Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing. (15th October 2021)

• Record Type: Journal Article
• Authors: Ghasempour, Fatemeh; Sekertekin, Aliihsan; Kutoglu, Senol Hakan
• Is Part Of: Journal of cleaner production. Volume 319(2021)
• Publication Date: 2021-10-15
• Subjects: Remote sensing -- Google earth engine -- Air pollution -- COVID-19 -- TROPOMI -- Turkey; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.128599
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Air pollution is one of the vital problems for the sustainability of cities and public health. The lockdown caused by the COVID-19 outbreak has become a natural laboratory, enabling to investigate the impact of human/industrial activities on the air pollution. In this study, we investigated the spatio-temporal density of TROPOMI-based nitrogen dioxide (NO2 ) and sulfur dioxide (SO2 ) products, and MODIS-derived Aerosol Optical Depth (AOD) from January 2019 to September 2020 (also covering the first wave of the COVID-19) over Turkey using Google Earth Engine (GEE). The results showed a significant decrease in NO2 and AOD, while SO2 unchanged and had slightly higher concentrations in some regions during the lockdown compared to 2019. The relationship between air pollutants and meteorological parameters during the lockdown showed that air temperature and pressure were highly correlated with air pollutants, unlike precipitation and wind speed. Moreover, Purchasing Managers' Index (PMI) data, indicator of economic/industrial activities, also provided poor correlation with air pollutants. TROPOMI-based NO2 and SO2 were compared with station-based pollutants for three sites (suburban, urban, and urban-traffic classes) in Istanbul, revealing 0.83, 0.70 and 0.65 correlation coefficients for NO2, respectively, while SO2 showed no significant correlation. Besides, AOD data were validated using two AERONET sites providing 0.86 and 0.82 correlation coefficients. Overall, the satellite-based data provided significant outcomes for the spatio-temporal evaluation of air quality, especially during the first wave of the COVID-19 lockdown.