Hourly ambient air pollutant concentrations (i.e., NO2, PM2.5, PM10, SO2, and O3) were measured during the lockdown (spanning from 1 January to 12 April) in 2020 and the same period averaged over the 2 previous years (2018–2019). The roadside and non-roadside pollutant data were respectively acquired from the Shanghai Environmental Protection Bureau (in charge of air monitoring) and the China's National Environmental Monitoring Center. Overall, 5.43% of the data were missing, and the total data used in this study comprises 14,703 h. The missing data were omitted from this study.

Meteorological conditions, in particular temperature relative humidity, planetary boundary-layer height, and wind direction), could also influence the pollutants levels by physical processes such as accumulation or dispersion and multiphase reactions for aerosol formation and growth [[39], [40], [41]]. Changes in the pollutant levels during the COVID-19 lockdown across Shanghai could be partially affected by the meteorological parameters during the period. However, air quality responses due to a marked emissions reduction can be studied by comparing the average pollutant concentrations under the same meteorological conditions [42,43]. The corresponding hourly temperature, wind speed, wind direction, relative humidity, and pressure data in the same time span (1 January to 12 April) from 2018 to 2020 were obtained from the UK Met Office (http://rp5.ru/), which collects data from a surface weather station. Meteorological effects were excluded at first when quantifying the impact of COVID-19 on air quality. Table 1 compared the meteorological parameters between a normal year and the pandemic year. We observed no significant (Pvalue > 0.05) difference in meteorological conditions during the COVID-19 lockdown compared to the normal year, but the surface temperature during the pandemic year was significant (Pvalue < 0.0001) higher than that in previous years. The average temperatures for 2018–2019 and 2020 was 9.32 °C and 10.44 °C, respectively.

Changes in meteorological parameters between a normal year and the pandemic year.

We analysed the data to assess the temporal variations in pollutants between the roadside and non-roadside stations during 2018–2019 and 2020. Furthermore, we assessed the pollutant variability in each station under different lockdown measures and compared their values to that of the same periods in previous years. A correlation analysis is a statistical technique that determines the strength and direction of correlations between variables. In this study, we used a Spearman's correlation analysis to evaluate the inter-relationships between the different air pollutants over the historical and current periods.

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