Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data
Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected...
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ftmdpi:oai:mdpi.com:/2072-4292/11/18/2096/ 2023-08-20T03:59:11+02:00 Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data Maram El-Nadry Wenzhao Li Hesham El-Askary Mohamed A. Awad Alaa Ramadan Mostafa 2019-09-08 application/pdf https://doi.org/10.3390/rs11182096 EN eng Multidisciplinary Digital Publishing Institute Urban Remote Sensing https://dx.doi.org/10.3390/rs11182096 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 18; Pages: 2096 AERONET MISR MODIS aerosol optical depth aerosols MENA region machine learning deep neural network health effect Text 2019 ftmdpi https://doi.org/10.3390/rs11182096 2023-07-31T22:35:28Z Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies. Text Aerosol Robotic Network MDPI Open Access Publishing Merra ENVELOPE(12.615,12.615,65.816,65.816) Remote Sensing 11 18 2096 |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
AERONET MISR MODIS aerosol optical depth aerosols MENA region machine learning deep neural network health effect |
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AERONET MISR MODIS aerosol optical depth aerosols MENA region machine learning deep neural network health effect Maram El-Nadry Wenzhao Li Hesham El-Askary Mohamed A. Awad Alaa Ramadan Mostafa Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data |
topic_facet |
AERONET MISR MODIS aerosol optical depth aerosols MENA region machine learning deep neural network health effect |
description |
Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies. |
format |
Text |
author |
Maram El-Nadry Wenzhao Li Hesham El-Askary Mohamed A. Awad Alaa Ramadan Mostafa |
author_facet |
Maram El-Nadry Wenzhao Li Hesham El-Askary Mohamed A. Awad Alaa Ramadan Mostafa |
author_sort |
Maram El-Nadry |
title |
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data |
title_short |
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data |
title_full |
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data |
title_fullStr |
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data |
title_full_unstemmed |
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data |
title_sort |
urban health related air quality indicators over the middle east and north africa countries using multiple satellites and aeronet data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11182096 |
long_lat |
ENVELOPE(12.615,12.615,65.816,65.816) |
geographic |
Merra |
geographic_facet |
Merra |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 11; Issue 18; Pages: 2096 |
op_relation |
Urban Remote Sensing https://dx.doi.org/10.3390/rs11182096 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs11182096 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
18 |
container_start_page |
2096 |
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1774718760452620288 |