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

22-year-long gap-free aerosol optical depth and near-surface PM2.5 concentration data at daily 1-km resolution covering global land areas. 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 grids.

Use Cases

• Modeling long-term air pollution trends based on 22 years of daily data.
• Analyzing global PM2.5 exposure and health impacts based on near-surface concentration estimates.
• Validating satellite-derived aerosol products based on gap-free AOD grids.
• Studying regional pollution events based on high-resolution 1-km daily data.
• Developing earth system science models based on integrated multimodal air quality measurements.

Strengths

• 22-year temporal coverage from 2000 to 2021.
• High spatial resolution of 1 kilometer.
• Gap-free daily data for global land areas.
• Integrates measurements from diverse 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.
• Data may reflect geographic bias inherent to the integration of available satellite and ground monitor sources.

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