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

22-year-long gap-free daily aerosol optical depth and PM2.5 concentration grids at 1-km resolution cover global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced this dataset using an improved big earth data analytic framework integrating multimodal satellite, ground monitor, and model data. A scene-aware ensemble learning graph attention network was developed to predict PM2.5 concentrations globally, including in regions with limited in-situ measurements.

Use Cases

• Modeling long-term global PM2.5 exposure based on the 22-year daily time series
• Analyzing spatial patterns of air pollution based on the global 1-km resolution grids
• Validating regional air quality models based on gap-free AOD and PM2.5 concentration data
• Studying the impact of environmental policies on air quality based on the multi-decadal coverage

Strengths

• 22-year temporal coverage from 2000 to 2021
• High spatial resolution of 1 kilometer per grid cell
• Gap-free global land coverage achieved via a multi-source integration framework

Limitations

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

Provenance

Source

East China Normal University

Collection Method

Derived via integration of multimodal AODs and air quality measurements from diverse satellites, ground monitors, and numerical models.

Time Range

2000 to 2021

Geography

Global land area