Rise In Air Pollution Essay

Rapid industrialisation over the last five decades has significantly contributed to the rise in air pollutants. Several researchers and policy makers have expressed concerns over declining quality of atmosphere, particularly at urban locations where population density is very high and emissions from vehicles and industry is constantly increasing. In particular, cities like Mumbai and Navi Mumbai that attract an influx of people and industries are vulnerable to rise in pollution arising out of increased aspirations and energy consumption. Rapid growth also causes unplanned urban and industrial development of city contributing to air pollution. As a fallout of economic growth in urban areas, high concentration of industries and thermal power

al.(2005) presented a web-based tool for air pollution prediction and control in Istanbul. The model relates the local meteorological data and air pollution indicators concentrations like SO2, PM10 and CO by using ANN. Yike Guo et al. (2006) monitored the air pollution pattern in London using the distributed K-means algorithm. Huang et.al (2013) studied the spatial distribution and source contribution of SO2 and NO2 pollution in Ulaanbaatar, Mongolia using multiple regression models. Chen et.al.(2007) present a Decision-Making Framework (DMF) for reducing ozone pollution in the metropolitan using mining and meta modelling tools. Vincent Ng et al.(2001) developed an agent framework to study the effect of meteorological data on the air pollutants. Athanasiadis et. al(2013) used the classification technique of data mining for supporting the decision making process of environmental management to monitor the ambient air quality and trigger alerts in case of emergency. Spatial Data mining provides an excellent tool for solving air pollution management problems. It uses sophisticated statistical analysis and modelling techniques to uncover predictive and descriptive patterns (Harms et al.,2002).McCabe et al.(2007) used the toolbox of statistical methods based on Generalised Additive Models (GAMs) to analyze local air quality problems in