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

Global land area daily gap-free PM2.5 and aerosol optical depth (AOD) concentration grids from 2000 to 2021. The dataset provides 22 years of data at a 1-kilometer resolution, generated by Kaixu Bai of East China Normal University using an attention-reinforced tensor construction framework and a scene-aware ensemble learning graph attention network (SCAGAT). 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 trends based on the 22-year daily time series.
• Analyzing spatial patterns of air pollution based on the global 1-km resolution grids.
• Validating regional air quality models based on gap-free satellite-derived AOD and PM2.5 data.
• Studying the relationship between aerosol optical depth and near-surface particulate matter based on the integrated multimodal data.

Strengths

• 22-year temporal coverage from 2000 to 2021.
• High 1-kilometer spatial resolution for global land area.
• Gap-free daily data for both PM2.5 and aerosol optical depth (AOD).
• Data generation uses an advanced framework integrating satellite, ground monitor, and model data.

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

Derived via 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