LGHAP: Monthly 1-km Gap-free Air Pollution Grids for China (2000–2020)

A 21-year-long gap-free dataset provides monthly aerosol optical depth (AOD), PM2.5, and PM10 concentration grids at 1-km resolution covering China's land area. The data was generated by Kaixu Bai at East China Normal University using a machine learning model to integrate multi-sensor AOD and related atmospheric data. It is distributed in NetCDF format with provided Python, Matlab, R, and IDL code for reading and visualization.

Use Cases

• Modeling long-term PM2.5 exposure for health impact studies based on the 21-year, gap-free concentration data.
• Analyzing spatial patterns of air pollution across China based on the 1-km resolution gridded data.
• Tracking seasonal and interannual variability of aerosols (AOD) based on the monthly time series from 2000 to 2020.
• Validating atmospheric transport models based on the gap-free, high-resolution pollutant concentration fields.

Strengths

• 21-year temporal coverage from 2000 to 2020 provides a long-term baseline for trend analysis.
• High 1-km spatial resolution allows for detailed regional and local studies.
• Gap-free data for AOD, PM2.5, and PM10 eliminates missing value interpolation challenges for users.

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

Generated via a machine learning regression model integrating multi-sensor AOD and related atmospheric datasets.

Time Range

2000–2020

Geography

Land area of China