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

LGHAP v2 is a 22-year-long gap-free dataset of aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering 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 integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. The dataset is archived in NetCDF format with individual yearly submissions, and Python, MATLAB, R, and IDL codes are provided for reading and visualization.

Use Cases

• Modeling long-term global PM2.5 exposure for public health studies based on the 22-year daily time series.
• Analyzing spatial patterns of air pollution at a 1-km resolution for environmental management.
• Validating regional air quality models using the gap-free, globally consistent PM2.5 grids.
• Studying the relationship between aerosol optical depth and ground-level PM2.5 concentrations using the integrated multimodal data.

Strengths

• Provides 22 years of continuous daily data from 2000 to 2021.
• Offers high spatial resolution at 1 km for global land coverage.
• Uses a scene-aware ensemble learning graph attention network (SCAGAT) to reduce modeling bias in regions with limited ground measurements.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Kaixu Bai, East China Normal University

Collection Method

Integration of multimodal AODs and air quality measurements from diverse satellites, ground monitors, and numerical models using an improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area