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  • Toward Understanding the Va...
    Jiang, Shuyi; Zhao, Chuanfeng; Fan, Hao

    Atmosphere, 02/2021, Letnik: 12, Številka: 2
    Journal Article

    Associated with the outbreak of new coronavirus in 2019 (COVID-19), lockdown measures were taken in spring 2020 in China, providing an ideal experiment to investigate the effects of emission controls on air quality. Using the observation data at 56 stations in Hebei province from the China National Environmental Monitoring Center from January 2019 to May 2020, along with the reanalysis meteorology data from ERA5, this study investigates the spatial and temporal variations of six air pollutants, and the clean and pollution events in COVID-19 period. Compared with the same periods in day and month in 2019 (SP19), the concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter of less than 10 μm (PM10), particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5), and carbon monoxide (CO) decreased by 39.2%, 38.2%, 42.1%, 39.8%, and 24.8% for lockdown period, respectively; and decreased by 13.7%, 8.9%, 16.8%, 13.4%, and 10.6% for post-lockdown period, respectively. In contrast, ozone (O3) increased by 8.0% and 5.5% for lockdown and post-lockdown periods, respectively. The diurnal variation analysis shows that the air pollutants other than O3 decrease more in the morning time (6:00–10:00 local time) than in the afternoon time (14:00–18:00 local time) during both lockdown and post-lockdown periods compared to SP19, implying the potential contribution from pollution-meteorology interaction. After lockdown period, SO2 and NO2 resumed quickly in most cities other than in Zhangjiakou, which is a city with few industries making it more sensitive to meteorology. The significant improvement of air quality during the lockdown period suggests that the whole air quality is highly dependent on the pollutant emissions, while the relatively weak reduction of pollution events imply that the pollution events are more dependent on adverse weather conditions.