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The Scientific World Journal
Volume 2013, Article ID 381410, 28 pages
Research Article

Seasonality and Dynamic Spatial Contagion of Air Pollution in 42 Chinese Cities

1Faculty of Management and Economics, Lianhua Campus, Kunming University of Science and Technology, Kunming 650093, China
2Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand
3Faculty of Transportation Engineering, Chenggong Campus, Kunming University of Science and Technology, Kunming 650504, China

Received 30 November 2012; Accepted 24 December 2012

Academic Editors: S. Bhattacharya and N. Fontanals

Copyright © 2013 Zhanqiong He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To monitor and improve the urban air quality, the Chinese government has begun to make many efforts, and the interregional cooperation to cut and improve air quality has been required. In this paper, we focus on the seasonality of the first and second moments of the daily air pollution indexes (APIs) of 42 Chinese sample cities over 10 years, from June 5, 2000 to March 4, 2010, and investigate the dynamic correlation of air pollution indexes (APIs) between 42 Chinese cities and their corresponding regional and national levels; comparison with the model without seasonal consideration is made. By adopting a DCC-GARCH model that accounts for the seasonality, we found that (i) the transformed DCC-GARCH model including seasonality dummies improves the estimation result in this study; (ii) the seasonality feature of the second moment follows that of the first moment, with the condition mean and variance of the second and autumn significantly lower than spring, whereas that of winter is higher than spring; (iii) the correlation between local APIs and their corresponding regional and national levels is dynamic; (iv) comparing with the DCC-GARCH model estimation, the transformed model does not change the feature of the dynamic correlations very much.