Abstract：
Wildfire smoke greatly impacts regional atmospheric systems, causing changes in the behavior of pollution. However, the impacts of wildfire smoke on pollution behavior are not easily quantifiable due to the complex nature of atmospheric systems. Air pollution correlation networks have been used to quantify air pollution behavior during ambient conditions. However, it is unknown how extreme pollution events impact these networks. Therefore, we propose a multidimensional air pollution correlation network framework to quantify the impacts of wildfires on air pollution behavior. The impacts are quantified by comparing two time periods, one during the 2023 Canadian wildfires and one during normal conditions with two complex network types for each period. In this study, the value network represents PM2.5 concentrations and the rate network represents the rate of change of PM2.5 concentrations. Wildfires\' impacts on air pollution behavior are captured by structural changes in the networks. The wildfires caused a discontinuous phase transition during percolation in both network types which represents non-random organization of the most significant spatiotemporal correlations. Additionally, wildfires caused changes to the connectivity of stations leading to more interconnected networks with different influential stations. During the wildfire period, highly polluted areas are more likely to form connections in the network, quantified by an 86 % and 19 % increase in the connectivity of the value and rate networks respectively compared to the normal period. In this study, we create novel understandings of the impacts of wildfires on air pollution correlation networks, show how our method can create important insights into air pollution patterns, and discuss potential applications of our methodologies. This study aims to enhance capabilities for wildfire smoke exposure mitigation and response strategies.
摘要：
野火烟雾极大地影响了区域大气系统，导致污染行为的变化。然而,由于大气系统的复杂性，野火烟雾对污染行为的影响不容易量化。空气污染相关网络已用于量化环境条件下的空气污染行为。然而,目前尚不清楚极端污染事件如何影响这些网络。因此,我们提出了一个多维空气污染相关网络框架来量化野火对空气污染行为的影响。通过比较两个时间段来量化影响，一个在2023年加拿大野火期间，一个在正常情况下，每个时期都有两种复杂的网络类型。在这项研究中,值网络表示PM2.5浓度，速率网络表示PM2.5浓度的变化率。野火对空气污染行为的影响是通过网络的结构变化来捕获的。野火在两种网络类型的渗流过程中都引起了不连续的相变，这代表了最重要的时空相关性的非随机组织。此外,野火导致站点的连通性发生变化，从而导致具有不同影响力的站点的更多互连网络。在野火时期,污染严重的地区更有可能在网络中形成连接，与正常时期相比，价值和比率网络的连通性分别增加了86％和19％。在这项研究中,我们对野火对空气污染相关网络的影响产生了新的理解，展示我们的方法如何对空气污染模式产生重要的见解，并讨论我们方法的潜在应用。这项研究旨在提高野火烟雾暴露缓解和应对策略的能力。
