LAPSO PM2.5 over Europe and US

Estimating Near-Surface Concentrations of Major Air Pollutants From Space: A Universal Estimation Framework LAPSO Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ( R2> 0.8 in polluted areas and uncertainty ≪5 μg/m3 for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.基于卫星遥感的近地表主要大气污染物浓度估算：一种通用估算框架LAPSO 与多数国家一样，中国在历经大规模大气污染治理攻坚后，仍面临严峻的大气污染挑战。从卫星观测数据反演近地表污染物浓度所存在的技术难点，制约了大尺度地表大气质量遥感监测技术的推广应用。 本研究旨在实现逐日精准的近地表大气污染物浓度估算（涵盖PM2.5、PM10、O3、NO2、SO2及CO），为此提出了一种名为卫星观测大气污染物学习（learning air pollutants from satellite observations，LAPSO）的稳健估算框架。LAPSO的核心原理是：借助基于深度学习技术的气象再分析资料，构建目标地表污染物浓度与卫星观测数据之间的非线性关联关系。 相较于其他算法，LAPSO框架优势显著：反演性能稳健、无需依赖化学传输模型（chemical transport models，CTMs）、硬件要求更低且界面友好易用。通过在中国1628个地面监测站点开展的大规模交叉验证可知，LAPSO的反演结果与地面实测数据吻合度极高：污染区域的决定系数R²>0.8，多数污染物的估算不确定性远低于5 μg/m³。该框架还能够精准捕捉各类大气污染物的时间演变特征。 通过与不同卫星平台的估算结果对比可知，搭载于哨兵-5P（Sentinel-5P）的对流层监测仪（TROPOspheric monitoring instrument，TROPOMI）在PM2.5估算方面表现略优。尽管卫星观测数据集的选择对O3估算结果无显著影响，但地面原位监测站点的数量与空间采样密度会对O3估算性能产生较大影响。 LAPSO能够以提升后的时空分辨率实现基于卫星遥感的近地表污染物浓度估算，其成功应用有望为区域及全球大气污染的连续动态监测提供有力支撑。