Abstract
The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe.
Highlights
We focused on Aerosol Optical Depth (AOD), which is defined as a measure of the columnar atmospheric aerosol content
MCD19A2 high-quality retrievals were used as the dependent variable, and since the Terra satellite is passing locally around 10:30 a.m. and the Aqua satellite passes around 1:30 p.m., we used the modeled AOD from Copernicus Atmosphere Monitoring Service (CAMS) at the closest three times per day to the satellites passing (9 a.m., 12 p.m., and 3 p.m.)
Due to the great number of MODISAOD -CAMSAOD pairs over land in the study area, representative subsets consisting of ~10% of the whole population were chosen using the Kolmogorov–Smirnov test to be used as learning dataset for a space-time model for each year
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Licensee MDPI, Basel, Switzerland.Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).The Severe Acute Respiratory Syndrome-COronaVIrus Diseases 2019 (SARS-COVID19) pandemic made humanity reconsider how to adapt their daily activities. By late June2020, the EU average infection rate was around 160 per million inhabitants [1]. In general, most European countries started applying restrictions in March 2020. These restrictions included lockdown, contain, various kinds of curfew, mandatory face masks, etc. By
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