TAP框架下2000年至今逐日1 km PM2.5的时空连续估计
Spatiotemporal continuous estimates of daily 1-km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework
摘要：
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。
英文摘要：
High spatial resolution PM2.5 data covering a long time period are urgently needed to support population exposure assessment and refined air quality management. In this study, we provided complete-coverage PM2.5 predictions with a 1-km spatial resolution from 2000 to the present under the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) framework. To support high spatial resolution modelling, we collected PM2.5 measurements from both national and local monitoring stations. To correctly reflect the temporal variations in land cover characteristics that affected the local variations in PM2.5, we constructed continuous annual geoinformation datasets, including the road maps and ensemble gridded population maps, in China from 2000 to 2021. We also examined various model structures and predictor combinations to balance the computational cost and model performance. The final model fused 10-km TAP PM2.5 predictions from our previous work, 1-km satellite aerosol optical depth retrievals and land use parameters with a random forest model. Our annual model had an out-of-bag R2 ranging between 0.80 and 0.84, and our hindcast model had a by-year cross-validation R2 of 0.76. This open-access 1-km resolution PM2.5 data product with complete coverage successfully revealed the local-scale spatial variations in PM2.5 and could benefit environmental studies and policy-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
