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

22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids with daily 1-km resolution covering global land areas from 2000 to 2021. The dataset, LGHAP v2, was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating multimodal AODs and air quality measurements from satellites, ground monitors, and models. Data is archived in NetCDF format with yearly submissions, and Python, MATLAB, R, and IDL codes are provided 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 spatial patterns of aerosol optical depth for climate science research based on the gap-free 1-km resolution grids.
• Validating regional air quality models using the integrated satellite, ground, and model-derived measurements mentioned in the description.
• Studying temporal trends in air pollution before and after major policy changes using the continuous daily data from 2000 to 2021.

Strengths

• 22-year temporal coverage from 2000 to 2021, enabling long-term trend analysis.
• High spatial resolution of 1 km and daily frequency for detailed spatiotemporal analysis.
• Gap-free global land coverage, addressing data scarcity in regions with limited monitoring.
• Integration of multimodal data sources including satellites, ground monitors, and numerical models.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and total file size are unknown, which may limit suitability assessment for specific compute resources.

Provenance

Source

Kaixu Bai, East China Normal University

Collection Method

Derived via an improved big earth data analytic framework integrating multimodal AODs and air quality measurements from diverse satellites, ground monitors, and numerical models, with PM2.5 predicted using a scene-aware ensemble learning graph attention network (SCAGAT).

Time Range

2000 to 2021

Geography

Global land area