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

Abstract

The absence of motor vehicle traffic and suspended manufacturing during the coronavirus disease 2019 (COVID-19) pandemic in China enabled assessment of the efficiency of air pollution mitigation. Up to 90% reduction of certain emissions during the city-lockdown period can be identified from satellite and ground-based observations. Unexpectedly, extreme particulate matter levels simultaneously occurred in northern China. Our synergistic observation analyses and model simulations show that anomalously high humidity promoted aerosol heterogeneous chemistry, along with stagnant airflow and uninterrupted emissions from power plants and petrochemical facilities, contributing to severe haze formation. Also, because of nonlinear production chemistry and titration of ozone in winter, reduced nitrogen oxides resulted in ozone enhancement in urban areas, further increasing the atmospheric oxidizing capacity and facilitating secondary aerosol formation.

Fig. 1. Spaceborne measurements of NO₂ from TROPOMI.

(A) Column-integrated NO₂ averaged over the COVID19 lockdown period (CLD) for three weeks during Jan. 23 to Feb. 13, 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, starting from the two days before the Chinese Lunar New Year (2019-LNY). Note that TROPOMI NO₂ is available only starting from June 2018. (C) The fractional changes between (A) and (B), calculated only for the regions with NO₂ in 2019-LNY greater than 0.2 DU. The symbols in the maps indicate the location of Wuhan, the most affected city by the COVID-19 disease. 1 Dobson Unit (DU) = 0.4462 mmol m⁻².

Fig. 2. Ground-based station observation of particulate matter (aerodynamic diameter less than 2.5 μm, PM_2.5), NO₂, SO₂, and ozone in eastern China including four megacities (A. Wuhan, B. Beijing, C. Guangzhou, and D. Shanghai).

The figure compares the three-week averages during the city lockdown period (CLD), the three-week averages before the city lockdown (pre-CLD), the five-year climatology for 2015-2019 during the same period with CLD in the Chinese lunar calendar that covers the Lunar New Year (CLIM-LNY), and the five-year climatology for 2015-2019 during the same period with CLD in the Gregorian calendar (CLIM). Error bars indicate the standard deviations over multiple years. (E) The map of surface PM_2.5 changes in 2020-CLD compared to CLIM-LNY based on the 1515 state monitoring stations (SI Fig. 2). The low-resolution patterns in the north and west are caused by the sparsity of stations. Two boxes indicate the Beijing-Tianjin-Hebei and central China regions. For ozone, 1 μg m⁻³ is approximately about 0.47 ppb under a standard condition.

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

(A) 1000-hPa relative humidity, (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 Beijing-Tianjin-Hebei region, and their sensitivity to the altered emissions, meteorological conditions, and chemical pathways.

(A) Time evolution of surface PM_2.5 concentrations in the ground-based observations (black dots), the baseline simulation (blue line), and the sensitivity simulation with the climatological (2015-2019) meteorological conditions (red line, see details in table S3). (B) The same with (A) but for ozone. (C) The simulated fractional changes in different aerosol species in response to changes in NO_x emissions, meteorological conditions, and the representation of heterogeneous chemistry. (D) The same with (C) but for gaseous pollutants including NO₂, SO₂, and O₃.