LGHAP v2: Global daily 1-km gap-free AOD grids (2018)

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）对于环境管理与地球系统科学分析具有重要意义。在当前发布的LGHAP v2版本数据集中，我们提供了2000年至2021年期间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，其空间分辨率为每日1公里。研究团队借助融合了注意力增强张量构建与自适应背景信息更新机制的改进型地球大数据分析框架，首先通过整合多模态气溶胶光学厚度数据以及来自多颗卫星、地面监测站与数值模式获取的空气质量观测数据，生成了无间隙的AOD格网数据。为了更精准地预测全球范围内的PM2.5浓度，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，缩写为SCAGAT），以解决在原位空气质量观测数据匮乏甚至缺失的区域存在的显著建模偏差问题。本数据集以网络通用数据格式（Network Common Data Form，缩写为NetCDF，常简称nc）归档，且每年的数据均单独存储为独立文件。同时还提供了Python、MATLAB、R及IDL代码，帮助用户读取并可视化LGHAP v2数据集。