Dai Yue-Hua, Zhou Wei-Xing
School of Business, East China University of Science and Technology, Shanghai 200237, China.
Department of Finance and Management Science, Carson College of Business, Washington State University, Pullman, WA99163, United States of America.
PLoS One. 2017 Aug 23;12(8):e0182724. doi: 10.1371/journal.pone.0182724. eCollection 2017.
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.
作为对公众健康的巨大威胁，中国的空气污染已引起广泛关注，且与经济发展同步持续加剧。尽管实时空气质量报告可用于更新我们对空气质量的认识，但污染物如何随时间演变以及污染物在空间上如何相关等问题仍是未解之谜。鉴于此，我们采用投影镶嵌图网络（PMFG）方法分析中国350个城市六种污染物的每小时数据，试图找出这些污染物在时间和空间上的相关性。在时间维度上，结果表明，除臭氧（O3）外，污染物具有日内模式强烈的共同特征，其日变化由两个收缩期和两个扩张期组成。此外，六种污染物的所有时间序列都具有很强的长期相关性，这种时间记忆效应有助于解释为什么雾霾天总是接踵而至。在空间维度上，相关结构表明臭氧的空间连接性最高。投影镶嵌图通过过滤相关矩阵中的层次结构并将团块细化为微小的空间集群，揭示了这种空间相关性与省级行政区划之间的关系。最后，我们检验了相关结构的稳定性，得出结论：除了细颗粒物（PM10）和臭氧外，其他污染物具有总体稳定的相关性，并且所有污染物在空间上都有略微更加分散的趋势。这些结果不仅增进了我们对空气污染物演变过程的理解，也为复杂网络方法在地理问题中的应用提供了启示。
