क्रियाविधि
The AQLI estimates the relationship between air pollution and life expectancy, allowing users to view the gain in life expectancy they could experience if PM2.5 concentrations in their region 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 global population and PM2.5 data to estimate the impact of particulate matter on life expectancy across the globe.
THE EFFECT OF PARTICULATE POLLUTION ON LIFE EXPECTANCY
THE EFFECT OF PARTICULATE POLLUTION ON LIFE EXPECTANCY
In China, the government initiated a policy after 1950 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 reliance on coal north of the Huai River, relative to the south—and therefore more pollution.
At the same time, a household registration system discouraged people from leaving the communities where they were born. This effectively meant that people exposed to particulate pollution didn’t 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 time.
They found that just North of the river people were living 3.1 years less than in the south due to air pollution concentrations that are 46 percent higher.
HOW DOES IT IMPACT HUMAN HEALTH?
HOW DOES IT IMPACT HUMAN HEALTH?
The researchers of the China studies only had data on levels of PM10, particles smaller than 10 micrometers in diameter, available to them at the time of their research. However, it is widely known that PM2.5, with particles smaller than 2.5 micrometers in diameter, is the most harmful type of particulate pollution
THE EFFECT OF PARTICULATE POLLUTION ON LIFE EXPECTANCY
THE EFFECT OF PARTICULATE POLLUTION ON LIFE EXPECTANCY
Being more relevant to discussions around health impacts, the AQLI team converted PM10’s impact on life expectancy to PM2.5’s impact using a PM2.5-to-PM10 ratio of 0.64, which closely aligns with conditions in China during the time period of the study.[5] This converts the metric to: every additional 10 μg/m3 of PM2.5 reduces life expectancy by 0.98 years.
The AQLI team then combines this information with global satellite data mapping PM2.5 concentrations over fine grid cells (0.01° x 0.01°) covering the Earth (a dataset constructed by the Atmospheric Composition Analysis Group, ACAG, at the Washington University in St. Louis). Each grid cell is about the length of New York City’s Central Park—or, 50 city blocks.
The satellite-derived PM2.5 dataset the AQLI team uses filters out the particulate particles that come from natural sources like dust and sea salt, focusing instead on the majority of particles—which are generated by vehicle exhaust, combustion of fossil fuels, burning crops and other human activity.
The AQLI team then combines the satellite estimates of PM2.5 concentrations with associated population data (from the LandScan Global Population Database). This allows them to understand the number of people impacted in a given location.
The population data is used to capture the pollution levels that most people in a region are breathing. In other words, regions with low populations are given less influence on the average than regions with dense populations. This is called “population weighing.”
The AQLI team could have stopped there—calculating how much PM2.5 cuts off the average life expectancy in any given location. That would be alarming information, but it wouldn’t be tied to a policy benchmark, making it difficult for leaders to assess how to reduce pollution.
Instead, the team relates the metric to two significant benchmarks: One, the World Health Organization’s (WHO) guideline of 5 micrograms per cubic meter (μg/m3), which is the lowest level of long-term exposure that the WHO found—with high confidence—would not raise mortality.[1]
And, two, the country-specific national annual air quality standards.
So, the AQLI estimates the potential gain in life expectancy if PM2.5 concentrations in a given region were to meet either the WHO guideline or a country’s national air quality standard for PM2.5. That is—going back to the original metric that every additional 10 μg/m3 of PM2.5 reduces life expectancy by 0.98 years—the opposite is also true: Every reduction of 10 μg/m3 of PM2.5 increases life expectancy by 0.98 years.
So, if the benchmark is 5 µg/m³ and PM2.5 in a region is 15 µg/m³ (10 µg/m³ above 5 µg/m³) then residents could live 0.98 years longer if it reduced PM2.5 to meet the 5 µg/m³.
To give another example, if the benchmark is 5 µg/m³ and PM2.5 in a region is 84.34 µg/m³—as it was in Delhi in 2022—then residents could live 7.77 years longer if it reduced PM2.5 to meet the 5 µg/m³.
Visit the Map
Visit the Map
Users of the AQLI’s online platform can also enter their own percent reduction in pollution concentrations and see the gains in life expectancy that would result.
The AQLI measures pollution’s impact on life expectancy beginning in 1998—the earliest year for which the satellite data is available. Each year, the AQLI updates its data with the latest satellite PM2.5 data and corresponding life expectancy impacts.
Research behind the AQLI
Research behind the AQLI
The AQLI is based on a pair of studies set in China 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.
FREQUENTLY ASKED QUESTIONS
FREQUENTLY ASKED QUESTIONS
FOOTNOTES
he 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.