The advent of globally consistent age-resolved estimates of mortality from PMfrom the Global Burden of Disease (GBD) collaboration (3,4) now enables systematic assessment of the global variation in the decrement from air pollution. Here, we build upon the actuarial approach of Brunekreef, Dockery, and Pope, who combined data on baseline survival curves with illustrative examples of excess mortality risk from PMto arrive at approximate estimates of life expectancy decrements for simplified exposure scenarios. (10,18)

How air pollution affects human longevity has been a topic of continued interest for analysts at the science–policy interface of air pollution over at least the past five decades. (5−13) For the lay public and policymakers alike, health risks that substantially reduce survival time are more compelling than those that merely hasten death by a few days. In the 1980s and 1990s, much research investigated the so-called “harvesting” hypothesis that air pollution might most strongly influence the mortality of those who were already at risk of imminent death. (8−10) By the mid-2000s, the weight of evidence from several large, carefully designed long-term cohort studies suggested a substantial decrement in survival associated with air pollution mortality, because the risks of long-term PMexposure were approximately an order of magnitude greater than risks from day-to-day air quality variation. (9,14) Baccarelli and colleagues demonstrated that the U.S. communities with the most exceptional aging (e.g., populations of >85 or >100) had low ambient air pollution in addition to low rates of smoking, poverty, and obesity, providing suggestive evidence of the benefits of clean air for longevity. (15) Several groups have estimated the relationship between changes in air pollution and changes in life expectancy. (11−13,16,17) For example, Correia et al. (12) used a differences-in-differences approach to model the relationship between PMand life expectancy for 545 U.S. counties and determined that decadal-scale improvements in regional air quality resulted in ∼0.35 year of increased life expectancy at birth per 10 μg mchange in PM. Similarly, Ebenstein et al. used a regression discontinuity approach to evaluate spatial contrasts in PMacross China to estimate ∼0.64 year of increased life expectancy per 10 μg mincrement in PM

Exposure to ambient fine particulate matter (PM) air pollution causes important adverse health outcomes that result in premature death, including ischemic heart disease, strokes, lung cancer, chronic obstructive pulmonary disease, and respiratory infections. Despite the well-documented global burden of disease from PM (1−3) (∼4.1 million deaths in 2016), (4) prior research has not systematically explored how global variations in PMexposure affect life expectancy. Here, we use an actuarial modeling approach and data from the Global Burden of Disease (GBD) 2016 study to address the question: "How much does PMair pollution shorten human life expectancy around the world?"

Following Apte et al., (21) we performed a mortality analysis for ambient PMwherein we simulate the disease burden for alternative hypothetical exposure distributions where global PMis limited to specific target concentration(s). To do so, we updated the gridded PMmortality model of Apte et al. (21) with year-2016 data to reproduce the central-tendency GBD 2016 results for ambient PMto within ±1–2% for each country. We then re-estimated attributable mortality for PMunder hypothetical scenarios in which the ambient exposure concentration distribution for each grid cell was assigned to an alternative concentration, such as the World Health Organization (WHO) annual-average air quality guideline PMconcentration of 10 μg m

We used data from the Global Burden of Disease 2016 study to obtain age-specific attributable death rates for each country for ambient PM. Briefly, the GBD approach (3,21) involves estimating age-specific attributable mortality for each analysis region as the product of (i) age-resolved populations, (ii) age-specific background disease rates for five key causes, and (iii) regionally population-weighted age-specific population attributable fractions for PMmortality for each of the five causes, computed on the basis of nonlinear integrated-exposure-response (IER) functions (22,23) and a 0.1° × 0.1° gridded PMexposure surface. (24−26) The five GBD causes of death for which PMis a risk factor are ischemic heart disease, cerebrovascular disease (stroke), chronic obstructive pulmonary disease, lung cancer, and lower respiratory infections. As described by Cohen et al., (3) relative risks for the IER functions are estimated relative to a distribution of the theoretical minimum risk exposure level (TMREL) that ranges from 2.4 to 5.9 μg of PM, consistent with the lowest concentrations observed in long-term epidemiological studies. We input age-specific mortality into our life table analysis as the sum of the five cause-specific death rates. Uncertainties in the GBD approach include (i) the fact that underlying cause-specific mortality data are modeled and therefore uncertain for some countries, (27) (ii) contributions to attributable mortality from diseases other than the five major causes considered here, and (iii) the assumption that the IER functions reasonably describe mortality risk from PMover the full ambient concentration spectrum. (21,28) In addition to using the published GBD attributable death rates for ambient PM, we obtained (4,29) similar age-specific mortality data sets for other risk factors (e.g., tobacco smoking) and other major causes of death (e.g., cancers) to provide comparison and context.

Figure 1. Example survival curves for observed life tables (solid lines) and simulated cause-deleted life tables (dashed lines) where ambient PM 2.5 exposure is eliminated as a mortality risk factor. Life expectancy e 0 can be visualized as the integral of the survival curve over the age spectrum. Life expectancy for the counterfactual case is increased after removing PM 2.5 as a mortality risk. For a given country, the reduction in life expectancy attributed to PM 2.5 (ΔLE) relative to a counterfactual scenario with no excess mortality risk from PM 2.5 can be visualized as the area between the solid and dashed curves.

We use a cause-deleted life table approach to simulate the life expectancy decrement that is attributable to PMrisk factors. (10,18−20) This approach involves four steps: (i) estimating the age-specific death rate attributable to ambient PMfor each location, (ii) assuming that in the absence of this risk factor, age-specific death rates would be proportionally lower, (iii) recomputing a counterfactual “cause-deleted” or “cause-eliminated” life table that would exist in the absence of this risk factor (see the Supporting Information ), (19,20) and (iv) estimating the counterfactual life expectancy at birth (′). The life table approach requires an assumption that the baseline health status of those who die prematurely from air pollution is similar to that of the general population. (10,18−20) Finally, we attribute the difference between the baseline life expectancy and the cause-deleted counterfactual life expectancy to the life expectancy decrement caused by PM: ΔLE =′ – 1 Figure illustrates baseline and cause-deleted survival curves for two countries.

We used a standard life table method (19) to estimate the baseline life expectancy at birthfor each of 185 countries. For each country, we estimated abridged (i.e., multiyear interval) life tables using all-cause death rates for 23 age strata in the Global Burden of Disease 2016 data set. The Supporting Information describes procedures for computing life tables and baseline life expectancy from age-specific death rates.The standard life expectancy at birth () can be interpreted as the expected lifespan for an individual born into a population where current age-specific death rates are held constant over time. As such, the life expectancy metric is an actuarial construct (in reality, the structure of mortality for most populations is dynamic) but one that usefully summarizes the comparative survival of different populations in time and space. Our analyses emphasize one common life expectancy metric, life expectancy at birth (). We refer to the general concept of life expectancy with the acronym LE and to decrements in life expectancy at birth that arise from pollution exposure as ΔLE.

3. Results and Discussion ARTICLE SECTIONS Jump To

2.5 are unequally distributed ( 2.5 concentrations exceed the WHO guideline concentration of 10 μg m–3. Ambient PM 2.5 concentrations for the 10th and 90th percentiles of the global concentration–population distribution span nearly an order of magnitude (11 and 97 μg m–3, respectively). In 2016, global the population-weighted median life expectancy at birth was 72.6 years [interquartile range (IQR) of 68.2–76.3 years]. For high-income countries, the average life expectancy was 80.9 years ( Table 1 ). The lowest life expectancies are generally in sub-Saharan Africa (average of ∼62.8 years). Global population exposures to ambient PMare unequally distributed ( Figure 2 a). For the 2016 population, (26) 95% of the global population lived in regions where PMconcentrations exceed the WHO guideline concentration of 10 μg m. Ambient PMconcentrations for the 10th and 90th percentiles of the global concentration–population distribution span nearly an order of magnitude (11 and 97 μg m, respectively).

Table 1. Global and Regional Life Expectancy and Life Expectancy Decrements for Selected Risk Factors and Causes of Death global East Asia South Asia North Africa and Middle East sub-Saharan Africa Latin America high income baseline LE (years) 72.5 76.3 68.7 73.1 62.8 75.8 80.9 all air pollution 1.65 1.90 2.54 1.54 1.97 0.73 0.40 ambient PM 2.5 1.03 1.24 1.56 1.29 0.94 0.54 0.37 ambient ozone 0.05 0.07 0.10 0.03 0.01 0.02 0.03 household air pollution 0.72 0.71 1.22 0.30 1.32 0.20 0.01 tobacco 1.82 2.39 1.51 1.60 0.73 1.23 1.82 water sanitation 0.57 0.02 1.02 0.19 1.53 0.13 0.01 dietary risks 2.67 3.10 2.58 3.13 1.54 1.82 1.91 unsafe sex 0.37 0.08 0.16 0.04 2.03 0.27 0.07 all cancer 2.37 3.03 1.26 1.70 1.52 2.31 3.53 lung cancer 0.41 0.67 0.12 0.26 0.09 0.26 0.72 breast cancer 0.14 0.09 0.10 0.14 0.12 0.16 0.23

Figure 2 Figure 2. Relationship among the global distribution of ΔLE, the life expectancy decrement from PM 2.5 , and global PM 2.5 concentrations C. ΔLE is generally higher in countries with higher PM 2.5 levels. (a) Global distribution of population with respect to annual-average PM 2.5 for year 2016. Plotted data reflect local smoothing of bin-width-normalized distributions computed over 400 logarithmically spaced bins: equal-sized plotted areas reflect equal populations. Each country is colored proportionally to the ΔLE from PM 2.5 exposure. (b) Cumulative distribution of ΔLE over the global population. The global population-weighted median value for ΔLE is 1.22 years, corresponding to conditions in China. Shading for each country shows the national population-weighted mean PM 2.5 , illustrating how ΔLE has a strong but imperfect association with PM 2.5 . (c) National decrements in ΔLE vs PM 2.5 . Owing to the supralinear concentration–response relationship of mortality with PM 2.5 , the slope of this distribution is higher for countries with lower average PM 2.5 concentrations.

2.5 . Globally, ambient PM 2.5 pollution was associated with a population-weighted mean decrement in global life expectancy of 1.03 years. Among 185 countries, the population-weighted median decrement in life expectancy from PM 2.5 (ΔLE) was 1.22 years [IQR of 0.67–1.51 years ( 2.5 is especially large in polluted countries of Asia, Africa, and the Middle East, including Bangladesh (1.87 years), Egypt (1.85 years), Pakistan (1.56 years), India (1.53 years), Saudi Arabia (1.48 years), Nigeria (1.28 years), and China (1.25 years). 2 Figures and 3 show the global distribution of life expectancy impacts from PM. Globally, ambient PMpollution was associated with a population-weighted mean decrement in global life expectancy of 1.03 years. Among 185 countries, the population-weighted median decrement in life expectancy from PM(ΔLE) was 1.22 years [IQR of 0.67–1.51 years ( Figure 2 b)]. As shown in Figure 3 , the life expectancy impact of ambient PMis especially large in polluted countries of Asia, Africa, and the Middle East, including Bangladesh (1.87 years), Egypt (1.85 years), Pakistan (1.56 years), India (1.53 years), Saudi Arabia (1.48 years), Nigeria (1.28 years), and China (1.25 years).

Figure 3 Figure 3. Global maps of the life expectancy decrement ΔLE from PM 2.5 . Panel a shows baseline ΔLE for year-2016 concentrations (global population-weighted mean and median of 1.03 and 1.22 years, respectively). Panel b shows hypothetical gains in life expectancy for an alternative exposure distribution where concentrations are limited to a maximum of 10 μg m–3, the WHO air quality guideline concentration (global-average ΔLE of ∼0.59 year). See also Table S2.

2.5 are positively correlated with national-average PM 2.5 concentrations [r = 0.79 ( 2.5 concentrations below 25 μg m–3 (including nearly all high-income countries), ΔLE and population-weighted mean PM 2.5 track closely and approximately linearly, with a slope that is roughly consistent with the directly measured relationship between LE and PM 2.5 of Correia et al. (average of 0.35 year LE increase per 10 μg m–3 reduction in exposure for 545 U.S. counties). 2.5 generally lead to a decreasing marginal risk change per increment in PM 2.5 . Further, national differences in the structure of underlying disease burden modulate the relationship between PM 2.5 and life expectancy, contributing to the scatter in 2.5 is sensitive to age-specific death rates in each country, while death rates from PM 2.5 (but not ΔLE) are also strongly influenced by the age distribution of a country’s population (see Life expectancy decrements from PMare positively correlated with national-average PMconcentrations [= 0.79 ( Figure 2 c)]. For countries with PMconcentrations below 25 μg m(including nearly all high-income countries), ΔLE and population-weighted mean PMtrack closely and approximately linearly, with a slope that is roughly consistent with the directly measured relationship between LE and PMof Correia et al. (average of 0.35 year LE increase per 10 μg mreduction in exposure for 545 U.S. counties). (12) At higher concentrations, the nonlinear integrated exposure–response functions used to estimate mortality attributable to PMgenerally lead to a decreasing marginal risk change per increment in PM. Further, national differences in the structure of underlying disease burden modulate the relationship between PMand life expectancy, contributing to the scatter in Figure 2 c. In particular, ΔLE from PMis sensitive to age-specific death rates in each country, while death rates from PM(but not ΔLE) are also strongly influenced by the age distribution of a country’s population (see Figure S1 ).

2.5 , the full set of air pollution risk factors [including ambient O 3 and household air pollution (HAP)] decreases global life expectancy by an average of 1.65 years. In regions where both ambient PM 2.5 and HAP are major risk factors, the ΔLE for the combined set of household and ambient air pollutants is even larger (2.5 years in South Asia and 2.0 years in sub-Saharan Africa). For context, other major global risk factors for reduced life expectancy include dietary risks (2.7 years), tobacco smoking (1.8 years), unsafe water and sanitation (0.57 year), and unsafe sex (0.37 year). Globally, cancers result in ∼2.4 years of reduced life expectancy, while the most common cancer types (e.g., lung and breast) individually reduce life expectancy by ∼0.2–0.4 year. In the United States, the ΔLE for PM 2.5 (0.38 year) is substantially larger than the impact of breast cancer (0.23 year), while in South Asia, the ΔLE for PM 2.5 (1.6 years) substantially exceeds the combined impact of all cancers (1.3 years). In short, the burden of disease from air pollution results in life expectancy decrements of a magnitude similar to those of other high-priority risk factors and diseases. To place our findings in context, we used published GBD cause- and age-specific mortality data to estimate the life expectancy decrements that are attributable to other key diseases and risks ( Table 1 ). Relative to our core global finding of a global mean ΔLE of 1.03 years for ambient PM, the full set of air pollution risk factors [including ambient Oand household air pollution (HAP)] decreases global life expectancy by an average of 1.65 years. In regions where both ambient PMand HAP are major risk factors, the ΔLE for the combined set of household and ambient air pollutants is even larger (2.5 years in South Asia and 2.0 years in sub-Saharan Africa). For context, other major global risk factors for reduced life expectancy include dietary risks (2.7 years), tobacco smoking (1.8 years), unsafe water and sanitation (0.57 year), and unsafe sex (0.37 year). Globally, cancers result in ∼2.4 years of reduced life expectancy, while the most common cancer types (e.g., lung and breast) individually reduce life expectancy by ∼0.2–0.4 year. In the United States, the ΔLE for PM(0.38 year) is substantially larger than the impact of breast cancer (0.23 year), while in South Asia, the ΔLE for PM(1.6 years) substantially exceeds the combined impact of all cancers (1.3 years). In short, the burden of disease from air pollution results in life expectancy decrements of a magnitude similar to those of other high-priority risk factors and diseases.

2.5 affects survival from age 60 to 85, expressed as the metric 25 q 60 ( 2.5 , baseline survival rates for this 25-year interval are high (∼50%) and PM 2.5 exposure reduces 25 q 60 by ∼3%. In contrast, for high-PM 2.5 , high-mortality countries (e.g., South Asia), 25 q 60 at baseline is low (∼20–30%) and the impact of PM 2.5 on elderly survival is quite large. For example, across South Asia, the probability of surviving from age 60 to 85 would have been 20% higher if PM 2.5 exposure were removed as a mortality risk factor. Because air pollution has a disproportionate effect on the elderly, air pollution reduces life expectancy predominantly by increasing the probability of death above age 60 ( Figure S2 ). We utilized our estimated standard life tables for each country to understand how PMaffects survival from age 60 to 85, expressed as the metric Table S1 ). In high-income countries with a low PM, baseline survival rates for this 25-year interval are high (∼50%) and PMexposure reducesby ∼3%. In contrast, for high-PM, high-mortality countries (e.g., South Asia),at baseline is low (∼20–30%) and the impact of PMon elderly survival is quite large. For example, across South Asia, the probability of surviving from age 60 to 85 would have been 20% higher if PMexposure were removed as a mortality risk factor.

2.5 might result in increased life expectancy, we estimate ΔLE for alternative global exposure distributions ( 2.5 concentrations is held constant over time at a specific value. If PM 2.5 concentrations worldwide were limited to the WHO air quality guideline concentration of 10 μg m–3, global life expectancy would be on average 0.59 year longer. The benefit of reaching this stringent target would be especially large in countries with the highest current levels of pollution, with approximately 0.8–1.4 years of additional survival in countries such as Egypt, India, Pakistan, Bangladesh, China, and Nigeria. In contrast, many high-income countries already nearly meet the WHO guideline and would have much smaller LE benefits. Because limiting the maximum PM 2.5 concentration in one area may also have air quality benefits for less polluted surroundings, our estimates may understate the possible LE benefits of reaching specific air quality guidelines. Halving PM 2.5 globally would increase e 0 globally by 0.33 year, and about 0.40–0.55 year in the most polluted countries of Asia and Africa. These benefits are large in absolute magnitude. However, because the relationship for PM 2.5 and mortality has a declining slope at higher concentrations, 2.5 for the highly polluted countries is only 25–30% of the total national ΔLE for PM 2.5 . To illustrate how improvements in PMmight result in increased life expectancy, we estimate ΔLE for alternative global exposure distributions ( Figure 3 and Table S2 ). These simulations must be interpreted with care (see below), as they most properly reflect the LE for a hypothetical alternative reality where the distribution of PMconcentrations is held constant over time at a specific value. If PMconcentrations worldwide were limited to the WHO air quality guideline concentration of 10 μg m, global life expectancy would be on average 0.59 year longer. The benefit of reaching this stringent target would be especially large in countries with the highest current levels of pollution, with approximately 0.8–1.4 years of additional survival in countries such as Egypt, India, Pakistan, Bangladesh, China, and Nigeria. In contrast, many high-income countries already nearly meet the WHO guideline and would have much smaller LE benefits. Because limiting the maximum PMconcentration in one area may also have air quality benefits for less polluted surroundings, our estimates may understate the possible LE benefits of reaching specific air quality guidelines. Halving PMglobally would increaseglobally by 0.33 year, and about 0.40–0.55 year in the most polluted countries of Asia and Africa. These benefits are large in absolute magnitude. However, because the relationship for PMand mortality has a declining slope at higher concentrations, (21−23,28,30−33) the LE benefit of halving PMfor the highly polluted countries is only 25–30% of the total national ΔLE for PM