LGHAP v2: Global Daily 1-km AOD and PM2.5 Grids (2000-2021)

LGHAP v2 provides 22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced the dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework. Data are archived in NetCDF format with individual submissions per year, and Python, MATLAB, R, and IDL codes are provided for reading and visualization.

Use Cases

• Modeling long-term global air pollution trends based on the 22-year daily time series.
• Analyzing spatial patterns of aerosol optical depth at 1-km resolution across global land areas.
• Predicting near-surface PM2.5 concentrations using the described scene-aware ensemble learning graph attention network (SCAGAT).
• Conducting earth system science analysis with gap-free, high-resolution pollutant grids.

Strengths

• 22-year temporal coverage from 2000 to 2021
• Daily data at 1-km spatial resolution covering global land areas
• Integration of data from satellites, ground monitors, and numerical models

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment

Provenance

Source

East China Normal University

Collection Method

Integration of multimodal AODs and air quality measurements using an improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area