AI 中文总结
针对传统统计模型对空气污染数据复杂特征捕捉不足及现有文献的缺陷，应用超统计框架分析英国五年空气污染物浓度数据集，拟合效果佳，发现拟合参数依地点等而异，还研究了自相关函数及O3的异常分布。
AI 中文摘要
传统统计模型难以完全捕捉现实世界空气污染数据复杂的时空动态、间歇性波动和重尾分布。现有文献常聚焦极端事件，忽视低污染状态的持续性和时间记忆效应。为填补这些空白，我们应用非平衡统计物理的超统计框架，分析英国2020 - 2025年每小时空气污染物浓度的五年综合数据集。理论模型能很好拟合实测分布。拟合参数因测量地点而异，在三维参数空间形成特征模式，且取决于污染物类型和环境条件。我们还研究了自相关函数，发现白天和夜间自相关函数衰减存在差异。研究主要聚焦NO、NO2、PM2.5、PM10的动态，也报告了O3的一些异常分布。
英文摘要
Conventional statistical models often struggle to fully capture the complex spatio-temporal dynamics, intermittent fluctuations, and heavy-tailed distributions characteristic of real-world air pollution data. Furthermore, existing literature frequently focuses on extreme events, overlooking the persistence of low-pollution states and temporal memory effects. To address these gaps, we apply superstatistical frameworks from non-equilibrium statistical physics to analyse a comprehensive five-year dataset (2020-2025) of hourly air pollutant concentrations across the United Kingdom. Excellent fits of experimentally measured distributions are obtained from our theoretical models. We observe large heterogeneities of the best fitting parameters depending on the locations where the measurements are performed. These parameters form characteristic patterns in the 3-dimensional parameter space and depend on the type of pollutant considered, as well as on the environmental conditions (high traffic, industrial, or rural surroundings). We also investigate autocorrelation functions and provide evidence for differences in day-time and night-time decays of the autocorrelation function. Our investigation mainly focuses onto the dynamics of NO, NO2, PM2.5, PM10, but we also report on some anomalous distributions observed for O3.
