Skip to main content
SpringerLink
Log in

Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021

  • Article
  • Published:
Science China Earth Sciences Aims and scope Submit manuscript

Abstract

Air pollutant concentration is a function of emission rates and meteorology. To accurately evaluate the effect of control measures, the meteorological covariate must be corrected from the observations. This study quantified the impacts of emission abatement and meteorological condition on the interannual variations of SO2, NO2, CO, O3, PM10 and PM2.5 concentrations in 31 major Chinese cities using an optimized machine learning-based meteorological normalization technique. Overall, the annual average concentrations of SO2, NO2, CO, PM10 and PM2.5 were reduced by 86%, 51%, 99%, 86% and 88% from 2015 to 2020, respectively, in the studied cities, attributable to their emission reductions. However, the concentration of O3 was found with no significant decrease with the reduction of precursors. Emission abatement notably improved air quality between 2015 and 2018. Such a decline in emissions tended to progressively slow down since 2018. Overall, the meteorological conditions in 2016–2017 and 2018–2019 were unfavorable for a better air quality, while it became favorable in 2020–2021. Specifically, emission abatement in 2021 further lowered the concentrations of SO2, NO2, CO, and PM2.5, while the emission of PM10 increased. And changes in precursors emissions worsened O3 air quality. To meet the demand of improving air quality, more aggressive abatement measures need to be formulated to synergistically reduce NOx, volatile organic compounds, and coarse particles.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Beevers S D, Carslaw D C, Westmoreland E J, Mittal H. 2009. Air pollution and emissions trends in London. King’s College London, Environmental Research Group Leeds University, Institute for Transport Studies

  • Carslaw D. 2017. Worldmet: Import Surface Meteorological Data from NOAA Integrated Surface Database (ISD)

  • Chen L, Guo B, Huang J, He J, Wang H, Zhang S, Chen S X. 2018. Assessing air-quality in Beijing-Tianjin-Hebei region: The method and mixed tales of PM2.5 and O3. Atmos Environ, 193: 290–301

    Google Scholar 

  • Chen Z, Chen D, Kwan M P, Chen B, Gao B, Zhuang Y, Li R, Xu B. 2019. The control of anthropogenic emissions contributed to 80 % of the decrease in PM2.5 concentrations in Beijing from 2013 to 2017. Atmos Chem Phys, 19: 13519–13533

    Google Scholar 

  • Cohen A J, Brauer M, Burnett R, Anderson H R, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L, Dandona R, Feigin V, Freedman G, Hubbell B, Jobling A, Kan H, Knibbs L, Liu Y, Martin R, Morawska L, Pope Iii C A, Shin H, Straif K, Shaddick G, Thomas M, van Dingenen R, van Donkelaar A, Vos T, Murray C J L, Forouzanfar M H. 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the global burden of diseases study 2015. Lancet, 389: 1907–1918

    Google Scholar 

  • Cole M A, Elliott R J R, Liu B. 2020. The impact of the Wuhan Covid-19 lockdown on air pollution and health: a machine learning and augmented synthetic control approach. Environ Resource Econ, 76: 553–580

    Google Scholar 

  • Dai Q, Liu B, Bi X, Wu J, Liang D, Zhang Y, Feng Y, Hopke P K. 2020. Dispersion normalized PMF provides insights into the significant changes in source contributions to PM2.5 after the COVID-19 outbreak. Environ Sci Technol, 54: 9917–9927

    Google Scholar 

  • Dai Q, Hou L, Liu B, Zhang Y, Song C, Shi Z, Hopke P K, Feng Y. 2021. Spring Festival and COVID-19 lockdown: Disentangling PM sources in major Chinese cities. Geophys Res Lett, 48: e2021GL093403

    Google Scholar 

  • Deng X W, Zeng X M. 2021. Spatial and temporal analysis of the “Spring Festival effect” on air pollutants in 31 cities of China (in Chinese). J Earth Environ, 12: 159–169

    Google Scholar 

  • Ding J, Dai Q, Fan W, Lu M, Zhang Y, Han S, Feng Y. 2023. Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021. J Environ Sci, 126: 506–516

    Google Scholar 

  • Gardner M W, Dorling S R. 2000. Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmos Environ, 34: 21–34

    Google Scholar 

  • Grange S K. 2017. Normalweatherr: Package to conduct meteorological/weather normalisation on air quality data (deprecated). https://github.com/skgrange/normalweatherr

  • Grange S K. 2018. Rmweather: Tools to Conduct Meteorological Normalisation on Air Quality Data. R package version 0.1.2. https://CRAN.R-project.org/package=rmweather

  • Grange S K, Carslaw D C. 2019. Using meteorological normalisation to detect interventions in air quality time series. Sci Total Environ, 653: 578–588

    Google Scholar 

  • Grange S K, Carslaw D C, Lewis A C, Boleti E, Hueglin C. 2018. Random forest meteorological normalisation models for Swiss PM10 trend analysis. Atmos Chem Phys, 18: 6223–6239

    Google Scholar 

  • Grange S K, Lee J D, Drysdale W S, Lewis A C, Hueglin C, Emmenegger L, Carslaw D C. 2021. COVID-19 lockdowns highlight a risk of increasing ozone pollution in European urban areas. Atmos Chem Phys, 21: 4169–4185

    Google Scholar 

  • Han B, Yao T, Li G, Song Y, Zhang Y, Dai Q, Yu J. 2022. Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach. Environ Res, 214: 114117

    Google Scholar 

  • Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Chiara G, Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R, Fuentes M, Geer A, Haimberger L, Healy S, Hogan R J, Hólm E, Janisková M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, Rosnay P, Rozum I, Vamborg F, Villaume S, Thépaut J. 2020. The ERA5 global reanalysis. QJR Meteorol Soc, 146: 1999–2049

    Google Scholar 

  • Hou L, Dai Q, Song C, Liu B, Guo F, Dai T, Li L, Liu B, Bi X, Zhang Y, Feng Y. 2022. Revealing drivers of haze pollution by explainable machine learning. Environ Sci Technol Lett, 9: 112–119

    Google Scholar 

  • Huang X F, Cao L M, Tian X D, Zhu Q, Saikawa E, Lin L L, Cheng Y, He L Y, Hu M, Zhang Y H, Lu K D, Liu Y H, Daellenbach K, Slowik J G, Tang Q, Zou Q L, Sun X, Xu B Y, Jiang L, Shen Y M, Ng N L, Prévôt A S H. 2021. Critical role of simultaneous reduction of atmospheric odd oxygen for winter haze mitigation. Environ Sci Technol, 55: 11557–11567

    Google Scholar 

  • Huang Z, Sha Q’, Zhu M, Xu Y, Yu F, Liu H, Zhou W, Zhang X, Zhang X, Rao S, Jiang F, Liu J, Zheng J. 2022. Status and quality evaluation of precursor emission inventories for PM2.5 and ozone in China (in Chinese). Chin Sci Bull, 67: 1978–1994

    Google Scholar 

  • Jiang Q, Sun Y L, Wang Z, Yin Y. 2015. Aerosol composition and sources during the Chinese Spring Festival: Fireworks, secondary aerosol, and holiday effects. Atmos Chem Phys, 15: 6023–6034

    Google Scholar 

  • Lai Y, Brimblecombe P. 2020. Changes in air pollution and attitude to fireworks in Beijing. Atmos Environ, 231: 117549

    Google Scholar 

  • Li K, Jacob D J, Liao H, Zhu J, Shah V, Shen L, Bates K H, Zhang Q, Zhai S. 2019. A two-pollutant strategy for improving ozone and particulate air quality in China. Nat Geosci, 12: 906–910

    Google Scholar 

  • Li M, Liu H, Geng G, Hong C, Liu F, Song Y, Tong D, Zheng B, Cui H, Man H, Zhang Q, He K. 2018. Corrigendum to Anthropogenic emission inventories in China: A review. Natl Sci Rev, 5: 603

    Google Scholar 

  • Liang P, Chen B, Yang X, Liu Q, Li A, Mackenzie L, Zhang D. 2022. Revealing the dust transport processes of the 2021 mega dust storm event in northern China. Sci Bull, 67: 21–24

    Google Scholar 

  • Liang X, Zou T, Guo B, Li S, Zhang H, Zhang S, Huang H, Chen S X. 2015. Assessing Beijing’s PM2.5 pollution: Severity, weather impact, APEC and winter heating. Proc R Soc A, 471: 20150257

    Google Scholar 

  • Liu Z, Wang H, Shen X, Peng Y, Shi Y, Che H, Wang G. 2019. Contribution of meteorological conditions to the variation in winter PM2.5 concentrations from 2013 to 2019 in Middle-Eastern China. Atmosphere, 10: 563

    Google Scholar 

  • Lu X, Zhang L, Chen Y, Zhou M, Zheng B, Li K, Liu Y, Lin J, Fu T M, Zhang Q. 2019. Exploring 2016–2017 surface ozone pollution over China: Source contributions and meteorological influences. Atmos Chem Phys, 19: 8339–8361

    Google Scholar 

  • Lu X, Zhang L, Wang X, Gao M, Li K, Zhang Y, Yue X, Zhang Y. 2020. Rapid increases in warm-season surface ozone and resulting health impact in China since 2013. Environ Sci Technol Lett, 7: 240–247

    Google Scholar 

  • Lunberg Scott, Gabriel Erion, Chen Hugh, DeGrave Alex, Jordan Prutkin. 2020. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell, 2: 56–67

    Google Scholar 

  • Lv Z, Wang X, Deng F, Ying Q, Archibald A T, Jones R L, Ding Y, Cheng Y, Fu M, Liu Y, Man H, Xue Z, He K, Hao J, Liu H. 2020. Source–receptor relationship revealed by the halted traffic and aggravated haze in Beijing during the COVID-19 lockdown. Environ Sci Technol, 54: 15660–15670

    Google Scholar 

  • Mousavinezhad S, Choi Y, Pouyaei A, Ghahremanloo M, Nelson D L. 2021. A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions. Atmos Res, 257: 105599

    Google Scholar 

  • Murray C J L, Aravkin A Y, Zheng P, Abbafati C, Abbas K M. et al., 939 authors 2020. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019. Lancet, 396: 1223–1249

    Google Scholar 

  • Pang N, Gao J, Zhao P, Wang Y, Xu Z, Chai F. 2021. The impact of fireworks control on air quality in four Northern Chinese cities during the Spring Festival. Atmos Environ, 244: 117958

    Google Scholar 

  • Qu L, Liu S, Ma L, Zhang Z, Du J, Zhou Y, Meng F. 2020. Evaluating the meteorological normalized PM2.5 trend (2014–2019) in the “2+26” region of China using an ensemble learning technique. Environ Pollution, 266: 115346

    Google Scholar 

  • Seinfeld J, Pandis S. 2016. Atmospheric Chemistry and Physics From Air Pollution to Climate Change (3rd). John Wiley & Sons, Inc

  • Shi Z, Song C, Liu B, Lu G, Xu J, Van Vu T, Elliott R J R, Li W, Bloss W J, Harrison R M. 2021. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Sci Adv, 7: eabd6696

    Google Scholar 

  • Sicard P, De Marco A, Agathokleous E, Feng Z, Xu X, Paoletti E, Rodriguez J J D, Calatayud V. 2020. Amplified ozone pollution in cities during the COVID-19 lockdown. Sci Total Environ, 735: 139542

    Google Scholar 

  • Sloane C S. 1984. Meteorologically adjusted air quality trends: Visibility. Atmos Environ (1967), 18: 1217–1229

    Google Scholar 

  • Song L, Bi X, Zhang Z, Li L, Dai Q, Zhang W, Li H, Wang X, Liang D, Feng Y. 2022. Impact of sand and dust storms on the atmospheric environment and its source in Tianjin-China. Sci Total Environ, 825: 153980

    Google Scholar 

  • Stein A F, Draxler R R, Rolph G D, Stunder B J B, Cohen M D, Ngan F. 2015. NOAA’s hysplit atmospheric transport and dispersion modeling system. Bull Am Meteorol Soc, 96: 2059–2077

    Google Scholar 

  • Thompson M L, Reynolds J, Cox L H, Guttorp P, Sampson P D. 2001. A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmos Environ, 35: 617–630

    Google Scholar 

  • Venter Z S, Aunan K, Chowdhury S, Lelieveld J. 2020. COVID-19 lockdowns cause global air pollution declines. Proc Natl Acad Sci USA, 117: 18984–18990

    Google Scholar 

  • Vu T V, Shi Z, Cheng J, Zhang Q, He K, Wang S, Harrison R M. 2019. Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique. Atmos Chem Phys, 19: 11303–11314

    Google Scholar 

  • Wang S, Su H, Chen C, Tao W, Streets D G, Lu Z, Zheng B, Carmichael G R, Lelieveld J, Pöschl U, Cheng Y. 2020. Natural gas shortages during the “coal-to-gas” transition in China have caused a large redistribution of air pollution in winter 2017. Proc Natl Acad Sci USA, 117: 31018–31025

    Google Scholar 

  • Wang Z F, Li J, Wang Z, Yang W Y, Tang X, Ge B Z, Yan P Z, Zhu L L, Chen X S, Chen H S, Wand W, Li J J, Liu B, Wang X Y, Wand W, Zhao Y L, Lu N, Su D B. 2014. Modeling study of regional severe hazes over Mid-Eastern China in January 2013 and its implications on pollution prevention and control. Sci China Earth Sci, 57: 3–13

    Google Scholar 

  • Wu Q, Li T, Zhang S, Fu J, Seyler B C, Zhou Z, Deng X, Wang B, Zhan Y. 2022. Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods. Atmos Environ, 278: 119083

    Google Scholar 

  • Wu Q Z, Wang Z F, Xu W S, Huang J P, Gbaguidi A. 2010. Multi-model simulation of PM10 during the 2008 Beijing Olympic Games: Effectiveness of emission restriction (in Chinese). Acta Scient Circums, 9: 1739–1748

    Google Scholar 

  • Xiao Q, Zheng Y, Geng G, Chen C, Huang X, Che H, Zhang X, He K, Zhang Q. 2021. Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018. Atmos Chem Phys, 21: 9475–9496

    Google Scholar 

  • Yin C, Deng X, Zou Y, Solmon F, Li F, Deng T. 2019. Trend analysis of surface ozone at suburban Guangzhou, China. Sci Total Environ, 695: 133880

    Google Scholar 

  • Zhang Q, Geng G. 2019. Impact of clean air action on PM2.5 pollution in China. Sci China Earth Sci, 62: 1845–1846

    Google Scholar 

  • Zhang Q, Zheng Y, Tong D, Shao M, Wang S, Zhang Y, Xu X, Wang J, He H, Liu W, Ding Y, Lei Y, Li J, Wang Z, Zhang X, Wang Y, Cheng J, Liu Y, Shi Q, Yan L, Geng G, Hong C, Li M, Liu F, Zheng B, Cao J, Ding A, Gao J, Fu Q, Huo J, Liu B, Liu Z, Yang F, He K, Hao J. 2019. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc Natl Acad Sci USA, 116: 24463–24469

    Google Scholar 

  • Zhang X Y, Sun J Y, Wang Y Q, Li W J, Zhang Q, Wang W G, Quan J N, Cao G L, Wang J Z, Yang Y Q, Zhang Y M. 2013. Factors contributing to haze and fog in China (in Chinese). Chin Sci Bull, 58: 1178–1187

    Google Scholar 

  • Zhang X, Xu X, Ding Y, Liu Y, Zhang H, Wang Y, Zhong J. 2019. The impact of meteorological changes from 2013 to 2017 on PM2.5 mass reduction in key regions in China. Sci China Earth Sci, 62: 1885–1902

    Google Scholar 

  • Zhang Y F, Zhu T, Feng Y C, Han S Q, Li X, Liu C X. 2009. Evaluation model for the effectiveness of air pollution control and its application (in Chinese). China Environ Sci, 29: 225–230

    Google Scholar 

  • Zhang Y J, Lei R Y, Cui S J, Wang H L, Chen M D, Ge X L. 2022. Spatiotemporal trends and impact factors of PM2.5 and O3 pollution in major cities in China during 2015–2020 (in Chinese). Chin Sci Bull, 67: 2029–2042

    Google Scholar 

  • Zheng B, Tong D, Li M, Liu F, Hong C, Geng G, Li H, Li X, Peng L, Qi J, Yan L, Zhang Y, Zhao H, Zheng Y, He K, Zhang Q. 2018. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos Chem Phys, 18: 14095–14111

    Google Scholar 

  • Zhong J, Zhang X, Dong Y, Wang Y, Liu C, Wang J, Zhang Y, Che H. 2018. Feedback effects of boundary-layer meteorological factors on cumulative explosive growth of PM2.5 during winter heavy pollution episodes in Beijing from 2013 to 2016. Atmos Chem Phys, 18: 247–258

    Google Scholar 

  • Zhong Q, Ma J, Shen G, Shen H, Zhu X, Yun X, Meng W, Cheng H, Liu J, Li B, Wang X, Zeng E Y, Guan D, Tao S. 2018. Distinguishing emission-associated ambient air PM2.5 concentrations and meteorological factor-induced fluctuations. Environ Sci Technol, 52: 10416–10425

    Google Scholar 

  • Zhu T, Wan W, Liu J, Xue T, Gong J, Zhang S. 2022. Insights into the new WHO Global Air Quality Guidelines (in Chinese). Chin Sci Bull, 67: 697–706

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge many domestic and overseas colleagues for their helpful guidance and supports. This work was supported by the National Key R&D Program of China (Grant No. 2022YFC3703001), the China Postdoctoral Science Foundation (Grant No. 2022T150334) and the National Natural Science Foundation of China (Grant Nos. 42177085 & 42177465).

Author information

Authors and Affiliations

  1. State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China

    Qili Dai, Tianjiao Dai, Linlu Hou, Linxuan Li, Xiaohui Bi, Yufen Zhang & Yinchang Feng

  2. China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin, 300350, China

    Qili Dai, Tianjiao Dai, Linlu Hou, Linxuan Li, Xiaohui Bi, Yufen Zhang & Yinchang Feng

Authors
  1. Qili Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianjiao Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linlu Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Linxuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaohui Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yufen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yinchang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yufen Zhang or Yinchang Feng.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dai, Q., Dai, T., Hou, L. et al. Quantifying the impacts of emissions and meteorology on the interannual variations of air pollutants in major Chinese cities from 2015 to 2021. Sci. China Earth Sci. 66, 1725–1737 (2023). https://doi.org/10.1007/s11430-022-1128-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11430-022-1128-1

Keywords

Search

Navigation