V5.GL.05.02
This version is recommended for users requiring our traditional 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, and MAIAC) that use observations from multiple satellite-based NASA instruments (MODIS/Terra, MODIS/Aqua, MISR/Terra, SeaWiFS/SeaStar, VIIRS/SNPP, and VIIRS/NOAA20) with the GEOS-Chem chemical transport model (http://geos-chem.org), and subsequently calibrating to global ground-based observations using a Geographically Weighted Regression (GWR), as detailed in the below reference for V5.GL.01. V5.GL.05.02 follows the methodology of V5.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 2023, updates satellite retrieval versions, and includes retrievals from the SNPP/NOAA VIIRS instruments.
Reference:
Aaron van Donkelaar, Melanie S. Hammer, Liam Bindle, Michael Brauer, Jeffery R. Brook, Michael J. Garay, N. Christina Hsu, Olga V. Kalashnikova, Ralph A. Kahn, Colin Lee, Robert C. Levy, Alexei Lyapustin, Andrew M. Sayer and Randall V. Martin (2021). Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty Environmental Science & Technology, 2021, doi:10.1021/acs.est.1c05309. [Link]
Hammer, M. S., van Donkelaar, A., Bindle, L., Sayer, A. M., Lee, J., Hsu, N. C., Levy, R.C., Sawyer, V., Garay, M. J., Kalashnikova, O. V., Kahn, R. A., Lyapustin, A., and Martin, R. V.: Assessment of the impact of discontinuity in satellite instruments and retrievals on global PM2.5 estimates. Remote Sensing of Environment, Volume 294, 2023, 113624, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2023.113624. [Link]
Scientific Datasets:
Annual and monthly datasets are provided in NetCDF [.nc] format. Gridded files use the WGS84 projection. Please contact our Support Team (support@satpm.org) for further information.
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-V5GL0502-GWRPM25]
Annual and monthly mean PM₂.₅ [µg/m3] at 0.1° × 0.1°:
[https://wustl.box.com/v/ACAG-V5GL0502-GWRPM25c0p05]
Annual and monthly mean PM₂.₅ Uncertainty [µg/m3] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V5GL0502-GWRPM25SIGMA]
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 V5.GL.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 data V5.GL.05.02 are licensed under CC BY 4.0