In an era where air pollution poses a significant threat to both the environment and public health, we present a network-based approach to unravel the dynamics of extreme pollution events. Leveraging data from 741 monitoring stations in the contiguous United States, we have created dynamic networks using time-lagged correlations of hourly particulate matter (PM_2.5) data. The established spatial correlation networks reveal significant PM_2.5 anomalies during the 2020 and 2021 wildfire seasons, demonstrating the approach’s sensitivity to detecting regional pollution phenomena. The methodology also provides insights into smoke transport and network response, highlighting the persistence of air quality issues beyond visible smoke periods. Additionally, we explored meteorological variables’ impacts on network connectivity. This study enhances understanding of spatiotemporal pollution patterns, positioning spatial correlation networks as valuable tools for environmental monitoring and public health surveillance.

在空气污染对环境和公众健康构成重大威胁的时代，我们提出了一种基于网络的方法来揭示极端污染事件的动态。利用来自美国本土 741 个监测站的数据，我们利用每小时颗粒物 (PM _2.5 ) 数据的时滞相关性创建了动态网络。建立的空间相关网络揭示了 2020 年和 2021 年野火季节的显着 PM _2.5 异常，证明了该方法对检测区域污染现象的敏感性。该方法还提供了对烟雾传输和网络响应的见解，强调了在可见烟雾时段之外空气质量问题的持续存在。此外，我们还探讨了气象变量对网络连接的影响。这项研究增强了对时空污染模式的理解，将空间相关网络定位为环境监测和公共卫生监测的宝贵工具。