LGHAP: Daily 1-km PM2.5 Grids for China (2000–2020)

21 years of daily, gap-free PM2.5 concentration data at a 1-km spatial resolution cover the land area of China from 2000 to 2020. The dataset was generated by applying a machine learning regression model to integrate aerosol optical depth (AOD) and related data from multiple sensors and platforms. It is provided in NetCDF format with supporting code for reading and visualization in Python, Matlab, R, and IDL.

Use Cases

• Analyzing long-term trends in fine particulate matter exposure based on the 21-year daily time series.
• Assessing regional air pollution disparities and environmental justice using the 1-km resolution spatial grids.
• Validating atmospheric chemistry and air quality models with gap-free, high-resolution observational data.
• Studying the health impacts of PM2.5 by linking concentration data with epidemiological records.
• Informing environmental management and policy decisions with spatially detailed historical pollution data.

Strengths

• Provides a long-term, 21-year time series (2000–2020) for longitudinal analysis.
• Offers high spatial detail with daily data at a 1-kilometer resolution.
• Data is gap-free, enhancing usability for continuous spatial and temporal modeling.
• Includes utility code for multiple programming environments (Python, Matlab, R, IDL).

Limitations

• Specific column names and the exact data structure within the NetCDF files are not detailed in the provided metadata.
• The dataset's size, row count, and precise last update date are unknown.
• Spatial coverage is limited to the land area of China.

Provenance

Source

Kaixu Bai, East China Normal University

Collection Method

Generated from daily gap-free AOD and related datasets (e.g., air pollutants, atmospheric visibility) via a machine learning regression model.

Time Range

2000–2020

Geography

Land area of China