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.

Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.

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