Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Andersen, Hendrik; Cermak, Jan; Stirnberg, Roland; Fuchs, Julia; Kim, Miae; Pauli, Eva
• Is Part Of: Tellus. Volume 73:Issue 1(2021)
• Page Start: 1
• Page End: 14
• Publication Date: 2021-01-01
• Subjects: Atmospheric aerosols -- COVID-19 -- satellite remote sensing -- machine learning; Atmospheric chemistry -- Periodicals; Atmospheric physics -- Periodicals; Meteorology -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; Chimie de l'atmosphère -- Périodiques; Météorologie physique -- Périodiques; Météorologie -- Périodiques; Air -- Pollution -- Meteorological aspects; Atmospheric chemistry; Atmospheric physics; Meteorology; Meteorologie; Chimie de l'atmosphère; Météorologie physique; Météorologie; Meteorology; Electronic journals; Computer network resources; Periodicals; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 551.505
• DOI: 10.1080/16000889.2021.1971925
• Languages: English
• ISSNs: 0280-6509

Abstract

Abstract: Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.