is nonlinearly affected by pollution source emission, atmospheric physicochemical, and meteorological conditions. With the development of online observation technology, quantifing the effect of driving factors on PM2.5
in actual atmosphere through observation datas becomes a research hotspot. Based on the online observation data of the atmospheric environment comprehensive observation superstation of Nankai University from November 2018 to October 2020, this paper analyzes the pollution characteristics of PM2.5
; and uses the PMF model to identify the source and contribution of PM2.5
, the results show that the contribution of secondary sources during the observation period is the highest (44.7%), followed by coal-fired sources (23.6%), dust sources (9.9%), biomass combustion (7.2%), and the contribution of industrial sources is the lowest (3.6%); This paper also explores the effects of drivers such as emission sources, atmospheric oxidation capacity, and meteorological conditions on PM2.5
through machine learning model. The results show that the effect of emission sources (54.2%) is higher than other drivers, followed by the effect of meteorological conditions (32.6%), and the effect of atmospheric oxidation capacity (13.2%) is lower. At present, primary source emission is still the most important cause of PM2.5
pollution, and it is necessary to focus on the treatment of primary sources in the prevention and control of air pollution.