Characterizing air quality data from complex network perspective.
Air quality depends mainly on changes in emission of pollutants and their precursors. Understanding its characteristics is the key to predicting and controlling air quality. In this study, complex networks were built to analyze topological characteristics of air quality data by correlation coefficient method. Firstly, PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) indexes of eight monitoring sites in Beijing were selected as samples from January 2013 to December 2014. Secondly, the C-C method was applied to determine the structure of phase space. Points in the reconstructed phase space were considered to be nodes of the network mapped. Then, edges were determined by nodes having the correlation greater than a critical threshold. Three properties of the constructed networks, degree distribution, clustering coefficient, and modularity, were used to determine the optimal value of the critical threshold. Finally, by analyzing and comparing topological properties, we pointed out that similarities and difference in the constructed complex networks revealed influence factors and their different roles on real air quality system.
Center for Energy Development and Environmental Protection, Jiangsu University, Zhenjiang, Jiangsu, 212013, China. fan131@ujs.edu.cn.
Xinghua
X
Fan
Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.
Li
L
Wang
Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.
Huihui
H
Xu
Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.
Shasha
S
Li
Nonlinear Scientific Research Center, Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.|School of Mathematical Sciences, Nanjing Normal University, Nanjing, 210046, China.
Lixin
L
Tian
D000388:Air / Q000032:analysis / Q000592:standards; D000393:Air Pollutants / Q000032:analysis*; D000068476:Beijing; D016000:Cluster Analysis; D004784:Environmental Monitoring / Q000379:methods*; D016571:Neural Networks, Computer; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*
D000393:Air Pollutants; D052638:Particulate Matter
Adjacent matrix; Air quality; Clustering coefficient; Complex network; Correlation coefficient; Degree distribution; Phase space reconstruction; Time series