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

22-year-long daily gap-free grids of aerosol optical depth and near-surface PM2.5 concentrations 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 integrating satellite, ground monitor, and model data. Data is provided in NetCDF format with 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 time series.
• Analyzing spatial patterns of aerosol pollution for environmental management based on the 1-km resolution global land coverage.
• Validating air quality models and satellite retrievals based on the gap-free AOD and PM2.5 concentration grids.
• Studying the relationship between AOD and surface-level PM2.5 concentrations across different regions.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term record for trend analysis.
• Daily 1-km resolution global land coverage offers high spatial and temporal detail.
• Data is gap-free for both AOD and PM2.5 concentrations, addressing a common limitation in remote sensing products.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Data may reflect geographic bias inherent to the integration of multimodal sources with varying coverage.

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 and adaptive background information updating framework.

Time Range

2000 to 2021

Geography

Global land area