Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe
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...
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MDPI AG
2021-08-01
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Online Access: | https://www.mdpi.com/2072-4292/13/15/3027 |
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author | Saleem Ibrahim Martin Landa Ondřej Pešek Karel Pavelka Lena Halounova |
author_facet | Saleem Ibrahim Martin Landa Ondřej Pešek Karel Pavelka Lena Halounova |
author_sort | Saleem Ibrahim |
collection | DOAJ |
description | 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. |
first_indexed | 2024-03-10T09:09:30Z |
format | Article |
id | doaj.art-c6e6d15ce20a4d2ba976e897c40e365b |
institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-10T09:09:30Z |
publishDate | 2021-08-01 |
publisher | MDPI AG |
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series | Remote Sensing |
spelling | doaj.art-c6e6d15ce20a4d2ba976e897c40e365b2023-11-22T06:07:46ZengMDPI AGRemote Sensing2072-42922021-08-011315302710.3390/rs13153027Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over EuropeSaleem Ibrahim0Martin Landa1Ondřej Pešek2Karel Pavelka3Lena Halounova4Department of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech RepublicDepartment of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech RepublicDepartment of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech RepublicDepartment of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech RepublicDepartment of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech RepublicThe 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.https://www.mdpi.com/2072-4292/13/15/3027aerosol optical depthCAMSCOVID-19machine learningMODIS |
spellingShingle | Saleem Ibrahim Martin Landa Ondřej Pešek Karel Pavelka Lena Halounova Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe Remote Sensing aerosol optical depth CAMS COVID-19 machine learning MODIS |
title | Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe |
title_full | Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe |
title_fullStr | Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe |
title_full_unstemmed | Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe |
title_short | Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe |
title_sort | space time machine learning models to analyze covid 19 pandemic lockdown effects on aerosol optical depth over europe |
topic | aerosol optical depth CAMS COVID-19 machine learning MODIS |
url | https://www.mdpi.com/2072-4292/13/15/3027 |
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