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

A 22-year-long global land dataset provides daily, 1-km resolution aerosol optical depth (AOD) and near-surface PM2.5 concentration grids from 2000 to 2021. Kaixu Bai from East China Normal University created it by integrating multimodal satellite, ground monitor, and numerical model data using an attention-reinforced tensor construction framework. The data is archived in NetCDF format with yearly submissions and includes Python, MATLAB, R, and IDL codes for reading and visualization.

Use Cases

• Modeling long-term global PM2.5 exposure for public health studies based on the 22-year daily concentration data.
• Analyzing spatiotemporal patterns of aerosol optical depth for climate research based on the gap-free 1-km resolution grids.
• Validating regional air quality models using the integrated satellite and ground-based measurements described.
• Studying the impact of pollution events on environmental systems using the scene-aware ensemble learning model outputs.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term record for trend analysis.
• Daily 1-km resolution over global land area offers high spatiotemporal detail.
• Data integration from diverse satellites, ground monitors, and numerical models likely improves completeness.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• The dataset's specific size and row count are unknown, which may limit suitability assessment.

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 improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area