Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data

Authors

Maram El-Nadry, Alexandria University
Wenzhao Li, Chapman UniversityFollow
Hesham el-Askary, Chapman UniversityFollow
Mohamed A. Awad, Alexandria University
Alaa Ramadan Awad, Alexandria University

Document Type

Article

Publication Date

9-8-2019

Abstract

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.

Comments

This article was originally published in Remote Sensing, volume 11, issue 18, in 2019. https://doi.org/10.3390/rs11182096

Recommended Citation

El-Nadry, M.; Li, W.; El Askary, H.; Awad, M.A..; Mostafa, A.R. Urban health related air quality indicators over the Middle East and North Africa countries using multiple satellites and AERONET dataRemote Sens. 2019, 11(18), 2096. https://doi.org/10.3390/rs11182096

Peer Reviewed

1

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The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.