Massive growth has created challenges for quality of city life. The tremendous increase in vehicular pollution and industrial air nuisance has led to the concentration of air pollutants in major metropolitan cities. Preserving and monitoring air quality has become one of the most essential activities under sustainable development. Air pollution levels in many cities are at alarming levels and peak points according to World Health Organization (WHO). Limit for gaseous pollutants and particulate matter have almost been breached in hundreds of cities. In this paper, we hereby propose a heuristic model by clubbing meteorological factors (MFs) and air pollutant concentration (APCs) to infer air quality in a region. With this consideration, we are applying machine learning trends to predict whether the region is habitable or not and thus identify the chances of occurrence of air borne diseases. We target our air pollution prediction method to be prototyped and tested at the city of Delhi, India as it is at the forefront, battling against air pollution and such extensive experiments of real time air pollution forecasting on real data would demonstrates the effectiveness of the proposed method.
Volume 11 | 03-Special Issue
Pages: 1584-1589