• Sayali Nemade
• Chandrashekhar Mankar

Air allows our planet Earth to inhale – it is the mixture of gases that adds to the air giving life to the planet and animals that make Earth such an ebullient place. However, the gases that you are breathing in day by day could be endlessly killing you. According to the World Health Organization, around 2 million people give out from the impact of contaminated air every year. With the evolution of the economy and society all over the world, almost all metropolitan cities are experiencing increased concentrations of ground-level air pollutants. Impelled by the increasing awareness of the health aspects due to air pollution exposure especially by most sensitive groupings of population such as children and the senior citizens, short–term air pollution forecasts are provided in greater amounts by local authorities. The Air Quality Index (AQI) is a number given by governmental bureaus to describe the quality of the air at a given location. AQI is used for local and regional air quality management in metropolitan cities all across the world. Based on data from the last 6 years (2012-2018), the analysis shows that it applies to both the daily limit of 50 micrograms per cubic meter and the permissible number of daily concentration fat chances to 35 per year. Also, the average annual levels of PM10 exceeded the prescribed standard of no more than 40 micrograms per cubic meter. For the assurance of air contamination effluence, compact analyzing frameworks are working in numerous urban areas. Making use of brainpower tactics, in particular master frameworks and learning based procedures can be thought of an encouraging methodology. An information based approach offers another choice to the numerical models. In this paper, a linear regression model is proposed in which we take data from last 50 days and after analyzing it we are predicting levels of different air pollutants 5 days in advance.

Air Pollution, Air Quality Index (AQI), Linear Regression, Forecasting.

"Forecasting Air Quality for Different Air Pollutant Levels in Urban Areas Using Linear Regression Modeling", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.598-608, April-2019, Available :http://www.jetir.org/papers/JETIR1904F94.pdf

"Forecasting Air Quality for Different Air Pollutant Levels in Urban Areas Using Linear Regression Modeling", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp598-608, April-2019, Available at : http://www.jetir.org/papers/JETIR1904F94.pdf