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

LGHAP v2 provides 22 years of daily, 1-kilometer resolution gap-free aerosol optical depth and near-surface PM2.5 concentration data 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 from satellites, ground monitors, and numerical models. A scene-aware ensemble learning graph attention network was developed to predict PM2.5 concentrations, particularly for regions with limited ground measurements.

Use Cases

• Modeling long-term air pollution trends based on the 22-year daily time series.
• Analyzing spatial patterns of PM2.5 concentrations across global land areas based on the 1-km resolution grids.
• Validating satellite-derived aerosol optical depth products using the integrated multimodal data sources.
• Assessing population exposure to fine particulate matter based on the gap-free global coverage.

Strengths

• 22-year temporal coverage from 2000 to 2021.
• High spatial resolution of 1 kilometer for global land coverage.
• Data is gap-free, addressing a common limitation in remote sensing products.
• Integrates data from diverse sources including 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

East China Normal University

Collection Method

Integration of multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework.

Time Range

2000 to 2021

Geography

Global land area