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

LGHAP v2 is a 22-year-long gap-free dataset of aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 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 are 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 daily 1-km resolution data.
• Analyzing spatiotemporal patterns of aerosol optical depth for climate science research based on the gap-free global grids.
• Validating regional air quality models and satellite retrievals based on the integration of multimodal measurements.
• Assessing environmental policy effectiveness over two decades based on the 22-year time series.

Strengths

• Provides 22 years of continuous daily data from 2000 to 2021.
• Offers high spatial resolution of 1 kilometer for global land coverage.
• Integrates data from multiple sources including satellites, ground monitors, and numerical models.
• Uses a scene-aware ensemble learning graph attention network (SCAGAT) to address modeling bias in data-sparse regions.

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 diverse satellite and ground-based sources.

Provenance

Source

Kaixu Bai, East China Normal University

Collection Method

Integration of multimodal AODs and air quality measurements from diverse satellites, ground monitors, and numerical models using an improved big earth data analytic framework.

Time Range

2000 to 2021

Geography

Global land area