LGHAP v2: Global Daily 1-km Gap-Free Aerosol Optical Depth Grids (2000-2021)

22-year-long gap-free aerosol optical depth (AOD) 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 of East China Normal University using an improved big earth data analytic framework that integrates multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. Data for each year is archived individually in NetCDF format, with Python, MATLAB, R, and IDL codes provided for reading and visualization.

Use Cases

• Modeling long-term global PM2.5 exposure based on the provided near-surface concentration data.
• Analyzing daily aerosol optical depth trends at 1-km resolution for environmental management.
• Developing scene-aware ensemble learning models for regions with limited air quality measurements, as referenced in the description.
• Conducting spatiotemporal analysis of air pollutants using the gap-free, high-resolution global grids.

Strengths

• 22-year temporal coverage from 2000 to 2021.
• Daily data at a high 1-km spatial resolution.
• Global land area coverage.
• Data is gap-free, derived from an integration of multimodal 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.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Kaixu Bai, East China Normal University, via paperswithcode.

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