Satellite-Derived PM2.5 Estimates Spatially Aggregated to Polygons

Spatial aggregations of PM2.5 estimates generated by the Atmospheric Composition Analysis Group at Washington University St. Louis using V5GL and V6GL algorithms. The data is processed from grid/raster NetCDF files into polygon shapefiles. The code for performing the spatial aggregation is available on GitHub.

Use Cases

• Modeling population exposure to fine particulate matter based on satellite-derived PM2.5 estimates.
• Comparing air pollution levels across administrative or census boundaries using aggregated polygon data.
• Validating ground-based air quality monitoring stations with satellite-derived, spatially aggregated data.
• Assessing long-term trends in PM2.5 concentrations for specific regions based on the V5GL/V6GL algorithm outputs.

Strengths

• Spatial aggregations are performed using publicly available code on GitHub, promoting reproducibility.
• Data originates from the established Atmospheric Composition Analysis Group at Washington University St. Louis.

Limitations

• The specific spatial and temporal coverage, resolution, and row count of the aggregated data are unknown.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Atmospheric Composition Analysis Group at Washington University St. Louis.

Collection Method

Satellite-derived PM2.5 estimates processed via V5GL and V6GL algorithms and aggregated from raster to polygons.

Freshness

Last updated 2026-06-25 18:07:31; freshness should be verified.