Air pollution is one of the leading causes of death globally, and continues to have a detrimental effect on our health. In light of these impacts, an extensive range of statistical modelling approaches has been devised in order to better understand air pollution statistics. However, the time-varying statistics of different types of air pollutants are far from being fully understood. The observed probability density functions (PDFs) of concentrations depend very much on the spatial location and on the pollutant substance. In this paper, we analyse a large variety of data from 3544 different European monitoring sites and show that the PDFs of nitric oxide ($NO$), nitrogen dioxide ($NO2$) and particulate matter ($PM10$ and $PM2.5$) concentrations generically exhibit heavy tails and are asymptotically well approximated by $q$-exponential distributions with a given width parameter $λ$. We observe that the power-law parameter $q$ and the width parameter $λ$ vary widely for the different spatial locations. For each substance, we find different patterns of parameter clouds in the $(q, λ)$ plane. These depend on the type of pollutants and on the environmental characteristics (urban/suburban/rural/traffic/industrial/background). This means the effective statistical physics description of air pollution exhibits a strong degree of spatial heterogeneity.
Modelling spatio-temporal trends of air pollution in Africa
African Institute for Mathematical Sciences（非洲数学科学研究所）；Institute of Applied Statistics, University of Burundi（布隆迪大学应用统计研究所）；Ecole Normale Superieur de Bujumbura（布琼布拉高等师范学院）
Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multiple Data Sources
Tsinghua University（清华大学）；Chinese Academy of Environmental Planning（中国环境规划院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Spatiotemporal continuous estimates of daily 1-km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework
因此，迫切需要覆盖较长时间段的高空间分辨率PM2.5数据，以支持人口暴露评估和精细化空气质量管理。本文在TAP（Tracking Air Pollution in China，http：//tapdata.org.cn/）框架下，对2000年至今的PM2.5进行了空间分辨率为1 km的全覆盖预测。为了支持高空间分辨率建模，我们收集了来自国家和地方监测站的PM2.5测量值。为了正确反映影响PM2.5局部变化的土地覆盖特征的时间变化，我们构建了2000 - 2021年中国连续的年度地理信息数据集，包括道路图和集合网格人口图。我们还研究了各种模型结构和预测器组合，以平衡计算成本和模型性能。最终的模型融合了10公里TAP PM2.5预测从我们以前的工作，1公里卫星气溶胶光学深度反演和土地利用参数与随机森林模型。我们的年度模型的开箱R2范围在0.80和0.84之间，我们的后报模型的年度交叉验证R2为0.76。这一开放获取的1公里分辨率PM2.5数据产品覆盖面广，成功揭示了PM2.5在局地尺度上的空间变化，有利于环境研究和政策制定。
