Open AccessAnalysis of Air Pollution Influencing Factors of PM2.5 Secondary Particles by Random Forest
Author(s) -
Hongbo Zheng,
Zhengyu Wang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/804/4/042065
Subject(s) - quarter (canadian coin) , figuring , environmental science , random forest , pollution , meteorology , air pollution , statistics , geography , mathematics , computer science , physics , chemistry , ecology , machine learning , biology , archaeology , organic chemistry , optics
Aiming at figuring out the influencing factors of PM 2.5 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 PM 2.5 , 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 NO 2 is the primary factor of PM 2.5 in the first and fourth quarter, and O 3 is the primary factor in the third quarter. From the perspective of big data, this is a persuasive way to provide reference for PM 2.5 pollution control in different quarters.