Calculating Life Years Lost Per Person
The AQLI estimates the relationship between air pollution and life expectancy, allowing users to view the gain in life expectancy they could experience if their community met World Health Organization (WHO) guidelines, national standards or some other standard. It does so by leveraging results from a pair of studies set in China. The results of the studies are combined with detailed global population and PM2.5 data to estimate the impact of particulate matter on life expectancy across the globe.
Grid-Level Particulates and Population Estimates
The data sources used to construct the AQLI were chosen for their geographic completeness and their methodological consistency between data points across countries.
The AQLI incorporates twenty years of annual ambient particulate pollution (PM2.5) concentration estimates. This satellite-derived data, provided by Van Donkelaar et al. (2016), covers the globe at the high resolution of 10km x 10km — in other words, for each year, there is a data point for every area about 1/8 the size of New York City, 1/15 the size of Delhi, or 1/40 the size of urban Beijing.
Throughout the AQLI, we report PM2.5 that excludes dust and sea salt, which can be interpreted as concentrations stemming primarily from human activity (such as automobile emissions, power plants, or industrial activities) rather than natural sources (such as dust). This allows us to focus on the subset of particulate pollution which has a more similar composition to the particulates studied in Ebenstein et al. (2017) that predominantly relies on variation due to difference in coal combustion, and which can be most easily targeted by public policies.
The AQLI uses population data from the 2015 LandScan Global Population Database, which uses spatial methods to disaggregate census population counts in each country into grid cells of length 30 arc-seconds. These grid cells are about 1 km2 around the Equator, and smaller elsewhere. After combining the detailed population data with the satellite estimates of PM2.5concentrations, the result is a global gridded database of ambient PM2.5 concentrations with associated population counts. The population counts are used as weights when aggregating PM2.5 concentrations and life expectancy results from the grid level up to the local, state, national, and global averages.
When aggregating pollution and life expectancy gains for any year, the AQLI always uses the 2015 population data. This is so that changes in pollution levels and life expectancy gains across time reflect real changes in the concentration of particulates in the air, and are not confounded with changes in the population distribution over time. Thus, for example, life expectancy impacts reported for 1998 are to be interpreted as the life expectancy impacts that people alive today would experience if particulate concentrations were at 1998’s levels instead of at current concentrations.
The AQLI is updated each year with the latest available PM2.5 and population data.
WHO Guideline and National Standards for PM2.5:
The AQLI measures potential gains in life expectancy by lowering PM2.5 concentrations to meet either the WHO guideline for particulate matter concentrations or nationally administered air quality standards (National Standards). The WHO’s guideline is 10 micrograms per cubic meter (μg/m3), which corresponds to the lowest level of long-term exposure that the WHO found to raise mortality with greater than 95% confidence. Country-specific nationally administered annual standards were identified for 86 countries and range from 8–40 μg/m3. For the remaining countries for which we could not identify a national standard, we indicate on the map tool that they lack a national PM2.5 standard and do not calculate gains in life expectancy relative to national standard. Additionally, the AQLI allows users to enter their own percent reduction in pollution concentrations and see the gains in life expectancy that would result.
The Effect of Particulate Pollution on Life Expectancy
The AQLI is based on a study by Michael Greenstone, Avraham Ebenstein, Maoyong Fan, Guojun He, and Maigeng Zhou (2017) that, thanks to a unique social setting, were able to measure the effect of sustained exposure to high levels of pollution on a person’s life expectancy.
In China, areas in the north have traditionally experienced higher levels of pollution in part because of a government policy initiated during the planning period (i.e., 1950 to 1980) that gave those living north of the Huai River, where it is colder, free coal to power boilers for heating. While the policy’s purpose was to provide warmth in the winter to those who needed it the most, it resulted in a high and differential reliance on coal north of the Huai River, relative to the south. The legacy of the policy remains today, with very different rates of indoor heating north and south of the Huai River as the north continued to rely on the coal heating systems. . At the same time, the hukou household registration system discouraged people from leaving the communities where they were born. This effectively meant that people exposed to particulate pollution could not migrate to areas with cleaner air. Combined, these two policies created a unique demarcation line where the researchers were able to study the impact of high levels of pollution over a long period of time and to isolate that impact from other factors that affect life expectancy.
The more recent of the pair of studies indicates that sustained exposure to an additional 10 μg/m3 of PM10 reduces life expectancy by 0.64 years. Although the study was based solely on a Chinese setting, together, the regions and years covered in the study saw a wide range of pollution levels: in the areas within five degrees latitude of the Huai River, the range of PM10 levels within a standard deviation of the mean is 75-148 μg/m3 of PM10 (approximately equal to 48-96 μg/m3 of PM2.5). The full range within five degrees latitude of the river is 27-307 μg/m3 of PM10 (approximately equal to 18-200 μg/m3 of PM2.5). Thus, the relationship between life expectancy and particulate pollution that underlies the AQLI is derived from a PM2.5 distribution similar to the observed global distribution, providing a credible basis for generalizing the measured pollution-life expectancy relationship from Ebenstein et al. (2017).
Figure: PM10 Concentrations in China and the Huai River Dividing Line in Ebenstein et al. (2017)
Estimating Life Expectancy Gains by Meeting National Standard or WHO Guideline
To use the results of Ebenstein et al. (2017) in building the AQLI, we first convert to PM2.5. Due to data availability constraints, Ebenstein et al. (2017) measure the impact of particulate pollution on life expectancy in terms of levels of PM10, particles smaller than 10 micrometers in diameter. Since global air pollution data only measure PM2.5, the most harmful type of particulate pollution, we convert the study’s estimates to units of PM2.5 using a 0.65 PM2.5-to-PM10 ratio, which closely aligns with conditions in China during the time period of the study. This translates to:
In other words, life expectancy is reduced 0.98 years per 10 μg/m3 of sustained exposure to PM2.5.
Following the epidemiology literature, the AQLI assumes a linear relationship between long-term exposure to PM2.5 and life expectancy throughout the observed PM2.5 distribution. Though it is possible that the pollution-life expectancy relationship is nonlinear over certain ranges of PM2.5 concentrations and/or that there is a threshold below which PM2.5 has no effect, we are unaware of credible empirical evidence that would cause a rejection of the linearity assumption. Therefore, to estimate the potential gain in life expectancy within each grid-cell, the AQLI increases the loss in life expectancy by 0.98 years for every 10 μg/m3 of additional long-term exposure above the reference standard (either the WHO guideline or national standard, or a user entered standard).
For both pollution concentrations and loss in life expectancy, the AQLI aggregates grid-cell-level estimates to national and sub-national administrative boundaries. Aggregations are population-weighted. For example, in 2016, the annual average PM2.5 level in Beijing was 69 μg/m3, and the level in Guangzhou was 34 μg/m3. When calculating China’s national 2016 annual average PM2.5 level, Beijing’s PM2.5 level is given about 50% more weight than Guangzhou’s, because Beijing’s population is about 1.5 times that of Guangzhou. Thus, China’s national average PM2.5 level of 40 μg/m3 means that on average, each person in China was exposed to an annual average PM2.5 level of 40 μg/m3 in 2016. These aggregated values are what is shown in the map tool at the national and sub-national levels.
A Note on the AQLI’s Pollution Data
Users may find that the AQLI’s pollution data differs from other databases, such as those used by the WHO and the Global Burden of Disease (GBD). One difference is in raw data. The AQLI uses annual data for each year from 1998-2016 instead of three-year rolling averages. However, compared to the WHO’s data, the available annual data uses a less sophisticated method of calibrating satellite and ground measurements. These result in the AQLI’s data source generally reporting lower pollution levels than the WHO. In addition, the AQLI removes dust and sea salt, while the WHO and the GBD do not, leading to a further difference. For example, removing dust and sea salt reduces reported PM2.5 in Delhi by about 15%, and by about 8% for Beijing. In North Africa and the Middle East, Sub-Saharan Africa, and northwestern China, they are a significant portion of total PM2.5 concentrations, leading to even larger differences.
Since the AQLI’s satellite-derived PM2.5 data begins in 1998 and ends before the present year, the Policy Impacts pages also make use of monitor-based pollution data to measure the impact of policies enacted before 1998 and/or that continue to be associated with significant changes in pollution levels today. Users reading the Policy Impact pages or comparing AQLI data to their local air quality monitors’ annual averages may note discrepancies between monitor- and satellite-derived measurements. Aside from differences due to the AQLI’s exclusion of dust and sea salt, discrepancies may also arise from limitations of satellites. For example, satellites can measure air pollution only on relatively cloud-free days; in many areas, pollution is most severe in winter, when there are few such days. In such cases, monitor measurements are likely to be more accurate. However, monitor measurements are unavailable in many countries, and measurement technologies and methodologies are inconsistent from place to place, rendering them difficult to compare. Since satellite-derived pollution data are available for around the world and based on a single methodology, it is the primary data of the AQLI.
The ratio of 0.65 is based on a careful review of studies that report historical PM2.5-to-PM10 ratios in China during a similar timeframe as Ebenstein (2017). Two nationally representative studies are of particularly interest. Wang et al. (2015) measures PM2.5-to-PM10 ratios at 24 monitoring stations across the country between 2006 and 2014 and reports total averages by station/city. A back of the envelope population weighted-average calculation using these averages indicates a PM2.5-to-PM10 ratio of 0.73. Importantly, the list of cities in this study does not include some major metropolitan areas (e.g. Beijing), although many surrounding areas are included. Zhou et al. (2015) compiles a comprehensive nationwide database of all published literature (128 articles) which studied PM2.5 and PM10 mass concentrations from 1988 – 2010 and finds a PM2.5-to-PM10 ratio of 0.65 based on 589 pairs of data covering 57 cities and regions. Finally, we also considered the mass ratio PM2.5/PM10 of 0.66 used by the World Health Organization for China in its ambient pollution database. Given the comprehensiveness of Zhou et al. (2015) and how close its findings are to the WHO value (0.65 versus 0.66), we use 0.65 as the baseline PM2.5-to-PM10 ratio for the AQLI.
Comparisons with Other Mortality Causes and Risks in Fact 8
Based on the AQLI’s results, if 2016 levels of ambient particulate pollution are sustained around the world, the life expectancy of everyone alive today would be on average 1.8 years lower than if particulate concentrations everywhere complied with the WHO guideline. In Fact 8, we compare this finding with the life expectancy impacts of other causes and risks of premature death. We do so using life tables, the same epidemiological approach that the Huai River studies used to calculate the relationship between particulate pollution and life expectancy.
A summary of this approach is as follows. From the WHO, we obtain a life table of 2016 mortality data such as probability of death and life expectancy remaining for each sex and age interval 0-1, 1-4, 5-9, …, 80-84, and 85+. We call these the “baseline” data. From the Global Burden of Disease 2016 (GBD), for a variety of causes and risks of mortality (e.g. smoking, malaria), we obtain the rates of death in 2016 due to that cause or risk within each sex and age interval 0-1, 1-4, 5-9, …, 75-79, and 80+. For both of these data sets, we aggregate the highest age intervals into a single 80+ interval for consistency.
Now, using the baseline life table and following the procedure first outlined by Greenwood (1922) and Chiang (1984), we calculate average life expectancy for a male and a female born in 2016, assuming that mortality risks in each age interval remain constant into the future. Using the 2016 sex ratio at birth, we take a weighted average to aggregate the life expectancies by sex into a single average baseline life expectancy. This number accounts for the life expectancy impacts of all mortality causes and risks, based on their actual burdens in 2016.
To calculate what life expectancy at birth in 2016 would hypothetically have been if a particular condition (e.g. smoking or malaria) did not exist, we subtract rates of death due to that condition from the life table baseline rates, then follow the same procedure as above to obtain the counterfactual average life expectancy at birth. The difference between the baseline life expectancy and this counterfactual life expectancy is the life expectancy impact of that condition, which we compare to the AQLI’s result of 1.8 years for particulate pollution.
The data and code that produced these calculations can be found here.
 WHO, 2006
 Many national standards were identified from Kutlar et al. (2017)
 Chen et al., 2013; Ebenstein et al., 2017
 The ratio of 0.65 is based on a careful review of studies that report historical PM2.5-to-PM10 ratios in China during a similar timeframe as Ebenstein et al. (2017). Two nationally representative studies are of particularly interest. Wang et al. (2015) measures PM2.5-to-PM10 ratios at 24 monitoring stations across the country between 2006 and 2014 and reports total averages by station/city. A back of the envelope population weighted-average calculation using these averages indicates a PM2.5-to-PM10 ratio of 0.73. Importantly, the list of cities in this study does not include some major metropolitan areas (e.g. Beijing), although many surrounding areas are included. Zhou et al. (2015) compiles a comprehensive nationwide database of all published literature (128 articles) which studied PM2.5 and PM10 mass concentrations from 1988 – 2010 and finds a PM2.5-to-PM10 ratio of 0.65 based on 589 pairs of data covering 57 cities and regions. Finally, we also considered the mass ratio PM2.5/PM10 of 0.66 used by the World Health Organization for China in its ambient pollution database. Given the comprehensiveness of Zhou et al. (2015) and how close its findings are to the WHO value (0.65 versus 0.66), we use 0.65 as the baseline PM2.5-to-PM10 ratio for the AQLI.
 See, for example, Global Burden of Disease (2016).
 WHO, 2018
 U.N. Population Division, 2017
Bright, E.A., Rose, A.N., and Urban, M.L. (2016). LandScan 2015 [Data file]. Oak Ridge National Laboratory. Retrieved from https://landscan.ornl.gov/
Chen et al. (2013) “Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy,” Proceedings of the National Academy of Sciences, 110(32): 12936-12941.
Ebenstein et al. (2017) “New evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy,” Proceedings of the National Academy of Sciences, 114(39): 10384-10389.
Global Burden of Disease. (2016). Retrieved from http://ghdx.healthdata.org/gbd-2016
Kutlar, J.M, Eeftens, M., Gintowt, E., Kappeler, R., Kunzli, N. (2017). Time to Harmonize National Ambient Air Quality Standards. International Journal of Public Health 62(4), 453–462.
UN Population Division. (2017). _World Population Division: 2017 Revision_. Retrieved from https://population.un.org/wpp/DataQuery
van Donkelaar, 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.
Wang et al. (2015) “Spatial and temporal variations of the concentration of PM10, PM2.5 and PM1 in China” Atmospheric Chemistry and Physics, 15:13585-13598.
World Health Organization (2006) “WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide” available at: http://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf
World Health Organization. (2018). Life tables by WHO region: Global [Data file]. Available at http://apps.who.int/gho/data/
Zhou et al. (2015) “Concentrations, correlations and chemical species of PM2.5/PM10 based on published data in China: Potential implications for the revised particulate standard” Chemosphere, 144(2016): 518-526.