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

机译:利用地面空气质量监测数据可视化京津冀地区PM2.5时间序列的城际相关性

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摘要

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.
机译:京津冀地区面临严重的细颗粒物(PM2.5)问题。迄今为止,在理解PM2.5问题方面已经取得了相当大的进步,包括时空特征,驱动因素和健康影响。但是,关于该地区不同城市中PM2.5浓度之间的动态相互作用和相互关系的研究很少。为了解决研究空白,本研究发现了PM2.5时间序列之间存在时滞的城市间关联现象,并提出了基于该现象的可视化框架,以可视化城市之间PM2.5浓度之间的相互作用。使用该框架产生的可视化结果表明,该地区不同城市的PM2.5时间序列之间存在明显的时间滞后关系。可视化还显示,在较冷的月份以及更接近的城市之间,相关性更加显着,并且相关PM2.5时间序列的时间顺序存在季节性变化。进一步的分析表明,PM2.5时间序列的时滞城市间相关性最有可能是由于天气的气象变化。我们认为,可视化显示了京津冀地区城市之间的空气污染之间的相互作用以及天气状况对PM2.5污染的显着影响。可视化框架可以帮助确定空气污染的区域性运输途径,也可能有助于勾勒出PM2.5污染相互作用的区域以进​​行影响分析。

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