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Exposure to air pollution from human sources

What you should know about this indicator

  • Outdoor air pollution, specifically PM2.5, is a major environmental risk factor for health issues such as respiratory and cardiovascular diseases. It is estimated to contribute to millions of premature deaths globally each year.
  • The World Health Organization (WHO) recommends that annual average PM2.5 levels should be below 5 µg/m³ to minimize health impacts.
  • The Air Quality Life Index (AQLI) uses satellite-derived data to estimate PM2.5 levels, which may differ from local ground monitor readings.
  • This data uses population-weighted averages to reflect the exposure of people in a region. It also excludes natural sources of PM2.5, such as dust and sea salt, to focus on human-caused pollution.
  • This data is not directly comparable to data from the World Bank’s World Development Indicators (WDI) or the WHO’s Global Health Observatory (GHO), which include natural sources of PM2.5.
  • You can learn more about the data and methodology in the AQLI methodology and research design document.

How is this data described by its producer?

The data for the annual average concentrations of PM2.5 comes from a satellite-derived particulate pollution concentration dataset constructed by Donkelaar et al. (2021).

Users may find that the AQLI’s satellite-derived pollution data differs from what their governments or local air quality monitors report. Some of the discrepancies are by design, reflecting differences in the operational definition of particulate pollution level:

Dust and sea salt

Whereas monitors pick up all kinds of particulates, the AQLI intentionally uses raw PM₂.₅ data from which the shares of mineral dust and sea salt have been removed in order to target human-caused pollution. This decreases the AQLI’s reported pollution, especially in arid regions such as North Africa, the Middle East, Sub-Saharan African, and northwestern China.

Area average vs. point estimates

The satellite-derived data measures the average air pollution within a block of a grid. In contrast, air pollution monitors measure the air pollution level at their specific point locations—downwind of the brick kiln or in a tree in the park. If one were to average monitor readings in a region, the average could be higher or lower than one specific point measure.

Population-weighting

We use population weighting to calculate average exposures in a region. Population weighting is done by multiplying the air pollution level (PM₂.₅) in each cell by the population in that grid. These values are then added up for all grids within a region, and the total is divided by the sum of the populations of all grids.

Resolution

While the 0.01 x 0.01-degree grid provides a fine resolution for a global air pollution dataset, it may misrepresent pollution levels in areas with sharp changes in pollution and population density at smaller scales. This issue arises in areas with significant pollution and population variation, such as cities near large mountains. An extreme example is Ulaanbaatar, Mongolia. The city’s elongated shape and location in the Tuul River Valley mean each grid cell used to calculate its average pollution level includes both the highly polluted urban area and the cleaner surrounding mountains. This lowers the grid’s average pollution levels, and population-weighting does not sufficiently reduce the influence of the less-polluted mountain areas. As a result, AQLI estimates much lower PM₂.₅ levels (19 μg/m³ in 2016) compared to monitor data (92 μg/m³), with the latter better reflecting the air residents breathe.

More information can be found in the AQLI methodology and research design document.

Exposure to air pollution from human sources
Annual population-weighted average concentration of in the air, measured in micrograms per cubic meter (µg/m³). Fine particulate matter (PM2.5) are particles with a diameter of 2.5 micrometers or less, which can pose significant health risks. This data excludes natural sources of PM2.5, such as dust and sea salt, to focus on human-caused pollution.
Source
Energy Policy Institute at the University of Chicago (EPIC) (2026) – with minor processing by Our World in Data
Last updated
March 30, 2026
Next expected update
March 2027
Date range
1998–2024
Unit
micrograms per cubic meter

Sources and processing

This data is based on the following sources

Energy Policy Institute at the University of Chicago (EPIC) – Air Quality Life Index (AQLI)

The Air Quality Life Index (AQLI) by the Energy Policy Institute at the University of Chicago showcases the potential for life expectancy gains if air pollution levels were reduced. Fine particle pollution (PM2.5) shortens people's lives when they breathe them in, the AQLI projects how many more years people could live if pollution was reduced to meet the World Health Organization's guidelines (an annual exposure of 5 µg/m3). Using global, satellite-derived data, the AQLI provides a detailed picture of air pollution levels and their health impact around the world, tracking changes from 1998 to 2022. This information can be used to inform policies and actions that aim to improve air quality and public health.

Retrieved on
March 30, 2026
Retrieved from
https://aqli.epic.uchicago.edu/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Air Quality Life Index (AQLI) by the Energy Policy Institute at the University of Chicago (https://aqli.epic.uchicago.edu/) (2024)

The Air Quality Life Index (AQLI) by the Energy Policy Institute at the University of Chicago showcases the potential for life expectancy gains if air pollution levels were reduced. Fine particle pollution (PM2.5) shortens people's lives when they breathe them in, the AQLI projects how many more years people could live if pollution was reduced to meet the World Health Organization's guidelines (an annual exposure of 5 µg/m3). Using global, satellite-derived data, the AQLI provides a detailed picture of air pollution levels and their health impact around the world, tracking changes from 1998 to 2022. This information can be used to inform policies and actions that aim to improve air quality and public health.

Retrieved on
March 30, 2026
Retrieved from
https://aqli.epic.uchicago.edu/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Air Quality Life Index (AQLI) by the Energy Policy Institute at the University of Chicago (https://aqli.epic.uchicago.edu/) (2024)

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline

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Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Exposure to air pollution from human sources”, part of the following publication: Hannah Ritchie and Max Roser (2022) - “Outdoor Air Pollution”. Data adapted from Energy Policy Institute at the University of Chicago (EPIC). Retrieved from https://archive.ourworldindata.org/20260512-161845/grapher/human-caused-air-pollution-exposure.html [online resource] (archived on May 12, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Energy Policy Institute at the University of Chicago (EPIC) (2026) – with minor processing by Our World in Data

Full citation

Energy Policy Institute at the University of Chicago (EPIC) (2026) – with minor processing by Our World in Data. “Exposure to air pollution from human sources” [dataset]. Energy Policy Institute at the University of Chicago (EPIC), “Air Quality Life Index (AQLI)” [original data]. Retrieved May 12, 2026 from https://archive.ourworldindata.org/20260512-161845/grapher/human-caused-air-pollution-exposure.html (archived on May 12, 2026).

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