Global Particulate Matter (PM) 2.5 between 1998-2016

This layer contains particulate matter 2.5 (PM 2.5) air quality data globally between 1998 and 2016 based on NASA SEDAC gridded data. The data is aggregated to countries, administrative 1 boundaries, and 50 km hex bins. This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.
The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.

The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:

  • What is the average annual PM 2.5 value over 19 years? (1998-2016)
  • What is the annual average PM 2.5 value for each year from 1998 to 2016?
  • What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)
  • Are there hot spots (or cold spots) of PM 2.5 over the 19 years?
  • How many people are impacted by the air quality in an area?
  • What is the death rate caused by the joint effects of air pollution?
Choose a different attribute to symbolize in order to reveal any of the patterns above.
A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. 
Citations: 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. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020

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. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.

Boundaries and population figures:
  • Antarctica is excluded from all maps because it was not included in the original NASA grids.
  • 50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analyses
  • Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator
  • Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. 
Data processing notes:
  1. NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.
  2. For each geography level, the following was performed: 
    1. Zonal Statistics were run against the mosaic as a multidimensional layer.
    2. A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.
    3. The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.
  3. The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.
  4. The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.
  5. Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. 
  6. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. 
    1. Within each hex bin, the total population figure and average PM 2.5 were multiplied.
    2. The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.
    3. The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.
  7. Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.
  8. Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.
To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie. 

Datasets available for download

Additional Info

Field Value
Last Updated March 24, 2026, 21:07 (UTC)
Created March 24, 2026, 21:04 (UTC)
Domain / Topic
Domain or topic of the dataset being cataloged.
Title
Title for the Dataset.
Global Particulate Matter (PM) 2.5 between 1998-2016
Description
A description of the dataset.

This layer contains particulate matter 2.5 (PM 2.5) air quality data globally between 1998 and 2016 based on NASA SEDAC gridded data. The data is aggregated to countries, administrative 1 boundaries, and 50 km hex bins. This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.
The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.

The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:

  • What is the average annual PM 2.5 value over 19 years? (1998-2016)
  • What is the annual average PM 2.5 value for each year from 1998 to 2016?
  • What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)
  • Are there hot spots (or cold spots) of PM 2.5 over the 19 years?
  • How many people are impacted by the air quality in an area?
  • What is the death rate caused by the joint effects of air pollution?
Choose a different attribute to symbolize in order to reveal any of the patterns above.
A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. 
Citations: 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. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020

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. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.

Boundaries and population figures:
  • Antarctica is excluded from all maps because it was not included in the original NASA grids.
  • 50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analyses
  • Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator
  • Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. 
Data processing notes:
  1. NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.
  2. For each geography level, the following was performed: 
    1. Zonal Statistics were run against the mosaic as a multidimensional layer.
    2. A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.
    3. The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.
  3. The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.
  4. The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.
  5. Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. 
  6. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. 
    1. Within each hex bin, the total population figure and average PM 2.5 were multiplied.
    2. The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.
    3. The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.
  7. Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.
  8. Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.
To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie. 
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5946064.0
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2020-08-14
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public
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This work is licensed under the Esri Master License Agreement. View Summary | View Terms of Use
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https://climat.esri.ca/datasets/arcgis-content::global-particulate-matter-pm-2-5-between-1998-2016
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lisa_berry
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ArcGIS Living Atlas Team
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ArcGIS Living Atlas Team
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lisa_berry
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Accessed At
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2023-07-27
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01a55265757f402a8c4a3eaa2845cd0c
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English
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https://climat.esri.ca/datasets/arcgis-content::global-particulate-matter-pm-2-5-between-1998-2016
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ArcGIS Living Atlas Team
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