Source: http://fizz.phys.dal.ca/~atmos/martin/?page_id=140
Timestamp: 2019-04-20 01:01:46+00:00

Document:
Previous, obsolete versions are also available.
We estimate ground-level fine particulate matter (PM2.5) total and compositional mass concentrations over North America 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 regional ground-based observations of both total and compositional mass using Geographically Weighted Regression (GWR) as detailed in the below reference.
Annual datasets are provided in NetCDF [.nc] or a zipped ArcGIS-compatible ASCII [.asc.zip] file. Note that the unzipped ASCII files can be cumbersome. Gridded files use the WGS84 projection. Compositional estimates are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). Please contact Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca) 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 (0.01° × 0.01°), but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.
The monthly V4.NA.02 PM2.5 total mass and composition described in van Donkelaar et al., ES&T 2019 are available via anonymous ftp to: ftp://stetson.phys.dal.ca/Aaron/V4NA02/Monthly/. Users are reminded that these datasets are intended for long-term, large-scale studies. Increased uncertainties are expected when used at finer spatial/temporal resolution.
This dataset employs the same methodology used for V4.NA.02 to produce combined geophysical-statistical estimates of PM2.5 over China using the recently expanded PM2.5 measurement network in this region from May 2014 to December 2016, and extends these values back to 2000 using the interannual changes between the GM observed and non-GM observed time periods based on the geophysical satellite-derived values of van Donkelaar et al. (2015).
Ground-based PM2.5 measurements were obtained from http://beijingair.sinaapp.com/ over mainland China. These data are captured by individuals from instantaneous data records on the website of the Chinese EPA. Taiwanese PM2.5 measurements were downloaded from https://taqm.epa.gov.tw/taqm/tw/YearlyDataDownload.aspx.
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. Please contact Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca) for further information.
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.
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 Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca) 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.02, available above.
Version information is available below. 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.
V4.GL.02 V4.GL.02.NoGWR van Donkelaar, A., R. V. Martin, et al. (2016). “Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.” Environmental Science & Technology 50(7): 3762-3772.
van Donkelaar, A., R. V. Martin, et al. (2015). “Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter.” Environmental Health Perspectives 123(2): 135-143.
V4.GL.01 van Donkelaar, A., R. V. Martin, et al. (2016). “Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.” Environmental Science & Technology 50(7): 3762-3772.
V4.NA.01 van Donkelaar, A., R. V. Martin, et al. (2015). “High-resolution satellite-derived PM2.5 from optimal estimation and geographically weighted regression over North America.” Environmental Science & Technology 49(17): 10482-10491.
V3.01 van Donkelaar, A., R. V. Martin, et al. (2015). “Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter.” Environmental Health Perspectives 123(2): 135-143.
V2.01 van Donkelaar, A., R. V. Martin, et al. (2013). “Optimal estimation for global ground-level fine particulate matter concentrations.” Journal of Geophysical Research 118: 1–16.
V1.01 van Donkelaar, A., R. V. Martin, et al. (2010). “Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application.” Environmental Health Perspectives 118(6).
V0.01 van Donkelaar, A., R. V. Martin, et al. (2006). “Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing.” Journal of Geophysical Research-Atmospheres 111(D21).
Note: V4.GL.01 provided identical PM2.5 estimates as V4.GL.02 for years 2008 and onward, but did not incorporate the additional temporal information from Boys et al (2014) and van Donkelaar et al. (2015). V4.GL.01 filenames do not contain the ‘wUni’ addendum.
We estimate ground-level fine particulate matter (PM2.5) over North America by combining a 0.01 degree x 0.01 degree resolution optimal estimate-based Aerosol Optical Depth (AOD) retrieval from the NASA MODIS instrument with aerosol vertical profile and scattering properties simulated by the GEOS-Chem chemical transport model. We then use a geographically weighted regression (GWR) that incorporates ground-based observations to adjust for any residual bias in the satellite-derived PM2.5 estimates. Value prior to 2004 use apply the temporal variation of Boys et al., to the GWR-adjusted period.
A small selection of regional Mean Satellite-Derived PM2.5 estimates have been cut for import into Google Earth. Click on placemarks labelled ‘PM2.5′ for the legend. For additional regions, please contact Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca).
Global resolved datasets are provided in an ArcGIS-compatible ASCII file. Other extractions can often be produced upon request. Please contact Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca) for further information.
A small selection of regional Mean Satellite-Derived PM2.5 estimates have been cut for import into Google Earth. These correspond to 2001-2010 concentrations adjusted to 35% relative humidity. Click on placemarks labelled ‘PM2.5′ for the legend. For additional regions, please contact Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca).
Global resolved datasets are provided in an ArcGIS-compatible ASCII file. Country means are provided in a comma separated ascii (.csv) format. Other extractions can often be produced upon request. Please contact Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca) for further information.
All data are provided in a GIS-friendly ASCII format.
Ground-level PM2.5 observations collected for validation of van Donkelaar et al. (above) are also publicly available in an Excel spreadsheet [.xlsx].
A small selection of regional Annual Mean Satellite-Derived PM2.5 estimates have been cut for import into Google Earth (the whole dataset is too big for a single file). These correspond to mean 2001-2006 values adjusted to the given relative humidities (RH). For additional regions, please contact Aaron van Donkelaar (Aaron.van.Donkelaar@dal.ca).
All data are provided in a comma-separated, ASCII file. The first row/column indicate longitude/latitude centres, respectively. Missing values or water are denoted by -999. Thanks to Andy Davidson for providing the GIS-friendly .TIF.zip and .ASCII.zip files.

References: V. 
 V. 
 V. 
 V. 
 V. 
 V. 
 V. 
 V.