Air pollution has become a major issue and caused widespread environmental and health problems. Aerosols or particulate matters are an important component of the atmosphere and can transport under complex meteorological conditions. Based on the data of PM 2.5 observations, we develop a network approach to study and quantify their spreading and diffusion patterns. We calculate cross-correlation functions of the time lag between sites within different seasons. The probability distribution of correlation changes with season. It is found that the probability distributions in four seasons can be scaled into one scaling function with averages and standard deviations of correlation. This seasonal scaling behavior indicates that there is the same mechanism behind correlations of PM 2.5 concentration in different seasons. Further, the weighted degrees reveal the strongest correlations of PM 2.5 concentration in winter and in the North China Plain for the positive correlation pattern that is mainly caused by the transport of PM 2.5. These directional degrees show net influences of PM 2.5 along Gobi and inner Mongolia, the North China Plain, Central China, and Yangtze River Delta. The negative correlation pattern could be related to the large-scale atmospheric waves.
ASJC Scopus subject areas
- Physics and Astronomy (all)