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）
Air pollution has altered the Earth radiation balance, disturbed the ecosystem and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant dataset with timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, tapdata.org) that combines information from multiple data sources, including ground measurements, satellite retrievals, dynamically updated emission inventories, operational chemical transport model simulations and other ancillary data. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities.
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。
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）
Lagged sea-surface-temperature precursors of the leading PM2.5 mode in China
Yunnan Key Laboratory of Complex Systems and Brain-Inspired Intelligence, Kunming University of Science and Technology（云南复杂系统与脑启发智能重点实验室，昆明理工大学）；Faculty of Science, Kunming University of Science and Technology（昆明理工大学理学院）
