International Journal of Environmental Monitoring and Analysis
Volume 6, Issue 3, June 2018, Pages: 110-115
Received: Oct. 17, 2018;
Published: Oct. 18, 2018
Views 376 Downloads 30
Xinghua Fan, Faculty of Science, Jiangsu University, Zhenjiang, China
Qi Zhang, Faculty of Science, Jiangsu University, Zhenjiang, China
Li Wang, Helie Middle School, Wuxi, China
Jiuli Yin, Faculty of Science, Jiangsu University, Zhenjiang, China
As air quality is closely related to human life and physical and mental health, the data of air quality has become a concern of the entire society. This study analyzes the characteristics of air quality data from a visibility graph networks point of view. The authors select eight monitoring stations in Beijing as samples. The time series of air quality data is mapped to a complex network based on the visibility graph algorithm. First, the authors study the topological structure of the networks for all the monitoring stations. Comparison results show that all constructed networks have similar structures in terms of the average path length, the network diameter, average clustering coefficient, density and the average degrees. Then the authors study the evolution of the visibility graph network for Huairou Town station for a long period of time. On the one hand, the value of the node degree indicates that the most important dates for air quality are the end of April, the beginning of May and the first three weeks of winter. On the other hand, the small-world properties of the networks reveals that the air quality data for the year 2014 is more stable without extreme fluctuations. This finding is consistent with the conclusion that air quality is largely affected by the weather while human activities play a more and more important role.
Visibility Graph Network Analysis of Air Quality Data, International Journal of Environmental Monitoring and Analysis.
Vol. 6, No. 3,
2018, pp. 110-115.
S. Zheng, M. E. Kahn, Understanding China’s urban pollution dynamics, Journal of Economic Literature 51 (3) (2013) 731–72.
M. Greenstone, R. Hanna, Environmental regulations, air and water pollution, and infant mortality in India, American Economic Review 104 (10) (2014) 3038–72.
R. A. Rohde, R. A. Muller, Air pollution in China: Mapping of concentrations and sources, PLOS ONE 10 (8) (2015) 1–14.
Z. Yang, M. Tang, Does the increase of public transit fares deteriorate air quality in Beijing?, Transportation Research Part D: Transport and Environment 63 (2018) 49 – 57.
X. Liu, J. Li, Y. Qu, T. Han, L. Hou, J. Gu, C. Chen, Y. Yang, X. Liu, T. Yang, et al., Formation and evolution mechanism of regional haze: a case study in the megacity Beijing, China, Atmospheric Chemistry & Physics 13 (9).
S. Wang, M. Zhao, J. Xing, Y. Wu, Y. Zhou, Y. Lei, K. He, L. Fu, J. Hao, Quantifying the air pollutants emission reduction during the 2008 Olympic Games in Beijing, Environmental science & technology 44 (7) (2010) 2490–2496.
Z. Chen, B. Xu, J. Cai, B. Gao, Understanding temporal patterns and characteristics of air quality in Beijing: A local and regional perspective, Atmospheric Environment 127 (2016) 303–315.
M. Elangasinghe, N. Singhal, K. Dirks, J. Salmond, S. Samarasinghe, Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering, Atmospheric Environment 94 (0) (2014) 106 – 116.
Y. Li, T. Lin, F. Wang, T. Ji, Z. Guo, Seasonal variation of polybrominated diphenyl ethers in PM2.5 aerosols over the East China Sea, Chemosphere 119 (2015) 675 – 681.
J. Chen, J. Lu, J. C. Avise, J. A. DaMassa, M. J. Kleeman, A. P. Kaduwela, Seasonal modeling of PM2.5 in California’s San Joaquin Valley, Atmospheric Environment 92 (2014) 182 – 190.
R. Zhang, J. Jing, J. Tao, S.-C. Hsu, G. Wang, J. Cao, C. S. L. Lee, L. Zhu, Z. Chen, Y. Zhao, et al., Chemical characterization and source apportionment of PM 2.5 in Beijing: seasonal perspective, Atmospheric Chemistry and Physics 13 (14) (2013) 7053–7074.
X. Li, J. Wu, M. Elser, F. Tian, J. Cao, I. El-Haddad, R. Huang, X. Tie, A. S. Prévôt, G. Li, Contributions of residential coal combustion to the air quality in Beijing–Tianjin–Hebei (BTH), China: a case study, Atmospheric Chemistry and Physics 18 (14) (2018) 10675–10691.
Q. Liu, J. Baumgartner, Y. Zhang, Y. Liu, Y. Sun, M. Zhang, Oxidative potential and inflammatory impacts of source apportioned ambient air pollution in Beijing, Environmental science & technology 48 (21) (2014) 12920–12929.
F. Huang, X. Li, C. Wang, Q. Xu, W. Wang, Y. Luo, L. Tao, Q. Gao, J. Guo, S. Chen, et al., PM2.5 spatiotemporal variations and the relationship with meteorological factors during 2013-2014 in Beijing, China, PloS one 10 (11) (2015) e0141642.
D. Wu, Y. Xu, S. Zhang, Will joint regional air pollution control be more cost-effective? An empirical study of China’s Beijing–Tianjin–Hebei region, Journal of Environmental Management 149 (2015) 27 – 36.
X. Fan, L. Wang, H. Xu, S. Li, L. Tian, Characterizing air quality data from complex network perspective, Environmental Science and Pollution Research 23 (4) (2016) 3621–3631.
E. Zhuang, M. Small, G. Feng, Time series analysis of the developed financial markets’ integration using visibility graphs, Physical A: Statistical Mechanics and its Applications 410 (2014) 483 – 495.
L. Lacasa, B. Luque, F. Ballesteros, J. Luque, J. C. Nuo, From time series to complex networks: The visibility graph, Proceedings of the National Academy of Sciences 105 (13) (2008) 4972–4975.
M. Ahmadlou, H. Adeli, A. Adeli, New diagnostic EEG markers of the Alzheimer’s disease using visibility graph, Journal of neural transmission 117 (9) (2010) 1099–1109.
D. J. Watts, S. H. Strogatz, Collective dynamics of ‘small-world’ networks, nature 393 (6684) (1998) 440.
M. Sun, Y. Wang, C. Gao, Visibility graph network analysis of natural gas price: The case of north American market, Physical A: Statistical Mechanics and its Applications 462 (2016) 1–11.
A. Vázquez, R. Pastor-Satorras, A. Vespignani, Large-scale topological and dynamical properties of the internet, Physical Review E 65 (6) (2002) 066130.
J. Liu, W. Li, J. Wu, Y. Liu, Visualizing the intercity correlation of PM2. 5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data, PloS one 13 (2) (2018) e0192614.