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

Global land area daily gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration data from 2000 to 2021 at 1-km resolution. The dataset was produced by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. It is provided in NetCDF format with Python, MATLAB, R, and IDL codes for reading and visualization.

Use Cases

• Analyze long-term global air pollution trends based on the 22-year daily time series.
• Model PM2.5 exposure for health impact studies based on the gap-free, high-resolution concentration grids.
• Validate regional air quality models based on the globally consistent AOD data.
• Study aerosol transport and climate interactions based on the integrated multi-modal satellite and model data.

Strengths

• Provides 22 years of daily data from 2000 to 2021.
• Offers global land coverage at a high 1-kilometer spatial resolution.
• Integrates data 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 large-scale ML training.

Provenance

Source

East China Normal University

Collection Method

Derived via an integration of multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework.

Time Range

2000 to 2021

Geography

Global land area