Satellite-derived PM2.5 Archive

Contents:

V6.GL.01 is recommended for all regions and is available for 2000-2019

V5.GL.03 is recommended for all regions
V4.NA.03 is available for compositional use over North America

Previous versions, including V4.NA.02.MAPLE, are also available.

Additions tools developed by users for accessing these datasets are available here

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Global Estimates (V6.GL.01):

We estimate annual and monthly ground-level fine particulate matter (PM_2.5) for 2000-2019 by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS 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.

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]

Annual and monthly datasets are provided in NetCDF [.nc] format. Gridded files use the WGS84 projection. Please contact Siyuan Shen (s.siyuan@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Lijian Han (ljhan@rcees.ac.cn) has prepared an example R-based code that converts the below netcdf files to raster for import into GIS-based programs that lack netcdf support. It can be accessed via this link: [NC to Geotif.txt]. Please contact Lijian if you have further questions.

Annual and monthly mean PM_2.5 [ug/m³] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V6GL01-CNNPM25]
Annual and monthly mean PM_2.5 [ug/m³] at 0.1° × 0.1°:
[https://wustl.box.com/v/ACAG-V6GL01-CNNPM25c0p10]

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.01 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.

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Global/Regional Estimates (V5.GL.03):

We estimate annual and monthly ground-level fine particulate matter (PM_2.5) for 1998-2021 by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, 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.03 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, and extends temporal coverage through 2021.

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]

Scientific Datasets:
Annual and monthly datasets are provided in NetCDF [.nc] format. Gridded files use the WGS84 projection. Please contact Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Lijian Han (ljhan@rcees.ac.cn) has prepared an example R-based code that converts the below netcdf files to raster for import into GIS-based programs that lack netcdf support. It can be accessed via this link: [NC to Geotif.txt]. Please contact Lijian if you have further questions.

Annual and monthly mean PM_2.5 [ug/m³] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V5GL03-GWRPM25]
Annual and monthly mean PM_2.5 [ug/m³] at 0.1° × 0.1°:
[https://wustl.box.com/v/ACAG-V5GL03-GWRPM25c0p10]

Annual and monthly mean PM_2.5 Uncertainty [ug/m³] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V5GL03-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.02 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.———————————————————————————

North American Regional Estimates (V4.NA.03):

We estimate ground-level fine particulate matter (PM_2.5) 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 for V4.NA.02. V4.NA.03 further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project, and uses V4.GL.03 PM2.5 estimates as geophysical input. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.03 total PM_2.5.

Reference:
Hammer, M. S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A. M.; Hsu, N. C.; Levy, R. C.; Garay, M. J.; Kalashnikova, O. V.; Kahn, R. A.; Brauer, M.; Apte, J. S.; Henze, D. K.; Zhang, L.; Zhang, Q.; Ford, B.; Pierce, J. R.; and Martin, R. V., Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)., Environ. Sci. Technol, doi: 10.1021/acs.est.0c01764, 2020. [Link]

van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392. [Link]

Scientific Datasets:
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, based on V4.NA.02, are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis. Please contact Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Annual mean PM_2.5 [ug/m³]: [.nc] [.asc.zip]
Annual mean SO₄²⁺ [%]: [.nc] [.asc.zip]
Annual mean NO₃^– [%]: [.nc] [.asc.zip]
Annual mean NH₄⁺ [%]: [.nc] [.asc.zip]
Annual mean OM [%]: [.nc] [.asc.zip]
Annual mean BC [%]: [.nc] [.asc.zip]
Annual mean SOIL [%]: [.nc] [.asc.zip]
Annual mean SS [%]: [.nc] [.asc.zip]

Monthly V4.NA.03 total mass PM_2.5 is available from: https://wustl.box.com/v/ACAG-V4NA03-PM25.

Monthly V4.NA.02 PM_2.5 composition described in van Donkelaar et al., ES&T 2019 are also available [here]. Percentages are denoted with a ‘p’ after component identifiers within filenames and recommended to be used with the V4.NA.03 dataset. 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.

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Previous Version of Global Estimates:

Version History

Version	Reference
V5.GL.02 (V5.GL.01 with 2020 update)	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]
V5.GL.01	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]
V4.NA.03 V4.EU.03 V4.CH.03	Hammer, M. S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A. M.; Hsu, N. C.; Levy, R. C.; Garay, M. J.; Kalashnikova, O. V.; Kahn, R. A.; Brauer, M.; Apte, J. S.; Henze, D. K.; Zhang, L.; Zhang, Q.; Ford, B.; Pierce, J. R.; and Martin, R. V., Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)., Environ. Sci. Technol, doi: 10.1021/acs.est.0c01764, 2020. [Link] van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, in press, doi:10.1021/acs.est.8b06392. [Link]
V4.NA.02.MAPLE	van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392. [Link]
V4.NA.02 V4.EU.02 V4.CH.02	van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392. [Link]
GWR_V4.GL.03 V4.GL.03	Hammer, M.S., A., van Donkelaar, et al. (2020). “Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018).” Environmental Science & Technology doi:10.1021/acs.est.0c01764. [Link]
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 PM_2.5 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.

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Global/Regional Estimates (V5.GL.02):

We estimate annual and monthly ground-level fine particulate matter (PM_2.5) for 1998-2020 by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, 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.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, and extends temporal coverage through 2020.

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]

Scientific Datasets:
Annual and monthly datasets are provided in NetCDF [.nc] format. Gridded files use the WGS84 projection. Please contact Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Lijian Han (ljhan@rcees.ac.cn) has prepared an example R-based code that converts the below netcdf files to raster for import into GIS-based programs that lack netcdf support. It can be accessed via this link: [NC to Geotif.txt]. Please contact Lijian if you have further questions.

Annual and monthly mean PM_2.5 [ug/m³] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V5GL02-GWRPM25]
Annual and monthly mean PM_2.5 [ug/m³] at 0.1° × 0.1°:
[https://wustl.box.com/v/ACAG-V5GL02-GWRPM25c0p10]

Annual and monthly mean PM_2.5 Uncertainty [ug/m³] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V5GL02-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.02 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.

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Global/Regional Estimates (V5.GL.01):

We estimate annual and monthly ground-level fine particulate matter (PM_2.5) for 1998-2019 by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a Geographically Weighted Regression (GWR), as detailed in the below reference for V5.GL.01.

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]

Scientific Datasets:
Annual and monthly datasets are provided in NetCDF [.nc] format. Gridded files use the WGS84 projection. Please contact Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Annual and monthly mean PM_2.5 [ug/m³] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V5GL01-GWRPM25]
Annual and monthly mean PM_2.5 [ug/m³] at 0.1° × 0.1°:
[https://wustl.box.com/v/ACAG-V5GL01-GWRPM25c]

Annual and monthly mean PM_2.5 Uncertainty [ug/m³] at 0.01° × 0.01°:
[https://wustl.box.com/v/ACAG-V5GL01-GWRPM25E]

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China Regional Estimates (V4.CH.03):

This dataset employs the same methodology used for V4.NA.03 to produce combined geophysical-statistical estimates of PM_2.5 over China using the recently expanded PM_2.5 measurement network in this region from May 2014 to December 2018, and extends these values back to 2000 using the interannual changes between the GM observed and non-GM observed time periods based on the V4.GL.03 geophysical satellite-derived values of Hammer et al. (2020).

Ground-based PM_2.5 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 PM_2.5 measurements were downloaded from https://taqm.epa.gov.tw/taqm/tw/YearlyDataDownload.aspx.

References:
Hammer, M. S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A. M.; Hsu, N. C.; Levy, R. C.; Garay, M. J.; Kalashnikova, O. V.; Kahn, R. A.; Brauer, M.; Apte, J. S.; Henze, D. K.; Zhang, L.; Zhang, Q.; Ford, B.; Pierce, J. R.; and Martin, R. V., Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)., Environ. Sci. Technol, doi: 10.1021/acs.est.0c01764, 2020. [Link]

van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, in press, doi:10.1021/acs.est.8b06392. [Link]

Scientific Datasets:
Gridded 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.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

A summary of annual population- and geographically-weighted provincial estimates are available here:
[ChinaPM25-V4CH03-PROVINCIAL-2000-2018.csv]

Year	PM2.5
2000	[.nc] [.asc.zip]
2001	[.nc] [.asc.zip]
2002	[.nc] [.asc.zip]
2003	[.nc] [.asc.zip]
2004	[.nc] [.asc.zip]
2005	[.nc] [.asc.zip]
2006	[.nc] [.asc.zip]
2007	[.nc] [.asc.zip]
2008	[.nc] [.asc.zip]
2009	[.nc] [.asc.zip]
2010	[.nc] [.asc.zip]
2011	[.nc] [.asc.zip]
2012	[.nc] [.asc.zip]
2013	[.nc] [.asc.zip]
2014	[.nc] [.asc.zip]
2015	[.nc] [.asc.zip]
2016	[.nc] [.asc.zip]
2017	[.nc] [.asc.zip]
2018	[.nc] [.asc.zip]

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European Regional Estimates (V4.EU.03):

This dataset employs the same methodology used for V4.NA.03 to produce combined geophysical-statistical estimates of PM_2.5 over Europe using available PM_2.5 measurements in this region. Ground-based PM_2.5 measurements were obtained from the European Environment Agency Air Quality e-Reporting system (https://www.eea.europa.eu/data-and-maps/data/aqereporting).

References:
Hammer, M. S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A. M.; Hsu, N. C.; Levy, R. C.; Garay, M. J.; Kalashnikova, O. V.; Kahn, R. A.; Brauer, M.; Apte, J. S.; Henze, D. K.; Zhang, L.; Zhang, Q.; Ford, B.; Pierce, J. R.; and Martin, R. V., Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)., Environ. Sci. Technol, doi: 10.1021/acs.est.0c01764, 2020. [Link]

van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, in press, doi:10.1021/acs.est.8b06392. [Link]

Scientific Datasets:
Gridded 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.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Year	PM2.5
2001	[.nc] [.asc.zip]
2002	[.nc] [.asc.zip]
2003	[.nc] [.asc.zip]
2004	[.nc] [.asc.zip]
2005	[.nc] [.asc.zip]
2006	[.nc] [.asc.zip]
2007	[.nc] [.asc.zip]
2008	[.nc] [.asc.zip]
2009	[.nc] [.asc.zip]
2010	[.nc] [.asc.zip]
2011	[.nc] [.asc.zip]
2012	[.nc] [.asc.zip]
2013	[.nc] [.asc.zip]
2014	[.nc] [.asc.zip]
2015	[.nc] [.asc.zip]
2016	[.nc] [.asc.zip]
2017	[.nc] [.asc.zip]
2018	[.nc] [.asc.zip]

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Global Estimates (V4.GL.03 / V4.GL.03.NoGWR):

We estimate global annual surface fine particulate matter (PM_2.5) concentrations for 1998-2019. Aerosol optical depth (AOD) from the NASA MODIS C6.1, MISR v23, MAIAC C6, and SeaWiFS satellite products are combined and related to surface PM2.5 concentrations using geophysical relationships between surface (PM_2.5) and AOD simulated by the GEOS-Chem chemical transport model. These estimates are subsequently calibrated to global ground-based observations of (PM_2.5) from the World Health Organization using Geographically Weighted Regression (GWR). These estimates identify significant regional trends, as detailed in the reference below.

References:
Hammer, M. S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A. M.; Hsu, N. C.; Levy, R. C.; Garay, M. J.; Kalashnikova, O. V.; Kahn, R. A.; Brauer, M.; Apte, J. S.; Henze, D. K.; Zhang, L.; Zhang, Q.; Ford, B.; Pierce, J. R.; and Martin, R. V., Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)., Environ. Sci. Technol, doi: 10.1021/acs.est.0c01764, 2020. [Link]

Errata: In Table S5, the global population weighted mean GEO PM_2.5, HBR PM_2.5, and GBD PM_2.5 values of 41.8, 46.9, and 55.7 should be corrected to 35.3, 40.4, and 46.3.

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. Country means are also provided in a comma separated ascii (.csv) format. Dust and Sea-Salt Removed PM_2.5 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 Melanie Hammer (melanie.hammer@wustl.edu), cc Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu)

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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Studies with a focus on North America, Europe or China are recommended to use V4.NA.03, V4.EU.03 and V4.CH.03, available above.

Version information is available below. GWR_V4.GL.03 refer to those datasets that incorporate ground-based observations via a Geographically Weighted Regression, as described in Hammer et al., ES&T 2020. V4.GL.03 refer to those data that do not incorporate ground-based observations, also described in Hammer et al., ES&T 2020.

All Composition PM_2.5:

Satellite-Derived PM_2.5, 1998, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 1999, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2000, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2001, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2002, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2003, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2004, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2005, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2006, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2007, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2008, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2009, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2010, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2011, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2012, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2013, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2014, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2015, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2016, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2017, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2018, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2019, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Dust and Sea-Salt Removed:

Satellite-Derived PM_2.5, 1998, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 1999, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2000, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2001, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2002, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2003, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2004, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2005, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2006, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2007, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2008, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2009, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2010, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2011, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2012, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2013, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2014, at 35% RH [ug/m3]
0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2015, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2016, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2017, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2018, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

Satellite-Derived PM_2.5, 2019, at 35% RH [ug/m3]

0.05° × 0.05° [.nc] [.asc.zip] [.csv]
0.05° × 0.05° w GWR adjustment [.nc] [.asc.zip] [.csv]
0.01° × 0.01° w GWR adjustment [.nc] [.asc.zip] [.csv]

———————————————————————————

North American Regional Estimates (V4.NA.02.MAPLE):

We estimate ground-level fine particulate matter (PM_2.5) 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 for V4.NA.02. V4.NA.02.MAPLE further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project. This adjustment was of particular value over low concentrations. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.02.MAPLE total PM_2.5.

Reference:
van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392. [Link]

Scientific Datasets:
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). A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis. Please contact Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu) for further information.

V4.NA.02.MAPLE is also permenantly available via: https://zenodo.org/record/6557778 with doi 10.5281/zenodo.6557777.

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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Annual mean PM_2.5 [ug/m³]: [.nc] [.asc.zip]
Annual mean SO₄²⁺ [%]: [.nc] [.asc.zip]
Annual mean NO₃^– [%]: [.nc] [.asc.zip]
Annual mean NH₄⁺ [%]: [.nc] [.asc.zip]
Annual mean OM [%]: [.nc] [.asc.zip]
Annual mean BC [%]: [.nc] [.asc.zip]
Annual mean SOIL [%]: [.nc] [.asc.zip]
Annual mean SS [%]: [.nc] [.asc.zip]

Monthly V4.NA.02.MAPLE total mass PM_2.5 is available from: here.

Annual and monthly V4.NA.02 PM_2.5 total mass and composition described in van Donkelaar et al., ES&T 2019 are available from: here. Percentages are denoted with a ‘p’ after component identifiers within filenames. V4.NA.02.MAPLE total mass PM_2.5 is available from: here. 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.

———————————————————————————

China Regional Estimates (V4.CH.02):

This dataset employs the same methodology used for V4.NA.02 to produce combined geophysical-statistical estimates of PM_2.5 over China using the recently expanded PM_2.5 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 PM_2.5 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 PM_2.5 measurements were downloaded from https://taqm.epa.gov.tw/taqm/tw/YearlyDataDownload.aspx.

References:
van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, in press, doi:10.1021/acs.est.8b06392. [Link]

van Donkelaar, A., R. V. Martin, et al. (2015) 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. Please contact Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Annual mean PM_2.5 [ug/m³]: [.nc] [.asc.zip]

A summary of annual population- and geographically-weighted provincial estimates are available here:
[ChinaPM25-V4CH02-PROVINCIAL-2000-2017.csv]

———————————————————————————

European Regional Estimates (V4.EU.02):

This dataset employs the same methodology used for V4.NA.02 to produce combined geophysical-statistical estimates of PM_2.5 over Europe using available PM_2.5 measurements in this region. Ground-based PM_2.5 measurements were obtained from the European Environment Agency Air Quality e-Reporting system (https://www.eea.europa.eu/data-and-maps/data/aqereporting).

References:
van Donkelaar, A., R. V. Martin, et al. (2019). Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors. Environmental Science & Technology, in press, doi:10.1021/acs.est.8b06392. [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. Please contact Aaron van Donkelaar (aaron.vandonkelaar@wustl.edu) 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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Annual mean PM_2.5 [ug/m³]: [.nc] [.asc.zip]

———————————————————————————
Global Estimates (V4.GL.02 / V4.GL.02.NoGWR):

We estimate ground-level fine particulate matter (PM_2.5) 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 PM_2.5 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 PM_2.5 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.vandonkelaar@wustl.edu) 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 PM_2.5 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.

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.

Annual Mean All Composition PM_2.5:
Geophysical PM_2.5 at 0.1° × 0.1° [ug/m³]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM_2.5 at 0.1° × 0.1° [ug/m³]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM_2.5 at 0.01° × 0.01° [ug/m³]: [.nc] [.asc.zip] [.kmz image] [.csv summary]

Annual Mean Dust and Sea-Salt Removed PM_2.5:
Geophysical PM_2.5 at 0.1° × 0.1° [ug/m³]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM_2.5 at 0.1° × 0.1° [ug/m³]: [.nc] [.asc.zip] [.kmz image] [.csv summary]
Hybrid PM_2.5 at 0.01° × 0.01° [ug/m³]: [.nc] [.asc.zip] [.kmz image] [.csv summary]

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North American Estimates with Ground-Monitor Based Adjustment (V4.NA.01):

We estimate ground-level fine particulate matter (PM_2.5) 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 PM_2.5 estimates. Value prior to 2004 use apply the temporal variation of Boys et al., to the GWR-adjusted period.

Reference:
van Donkelaar, A., R. V. Martin, R. J. D. Spurr and R. T. Burnett, High-resolution satellite-derived PM_2.5 from optimal estimation and geographically weighted regression over North America, Environ. Sci. and Tech., doi 10.1021/acs.est.5b02076 [Link]

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]

Google Earth Datasets:
A small selection of regional Mean Satellite-Derived PM_2.5 estimates have been cut for import into Google Earth. Click on placemarks labelled ‘PM_2.5′ for the legend.

Satellite-Derived PM_2.5 with GWR, North America, 3-yr mean, at 35%RH [ug/m3] [.kmz]

Scientific Datasets:
Global resolved datasets are provided in an ArcGIS-compatible ASCII file. Other extractions can often be produced upon request.

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 PM_2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution.

Satellite-Derived PM_2.5 with GWR, North America, 3-yr mean, at 35%RH [ug/m3] Resolved [.asc.zip]

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Version 3.01

References:
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 an ArcGIS-compatible ASCII file. Country means are provided in a comma separated ascii (.csv) format. Other extractions can often be produced upon request.

All Composition:
Satellite-Derived PM_2.5, 3-yr mean, at 35% RH [ug/m3] Resolved [.asc.zip] Country Mean [.csv]
Satellite-Derived PM_2.5 Trend, 1998-2012, at 35% RH [ug/m3] Resolved [.asc.zip]

Dust and Sea-Salt Removed:
Satellite-Derived PM_2.5, 3=yr mean, at 35% RH [ug/m3] Resolved [.asc.zip] Country Mean [.csv]

Format:
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].

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Version 1.01

References:
van Donkelaar, A., R.V. Martin, M. Brauer, R. Kahn, R. Levy, C. Verduzco, and P.J. Villeneuve, Global estimates of exposure to fine particulate matter concentrations from satellite-based aerosol optical depth, Environ. Health Perspec., doi:10.1289/ehp.0901623, 118(6), 2010. [Full Text (PDF)]

Google Earth Datasets:
A small selection of regional Annual Mean Satellite-Derived PM_2.5 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).

Google Earth Datasets:
A small selection of regional Mean Satellite-Derived PM_2.5 estimates have been cut for import into Google Earth, available [here]. These correspond to 2001-2010 concentrations adjusted to 35% relative humidity. Click on placemarks labelled ‘PM_2.5′ for the legend.

Available Scientific Datasets:
Annual Mean Satellite-Derived PM_2.5, 2001-2006, at 35% RH [ug/m3] [here]
Annual Mean Satellite-Derived PM_2.5, 2001-2006, at 50% RH [ug/m3] [here]
Annual Mean Satellite-Derived PM_2.5 Total Number of Observations [counts], 2001-2006 [here]
Annual Mean Satellite-Derived PM_2.5 Uncertainty [%], 2001-2006 [here]
Annual Mean Combined MODIS/MISR AOD [unitless], 2001-2006 [here]

Format:
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.

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We sincerely appreciate the efforts by our users to develop tools that facilitate access of our PM2.5 estimates. While we don’t directly service these codes, we gratefully make them available for others to use. Please contact the developer indicated if you have any questions.

– Lijian Han (ljhan@rcees.ac.cn) has prepared an example R-based code that converts the netcdf files we provide into raster for import into GIS-based programs that lack netcdf support. It can be accessed via this link: [NC to Geotif.txt].

– MITRE’s Health Equity MIP has developed an open-source R package for particulate matter 2.5 data called [ACAGPM]. This package aligns ACAG’s particulate matter 2.5 data with 2019 census geographies, allowing users to efficiently analyze PM2.5 as it relates to social determinants of health and other variables. For more information, please reach out to the lead contributors indicated via github.