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. 2020 Oct 19;47(20):e2020GL090041. doi: 10.1029/2020GL090041

Abnormally Shallow Boundary Layer Associated With Severe Air Pollution During the COVID‐19 Lockdown in China

Tianning Su 1,✉, Zhanqing Li 1,✉, Youtong Zheng 1, Qingzu Luan 1,2, Jianping Guo 3
PMCID: PMC7537024  PMID: 33041384

Abstract

After the 2020 Lunar New Year, the Chinese government implemented a strict nationwide lockdown to inhibit the spread of the Coronavirus Disease 2019 (COVID‐19). Despite the abrupt decreases in gaseous emissions caused by record‐low anthropogenic activities, severe haze pollution occurred in northern China during the COVID lockdown. This paradox has attracted the attention of both the public and the scientific community. By analyzing comprehensive measurements of air pollutants, planetary boundary layer (PBL) height, and surface meteorology, we show that the severe air pollution episode over northern China coincided with the abnormally low PBL height, which had reduced by 45%, triggering strong aerosol‐PBL interactions. After dynamical processes initiated the temperature inversion, the Beijing metropolitan area experienced a period with continuously shallow PBLs during the lockdown. This unprecedented event provided an experiment showcasing the role of meteorology, in particular aerosol‐PBL interactions in affecting air quality.

Keywords: boundary layer, aerosol, air pollution, COVID‐19

Key Points

  • Based on nationwide observations, variations in air pollution and meteorology are discussed during the COVID lockdown

  • The severe air pollution during the lockdown coincided with abnormally shallow PBL triggering strong aerosol‐PBL interactions

  • Beijing experienced a period with continuously shallow PBLs initiated by dynamical processes, which warrant the formation of haze event

1. Introduction

The outbreak of the Coronavirus Disease 2019 (COVID‐19) in early 2020 had tremendous social and economic impacts on China (Tian et al., 2020; C. Wang, Horby, et al., 2020; Zu et al., 2020). The Chinese government imposed an unpreceded nationwide mandatory lockdown to contain its spread shortly after its first occurrence. As the early epicenter, the first lockdown was implemented in Wuhan on 23 January. Following that, all other major cities went into lockdown after the Lunar New Year (LNY). Despite differences in local measures, strict COVID lockdowns (CLDs) lasted for at least 3 weeks nationwide. During the CLD period, commercial activities, traffic, and travel were restricted, leading to a substantial reduction in emissions of primary air pollution.

As expected, anthropogenic emissions declined considerably during the CLD period. For instance, the Ozone Monitoring Instrument observed a 48% drop in tropospheric column densities of NO2 over eastern China during the CLD (Liu et al., 2020). Also reported was a similar decrease in tropospheric NO2 columns from analyses of TROPOspheric Monitoring Instrument data (Bauwens et al., 2020; Shi & Brasseur, 2020). However, surface aerosol loading did not have a similar reduction despite the substantial decrease in primary pollutant emissions (P. Wang, Chen, et al., 2020). There were episodes of severe air pollution in northern China during the CLD (Le et al., 2020; Sun et al., 2020), incurring a public outcry questioning the effects of the drastic emission control measures implemented in China that could have adverse impacts on the financial well‐being in some sectors.

This apparent paradox drew immediate attention to the scientific community who has come up with various sound explanations/hypotheses, one of which was attributed to the formation of secondary pollution (Chang et al., 2020; Huang et al., 2020). While enhanced secondary pollution may play a significant role in the severe air pollution during the CLD, it remains an open question as to if it was the only major factor. If not, what are other factors and their contributions to the severe haze event with the concentrations of atmospheric particulate matter (PM) with diameters less than 2.5 μm (PM2.5) more than 100 μg m−3? Le et al. (2020) employed reanalysis data and model simulations in an attempt to attribute the severe air pollution to meteorology and heterogeneous chemistry. However, reanalysis data have considerable uncertainties, especially in the treatment of physical processes in the planetary boundary layer (PBL) (Alapaty et al., 1997). The PBL height (PBLH), one of the most fundamental PBL quantity, is not modeled accurately (e.g., Banks et al., 2015; Chu et al., 2019; Guo et al., 2016; Su et al., 2017). More importantly, PBLH is associated with aerosol vertical mixing, affecting the concentration of air pollutions emitted near the surface through various interactions and feedback mechanisms (Dong et al., 2017; Lou et al., 2019; Su et al., 2018; Wang et al., 2013).

To quantify the role of the PBL on the severe haze episode that occurred during the CLD, we employed a nationwide PBLH database along with other observations of pollutants and surface meteorological parameters. The abnormally shallow PBL occurring in northern China could account for the unexpected severe air pollution episode, which likely offset the reduction in primary emissions. The incident serves as a natural test bed for understanding aerosol‐PBL interactions in a historically low‐emission scenario.

2. Data and Methods

2.1. Data Sets

In this study, we acquired the data from 1,138 environmental stations and 55 radiosonde stations in eastern China. Figure S1 in the supporting information shows the topography and locations of stations. Measured routinely at the environmental stations are carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and PM2.5 concentrations at 1‐hr intervals, released to the public with sound quality (Liang et al., 2016; Wei et al., 2019, 2020). The China Meteorological Administration is responsible for maintaining the radiosonde stations, in which the vertical profiles of pressure, water vapor, temperature, and wind are routinely measured at 08:00 and 20:00 Beijing Time (BJT = UTC + 8), as well as at 1400 BJT during summer only. The original resolution of radiosonde data varies with the ascending height of the balloon (Guo et al., 2016; Zhang et al., 2018), unified to a fixed resolution of 5 hPa (Liu & Liang, 2010). At the radiosonde sites, hourly measurements of meteorological variables also are available at the surface level (Guo et al., 2017, 2019). Given the availability of both environmental and meteorological data, valid data periods are 2013–2020 in Beijing and 2016–2020 in other places. In this study, we employ the daily means of environmental and meteorological quantities, averaged over 0800–1900 BJT, when most commercial activities and transport sectors were suppressed during the CLD.

2.2. Determination of PBLH

Radiosonde‐measured temperature and pressure profiles determined the vertical profiles of potential temperature. Since radiosonde soundings were not available at noon, we adopted the parcel method proposed by Holzworth (1964). This widely used method continuously tracks PBLHs, based on a morning radiosonde sounding combined with daily meteorological data (Karimian et al., 2016; Li et al., 2020; Zhang et al., 2014).

Due to the overshooting of rising parcels, the potential temperature at PBL top is generally higher than the surface value (Liu & Liang, 2010). Therefore, we modified the parcel method by adding a threshold. Assuming that surface temperatures increase during the morning, the parcel method also assumes that an air parcel is lifted adiabatically from the near surface to the upper part of PBL and keeps an approximately constant potential temperature. Following the idea of 1.5‐θ‐increase method (Nielsen‐Gammon et al., 2008), a threshold of 1.5 K is added to the original method. The upper boundary of PBL is thus defined as the height where the environmental potential temperature first exceeds the current surface potential temperature by more than 1.5 K. In particular, the environmental potential temperature profile is determined by the radiosonde launched at 0800 BJT at radiosonde sites, while the surface potential temperature is obtained from surface‐based meteorological measurements.

Note that the PBL undergoes regime transition in a diurnal cycle. It typically transforms from a stable boundary layer to a neutral or convective boundary layer during the day and then becomes a stable boundary layer again at night. Due to this diurnal feature, we only retrieve the noontime PBL, which is well developed and most representative of daily development due to the strong turbulent mixing (Stull, 1988; Yang et al., 2013). Hereafter, we define the noontime as 1100–1500 BJT. Among the 55 sites considered, 53 of them have continuous 4‐year records of PBLH (2016–2020).

The assumptions made in the parcel method will lead to some biases in the estimation of the PBLH. Regarding this issue, we evaluated the noontime PBLH derived from the parcel method through comparisons with those calculated using real‐time radiosonde data from 1400 BJT in Beijing during the summer (Figure S2). The PBLHs derived from the parcel method agree well with those derived from the 1.5‐θ‐increase method or the Liu and Liang (2010) method, with high correlation coefficients (~0.8).

3. Results

3.1. Nationwide Changes in Air Pollution and Meteorology

We focus on the CLD, defined as the period between 26 January and 17 February 2020, when anthropogenic emissions dramatically declined (Huang et al., 2020; Liu et al., 2020). We compared the surface pollutant concentrations with climatological means for the same period. Hereafter, climatological means refer to the same period in the Chinese lunar calendar but from 2016 to 2019 to potentially account for the holiday effect (Le et al., 2020; Zhang et al., 2010). Figure 1 presents the ratios of changes in NO2, SO2, CO, and PM2.5. On average, surface concentrations of NO2, SO2, and CO decreased by 45%, 47%, and 20%, respectively. The reduced primary emissions directly lead to the dramatic decrease in gaseous pollution. Moreover, despite the considerable reduction in emissions during the CLD, PM2.5 increased by 19% in the northern part of China, with large regional discrepancies (Figure 1d).

Figure 1.

Figure 1

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Percentage differences in (a) NO2, (b) SO2, (c) CO, (d) PM2.5, (e) wind speed (WS), (f) relative humidity (RH), (g) rainfall amount, and (h) planetary boundary layer height (PBLH) between mean values during the COVID‐19 lockdown (26 January to 17 February 2020) and the climatological mean during the same period of the years 2016 to 2019. The PBLH is retrieved during noontime (1100–1500 Beijing Time, or BJT) due to the diurnal transition, while other parameters are averaged during daytime (0800–1900 BJT).

Figures 1e–1h show simultaneous changes in meteorological variables (i.e., wind speed [WS], relative humidity [RH], rainfall amount, and PBLH). In general, WS exhibited a 0–10% decrease during the CLD period, except for a few increasing trends. RH did not notably change in southern China but considerably increased in northern China on the order of a mean of 25% and a range of 10–70%. A moist environment would facilitate the formation of secondary aerosols (Wang et al., 2016). Rainfall is unlikely a major driving force behind the spatial pattern of air pollutants because both southern and northern China generally experienced more precipitation during the CLD to varying degrees. By contrast, the PBLH showed dramatic changes: significant decreases during the CLD in northern China but increases in central China. Meanwhile, the changes in surface temperature are similar to the changes in PBL to some extent (Figure S3). Shallow PBL is generally associated with the low surface temperature.

Regional differences in the ratios of changes in pollutants and meteorology are notable, especially the sharp contrast between northern China (latitudes above 38°N) and central/southern China (latitudes below 38°N). We averaged the PM2.5, NO2, PBLH, WS, and RH anomalies during the CLD within each region, and all anomalies are normalized by climatological means: (values in 2020 − Climatology)/Climatology. Figure 2a reveals that the gaseous pollution diminished considerably during the CLD, presumably as a result of the lockdown, and gradually recovered to normal levels after the CLD. Near the end of the CLD, NO2 and PM2.5 both decreased by more than 50% in different parts of China. In particular, over central and southern China, PM2.5 generally decreased by 30%, and NO2 decreased by 50% during the CLD. However, this was not the case in northern China where PM2.5 concentrations were higher than normal. This is linked to meteorological conditions (Figure 2b). In northern China, both the humid environment and low WSs during the CLD favored the formation and accumulation of aerosols. The PBLH in northern China had the most significant decrease, that is, a 45% decrease. In central/southern China, changes in these meteorological variables were in similar directions but with lesser magnitudes. Comparing the change ratios in northern China to those in central/southern China, WS was 9.7% lower, RH was 19.8% higher, and the PBLH was 39.1% lower. The PBLH could play a more dominant role in enhancing air pollution during the CLD, discussed next.

Figure 2.

Figure 2

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(a) PM2.5 (red line) and NO2 (blue line) anomalies and (b) planetary boundary layer height (PBLH, red line), wind speed (WS, blue line), and relative humidity (RH, green line) anomalies over China. In (a) and (b), a 9‐day smoothing window was applied to all anomalies. Colored solid lines represent results averaged over northern China (latitudes above 38°N), and colored dashed lines represent results averaged over central and southern China (latitudes below 38°N). Zero lines represent the climatology averaged over the same COVID lockdown (CLD) period of the years 2016 to 2019. Pink areas represent the Lunar New Year (LNY), and gray areas represent the CLD period.

3.2. Synergistic Changes in the PBLH and PM2.5 Concentration

Because northern China experienced an unusual increase in PM2.5 levels during the CLD, we investigated two regions of interests: Beijing and northeast China (Figure S1). Table S1 lists the radiosonde stations in these two regions. Data from environmental and radiosonde stations are matched if the stations are located within 25 km of each other. Figures 3a and 3b show the general relationships between PBLH and PM2.5 during wintertime in Beijing and northeast China. Following Su et al. (2018), an inverse function (i.e., fx=Ax+B) describes the relationship between PBLH and PM2.5 (more details are given in the supporting information). During wintertime, the relationship was highly nonlinear. The PM2.5 concentration increased rapidly with decreasing PBLH for PBLHs lower than 1 km. This dependence weakened for higher PBLHs. Although it is not an absolute value, 1 km appears to be the turning point dictating the PM2.5‐PBLH relationship.

Figure 3.

Figure 3

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The relationship between the planetary boundary layer height (PBLH) and PM2.5 over (a) Beijing and (b) northeast China during wintertime. The black dots and whiskers represent average values and standard deviations in each bin, respectively. The vertical black dashed line shows the turning point of the PBLH (1 km). The thick red lines and thick blue lines indicate regressions before and after the turning point, respectively. The fitting functions and coefficient correlations of the inverse fittings are given at the top of each panel. Red and blue areas in panels (c) and (d) represent the probability density functions (PDFs) of the PBLH during the COVID lockdown (CLD) and the climatology, respectively. The red and blue dash lines represent the mean PBLH during the CLD and the climatological mean, respectively.

To gain further insight into the potential role of the PBL in regulating air pollution during the CLD, we compared the probability density functions of the PBLH during the CLD with that during the same period in the lunar calendar during the previous 4 years. The PBLH was much lower in both Beijing and northeast China during the CLD compared with the mean of the previous 4 years. The frequency of PBLHs lower than the turning point was significantly higher during the CLD. According to the climatology, the PBLH was mostly above the turning point, corresponding to a weak interaction scenario. The shallow‐PBL cases during the CLD appear to have triggered a strong aerosol‐PBL interaction, leading to excessively high near‐surface aerosol loadings. Due to the positive feedback loop (Bond et al., 2013; Ding et al., 2016; Petäjä et al., 2016; Su et al., 2020; Z. Li et al., 2017), the low‐PBL effect amplified, contributing to the drastic changes in PM2.5 when the PBLH was below the turning point.

Figure 4 shows the time series of normalized PM2.5 and 1/PBLH in Beijing and northeast China. The normalization is done by subtracting the monthly mean, removing the trend, and subtracting the mean value in the lunar calendar. Highly consistent changes in 1/PBLH and PM2.5 are found in the two regions. The PM2.5 concentration was higher than normal during the CLD in both Beijing and northeast China, which is associated with the increase in 1/PBLH. Note that there are some discrepancies between the time series of PM2.5 and PBLH. The precipitation effectively reduced the pollution level at the end of CLD haze event. Meanwhile, PBLH still maintains the relatively low values due to the suppressed surface fluxes. Therefore, a decrease in PM2.5 is ahead of the decrease in 1/PBLH during the CLD haze event. In addition, the relationships between meteorological factors and PM2.5 are established, following standardized multiple regressions whose coefficients denote the relative importance of the individual factors (Table S2). PBLH turns out to have the strongest partial correlation with the daily PM2.5 concentration. The other factors also matter.

Figure 4.

Figure 4

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Time series of 1/PBLH (planetary boundary layer height, red lines) and PM2.5 (blue lines) over (a) Beijing and (b) northeast China, with a 3‐day smoothing window. Seasonal cycles, trends, and climatology have been removed in the time series. Pink areas represent the Lunar New Year (LNY), and gray areas represent the COVID lockdown (CLD) period. The green areas indicate the periods with daily rainfall above 1 mm. A notable haze event that occurred in Beijing during the CLD is pointed out in (a).

In the middle of the CLD, Beijing experienced a heavy haze episode. Table S3 lists the daily air pollution and meteorological parameters during the CLD. During the CLD haze event, there were five consecutive days with noontime PBLHs lower than 0.7 km, which is rare, having only occurred three times during winter from 2013 to 2019 (detailed in Table S3). For other shallow PBL periods in the previous 7 years, the mean PM2.5 is 270 μg m−3, with a standard deviation of 125 μg m−3. A continuously low PBLH can generate an unfavorable environment for the dissipation of pollutants, thus compounding severe air pollution in Beijing.

Further investigated were historical cases with similar meteorological conditions in Beijing, as detailed in the supporting information. Figure 5 presents the distribution of daytime PM2.5 under these similar conditions. Even though WS and RH during the CLD favored the accumulation of near‐surface pollutants, these conditions did not necessarily lead to a high surface PM2.5 concentration. Due to the unprecedented reduction of various emissions during the CLD, the PM2.5 level appears to be the lowest scenario compared with the previous level under similar meteorological conditions.

Figure 5.

Figure 5

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The red dot and whisker represent the average value and standard deviation of daytime PM2.5 during the COVID lockdown (CLD) haze event in Beijing. Based on wintertime data from 2013 to 2019, days with a similar planetary boundary layer height (PBLH), wind speed (WS), relative humidity (RH), and all three are selected (less than 20% difference). The distribution of daytime PM2.5 is presented under these similar conditions. The black dot and whisker represent the average value and standard deviation, while the width of the color‐shaded areas represents the smoothed distribution of daytime PM2.5. The mean PM2.5 values for these categories are given at the top of the figure (unit: μg m−3).

The abnormally shallow PBL during the CLD haze event is attributed to atmospheric dynamics. Using Modern‐Era Retrospective analysis for Research and Applications Version 2 reanalysis data (Gelaro et al., 2017), we examine the composite mean meteorological field during the CLD haze event in Figure S4. During this period, Beijing is located near the southeast flank of a low‐pressure system. The southwesterly winds advected warm air masses over the cold surface, which stabilized the PBL. This process is commonly known as the mechanism behind formations of frontal inversion (Zhang et al., 2009). The dynamic processes caused a heating effect on the upper PBL and a cooling effect near the surface during this period (Figure S5), notably reducing the PBLH. This is also consistent with previous studies showing the frequent occurrences of more stable PBLs under similar synoptic patterns (e.g., Miao et al., 2017; Wu et al., 2017).

4. Conclusions

The nationwide lockdown of economic activities in China provided a unique opportunity to differentiate the impact of emissions and meteorology on severe air pollution episodes that occurred in northern China. To date, reported findings have chiefly focused on the role of secondary aerosols. Using comprehensive ground‐based observations of air pollutants, PBLH, and other meteorological variables, we argue that the abnormally shallow PBL during the CLD was likely a key player in dictating PM2.5 in northern China.

Compared with the climatology during the same period in the previous 4 years, the noontime PBLH in central China increased during the CLD but decreased by more than 40% in northern China, triggering a strong aerosol‐PBL interaction. As the most prominent city in northern China, Beijing experienced a persistent low PBLH during the CLD, leading to a severely polluted episode relative to historical data. In addition to the effects of PBL, secondary aerosols may further exacerbate surface pollution (Huang et al., 2020; Le et al., 2020; Sun et al., 2020).

The in‐depth observation‐based analysis presented here may help explain the high‐profile haze event that occurred in Beijing during the CLD. The aerosol‐PBL interaction was likely a key mechanism behind the severe pollution in northern China, given the exceptionally low‐emission scenario. This may resolve the paradox of the well‐established relationship between air pollution and primary emissions during this special period. In the long run, however, emissions remain a critical factor in driving the variation in PM2.5.

Supporting information

Supporting Information S1

Click here for additional data file. (1.8MB, docx)

Acknowledgments

This study is supported by the National Science Foundation (AGS1837811) and the National Key R&D Program of China (2017YFC1501702). We appreciate the provision of meteorological and radiosonde measurements by the China Meteorological Administration and for the provision of air pollution measurements by the Ministry of Ecology and Environment of China. We sincerely thank the MERRA team for their data sets.

Su, T. , Li, Z. , Zheng, Y. , Luan, Q. , & Guo, J. (2020). Abnormally shallow boundary layer associated with severe air pollution during the COVID‐19 lockdown in China. Geophysical Research Letters, 47, e2020GL090041. 10.1029/2020GL090041

Contributor Information

Tianning Su, Email: tianning@umd.edu.

Zhanqing Li, Email: zli@atmos.umd.edu.

Data Availability Statement

MERRA‐2 reanalysis data can be found online at (https://disc.gsfc.nasa.gov/datasets/M2I1NXASM_5.12.4/summary?keywords=merra2). Hourly air pollution measurements are released by the Ministry of Ecology and Environment of China (https://aqicn.org/data‐platform/covid19/). Surface meteorological data and radiosonde are provided by the China Meteorological Data Service Center of the China Meteorological Administration (http://data.cma.cn/en/?r=data/detail&dataCode=B.0011.0001C). The data sets can be found on these websites or request from the lead author (tianning@umd.edu).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information S1

Click here for additional data file. (1.8MB, docx)

Data Availability Statement

MERRA‐2 reanalysis data can be found online at (https://disc.gsfc.nasa.gov/datasets/M2I1NXASM_5.12.4/summary?keywords=merra2). Hourly air pollution measurements are released by the Ministry of Ecology and Environment of China (https://aqicn.org/data‐platform/covid19/). Surface meteorological data and radiosonde are provided by the China Meteorological Data Service Center of the China Meteorological Administration (http://data.cma.cn/en/?r=data/detail&dataCode=B.0011.0001C). The data sets can be found on these websites or request from the lead author (tianning@umd.edu).

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