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Top1. Introduction
With the evolution of the economy and society everywhere on the planet, the world is experiencing increased concentrations of air pollutants. Air quality has a direct bearing on how people live and breathe. Currently, the environmental downside is the most severe problem that features a major influence on human health and ecosystem. Air pollution is increasing day by day, adversely affecting the world economy, degrading the quality of life and resulting in a major productivity loss. At present, this is one of the most critical problem. It has a significant impact on human health and ecosystem. Recently there have been scenarios where the air pollution has surged to significant levels and had a severe detrimental effect on human health. The Amazon forest fires, severe air quality degradation in Delhi, India and the fires in the Australian forests are some of the biggest air pollution hazards in recent times. Various efforts are placed by government towards the management of pollution, and much success has been obtained within the same (Gulia, Shiva Nagendra, Khare, & Khanna, 2015). Human health problem is one of the necessary consequences of air pollution, particularly in urban areas. The global warming from phylogeny greenhouse gas emissions may be a long run consequence of air. Correct air quality prediction can cut back the effect of a pollution peak on the encircling population and ecosystem, hence rising air quality prediction is a very important goal for society(Bellinger, Mohomed Jabbar, Zaïane, & Osornio-Vargas, 2017). Most recent air quality prediction uses effortless methods viz box models, Gaussian models and linear statistical models. All of the above models are quite simple to implement and enable for the quick calculation of predictions (Moltchanov et al., 2015). Nevertheless, they normally don’t describe interactivity & non-linear relationships that command the transfer and nature of adulterants in air. With these provocations, machine learning approaches like outlier detection have become favoured in air quality prediction and other environmental related areas (D. Zhu, Cai, Yang, & Zhou, 2018).