Data Analysis For Air Pollution Incidents

Air is necessity for human being to survive and polluted air by various noxious substances directly or indirectly influences the health of peoples. Polluted air is the result of the development of modern industry, urban population concentration and the quick increase on coal and petroleum. Air pollution is a big global issue, and it is threaten to human being's survival. In recent years, some researches have indicated that air pollution is becoming more and more serious and some diseases have close relationship with air pollution and its death ratio is increasing.Classical linear regression is the most widely-used method in the various statistic analyses, and it is one kind of mathematical statistics method which deals with the interdependent relationship of the variables. That interdependence of the variables exists in a great quantity in practical problem, and regression analysis is an effective mathematical method studying this interdependence.Generalized linear models are the generalization of the normal linear model, and it can be applied to the continuous data and discrete data it is more effective for the latter, such as attribute data, counting data. It has widely application in the field of biology, medical science and economy.This paper takes the generalized linear models as the theoretical basis and studies the relation between the times of air pollution event occurrence and various pollution index of air pollution produced by industry in 31 areas in 2005. Since the channel of producing pollution in industry is various, first we should get rid of the variable which exists the obvious collinearity, and then make the likelihood ratio test on the regression equation in order to pick out the influential variables of the times of air pollution event, and finally with the making use of the Poisson distribution model to form a regression relationship between them.