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Air Quality has been a research field for many investigators from varied disciplines in respect to global heating, climate change, health effect theories and others. Predicting air quality status is becoming more complex with time due to different air gases and other components. This paper aims at presenting machine learning models and techniques to predict air quality levels in cities providing accuracy measures to support data driven decision making in various sectors aligned with sustainable development, economic growth and social values. The research supports air quality policies formulation with a forward looking to eliminate global related consequences, save the world from the dangerous earth pollution and to close the gap in air quality index standardization with emphasis on cities sustainable development.
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