The important growth of industrial, transport, and agriculture activities, has not led only to the air quality and climate changes issues, but also to the increase of the potential natural disasters. The emission of harmful gases, particularly: the Vertical Column Density (VCD) of CO, SO2 and NOx, is one of the major factors causing the aforementioned environmental problems. Our research aims to contribute finding solution to this hazardous phenomenon, by using remote sensing (RS) techniques to monitor air quality which may help decision makers. However, RS data is not easy to manage, because of their huge amount, high complexity, variety, and velocity, Thus, our manuscript explains the different aspects of the used satellite data. Furthermore, this article has proven that RS data could be regarded as big data. Accordingly, we have adopted the Hadoop big data architecture and explained how to process efficiently RS environmental data.

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The world is witnessing important increases in industrial, transport and agriculture activities. This leads to economic growth, but, on the other hand, causes substantial damage in urban air, due to emissions of harmful gases, mainly CO, SO2, NO2 and the Particular Matter (PM). The World Health Organization (WHO) confirms that daily exposure to pollutants causes approximately three million deaths. It is therefore necessary to assess continuously the air quality. In this context, a Java-based application was developed to acquire data from EUMETSAT geostationary and Polar Orbit satellites, through the Mediterranean Dialogue Earth Observatory (MDEO) terrestrial station. This application filters, subsets, processes and visualizes products covering Morocco zone. Significant correlations were found between emissions and industrial activities related to power thermal plants, factories, transportation and ports.

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