LGHAP v2: Global daily 1-km gap-free AOD grids (2022)

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 24-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 2023. 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（以下简称LGHAP）对于环境管理与地球系统科学分析具有重要意义。在本次发布的LGHAP数据集（LGHAP v2）中，我们提供了2000年至2023年覆盖全球陆地区域的24年无间隙气溶胶光学厚度（Aerosol Optical Depth, AOD）与近地面PM2.5浓度数据，其时间分辨率为逐日、空间分辨率为1千米。本数据集依托改进的大型地球数据分析框架，结合注意力增强张量构建与自适应背景信息更新策略，首先通过整合多模态气溶胶光学厚度数据与来自多类卫星、地面监测站点及数值模式的空气质量观测资料，生成无间隙气溶胶光学厚度格网数据。为实现全球范围内PM2.5浓度的精准预测，研究团队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, SCAGAT），以修正仅拥有少量甚至无原位空气质量观测数据区域的建模偏差。该数据集以NetCDF（nc）格式存储，且按年份分卷归档为独立文件。同时还提供了Python、MATLAB、R及IDL代码，方便用户读取与可视化LGHAP v2数据集。