Urban Air Quality Prediction Using Regression Analysis

In the last several years, air pollution has risen steadily in urban environments. Cities like Gurugram, Faisalabad, Delhi, Beijing are few of the world's most polluted cities and have seen a dangerous rise in air pollution levels. Forecasting is important because of the human, ecologic and economic toll of pollution, and is a useful investment at individual and community levels. Accurate forecasting will help us plan in advance, decreasing the effects on health and the costs associated. Local weather conditions strongly affect air pollution levels. Generating deterministic models to study air pollutant behavior in environmental science research is often not very accurate because they are complex and need simulation at the molecular interaction level. Here comes machine learning to the rescue with high computing facilities to predict air pollution. This paper investigates how effective some available prediction models are in predicting the Air Quality Index(AQI) values given some input data, based on the pollution and meteorological information in New Delhi, India. We perform regression analysis on the dataset, and our results show which meteorological factors affect the AQI values more and how useful the predictive models are to help in air quality forecasting.