Abstract:In order to deal with the COVID-19 outbreak, Kunshan has strictly followed the national epidemic prevention regulations and has taken strict lockdown measures since January 24, 2020. Anthropogenic emissions, led by motor vehicle activity, have been reduced and air quality has changed as a result. Based on the environmental monitoring network, combined with the meteorological observation system, the integrated use of mathematical statistics and spatial analysis methods have been applied to investigate the impact of variations in human activity patterns on the air quality of Kunshan city before (from January 1, 2020 - January 20) and during the lockdown (from January 27, 2020 - February 15). The results show that great progress has been made in pollution control comparing with the same period (from January 1 to February 15) in the past three years, in that the days of exceeding the standard of particulate matter (PM2.5 and PM10) have decreased by 4 days. The daily maximum 8-hour ozone concentration (MDA8 O3), however, has increased by 14%, indicating that severe O3 pollution is about to be a problem throughout whole year rather than limited in summertime. Due to the reduction of emissions mainly from transportation sector during the lockdown, the concentration of nitrogen dioxide (NO2), an important precursor of O3, and particulate matter decreased significantly while MDA8 O3 increased during this period (62%). It is found that severe pollution events may occur even in the case of emission reduction, so the influence of meteorological conditions on air quality cannot be ignored. It is found that serious pollution events may occur even in the case of emission reduction, so the influence of meteorological conditions on air quality cannot be ignored. This study further improves the insights into the characteristics of main pollutants in Kunshan in winter, and explores the impact of anthropogenic emissions and meteorological factors on pollutants, which will provide a scientific reference for the simulation and prediction of aerosol at the urban scale in the Yangtze River Delta, China.