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

A Long-term Gap-free High-resolution Air Pollutants concentration dataset provides daily, 1-kilometer resolution grids of aerosol optical depth and near-surface PM2.5 concentrations covering global land areas from 2000 to 2021. It was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. The dataset is archived in NetCDF format with helper codes for Python, MATLAB, R, and IDL.

Use Cases

• Modeling long-term global PM2.5 exposure based on the 22-year daily time series.
• Analyzing spatial patterns of air pollution based on the 1-km resolution global land coverage.
• Validating regional air quality models based on the gap-free AOD and PM2.5 concentration grids.
• Studying the impact of pollution events based on the daily temporal resolution.

Strengths

• 22-year temporal coverage from 2000 to 2021.
• Daily temporal resolution and 1-kilometer spatial resolution.
• Global land coverage.
• Gap-free data derived from multimodal satellite, ground, and model sources.

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 attention-reinforced tensor construction and adaptive background information updating scheme.

Time Range

2000 to 2021

Geography

Global land area