Global Satellite-derived Monthly PM₂.₅ Total Mass (0.01x0.01) - V6GL (Selected as AGU Impactful Datasets)

Overview

We estimate annual and monthly ground-level fine particulate matter ($\rm{PM_{2.5}}$) for 2000-2019 by combining Aerosol Optical Depth (AOD) retrievals (Dark Target, Deep Blue, MAIAC) that make use of observations from numerous satellite-based NASA instruments (MODIS/Terra, MODIS/Aqua, MISR/Terra, SeaWiFS/SeaStar, VIIRS/SNPP, and VIIRS/NOAA20) with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a residual Convolutional Neural Network (CNN), as detailed in the below reference for V6.GL.01. SatPM V6.GL.03 follows the methodology of V6.GL.01 and V6.GL.02.04, but updates the ground-based observations used to calibrate the geophysical $\rm{PM_{2.5}}$ estimates for the entire time series, extends temporal coverage through 1998 – 2024, and includes retrievals from the SNPP VIIRS instrument. Also, previous versions were reported to contain abnormally low values in certain, rare circumstances. Also, the GEOS-Chem information (and the geophysical PM2.5) includes the latest updates on dust size and distribution.

Current Datasets

V6.GL.03 (1998-2024)

• Annual and monthly mean $\rm{PM_{2.5}}$ [ug/m3] at 0.01° × 0.01°: https://wustl.box.com/v/V6GL03-FineResolution
• Annual and monthly mean $\rm{PM_{2.5}}$ [ug/m3] at 0.1° × 0.1°: https://wustl.box.com/v/V6GL03-CoarseResolution
• Sat$\rm{PM_{2.5}}$ data is alternatively available via the Registry of Open Data on AWS: https://registry.opendata.aws/surface-pm2-5-v6gl/
• Processed Datasets:
  • Annual Global country-level mean $\rm{PM_{2.5}}$
  • Annual Canada provincial-level mean $\rm{PM_{2.5}}$
  • Annual China regional-level mean $\rm{PM_{2.5}}$.
  • Annual India regional-level mean $\rm{PM_{2.5}}$
  • Annual United States state-level mean $\rm{PM_{2.5}}$
• Sat$\rm{PM_{2.5}}$ V6.GL.03 data are licensed under CC BY 4.0

Archived Datasets

V6.GL.02.04 (1998-2023)

• Annual and monthly mean $\rm{PM_{2.5}}$ [ug/m3] at 0.01° × 0.01°: https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25

• Annual and monthly mean $\rm{PM_{2.5}}$ [ug/m3] at 0.1° × 0.1°: https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25c0p10

• Sat$\rm{PM_{2.5}}$ data is alternatively available via the Registry of Open Data on AWS: https://registry.opendata.aws/surface-pm2-5-v6gl/

• Processed Datasets:

  • Annual Global country-level mean $\rm{PM_{2.5}}$
  • Annual Canada provincial-level mean $\rm{PM_{2.5}}$
  • Annual China regional-level mean $\rm{PM_{2.5}}$.
  • Annual India regional-level mean $\rm{PM_{2.5}}$
  • Annual United States state-level mean $\rm{PM_{2.5}}$

• Sat$\rm{PM_{2.5}}$ V6.GL.02.04 data are licensed under CC BY 4.0

Reference and citation

Shen, S. Li, C. van Donkelaar, A. Jacobs, N. Wang, C. Martin, R. V.: Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning. (2024) ACS ES&T Air. DOI: 10.1021/acsestair.3c00054. Link