Multiple regression analysis on PM_(2.5) impact factors based on geographic conditions monitoring data

As air pollution in China has been increasingly exacerbated,the relevant factors analysis and modeling on the PM2.5sources and distribution is becoming a significant research direction.In this paper,Shijiazhuang City in Hebei Province was chosen as the research area,based on the analysis of relevant factors,regression modeling was processed on Shijiazhuang PM2.5 concentration and certain geographical condition data,such as ground dust distribution,distribution of industrial enterprises in key industry,land cover and road data,using multiple linear regression analysis method and multivariate nonlinear regression analysis method.After comparative analysis,the optimal modeling method and the relationship between PM2.5and its significant impact factors were figured out with the measure of the coefficient of determination R2.The results showed that the multiple nonlinear regression analysis method could get a better fitting result,and the ground dust,unused land,and area of artificial were important factors positively correlated to PM2.5reflected from the model,which would provide a reference for the understanding of the origin and distribution of PM2.5in the air.