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. 2020 Jul 1;99:346–353. doi: 10.1016/j.jes.2020.06.031

Significant concurrent decrease in PM2.5 and NO2 concentrations in China during COVID-19 epidemic

Biwu Chu 1,2,3, Shuping Zhang 1,3, Jun Liu 1,3, Qingxin Ma 1,2,3, Hong He 1,2,3,⁎
PMCID: PMC7328636  PMID: 33183713

Abstract

The strict control measures and social lockdowns initiated to combat COVID-19 epidemic have had a notable impact on air pollutant concentrations. According to observation data obtained from the China National Environmental Monitoring Center, compared to levels in 2019, the average concentration of NO2 in early 2020 during COVID-19 epidemic has decreased by 53%, 50%, and 30% in Wuhan city, Hubei Province (Wuhan excluded), and China (Hubei excluded), respectively. Simultaneously, PM2.5 concentration has decreased by 35%, 29%, and 19% in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded), respectively. Less significant declines have also been found for SO2 and CO concentrations. We also analyzed the temporal variation and spatial distribution of air pollutant concentrations in China during COVID-19 epidemic. The decreases in PM2.5 and NO2 concentrations showed relatively consistent temporal variation and spatial distribution. These results support control of NOx to further reduce PM2.5 pollution in China. The concurrent decrease in NOx and PM2.5 concentrations resulted in an increase of O3 concentrations across China during COVID-19 epidemic, indicating that coordinated control of other pollutants is needed.

Keywords: Covid-19 epidemic, Air quality, Temporal variation and spatial distribution, Coordinated control, PM2.5, O3

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Introduction

Coronavirus disease 2019 (COVID-19) has spread rapidly all over the world. As of 12 May 2020, the total accumulative cases of COVID-19 epidemic exceeded four million. In China, this novel pneumonia-like disease of unknown origin was first reported in December 2019 in Wuhan city, Hubei Province. Soon afterwards, due to dramatical increase of COVID-19 case numbers, the government officially closed urban transportation system in Wuhan on 23 January 2020. Subsequently, all 31 provincial regions in Chinese mainland, including Hubei (with its capital city Wuhan), began initiating their first-level response to a major public health emergency. By 25 January 2020, which was the start of the Chinese spring festival (i.e., Chinese New Year, CNY), an increasing number of control measures were introduced by local governments to reduce gatherings and travel. The strictest control measures lasted until 23 February 2020, after the peak in COVID-19 case numbers in China. Then, based on the sustained downward trend in COVID-19 case numbers, the Chinese government allowed the resumption of livelihoods and industries by stages and districts. In late March of 2020, the transmission of COVID-19 experienced a substantial decline in China, while cases imported from foreign countries became the dominate source. By 2 May 2020, the 31 provincial regions in Chinese mainland, including Hubei, terminated their public health emergency first-level response.

The measures and lockdowns introduced to stop the spread of COVID-19 in China have had a remarkable impact on industrial production and social life, and therefore have likely had an effect on pollutant emissions and environmental air quality. Unlike stringent emission controls, the declines in pollutant emissions during COVID-19 epidemic have primarily come from changes in social life and light industry, with power generators and heavy industry expected to be less affected. Stringent emission controls have been shown to be effective for temporary improvement of air quality during specific events, such as the 2008 Beijing Olympic Games (Wang et al., 2010; Xing et al., 2011) and 2014 Asia-Pacific Economy Cooperation (APEC) China Summit (Sun et al., 2016; Xu et al., 2019). The COVID-19 epidemic has provided the opportunity to investigate the influence of changes in societal production and life on air quality. In this study, based on analysis of concentrations of nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), and particulate matter with an aerodynamic diameter less than 10 μm (PM10) obtained from the China National Environmental Monitoring Center (CNEMC), we evaluated changes in air quality in Wuhan, Hubei, and China. This study could have considerable implications for air pollution control in China and possibly elsewhere.

1. Materials and methods

1.1. Monitoring network

Established by the Ministry of Ecology and Environment of China (MEE), the CNEMC network (http://106.37.208.233:20035/) began measuring surface PM2.5, PM10, SO2, NO2, O3, and CO concentrations in China in 2013. Both PM2.5 and PM10 mass concentrations are measured using oscillating microbalance and/or β absorption; SO2 concentrations are measured by ultraviolet (UV) fluorescence; CO and O3 concentrations are measured using infrared and UV absorption, respectively; and, NO2 concentrations are measured by a molybdenum converter and chemiluminescence. Details of the specifications and procedures of the measurements are listed on the MEE website: http://kjs.mee.gov.cn/hjbhbz/bzwb/jcffbz/201308/t20130802_256852.shtml (2019/08/18), and http://kjs.mee.gov.cn/hjbhbz/bzwb/jcffbz/201308/t20130802_256853.shtml (2019/08/18). The network now includes 1 656 sites in 378 cities. In the current study, hourly data from 365 cities with measurements from 2015 to early 2020 were used.

1.2. Data quality control

Similar data quality control methods as employed in previous studies (Shi et al., 2018; Wu et al., 2018) were used to reduce errors in the CNEMC monitoring network dataset. First, zero or negative concentrations were set as missing. Second, extreme outlier concentrations were deleted (i.e., SO2 > 1 428 µg/m3, NO2 > 1 026 µg/m3, CO > 62.5 mg/m3, PM2.5 > 10 000 µg/m3, PM10 > 10 000 µg /m3, O3 > 1 071 µg/m3). Third, identical data repeated three or more times, which were likely duplicated by the monitoring network reporting system due to communication error (Rohde and Muller, 2015), were also set as missing, except for the first value. Fourth, temporally inconsistent outliers were removed following Wu et al. (2018).

2. Results and discussion

2.1. Changes in air pollutant concentrations

Our results indicated a concurrent significant change in air pollutant concentrations in China with the introduction of control measures and societal lockdowns to limit COVID-19 spread. Based on the increase in COVID-19 cases and corresponding control measures in China, we first selected the period from 21 January to 23 March as a whole to analyze changes in pollutant concentrations in early 2020 with COVID-19 epidemic. Although CNY could have a significant influence on air quality (Zhang et al., 2010), this event was included within the study time-frame in both 2019 and 2020. Compared with the same period in 2019, the COVID-19 epidemic control measures in early 2020 coincided with significant reductions in NO2 concentrations in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded) (by 53%, 50%, and 30%, respectively; Fig. 1 ). These reductions are comparable with the decrease of NO2 column concentrations observed with satellites (Bauwens et al., 2020). An important reason for the decrease of NO2 should be the significant reductions (40%-80%) of city traffic in eastern and northern China during COVID-19 epidemic (Wang et al., 2020). Coinciding with the decrease in NO2, PM2.5 concentrations also showed an obvious decrease compared to levels in 2019, with reductions of 35%, 29%, and 19% in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded), respectively. The PM10 concentrations also decreased, with similar percentage reductions as that of PM2.5. This might indicate that the secondary aerosol and primary aerosol decreased with similar percentages during COVID-19 epidemic since higher percentage of primary aerosol contributed to PM10 than that to PM2.5 (Seinfeld and Pandis, 2006). The percentage changes in NO2 and PM2.5 concentrations were similar in background area with those in urban area. Compared to levels in 2019, the concentrations of SO2 and CO also decreased during COVID-19 epidemic in 2020, but at much lower rates than that observed for NO2, PM2.5 and PM10. The decreases in SO2 and CO were not significant in background area of Wuhan or Hubei (Table S1). In general, NO2 and PM2.5 showed marked decreases and SO2 and CO exhibited much smaller decreases. These phenomena indicated that the emission from stationary sources, such as coal-fired power plants, iron and steel production, didn't decreased as much as traffic. In contrast, due to less NO to react with O3 (Seinfeld and Pandis, 2006), as well as less heterogeneous HO2 radical loss and higher actinic flux with lower concentration of particle (Li et al., 2019b), O3 demonstrated a notable increase, with a higher rate observed in Wuhan (58%) than Hubei or China.

Fig. 1.

Fig. 1

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Air pollutant concentrations during spread of COVID-19 in Wuhan, Hubei Province (Wuhan excluded), and China (Hubei excluded) in early 2020 and the changes (%) during COVID-19 epidemic compared to corresponding periods (21 January to 23 March) in 2019.

2.2. Spatial distribution of air pollutant concentrations

Fig. 2 shows the spatial distribution of air pollutant concentrations from 21 January to 23 March in both 2019 and 2020 as well as the percentage changes. The decreases in NO2 and PM2.5 showed similar spatial distributions. The most significant decreases in NO2 and PM2.5 occurred in the North China Plain and Yangtze River Delta, covering most of east-central China. SO2 also showed a decreasing trend, with the most obvious decrease observed in the North China Plain. Changes in CO concentrations were not significant enough to show obvious distribution characteristics. In contrast, O3 concentrations increased across most of China, with the most significant increase observed in the Yangtze River Delta, especially Hubei Province.

Fig. 2.

Fig. 2

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Spatial distribution of air pollutant concentrations from 21 January to 23 March in 2019 and 2020, and changes in percentage of concentrations.

2.3. Temporal variation in pollutant concentrations

To further analyze the influence of COVID-19 epidemic control measures and lockdowns on air quality, temporal variations in pollutant concentrations in different stages of COVID-19 spread were analyzed. Five periods, i.e. P1 to P5, were selected as different stages of COVID-19 outbreak in early 2020. P1 (7 December 2019 to 20 January 2020) was the early stage before the outbreak of COVID-19 epidemic in China. In P2 (21 January to 6 February), COVID-19 case numbers dramatically increased, coincided with an increasing number of control measures. The government officially closed Wuhan before CNY and all 31 provincial regions in Chinese mainland began initiating their first-level response to a major public health emergency in this stage. In P3 (7 February to 22 February), the peak in COVID-19 case numbers appeared in China, coincided with strictest control measures in this stage. In P4 (23 February to 23 March), livelihoods and industries began to resume by stages and districts with the sustained downward trend in COVID-19 epidemic case numbers. In P5 (24 March to 22 April), the livelihoods and industries were basically restored to order and the first-level responses to public health emergency were terminated. As COVID-19 spread coincided with the CNY, we compared the temporal variations of pollutant concentrations in similar time periods relative to CNY from 2015 to 2019 (see more details in Table S2).

As shown in Fig. 3 and Figs. S1–S3, the concentrations of air pollutants in P1 were similar in 2015 to 2020. The concentrations of NO2 and PM2.5 showed a decrease a few days before CNY in P2 due to the national holiday. In 2015 to 2019, the concentrations of NO2 and PM2.5 returned to normal after two weeks when the holiday period ended in P3. In 2020, however, coinciding with the spread of COVID-19 and its targeted control measures, NO2 and PM2.5 levels remained low in P3 and P4. The trend of NO2 concentrations in Fig. 3 is consistent with the tropospheric NO2 column observations from the satellite (Zhang et al., 2020). After people resumed work in stages and by districts during P4, the concentrations of NO2 and PM2.5 returned to normal levels in P5. According to the temporal variation of pollutant concentrations, concentrations of NO2 and PM2.5 decreased most significantly in P3, although concentrations were also affected by the measures and lockdowns in P2 and P4, which were consistent with the strict degrees of control measures in different periods.

Fig. 3.

Fig. 3

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Temporal variations in NO2 and PM2.5 concentrations during COVID-19 epidemic in urban areas of Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded) in early 2020 and corresponding periods in 2015 to 2019. Day 0 indicates Chinese New Year (CNY) each year. P1 to P4 are the early stage before the outbreak, the rapid growth period, the fastigium, and the decline period of COVID-19 epidemic, respectively. P5 is the period when the livelihoods and industries were basically restored to order. Detail information of P1 to P5 in each year is listed in Table S2.

The declines in concentrations differed for different air pollutants, as shown in Fig. 4 and Tables S3–S5. NO2 concentrations showed the most significant decrease. In P3 when the strictest control measures were introduced, compared to levels in 2019, NO2 concentrations in 2020 decreased by 57%, 61%, and 51% in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded), respectively. Coinciding with the decline in NO2 levels, PM2.5 concentrations in P3 also showed an obvious decrease in 2020 compared to 2019, with 51%, 47%, and 39% reductions in Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded), respectively. The decrease of concentrations of SO2 and CO was not significant in 2020 compared to 2019 in Wuhan or Hubei (Tables S3–S5), even in P3, which may indicate that coal burning did not vary much under COVID-19 epidemic control measures.

Fig. 4.

Fig. 4

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NO2 and PM2.5 concentrations in different periods during COVID-19 epidemic in urban areas of Wuhan, Hubei (Wuhan excluded), and China (Hubei excluded) in early 2020 and corresponding periods relative to CNY in 2015 to 2019. Error bar is one standard variation of day-average concentrations in each period. Period 1 to 5 are the same as P1 to P5 in Fig. 3. Period 1 to 4 are the early stage before the outbreak, the rapid growth period, the fastigium, and the decline period of COVID-19 epidemic, respectively. Period 5 is the period when the livelihoods and industries were basically restored to order. Detail information of period 1 to 5 in each year is listed in Table S2.

As shown in Tables S3–S5, O3 levels primarily increased in P2. During this period, O3 concentrations increased by 40%–60% in Wuhan and Hubei in 2020 compared to levels in 2019. However, with continuous decrease in NO2 concentrations, O3 concentrations did not further increase in P3. In background areas in Wuhan and other cities in Hubei, NO2 concentrations decreased by about 70% and PM2.5 decreased by about 60% in P3 in 2020 compared to levels in 2019, whereas the O3 concentration only increased by 2%–4% (Tables S3–S4). These results indicate that a significant reduction in NOx may overcome the increase in O3 concentration due to the concurrent decrease in both NOx and PM2.5, thereby progressing toward the coordinated control of both PM2.5 and O3.

3. Summary and implications

The changes in air pollutant concentrations due to the introduction of COVID-19 epidemic control measures indicated that the resulting changes in societal production and life had a notable impact on pollutant emissions and air quality. Following social lockdown, traffic intensity decreased markedly (Wang et al., 2020), leading to a significant decrease in NO2 concentrations, given that power generators and heavy industry were less affected. NOx plays key roles in the formation of atmospheric secondary aerosol (Cheng et al., 2016; Chu et al., 2019; He et al., 2014; Russell et al., 1988), which is the main contributor of PM2.5 mass in China (Chen et al., 2020; Fu et al., 2016; Huang et al., 2014; Liu et al., 2015). Due to the more rapid decrease in SO2 emissions than NOx in recent years, aerosol pollution has shifted from sulfate-dominated to nitrate-dominated in many eastern Chinese cities (Li et al., 2019a; Wen et al., 2018) . NOx is also highly active in the formation of oxidants in the gas phase, such as O3 and OH and NO3 radicals (Seinfeld and Pandis, 2006), and contributes to oxidation capacity in heterogeneous and aqueous reactions (Chen et al., 2019; Cheng et al., 2016; He et al., 2014; Xue et al., 2016). The results of this study corroborated the above mechanisms. The relatively consistent temporal variation and spatial distributions of changes in concentration of PM2.5 and NO2 following the introduction of strict COVID-19 epidemic control measures indicated that PM2.5 is highly related to NO2.

SO2, NOx, NH3, and volatile organic compounds (VOCs) are the key precursors of secondary PM2.5 (Ding et al., 2016; Kerminen, 1999; Liu et al., 2013; Meng et al., 2011). At present, the economic costs for further substantial decreases in SO2 emissions will be huge after the universal desulphurization in coal-fired power plants in China (Zheng et al., 2018). VOCs and NH3 are derived from a wealth of sources, with a lack of effective control methods relative to NOx (Fu et al., 2017; Liu et al., 2015; Zheng et al., 2018). These situations, plus the finding of coincidental decreases in PM2.5 and NO2 concentrations in this study, support the control of NOx to further reduce PM2.5 pollution in China. The decrease in NOx and PM2.5 concentrations may cause an increase in O3 concentration, indicating the need for coordinated control of VOCs. However, in some areas where control of VOCs is difficult, a high reduction in NOx may overcome the increase of O3 concentration due to the decrease in both NOx and PM2.5, thus progressing toward coordinated control of PM2.5 and O3.

Declaration of Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 41877304), the National Key R&D Program of China(No. 2018YFC0506901), and the Youth Innovation Promotion Association, CAS (Nos. 2018060 and 2018055). The authors would like to thank Prof. Jingkun Jiang and Dr. Wei Zhou for their help collecting monitoring data from the CNEMC network, and thank Ms. Ting Chu for her help editing the figures.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jes.2020.06.031.

Appendix. Supplementary materials

mmc1.zip (1.5MB, zip)

References

  1. Bauwens M., Compernolle S., Stavrakou T., Müller J.-F., van Gent J., Eskes H. Impact of coronavirus outbreak on NO2 pollution assessed using TROPOMI and OMI observations. Geophys. Res. Lett. 2020 doi: 10.1029/2020GL087978. e2020GL087978. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Chen T., Chu B., Ge Y., Zhang S., Ma Q., He H. Enhancement of aqueous sulfate formation by the coexistence of NO2/NH3 under high ionic strengths in aerosol water. Environ. Pollut. 2019;252:236–244. doi: 10.1016/j.envpol.2019.05.119. [DOI] [PubMed] [Google Scholar]
  3. Chen T., Liu J., Liu Y., Ma Q., Ge Y., Zhong C. Chemical characterization of submicron aerosol in summertime Beijing: a case study in southern suburbs in 2018. Chemosphere. 2020;247 doi: 10.1016/j.chemosphere.2020.125918. [DOI] [PubMed] [Google Scholar]
  4. Cheng Y., Zheng G., Wei C., Mu Q., Zheng B., Wang Z. Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Sci. Adv. 2016;2 doi: 10.1126/sciadv.1601530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chu B., Wang Y., Yang W., Ma J., Ma Q., Zhang P. Effects of NO2 and C3H6 on the heterogeneous oxidation of SO2 on TiO2 in the presence or absence of UV–Vis irradiation. Atmos. Chem. Phys. 2019;19:14777–14790. [Google Scholar]
  6. Ding X., He Q.F., Shen R.Q., Yu Q.Q., Zhang Y.Q., Xin J.Y. Spatial and seasonal variations of isoprene secondary organic aerosol in China: significant impact of biomass burning during winter. Sci. Rep. 2016;6:20411. doi: 10.1038/srep20411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Fu X., Wang S.X., Xing J., Zhang X.Y., Wang T., Hao J.M. Increasing ammonia concentrations reduce the effectiveness of particle pollution control achieved via SO2 and NOX emissions reduction in east China. Environ. Sci. & Technol. Lett. 2017;4:221–227. [Google Scholar]
  8. Fu X.X., Wang X.M., Hu Q.H., Li G.H., Ding X., Zhang Y.L. Changes in visibility with PM2.5 composition and relative humidity at a background site in the pearl river delta region. J. Environ. Sci. 2016;40:10–19. doi: 10.1016/j.jes.2015.12.001. [DOI] [PubMed] [Google Scholar]
  9. He H., Wang Y., Ma Q., Ma J., Chu B., Ji D. Mineral dust and NOx promote the conversion of SO2 to sulfate in heavy pollution days. Sci. Rep. 2014;4:04172. doi: 10.1038/srep04172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Huang R.-J., Zhang Y., Bozzetti C., Ho K.-F., Cao J.-J., Han Y. High secondary aerosol contribution to particulate pollution during haze events in China. Nature. 2014;514:218–222. doi: 10.1038/nature13774. [DOI] [PubMed] [Google Scholar]
  11. Kerminen V.M. Roles of SO2 and secondary organics in the growth of nanometer particles in the lower atmosphere. J. Aerosol Sci. 1999;30:1069–1078. [Google Scholar]
  12. Li H.Y., Cheng J., Zhang Q., Zheng B., Zhang Y.X., Zheng G.J. Rapid transition in winter aerosol composition in Beijing from 2014 to 2017: response to clean air actions. Atmos. Chem. Phys. 2019;19:11485–11499. [Google Scholar]
  13. Li K., Jacob D.J., Liao H., Shen L., Zhang Q., Bates K.H. Anthropogenic drivers of 2013-2017 trends in summer surface ozone in China. Proc. Natl. Acad. Sci. USA. 2019;116:422–427. doi: 10.1073/pnas.1812168116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Liu T., Wang X., Deng W., Zhang Y., Chu B., Ding X. Role of ammonia in forming secondary aerosols from gasoline vehicle exhaust. Sci. China Chem. 2015;58:1377–1384. [Google Scholar]
  15. Liu X.G., Li J., Qu Y., Han T., Hou L., Gu J. Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China. Atmos. Chem. Phys. 2013;13:4501–4514. [Google Scholar]
  16. Meng Z.Y., Lin W.L., Jiang X.M., Yan P., Wang Y., Zhang Y.M. Characteristics of atmospheric ammonia over Beijing, China. Atmos. Chem. Phys. 2011;11:6139–6151. [Google Scholar]
  17. Rohde R.A., Muller R.A. Air pollution in China: mapping of concentrations and sources. PLoS ONE. 2015;10 doi: 10.1371/journal.pone.0135749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Russell A.G., McCue K.F., Cass G.R. Mathematical-modeling of the formation of nitrogen-containing pollutants. 2. Evaluation of the effect of emission controls. Environ. Sci. Technol. 1988;22:1336–1347. doi: 10.1021/es00168a004. [DOI] [PubMed] [Google Scholar]
  19. Seinfeld J.H., Pandis S.N. 2nd ed. John Wiley & Sons, Inc.; New York: 2006. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. [Google Scholar]
  20. Shi X.Q., Zhao C.F., Jiang J.H., Wang C.Y., Yang X., Yung Y.L. Spatial representativeness of PM2.5 concentrations obtained using observations from network stations. J. Geophys. Res.- Atmos. 2018;123:3145–3158. [Google Scholar]
  21. Sun Y.L., Wang Z.F., Wild O., Xu W.Q., Chen C., Fu P.Q. "APEC blue": secondary aerosol reductions from emission controls in Beijing. Sci. Rep. 2016;6:20668. doi: 10.1038/srep20668. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Wang P., Chen K., Zhu S., Wang P., Zhang H. Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour. Conserv. Recycl. 2020;158 doi: 10.1016/j.resconrec.2020.104814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Wang T., Nie W., Gao J., Xue L.K., Gao X.M., Wang X.F. Air quality during the 2008 Beijing Olympics: secondary pollutants and regional impact. Atmos. Chem. Phys. 2010;10:7603–7615. [Google Scholar]
  24. Wen L., Xue L., Wang X., Xu C., Chen T., Yang L. Summertime fine particulate nitrate pollution in the North China Plain: increasing trends, formation mechanisms and implications for control policy. Atmos. Chem. Phys. 2018;18:11261–11275. [Google Scholar]
  25. Wu H.J., Tang X., Wang Z.F., Wu L., Lu M.M., Wei L.F. Probabilistic automatic outlier detection for surface air quality measurements from the China national environmental monitoring network. Adv. Atmos. Sci. 2018;35:1522–1532. [Google Scholar]
  26. Xing J., Zhang Y., Wang S., Liu X., Cheng S., Zhang Q. Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics. Atmos. Environ. 2011;45:1786–1798. [Google Scholar]
  27. Xu W., Liu X.J., Liu L., Dore A.O., Tang A.H., Lu L. Impact of emission controls on air quality in Beijing during APEC 2014: Implications from water-soluble ions and carbonaceous aerosol in PM2.5 and their precursors. Atmos. Environ. 2019;210:241–252. [Google Scholar]
  28. Xue J., Yuan Z.B., Griffith S.M., Yu X., Lau A.K.H., Yu J.Z. Sulfate formation enhanced by a cocktail of high NOx, SO2, particulate matter, and droplet pH during haze-fog events in megacities in China: an observation-based modeling investigation. Environ. Sci. Technol. 2016;50:7325–7334. doi: 10.1021/acs.est.6b00768. [DOI] [PubMed] [Google Scholar]
  29. Zhang M., Wang X.M., Chen J.M., Cheng T.T., Wang T., Yang X. Physical characterization of aerosol particles during the Chinese New Year's firework events. Atmos. Environ. 2010;44:5191–5198. [Google Scholar]
  30. Zhang R., Zhang Y., Lin H., Feng X., Fu T.-M., Wang Y. NOx emission reduction and recovery during COVID-19 in east China. Atmosphere (Basel) 2020;11:433. [Google Scholar]
  31. Zheng B., Tong D., Li M., Liu F., Hong C., Geng G. Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 2018;18:14095–14111. [Google Scholar]

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