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. 2022 Apr 22;127(8):e2021JD036191. doi: 10.1029/2021JD036191

The Heavy Particulate Matter Pollution During the COVID‐19 Lockdown Period in the Guanzhong Basin, China

Xia Li 1,2, Naifang Bei 3, Jiarui Wu 1, Suixin Liu 1, Qiyuan Wang 1, Jie Tian 1, Lang Liu 1, Ruonan Wang 1,2, Guohui Li 1,4,✉
PMCID: PMC9111303  PMID: 35600237

Abstract

Nationwide restrictions on human activities (lockdown) in China since 23 January 2020, to control the 2019 novel coronavirus disease pandemic (COVID‐19), has provided an opportunity to evaluate the effect of emission mitigation on particulate matter (PM) pollution. The WRF‐Chem simulations of persistent heavy PM pollution episodes from 20 January to 14 February 2020, in the Guanzhong Basin (GZB), northwest China, reveal that large‐scale emission reduction of primary pollutants has not substantially improved the air quality during the COVID‐19 lockdown period. Simultaneous reduction of primary precursors during the lockdown period only decreases the near‐surface PM2.5 mass concentration by 11.6% (12.6 μg m−3), but increases ozone (O3) concentration by 9.2% (5.5 μg m−3) in the GZB. The primary organic aerosol and nitrate are the major contributor to the decreased PM2.5 in the GZB, with the reduction of 28.0% and 21.8%, respectively, followed by EC (10.1%) and ammonium (7.2%). The increased atmospheric oxidizing capacity by the O3 enhancement facilitates the secondary aerosol (SA) formation in the GZB, increasing secondary organic aerosol and sulphate by 6.5% and 3.3%, respectively. Furthermore, sensitivity experiments suggest that combined emission reduction of NOX and VOCs following the ratio of 1:1 is conducive to lowering the wintertime SA and O3 concentration and further alleviating the PM pollution in the GZB.

Keywords: secondary aerosols, atmospheric oxidizing capacity, particulate matter pollution, Guanzhong basin, COVID‐19

Key Points

  • The COVID‐19 lockdown decreases PM2.5 concentration by 11.6% in the Guanzhong Basin (GZB), but increases O3 level by 9.2%

  • The enhanced atmospheric oxidizing capacity promotes the secondary aerosol formation in the GZB during the COVID‐19 lockdown period

  • Combined emission reduction of NOx and VOCs is conducive to mitigating the wintertime particulate matter pollution in the GZB

1. Introduction

The novel coronavirus disease pandemic abruptly breaking out in the late December 2019 (COVID‐19) has been spreading worldwide, leading to hundreds of thousands of deaths (Huang et al., 2020; Tian et al., 2020; R. Y. Zhang, et al., 2020). In order to efficiently prevent the spread of the virus, Chinese government has firstly imposed the nationwide lockdown strategy to restraint human activities since 23 January 2020, which has substantially decreased primary air pollutants emissions, especially those from industry and traffic sectors (Le et al., 2020; F. Liu, et al., 2020; Shi & Brasseur, 2020). It has been reported that the regional mean NO2 concentration over eastern China has been reduced by 71.9% during the first stage of lockdown from 23 January to 13 February 2020, when compared to the same time period in 2019 (Le et al., 2020). Despite dramatic reductions of the primary air pollutants, heavy particulate matter (PM) pollution has occurred in some areas of China during the lockdown period (Chang et al., 2020; Li et al., 2020; Miyazaki et al., 2020; Xu et al., 2020). Huang et al. (2020) have found that the PM2.5 (PM with aerodynamic diameter equal to or less than 2.5 μm) concentrations in north China exhibit an increasing trend during the lockdown period when compared with prior 3 weeks, especially in the Beijing‐Tianjin‐Hebei region. Li et al. (2020) have revealed that, although PM2.5 concentrations in the Yangtze River Delta region during the first stage of prevention period have decreased by 31.8% against the same time period in 2019, the daily average PM2.5 concentration ranges from 15 to 79 μg m−3, showing high levels of the background and residual pollutants.

Previous studies have demonstrated that short‐term air quality has been substantially improved through mitigating local emissions of primary air pollutants, for example, in Beijing for the 2008 Olympics, 2014 Asia‐Pacific Economic Cooperation, and 2015 China Victory Day Parade, Nanjing for the 2014 Youth Olympic Games, and Guangzhou for the 2010 Asian Games (Huang et al., 2017; Liu et al., 2013; Wang et al., 2010; Xu et al., 2015, 2017). Heavy air pollution is generally a synergetic effect of adverse meteorological conditions and accumulated air pollutants, involving complicated physical and chemical processes, as well as special topography (An et al., 2019; Bei et al., 2017, 2020; Long et al., 2018; Wang et al., 2016). Therefore, with occurrence of unfavorable meteorological situations, that is, stagnant and humid air, how to mitigate anthropogenic emissions is important to effectively alleviate air pollution. The key strategy is to avoid the chemical and physical feedback induced by the emission mitigation to deteriorate air pollution.

The Guanzhong Basin (GZB) is located in northwestern China and surrounded by the Loess Plateau in the north and Qinling Mountains in the south, with a narrow opening to the east. The GZB has experienced severe air pollution in recent 20 years, caused by high anthropogenic emissions due to the intensive growth of industries and rapid city expansions, frequent occurrence of unfavorable synoptic patterns, and the unique topography (Bei, Li, et al., 2016, Bei, Xiao, et al., 2016; Feng et al., 2016, 2018). Although strict emission mitigation strategies have been carried out in the GZB, heavy air pollution events still frequently occur, particularly during wintertime. In January 2020, the unfavorable synoptic patterns are subject to controlling the GZB and the PM pollution is severe before the nationwide lockdown. However, although anthropogenic emissions of primary pollutants have decreased considerably during the lockdown period in the GZB, heavy haze with high levels of PM2.5 still engulfs the basin (Tian et al., 2021).

In this study, we conduct the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) simulations to evaluate the impact of the large‐scale emission reduction of primary pollutants on the heavy PM pollution in the GZB during the COVID‐19 lockdown period and further investigate the response of secondary aerosol (SA) and daytime O3 concentration to the combined emission reduction of NOX and VOCs to explore effective measure for alleviating the wintertime PM pollution in the GZB.

2. Model and Data

2.1. WRF‐Chem Model and Configurations

The WRF‐Chem model used in the present study has been modified by Li et al. (2010, 2012), Li, Bei, et al. (2011), Li, Zavala, et al. (2011) based on the original version (Fast et al., 2006; Grell et al., 2005). The model consists of a gas‐phase chemical module fitting for various chemical mechanisms and the AERO5 aerosol module adopted from the CMAQ/models‐3 (Community Multiscale Air Quality/Third‐Generation Air Quality Modeling System; Binkowski & Roselle, 2003; Foley et al., 2010). The method in the CMAQ and Wesely (1989) is applied to calculate the wet and dry deposition of gaseous and particulate species, respectively. The non‐traditional VBS (volatility basis‐set) modeling method and ISORROPIA is used to predict organic and inorganic aerosols, respectively (Nenes et al., 1998; Tsimpidi et al., 2010). The heterogeneous reactions involving aerosol liquid water include SO2 oxidation by O2 catalyzed by Fe3+, N2O5 hydrolysis, and the irreversible uptake of glyoxal and methylglyoxal (G. H. Li, et al., 2017; L. Liu, et al., 2020; Xing et al., 2019). The secondary formation of aerosol, including nucleation and rapid growth, is considered in model simulations. The new particle formation (NPF) is calculated due to the binary nucleation of H2SO4 and H2O vapor. The nucleation rate is parameterized as a function of temperature, relative humidity (RH), and the vapor‐phase H2SO4 concentration (Kulmala et al., 1998), and the new particles are assumed to have a diameter of 2.0 nm. Aerosol effects on photolysis and aerosol‐radiation feedback are also considered in model simulations (Li et al., 2005, Li, Bei, et al. (2011); Wu et al., 2019, 2020). The main physical parameterizations employed in model simulations include the WRF Single‐Moment 6‐class graupel scheme (Hong & Lim, 2006), Mellor‐Yamada‐Janjić (MYJ) turbulent kinetic energy (TKE) planetary boundary layer (PBL) scheme (Janjić, 2002), MYJ surface scheme (Janjić, 2002), Unified Noah Land‐surface model (Chen & Dudhia, 2001), and Goddard longwave and shortwave scheme (Chou et al., 2001; Chou & Suarez, 1999).

Persistent and heavy PM pollution episodes from 20 January to 14 February 2020, occurred in the GZB during the first stage of the COVID‐19 lockdown, are simulated using the WRF‐Chem model. The simulation domain is centered at 34.25°N, 109°E, including 300 × 300 grid cells (Figure 1). The model adopts a horizontal resolution of 6 km and 35 sigma vertical levels in simulations. The NCEP (National Centers for Environmental Prediction) 1° × 1° reanalysis data is used for meteorological boundary and initial conditions and the WACCM (Whole Atmosphere Community Climate Model; Marsh et al., 2013; Neale et al., 2013) output data with a 6 hr interval is used for chemical initial and boundary conditions. The biogenic emissions are online predicted by MEGAN (Model of Emissions of Gases and Aerosol from Nature; Guenther et al., 2006).

Figure 1.

Figure 1

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(a) Map showing the location of the simulation domain and (b) WRF‐Chem model simulation domain with topography. The bold blue solid line in (a) and (b) shows the boundary of the Guanzhong Basin (GZB). In (b), the black dots represent ambient monitoring sites in the GZB. The filled blue triangle shows the Jinghe meteorological station in Xi'an, and the filled red rectangle denotes the deployment location of the ACSM at IEECAS in Xi'an.

The anthropogenic emission inventory (AEI) used in the present study is developed by Zhang et al. (2009) and M. Li, et al. (2017) with the base year of 2013 (Feng et al., 2016). Considering the substantial changes in the AEI due to the implementation of the clean air actions in China (Zhang et al., 2019), the AEI has been adjusted to the year of 2020 according to the trends provided by Zheng et al. (2018). Huang et al. (2020) have estimated that the average emission reduction ratio of CO, NOX, SO2, VOCs, PM2.5, OC, and EC in Shaanxi province caused by the lockdown is 19%, 45%, 18%, 34%, 13%, 22%, and 5%, respectively. Therefore, in the base simulation (F Base ), the AEI is further adjusted as suggested by Huang et al. (2020). The results of F Base are used to compare with observations and measurements for model validations. In order to evaluate the impact of the primary emission reduction due to the lockdown on the SA formation and further the PM pollution in the GZB, a sensitivity simulation (F Sens ) is further conducted with the AEI in the base year of 2020 with no emission reduction.

2.2. Observation Data

Three‐hour observations of the temperature, RH, wind speed and wind direction at the Jinghe meteorological station in Xi'an (Figure 1b) are obtained from the website of https://www.ncei.noaa.gov/maps/hourly. Hourly observed near‐surface mass concentrations of PM2.5, O3, NO2, SO2, and CO released by the Ministry of Environment and Ecology of China can be downloaded from the website of http://www.aqistudy.cn/. The quadrupole ACSM (Aerosol Chemical Speciation Monitor, Aerodyne Research Inc., Billerica, MA, USA) has been performed at the rooftop of the Institute of Earth Environment, Chinese Academy of Sciences (IEECAS; 34.23°N, 108.89°E) in Xi'an (Figure 1b), to online measure the non‐refractory submicron aerosol (NR‐PM1), mainly including OA, nitrate, sulphate, ammonium, and chloride. The measured mass spectra of OA are analyzed using the HERM (Hybrid Environmental Receptor Model, Chen & Cao, 2018), and six components have been identified, including hydrocarbon‐like organic aerosol (HOA), cooking organic aerosol (COA), coal combustion organic aerosol (CCOA), biomass burning organic aerosol (BBOA), less‐oxidized oxygenated organic aerosol (LO‐OOA), and more‐oxidized oxygenated organic aerosol (MO‐OOA). HOA, COA, CCOA and BBOA are classified as primary organic aerosol (POA), and LO‐OOA and MO‐OOA are treated as secondary organic aerosol (SOA; Tian et al., 2021).

2.3. Statistical Methods for Comparisons

The mean bias (MB), root mean square error (RMSE) and index of agreement (IOA) are used to validate the WRF‐Chem model performance in simulating the meteorological conditions, air pollutants, and aerosol species against observations and measurements. The formulae are as follows:

MB=1N∑i=1NPi−Oi,
RMSE=1N∑i=1NPi−Oi212,
IOA=1−∑i=1NPi−Oi2∑i=1N|Pi−O¯|+|Oi−O¯|2.

Where Pi and Oi are the predictions and observations of meteorological parameters and air pollutants, respectively. N is the total number of the predictions and observations used for comparisons, and O¯ represents the average of observations. IOA describes the relative difference between the model predictions and observations, ranging from 0 to 1, with one indicating perfect agreement of predictions and observations.

3. Results and Discussions

3.1. PM Pollution in the GZB During the COVID‐19 Lockdown Period

Increased PM2.5 concentrations in eastern China during the first stage of COVID‐19 lockdown have been observed, when compared with the same time period before COVID‐19 (Chang et al., 2020; Huang et al., 2020), but a 50% drop in NR‐PM1 from before to during the COVID‐19 control period in Lanzhou, northwest China, has been reported by Xu et al. (2020), suggesting distinct differences in the air pollution between east and west China. Figure 2 shows comparisons of the 3‐weeks average mass concentration of observed air pollutants in the GZB during the COVID‐19 lockdown period from 23 January to 13 February 2020 (CLD), the 3‐weeks average before CLD from 1 January to 22, 2020 (PRE‐CLD), the 5‐years average from 2015 to 2019 during the same time period with CLD in the Gregorian calendar (STP‐CLD), and the 5‐years average from 2015 to 2019 during the same time period with CLD in the Chinese lunar calendar covering the Lunar New Year (STP‐LNY). The average observed near‐surface PM2.5 mass concentration in the GZB during the CLD is 105.9 μg m−3, 11.8% (14.2 μg m−3) lower than that during the PRE‐CLD. Although the lockdown has reduced the PM2.5 levels in the GZB, the average PM2.5 concentration during the CLD is still 11.6% (11.0 μg m−3) and 17.8% (16.0 μg m−3) higher than that during the STP‐CLD and STP‐LNY, respectively.

Figure 2.

Figure 2

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Comparisons of the three‐weeks average of the air pollutants observations in the GZB during the COVID‐19 lockdown period from 23 January to 13 February 2020 (CLD), three‐weeks average before the CLD from 1–22 January, 2020 (PRE‐CLD), the 5‐years average from 2015 to 2019 during the same time period with CLD in the Gregorian calendar (STP‐CLD), and the 5‐years average from 2015 to 2019 during the same time period with CLD in the Chinese lunar calendar that covers the Lunar New Year (STP‐LNY).

The observed O3 concentration in the GZB during the CLD is around 66.7 μg m−3, which is increased by 106.4% when compared with PRE‐CLD (32.3 μg m−3), and 23.1 and 11.7 μg m−3 higher than that during the STP‐CLD and STP‐LNY, respectively. The observed near‐surface NO2 mass concentration during the CLD in the GZB is only around 23.9 μg m−3, which is 1.9 and 1.6 times lower than that during the STP‐CLD and STP‐LNY, respectively. The NO2 concentration in the GZB during the CLD is reduced by about 52.2% (26.1 μg m−3) when compared with the PRE‐CLD. F. Liu, et al. (2020) have reported that the average vertical column density of tropospheric NO2 in China shows a 48% drop from 20 days before the 2020 Lunar New Year to 20 days after, which is 21 ± 5% greater than that averaged from 2015 to 2019. Le et al. (2020) have also shown quite low column‐integrated NO2 amount throughout the whole China during the CLD period. The wintertime O3 production in urban areas in China is in a NOX‐saturated regime (NOX = NO + NO2) because of the lack of HOX radicals (Le et al., 2020; Seinfeld & Pandis, 2006). Additionally, reductions in NO emissions alleviate the daytime O3 titration (Levy et al., 2014; Seinfeld & Pandis, 2006). Therefore, the O3 enhancement in the GZB during the CLD is mainly caused by the remarkable reductions of NOX. Besides, previous studies have also attributed the anti‐correlation between PM2.5 and O3 to the aerosol radiative effect on the photochemical formation of O3 (Wu et al., 2020), as well as the aerosol sink for O3 precursors (Li et al., 2019).

The SO2 and CO concentrations in the GZB during the CLD are quite low with an average of 11.9 μg m−3 and 1.2 mg m−3, respectively. The SO2 concentration in the GZB during the CLD is only 1.7 μg m−3 lower than that during the PRE‐CLD. When compared with the average during STP‐CLD and STP‐LNY, the SO2 concentration in the GZB during the CLD is significantly decreased by 59.5% and 51.1%, respectively, indicating the beneficial effects of the series of air pollution control actions. The CO concentration in the GZB during the CLD is reduced by 17.9% (0.3 mg m−3) when compared with that during PRE‐CLD. With the similar trend as SO2, the CO concentration in the GZB during the CLD is also reduced by 27.5% and 20.6%, when compared with the STP‐CLD and STP‐LNY, respectively. Although the mass concentrations of NO2 and SO2 are quite low in the GZB during the CLD, the collaborative pollution with high levels of PM2.5 and O3 is severe, suggesting the limited contribution of primary emission reduction to the PM pollution in the GZB.

3.2. WRF‐Chem Model Performance

Considering the significant role of meteorological conditions in the formation, conversion, transmission, and dispersion of air pollutants in the atmosphere (Bei et al., 2017, 2020), the simulated and observed temporal profiles of temperature, RH, wind speed and wind direction at the Jinghe meteorological station in Xi'an from 20 January to 14 February 2020, have been compared to validate the WRF‐Chem model performance in simulating meteorological fields (Figure S1 in Supporting Information S1). The WRF‐Chem model well reproduces the temporal variations of the near‐surface temperature in Xi'an during the simulation period. The MB and RMSE is −0.1°C and 2.3°C, respectively, and the IOA is 0.90, indicating good agreements of simulations with observations. The simulated temporal variations of RH are also well consistent with observations, with the IOA of 0.82, but the model biases are large, with MB and RMSE of 5.9% and 14.8%, respectively. In addition, the model also reasonably tracks the diurnal variations of the near‐surface wind, with IOA of 0.68 and 0.64 for the wind speed and wind direction, respectively.

Figure S2 in Supporting Information S1 exhibits the distributions of predicted and observed near‐surface mass concentrations of PM2.5, O3, NO2, and SO2 along with the simulated wind fields averaged from 20 January to 14 February 2020. Generally, the predicted spatial pattern of PM2.5 concentrations is consistent with observations at ambient monitoring sites in the GZB. The WRF‐Chem model reasonably reproduces the higher PM2.5 concentrations in the central basin than surrounding areas. Apparently, during the study period, the weak northeast winds in the GZB facilitate the accumulation of PM2.5, causing heavy air pollution. The model also well yields the observed O3 concentrations varying from 40 to 80 μg m−3 in the basin. The observed and calculated NO2 and SO2 concentrations are quite low in the GZB during the simulation period, within 40 and 20 μg m−3, respectively, which is mainly caused by the emission reduction during the lockdown period. The simulated and observed diurnal profiles of near‐surface PM2.5, O3, NO2, SO2, and CO mass concentrations averaged over all monitoring sites in the GZB during the episodes are shown in Figure S3 in Supporting Information S1. The WRF‐Chem model performs reasonably well in simulating the diurnal variations of PM2.5 concentrations when compared to observations in the GZB. The MB and RMSE is only 0.5 and 23.3 μg m−3, respectively, and the IOA is 0.94. The predicted O3 and NO2 diurnal variations are also well consistent with observations, with IOA of 0.89 and 0.85, respectively. The model also yields good predictions for CO temporal variations, with the IOA of 0.93. By contrast, the deviations in SO2 simulations are considerably large, with the MB and RMSE of −0.8 and 3.8 μg m−3, respectively, and the IOA of 0.76. A large fraction of SO2 is emitted from point sources, like power plants and agglomerated industrial zones, thus the transport of SO2 is more sensitive to uncertainties in simulated wind fields.

Figure 3 presents the temporal variations of measured and simulated concentrations of OA, SOA, sulphate, nitrate, and ammonium aerosol at the IEECAS site in Xi'an during the episodes. The WRF‐Chem model reasonably predicts the diurnal variations of OA concentrations, with the MB of 0.40 μg m−3 and IOA of 0.76, but the deviations in OA simulations are large, with the RMSE of 21.9 μg m−3. OA in the atmosphere is mainly determined by primary emissions of coal combustion, biomass burning, cooking, and vehicles, and secondary formation, as well as regional transport (Li et al., 2018). Therefore, the OA simulations are markedly influenced by the uncertainties in anthropogenic emissions and the predicted wind fields (Bei et al., 2017). The model also well tracks the measured diurnal variations of SOA concentrations, with the IOA of 0.82. The modeled temporal variations of sulphate, nitrate, and ammonium aerosol are generally in good agreements with measurements, with IOAs exceeding 0.85. The good agreements of the simulated meteorological parameters and mass concentrations of air pollutants and aerosol species with observations and measurements in Xi'an and GZB reveal that the simulated wind fields and the AEI used in the present study are generally reasonable, providing a reliable base for further evaluations.

Figure 3.

Figure 3

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Comparisons of measured (black dots) and simulated (red lines) diurnal profiles of submicron (a) OA, (b) SOA, (c) sulphate, (d) nitrate, and (e) ammonium at the IEECAS site in Xi'an from 20 January to 14 February 2020.

Although the WRF‐Chem model performs considerably well in simulating meteorological parameters, air pollutants and aerosol species against observations and measurements during the study period, model biases still exist. In general, simulations using a chemical transport model always contain large uncertainties regarding emissions, meteorology, and chemistry represented in the model (Grell et al., 2005; Zhang et al., 2015). The uncertainty in meteorology lies mainly in the biases of the simulated wind fields and the planetary boundary layer (PBL) development, which determine accumulation or dispersion of air pollutants (Bei et al., 2017). The uncertainties in the AEI used in simulations are also responsible for the model biases. Firstly, with the implementation of strict emission control measures since 2013, the spatiotemporal distributions of anthropogenic emissions in China have been considerably changed (Zhang et al., 2019; Zheng et al., 2018), which have not been well reflected in the AEI used in this study. Secondly, during the CLD, the anthropogenic emissions in China have been estimated to be reduced substantially between January and March in 2020, with the largest reductions in February (Zheng et al., 2021). The reductions in anthropogenic emissions are dominated by the industry sector for SO2 and PM2.5 and are contributed approximately equally by the industry and transportation sectors for NOX, CO, and NMVOCs, which have not been well considered in the AEI, either. Thirdly, the model simulations do not consider the variation of diurnal profiles of the AEI during the CLD. As for the chemistry in the WRF‐Chem model, one of the major uncertainties concerns the controversy for the chemical mechanism leading to sulphate formation (Peng et al., 2021; Wang et al., 2018). In this study, the sulphate heterogeneous formation from SO2 is parameterized as a first‐order irreversible uptake by wet aerosols, with a reactive uptake coefficient of 0.5 × 10−4 (G. H. Li, et al., 2017), which is close to the result of laboratory experiments and observations in the field campaign reported by Wang et al. (2016, 2018). Previous studies have revealed that the SO2 heterogeneous oxidation can be catalyzed not only by transition metals, like Fe3+ (G. H. Li, et al., 2017) and Mn2+ (Huang et al., 2014), but also by NO2 with abundant NH3 (Wang et al., 2016). A most recent work has invoked the critical catalytic role of black carbon (BC) in sulphate production during severe haze events in China, demonstrating that the SO2 oxidation is efficiently catalyzed on BC particles in the presence of NO2, even at low SO2 and intermediate RH levels (F. Zhang, et al., 2020).

3.3. Impact of the Emission Mitigation on the PM Pollution During the CLD in the GZB

The contribution of emission reduction resulted from the CLD to the air pollutants in the GZB is calculated by differentiating F Sens with F Base . The near‐surface PM2.5 mass concentrations in the GZB are appreciably lowered by the CLD, especially during heavily polluted days (Figure S4a in Supporting Information S1). The average decrease of PM2.5 mass in the GZB due to the CLD varies from 5.0 to 40 μg m−3 (6.0%–20%), with the most decrease in the central basin (Figure 4a and Figure S5a in Supporting Information S1). The CLD increases the O3 concentration in the GZB by 1.0–15 μg m−3 (1.0%–50%; Figure 4b and Figure S5b in Supporting Information S1), but the O3 concentration in the eastern and southeastern part outside the basin is slightly decreased, which might be resulted from the decreases in VOCs emissions. It is worth noting that the O3 enhancement in the GZB during the CLD generally occurs during nighttime (Figure S4b in Supporting Information S1). The CLD decreases the near‐surface NO2 concentrations in the GZB by 3.0–20 μg m−3 (30%–60%; Figure 4c and Figure S5c in Supporting Information S1). The near‐surface concentrations of SO2 and CO in the GZB are also slightly reduced due to the CLD, especially during heavily polluted days (Figures S4d and S4e in Supporting Information S1). The SO2 decrease by the CLD in the basin is less than 5.0 μg m−3, varying from 10% to 30% (Figure 4d and Figure S5d in Supporting Information S1).

Figure 4.

Figure 4

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Contributions of emission reduction due to the CLD to the near‐surface (a) PM2.5, (b) O3, (c) NO2, and (d) SO2 concentration in the GZB averaged from 23 January to 13 February 2020.

The CLD generally decreases the OA concentrations in the GZB, especially during heavily polluted days (Figure S6a in Supporting Information S1), but slightly increases the SOA concentration, with the average enhancement less than 4.0 μg m−3 (Figure S7a in Supporting Information S1). The average SOA increase varies from 1.0% to 10% in the GZB during the CLD period (Figure 5a). The sulphate concentrations are also slightly increased during heavily polluted days (Figure S6c in Supporting Information S1), with the average enhancement less than 1.0 μg m−3 in the GZB (Figure 5b and Figure S7b). On the contrary, the CLD considerably reduces the nitrate concentration in the GZB by 1.0–8.0 μg m−3 (10%–30%; Figure 5c and Figure S7c in Supporting Information S1). The ammonium concentrations in the GZB are also decreased by less than 4.0 μg m−3 (3.0%–15%; Figure S7d in Supporting Information S1).

Figure 5.

Figure 5

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Contributions of emission reduction due to the CLD to the (a) SOA, (b) sulphate, (c) nitrate, and (d) ammonium concentration in the GZB averaged from 23 January to 13 February 2020.

The average contribution of emission reduction of primary pollutants during the CLD period to the aerosols and gas pollutants in the GZB is shown in Figure 6. The near‐surface PM2.5 reduction in the GZB due to the primary emission reduction is not substantial as expected, with an average decrease of around 11.6% (12.6 μg m−3), which is mainly due to the considerable decrease in POA (28.0% or 7.5 μg m−3) and nitrate aerosols (21.8% or 4.3 μg m−3). The results are generally consistent with a recent work by the CMAQ model simulations of the PM pollution episode during COVID‐19 lockdown period from 1 January to 12 February 2020 (Wang et al., 2020), which has proposed a 14.2 μg m−3 decrease in the average PM2.5 mass concentration in Xi'an during highly polluted days when the PM2.5 mass concentrations exceed 75 μg m−3 as compared with the case without CLD. Tian et al. (2021) have found that the POA concentrations in Xi'an are decreased by 17%–58% during the CLD period, in which the HOA shows the largest reduction due to the strict restrictions on motor vehicles. By contrast, smaller decreases in COA, BBOA, and CCOA are found, because the residential use of coal and biomass for heating and cooking are not restricted. During the CLD period, the ammonium aerosols, unspecified species (mainly dust), and EC in the GZB are also slightly decreased by 7.2%, 10.1%, and 10.1%, respectively. However, the SOA and sulphate aerosols in the GZB are increased by 6.5% (1.3 μg m−3) and 3.3% (0.4 μg m−3), respectively, which is mainly caused by the O3 enhancement. During the CLD period, SOA accounts for 53.2% of the total OA in the GZB, but 43.4% in the non‐CLD scenario (Figure S8 in Supporting Information S1), indicating the enhanced SOA formation and decreased POA emissions during CLD period.

Figure 6.

Figure 6

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Average contributions of emission reduction due to the CLD to aerosols and gas pollutants in the GZB from 23 January to 13 February 2020.

The primary emission reduction by the CLD causes around 45.6% decrease in the near‐surface NO2 concentration in the GZB, on average, consequently lowering the nitrate and ammonium formation. However, the ammonium reduction is not so evident as nitrate, which is resulted from the slight increase of NH3 (27.4% or 1.3 μg m−3). The decreased nitric caused by large reductions of NO2 leads to a surplus of ammonia in the atmosphere. The SO2 and CO concentrations in the GZB are also slightly reduced by 16.7% (1.2 μg m−3) and 10.4% (0.1 mg m−3), respectively, during the CLD period. However, the O3 concentration in the GZB is increased by 9.2% (5.5 μg m−3) due to the primary emissions reduction by the CLD. Generally, as the main precursor to form O3 and SOA, VOCs emission reduction should directly hinder the O3 and SOA formation. However, the increased O3 by the NOX reduction enhances the atmospheric oxidation capacity (AOC) and further promotes the SOA and sulphate formation, offsetting the slight decrease in SOA and sulphate caused by VOCs and SO2 emission reduction.

In the present study, the AOC is represented by the OX (=O3 + NO2) in the atmosphere (Feng et al., 2020; Wang et al., 2017). Figure 7 shows variations of the daytime (from 10:00 to 17:00, Beijing time, BJT) O3, NO2, and OX concentrations in the GZB during the wintertime from 2013 to 2018 and the PRE‐CLD and CLD period. The wintertime NO2 concentration in the GZB slightly decreases from 53.6 μg m−3 in 2013 to 41.8 μg m−3 in 2018, but the O3 concentration follows an increasing trend from 2013 to 2017 and then slightly decreases in 2018. From 2013 to 2018, the wintertime AOC in the GZB is at a high level and fluctuates following the variations of NO2 concentrations. During wintertime, the weak solar radiation in the GZB markedly decreases photolysis and slows up the photochemical formation of O3. During the heavy PM pollution period, the stable or stagnant lower atmosphere is unfavorable for the dispersion of air pollutants. The NO titration significantly influences the daytime O3 levels in the PBL. With the deterioration of PM2.5 pollution, the daytime O3 concentration decreases, but the NO2 concentration increases (Figure S9 in Supporting Information S1). However, during the CLD period, the near‐surface daytime O3 concentration in the GZB is quite higher than that during the wintertime from 2013 to 2018 (Figure 7 and S9 in Supporting Information S1). Although the NO2 concentration is quite low in the GZB during the CLD, the increased O3 levels greatly enhance the AOC, not only promoting the formation of sulphate and SOA, but also accelerating the NO2 conversion to form HNO3 and further counterbalancing the decreased HNO3 due to the NOX emission reduction.

Figure 7.

Figure 7

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Variations of the observed near‐surface mass concentration of daytime (from 10:00 to 17:00, Beijing time) O3, NO2, and OX (=O3 + NO2) in the GZB during the wintertime from 2013 to 2018 and the PRE‐CLD and CLD period.

Le et al. (2020) have reported that except for the complex chemistry of SA and O3, the meteorological influence is a possible factor explaining the enhanced PM2.5 and O3 during the CLD in northern China. As compared with the 5‐years average climatology for 2015–2019 during STP‐LNY, the increased RH, decreased wind speed, declined PBL height, and less precipitation in northern China during the CLD are conducive to the haze development. Besides, as positive feedback to the meteorological variations, aerosols can reduce PBL height and stabilize lower atmosphere via their radiative effects, and further promote the accumulation of air pollutants. In the GZB, the meteorological condition does not exhibit substantial variations from the PRE‐CLD to the CLD, with the average temperature increased from 2.1°C to 4.8°C, the RH decreased from 68.4% to 61.7%, the wind speed decreased from 1.3 m s−1 to 1.5 m s−1, and the PBL height increased from 401.5 to 424.1 m. In general, the meteorological conditions during the CLD are favorable for the diffusion of air pollutants when compared to the PRE‐CLD.

In addition, Guo et al. (2020) have proposed that the photochemical oxidation of vehicular exhaust yields abundant precursors and organics for efficient nucleation and growth of ultrafine particles. However, under polluted conditions, preexisting (secondary and primary) particles inhibit nucleation, leading to masked NPF, although photochemistry is still sufficient for nucleation. Therefore, reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions can promote the NPF. Previous studies have also shown that NPF is likely enhanced due to reduced primary PM emissions and elevated O3 levels (Lee et al., 2019; Wang et al., 2013; Zhang et al., 2012). During the CLD, the substantial decrease in primary particles and increase in O3 concentrations potentially enhance the NPF, but the emission reduction of precursors and organics from traffic sectors is not conducive to the nucleation and growth of ultrafine particles. Hence, further studies are needed to investigate the NPF during the CLD in the GZB.

3.4. Response of SA and Daytime O3 Concentration to the Combined Emission Reduction of NOX and VOCs in the GZB

During the CLD period, the considerable reduction of POA and nitrate aerosol in the GZB are partially canceled out by the enhancement in SOA and sulphate aerosol, which does not alleviate the PM pollution as expected. The enhanced SOA and sulphate formation is attributed to the increased AOC by the elevated O3 concentration. Therefore, reducing wintertime AOC constitutes the priority to decrease the O3 concentration and further alleviate PM pollution in the GZB under current conditions. Considering the dominant role of VOCs and NOX in the O3 formation, we design an emission reduction matrix including the F Base and 35 emission reduction scenarios in NOX and VOCs with 20% interval from 0% to 100% to develop isopleth diagrams of air pollutants in the GZB during the CLD period. It is worth noting that both AVOCs (VOCs from anthropogenic emissions) and BVOCs (VOCs from biogenic emissions) are considered in WRF‐Chem model simulations, but only AVOCs are reduced in the sensitivity simulations, given that BVOCs are unmanageable. In a conventional O3 EKMA (empirical kinetic modeling approach) diagram, the NOX‐limited and VOCs‐limited regimes are separated by a ridge line, following the maximum 1‐hr O3 mixing ratio produced by a given VOCs emission (Kinosian, 1982; Ou et al., 2016). According to the principle of EKMA profile, we make the isopleth diagrams for SA (including SOA, sulphate, nitrate, and ammonium) and daytime 8‐hr average O3 concentration from 10:00 to 17:00 (BJT) to evaluate the response of SA and daytime O3 concentration in the GZB to the combined emission reduction of NOX and VOCs during the CLD period.

As shown in Figure 8a, the SA concentration in the GZB during the CLD period is in a transitional regime from VOCs‐limited to NOX‐limited, with the most reduction of around 30% when NOX and VOCs emission in the GZB are both reduced by 80%. Correspondingly, the PM2.5 and daytime O3 concentrations are both reduced by 17.9% on average, with significant decrease of 39.5% and 41.4% in SOA and nitrate aerosol, respectively. It is worth noting that the combined emission reduction of 80% in both NOX and VOCs in the GZB causes a 54.2% (2.5 μg m−3) increase in the NH3 concentration, which is caused by the reduction of acid gases (mainly H2SO4 and HNO3) to release NH3 into gas phase. The daytime O3 in the GZB during CLD period is in VOCs‐limited regime, decreased by 8.5%–59.3% when the VOCs emission in the GZB is reduced from 20% to 80% with NOX emissions invariable. Meanwhile, the PM2.5 and SA are reduced by 2.3%–10.6% and 3.6%–22.8%, respectively, to which SOA is the main contributor. Considering the negative correlation between the daytime O3 concentration and the NOX emission in the GZB, combined reduction of VOCs and NOX emission following the ratio of 1:1 is beneficial for the SA and daytime O3 mitigation in the GZB in winter. In addition, ambient PM2.5 and O3 in the GZB during wintertime are not only locally formed, but also from transboundary transport outside GZB (Li & Feng, 2016; Li et al., 2019; Xue et al., 2014). Bei et al. (2016a, 2016b) have shown that when the PM pollution occurs in the GZB, the easterly/northeasterly winds are prevailing and subject to transporting air pollutants generated in eastern/northeastern regions to the basin, facilitating the heavy haze formation. Li et al. (2020) have revealed that the transboundary transport of emissions from Shanxi and Henan provinces accounts for around 22.2% of the total PM2.5 in the GZB during the heavy PM pollution episode from 6 December to 21, 2016. Furthermore, with the deterioration of the air quality in the GZB from being slightly polluted to severely polluted in terms of hourly PM2.5 concentrations, transboundary transport of emissions from Shanxi and Henan provinces plays an increasingly important role in the PM pollution, with the average PM2.5 contribution increasing from 8.0% to 27.5%. In the reduction scheme of 100% reduction in both NOX and VOCs emission in the GZB during the CLD, the PM2.5, SA, and daytime O3 concentrations are decreased by 24.4%, 40.6%, and 27.7%, respectively, indicating the important role of transboundary transport to the wintertime PM pollution in the GZB. Therefore, co‐control with neighboring regions to mitigate both primary pollutants and SA would be more efficient to alleviate the wintertime PM pollution in the GZB.

Figure 8.

Figure 8

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Isopleth diagrams for the mass concentrations of (a) secondary aerosols (SA, including SOA, sulphate, nitrate, and ammonium) and (b) daytime 8‐hr average O3 from 10:00 to 17:00 (BJT) in the GZB during the CLD period with NOX and VOCs emissions varying from 0% to 100%. The solid red line separates the NOX‐limited and VOC‐limited regime. The yellow dash lines show the emission reduction of 45% and 34% for NOX and VOCs during the CLD period in the GZB, respectively.

4. Conclusions

The lockdown strategy to prevent the spread of COVID‐19 has led to unprecedented anthropogenic emission reduction in China, especially in the traffic and industry sectors. Observations show that the near‐surface mass concentration of PM2.5 in the GZB during the CLD is 11.8% (14.2 μg m−3) lower than that during the PRE‐CLD, but 11.6% (11.0 μg m−3) and 17.8% (16.0 μg m−3) higher than that during the STP‐CLD and STP‐LNY, respectively. Although the observed near‐surface NO2 concentration in the GZB during the CLD has been reduced by about 52.2% (26.1 μg m−3) when compared with the PRE‐CLD, the observed O3 concentration in the GZB during the CLD has been increased by 106.4% when compared with PRE‐CLD (32.3 μg m−3), which is also 23.1 and 11.7 μg m−3 higher than that in the STP‐CLD and STP‐LNY, respectively, suggesting the limited contribution of primary emission reduction to the PM pollution in the GZB during the CLD period.

The WRF‐Chem model simulation of persistent PM pollution episodes in the GZB during the CLD period reveals enhanced secondary formation of aerosols by the elevated AOC with increased O3 concentrations by NOX reductions. The near‐surface mass concentration of PM2.5 in the GZB is not reduced as expected by the CLD, with an average decrease of 11.6%. The NO2 and SO2 concentration in the GZB during the CLD period is decreased by 45.6% and 16.7%, respectively, when compared with non‐CLD. However, the O3 concentration is increased by 9.2%, enhancing the AOC and further promoting the sulphate and SOA formation. The relatively large decrease in POA, nitrate, and ammonium is offset by the increase in sulphate and SOA, leading to a slight decrease in total PM2.5 mass concentration. Furthermore, sensitivity simulations show that the SA formation in the GZB during the CLD period is in a transitional regime from VOCs‐limited to NOX‐limited, and the daytime O3 is in VOCs‐limited regime. Considering the negative correlation between the daytime O3 concentration and the NOX emissions in the GZB, it is suggested that combined emission reduction of NOX and VOCs following the ratio of 1:1 is conducive to lowering the SA and daytime O3 concentration and further alleviating the PM pollution in the GZB during wintertime.

Supporting information

Supporting Information S1

Click here for additional data file. (11.3MB, pdf)

Acknowledgments

This work is financially supported by the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB40030203), and the National Natural Science Foundation of China (No. 41975175).

Li, X. , Bei, N. , Wu, J. , Liu, S. , Wang, Q. , Tian, J. , et al. (2022). The heavy particulate matter pollution during the COVID‐19 lockdown period in the Guanzhong Basin, China. Journal of Geophysical Research: Atmospheres, 127, e2021JD036191. 10.1029/2021JD036191

Data Availability Statement

The observed meteorological parameters are obtained from the website of https://www.ncei.noaa.gov/maps/hourly (NOAA, 2001) [Dataset]. The hourly observed near‐surface mass concentrations of PM2.5, O3, NO2, SO2, and CO released by the Ministry of Ecology and Environment of China can be accessed at Zenodo (https://zenodo.org/record/6404603#.YkafoC-KHVg; China MEP, 2013) [Dataset]. The measurements of the non‐refractory submicron aerosol (NR‐PM1) can be referenced in Tian et al. (2021). The NCEP 1° × 1° reanalysis data are acquired from the website of https://rda.ucar.edu (NCEP, 2000) [Dataset]. The 6‐hr output data of the WACCM are from the website of https://www.acom.ucar.edu/waccm/download.shtml (NCAR, 2020) [Dataset]. The MEIC (Multi‐resolution Emission Inventory for China) emission inventory are available at the website of http://meicmodel.org (Li, Zhang, et al., 2017; Zhang et al., 2009; Zheng et al., 2018) [Dataset].

References

  1. An, Z. S. , Huang, R. J. , Zhang, R. Y. , Tie, X. X. , Li, G. H. , Cao, J. J. , et al. (2019). Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes. Proceedings of the National Academy of Sciences of the United States of America, 116(18), 8657–8666. 10.1073/pnas.1900125116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bei, N. F. , Li, G. H. , Huang, R. J. , Cao, J. J. , Meng, N. , Feng, T. , et al. (2016). Typical synoptic situations and their impacts on the wintertime air pollution in the Guanzhong basin, China. Atmospheric Chemistry and Physics, 16(11), 7373–7387. 10.5194/acp-16-7373-2016 [DOI] [Google Scholar]
  3. Bei, N. F. , Li, X. P. , Tie, X. X. , Zhao, L. N. , Wu, J. R. , Li, X. , et al. (2020). Impact of synoptic patterns and meteorological elements on the wintertime haze in the Beijing‐Tianjin‐Hebei region, China from 2013 to 2017. The Science of the Total Environment, 704, 135210. 10.1016/j.scitotenv.2019.135210 [DOI] [PubMed] [Google Scholar]
  4. Bei, N. F. , Wu, J. R. , Elser, M. , Feng, T. , Cao, J. J. , El‐Haddad, I. , et al. (2017). Impacts of meteorological uncertainties on the haze formation in Beijing‐Tianjin‐Hebei (BTH) during wintertime: A case study. Atmospheric Chemistry and Physics, 17(23), 14579–14591. 10.5194/acp-17-14579-2017 [DOI] [Google Scholar]
  5. Bei, N. F. , Xiao, B. , Meng, N. , & Feng, T. (2016). Critical role of meteorological conditions in a persistent haze episode in the Guanzhong Basin, China. The Science of the Total Environment, 550, 273–284. 10.1016/j.scitotenv.2015.12.159 [DOI] [PubMed] [Google Scholar]
  6. Binkowski, F. S. , & Roselle, S. J. (2003). Models‐3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description. Journal of Geophysical Research, 108(D6), 4183. 10.1029/2001JD001409 [DOI] [Google Scholar]
  7. Chang, Y. H. , Huang, R. J. , Ge, X. L. , Huang, X. P. , Hu, J. L. , Duan, Y. S. , et al. (2020). Puzzling haze events in China during the coronavirus (COVID‐19) shutdown. Geophysical Research Letters, 47(12), e2020GL088533. 10.1029/2020GL088533 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chen, F. , & Dudhia, J. (2001). Coupling an advanced land surface‐hydrology model with the Penn State‐NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review, 129, 569–585. [DOI] [Google Scholar]
  9. Chen, L. W. , & Cao, J. J. (2018). PM2.5 source apportionment using a hybrid environmental receptor model. Environmental Science & Technology, 52(11), 6357–6369. 10.1021/acs.est.8b00131 [DOI] [PubMed] [Google Scholar]
  10. China MEP (Ministry of Environmental Protection, China) . (2013). Online monitoring and analysis platform of China air Quality [Dataset]. China MEP. Retrieved from https://www.aqistudy.cn/
  11. Chou, M. D. , & Suarez, M. J. (1999). A solar radiation parameterization for atmospheric studies. NASA/TM‐1999‐10460. NASA Technological Report, 15, 38pp. [Google Scholar]
  12. Chou, M. D. , Suarez, M. J. , Liang, X. Z. , Yan, M. H. , & Cote, C. (2001). A thermal infrared radiation parameterization for atmospheric studies. Max J, 19, 55pp. NASA/TM‐2001‐104606. [Google Scholar]
  13. Fast, J. D. W. I. G., Jr. , Easter, R. C. , Zaveri, R. A. , Barnard, J. C. , Chapman, E. G. , Chapman, E. G. , et al. (2006). Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology‐chemistry‐aerosol model. Journal of Geophysical Research, 111(D21), D21305. 10.1029/2005JD006721 [DOI] [Google Scholar]
  14. Feng, T. , Bei, N. F. , Zhao, S. Y. , Wu, J. R. , Li, X. , Zhang, T. , et al. (2018). Wintertime nitrate formation during haze days in the Guanzhong Basin, China: A case study. Environmental Pollution, 243, 1057–1067. 10.1016/j.envpol.2018.09.069 [DOI] [PubMed] [Google Scholar]
  15. Feng, T. , Li, G. H. , Cao, J. J. , Bei, N. F. , Shen, Z. X. , Zhou, W. J. , et al. (2016). Simulations of organic aerosol concentrations during springtime in the Guanzhong Basin, China. Atmospheric Chemistry and Physics, 16(15), 10045–10061. 10.5194/acp-16-10045-2016 [DOI] [Google Scholar]
  16. Feng, T. , Zhao, S. Y. , Zhang, X. , Wang, Q. Y. , Liu, L. , Li, G. H. , & Tie, X. (2020). Increasing wintertime ozone levels and secondary aerosol formation in the Guanzhong basin, central China. The Science of the Total Environment, 745, 140961. 10.1016/j.scitotenv.2020.140961 [DOI] [PubMed] [Google Scholar]
  17. Foley, K. , Roselle, S. , Appel, K. , Bhave, P. , Pleim, J. , Otte, T. , et al. (2010). Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3(1), 205–226. 10.5194/gmd-3-205-2010 [DOI] [Google Scholar]
  18. Grell, G. A. , Peckham, S. E. , Schmitz, R. , McKeen, S. A. , Frost, G. , Skamarock, W. C. , & Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmospheric Environment, 39(37), 6957–6975. 10.1016/j.atmosenv.2005.04.027 [DOI] [Google Scholar]
  19. Guenther, A. , Karl, T. , Harley, P. , Wiedinmyer, C. , Palmer, P. I. , & Geron, C. (2006). Estimates of global terrestrial isoprene emissions using MEGAN (model of emissions of gases and aerosols from nature). Atmospheric Chemistry and Physics, 6, 3181–3210. 10.5194/acp-6-3181-2006 [DOI] [Google Scholar]
  20. Guo, S. , Hu, M. , Peng, J. F. , Wu, Z. J. , Zamora, M. L. , Shang, D. J. , et al. (2020). Remarkable nucleation and growth of ultrafine particles from vehicular exhaust. Proceedings of the National Academy of Sciences of the United States of America, 117, 3427–3432. 10.1073/pnas.1916366117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hong, S. Y. , & Lim, J. O. J. (2006). The WRF single‐moment 6‐class microphysics scheme (WSM6). Asia‐Pacific Journal of Atmospheric Sciences, 42(2), 129–151. [Google Scholar]
  22. Huang, Q. , Wang, T. J. , Chen, P. L. , Huang, X. X. , Zhu, J. L. , & Zhuang, B. L. (2017). Impacts of emission reduction and meteorological conditions on air quality improvement during the 2014 Youth Olympic Games in Nanjing, China. Atmospheric Chemistry and Physics, 17(21), 13457–13471. 10.5194/acp-17-13457-2017 [DOI] [Google Scholar]
  23. Huang, X. , Ding, A. J. , Gao, J. , Zheng, B. , Zhou, D. R. , Qi, X. M. , et al. (2020). Enhanced secondary pollution offset reduction of primary emissions during COVID‐19 lockdown in China. National Science Review, 8(2), nwaa137. 10.1093/nsr/nwaa137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Huang, X. , Song, Y. , Zhao, C. , Li, M. M. , Zhu, T. , Zhang, Q. , & Zhang, X. (2014). Pathways of sulfate enhancement by natural and anthropogenic mineral aerosols in China. Journal of Geophysical Research: Atmospheres, 119(24), 14165–14179. 10.1002/2014JD022301 [DOI] [Google Scholar]
  25. Janjić, Z. I. (2002). Nonsingular implementation of the Mellor‐Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note, 437. [Google Scholar]
  26. Kinosian, J. R. (1982). Ozone‐precursor relationships from EKMA diagrams. Environmental Science & Technology, 16(12), 880–883. 10.1021/es00106a011 [DOI] [PubMed] [Google Scholar]
  27. Kulmala, M. , Laaksonen, A. , & Pirjola, L. (1998). Parameterizations for sulfuric acid/water nucleation rates. Journal of Geophysical Research, 103(D7), 8301–8307. 10.1029/97JD03718 [DOI] [Google Scholar]
  28. Le, T. H. , Wang, Y. , Liu, L. , Yang, J. N. , Yung, Y. L. , Li, G. H. , & Seinfeld, J. H. (2020). Unexpected air pollution with marked emission reductions during the COVID‐19 outbreak in China. Science, 369(6504), 702–706. 10.1126/science.abb7431 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lee, S. H. , Gordon, H. , Yu, H. , Lehtipalo, K. , Haley, R. , li, Y. X. , & Zhang, R. (2019). New particle formation in the atmosphere: From molecular clusters to global climate. Journal of Geophysical Research: Atmospheres, 124(13), 7098–7146. 10.1029/2018JD029356 [DOI] [Google Scholar]
  30. Levy, M. , Zhang, R. , Zheng, J. , Zhang, A. L. , Xu, W. , Gomez‐Hernandez, M. , et al. (2014). Measurements of nitrous acid (HONO) using ion drift‐chemical ionization mass spectrometry during the 2009 SHARP field campaign. Atmospheric Environment, 94, 231–240. 10.1016/j.atmosenv.2014.05.024 [DOI] [Google Scholar]
  31. Li, G. H. , Bei, N. F. , Cao, J. J. , Huang, R. J. , Wu, J. R. , Feng, T. , et al. (2017). A possible pathway for rapid growth of sulfate during haze days in China. Atmospheric Chemistry and Physics, 17(5), 3301–3316. 10.5194/acp-17-3301-2017 [DOI] [Google Scholar]
  32. Li, G. H. , Bei, N. F. , Tie, X. X. , & Molina, L. T. (2011). Aerosol effects on the photochemistry in Mexico City during MCMA‐2006/MILAGRO campaign. Atmospheric Chemistry and Physics, 11(11), 5169–5182. 10.5194/acp-11-5169-2011 [DOI] [Google Scholar]
  33. Li, G. H. , & Feng, T. (2016). Simulating the transport and source of PM2.5 during hazy days in the Guanzhong Basin, China. Journal of Earth Environment, 7(4), 412–424. 10.7515/JEE201604009 [DOI] [Google Scholar]
  34. Li, G. H. , Lei, W. F. , Bei, N. F. , & Molina, L. T. (2012). Contribution of garbage burning to chloride and PM2.5 in Mexico City. Atmospheric Chemistry and Physics, 12(18), 8751–8761. 10.5194/acp-12-8751-2012 [DOI] [Google Scholar]
  35. Li, G. H. , Lei, W. F. , Zavala, M. , Volkamer, R. , Dusanter, S. , Stevens, P. , & Molina, L. T. (2010). Impacts of HONO sources on the photochemistry in Mexico city during the MCMA‐2006/MILAGO campaign. Atmospheric Chemistry and Physics, 10(14), 6551–6567. 10.5194/acp-10-6551-2010 [DOI] [Google Scholar]
  36. Li, G. H. , Zavala, M. , Lei, W. F. , Tsimpidi, A. P. , Karydis, V. A. , Pandis, S. N. , et al. (2011). Simulations of organic aerosol concentrations in Mexico City using the WRF‐CHEM model during the MCMA‐2006/MILAGRO campaign. Atmospheric Chemistry and Physics, 11(8), 3789–3809. 10.5194/acp-11-3789-2011 [DOI] [Google Scholar]
  37. Li, G. H. , Zhang, R. Y. , Fan, J. , & Tie, X. X. (2005). Impacts of black carbon aerosol on photolysis and ozone. Journal of Geophysical Research, 110(D23), D23206. 10.1029/2005JD005898 [DOI] [Google Scholar]
  38. Li, K. , Jacob, D. J. , Liao, H. , Shen, L. , Zhang, Q. , & Bates, K. H. (2019). Anthropogenic drivers of 2013‐2017 trends in summer surface ozone in China. Proceedings of the National Academy of Sciences of the United States of America, 116(2), 422–427. 10.1073/pnas.1812168116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Li, K. , Jacob, D. J. , Liao, H. , Zhu, J. , Shah, V. , Shen, L. , et al. (2019). A two‐pollutant strategy for improving ozone and particulate air quality in China. Nature Geoscience, 12(11), 906–910. 10.1038/s41561-019-0464-x [DOI] [Google Scholar]
  40. Li, L. , Li, Q. , Huang, L. , Wang, Q. , Zhu, A. S. , Xu, J. , et al. (2020). Air quality changes during the COVID‐19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation. The Science of the Total Environment, 732, 139282. 10.1016/j.scitotenv.2020.139282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Li, M. , Zhang, Q. , Kurokawa, J. I. , Woo, J. H. , He, K. , Lu, Z. , et al. (2017). Mix: A mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS‐Asia and HTAP. Atmospheric Chemistry and Physics, 17(2), 935–963. 10.5194/acp-17-935-2017 [DOI] [Google Scholar]
  42. Li, X. , Wu, J. R. , Elser, M. , Feng, T. , Cao, J. J. , Ei‐Haddad, I. , et al. (2018). Contributions of residential coal combustion to the air quality in Beijing‐Tianjin‐Hebei (BTH), China: A case study. Atmospheric Chemistry and Physics, 18(14), 10675–10691. 10.5194/acp-18-10675-2018 [DOI] [Google Scholar]
  43. Li, X. , Wu, J. R. , Liu, L. , & Li, G. H. (2019). Simulating the sources of PM2.5 during heavy haze pollution episodes in the autumn and winter of 2016 in Xianyang City, China. Journal of Earth Environment, 10(4), 347–363. 10.7515/JEE182084 [DOI] [Google Scholar]
  44. Liu, F. , Page, A. , Strode, S. A. , Yoshida, Y. , Choi, S. , Zheng, B. , et al. (2020). Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID‐19. Science Advance, 6(28), eabc2992. 10.1126/sciadv.abc2992 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Liu, H. , Wang, X. M. , Zhang, J. P. , He, K. B. , Wu, Y. , & Xu, J. Y. (2013). Emission controls and changes in air quality in Guangzhou during the Asian Games. Atmospheric Environment, 76, 81–93. 10.1016/j.atmosenv.2012.08.004 [DOI] [Google Scholar]
  46. Liu, L. , Bei, N. F. , Hu, B. , Wu, J. R. , Liu, S. X. , Li, X. , et al. (2020). Wintertime nitrate formation pathways in the north China plain: Importance of N2O5 heterogeneous hydrolysis. Environmental Pollution, 266(2), 115287. 10.1016/j.envpol.2020.115287 [DOI] [PubMed] [Google Scholar]
  47. Long, X. , Bei, N. F. , Wu, J. R. , Li, X. , Feng, T. , Xing, L. , et al. (2018). Does afforestation deteriorate haze pollution in Beijing‐Tianjin‐Hebei (BTH), China? Atmospheric Chemistry and Physics, 18(15), 10869–10879. 10.5194/acp-18-10869-2018 [DOI] [Google Scholar]
  48. Marsh, D. R. , Mills, M. , Kinnison, D. , Lamarque, J. F. , Calvo, N. , & Polvani, L. (2013). Climate change from 1850 to 2005 simulated in CESM1(WACCM). Journal of Climate, 26(19), 7372–7391. 10.1175/JCLI-D-12-00558.1 [DOI] [Google Scholar]
  49. Miyazaki, K. , Bowman, K. , Sekiya, T. , Jiang, Z. , Chen, X. , Eskes, H. , et al. (2020). Air quality response in China linked to the 2019 novel coronavirus (COVID‐19) lockdown. Geophysical Research Letters, 47(19), e2020GL089252. 10.1029/2020GL089252 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce . (2000). NCEP FNL operational model global tropospheric analyses, continuing from July 1999. [Dataset]. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory (updated daily). (Accessed on March 31, 2022). 10.5065/D6M043C6 [DOI]
  51. NCAR Atmospheric Chemistry Observations & Modeling/National Center for Atmospheric Research/University Corporation for Atmospheric Research . (2020). Whole atmosphere community climate model (WACCM) model output [Dataset]. Research data archive at the national center for atmospheric research, computational and information systems laboratory (updated daily). Retrieved from https://rda.ucar.edu/datasets/ds313.6/
  52. Neale, R. B. , Richter, J. , Park, S. , Lauritzen, P. H. , Vavrus, S. J. , Rasch, P. J. , & Zhang, M. (2013). The mean climate of the community atmosphere model (CAM4) in forced SST and fully coupled experiments. Journal of Climate, 26(14), 5150–5168. 10.1175/jcli-d-12-00236.1 [DOI] [Google Scholar]
  53. Nenes, A. , Pandis, S. N. , & Pilinis, C. (1998). Isorropia: A new thermodynamic equilibrium model for multiphase multi‐component inorganic aerosols. Aquatic Geochemistry, 4(1), 123–152. 10.1023/A:1009604003981 [DOI] [Google Scholar]
  54. NOAA National Centers for Environmental Information . (2001). Global Surface Hourly [indicate subset used]. [Dataset]. NOAA National Centers for Environmental Information. Retrieved from https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00532/html
  55. Ou, J. M. , Yuan, Z. B. , Zheng, J. Y. , Huang, Z. J. , Shao, M. , Li, Z. K. , et al. (2016). Ambient ozone control in a photochemically active region: Short‐term despiking or long‐term attainment? Environmental Science & Technology, 50(11), 5720–5728. 10.1021/acs.est.6b00345 [DOI] [PubMed] [Google Scholar]
  56. Peng, J. F. , Hu, M. , Shang, D. J. , Wu, Z. J. , Du, Z. F. , Tan, T. Y. , et al. (2021). Explosive secondary aerosol formation during severe haze in the North China Plain. Environmental Science & Technology, 55(4), 2189–2207. 10.1021/acs.est.0c07204 [DOI] [PubMed] [Google Scholar]
  57. Seinfeld, J. H. , & Pandis, S. N. (2006). Atmospheric chemistry and physics: From air pollution to climate change (2nd ed.), John Wiley and Sons Inc. [Google Scholar]
  58. Shi, X. , & Brasseur, G. P. (2020). The response in air quality to the reduction of Chinese economic activities during the COVID outbreak. Geophysical Research Letters, 47(11), e2020GL088070. 10.1029/2020GL088070 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Tian, H. Y. , Liu, Y. H. , Li, Y. D. , Wu, C. H. , Chen, B. , Kraemer, M. U. G. , et al. (2020). An investigation of transmission control measures during the first 50 days of the COVID‐19 epidemic in China. Science, 368(6491), 638–642. 10.1126/science.abb6105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Tian, J. , Wang, Q. Y. , Zhang, Y. , Yan, M. Y. , Liu, H. K. , Zhang, N. N. , et al. (2021). Impacts of primary emissions and secondary aerosol formation on air pollution in an urban area of China during the COVID‐19 lockdown. Environment International, 150, 106426. 10.1016/j.envint.2021.106426 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Tsimpidi, A. P. , Karydis, V. A. , Zavala, M. , Lei, W. , Molina, L. , Ulbrich, I. M. , et al. (2010). Evaluation of the volatility basis‐set approach for the simulation of organic aerosol formation in the Mexico City metropolitan area. Atmospheric Chemistry and Physics, 10(2), 525–546. 10.5194/acp-10-525-2010 [DOI] [Google Scholar]
  62. Wang, G. H. , Zhang, F. , Peng, J. F. , Duan, L. , Ji, Y. M. , Marrero‐Ortiz, W. , et al. (2018). Particle acidity and sulfate production during severe haze events in China cannot be reliably inferred by assuming a mixture of inorganic salts. Atmospheric Chemistry and Physics, 18, 10123–10132. 10.5194/acp-18-10123-2018 [DOI] [Google Scholar]
  63. Wang, G. H. , Zhang, R. Y. , Gomez, M. E. , Yang, L. X. , Zamora, M. L. , Hu, M. , et al. (2016). Persistent sulfate formation from London Fog to Chinese haze. Proceedings of the National Academy of Sciences of the United States of America, 113(48), 13630–13635. 10.1073/pnas.1616540113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Wang, P. F. , Chen, K. Y. , Zhu, S. Q. , Wang, P. , & Zhang, H. L. (2020). Severe air pollution events not avoided by reduced anthropogenic activities during COVID‐19 outbreak. Resources, Conservation and Recycling, 158(4), 104814. 10.1016/j.resconrec.2020.104814 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Wang, S. X. , Zhao, M. , Xing, J. , Wu, Y. , Zhou, Y. , Lei, Y. , et al. (2010). Quantifying the air pollutants emission reduction during the 2008 Olympic Games in Beijing. Environmental Science & Technology, 44(7), 2490–2496. 10.1021/es9028167 [DOI] [PubMed] [Google Scholar]
  66. Wang, Y. C. , Huang, R. J. , Ni, H. Y. , Chen, Y. , Wang, Q. Y. , Li, G. H. , et al. (2017). Chemical composition, sources and secondary processes of aerosols in Baoji city of northwest China. Atmospheric Environment, 158, 128–137. 10.1016/j.atmosenv.2017.03.026 [DOI] [Google Scholar]
  67. Wang, Z. B. , Hu, M. , Yue, D. L. , He, L. Y. , Huang, X. F. , Yang, Q. , et al. (2013). New particle formation in the presence of a strong biomass burning episode at a downwind rural site in PRD, China. Tellus B: Chemical and Physical Meteorology, 65(1), 19965. 10.3402/tellusb.v65i0.19965 [DOI] [Google Scholar]
  68. Wesely, M. L. (1989). Parameterization of surface resistances to gaseous dry deposition in regional‐scale numerical models. Atmospheric Environment, 23(6), 1293–1304. 10.1016/0004-6981(89)90153-4 [DOI] [Google Scholar]
  69. Wu, J. R. , Bei, N. F. , Hu, B. , Liu, S. X. , Wang, Y. , Shen, Z. X. , et al. (2020). Aerosol‐photolysis interaction reduces particulate matter during wintertime haze events. Proceedings of the National Academy of Sciences of the United States of America, 117(18), 9755–9761. 10.1073/pnas.1916775117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wu, J. R. , Bei, N. F. , Hu, B. , Liu, S. X. , Zhou, M. , Wang, Q. Y. , et al. (2019). Aerosol‐radiation feedback deteriorates the wintertime haze in the North China Plain. Atmospheric Chemistry and Physics, 19(13), 8703–8719. 10.5194/acp-19-8703-2019 [DOI] [Google Scholar]
  71. Xing, L. , Wu, J. R. , Elser, M. , Tong, S. R. , Liu, S. X. , Li, X. , et al. (2019). Wintertime secondary organic aerosol formation in Beijing‐Tianjin‐Hebei (BTH): Contributions of HONO sources and heterogeneous reactions. Atmospheric Chemistry and Physics, 19(4), 2343–2359. 10.5194/acp-19-2343-2019 [DOI] [Google Scholar]
  72. Xu, J. Z. , Ge, X. L. , Zhang, X. H. , Zhao, W. H. , Zhang, R. X. , & Zhang, Y. Z. (2020). COVID‐19 impact on the concentration and composition of submicron particulate matter in a typical city of northwest China. Geophysical Research Letters, 47(19), e2020GL089035. 10.1029/2020GL089035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Xu, W. , Song, W. , Zhang, Y. Y. , Liu, X. J. , Zhang, L. , Zhao, Y. H. , et al. (2017). Air quality improvement in a megacity: Implications from 2015 Beijing parade blue pollution control actions. Atmospheric Chemistry and Physics, 17(1), 31–46. 10.5194/acp-17-31-2017 [DOI] [Google Scholar]
  74. Xu, W. Q. , Sun, Y. L. , Chen, C. , Du, W. , Han, T. T. , Wang, Q. Q. , et al. (2015). Aerosol composition, oxidation properties, and sources in Beijing: Results from the 2014 Asia‐Pacific economic cooperation summit study. Atmospheric Chemistry and Physics, 15(23), 13681–13698. 10.5194/acp-15-13681-2015 [DOI] [Google Scholar]
  75. Xue, W. B. , Fu, F. , Wang, J. N. , Tang, G. Q. , Lei, Y. , Yang, J. T. , et al. (2014). Numerical study on the characteristics of regional transport of PM2.5 in China. China Environmental Science, 34(6), 1361–1368. [Google Scholar]
  76. Zhang, F. , Wang, Y. , Peng, J. F. , Chen, L. , Sun, Y. L. , Duan, L. , et al. (2020). An unexpected catalyst dominates formation and radiative forcing of regional haze. Proceedings of the National Academy of Sciences of the United States of America, 117(13), 3960–3966. 10.1073/pnas.1919343117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Zhang, Q. , Streets, D. G. , Carmichael, G. R. , He, K. B. , Huo, H. , Kannari, A. , et al. (2009). Asian emissions in 2006 for the NASA INTEX‐B mission. Atmospheric Chemistry and Physics, 9(14), 5131–5153. 10.5194/acp-9-5131-2009 [DOI] [Google Scholar]
  78. Zhang, Q. , Zheng, Y. X. , Tong, D. , Shao, M. , Wang, S. X. , Zhang, Y. H. , et al. (2019). Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proceedings of the National Academy of Sciences of the United States of America, 116(49), 24463–24469. 10.1073/pnas.1907956116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Zhang, R. Y. , Khalizova, A. , Wang, L. , Hu, M. , & Xu, W. (2012). Nucleation and growth of nanoparticles in the atmosphere. Chemical Reviews, 112(3), 1957–2011. 10.1021/cr2001756 [DOI] [PubMed] [Google Scholar]
  80. Zhang, R. Y. , Li, Y. X. , Zhang, A. L. , Wang, Y. , & Molina, M. J. (2020). Identifying airborne transmission as the dominant route for the spread of COVID‐19. Proceedings of the National Academy of Sciences of the United States of America, 117(26), 25942–25943. 10.1073/pnas.2018637117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Zhang, R. Y. , Wang, G. H. , Guo, S. , Zamora, M. L. , Ying, Q. , Lin, Y. , et al. (2015). Formation of urban fine particulate matter. Chemical Reviews, 115(10), 3803–3855. 10.1021/acs.chemrev.5b00067 [DOI] [PubMed] [Google Scholar]
  82. Zheng, B. , Tong, D. , Li, M. , Liu, F. , Hong, C. P. , Geng, G. N. , et al. (2018). Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmospheric Chemistry and Physics, 18(19), 14095–14111. 10.5194/acp-18-14095-2018 [DOI] [Google Scholar]
  83. Zheng, B. , Zhang, Q. , Geng, G. , Chen, C. , Shi, Q. R. , Cui, M. , et al. (2021). Changes in China’s anthropogenic emissions and air quality during the COVID‐19 pandemic in 2020. Earth System Science Data, 13, 2895–2907. 10.5194/essd-13-2895-2021 [DOI] [Google Scholar]

Associated Data

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

Data Citations

  1. China MEP (Ministry of Environmental Protection, China) . (2013). Online monitoring and analysis platform of China air Quality [Dataset]. China MEP. Retrieved from https://www.aqistudy.cn/
  2. National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce . (2000). NCEP FNL operational model global tropospheric analyses, continuing from July 1999. [Dataset]. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory (updated daily). (Accessed on March 31, 2022). 10.5065/D6M043C6 [DOI]
  3. NCAR Atmospheric Chemistry Observations & Modeling/National Center for Atmospheric Research/University Corporation for Atmospheric Research . (2020). Whole atmosphere community climate model (WACCM) model output [Dataset]. Research data archive at the national center for atmospheric research, computational and information systems laboratory (updated daily). Retrieved from https://rda.ucar.edu/datasets/ds313.6/
  4. NOAA National Centers for Environmental Information . (2001). Global Surface Hourly [indicate subset used]. [Dataset]. NOAA National Centers for Environmental Information. Retrieved from https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00532/html

Supplementary Materials

Supporting Information S1

Click here for additional data file. (11.3MB, pdf)

Data Availability Statement

The observed meteorological parameters are obtained from the website of https://www.ncei.noaa.gov/maps/hourly (NOAA, 2001) [Dataset]. The hourly observed near‐surface mass concentrations of PM2.5, O3, NO2, SO2, and CO released by the Ministry of Ecology and Environment of China can be accessed at Zenodo (https://zenodo.org/record/6404603#.YkafoC-KHVg; China MEP, 2013) [Dataset]. The measurements of the non‐refractory submicron aerosol (NR‐PM1) can be referenced in Tian et al. (2021). The NCEP 1° × 1° reanalysis data are acquired from the website of https://rda.ucar.edu (NCEP, 2000) [Dataset]. The 6‐hr output data of the WACCM are from the website of https://www.acom.ucar.edu/waccm/download.shtml (NCAR, 2020) [Dataset]. The MEIC (Multi‐resolution Emission Inventory for China) emission inventory are available at the website of http://meicmodel.org (Li, Zhang, et al., 2017; Zhang et al., 2009; Zheng et al., 2018) [Dataset].

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