The Air Quality-Life Index™


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How much longer would you live if your country reduced air pollution to comply with your own national standard or the World Health Organization (WHO) standard?

The number of years saved if countries met the World Health Organization (WHO) standard for what is considered safe air quality.

Airborne particulate matter pollution is perhaps the greatest current environmental risk to health, leading to respiratory and heart disease, strokes and lung cancer. Currently, an estimated 4.5 billion people around the world are exposed to levels of particulate pollution that are at least twice what the World Health Organization considers safe.

The Air Quality-Life Index, or AQLITM, translates particulate pollution concentrations into their impact on lifespans. Specifically, the AQLI provides a reliable measure of the potential gain in life expectancy communities could see if their pollution concentrations are brought into compliance with global or national standards. The AQLI can serve as an important complement to the frequently used Air Quality Index (AQI) that is a complicated function of air pollution concentrations and does not map directly to health.

Scores of studies have demonstrated that air pollution negatively impacts our health. But, these studies tend to rely on data tracking people’s exposure over a short time period, while the most important question is about lifetime exposure. Here, there is almost no evidence, particularly at the high concentrations that prevail currently in many parts of the world. Further, the conventional wisdom is that it is very difficult to find settings where it is possible to isolate the effect of air pollution from other factors that impact health.

The AQLI is based on data from a pair of studies (Chen et al. 2013; Ebenstein et al. 2017) published in the Proceedings of the National Academy of Sciences (PNAS) that provide credible solutions to these challenges. The studies exploit a quasi-experiment where one segment of the population was inadvertently exposed to high particulate pollution concentrations during a period when migration was restricted, thus producing sustained differences in pollution exposure that is plausibly unrelated to other determinants of health.

Calculating Life Years Lost Per Person

The AQLI provides county-level (or equivalent) estimates of life years lost per person from ambient PM2.5 concentrations above World Health Organization (WHO) or nationally administered standards. The calculation of life years lost per person in each county is based on an aggregation of grid-level (1km x 1km) estimates of PM2.5 and population as follows:

  • Each grid cell is assigned a number of total life years lost equal to:

(Effect of PM2.5 on life expectancy) x (Grid population) x (Grid PM2.5 above standard)

Note: If the grid cell’s estimated PM2.5 is less than its associated standard, life years lost is set equal to zero.

  • Total life years lost in each grid are then summed to the county level, giving us the total number of life years lost per county. Dividing this number by the population of the county yields life years lost per person.

Converting the Effect of PM10 on Life Expectancy in Ebenstein et al. (2017) to Units of PM2.5

Our estimated impact of pollution on life expectancy is based on the finding in Ebenstein et al. (2017) that a 10 μg/m3 increase in sustained exposure to PM10 reduces life expectancy by 0.64 years. To apply this PM10 estimate to our global measures of PM2.5 concentrations, we estimate the ratio of PM2.5-to-PM10 in China using pollution monitor data in 2013 – 2015 from the national air quality monitoring network. The data provided to us contains annualized monitor-level measurements of the ratio of PM2.5-to-PM10 from 1,612 monitors across China in 2013 – 2015, for a total of 3,233 unique monitor readings. These monitor values are aggregated to a national ratio of PM2.5-to-PM10 by first averaging monitor readings within the county they are located, and then calculating a population weighted average ratio of PM2.5-to-PM10 across all counties. This procedure yields a national PM2.5-to-PM10 ratio of 0.62; applying this to the Ebenstein et al. (2017) estimate of 0.64 life years lost per 10 μg/m3 of PM10 yields an estimated effect 1.03 life years lost per 10 μg/m3 of PM2.5.

Global PM2.5 Estimates

Grid-level (1km x 1km) estimates of ambient PM2.5 concentrations across the world (as of 2015) were provided by the Atmospheric Composition Analysis Group and are produced using a combination of satellite, monitor- and simulation- based sources. These measures of PM2.5 represent, to our knowledge, one of the most comprehensive aggregations of PM2.5 on a global scale. Notably, the estimated concentrations of PM2.5 used in this index exclude dust and sea-salt, which are typically considered natural sources of PM2.5. Therefore, the estimates of ambient PM2.5 concentrations shown in the map can be interpreted as pollution resulting principally from human activity.

Global Population Estimates

Grid-level (1km x 1km) estimates of population across the world (as of 2011) were provided by LandScan Global Population Database.

PM2.5 Standards

The AQLI measures PM2.5 concentrations against two sets of PM2.5 standards. The first is the WHO standard of annual mean concentrations of 10 μg/m3. The second is based on nationally administrated standards, which can vary by country. We identified annual mean PM2.5 standards for 86 countries ranging from 8-40 μg/m3. For the remaining countries which we could not find national standards for, we assigned them a national standard equal to the average standard in these 86 countries (21.3 μg/m3).

Notes and Sources:

Kutlar Joss, Meltem et al. “Time to Harmonize National Ambient Air Quality Standards.” International Journal of Public Health 62.4 (2017): 453–462.

van Donkelaar, A. et al. “Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors.” Environmental Science and Technology 50.7 (2016): 3762–3772.

This product was made utilizing the LandScan (2011)™ High Resolution global Population Data Set copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with the United States Department of Energy.  The United States Government has certain rights in this Data Set.  Neither UT-BATTELLE, LLC NOR THE UNITED STATES DEPARTMENT OF ENERGY, NOR ANY OF THEIR EMPLOYEES, MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE DATA SET.

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