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Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China

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Science  07 Aug 2020:
Vol. 369, Issue 6504, pp. 702-706
DOI: 10.1126/science.abb7431
  • Fig. 1 Spaceborne measurements of NO2 from TROPOMI.

    (A) Column-integrated NO2 averaged over the 2020-CLD period for 3 weeks during 23 January to 13 February 2020. (B) Column-integrated NO2 averaged over the reference period in 2019. To account for the annual holiday, the 2019 reference period we choose is the same as that in 2020-CLD in the Chinese lunar calendar, including the Chinese Lunar New Year (2019-LNY). TROPOMI NO2 is available only starting from June 2018. (C) The fractional changes (DIFF) between (A) and (B), calculated only for the regions with NO2 in 2019-LNY greater than 0.2 Dobson units (DU). The symbols in the maps indicate the location of Wuhan, the city most affected by COVID-19. 1 DU = 0.4462 mmol m−2.

  • Fig. 2 Ground-based station observation of PM2.5, NO2, SO2, and ozone in eastern China, including four megacities.

    (A) Wuhan. (B) Beijing. (C) Guangzhou. (D) Shanghai. The figure compares the 3-week averages during the city lockdown period (CLD), the 3-week averages before 2020-CLD (pre-CLD), the 5-year climatology for 2015 to 2019 during the same period of 2020-CLD in the Chinese lunar calendar that covers the Lunar New Year (CLIM-LNY), and the 5-year climatology for 2015 to 2019 during the same period with 2020-CLD in the Gregorian calendar (CLIM). Error bars indicate SDs over multiple years. (E) Map of surface PM2.5 changes in 2020-CLD compared with CLIM-LNY based on the 1515 state monitoring stations (fig. S2). The low-resolution patterns in the north and west are caused by the sparsity of stations. Two boxes indicate the BTH and central China regions. For ozone, 1 μg m−3 is ~0.47 ppb under a standard condition.

  • Fig. 3 Fractional changes (%) in meteorological conditions between the 2020-CLD and CLIM-LNY during 2015 to 2019 based on the ERA5 reanalysis data.

    (A) Relative humidity of 1000 hPa, (B) 10-m wind speed (contours) and wind direction (vectors), (C) boundary-layer height, and (D) daily precipitation. Symbols in the maps indicate the location of the four major cities in Fig. 2.

  • Fig. 4 WRF-Chem simulated aerosol species and precursor gases during the COVID-19 city lockdown period in the BTH region and their sensitivity to the altered emissions, meteorological conditions, and chemical pathways.

    (A) Time evolution of surface PM2.5 concentrations in the ground-based observations (black dots), the baseline simulation (blue line), and the sensitivity simulation with the climatological (2015 to 2019) meteorological conditions (red line) (table S3). BJT, Beijing time; IOA, index of agreement; RMSE, root mean square error. (B) Same with (A) but for ozone. (C) Simulated fractional changes in different aerosol species in response to changes in NOx emissions, meteorological conditions, and the representation of heterogeneous chemistry. (D) Same with (C) but for gaseous pollutants including NO2, SO2, and O3.

Supplementary Materials

  • Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China

    Tianhao Le, Yuan Wang, Lang Liu, Jiani Yang, Yuk L. Yung, Guohui Li, John H. Seinfeld

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Figs. S1 to S9
    • Tables S1 to S3
    • References

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