LGHAP v2: Global Daily 1-km Gap-Free Air Pollution Data (2000-2021)

LGHAP v2 is a long-term, high-resolution air pollutants concentration dataset from East China Normal University. It provides 22 years of daily, gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids at a 1-km resolution covering global land areas from 2000 to 2021. The data was generated by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an improved big earth data analytic framework.

Use Cases

• Modeling long-term global PM2.5 exposure trends based on the 22-year daily concentration data.
• Analyzing spatial patterns of aerosol optical depth (AOD) at a 1-km resolution for environmental research.
• Training machine learning models for air quality prediction using the integrated satellite and ground monitor data.
• Conducting gap-free time-series analysis of air pollutants for specific geographic regions.

Strengths

• Provides 22 years of continuous daily data from 2000 to 2021.
• Offers global land coverage at a high 1-km spatial resolution.
• Data is gap-free, addressing a common limitation in remote sensing datasets.
• Integrates measurements 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

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

Freshness

Data covers up to 2021; last update date of the dataset record is unknown.

Geography

Global land area