Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Winter particulate pollution severity in North China driven by atmospheric teleconnections

Nature Geoscience volume 15, pages 349–355 (2022)Cite this article

Subjects

Abstract

Elevated levels of particulate matter in the atmosphere are hazardous to human health and the environment. Severe particulate pollution days, with daily mean PM2.5 concentrations exceeding 150 μg m−3, occurred frequently in North China, especially during the boreal winters of 2013–2019. Severe particulate pollution generally occurs under conducive weather patterns characterized by a stable atmosphere with weak winds, under which air pollutants emitted at the surface by human activities would accumulate. The occurrence of conducive weather patterns has been attributed to variations in numerous climate factors such as Arctic sea-ice cover, sea surface temperature and atmospheric teleconnections, but the dominant climate drivers remain unclear. Here, we show that the East Atlantic–West Russia teleconnection pattern and the Victoria mode of sea surface temperature anomalies are the top two dominant climate drivers that lead to conducive weather patterns in North China through the zonal and meridional propagations of Rossby waves. Our results suggest that, with the help of seasonal forecast from climate models, indices of these two drivers can be used to predict severe particulate pollution over North China for the coming winter, enabling us to protect human health by air-quality planning.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Conducive weather patterns favourable for formation of SPPDs.
Fig. 2: Time series of conducive weather patterns and reconstructed conducive weather patterns.
Fig. 3: Possible mechanism for T5.
Fig. 4: Possible mechanism for T7.
Fig. 5: Evaluation of the ability to predict the frequency of CWPs by EA/WR and VM.

Similar content being viewed by others

Data availability

The analytic data that support the major results are accessible at figshare (https://figshare.com/s/be3b2c64e0757e805bf7). The surface PM2.5 observations from the Chinese Ministry of Ecology and Environment can be obtained from http://106.37.208.233:20035/ and https://quotsoft.net/air/. The surface PM2.5 observations for the US Embassy in Beijing are downloaded from https://www.airnow.gov/international/us-embassies-and-consulates/#China$Beijing. The ERA5 reanalysis data are available from https://cds.climate.copernicus.eu/cdsapp#!/search. The NCEP1 renalysis data are available from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. The NCEP2 reanalysis data are available from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. The observed sea surface temperatures from the Hadley Centre are downloaded from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. Source data are provided with this paper.

Code availability

The Cost733class software is open source (http://cost733.met.no/).

References

  1. Cai, W., Li, K., Liao, H., Wang, H. & Wu, L. Weather conditions conducive to Beijing severe haze more frequent under climate change. Nat. Clim. Change 7, 257–262 (2017).

    Article  Google Scholar 

  2. Li, J., Liao, H., Hu, J. & Li, N. Severe particulate pollution days in China during 2013–2018 and the associated typical weather patterns in Beijing–Tianjin–Hebei and the Yangtze River delta regions. Environ. Pollut. 248, 74–81 (2019).

    Article  Google Scholar 

  3. Yang, Y., Liao, H. & Lou, S. Increase in winter haze over eastern China in recent decades: roles of variations in meteorological parameters and anthropogenic emissions. J. Geophys. Res. Atmos. 121, 13050–13065 (2016).

    Article  Google Scholar 

  4. Yue, X. et al. Ozone and haze pollution weakens net primary productivity in China. Atmos. Chem. Phys. 17, 6073–6089 (2017).

    Article  Google Scholar 

  5. Hu, J. et al. Premature mortality attributable to particulate matter in China: source contributions and responses to reductions. Environ. Sci. Technol. 51, 9950–9959 (2017).

    Article  Google Scholar 

  6. Hao, X. et al. Long-term health impact of PM2.5 under whole-year COVID-19 lockdown in China. Environ. Pollut. 290, 118118 (2021).

    Article  Google Scholar 

  7. Li, Z. et al. Aerosol and monsoon climate interactions over Asia. Rev. Geophys. 54, 866–929 (2016).

    Article  Google Scholar 

  8. Wang, Y. et al. Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Sci. China Earth Sci. 57, 14–25 (2014).

    Article  Google Scholar 

  9. Zhang, Q. et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl Acad. Sci. USA 116, 24463–24469 (2019).

    Article  Google Scholar 

  10. Huang, X. et al. Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. Natl Sci. Rev. 8, nwaa137 (2020).

    Article  Google Scholar 

  11. 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. 158, 104814 (2020).

  12. Zhao, X. J. et al. Analysis of a winter regional haze event and its formation mechanism in the North China Plain. Atmos. Chem. Phys. 13, 5685–5696 (2013).

    Article  Google Scholar 

  13. Liu, C. et al. A severe fog-haze episode in Beijing–Tianjin–Hebei region: characteristics, sources and impacts of boundary layer structure. Atmos. Pollut. Res. 10, 1190–1202 (2019).

    Article  Google Scholar 

  14. Chen, H. & Wang, H. Haze days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012. J. Geophys. Res. Atmos. 120, 5895–5909 (2015).

    Article  Google Scholar 

  15. Pei, L., Yan, Z., Sun, Z., Miao, S. & Yao, Y. Increasing persistent haze in Beijing: potential impacts of weakening East Asian winter monsoons associated with northwestern Pacific sea surface temperature trends. Atmos. Chem. Phys. 18, 3173–3183 (2018).

    Article  Google Scholar 

  16. Wu, P., Ding, Y. & Liu, Y. Atmospheric circulation and dynamic mechanism for persistent haze events in the Beijing–Tianjin–Hebei region. Adv. Atmos. Sci. 34, 429–440 (2017).

    Article  Google Scholar 

  17. An, Z. et al. Severe haze in northern China: a synergy of anthropogenic emissions and atmospheric processes. Proc. Natl Acad. Sci. USA 116, 8657–8666 (2019).

    Article  Google Scholar 

  18. Zhao, S., Feng, T., Tie, X., Li, G. & Cao, J. Air pollution zone migrates south driven by East Asian winter monsoon and climate change. Geophys. Res. Lett. 48, e2021GL092672 (2021).

    Google Scholar 

  19. Wu, G. et al. Advances in studying interactions between aerosols and monsoon in China. Sci. China Earth Sci. 59, 1–16 (2016).

    Article  Google Scholar 

  20. Wang, H., Chen, H. & Liu, J. Arctic sea ice decline intensified haze pollution in eastern China. Atmos. Ocean Sci. Lett. 8, 1–9 (2015).

    Google Scholar 

  21. Wang, J. et al. Two pathways of how remote SST anomalies drive the interannual variability of autumnal haze days in the Beijing–Tianjin–Hebei region, China. Atmos. Chem. Phys. 19, 1521–1535 (2019).

    Article  Google Scholar 

  22. Yin, Z. & Wang, H. The relationship between the subtropical western Pacific SST and haze over north-central North China Plain. Int. J. Climatol. 36, 3479–3491 (2016).

    Article  Google Scholar 

  23. Chen, S., Guo, J., Song, L., Cohen, J. B. & Wang, Y. Temporal disparity of the atmospheric systems contributing to interannual variation of wintertime haze pollution in the North China Plain. Int. J. Climatol. 40, 128–144 (2020).

    Article  Google Scholar 

  24. Shen, L., Jacob, D. J., Mickley, L. J., Wang, Y. & Zhang, Q. Insignificant effect of climate change on winter haze pollution in Beijing. Atmos. Chem. Phys. 18, 17489–17496 (2018).

    Article  Google Scholar 

  25. Yin, Z. & Wang, H. Role of atmospheric circulations in haze pollution in December 2016. Atmos. Chem. Phys. 17, 11673–11681 (2017).

    Article  Google Scholar 

  26. Zou, Y., Wang, Y., Xie, Z., Wang, H. & Rasch, P. J. Atmospheric teleconnection processes linking winter air stagnation and haze extremes in China with regional Arctic sea ice decline. Atmos. Chem. Phys. 20, 4999–5017 (2020).

    Article  Google Scholar 

  27. Zou, Y., Wang, Y., Zhang, Y. & Koo, J. H. Arctic sea ice, Eurasia snow, and extreme winter haze in China. Sci. Adv. 3, e1602751 (2017).

    Article  Google Scholar 

  28. Pei, L. & Yan, Z. Diminishing clear winter skies in Beijing towards a possible future. Environ. Res. Lett. 13, 124029 (2018).

    Article  Google Scholar 

  29. Chen, H., Wang, H., Sun, J., Xu, Y. & Yin, Z. Anthropogenic fine particulate matter pollution will be exacerbated in eastern China due to 21st century GHG warming. Atmos. Chem. Phys. 19, 233–243 (2019).

    Article  Google Scholar 

  30. Wallace, J. M. & Gutzler, D. S. Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Weather Rev. 109, 784–812 (1981).

    Article  Google Scholar 

  31. Horel, J. D. & Wallace, J. M. Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Weather Rev. 109, 813–829 (1981).

    Article  Google Scholar 

  32. Krichak, S. O., Breitgand, J. S., Gualdi, S. & Feldstein, S. B. Teleconnection–extreme precipitation relationships over the Mediterranean region. Theor. Appl. Climatol. 117, 679–692 (2013).

    Article  Google Scholar 

  33. Lim, Y.-K. The East Atlantic/West Russia (EA/WR) teleconnection in the North Atlantic: climate impact and relation to Rossby wave propagation. Clim. Dyn. 44, 3211–3222 (2014).

    Article  Google Scholar 

  34. Ding, R., Li, J., Tseng, Y.-h., Sun, C. & Guo, Y. The Victoria mode in the North Pacific linking extratropical sea level pressure variations to ENSO. J. Geophys. Res. Atmos. 120, 27–45 (2015).

    Article  Google Scholar 

  35. Bond, N. A., Overland, J. E., Spillane, M. & Stabeno, P. Recent shifts in the state of the North Pacific. Geophys. Res. Lett. 30, 2183 (2003).

    Article  Google Scholar 

  36. Ding, R., Li, J., Tseng, Y.-h., Sun, C. & Zheng, F. Linking a sea level pressure anomaly dipole over North America to the central Pacific El Niño. Clim. Dyn. 49, 1321–1339 (2017).

    Article  Google Scholar 

  37. Tanaka, S., Nishii, K. & Nakamura, H. Vertical structure and energetics of the western Pacific teleconnection pattern. J. Clim. 29, 6597–6616 (2016).

    Article  Google Scholar 

  38. Shen, G. et al. Impacts of air pollutants from rural Chinese households under the rapid residential energy transition. Nat. Commun. 10, 3405 (2019).

    Article  Google Scholar 

  39. Dang, R. & Liao, H. Severe winter haze days in the Beijing–Tianjin–Hebei region from 1985 to 2017 and the roles of anthropogenic emissions and meteorology. Atmos. Chem. Phys. 19, 10801–10816 (2019).

    Article  Google Scholar 

  40. Zhang, Y., Yin, Z. & Wang, H. Roles of climate variability on the rapid increases of early winter haze pollution in North China after 2010. Atmos. Chem. Phys. 20, 12211–12221 (2020).

    Article  Google Scholar 

  41. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  42. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).

    Article  Google Scholar 

  43. Kanamitsu, M. et al. NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Am. Meteorol. Soc. 83, 1631–1644 (2002).

    Article  Google Scholar 

  44. Rayner, N. A. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003).

    Article  Google Scholar 

  45. Barnston, A. G. & Livezey, R. E. Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Weather Rev. 115, 1083–1126 (1987).

    Article  Google Scholar 

  46. Huth, R. et al. Classifications of atmospheric circulation patterns: recent advances and applications. Ann. N. Y. Acad. Sci. 1146, 105–152 (2008).

    Article  Google Scholar 

  47. Zhang, J. P. et al. The impact of circulation patterns on regional transport pathways and air quality over Beijing and its surroundings. Atmos. Chem. Phys. 12, 5031–5053 (2012).

    Article  Google Scholar 

  48. Wang, X. & Zhang, R. Effects of atmospheric circulations on the interannual variation in PM2.5 concentrations over the Beijing–Tianjin–Hebei region in 2013–2018. Atmos. Chem. Phys. 20, 7667–7682 (2020).

    Article  Google Scholar 

  49. Xu, J. et al. The meteorological modulation on PM2.5 interannual oscillation during 2013 to 2015 in Shanghai, China. Sci. Total Environ. 572, 1138–1149 (2016).

    Article  Google Scholar 

  50. Philipp, A. et al. Cost733cat—a database of weather and circulation type classifications. Phys. Chem. Earth Part A/B/C 35, 360–373 (2010).

    Article  Google Scholar 

  51. Murawski, A., Bürger, G., Vorogushyn, S. & Merz, B. Can local climate variability be explained by weather patterns? A multi-station evaluation for the Rhine basin. Hydrol. Earth Syst. Sci. 20, 4283–4306 (2016).

    Article  Google Scholar 

  52. Boers, N. et al. Complex networks reveal global pattern of extreme-rainfall teleconnections. Nature 566, 373–377 (2019).

    Article  Google Scholar 

  53. Plumb, R. A. On the three-dimensional propagation of stationary waves. J. Atmos. Sci. 42, 217–229 (1985).

    Article  Google Scholar 

Download references

Acknowledgements

H.L. acknowledges the National Natural Science Foundation of China (grant nos. 42021004 and 91744311) and the Major Research Plan of the National Social Science Foundation (grant no. 18ZDA052). X.H. is supported by the National Natural Science Foundation of China (grant no. 42088101) and the Guangdong Major Project of Basic and Applied Basic Research (grant no. 2020B0301030004).

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China

    Jiandong Li, Hong Liao, Ke Li, Xu Yue, Yang Yang, Yuhao Mao, Lei Chen & Jia Zhu

  2. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

    Xin Hao

  3. Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Xin Hao

  4. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Yuhang Wang

  5. Key Laboratory of Physical Oceanography–Institute for Advanced Ocean Studies, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

    Wenju Cai

  6. Centre for Southern Hemisphere Oceans Research (CSHOR), CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

    Wenju Cai

  7. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/ Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC–FEMD), Nanjing University of Information Science and Technology (NUIST), Nanjing, China

    Haishan Chen

  8. Climate Change Research Center, Chinese Academy of Sciences, Beijing, China

    Yu Fu

  9. Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Yu Fu

Authors
  1. Jiandong Li
    View author publications

    Search author on:PubMed Google Scholar

  2. Xin Hao
    View author publications

    Search author on:PubMed Google Scholar

  3. Hong Liao
    View author publications

    Search author on:PubMed Google Scholar

  4. Yuhang Wang
    View author publications

    Search author on:PubMed Google Scholar

  5. Wenju Cai
    View author publications

    Search author on:PubMed Google Scholar

  6. Ke Li
    View author publications

    Search author on:PubMed Google Scholar

  7. Xu Yue
    View author publications

    Search author on:PubMed Google Scholar

  8. Yang Yang
    View author publications

    Search author on:PubMed Google Scholar

  9. Haishan Chen
    View author publications

    Search author on:PubMed Google Scholar

  10. Yuhao Mao
    View author publications

    Search author on:PubMed Google Scholar

  11. Yu Fu
    View author publications

    Search author on:PubMed Google Scholar

  12. Lei Chen
    View author publications

    Search author on:PubMed Google Scholar

  13. Jia Zhu
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.L., X.H. and H.L. conceived the study. J.L. and X.H. performed the data analysis. Y.W. and W.C. contributed to interpreting the scientific questions. J.L., X.H. and H.L. wrote the draft of the manuscript. All authors contributed to discussing and improving the manuscript.

Corresponding author

Correspondence to Hong Liao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Xujia Jiang and James Super, in collaboration with the Nature Geoscience team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Correlation between daily meteorology anomalies and winter PM2.5.

a, Topographic map of the North China Plain (shading, unit: m) and locations of cities (black dots) with observed PM2.5 concentrations used in this study. b-m, Distribution of correlation coefficients between daily mean PM2.5 concentrations and daily meteorological fields during DJFs from 2013 to 2019. Stippled regions in b-m denote those areas exceeding the 95% significance level based on the Student’s t-test. The blue line is the boundary of the BTH region.

Source data

Extended Data Fig. 2 Regression between daily meteorological anomalies and winter PM2.5.

Distribution of regression coefficients between daily mean PM2.5 concentrations with daily (a) U200, (b) Z500, (c) V850, (d) RH1000 (relative humidity at 1000 hPa) and (e) Deltem_850_250 (vertical difference in the temperature anomalies between 850 hPa and 250 hPa) during DJFs from 2013 to 2019. Stippled regions denote those areas exceeding the 95% significance level based on the Student’s t-test. The blue line is the boundary of the BTH region. The black rectangles in a-c are selected regions for each variable.

Source data

Extended Data Fig. 3 Composited weather patterns for T1–T7 during the DJFs of 2013–2019.

a-bb, Composite anomalies of (a-g) U200 (units: m s−1), (h-n) Z500 (units: m), (o-u) V850 (units: m s−1), and (v-bb) pressure-longitude cross sections of the relative humidity (shadings, units: %) and temperature (contour, unit: °C) for each weather type. The gray contours in a-g are the western jet streams calculated by the DJF means of U200 from 1979 to 2018. The cross sections in v-bb are averaged over 30–40°N, and areas between the two black dashed lines denote the BTH region.

Source data

Extended Data Fig. 4 Evaluation for different classifications.

a, The explained variation (EV) and pseudo-F (PF) values for different classifications. b-e, Box and whisker plots of the average PM2.5 concentrations (b, c) and frequencies of SPPDs (d, e) under each Type for 6 (b, d) and 7 (c, e) classes in a. The red dots in boxes (b, c) denote mean PM2.5 concentrations and their values are listed below boxes. The numbers above the histograms are the frequencies of each type in DJFs during 2013–2019, and those below the histograms are the SPPDs frequencies within each type.

Source data

Extended Data Fig. 5 Long-term variations of reconstructed conducive weather patterns using different reanalysis datasets.

a-c, Time series of occurrence frequencies for the R-CWPs in winters of 1979–2019 by using the ERA5 (red), NCEP1(blue), and NCEP2 (green) reanalysis datasets, respectively. The inset texts in the top right corner of each panel denote correlation coefficients between R-CWPs with ERA5 reanalysis and R-CWPs using NCEP1 and NCEP2 reanalysis in 1979-2019, respectively.

Source data

Extended Data Fig. 6 Reconstructed conducive weather patterns for T5 and T7 over the years of 1979–2019.

a-h, Composite anomaly distributions of (a, b) U200 (units: m s−1), (c, d) Z500 (units: m), (e, f) V850 (units: m s−1) and (g, h) pressure-longitude cross sections of the relative humidity (shadings, units: %) and temperature (contour, unit: °C) for the reconstructed T5 and T7 weather types. The gray contours in a and b are the western jet streams calculated by the DJF means of U200 from 1979 to 2018. The cross sections are averaged over 30–40°N, and areas between the two black dashed lines denote the BTH region. The “Pattern corr” in a-f denote the pattern correlation between the composites in Fig. 1c-h and a-f.

Source data

Extended Data Fig. 7 Relationship between T5 and atmospheric indices.

Heatmap of correlation coefficients between various atmospheric indices and frequency of reconstructed T5 weather pattern. Correlation coefficients between the time series of T5/T7 and climate indices in Extended Data Figs. 7-9 are all detrended to remove linear trends.

Source data

Extended Data Fig. 8 Relationship between T7 and atmospheric indices.

Heatmap of correlation coefficients between various atmospheric indices and frequency of reconstructed T5 weather pattern.

Source data

Extended Data Fig. 9 Relationship between T5/T7 and SST indices.

Heatmap of correlation coefficients between SST indices and frequency of reconstructed T5 (top panel) and T7 (bottom panel) weather pattern.

Source data

Supplementary information

Supplementary Information

Supplementary Texts 1 and 2, Tables 1 and 2, and Figs. 1–5.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Hao, X., Liao, H. et al. Winter particulate pollution severity in North China driven by atmospheric teleconnections. Nat. Geosci. 15, 349–355 (2022). https://doi.org/10.1038/s41561-022-00933-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41561-022-00933-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing