Air Quality Analysis & Prediction Using Machine Learning: Pune Smart City Case Study

The rise in air pollution emanating from fossil fuel consumption, tyre wear, quarrying and brick units and industries has its severe harmful effects on environment. Thus, it has made air pollution forecasting a crucial research area today. There is a need to analyze the trend and reasons for temporal variations in AQI levels of Pune city and also to find the locations worst affected by each of the 6 dominant pollutants. In this work, air pollutants concentration data of a year is extracted from Pune Smart City office, the Special Purpose Vehicle (SPV) for implementing Smart City Mission in Pune City. This work deals with pre-processing 1 year data concerning concentration levels of dominant AQI pollutants, then analysis and visualizations are done using data analytics tools of Tableau and Machine Learning decision tree algorithm. Correlation matrix features and Tableau visualizations gives deeper insights about the data. Supervised machine Learning algorithm - Random Forest and Time Series forecast model of tableau are built to predict the air quality and best fit the data with maximum accuracy. Easily understandable Tableau dashboard trends define the temporal variations clearly. Data analysis provides the method to predict the future air pollution levels so that preventive measures can be implemented by people as well as administration hence making the city literally smart.