Developing Pattern Prediction, Casual Analysis And Simulation Of PM2.5Pollution In Wuhan City

Atmospheric haze, the most severe air pollution in2013, is mainly caused by PM2.5. PM2.5particles have a broad range of source and a complex course of formation. In order to reflect variation tendency of pollution, it is of great urgency to reinforce pollution control and severe pollution prevention, as well as to launch pollution treatment projects.First, we used Markov chain to describe the developing pattern of PM2.5pollution in Wuhan City. We discussed this developing process from time series of PM2.5, and identified step length of Markov chain through calculation and comparison of autocorrelation coefficient. With inspection, we established a superimposing Markov chain model to predict developing pattern of PM2.5pollution in Wuhan City. The result showed that the possibility of PM2.5-pollution-free in Wuhan City is0.6on long terms.Second, after identified developing patter of PM2.5pollution in Wuhan City, we analyzed the cause of formation of PM2.5. We mainly analyzed secondary particle, which is the most important cause of PM2.5pollution, through the relationship between PM2.5and air quality index. The step-wise regression model after differential correction is:y*=-11.144+2.280x*co+0.752x*pm10-0.182x*o3+0.397x*no2Last, we used MATLAB software toolbox Simulink to construct a neural network model for the study, exercise and simulation of PM2.5pollution problem in Wuhan City. The rate of accuracy is88%.Through the research on the PM2.5pollution problem in Wuhan City, this paper provide new thoughts and methods to the forecasting and prevention of urban air pollution in an information society, as well as an effective, simple and convenient simulation software MATLAB.