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

22-year-long daily gap-free grids of aerosol optical depth and near-surface PM2.5 concentrations at 1-km resolution covering global land areas. Kaixu Bai from East China Normal University produced this dataset using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. Python, MATLAB, R, and IDL codes are provided to help users read and visualize the NetCDF files.

Use Cases

• Modeling long-term global PM2.5 exposure based on 22 years of daily data.
• Analyzing spatial patterns of air pollution based on 1-km resolution global land grids.
• Validating air quality models based on gap-free AOD and PM2.5 concentrations derived from multimodal sources.
• Studying temporal trends in aerosol optical depth based on daily data from 2000 to 2021.

Strengths

• 22-year temporal coverage from 2000 to 2021.
• High spatial resolution of 1 kilometer for global land area.
• Gap-free daily data derived from an integration of satellite, ground, and model sources.
• Includes companion code for reading and visualization in Python, MATLAB, R, and IDL.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

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 improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area