LGHAP v2: Global daily 1-km gap-free AOD grids (2015)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.长期无间隙高分辨率空气污染物浓度数据集（Long-term Gap-free High-resolution Air Pollutants concentration dataset，缩写为LGHAP）在环境管理与地球系统科学分析中具有重要应用价值。在本次发布的LGHAP v2版本数据集中，我们提供了2000年至2021年覆盖全球陆地区域的22年无间隙气溶胶光学厚度（Aerosol Optical Depth，缩写为AOD）与近地面PM2.5浓度数据，其空间分辨率为1公里、时间分辨率为每日。本研究采用改进的地球大数据分析框架，该框架集成了注意力增强张量构建与自适应背景信息更新机制，首先通过整合多源卫星、地面监测站与数值模式获取的多模态AOD数据与空气质量观测数据，生成了无间隙AOD格网数据。为实现全球范围内PM2.5浓度的精准预测，本研究进一步开发了场景感知集成学习图注意力网络（Scene-aware Ensemble Learning Graph Attention Network，缩写为SCAGAT），以解决原位空气质量观测数据匮乏甚至缺失区域存在的显著建模偏差问题。该系列数据集以网络通用数据格式（NetCDF，缩写为nc）存储，且每年的数据均作为独立数据集单独归档。同时还提供了Python、MATLAB、R及IDL代码，以帮助用户读取并可视化LGHAP v2数据集。