LGHAP v2: Global Daily 1-km Gap-Free Air Pollutant Grids (2000-2021)

22-year-long daily 1-km resolution gap-free aerosol optical depth and PM2.5 concentration grids covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced this dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. An improved big earth data analytic framework with attention-reinforced tensor construction and a scene-aware ensemble learning graph attention network were developed to generate these gap-free predictions.

Use Cases

• Modeling long-term global PM2.5 exposure based on gap-free daily 1-km concentration grids.
• Analyzing spatiotemporal trends in aerosol optical depth using 22 years of daily global land data.
• Validating regional air quality models with high-resolution, gap-free satellite-derived AOD data.
• Studying the health impacts of air pollution by linking historical PM2.5 concentrations to population data.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term perspective.
• Daily 1-km resolution offers high spatial and temporal granularity for global land analysis.
• Data integrates measurements from diverse sources including satellites, ground monitors, and numerical models.

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

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