Significance An estimated 4.5 billion people are currently exposed to particulate matter (PM) levels at least twice the concentration that the WHO considers safe. Existing evidence linking health to air pollution is largely based on populations exposed to only modest levels of PM and almost entirely composed of observational studies, which are likely to confound air pollution with other unobserved determinants of health. This study uses quasiexperimental variation in particulate matter smaller than 10 μm (PM 10 ) generated by an arbitrary Chinese policy to find that a 10-μg/m3 increase in PM 10 reduces life expectancy by 0.64 years. The estimates imply that bringing all of China into compliance with its Class I standards for PM 10 would save 3.7 billion life-years.

Abstract This paper finds that a 10-μg/m3 increase in airborne particulate matter [particulate matter smaller than 10 μm (PM 10 )] reduces life expectancy by 0.64 years (95% confidence interval = 0.21–1.07). This estimate is derived from quasiexperimental variation in PM 10 generated by China’s Huai River Policy, which provides free or heavily subsidized coal for indoor heating during the winter to cities north of the Huai River but not to those to the south. The findings are derived from a regression discontinuity design based on distance from the Huai River, and they are robust to using parametric and nonparametric estimation methods, different kernel types and bandwidth sizes, and adjustment for a rich set of demographic and behavioral covariates. Furthermore, the shorter lifespans are almost entirely caused by elevated rates of cardiorespiratory mortality, suggesting that PM 10 is the causal factor. The estimates imply that bringing all of China into compliance with its Class I standards for PM 10 would save 3.7 billion life-years.

Is airborne particulate matter (PM) today’s greatest environmental risk to human health? Among the 5.9 billion people who live in countries where PM readings are available, 4.5 billion are currently exposed to PM concentrations that are at least twice the concentration that the WHO considers safe (1). The health effects of inhaling PM have been widely studied and found to be robustly associated with elevated risk of heart disease, stroke, and lung cancer (2⇓–4). However, questions regarding the health effects of PM and its appropriate regulation continue to be of tremendous scientific and policy relevance, because it is apparent that the existing evidence has not convinced countries to adopt and enforce tough emission standards. Current policies regarding PM also influence the probability that the world will face disruptive climate change, because the combustion of fossil fuels that causes PM also causes greenhouse gas emissions.

At least three limitations have plagued the existing evidence linking health to air pollution, especially at the concentrations that prevail in many of today’s developing countries. First, the literature is almost entirely composed of observational studies, comparing populations across locations with varying exposure to pollution. These studies are likely to confound air pollution with unobserved determinants of health that are correlated with pollution exposure (e.g., income, hospital quality, water pollution, etc.), such as has been emphasized in a recent Science article (5). Second, the available evidence is largely based on examinations of populations exposed to the modest levels of PM that are commonly observed in developed countries, where reliable pollution and health data are more readily available (6). PM concentrations in many developing countries (e.g., India and China) are 5–10 times higher than in developed countries; consequently, the existing evidence has little empirical relevance for these countries if there is a nonlinear relationship between health and pollution. Third, the most important questions about pollution center on the impacts of sustained exposure (e.g., lifetime exposure). However, there have been few opportunities to measure long-run exposure to air pollution. [Notable exceptions include the works by Dockery et al. (3) and Pope et al. (6, 7). However, these studies have at least one of the following limitations: they (i) exploit observational variation in PM, (ii) use small samples, (iii) assume no selective migration, or (iv) focus on low levels of pollution found in the United States.] As a consequence, the existing literature focuses on shorter-run variation in PM exposure and often examines outcomes (e.g., hospitalization, infant outcomes) that are only indirectly related to longer-run outcomes, like life expectancy.

This paper estimates the effect of sustained exposure to particulate matter smaller than 10 μm (PM 10 ) on life expectancy with recent data from China and, in so doing, addresses each of the previous literature’s limitations. First, the quasiexperimental research design is based on China’s Huai River Policy. The policy was instituted during the 1950s when economic resources were allocated through central planning and dictated that areas to the north of the Huai River received free or highly subsidized coal for indoor heating. This led to the construction of a coal-powered centralized heating infrastructure only in cities north of the Huai River, and no equivalent system is in cities to the south; the legacy of that policy is evident even today, with very different rates of indoor heating north and south of the Huai River. Consequently, the findings are derived from a regression discontinuity (RD) design based on distance from the Huai River.

Second, the average PM 10 concentration in China during the examined period is 103 μg/m3 (i.e., more than five times the WHO standard), and, therefore, the results are informative for the 1.36 billion people currently living in China as well as the several billion other people who are exposed to high PM 10 concentrations around the world. Furthermore, the analysis is conducted with the most comprehensive data file ever compiled on health and pollution in a developing country (Fig. 1).

Fig. 1. China’s Huai River/Qinling Mountain Range winter heating policy line and PM 10 concentrations. Black dots indicate the DSP locations. Coloring corresponds to interpolated PM 10 levels at the 12 nearest monitoring stations, where green, yellow, and red indicate areas with relatively low, moderate, and high levels of PM 10 , respectively. Areas left in white are not within an acceptable range of any station.

Third, the Huai River Policy produces sustained differences in PM 10 concentrations between the areas north and south of the river that date back to China’s first measurement of airborne PM in the early 1980s (and very likely earlier) and persist even today. Importantly, the mortality data cover the years 2004–2012 when restrictions on migration had been loosened, but Disease Surveillance Points (DSP) mortality was still generally recorded at an individual’s birthplace or hukou (additional details are in SI Appendix). The possibility of individuals moving away from their hukou, as a compensatory response to high levels of air pollution, but still having their death recorded at their birthplace means that this study provides an answer to a central question: what are the effects of PM 10 concentrations at a person’s birthplace on their life expectancy? Because individuals can migrate and undertake other compensatory responses to air pollution, the resulting estimates of the effect of PM 10 on life expectancy are more likely to be externally valid to other countries than estimates from earlier periods when Chinese migration was greatly restricted (8).

The analysis indicates that PM 10 exposure causes people to live substantially shorter and sicker lives at the concentrations present today in China and other developing countries. We estimate that the Huai River Policy generates an increase in PM 10 exposure of 41.7 μg/m3 [95% confidence interval (95% CI) = 16.4–67.0] and a decline of 3.1 years (95% CI = 1.3–4.9) in life expectancy just to the north of the river. Furthermore, the elevated mortality rates are concentrated among cardiorespiratory causes of death, whereas there is little evidence of a difference in mortality rates for causes that are not plausibly related to air pollution. More broadly, the results suggest that long-term exposure to an additional 10 μg/m3 is associated with a 0.64-year (95% CI = 0.21–1.07) decline in life expectancy.

This paper improves on the work by Chen et al. (8), which also exploits the Huai River Policy to measure the health impact of total suspended particulates (TSPs), by examining the health consequences of smaller particles that comprise PM 10 and using significantly more accurate measures of mortality from a more recent time period (2004–2012) with a coverage population roughly eight times larger. We also probed the robustness of the results in several new and important ways, including but not limited to adjustment for important health behaviors (e.g., smoking), the choice of parametric vs. nonparametric estimation methods (9), a placebo exercise that finds that the discontinuity in pollution and life expectancy is only evident at the river itself, and a bounding exercise that allows for the possibility that pollution is correlated with an unobservable determinant of health (10).

The rest of the paper proceeds as follows. Data Sources describes the data sources, whereas Econometric Model outlines the empirical strategy. Empirical Results presents the results, and Conclusion provides some conclusions.

Data Sources The heart of the analysis is based on two data files that together provide location-specific information on life expectancy, health, and air pollution. The mortality and life expectancy data come from the Chinese Center for Disease Control and Prevention (Chinese CDC) DSP survey. The DSP is a remarkably high-quality nationally representative survey; it provides detailed cause of death data (verified by verbal autopsy) for a coverage population of over 73 million people at 161 separate locations for each year between 2004 and 2012. The cause of death information recorded in the autopsies is used to assign all deaths to either cardiorespiratory causes of death (i.e., heart, stroke, lung cancers, and respiratory illnesses) that are plausibly related to air pollution exposure or noncardiorespiratory causes (i.e., cancers other than lung and all other causes). The latter are used as a placebo-style test. The air pollution data were compiled by combining print and electronic resources for six main pollutants over the period 1981–2012. To estimate the impacts of long-run exposure to pollution, the location-level panel data are collapsed to a 154-observation, location-level, cross-sectional dataset; the panel data are collapsed in this manner, because the Huai River RD design is fundamentally a cross-sectional design. These collapsed measures of PM 10 for each DSP are available in SI Appendix and are based on what we believe is the most comprehensive archive of Chinese air pollution ever assembled. Three other datasets are used to adjust the estimates for potential confounders. Measures of health-relevant behavior, such as smoking prevalence and dietary patterns, are taken from a Chinese CDC survey conducted in 2010. This survey is almost ideal for this study’s purposes, because it was designed to capture patterns in behavior specifically for the coverage population at each DSP location. We use the 2005 census as a secondary dataset, which enables us to adjust the estimates for demographic covariates included in the census and not available from the Chinese CDC survey (e.g., share minority, average years of education). Finally, we consider other local factors that are plausibly related to health in China, including surface water pollution grade from China’s Environmental Yearbooks (2004–2012). SI Appendix describes the data in greater detail.

Econometric Model We use two approaches to estimating the relationship between PM 10 and health outcomes. The first approach is a “conventional” strategy that uses ordinary least squares (OLS) to fit the following equation to the cross-sectional data file: Y j = β 0 + β 1 P M j + X j Г + ε j , [1]where j references a DSP location in China. PM j is the PM 10 concentration in city j, X j is a vector of the observable characteristics of the location that might influence health outcomes other than air quality, and ε j is a disturbance term. The dependent variable Y j is either a measure of mortality rates in DSP location j or its residents’ life expectancy, which is a simple function of age-specific mortality rates. The coefficient β 1 measures the effect of PM 10 exposure on mortality after controlling for the available covariates. Consistent estimation of β 1 requires that unobserved determinants of mortality do not covary with PM j after adjustment for X j . Thus, the conventional approach rests on the assumption that linear adjustment for the limited set of variables available in the census removes all sources of confounding. Previous research has raised substantive concerns about the validity of this assumption (5, 11). Furthermore, pollution concentrations are measured with error, and it is well-known that classical measurement error will attenuate the coefficient associated with PM 10 . The second approach leverages the RD design implicit in the Huai River Policy to measure its impact on PM 10 concentrations and life expectancy. The RD design was developed more than five decades ago and has been used successfully to test the causal nature of relationships in a wide range of fields, including psychology, education, statistics, biostatistics, and economics (12, 13). This paper’s RD design exploits the Huai River Policy that provides free or heavily subsidized coal for indoor heating north of the river and no subsidies to the south. Specifically, we separately test whether the Huai River Policy caused discontinuous changes in PM 10 and life expectancy to the north of the river. The respective necessary assumptions are that any unobserved determinants of PM 10 or mortality change smoothly as they cross the river. If the relevant assumption is valid, adjustment for a sufficiently flexible polynomial in distance from the river or local linear regressions on either side of the river will remove all potential sources of bias and allow for causal inference. In practice, we estimate the following parametric equations to test for the impacts of the Huai River Policy: P M j = α 0 + α 1 N j + f ( L j ) + N j f ( L j ) + X j γ + u j [2a] Y j = δ 0 + δ 1 N j + f ( L j ) + N j f ( L j ) + X j φ + ε j , [2b]where j references a DSP location in China. PM j is the average annual ambient concentration of PM 10 in location j over the period 2004–2012, and Y j is a measure of location j’s mortality rate or life expectancy at birth. N j is an indicator variable equal to one for locations that are north of the Huai River line, f ( L j ) is a polynomial in degrees north of the Huai River that is interacted with N j (chosen based on goodness of fit criteria), and X j is a vector of the demographic and city characteristics other than air quality that are associated with mortality rates. An alternative estimation strategy for the RD approach involves nonparametric identification of [2a] and [2b]. For example, consider the following setup for estimation by local linear regression: P M j = α 0 + α 1 N j + α 2 L j + α 3 N j L j + u j [3a] Y j = δ 0 + δ 1 N j + δ 2 L j + δ 3 N j L j + ε j , [3b]such that L j is within h latitude degrees of the Huai River. We rely on a choice rule, which determines the optimal h as a function of the data (14, 15). Additionally, we report results using three separate kernels or weighting functions—triangle, uniform, and Epanechnikov. Finally, we also report results from the parametric RD approach described in Eqs. 2a and 2b that restricts the sample to locations within 5° latitude of the Huai River; this sample restriction is an informal way of implementing the local linear methods that use bandwidths and kernels to focus comparisons near the discontinuity. Empirical Results reports on estimation of Eqs. 2a, 2b, 3a, and 3b for PM 10 , life expectancy at birth, and several other outcomes of interest, such as cardiorespiratory mortality. The parameters of interest are α 1 and δ 1 , which provide an estimate of whether there is a discontinuity in outcomes at locations just to the north of the Huai River relative to locations to the south. If the RD assumptions hold, estimates of δ 1 will provide an unbiased estimate of the life expectancy consequences of birth in a location just to the north of the Huai River. Importantly, this parameter is not a laboratory-style estimate of the consequences of exposure to air pollution where all other factors are held constant, because it reflects individuals’ actions to protect themselves from the resulting health problems of pollution. Although the laboratory-style estimate might be of interest for researchers interested in how PM affects the human body, its relevance for understanding the real world consequences of air pollution is unclear. In fact, an appealing feature of the estimates of δ 1 is that they reflect all of the compensatory behavior that individuals undertake to protect themselves from air pollution, including migration to less polluted locations and other defensive measures, such as purchasing indoor air purifiers (16). Importantly, the results in Eqs. 2a, 2b, 3a, and 3b can each be used to develop estimates of the impact of PM 10 concentrations on life expectancy. Specifically, if the Huai River Policy only influences mortality through its impact on PM 10 , then it is valid to treat Eq. 2a as the first stage in a two-stage least squares (2SLS) system of equations. The appeal of the 2SLS approach is that it produces estimates of the impact of units of PM 10 on life expectancy, and therefore, the results are applicable to other settings (e.g., India and other developing countries with comparable PM 10 concentrations). The second-stage equation is Y j = β 0 + β 1 P M ^ j + f ( L j ) + N j f ( L j ) + X j φ + ε j , [2c]where P M ^ j represents the fitted values from estimating [2a]; the other variables are as described above. The 2SLS approach offers the prospect of solving the confounding or omitted variables problem associated with the estimation of the air pollution health effects relationship and is a solution to the attenuation bias associated with the mismeasurement of PM 10 . A nonparametric analog to [2c] can be estimated by taking the ratio of the estimated discontinuity in life expectancy to the estimated discontinuity in PM 10 , with both estimated by local linear regression. The result is an instrumental variable (IV) Wald estimate of the impact of PM 10 on life expectancy that is analogous to the 2SLS estimates produced in [2c], and it is based on the recommendations of Calonico et al. (17) for implementing a “fuzzy RD.” Generally, this approach is used to assess the impact of a binary treatment where the probability of treatment rises at some threshold but being above or below the threshold does not fully determine treatment status. In our context, exposure to PM 10 increases significantly at the Huai River, but pollution exists south and north of the river, making our context naturally analogous to a fuzzy RD, where the ratio is estimated as the ratio of two “sharp” discontinuities; in practice, we use the optimal bandwidth for life expectancy as the bandwidth for both life expectancy and PM 10 . The work by Calonico et al. (15) has additional details.

Conclusion The analysis suggests that the Huai River Policy, which had the laudable goal of providing indoor heat, had disastrous consequences for human health. Specifically, it led to PM 10 concentrations that are 41.7 μg/m3 (95% CI = 16.4–67.0) or 46% higher in the north and reductions in life expectancies of 3.1 years (95% CI = 1.3–4.9) in the north caused by elevated rates of cardiorespiratory mortality. More broadly, the results suggest that sustained exposure to an additional 10 μg/m3 PM 10 is associated with a 0.64-year (95% CI = 0.21–1.07) decline in life expectancy. The implications of these results for human wellbeing are potentially enormous. The application of the paper’s estimates suggests that bringing all of China into compliance with their Class I PM 10 standard of 40 μg/m3 would lead to a gain of 3.7 billion life-years for their current population. Furthermore, a growing body of evidence finds that individuals devote substantial resources to protecting themselves from air pollution, and these defensive expenditures represent very real costs of air pollution that are in addition to the direct mortality and morbidity effects (16, 18). The risks to life expectancy from PM exposure are not confined to China. In total, more than 4.5 billion people live in countries with average PM 10 concentrations that are at least twice the concentration that the WHO considers safe. This paper’s results suggest that, for most people in the world, there is currently no greater environmental risk to health than airborne PM.

Acknowledgments We thank our seminar participants and colleagues for their insightful comments, including Songnian Chen, Rema Hanna, Douglas Miller, Paulina Oliva, and Nicholas Sanders. Faraz Hayat, Harshil Sahai, Patrick Schwarz, and Dan Stuart provided outstanding research assistance. This study is supported by China National Science and Technology Pillar Program 2013 Grant 2013BAI04B02 from the Ministry of Science and Technology and a School-Based Initiatives Research Grant from the Hong Kong University of Science and Technology.

Footnotes Author contributions: A.E., M.F., M.G., G.H., and M.Z. designed research, performed research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Data deposition: The measures of PM 10 for each DSP location and the program files that produce the tables and figures have been posted as Dataset S1. Mortality measurements are available upon request and at the discretion of the Chinese Center for Disease Control and Prevention.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1616784114/-/DCSupplemental.