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

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.

Change Detection of Soil Formation Rate in Space and Time Based on Multi Source Data and Geospatial Analysis Techniques

“…MLR was mainly used to study the correlation between a dependent variable and multiple independent variables [49][50][51], and had a wide range of applications. The basic structure of multiple linear regression model was as follows:…”

COST Lecture 2019 AE GM Barcelona: International Network to Encourage the Use of Monitoring and Forecasting Dust Products (InDust)

“…Within the air quality community, there are ongoing discussions about the methodologies currently available to quantitatively report on contributions of this natural source to ambient particulate matter levels in Europe, in compliance with the EU Air Quality Directive (2008/50/CE). A matter of discussion is also how the dust forecasting models can help in the design of early warning systems (Solomos et al 2018;Gama et al 2019;Gama et al 2020;Marmureanu et al 2019;El-Nadry et al 2019;Wenzhao et al 2019). Mei et al's (2020) article bridges the gap between the dust modelling communities and the providers of satellite dust observations, improving data quality and ensuring data standards compliance.…”

Earth Observation and Cloud Computing in Support of Two Sustainable Development Goals for the River Nile Watershed Countries

“…For instance, Donchyts et al [20] employed the GEE for mapping global surface water changes over the past 30 years with a high spatial resolution (30 m), an effort that would not have been possible without the powerful processing and analytical capabilities from cloud computing. GEE is also widely used for environmental research in the Nile watershed and adjacent regions [14,[21][22][23][24][25]. In particular, it is a promising tool that allows researchers from developing countries (e.g., Nile Basin countries) to have the same ability to undertake state-of-art studies as those in the most advanced nations.…”