V4.GL.02/V4.GL.02.NoGWR
Global/Regional Estimates
- retired -
We estimate ground-level fine particulate matter (PM₂.₅) by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to global ground-based observations of PM₂.₅ using Geographically Weighted Regression (GWR) as detailed in the below reference.
References:
van Donkelaar, A., R.V Martin, M.Brauer, N. C. Hsu, R. A. Kahn, R. C Levy, A. Lyapustin, A. M. Sayer, and D. M Winker, Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors, Environ. Sci. Technol, doi: 10.1021/acs.est.5b05833, 2016. [Link]
Estimates prior to 2008 incorporate temporal information from:
Boys, B.L., Martin, R.V., van Donkelaar, A., MacDonell, R., Hsu, N.C., Cooper, M.J., Yantosca,R.M., Lu, Z., Streets,D.G., Zhang,Q., Wang,S., Fifteen-year global time series of satellite-derived fine particulate matter, Environ. Sci. Technol, 10.1021/es502113p, 2014. [Link]
van Donkelaar, A., R. V. Martin, M. Brauer and B. L. Boys, Global fine particulate matter concentrations from satellite for long-term exposure assessment, Environmental Health Perspectives, 123, 135-143, DOI:10.1289/ehp.1408646, 2015. [Link]
Scientific Datasets:
Global resolved datasets are provided in ArcGIS-compatible NetCDF [.nc] or zipped ASCII [.asc.zip] file. Note that the unzipped ASCII files can be cumbersome. Gridded files use the WGS84 projection. Corresponding files for Google Earth are also provided [.kmz]. Country means are also provided in a comma separated ascii (.csv) format. Dust and Sea-Salt Removed PM₂.₅ estimates apply simulated compositional information to our full-composition values, following van Donkelaar et al., EHP, 2015. Other extractions can often be produced upon request. Please contact our Support Team (support@satpm.org) for further information.
Note that these estimates are primarily intended to aid in large-scale studies. Gridded datasets are provided to allow users to agglomerate data as best meets their particular needs. Datasets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM₂.₅ gradients at the gridded resolution due to influence by information sources at coarser resolution.
Studies with a focus on North America are recommended to use V4.NA.03, available above.
V4.GL.02 refer to those datasets that incorporate ground-based observations via a Geographically Weighted Regression, as described in van Donkelaar et al., ES&T 2016. V4.GL.02.NoGWR refer to those data that do not incorporate ground-based observations, also described in van Donkelaar et al., ES&T 2016.
Annual Mean All Composition PM₂.₅:
Geophysical PM₂.₅ at 0.1° × 0.1° [µg/m3]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM₂.₅ at 0.1° × 0.1° [µg/m3]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM₂.₅ at 0.01° × 0.01° [µg/m3]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Annual Mean Dust and Sea-Salt Removed PM₂.₅:
Geophysical PM₂.₅ at 0.1° × 0.1° [µg/m3]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM₂.₅ at 0.1° × 0.1° [µg/m3]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM₂.₅ at 0.01° × 0.01° [µg/m3]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
SatPM₂.₅ data V4.GL.02/V4.GL.02.NoGWR are licensed under CC BY 4.0