LGHAP v2: Global Daily 1-km Gap-free AOD and PM2.5 Data (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. The dataset, LGHAP v2, 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. A scene-aware ensemble learning graph attention network (SCAGAT) was developed to predict PM2.5 concentrations, particularly for regions with limited in situ measurements.

Use Cases

• Modeling long-term global air pollution trends based on the 22-year daily time series.
• Analyzing spatial patterns of aerosol optical depth and PM2.5 at a 1-km resolution.
• Validating regional air quality models using the gap-free, globally consistent AOD grids.
• Studying the relationship between satellite-derived AOD and ground-level PM2.5 concentrations using the integrated multimodal data.

Strengths

• 22 years of continuous daily data from 2000 to 2021.
• Global land coverage at a high spatial resolution of 1 kilometer.
• Gap-free data generation via integration of satellite, ground, and model sources.
• Includes both AOD and near-surface PM2.5 concentration estimates.

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 attention-reinforced tensor construction framework.

Time Range

2000 to 2021

Geography

Global land area