Visualizing the intercity correlation of PM2.5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data

• Published in: PloS one Vol. 13; no. 2; p. e0192614
• Main Authors: Liu, Jianzheng, Li, Weifeng, Wu, Jiansheng, Liu, Yonghong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.02.2018; Public Library of Science (PLoS)
• Subjects: Air monitoring; Air pollution; Air quality; Air quality assessments; Airborne particulates; Annual variations; Architectural engineering; Behavioral sciences; Cities; Computer and Information Sciences; Correlation; Earth Sciences; Ecology and Environmental Sciences; Engineering and Technology; Environmental monitoring; Gene expression; General circulation models; Impact analysis; Laboratories; Meteorological conditions; Particulate matter; Physical Sciences; Pollution; Pollution effects; Seasonal variations; Temporal variations; Time series; Urban planning; Ventilation; Visualization; Hong Kong China; Beijing China; United States > US; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0192614

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.