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

LGHAP v2 is a long-term, gap-free dataset providing daily 1-km resolution grids of aerosol optical depth and near-surface PM2.5 concentrations across global land areas from 2000 to 2021. It was produced by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates satellite, ground monitor, and numerical model data. The dataset is archived in NetCDF format with provided code 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 1-km resolution across global land areas.
• Validating regional air quality models using the gap-free, multi-source integrated AOD and PM2.5 data.
• Studying the relationship between aerosol optical depth and near-surface particulate matter 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