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

LGHAP v2 provides a 22-year-long, gap-free dataset of aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced it using an improved big earth data analytic framework integrating multimodal AODs and air quality measurements from satellites, ground monitors, and models. The data is archived 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 22-year daily concentration data.
• Analyzing spatiotemporal trends in aerosol optical depth using the gap-free 1-km resolution grids.
• Validating regional air quality models with the globally integrated satellite and ground monitor data.
• Assessing environmental policy impacts on air pollution using the consistent, long-term time series.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term baseline.
• Daily 1-km resolution offers high spatiotemporal detail for global land areas.
• 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.
• Row count is unknown, which may limit suitability assessment for specific ML tasks.

Provenance

Source

East China Normal University

Collection Method

Integration of multimodal AODs and air quality measurements using an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes.

Time Range

2000 to 2021

Geography

Global land area