Global Annual PM2.5 Grids from Satellite AOD, 1998-2016

Annual ground-level fine particulate matter (PM2.5) concentration grids with dust and sea-salt removed, derived from satellite aerosol optical depth (AOD) retrievals. The dataset provides global coverage at 0.01-degree resolution for the years 1998 through 2016. It was produced by NASA's ESDIS by combining data from MODIS, MISR, and SeaWiFS instruments and applying Geographically Weighted Regression with ground-based measurements.

Use Cases

• Modeling long-term population exposure to PM2.5 by analyzing the annual concentration grids across the 19-year time range.
• Validating chemical transport models like GEOS-Chem by comparing the satellite-derived, GWR-adjusted PM2.5 estimates against independent ground measurements.
• Studying regional air pollution trends by examining the spatiotemporal patterns in the 0.01-degree resolution GeoTIFF files.
• Assessing the impact of satellite sensor fusion by comparing PM2.5 estimates derived from the combined MODIS, MISR, and SeaWiFS AOD retrievals.

Strengths

• 19 years of continuous annual data from 1998 to 2016 provides a long-term record for trend analysis.
• Global spatial coverage at a 0.01-degree grid resolution allows for detailed large-scale mapping.
• Integration of AOD data from three distinct satellite instruments (MODIS, MISR, SeaWiFS) improves data completeness.
• Use of Geographically Weighted Regression with ground measurements aims to reduce bias in satellite-derived PM2.5 values.

Limitations

• Data does not fully resolve PM2.5 gradients at its 0.01-degree grid resolution due to influence from coarser-resolution source data.
• The dataset ends in 2016, making it temporally stale for analyzing recent air quality conditions.
• PM2.5 estimates are primarily intended for large-scale studies and may not capture hyper-local pollution sources.

Provenance

Source

NASA Earthdata, produced by ESDIS.

Collection Method

Satellite AOD retrievals from MODIS, MISR, and SeaWiFS, processed through the GEOS-Chem chemical transport model and adjusted via Geographically Weighted Regression with ground-based measurements.

Time Range

1998 to 2016

Freshness

Static dataset with a final update year of 2016.

Geography

Global