Assessing the COVID-19 Control Measures Impact on Air Quality in China using Time Series Decomposition to account for Trends and Seasonality

Abstract:

The outbreak of Coronavirus Disease 2019 (COVID-19) in China in January 2020 prompted substantial outbreak control measures including social distancing, suspensions of public transport and industry, and widespread restrictions of movement(‘lockdowns’), that have led to a decrease in industrial activity and air pollution emissions for around two months. We use a 5-year dataset from China’s National Environmental Monitoring Centre to assess the impact of control measures on air quality. Pollutant concentration time series are decomposed to account for the inter-annual trend, seasonal cycles and the effect of Lunar New Year (LNY), which coincided with the COVID-19 outbreak. During 2015-2019, there were significant negative trends in particulate matter (PM_2.5, -6% year^-1) and sulfur dioxide (SO₂, -12% year^-1) and nitrogen dioxide (NO₂, -2.2% year^-1) across China, whereas there were positive trends in ozone (O₃, +2.8% year^-1). We quantify the change in air quality during the LNY holiday week, during which pollutant concentrations increase on LNY’s day, followed by reduced concentrations in the rest of the week. After accounting for interannual trends, seasonal variation and LNY we find that across China, NO₂ experienced the largest and most sustained decrease during the lockdown period (-27.0%), while PM_2.5 and PM₁₀ concentrations were effected to a lesser extent (10.5 % and 21.4%, respectively) whereas but O₃ concentrations were not substantially affected (0.2%). The largest improvements in air quality were in Hubei province where the outbreak originated from Wuhan, and NO₂ concentrations were 50.5% lower than expected during the lockdown. Concentrations of affected pollutants returned to expected levels during April, after control measures were relaxed.