文献类型：期刊文献

英文题名：Temporal and spatial correlation patterns of air pollutants in Chinese cities

作者：Dai, Yue-Hua[1,2]; Zhou, Wei-Xing[1,3,4]

机构：[1] School of Business, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Finance and Management Science, Carson College of Business, Washington State University, Pullman, WA99163, United States; [3] Department of Mathematics, East China University of Science and Technology, Shanghai, 200237, China; [4] Research Center for Econophysics, East China University of Science and Technology, Shanghai, 200237, China

年份：2019

外文期刊名：arXiv

收录：EI(收录号：20200217430)

语种：英文

外文关键词：Complex networks - Health risks

摘要：As a huge threat to the public health, China’s air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants’ hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O3, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O3 is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM10 and O3, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants’ evolutionary process, but also shed lights on the application of complex network methods into geographic issues. Copyright ? 2019, The Authors. All rights reserved.