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

22-year-long gap-free daily PM2.5 concentration grids at 1-km resolution cover global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced this dataset using an attention-reinforced tensor construction framework that integrates satellite, ground monitor, and model data. A scene-aware ensemble learning graph attention network (SCAGAT) was developed to reduce modeling bias in regions with sparse ground measurements.

Use Cases

• Modeling long-term human exposure to fine particulate matter based on global daily 1-km PM2.5 grids.
• Analyzing spatial and temporal trends in aerosol optical depth (AOD) for climate studies based on gap-free global data.
• Calibrating regional air quality models using the integrated multi-source data framework described.
• Studying the impact of environmental policies on air pollution levels over two decades using the 22-year time series.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term baseline for trend analysis.
• Daily data at 1-km spatial resolution offers high granularity for global land areas.
• Dataset integrates multimodal data from satellites, ground monitors, and numerical models to create gap-free grids.
• Includes Python, MATLAB, R, and IDL code to assist users with reading and visualizing the NetCDF data.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Data may reflect geographic bias inherent to the integration of sources with varying global coverage.

Provenance

Source

Kaixu Bai, East China Normal University

Collection Method

Produced via an improved big earth data analytic framework integrating satellite, ground monitor, and model data.

Time Range

2000 to 2021

Geography

Global land area