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

22-year-long gap-free aerosol optical depth and near-surface PM2.5 concentrations with daily 1-km resolution covering global land area 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 multimodal AODs from satellites, ground monitors, and numerical models. Data for each year is archived individually in NetCDF format, with Python, MATLAB, R, and IDL codes provided for reading and visualization.

Use Cases

• Modeling long-term PM2.5 exposure for epidemiological health studies based on the 22-year daily time series.
• Analyzing global and regional air pollution trends and patterns based on the 1-km resolution global land coverage.
• Validating and improving atmospheric chemical transport models based on the gap-free aerosol optical depth grids.
• Assessing environmental policy impacts on air quality over time based on the continuous, gap-filled data record.

Strengths

• 22-year temporal coverage from 2000 to 2021 provides a long-term baseline for trend analysis.
• Daily 1-km resolution over global land area offers high spatiotemporal detail.
• Data is gap-free, addressing a common limitation in satellite-derived air quality products.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

East China Normal University

Collection Method

Integration of multimodal AODs from diverse satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework.

Time Range

2000 to 2021

Geography

Global land area