V6.GL.02.04
This version is recommended for users interested in our most state of the science global algorithm, and is available for 1998-2023
We estimate annual and monthly ground-level fine particulate matter (PM₂.₅) for 1998-2023 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. V6.GL.02.04 follows the methodology of V6.GL.01 but updates the ground-based observations used to calibrate the geophysical PM₂.₅ estimates for the entire time series, extends temporal coverage through 1998 – 2023, and includes retrievals from the SNPP VIIRS instrument. Also, previous versions were reported to contain abnormally low values in certain, rare circumstances. This limitation has been addressed in V6.GL.02.04 using a modified padding strategy and stronger geophysical constraints.
Reference:
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]
Scientific Datasets:
Annual and monthly datasets are provided in NetCDF [.nc] format. Gridded files use the WGS84 projection.
Note that these estimates are primarily intended to aid in large-scale studies. Annual and coarse-resolution averages correspond to a simple mean of within-grid values. Gridded datasets are provided to allow users to agglomerate data as best meets their particular needs. High-resolution (0.01° × 0.01°) datasets are gridded at the finest resolution of the information sources that were incorporated but are unlikely to fully resolve PM₂.₅ gradients at the gridded resolution due to influence by information sources at coarser resolution.
Annual and monthly mean PM₂.₅ [µg/m3] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25]
Annual and monthly mean PM₂.₅ [µg/m3] at 0.1° × 0.1°:
[https://wustl.box.com/v/ACAG-V6GL0204-CNNPM25c0p10]
This SatPM₂.₅ data is alternatively available via the Registry of Open Data on AWS:
[https://registry.opendata.aws/surface-pm2-5-v6gl/]
Processed Datasets:
These summary files are processed from the Scientific Datasets above for ease of accessibility. Population-weighted estimates and total population describe only those people covered by the V6.GL.02.04 dataset and are provided by GPWv4. Country borders are defined following GAD3.6.
Annual Global country-level mean PM₂.₅
Annual Canada provincial-level mean PM₂.₅
Annual China regional-level mean PM₂.₅
Annual India regional-level mean PM₂.₅
Annual United States state-level mean PM₂.₅
SatPM₂.₅ V6.GL.02.04 are licensed under CC BY 4.0