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

22-year-long gap-free daily PM2.5 concentration grids at 1-km resolution covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University created this dataset using an improved big earth data analytic framework integrating satellite, ground monitor, and numerical model data. The dataset is archived in NetCDF format with provided Python, MATLAB, R, and IDL codes for reading and visualization.

Use Cases

• Modeling long-term global PM2.5 exposure trends based on the 22-year daily time series.
• Analyzing spatial patterns of air pollution at a high 1-km resolution across continents.
• Validating regional air quality models using the gap-free, multi-source integrated data.
• Studying the relationship between aerosol optical depth (AOD) and near-surface PM2.5 concentrations.

Strengths

• Provides 22 years of continuous daily data from 2000 to 2021.
• Offers high spatial resolution at 1 kilometer per grid cell.
• Covers global land areas, enabling worldwide analysis.
• Uses a multi-source integration method combining satellite, ground, and model data to create gap-free grids.

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 attention-reinforced tensor construction framework.

Time Range

2000 to 2021

Geography

Global land area