Abstract
Aiming at figuring out the influencing factors of PM2.5 secondary particles, there is a new way of using random forest model to handle it. Compared with using correlation to measure the relationship between various single factors and PM2.5, in this paper, the random forest can comprehensively consider the overall factors including meteorological and other air pollution factors. In a case of Dalian, the results show that NO2 is the primary factor of PM2.5 in the first and fourth quarter, and O3 is the primary factor in the third quarter. From the perspective of big data, this is a persuasive way to provide reference for PM2.5 pollution control in different quarters.
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