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

22-year-long gap-free aerosol optical depth and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai at East China Normal University, integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework.

Use Cases

• Modeling long-term air pollution exposure based on daily 1-km gap-free AOD grids.
• Analyzing global PM2.5 concentration trends using the scene-aware ensemble learning graph attention network (SCAGAT) predictions.
• Validating regional air quality models with gap-free data derived from satellite and ground monitor integration.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term perspective.
• Daily 1-km resolution offers high spatiotemporal granularity for global land areas.
• Integration of multimodal sources (satellites, ground monitors, models) likely improves data completeness.

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 diverse satellites, ground monitors, and numerical models using an improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area