast China. Among the three cities, Harbin, the capital of Heilongjiang Province, is located in the highest latitude area, having the lowest temperature, with a flat terrain. Over 10.9 million people live in Harbin. The southeastern part of Harbin is located in the hills; meanwhile, mountainous area occupied the north part. The Songhua River flows through the middle part of Harbin. Changchun is located in the middle part of Northeast China with over 7.5 million people. The Changchun area, apart from a small area of low hills in the east, is mostly comprised of terraces. The terrain of Changchun is flat and convenient for transportation (Fang et al., 2001). In Changchun, from November to March, coal burning phenomenon for heating purpose is widespread. The heating area is scattered in one-fourth of the city area (Fang et al., 2001). Shenyang, the largest city in Northeast China, has over 8.3 million people (Xu et al., 2000; Han et al., 2010). It is located in the southeastern part of Northeast China, mainly in the plains, with mountains and hills concentrated in the southeast. Shenyang shows similar meteorological characteristics with the other two cities mentioned above: a monsoon-influenced humid continental climate, which has monsoon-caused hot, humid summers and dry, cold winters driven by the Siberian anticyclone (Han et al., 2010).

Data Source and Treatment

The time period of the studied PM2.5 data ranges from 2015 to 2017, provided by the China Meteorological Administration. The continuous hourly PM2.5 concentration data has been monitored by the 33 stations: 12 sites in Harbin, 10 sites in Changchun, and 11 sites in Shenyang. As can be seen in Fig. 1, the sites are evenly distributed throughout the cities. Thus the hourly average value of these sites can reasonably represent the PM2.5 concentration of the three cities. To keep consistency with the meteorological data, the PM2.5 concentration was calculated at 08:00 and 20:00 local time. The PM2.5 data is classified into three states: the non-pollution state (PM2.5 ≤ 75 µg m3), pollution state (PM2.5 > 75 µg m3), and heavy pollution state (PM2.5 > 150 µg m3) (Ma et al., 2017; Tian et al., 2017).

Meteorological data comes from the radiosonde measurements provided by the Integrated Global Radiosonde Archive developed by the American National Climatic Data Center (NCDC) (Durre et al., 2006). The humidity data and wind data are extracted specifically with wind velocity and wind direction at 850 hPa. In general, temperature drops as the altitude increases, beneficial to the convective phenomenon which accelerates the pollutants in the lower layer moving and diffusing upward. However, when temperature inversion occurs, the upward movement of the lower atmosphere is weakened, causing the accumulation of atmospheric pollutants in the lower layer. Temperature inversion phenomenon can be detected by the temperature profile of the atmosphere. Two factors, namely the temperature difference of the inversion layer (TI_T) and the depth of the inversion layer (TI_D), are used to describe the intensity of temperature inversion. In this research, the two factors of the inversion layer are derived from the Integrated Global Radiosonde Archive (IGRA) soundings data. We first analyze the temperature data for different air layers and find if there exists a temperature sequence segmentation in the profile that has monotonic increasing trend with the increase of the layer heights. If exist, the temperature inversion is defined. Then the temperature difference is calculated by subtracting the temperatures at the top and bottom of the inversion layers. And the depth of the inversion layer is calculated by subtracting the heights at the top and bottom of the inversion layers.

HPBL data used in this research is provided by National Centers for Environmental Prediction (NCEP) and Final (FNL) Reanalysis data. It is collected at 06:00 and 18:00 UTC. The HPBL data is a grid dataset and the horizontal resolution of HPBL is 1 × 1° (Zang et al., 2017). In this research, the HPBL values of Harbin, Changchun and Shenyang are calculated using the inverse distance weighting method based on the values in four closest grid points.


In order to decouple the contribution of different meteorological factors, a multiple regression model was used to analyze the correlations between PM2.5 concentration and meteorological factors. Based on atmospheric dispersion equations, the particulate concentration is correlated with meteorological parameters in multiplicative form other than additive form (Hien et al., 2002; Chaloulakou et al., 2003; Gu et al., 2018). The multiple regression analysis model can be constructed as follows:

where B represents the monitored PM2.5 concentration, k represents autochthonic contribution to PM2.5. k is considered to remain unchanged during each season. f(τn) represents the meteorological impact on PM2.5. Furthermore, the regression model can be expressed as follows:

The exponent αi represents the response of particulate concentration B to the rate of change in meteorological parameter Pi. f(τn) was calculated using five meteorological factors (Pi) in this model: wind velocity (WV), HPBL, TI_T, TI_D and RH. As a result, the model can be expressed as follows:

Then, take the logarithm for both sides of this equation. Eq. (3) can be further transformed to a multiple linear regression model:

where ln k, αi and ε represent the intercept, regression coefficients, and the error term respectively. WV, RH, TI_T, TI_D, and HPBL were selected in this research as the studied meteorological parameters which influence the PM2.5 concentration characteristics in Northeast China. To find the determinant(s) of the PM2.5 concentrations among various indicators, the data were analyzed by a stepwise multiple regression method with a significance level of 95% (p = 0.05) set for the regression coefficients.

The regression coefficient αi can only represent the positive or negative relationship between the PM2.5 concentration and the meteorological factors. To find the most important factors that impact the PM2.5 concentration in different cities of Northeast China, we calculated the standardized coefficient of the regression model. The standardized coefficient can compare the relative importance of different kind of factors to the PM2.5 concentration. The bigger the absolute value is, the more important the meteorological factor will be (Newman and Browner, 1991).


Variation of the PM2.5 Concentrations Correlated with Different Meteorological Factors

Massive biomass burning and coal consumption together with meteorological conditions potentially cause the high level of PM2.5 pollution in Northeast China’s winter (Han et al., 2010; Segura et al., 2013). Studies show that PM2.5 pollution in Northeast China originates from anthropogenic activities, such as straw burning and coal combustion for both industrial and domestic purpose (Zhang et al., 2010; Guan et al., 2018). Meanwhile, individual meteorological factors such as relative humidity, wind speed and temperature inversion have non-negligible impacts on local PM2.5 concentrations (Cheng and Lam, 1998; Ruellan and Cachier, 2001; Wen et al., 2018). The distribution and variation of the source of pollution are usually stable during each season. However, the dilution and accumulation abilities impacted by the meteorological conditions can change significantly. Investigating the impacts of meteorological factors is beneficial to a better understanding of the variations of PM2.5 mass concentrations.

As shown in Fig. 2, the correlation between PM2.5 concentration, and wind direction and speed were similar in the three cities. PM2.5 concentration is negatively associated with wind speed (Chen et al., 2017; Wen et al., 2018). High PM2.5 concentration tends to appear in a low wind speed situation. In Harbin, Changchun, and Shenyang, 79.2%, 79.1%, and 80.4% of the heavy PM2.5 pollution occurs in the daywhen the wind speed is less than 20 knots (a knot is a unit typically used to indicate wind speed, and thus used in this research; 1 knot = 1.852 km h1). Biomass burning in spring and autumn and coal combustion for domestic heating in winter produce a great deal of air pollutants (Zhang et al., 2010; Bao et al., 2015). Higher wind speed is likely to promote the spread of pollutant, which is beneficial for the dilution of PM2.5 pollutants (Wen et al., 2018). The westerly wind prevails during pollution days in Northeast China. In Harbin, over 70% of pollution days (PM2.5 > 75 µg m3) are related with the wind which has a direction between 240–330°. As for Changchun and Shenyang, the dominant wind direction in pollution days is concentrated at 210–330°. In general, Harbin, Changchun, and Shenyang are mainly dominated by the southwest wind during the whole year; meanwhile the northwest wind is the prevailing wind in winter. 

Fig. 2. Relationships between atmospheric pollutant concentrations, wind direction, and wind speed in (a) Harbin, (b) Changchun, and (c) Shenyang. Direction frequency and average PM2.5 concentrations in different direction during pollution days in (d) Harbin, (e) Changchun, and (f) Shenyang.Fig. 2. Relationships between atmospheric pollutant concentrations, wind direction, and wind speed in (a) Harbin, (b) Changchun, and (c) Shenyang. Direction frequency and average PM2.5 concentrations in different direction during pollution days in (d) Harbin, (e) Changchun, and (f) Shenyang.

As shown in Fig. 3, in terms of statistics, there is a general negative correlation between HPBL and PM2.5 concentrations for Harbin, Changchun, and Shenyang. Heavy PM2.5 pollution usually occurs in the lower HPBL (< 500 m). HPBL plays a vital role in the variation of PM2.5 concentration. The barrier effect at the top of the planetary boundary layer hinders air pollutants from being transported to the free troposphere, so that aerosol particles are constrained in the PBL (You et al., 2015; Li et al., 2017a;). Lower HPBL is usually related with higher concentrations of PM2.5, and higher HPBL is usually associated with smaller ground level PM2.5 concentration as a result of large vertical mixing ability (Yao et al., 2012). 

Fig. 3. Correlation between PM2.5 and the height of the planetary boundary layer in Harbin, Changchun, and Shenyang for (a) pollution days (> 75 µg m–3) and (b) non-pollution days (≤ 75 µg m–3).Fig. 3. Correlation between PM2.5 and the height of the planetary boundary layer in Harbin, Changchun, and Shenyang for (a) pollution days (> 75 µg m–3) and (b) non-pollution days (≤ 75 µg m3).

As can be seen in Fig. 4, PM2.5 concentration is significantly and positively correlated with the intensity of temperature inversion which is described by TI_T and TI_D. In Harbin, Changchun, and Shenyang, 60.6%, 44.8%, and 56.9% of PM2.5 pollution days occur with temperature inversion phenomenon, respectively. Only 14.4%, 18.4%, and 15.4% of PM2.5 pollution days have no temperature inversion. PM2.5 concentration rises significantly when TI_T is greater than 6°C in Harbin and Changchun. However, the relationship between TI_T and PM2.5 in Shenyang is not so significant in pollution days. The TI_D in Shenyang is generally thicker than that of other two cities. Heavy pollution usually occurs when TI_D is bigger than 600 m in Harbin and Shenyang. Compared to summer, the inversion layer in winter is thicker and has a longer duration. The atmosphere remains static once temperature inversion occurs. In static atmosphere, air pollutants are not easily dispersed vertically and, thus, accumulated near the surface. Additionally, a large temperature difference within a thin inversion layer can also cause high concentrations of PM2.5 in the ground layer. 

Fig. 4. Statistics of PM2.5 concentrations with temperature inversion. Variation of the PM2.5 concentrations correlated with the temperature difference of the inversion layer (TI_T) during (a) pollution days (> 75 µg m–3) and (b) non-pollution days (≤ 75 µg m–3). Variation of the PM2.5 concentrations correlated with the depth of temperature inversion (TI_D) during (c) pollution days (> 75 µg m–3) and (d) non-pollution days (≤ 75 µg m–3).Fig. 4. Statistics of PM2.5 concentrations with temperature inversion. Variation of the PM2.5 concentrations correlated with the temperature difference of the inversion layer (TI_T) during (a) pollution days (> 75 µg m3) and (b) non-pollution days (≤ 75 µg m3). Variation of the PM2.5 concentrations correlated with the depth of temperature inversion (TI_D) during (c) pollution days (> 75 µg m3) and (d) non-pollution days (≤ 75 µg m3).

As can be seen in Fig. 5, the relationship between RH and PM2.5 in Northeast China is not as significant as other air pollution areas. There is even a negative correlation between PM2.5 and RH. Possible reasons are that rainy and snowy weather accompanied with moist air can deposit the particulate pollutants so that air becomes clear, and when the air pollution is caused by the dust particles, the moisture in the air remains constantly low (Sun et al., 2013). 

Fig. 5. Variation of the PM2.5 concentrations correlated with different RH condition during (a) pollution days (> 75 µg m–3) and (b) non-pollution days (≤ 75 µg m–3) in Harbin, Changchun, and Shenyang respectively. The RH is divided into 5 segments (i.e., 0–20, 20–40, 40–60, 60–80, and 80–100).Fig. 5. Variation of the PM2.5 concentrations correlated with different RH condition during (a) pollution days (> 75 µg m3) and (b) non-pollution days (≤ 75 µg m3) in Harbin, Changchun, and Shenyang respectively. The RH is divided into 5 segments (i.e., 0–20, 20–40, 40–60, 60–80, and 80–100).

Quantitative Multiple Regression Analysis between PM2.5 Concentration and Meteorological Factors

PM2.5 concentration has dropped continuously during the past three years in Northeast China. However, daily PM2.5 levels varied remarkably, rising and falling rapidly with alternating sharp peaks and deep troughs (Wen et al., 2018). In Table 1, the confidence coefficient for meteorological factors above 95% confidence level (p-value < 0.05) indicates the factors significantly impact the daily variation of PM2.5 concentration to some extent. Wind speed, HPBL, and temperature inversion are important meteorological factors affecting the PM2.5 pollution in Northeast China. During pollution days, the regression coefficients of wind speed and HPBL are negative values, which show a clear negative influence of wind speed and HPBL on fine particle concentrations. Meanwhile, the values for the coefficients of TI_T and TI_D are positive, which indicates that temperature inversion has a positive impact on the accumulation of PM2.5. A meteorological factor plays the dominant role in affecting PM2.5 pollution when its standardized coefficient has the biggest absolute value. By comparing the standardized coefficients of different factors in the regression model, the most important factors are concluded. Wind speed is the key meteorological factor impacting PM2.5 concentrations in Harbin and Shenyang. Meanwhile, HPBL is the key factor in Changchun. 

Straw burning and pollutant emission, near-surface temperature inversion, and static wind together with other extreme meteorological conditions contribute to the PM2.5 pollution, especially in autumn and winter. The breeze hinders the dispersion and transport of the atmospheric pollutants released by straw burning. On the contrary, high wind speed is beneficial to the horizontal dilution of the pollutants. On the other hand, the turbulence is strengthened with the increase of wind speed, which is also beneficial to the pollutant dilution and spreading. There are significant differences between the situations with and without the existence of temperature inversion. Temperature inversion decreases the upward movement of the lower atmosphere and causes the pollutants trapped in the lower atmosphere. Frequent atmospheric conditions with temperature inversion in winter can cause the accumulation of atmospheric particles (He et al., 2001; Han et al., 2010). The convection between upper and lower layers decreases, so that the atmospheric pollutants in the near-surface layer can hardly be transported upward. At the same time, the pollutants never stop accumulating, which exacerbates air pollution. The diffusion and transportation of pollutants in the lower atmosphere depends to a large extent on the boundary layer structure. Within the boundary layer, turbulence can fully mix particles and gases. All the three factors have significant influence on the PM2.5 concentrations in Northeast China. As can be seen in the multiple regression result (Table 1), during pollution days, all the three cities have the wind speed as one of the major impact factors. However, different from other two cities, the temperature inversion is not a major impact factor for Changchun. Such result may be caused by the fact that the altitude of Changchun (236.8 m) is higher than Harbin (171.7 m) and Shenyang (41.6 m). And the temperature inversion has the characteristic that it occurs more obviously in the lowland (or valley) area than the highland area.

As can be seen from Table 2, during non-pollution days, HPBL and temperature inversion are important factors affecting PM2.5 in Harbin and Changchun. In Shenyang, TI_D is the only important determinant impacting PM2.5 concentration. In summer, the thickened mixing layer improves the dispersion of pollutants in the atmosphere. In winter, frequent and persistent thermal inversions at ground level drive considerable amount of air pollutants to accumulate in the lower layer of the atmosphere (Marcazzan et al., 2001). Although wind speed, HPBL, and TI_T are important factors of PM2.5 concentration, their correlations with PM2.5 concentration during non-pollution days are not significant. 

Fig. 6 shows the occurrence of PM2.5 pollution and non-pollution days in different meteorological conditions from 2015 to 2017. The meteorological factors for different cities are selected based on the significance level of 95% in Table 1. For Harbin, HPBL, WV, and TI_T are major impact factors. PM2.5 pollution usually occurs, with a possibility of 91.6%, when the wind speed is under 20 knots, HPBL is lower than 1500 m and TI_T is higher than 8°C. On the contrary, the possibility of occurrence of PM2.5 pollution is only 27.2% for the whole year. For Changchun, PM2.5 pollution is majorly impacted by wind speed and HPBL, the possibility is 61.7% when the wind speed is under 20 knots and HPBL is lower than 600 m. Meanwhile, the possibility is only 22.7% for the whole year. As for Shenyang, the possibility of the occurrence of PM2.5 pollution rises from 24.9% to 70.3% when the wind speed is under 20 knots, and TI_D is greater than 775 m. 

Fig. 6. The occurrence of PM2.5 pollution and non-pollution days in different meteorological conditions from 2015 to 2017 in (a) Harbin, (b) Changchun, and (c) Shenyang.Fig. 6. The occurrence of PM2.5 pollution and non-pollution days in different meteorological conditions from 2015 to 2017 in (a) Harbin, (b) Changchun, and (c) Shenyang.


To evaluate the effect of meteorological conditions on PM2.5 pollution in Northeast China, we applied a regression method to long-term ground-based PM2.5 data and radiosonde measurements. We found that the wind speed, HPBL, and temperature inversion were the primary factors influencing the fine particulate pollution in this region. Whereas the wind speed exhibited the strongest correlations with the PM2.5 levels in Harbin and Shenyang, the HPBL played the key role in Changchun, with the meteorological data showing significant negative effects from both of these factors on the fine particle concentration. By contrast, the temperature inversion, described using TI_T and TI_D, was positively correlated with the concentration.

In Harbin, the probability of PM2.5 pollution was 91.6% when the wind speed was less than 20 knots, the HPBL was below 1500 m, and TI_T was greater than 8°C. In Changchun, the probability was 61.7% when the wind speed was less than 20 knots and the HPBL was below 600 m. Finally, in Shenyang, the probability was 70.3% when the wind speed was less than 20 knots and TI_D was more than 775 m. Unlike other typical polluted areas, the variation in the PM2.5 concentration during polluted days in Northeast China little depended on the RH.

In addition to anthropogenic emissions, which necessitate local control strategies, the ambient meteorological conditions play a large role in PM2.5 pollution in Northeast China and must be considered in order to efficiently decrease air pollution in this region. For example, when temperature inversion occurs, reducing coal and petroleum consumption should be contemplated as a potential preventative measure.

Our study provides preliminary results on the major meteorological factors that affect the accumulation and dispersion of PM2.5 pollution. Further studies may examine additional meteorological parameters and expand the observation period. Furthermore, we used the nocturnal HPBL data which is usually dominated by the residual layer, and the relevancy between PM2.5 and HPBL may not be true in nighttime. This issue can also be addressed in the future.


This work is supported by the National Key Research and Development Program of China (2017YFC0212302). We would like to thank the Ministry of Environmental Protection of China for providing the PM2.5 observation data ( used in this study. We highly appreciate the National Oceanic and Atmospheric Administration (NOAA) as well as the Integrated Global Radiosonde Archive (IGRA) for giving us access to the historical radiosonde data. We would also like to show great gratitude to the National Center for Atmospheric Research (NCAR), associated with the University Corporation for Atmospheric Research (UCAR), for the generous provision of HPBL data. Without these institutions the experiments could not be conducted.


  1. Bao, J.Z., Yang, X.P., Zhao, Z.Y., Wang, Z.K., Yu, C.H. and Li, X.D. (2015). The spatial-temporal characteristics of air pollution in China from 2001–2014. Int. J. Environ. Res. Public Health 12: 15875–15887. [Publisher Site]

  2. Chaloulakou, A., Kassomenos, P., Spyrellis, N., Demokritou, P. and Koutrakis, P. (2003). Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece. Atmos. Environ. 37: 649–660. [Publisher Site]

  3. Chen, T., Guo, J., Li, Z., Zhao, C., Liu, H., Cribb, M., Wang, F. and He, J. (2016). A CloudSat perspective on the cloud climatology and its association with aerosol perturbations in the vertical over Eastern China. J. Atmos. Sci. 73: 3599–3616. [Publisher Site]

  4. Chen, W.W., Tong, D., Zhang, S.C., Dan, M., Zhang, X.L. and Zhao, H.M. (2015). Temporal variability of atmospheric particulate matter and chemical composition during a growing season at an agricultural site in northeastern China. J. Environ. Sci. 38: 133–141. [Publisher Site]

  5. Chen, W.W., Tong, D.Q., Dan, M., Zhang, S.C., Zhang, X.L. and Pan, Y.P. (2017). Typical atmospheric haze during crop harvest season in northeastern China: A case in the Changchun region. J. Environ. Sci. 54: 101–113. [Publisher Site]

  6. Chen, Z., Xie, X., Cai, J., Chen, D., Gao, B., He, B., Cheng, N. and Xu, B. (2018). Understanding meteorological influences on PM2.5 concentrations across China: A temporal and spatial perspective. Atmos. Chem. Phys. 18: 5343–5358. [Publisher Site]

  7. Cheng, S.Q. and Lam, K.C. (1998). An analysis of winds affecting air pollution concentrations in Hong Kong. Atmos. Environ. 32: 2559–2567. [Publisher Site]

  8. Dickerson, R.R., Li, C., Li, Z., Marufu, L.T., Stehr, J.W., McClure, B., Krotkov, N., Chen, H., Wang, P., Xia, X., Ban, X., Gong, F., Yuan, J. and Yang, J. (2007). Aircraft observations of dust and pollutants over northeast China: Insight into the meteorological mechanisms of transport. J. Geophys. Res. 112: D24S90. [Publisher Site]

  9. Durre, I., Vose, R.S. and Wuertz, D.B. (2006). Overview of the integrated global radiosonde archive. J. Clim. 19: 53–68. [Publisher Site]

  10. Fan, J., Wang, Y., Rosenfeld, D. and Liu, X. (2016). Review of aerosol–cloud interactions: Mechanisms, significance, and challenges. J. Atmos. Sci. 73: 4221–4252. [Publisher Site]

  11. Fang, C.S., Zhang, Z.D., Jin, M.Y., Zou, P.C. and Wang, J. (2017). Pollution characteristics of PM2.5 aerosol during haze periods in Changchun, China. Aerosol Air Qual. Res. 17: 888–895. [Publisher Site]

  12. Fang, F.M., Wang, Q.C., Liu, R.H., Ma, Z.W. and Hao, Q.J. (2001). Atmospheric particulate mercury in Changchun City, China. Atmos. Environ. 35: 4265–4272. [Publisher Site]

  13. Garratt, J.R. (1994). The atmospheric boundary layer. Earth Sci. Rev. 37: 89–134. [Publisher Site]

  14. Gu, X.F., Bao, F.W., Cheng, T.H., Chen, H., Wang, Y. and Guo, H. (2018). The impacts of regional transport and meteorological factors on aerosol optical depth over Beijing, 1980-2014. Sci. Rep. 8: 5113. [Publisher Site]

  15. Guan, Q.Y., Li, F.C., Yang, L.Q., Zhao, R., Yang, Y.Y. and Luo, H.P. (2018). Spatial-temporal variations and mineral dust fractions in particulate matter mass concentrations in an urban area of northwestern China. J. Environ. Manage. 222: 95–103. [Publisher Site]

  16. Guo, J., Deng, M., Lee, S.S., Wang, F., Li, Z., Zhai, P., Liu, H., Lv, W., Yao, W. and Li, X. (2016). Delaying precipitation and lightning by air pollution over the Pearl River Delta. Part I: Observational analyses. J. Geophys. Res. 121: 6472–6488. [Publisher Site]

  17. Guo, J., Su, T., Chen, D., Wang, J., Li, Z., Lv, Y., Guo, X., Liu, H., Cribb, M. and Zhai, P. (2019). Declining summertime local-scale precipitation frequency over China and the United States, 1981–2012: The disparate roles of aerosols. Geophys. Res. Lett. 46: 13281–13289. [Publisher Site]

  18. Han, B., Kong, S.F., Bai, Z.P., Du, G., Bi, T., Li, X., Shi, G.L. and Hu, Y. (2010). Characterization of elemental species in PM2.5 samples collected in four cities of Northeast China. Water Air Soil Pollut. 209: 15–28. [Publisher Site]

  19. He, K.B., Yang, F.M., Ma, Y.L., Zhang, Q., Yao, X.H., Chan, C.K., Cadle, S., Chan, T. and Mulawa, P. (2001). The characteristics of PM2.5 in Beijing, China. Atmos. Environ. 35: 4959–4970. [Publisher Site]

  20. Heal, M.R., Kumar, P. and Harrison, R.M. (2012). Particles, air quality, policy and health. Chem. Soc. Rev. 41: 6606–6630. [Publisher Site]

  21. Hien, P.D., Bac, V.T., Tham, H.C., Nhan, D.D. and Vinh, L.D. (2002). Influence of meteorological conditions on PM2.5 and PM2.5-10 concentrations during the monsoon season in Hanoi, Vietnam. Atmos. Environ. 36: 3473–3484. [Publisher Site]

  22. Jung, K.H., Patel, M.M., Moors, K., Kinney, P.L., Chillrud, S.N., Whyatt, R., Hoepner, L., Garfinkel, R., Yan, B. and Ross, J. (2010). Effects of heating season on residential indoor and outdoor polycyclic aromatic hydrocarbons, black carbon, and particulate matter in an urban birth cohort. Atmos. Environ. 44: 4545–4552. [Publisher Site]

  23. Kan, H.D., Chen, R.J. and Tong, S.L. (2012). Ambient air pollution, climate change, and population health in China. Environ. Int. 42: 10–19. [Publisher Site]

  24. Li, L., Tan, Q., Zhang, Y., Feng, M., Qu, Y., An, J. and Liu, X. (2017a). Characteristics and source apportionment of PM2.5 during persistent extreme haze events in Chengdu, southwest China. Environ. Pollut. 230: 718–729. [Publisher Site]

  25. Li, Z., Guo, J., Ding, A., Liao, H., Liu, J., Sun, Y., Wang, T., Xue, H., Zhang, H. and Zhu, B. (2017b). Aerosol and boundary-layer interactions and impact on air quality. Natl. Sci. Rev. 4: 810–833. [Publisher Site]

  26. Li, Z., Wang, Y., Guo, J., Zhao, C., Cribb, M.C., Dong, X., Fan, J., Gong, D., Huang, J., Jiang, M., Jiang, Y., Lee, S.S., Li, H., Li, J., Liu, J., Qian, Y., Rosenfeld, D., Shan, S., Sun, Y., Wang, H., Xin, J., Yan, X., Yang, X., Yang, X.Q., Zhang, F. and Zheng, Y. (2019). East Asian Study of Tropospheric Aerosols and their Impact on Regional Clouds, Precipitation, and Climate (EAST-AIRCPC). J. Geophys. Res. 124: 13026–13054. [Publisher Site]

  27. Liang, X., Zou, T., Guo, B., Li, S., Zhang, H.Z., Zhang, S.Y., Huang, H. and Chen, S.X. (2015). Assessing Beijing's PM2.5 pollution: Severity, weather impact, APEC and winter heating. Proc. R. Soc. London, Ser. A 471: 20150257. [Publisher Site]

  28. Liu, L., Guo, J., Miao, Y., Liu, L., Li, J., Chen, D., He, J. and Cui, C. (2018). Elucidating the relationship between aerosol concentration and summertime boundary layer structure in central China. Environ. Pollut. 241: 646–653. [Publisher Site]

  29. Lohmann, U. and Feichter, J. (2001). Can the direct and semi-direct aerosol effect compete with the indirect effect on a global scale? Geophys. Res. Lett. 28: 159–161. [Publisher Site]

  30. Lou, M., Guo, J., Wang, L., Xu, H., Chen, D., Miao, Y., Lv, Y., Li, Y., Guo, X., Ma, S. and Li, J. (2019). On the relationship between aerosol and boundary layer height in summer in China under different thermodynamic conditions. Earth Space Sci. 6: 887–901. [Publisher Site]

  31. Ma, Q.X., Wu, Y.F., Zhang, D.Z., Wang, X.J., Xia, Y.J., Liu, X.Y., Tian, P., Han, Z.W., Xia, X.G., Wang, Y. and Zhang, R.J. (2017). Roles of regional transport and heterogeneous reactions in the PM2.5 increase during winter haze episodes in Beijing. Sci. Total Environ. 599: 246–253. [Publisher Site]

  32. Marcazzan, G.M., Vaccaro, S., Valli, G. and Vecchi, R. (2001). Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy). Atmos. Environ. 35: 4639–4650. [Publisher Site]

  33. Miao, Y., Guo, J., Liu, S., Liu, H., Zhang, G., Yan, Y. and He, J. (2017). Relay transport of aerosols to Beijing-Tianjin-Hebei region by multi-scale atmospheric circulations. Atmos. Environ. 165: 35–45. [Publisher Site]

  34. Newman, T.B. and Browner, W.S. (1991). In defense of standardized regression coefficients. Epidemiology 2: 383–386. [Publisher Site]

  35. Petäjä, T., Järvi, L., Kerminen, V.m., Ding, A., Sun, J., Nie, W., Kujansuu, J., Virkkula, A., Yang, X., Fu, C., Zilitinkevich, S. and Kulmala, M. (2016). Enhanced air pollution via aerosol-boundary layer feedback in China. Sci. Rep. 6: 18998. [Publisher Site]

  36. Rosenfeld, D., Sherwood, S., Wood, R. and Donner, L. (2014). Climate effects of aerosol-cloud interactions. Science 343: 379. [Publisher Site]

  37. Ruellan, S. and Cachier, H. (2001). Characterisation of fresh particulate vehicular exhausts near a Paris high flow road. Atmos. Environ. 35: 453–468. [Publisher Site]

  38. Segura, S., Estellés, V., Esteve, A.R., Utrillas, M.P. and Martínez-Lozano, J.A. (2013). Analysis of a severe pollution episode in Valencia (Spain) and its effect on ground level particulate matter. J. Aerosol Sci. 56: 41–52. [Publisher Site]

  39. Seinfeld, J.H., Pandis, S.N. and Noone, K. (1998). Atmospheric chemistry and physics: From air pollution to climate change. Wiley, New York.

  40. Sun, Y.L., Wang, Z.F., Fu, P.Q., Jiang, Q., Yang, T., Li, J. and Ge, X.L. (2013). The impact of relative humidity on aerosol composition and evolution processes during wintertime in Beijing, China. Atmos. Environ. 77: 927–934. [Publisher Site]

  41. Tian, M., Wang, H.B., Chen, Y., Zhang, L.M., Shi, G.M., Liu, Y., Yu, J.Y., Zhai, C.Z., Wang, J. and Yang, F.M. (2017). Highly time-resolved characterization of water-soluble inorganic ions in PM2.5 in a humid and acidic mega city in Sichuan Basin, China. Sci. Total Environ. 580: 224–234. [Publisher Site]

  42. Twomey, S. (1977). The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci. 34: 1149–1152. [Publisher Site]

  43. Wang, H., Xu, J., Zhang, M., Yang, Y., Shen, X., Wang, Y., Chen, D. and Guo, J. (2014a). A study of the meteorological causes of a prolonged and severe haze episode in January 2013 over central-eastern China. Atmos. Environ. 98: 146–157. [Publisher Site]

  44. Wang, J. and Christopher, S.A. (2003). Intercomparison between satellite‐derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophys. Res. Lett. 30: 267–283. [Publisher Site]

  45. Wang, Y.G., Ying, Q., Hu, J.L. and Zhang, H.L. (2014b). Spatial and temporal variations of six criteria air pollutants in 31 provincial capital cities in China during 2013-2014. Environ. Int. 73: 413–422. [Publisher Site]

  46. Wen, X., Zhang, P.Y. and Liu, D.Q. (2018). Spatiotemporal variations and influencing factors analysis of PM2.5 concentrations in Jilin Province, Northeast China. Chin. Geog. Sci. 28: 810–822. [Publisher Site]

  47. Xu, T., Song, Y., Liu, M., Cai, X., Zhang, H., Guo, J. and Zhu, T. (2019). Temperature inversions in severe polluted days derived from radiosonde data in North China from 2011 to 2016. Sci. Total Environ. 647: 1011–1020. [Publisher Site]

  48. Xu, Z., Yu, D., Jing, L. and Xu, X. (2000). Air pollution and daily mortality in Shenyang, China. Arch. Environ. Health 55: 115–120. [Publisher Site]

  49. Yang, X., Zhao, C., Guo, J. and Wang, Y. (2016). Intensification of aerosol pollution associated with its feedback with surface solar radiation and winds in Beijing. J. Geophys. Res. 121: 4093–4099. [Publisher Site]

  50. Yao, L., Lu, N. and Jiang, S. (2012). Artificial Neural Network (ANN) for multi-source PM2.5 estimation using surface, MODIS, and meteorological data. 2012 International Conference on Biomedical Engineering and Biotechnology, Macao, 2012, pp. 1228–1231.

  51. You, W., Zang, Z.L., Pan, X.B., Zhang, L.F. and Chen, D. (2015). Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models. Sci. Total Environ. 505: 1156–1165. [Publisher Site]

  52. Zang, Z.L., Wang, W.Q., You, W., Li, Y., Ye, F. and Wang, C.M. (2017). Estimating ground-level PM2.5 concentrations in Beijing, China using aerosol optical depth and parameters of the temperature inversion layer. Sci. Total Environ. 575: 1219–1227. [Publisher Site]

  53. Zhang, G., Li, J., Li, X.D., Xu, Y., Guo, L.L., Tang, J.H., Lee, C.S.L., Liu, X.A. and Chen, Y.J. (2010). Impact of anthropogenic emissions and open biomass burning on regional carbonaceous aerosols in South China. Environ. Pollut. 158: 3392–3400. [Publisher Site]

  54. Zhang, P.F., Dong, G.H., Sun, B.J., Zhang, L.W., Chen, X., Ma, N.N., Yu, F., Guo, H.M., Huang, H., Lee, Y.L., Tang, N.J. and Chen, J. (2011). Long-term exposure to ambient air pollution and mortality due to cardiovascular disease and cerebrovascular disease in Shenyang, China. PLoS One 6: e20827. [Publisher Site]

  55. Zhang, Q., Quan, J.N., Tie, X.X., Li, X., Liu, Q., Gao, Y. and Zhao, D.L. (2015). Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China. Sci. Total Environ. 502: 578–584. [Publisher Site]

Aerosol Air Qual. Res. 20:1618-1628. https://doi.org/10.4209/aaqr.2019.10.0534 

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