Spatio-temporal association mining of intercity PM 2.5 pollution: Hubei Province in China as an example.

The complex interaction between emissions, meteorology, and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can reveal potential laws, providing new possibilities for understanding the evolution and causes of air pollution. Based on the Granger method and block modeling analysis, this paper explored the intercity spillover effects of hourly PM 2.5 in Hubei Province, China, to determine the specific role (i.e., overflow, limited overflow, bilateral, inflow, and limited inflow) of each city on regional pollution formation. Furthermore, a dynamic Apriori algorithm considering time-lag effects was used to mine the spatio-temporal associations of extreme PM 2.5 pollution events among different cities. Results suggest that the northern and central cities with high-level PM 2.5 concentration in Hubei have a significant spillover effect, whereas the eastern and southern cities generally play a role as the sink of pollutants. Based on the association rules of extreme PM 2.5 pollution, four main pollutant transport channels were excavated and well matched with the trajectories extracted by the atmospheric model. This paper provides new insights for exploring the interaction of intercity particulate pollution, which is a supplement and cross-validation of the model results.