Correlation and scaling behaviors of fine particulate matter (PM_2.5) concentration in China

¹ Data Science Research Center, Faculty of Science, Kunming University of Science and Technology Kunming 650500, Yunnan, China
² CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences P. O. Box 2735, Beijing 100190, China
³ Department of Physics, University of Helsinki - P.O. Box 48, 00014 Helsinki, Finland
⁴ Department of Physics, Bar-Ilan University - Ramat-Gan 52900, Israel
⁵ School of Physical Sciences, University of Chinese Academy of Sciences - Beijing 100049, China

^(a) chenxs@itp.ac.cn

Received: 11 March 2018
Accepted: 10 June 2018

Abstract

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.