Date Published: February 13, 2018
Publisher: Public Library of Science
Author(s): Jianzheng Liu, Weifeng Li, Jiansheng Wu, Yonghong Liu, Xiaolei Ma.
The Beijing-Tianjin-Hebei area faces a severe fine particulate matter (PM2.5) problem. To date, considerable progress has been made toward understanding the PM2.5 problem, including spatial-temporal characterization, driving factors, and health effects. However, little research has been done on the dynamic interactions and relationships between PM2.5 concentrations in different cities in this area. To address the research gap, this study discovered a phenomenon of time-lagged intercity correlations of PM2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interaction in PM2.5 concentrations between cities. The visualizations produced using the framework show that there are significant time-lagged correlations between the PM2.5 time series in different cities in this area. The visualizations also show that the correlations are more significant in colder months and between cities that are closer, and that there are seasonal changes in the temporal order of the correlated PM2.5 time series. Further analysis suggests that the time-lagged intercity correlations of PM2.5 time series are most likely due to synoptic meteorological variations. We argue that the visualizations demonstrate the interactions of air pollution between cities in the Beijing-Tianjin-Hebei area and the significant effect of synoptic meteorological conditions on PM2.5 pollution. The visualization framework could help determine the pathway of regional transportation of air pollution and may also be useful in delineating the area of interaction of PM2.5 pollution for impact analysis.
The Beijing-Tianjin-Hebei area, which is the national capital region of China, is considered one of the most urbanized and developed areas in the country. However, despite its remarkable economic prosperity, it now has the reputation of being a “nuclear winter” region, as reported by the media, due to severe fine particulate matter (PM2.5) pollution . Obviously, PM2.5 pollution not only undermines the reputation of the Beijing-Tianjin-Hebei area in terms of its economic prosperity, but more importantly, it also causes considerable public concern regarding health and poses critical challenges related to the sustainable development of cities within the region.
What this study attempts to visualize is a phenomenon that we defined as time-lagged intercity correlation of the PM2.5 time series. We have found that strong associations exist between PM2.5 time series in nearby cities when examining the patterns of the PM2.5 time series. The strength of the associations varies considerably between different cities and in different months. We also found that there are obvious time lags between PM2.5 time series in nearby cities. To illustrate this time-lagged intercity correlation relationship, an example is given using the Beijing and Qinhuangdao PM2.5 time series in January 2014. As seen in Fig 1A, the PM2.5 time series of Beijing and Qinhuangdao had very similar trends, and there was an obvious time delay between the two time series. It was easily found that their best alignment can be obtained by shifting the Qinhuangdao PM2.5 time series to the left by approximately 4 h.
This study discovered a phenomenon of time-lagged intercity correlations of PM2.5 time series and proposed a visualization framework based on this phenomenon to visualize the interactions in PM2.5 concentrations between cities. Using this framework, this study visualized the intercity correlation of PM2.5 time series between cities in the Beijing-Tianjin-Hebei region. The visualization results show that significant correlations exist between PM2.5 time series of different cities in this region, and correlations are more significant in colder months and between cities that are closer. The visualizations also show that there are seasonal changes in the temporal order of the correlated PM2.5 time series. Further analysis suggests that the intercity correlations of PM2.5 time series are probably due to synoptic meteorological variations, which corroborate with previous studies. In addition to the visualization framework, which can be used in several potential applications, the major contribution of this study is that the visualizations revealed the significant underlying dynamic relationships of PM2.5 concentrations between cities and provided visual evidence for interactions of air pollution between nearby cities.