Abstract:

We have analyzed the main components of PM2.5 with time series analysis based on the data which were collected in Beijing city from the http://www.cnemc.cn/. We have processed the stationarity, pure randomness of the data and the orders, parameters and significance testing of the model. Meanwhile we also predicted the PM2.5 concentration based on the rules which were got from the data. The results of prediction have showed that the error was in the reasonable range. We also analyzed the PM2.5 relative analysis and explored the dynamic relation of PM2.5 with other pollutants such as SO_{2}、NO_{2}、CO、O_{3}、PM10 on the Wanshougong position through vector auto-regressive model (VAR). It has been illustrated that the concentration of PM2.5 could be influenced by SO_{2}、NO_{2}、CO、O_{3} and PM10 with the other days. We found that PM2.5 was influenced mostly by PM10 with a long time. The influence of O_{3} and SO_{2} to PM2.5 concentration was remarkable at the second and third periods.

Key words: PM2.5, time series analysis, vector auto-regressive model