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

22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids from 2000 to 2021. The dataset provides daily 1-km resolution data covering global land areas, created by Kaixu Bai of East China Normal University. It was generated by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework.

Use Cases

• Modeling long-term global air pollution trends based on the 22-year daily time series
• Analyzing spatial patterns of aerosol optical depth based on the global 1-km resolution grids
• Predicting PM2.5 concentrations in data-sparse regions using the described scene-aware ensemble learning graph attention network (SCAGAT)
• Studying the integration of satellite and ground-based air quality measurements based on the multimodal data fusion approach

Strengths

• 22-year temporal coverage from 2000 to 2021
• High spatial resolution of 1 kilometer per grid cell
• Global land area coverage
• Gap-free data generation via an improved big earth data analytic framework

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.

Time Range

2000 to 2021

Geography

Global land area