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

22-year-long daily gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids at 1-km resolution 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 and air quality measurements from satellites, ground monitors, and numerical models. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.

Use Cases

• Modeling long-term global PM2.5 exposure for public health studies based on the 22-year daily time series.
• Analyzing spatial patterns of aerosol pollution for environmental management based on the global 1-km resolution grids.
• Developing and validating air quality prediction models based on the gap-free AOD and PM2.5 data derived from multimodal sources.
• Studying the relationship between satellite-derived AOD and ground-level PM2.5 concentrations across different global regions.

Strengths

• 22 years of daily data from 2000 to 2021 provides a long-term record for trend analysis.
• Global land coverage at a high 1-kilometer spatial resolution.
• Gap-free data generation via an attention-reinforced tensor construction and adaptive background information updating scheme.
• Integration of 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 improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area