Air pollution: state of art and prospects using data mining

Now-a-days air pollution is increasing at such a pace, that it has become a threat to environment as well as human health. So it is the need to analyze the air quality and conclude some measures to improve the quality of air. The elder people, new born babies, and the ones suffering from “chronic cardiopulmonary” diseases, “influenza” and “asthma” are the most exposed to mortality and serious morbidity effects from air pollutants. Other people are vulnerable to moderately serious health effects like decreased lung function, transient increases in respiratory symptoms, and other physiologic changes. Chronic exposure related studies suggest relatively broad vulnerability to cumulative effects of long-term repeated exposure to fine particles present in air, resulting in substantive estimates of population average loss of life expectancy in highly polluted environments. At present there are many techniques that can be used to analyze the data but the question arises that which technique is most précised. In this paper four different techniques of data mining i.e., Association Rule Mining, Clustering, Classification and Regression, which have been used in the past to predict the PM concentration, effect on mortality and diseases due to air pollution etc. have been studied.