LGHAP v2: Global Daily 1-km Aerosol and PM2.5 Data (2000-2021)

A 22-year-long global land dataset provides daily, 1-km resolution gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids from 2000 to 2021. It was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. The dataset is archived in NetCDF format, with data for each year stored as an individual submission, and includes Python, MATLAB, R, and IDL codes for reading and visualization.

Use Cases

• Modeling long-term global air pollution trends based on the 22-year daily time series.
• Analyzing spatial patterns of aerosol optical depth at high resolution based on the 1-km global land grids.
• Predicting PM2.5 concentrations in data-sparse regions based on the scene-aware ensemble learning graph attention network (SCAGAT) methodology described.
• Conducting environmental impact assessments based on the integration of satellite, ground, and model data sources.

Strengths

• Provides 22 years of continuous daily data from 2000 to 2021.
• Offers high spatial resolution at 1 km for global land areas.
• Integrates data from multiple sources: satellites, ground monitors, and numerical models.

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

Integration of multimodal AODs and air quality measurements via an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes.

Time Range

2000 to 2021

Geography

Global land area