| Summary: | Air pollution is one of the most serious environmental problems in the world. At present, air quality models are focused on pollutant prediction and source discovery, lacking the analysis of transmission paths and independent monitoring station characteristics. In this paper, complex network based on statistics is applied in air quality field and a new model is proposed based on it. Path existential network and pollutant transmission path set are generated integrating the data of air quality and meteorological monitoring stations. Mapping relation is established among them to generate real-time air quality network, then a complex network in a whole cycle is obtained through statistics. Experiments based on PM2.5 pollution data in Jing-Jin-Ji region demonstrate the rationality of the proposed model. Three characteristics of complex network: scale-free, small-world and community aggregation are verified. Characteristic detection and key station mining provide guidance for air protection in reality. In the network, we can reduce pollution effect by blocking a few important transmission paths between communities. The results provide reference for site selection of new monitoring station, dynamic evolution and pollution degradation.
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