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04 Jan 2021
04 Jan 2021
Abstract. The rapid response to COVID-19 pandemic led to the unprecedented decreases of economic activities, thereby reducing the pollutant emissions. A random forest (RF) model was applied to determine the respective contributions of meteorology and anthropogenic emissions to the changes of air quality. The result suggested the strict lockdown measures significantly decreased primary components such as Cr (−201 %) and Fe (−154 %) in PM2.5, whereas the higher relative humidity (RH) and NH3 level, and the lower air temperature (T) enhanced the production of secondary aerosol including SO42− (47.2 %), NO3− (38.6 %), and NH4+ (22.7 %). Positive matrix factorization (PMF) result suggested that the contribution ratios of secondary formation (SF), industrial process (IP), biomass burning (BB), coal combustion (CC), and road dust (RD) changed from 35.2 %, 28.9 %, 19.4 %, 11.8 %, and 4.75 % before COVID-19 outbreak to 42.7 %, 20.5 %, 19.45 %, 9.80 %, and 7.56 %, respectively. The rapid increase of the contribution ratio derived from SF to PM2.5 implied the intermittent haze events during COVID-19 period were characterized with secondary aerosol pollution, which was mainly contributed by the unfavorable meteorological conditions and high NH3 level.
Rui Li et al.
Status: open (until 01 Mar 2021)
I found the approach interesting and would like to see this article published. It shows a method to quantify the influence of meteorological parameters on pollutant time series and thus helps to better quantify the impact of COVID-19 lockdown measures on air pollution. There are, however, a few points that require better explanation or change before publication:
Rui Li et al.
Rui Li et al.
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An interactive open-access journal of the European Geosciences Union