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

22-year-long daily gap-free grids of aerosol optical depth (AOD) and near-surface PM2.5 concentrations at 1-km resolution covering global land areas from 2000 to 2021. The dataset 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 models. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes 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 for environmental management based on the 1-km resolution global land coverage.
• Developing and validating remote sensing algorithms for aerosol retrieval based on the integrated multimodal AOD data.
• Studying the relationship between AOD and ground-level PM2.5 concentrations based on the gap-free, co-located grids.

Strengths

• 22 years of daily temporal coverage from 2000 to 2021.
• High 1-kilometer spatial resolution covering global land areas.
• Gap-free data generation via an integration of satellite, ground, and model measurements.
• 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.
• Last update date is unknown; freshness unverified.

Provenance

Source

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

Freshness

Data covers up to 2021; last platform update date is unknown.

Geography

Global land area