LGHAP v2: Global Daily Gap-Free PM2.5 Grids (2000-2021)

22-year-long daily gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids at 1-km resolution covering global land area from 2000 to 2021. The dataset was produced by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and numerical model data. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.

Use Cases

• Modeling long-term global PM2.5 exposure trends based on the 22-year daily time series.
• Analyzing spatial patterns of air pollution at high resolution based on the 1-km global land grids.
• Validating regional air quality models based on gap-free AOD and PM2.5 concentration data.
• Assessing environmental policy impacts on air quality based on the global, long-term concentration data.

Strengths

• Covers a 22-year time range from 2000 to 2021.
• Provides daily data at a high 1-km spatial resolution.
• Uses a scene-aware ensemble learning graph attention network (SCAGAT) to account for modeling bias in regions with limited ground measurements.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Kaixu Bai, East China Normal University

Collection Method

Integration of multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area