To investigate the air quality change during the COVID-19 pandemic, we analyzed spatiotemporal variations of six criteria pollutants in nine typical urban agglomerations in China using ground-based data and examined… Click to show full abstract
To investigate the air quality change during the COVID-19 pandemic, we analyzed spatiotemporal variations of six criteria pollutants in nine typical urban agglomerations in China using ground-based data and examined meteorological influences through correlation analysis and backward trajectory analysis under different responses. Concentrations of PM2.5, PM10, NO2, SO2 and CO in urban agglomerations respectively decreased by 18%-45% (30%-62%), 17%-53% (22%-39%), 47%-64% (14%-41%), 9%-34% (0%-53%) and 16%-52% (23%-56%) during Lockdown (Post-lockdown) period relative to Pre-lockdown period. PM2.5 pollution events occurred during Lockdown in Beijing-Tianjin-Hebe (BTH) and Middle and South Liaoning (MSL), and daily O3 concentration rose to grade Ⅱ standard in Post-lockdown period. Distinct from the nationwide slump of NO2 during Lockdown period, a rebound (∼40%) in Post-lockdown period was observed in Cheng-Yu (CY), Yangtze River Middle-Reach (YRMR), Yangtze River Delta (YRD) and Pearl River Delta (PRD). With slightly higher wind speed compared with 2019, the reduction of PM2.5 (51%-62%) in Post-lockdown period is more than 2019 (15%-46%) in HC (Harbin-Changchun), MSL, BTH, CP (Central Plain) and SP (Shandong-Peninsula), suggesting lockdown measures are effective to PM2.5 alleviation. Although O3 concentrations generally increased during the lockdown, its increment rate declined compared with 2019 under similar sunlight duration and temperature. Additionally, unlike HC, MSL and BTH, which suffered from additional (> 30%) air masses from surrounding areas after the lockdown, the polluted air masses reaching YRD and PRD mostly originated from the long-distance transport, highlighting the importance of joint regional governance.
               
Click one of the above tabs to view related content.