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

22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas from 2000 to 2021. 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. Python, MATLAB, R, and IDL codes are provided to help users read and visualize the data.

Use Cases

• Modeling global air pollution exposure based on daily 1-km PM2.5 grids.
• Analyzing long-term aerosol trends based on 22-year gap-free AOD data.
• Developing scene-aware machine learning models for regions with sparse ground measurements.
• Integrating satellite and ground-based air quality data for environmental management.
• Visualizing global air pollutant concentrations using provided Python, MATLAB, R, or IDL codes.

Strengths

• 22-year temporal coverage from 2000 to 2021.
• Daily 1-km spatial resolution covering global land areas.
• Gap-free grids derived from multimodal satellite, ground, and model data.
• Includes both aerosol optical depth (AOD) and near-surface PM2.5 concentration variables.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.
• 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