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

22-year-long gap-free aerosol optical depth and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas. Kaixu Bai from East China Normal University produced this dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. An ensemble learning graph attention network was developed to predict PM2.5 concentrations, especially for regions with limited ground measurements.

Use Cases

• Modeling long-term global air pollution trends based on 22 years of daily data.
• Analyzing spatial patterns of aerosol optical depth at 1-km resolution.
• Studying correlations between satellite-derived AOD and ground-level PM2.5 concentrations.
• Validating atmospheric and climate models with gap-free global air pollutant grids.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term record.
• Daily 1-km resolution offers high spatial and temporal detail for global land areas.
• Data integration from satellites, ground monitors, and models aims to create gap-free grids.
• Provided Python, MATLAB, R, and IDL codes assist with data reading and visualization.

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 diverse source platforms.

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 attention-reinforced tensor construction framework.

Time Range

2000 to 2021

Geography

Global land area