AI 中文总结
研究PM2.5污染系统中的临界性与动态恢复力，引入FMMR过程，通过观测和数据揭示临界特征，经网格化比较发现不同地区恢复力差异，为以恢复力为导向的空气质量评估提供定量基础。
AI 中文摘要
基于浓度的指标支撑着空气质量评估，而动态持续性和恢复描述了高PM2.5事件消散的速度以及它们保留记忆的强度。本文引入有限记忆乘法反转（FMMR）过程，将PM2.5变异性的对数正态浓度主干与事件复发、时间记忆、方差放大和局部动态恢复力联系起来。通过站点观测和再分析数据，高PM2.5状态显示出一系列连贯的临界特征。这些共同出现的信号揭示了PM2.5污染系统中的动态临界性。通过在人口密集和受排放影响地区的网格化比较表明，具有相似PM2.5负担的地区恢复能力可能不同。这些发现为以恢复力为导向的空气质量评估提供了定量基础。
英文摘要
Concentration-based metrics underpin air-quality assessment, while dynamical persistence and recovery describe how rapidly high-PM2.5 episodes dissipate and how strongly they retain memory. Here we introduce a finite-memory multiplicative reversion (FMMR) process that links the lognormal concentration backbone of PM2.5 variability with event recurrence, temporal memory, variance amplification and local dynamical resilience. Across station observations and reanalysis data, elevated PM2.5 regimes show a coherent set of critical signatures: stronger memory, rising autocorrelation, broader upper tails, amplified variance, reduced resilience and more clustered exceedance events. Together, these co-occurring signals reveal dynamical criticality in PM2.5 pollution systems, with critical slowing down expressed as a loss of restoring capacity under high-pollution conditions. A gridded comparison across populated and emission-influenced regions further shows that areas with similar PM2.5 burden can differ in recovery capacity, while eastern China has shifted toward higher resilience during recent air-quality improvements and India and West Africa occupy lower-resilience states. By identifying where pollution burden and recovery capacity diverge, these findings establish dynamical persistence and resilience as complementary dimensions of PM2.5 risk and provide a quantitative basis for resilience-oriented air-quality assessment.
Comments33 pages, 4 figures
