Long-Term Exposure to Low-Level NO2 and Mortality among the Elderly Population in the Southeastern United States

Background: Mounting evidence has shown that long-term exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and ozone (O3) can increase mortality. However, the health effects associated with long-term exposure to nitrogen dioxide (NO2) are less clear, in particular the evidence is scarce for NO2 at low levels that are below the current international guidelines. Methods: We constructed a population-based full cohort comprising all Medicare beneficiaries (aged ≥65, N=13,590,387) in the southeastern United States from 2000 to 2016, and we then further defined the below-guideline cohort that included only those who were always exposed to low-level NO2, that is, with annual means below the current World Health Organization guidelines (i.e., ≤21 ppb). We applied previously estimated spatially and temporally resolved NO2 concentrations and assigned annual means to study participants based on their ZIP code of residence. Cox proportional hazards models were used to examine the association between long-term exposure to low-level NO2 and all-cause mortality, adjusting for potential confounders. Results: About 71.1% of the Medicare beneficiaries in the southeastern United States were always exposed to low-level NO2 over the study period. We observed an association between long-term exposure to low-level NO2 and all-cause mortality, with a hazard ratio (HR)= 1.042 (95% CI: 1.040, 1.045) in single-pollutant models and a HR= 1.047 (95% CI: 1.045, 1.049) in multipollutant models (adjusting for PM2.5 and O3), per 10-ppb increase in annual NO2 concentrations. The penalized spline indicates a linear exposure–response relationship across the entire NO2 exposure range. Medicare enrollees who were White, female, and residing in urban areas were more vulnerable to long-term NO2 exposure. Conclusion: Using a large and representative cohort, we provide epidemiological evidence that long-term exposure to NO2, even below the national and global ambient air quality guidelines, was approximately linearly associated with a higher risk of mortality among older adults, independent of PM2.5 and O3 exposure. Improving air quality by reducing NO2 emissions, therefore, may yield significant health benefits. https://doi.org/10.1289/EHP9044


Introduction
Air pollution is among the most critical environmental and public health concerns worldwide (Chen and Kan 2008). The adverse health effects of exposure to ambient fine particulate matter [PM ≤2:5 lm in aerodynamic diameter (PM 2:5 )] (Akintoye et al. 2016;Chen and Hoek 2020;Shi et al. 2020) and ozone (O 3 ) (Turner et al. 2016) have been widely reported (Cesaroni et al.2012;Cohen et al. 2017;Danesh Yazdi et al. 2021;Silva et al. 2016;Wei et al. 2020) in previous epidemiological studies; however, the relationship between ambient nitrogen dioxide (NO 2 ) exposure and mortality is less understood. Although the risk associated with acute NO 2 exposure and premature mortality has been studied more extensively (Chen et al. 2012;Chiusolo et al. 2011;Mills et al. 2016;Samoli et al. 2006), the evidence remains limited for long-term NO 2 exposure. Fewer epidemiological studies have investigated the mortality risks associated with long-term NO 2 concentrations (Eum et al. 2019;Faustini et al. 2014;Hoek et al. 2013). NO 2 gas, as one of the highly reactive nitrogen oxides (NO x ), primarily derives from high-temperature combustion processes. In the United States, motor vehicle emissions are the predominant source of NO 2 , and high levels of NO 2 are observed along highways and in cities . NO 2 can be coemitted on roadways with other traffic-related tailpipe and nontailpipe emissions, such as black carbon, organic carbon, and trace metals (WHO 2021). NO 2 is therefore often considered a surrogate for traffic-related air pollutants (Alotaibi et al. 2019). Other major sources of NO 2 also include power plants and off-road equipment (U.S. EPA 2011(U.S. EPA , 2020. Recent evidence suggests that long-term exposure to NO 2 may be linked to premature mortality (COMEAP 2018; U.S. EPA 2019). Two recent systematic reviews, Huangfu and Atkinson (2020) and Huang et al. (2021), both reported a positive association between long-term NO 2 exposure and all-cause mortality, and they noted that more large-scale cohort studies exploring the concentrationresponse relationship are encouraged (Huang et al. 2021;Huangfu and Atkinson 2020). In addition, several more recent large cohort studies have reported positive associations between NO 2 and allcause and cause-specific mortality, expanding the evidence base globally, including studies conducted in Europe , Canada (Paul et al. 2020;Zhang et al. 2021), Netherlands (Klompmaker et al. 2020;Klompmaker et al. 2021), Denmark (So et al. 2020), Greece (Kasdagli et al. 2021), Japan (Yorifuji and Kashima 2020), and South Korea (Jung et al. 2020). Yet, among the growing body of literature, a high degree of regional heterogeneity has been observed. A few studies have assessed NO 2 exposure across a broad geographic area (Heinrich et al. 2013;Jerrett et al. 2013;Maheswaran et al. 2010), albeit at a relatively coarse spatial resolution. Thus, previous studies are limited in their ability to quantify the spatial variability of long-term exposure resulting from local variations in traffic-related emissions, which may impact overall measures of association. Further, although many studies have used multipollutant models to estimate associations with NO 2 after adjusting for other pollutants, these studies reported mixed results (Faustini et al. 2014;Huang et al. 2021;Stieb et al. 2021), and the independent association between NO 2 and all-cause mortality remains unclear (COMEAP 2018).
To protect public health from adverse health outcomes induced by air pollution, the World Health Organization (WHO) has set evidence-based guidelines on ambient air pollution to inform environmental policy and national air quality targets (WHO 2006). The target guidelines for NO 2 is currently set at an annual average of 40 lg=m 3 ( ∼ 21 ppb) (WHO 2006). Similarly, in the United States, the National Ambient Air Quality Standards (NAAQS) are set and periodically revised by the U.S. Environmental Protection Agency on the basis of the best available scientific evidence, and the current NAAQS for annual mean NO 2 is 53 ppb (U.S. EPA 2021). However, it is not clear whether these standards are actually safe enough to protect public health.
We recently estimated temporally and spatially resolved NO 2 concentrations in the United States through an ensemble model that integrates multiple machine learning algorithms-including neural network, random forest, and gradient boosting-with a variety of predictor variables (e.g., satellite remote sensing and chemical transport models) . This approach allows one to estimate daily NO 2 at a 1 km × 1 km resolution across the contiguous United States from 2000 to 2016 with an excellent prediction model performance. Therefore, we were able to assess long-term exposures of NO 2 for population-based cohort studies, with residents living far from monitors, as well as for those potentially exposed to low-level NO 2 .
To address these critical gaps in knowledge, we conducted a large population-based cohort study encompassing all Medicare beneficiaries (≥65 years of age) from 2000 to 2016 in the southeastern United States, using a high-resolution spatiotemporal ensemble model that can better capture air pollution data in rural and suburban areas. Focusing on the independent health effect of long-term exposure to low-level NO 2 , we performed a multipollutant analysis to estimate the risk of all-cause mortality among the Medicare population associated with exposure (yearly average) to concentrations of NO 2 below the WHO annual guidelines of 40 lg=m 3 ( ∼ 21 ppb) in an effort to better clarify the potential mortality risks attributable to air pollution levels below the current national and global ambient air quality guidelines.

Study Population
The study population comprised all Medicare beneficiaries who were ≥65 years of age over from 2000 to 2016 in seven southeastern U.S. states (Alabama, Florida, Georgia, Mississippi, North Carolina, South Carolina, and Tennessee). We constructed an open cohort from 1 January 2000 to 31 December 2016, with all-cause mortality as the outcome. We obtained individual-level characteristics, including the year and age of Medicare enrollment, date of death, current age, sex, race, ZIP code of residence, and Medicaid eligibility [a proxy for socioeconomic status (SES), that is, an individual eligible for both Medicare and Medicaid is likely to be of lower SES], from the Medicare beneficiary denominator file derived from the Centers for Medicare and Medicaid Services (CMS). The ZIP code of residence and calendar year were used for further exposure assignment. We further restricted the population to Medicare beneficiaries who were always exposed to low-level NO 2 (annual mean ≤21 ppb) over the study period (i.e., the lowexposure cohort). This study was approved by the CMS under the data use agreement (RSCH20-55733) and the institutional review board of Emory University (STUDY00000316), and a waiver of informed consent was granted.

Exposure
We applied previously estimated daily NO 2 concentrations at a 1 km × 1 km resolution in the United States from 2000 to 2016 using an ensemble model that integrated multiple machine learning algorithms and predictor variables . Briefly, we respectively fit a neural network, a random forest, and a gradient boosting model with input predictor variables (satellite remote sensing, chemical transport models, meteorological variables, and multiple land-cover terms) and monitored NO 2 measurements to generate daily NO 2 predictions. This ensemble learning approach yielded a cross-validated mean R 2 of 0.79 and an average root mean square error of 7:2 ppb. For each ZIP code, daily NO 2 concentrations were averaged across all covered 1 km × 1 km grid cells with centroids that fell within the ZIP code boundary. We further calculated annual means (time-varying 1-y averages) and assigned these to Medicare beneficiaries according to their ZIP code of residence.

Covariates
Daily PM 2:5 and O 3 concentrations were previously estimated at a 1 km × 1 km resolution in the United States from 2000 to 2016 using the same ensemble model (Di et al. 2019;Requia et al. 2020). This trained model produced cross-validated mean R 2 values of 0.84 and 0.90 for PM 2:5 and O 3 , respectively. We then aggregated daily predictions based on all covered 1-km 2 grid cells, and further calculated annual averages for PM 2:5 and warm-season averages for O 3 for each year relative to the ZIP code of residence. The warm season, defined as 1 May to 31 October, is a specific time window for examining the health effects of O 3 because the warm climate favors the formation and accumulation of O 3 in the atmosphere (Zhang et al. 2019).
We , and matched these variables to the ZIP code scale. The eight variables included median home value, percentage of owner-occupied housing units, median household income, population density, percentage of Black population, percentage of Hispanic population, percentage of those with a low education level (i.e., with less than a high school degree), and the percentage of those below the poverty level. Behavioral risk factors, including body mass index (BMI) and percentage of those who have ever smoked, were obtained at the county level from the Behavioral Risk Factor Surveillance System (BRFSS) between 2000 and 2016 (CDC 2020). We assigned county-level variables to a given ZIP code if the centroid was located within the county boundary. We linearly interpolated or extrapolated any missing data based on the available data (Junninen et al. 2004), in other words, all area-level variables were time-varying. These annual average data were assigned to individuals according to the ZIP code of residence.
Daily 1-km 2 resolution air temperature data were acquired for the southeastern United States between 2000 and 2016 from a national meteorology data product (Daymet, version 4) (Thornton et al. 2020). Daily temperature data were averaged for each ZIP code, and seasonal averages, including the mean temperature for summer (June-August) and winter (December-February), were calculated for each ZIP code and each year. We then assigned the seasonal mean temperature estimates to each participant according to the ZIP code of residence. Because more evidence has been found that seasonal temperature, particularly summer mean and winter mean temperature, has been associated with both all-cause mortality and air pollution levels (Duan et al. 2019;Park et al. 2011), we adjusted for summer and winter mean temperature in our main analyses.

Statistical Analysis
A counting process survival data set, based on the scheme presented by Andersen and Gill (1982), was constructed using the individual-level data. Namely, each observation represented a single person-year of mortality follow-up, with follow-up taking place at the beginning of the calendar year, whereas deaths were assessed at the end of each calendar year. We fit a series of single-, bi-, and tri-pollutant Cox proportional hazards models, with years of follow-up as the time scale, to estimate hazard ratios (HR) per 10-ppb increase in annual mean NO 2 exposure associated with all-cause mortality among the elderly population in both cohorts. All models were stratified by 5-y age categories, sex (female, male), and race (White, Black, and other), as well as by Medicaid eligibility, adjusting for indicators of calendar year, summer and winter mean temperature, median home value, median household income, population density, the proportion of owner-occupied housing units, and other demographic and behavioral risk factors, including the percentage of Black and Hispanic populations, education level, population below poverty level, BMI, and the proportion of those who were ever smokers.
To identify the most vulnerable subgroups, we evaluated effect modification by sex (female vs. male), race (White vs. Black vs. other), age (≥80 vs. <80 y), Medicaid eligibility (dual vs. nondual eligibility), urbanicity (quartiles of population density), and arealevel SES indicator (a measure showing socioeconomic status; high SES vs. low SES) in tri-pollutant Cox models, by including interaction terms between these potential effect modifiers and NO 2 . We included race as a covariate and effect modifier in our analysis to reflect the racial disparity. We applied the Wald test (Kaufman and MacLehose 2013) to assess whether one subgroup had a larger effect than another, and the p < 0:05 was chosen to suggest statistical significance. Dual eligibility subgroups refer to individuals who were eligible for both Medicare and Medicaid benefits, nondual otherwise. Area-level SES was defined as either below or above the median of the distribution of percentage below the poverty level. To assess the potential nonlinearity of the exposure-response relationship, we fit penalized spline regressions with penalized splines for NO 2 , adjusting for all covariates and copollutants.
We performed the m-of-n bootstrap method to calculate statistically robust confidence intervals and account for potential spatial dependency in the Cox model. Given that the model treats the observations as independent, it may not adequately capture spatial patterns. M-of-n bootstrapping was performed by randomly sampling m ZIP codes of a total of n ZIP codes for each bootstrap replicate (m = 2 times the square root of n, 500 replicates in total) (Bickel et al.2012). Doing so, breaks down the underlying spatial dependence as randomly sampled ZIP codes were not necessarily adjacent in each bootstrapped sample, yielding more robust standard errors and thus 95% confidence intervals. Therefore, it is least likely that our findings are influenced by spatial correlation.
We conducted several sensitivity analyses to assess the robustness of our results. First, we fit alternative models, excluding different covariates, including co-pollutant, time trends, SES, meteorology variables, behavioral risk factors, and baseline hazard stratification. We also tested how sensitive our models might be to adjust for space with a spatial smoother and with a statelevel adjustment. We then compared the results of these models to examine the influence of potential confounders. Second, we evaluated the potential heterogeneity of associations by each U.S. state. Third, we fit single-pollutant penalized spline models, and tested whether the exposure-response relationship held under both scenarios (i.e., single-pollutant vs. multipollutant models).
The Medicare data set was stored and analyzed in the Rollins High-Performance Computing Cluster at Emory University, with Health Insurance Portability and Accountability Act compliance. R software, version 4.0.2 (R Development Core Team) was used in this study. The results were rounded to three decimal places to differentiate the upper and lower bounds of the confidence intervals. The estimated results with p < 0:05 were considered statistically significant.

Results
We included a total of 13,590,387 Medicare enrollees residing in 10,193 ZIP codes and 1,701 counties in the southeastern United States, with 107,291,652 person-years of follow-up in our full cohort study between 2000 through 2016. Each ZIP code had a mean [ ± standard deviation ðSDÞ] of 1,485 ± 2,815 Medicare beneficiaries. A total of 4,898,015 (36.0%) participants died between 2000 and 2016. Among the full cohort, 9,669,469 (71.1%) Medicare enrollees living in 7,541 ZIP codes were always exposed to annual mean NO 2 concentrations below WHO air quality guidelines (21 ppb), with 2,814,617 (29.1%) deaths in 69,077,046 person-years of follow-up. The median follow-up years for the full cohort and the below-WHO guidelines cohort were 8 and 7 y, respectively. Nearly all (99.95%) of the Medicare enrollees were always exposed to annual mean NO 2 concentrations below the U.S. NAAQS (53 ppb). Detailed characteristics of the study population and summary statistics for all covariates are presented in Table 1 and Table S1.
Overall, from 2000 to 2016, the mean annual NO 2 concentration across the southeastern United States was 13.7 ppb, with an interquartile range (IQR) of 9.3 ppb ( Table 2). The spatial distribution of long-term NO 2 concentrations is presented in Figure 1, which appears to depict patterns consistent with major roadways ( Figure S1) and NO 2 concentrations at 1-km resolution ( Figure  S2). The SDs of the 1-km NO 2 concentrations within ZIP code areas are shown in Figure S3. The temporal trend of long-term NO 2 concentrations by state is shown in Figure S4. At the beginning of the study period, the lowest annual NO 2 levels were observed in Mississippi, with the highest annual levels observed in Florida. We observed a declining trend of NO 2 concentrations over the study period, apart from elevated levels between 2009 and 2011.
Overall, long-term exposure to NO 2 , even at low levels, was significantly and positively associated with mortality in all statistical analyses (Table 3). In single-pollutant models, we observed a HR = 1.042 [95% confidence interval (CI): 1.040, 1.045] per 10-ppb increase in NO 2 concentrations. After adjusting for PM 2:5 and O 3 , the results for NO 2 were similar (the estimated results for PM 2:5 and O 3 are presented in Table S2). The observed relationship between long-term NO 2 concentrations and mortality appears to be approximately linear across the exposure distribution, because the concentration-response curve does not suggest a threshold for mortality at low concentrations of NO 2 , the slope of the curve does not level off at high concentrations at least in the range examined, and the nonlinearity derived from the penalized spline fitting is within the model uncertainty ( Figure 2).
In our sensitivity analyses, excluding time trends changed the HR the most compared with the main analysis (HR excluding time trend = 1.251; 95% CI: 1.248, 1.254; Table S4). Analyses stratifying by state yielded consistently positive associations between long-term NO 2 exposure and mortality, with the highest HR observed among Medicare beneficiaries in North Carolina (HR = 1.067; 95% CI: 1.060, 1.074; Table S5). Last, the singlepollutant and multipollutant penalized spline models yielded almost identical splines, both suggesting approximately linear exposure-response relationships for annual NO 2 and all-cause mortality (Figure 2; Figure S5).

Discussion
In this large-scale population-based cohort study, we observed a significant and independent association between long-term exposure to NO 2 and all-cause mortality among the Medicare population even at NO 2 levels below global and national guidelines. We further observed a roughly linear trend in mortality risk after adjusting for PM 2:5 and O 3 , indicating no apparent evidence of a threshold value. We also observed larger measures of association among White populations, women, and urban residents, indicating potential susceptibility among these groups.

Models
b Single-pollutant model: stratified by age at entry (5-y categories), sex (female, male), race (White, Black, and other), Medicaid eligibility, and adjusted for calendar year, summer and winter mean temperature, median home value, median household income, population density, the proportion of owner-occupied housing units, the percentage of Black and Hispanic populations, education level, population below poverty level, body mass index, and the proportion of those who were ever smokers. The descriptive statistics for these variables are provided in Table 1 and Table S1. The exposure-response relationship between long-term exposure to NO 2 and all-cause mortality, derived from tri-pollutant models with adjustment of annual mean of PM 2:5 , annual warm-season average of O 3 , age at entry (5-y categories), sex (female, male), race (White, Black, and other), Medicaid eligibility, calendar year, summer and winter mean temperature, median home value, median household income, population density, the proportion of owner-occupied housing units, the percentage of Black and Hispanic populations, education level, population below poverty level, body mass index, and the proportion of those who were ever smokers. The descriptive statistics for these variables are provided in Table 1 and Table S1. Shaded areas indicate the 95% confidence bands. Note: NO 2 , nitrogen dioxide; PM 2:5 , particulate matter <2:5 lm in aerodynamic diameter; O 3 , ozone.
Although other factors-such as geographical differences, pollutant composition, and relative urbanicity, in addition to methodological differences that challenge the ability to make comparisons across studies, may impact the variability of measures of association (Hoek et al. 2013), multipollutant models-may provide a more accurate characterization of associated effects. However, at present, it is unclear whether the mortality effects observed in previous studies reflect an independent NO 2 association (COMEAP 2018), underscoring the potentially important contribution of our analysis to the literature. More work is necessary in this regard to better determine the causality of health effects and whether longterm average concentrations of NO 2 are adequately representative of complex pollutant mixtures.
Few studies have assessed the shape of the exposure-response relationship between NO 2 and mortality (Huangfu and Atkinson 2020). Our findings demonstrating evidence of linearity across the exposure distribution are supported by results from other recent studies (Dirgawati et al. 2019;Hanigan et al. 2019), suggesting that long-term exposure to NO 2 , even at levels below current guidelines, is associated with increased mortality.
Another important finding from our study relates to the differential association observed in subgroup analyses. We found a higher average risk of mortality among White populations when compared with other races. One possible reason is that White populations, although less socially vulnerable and presumably, on average, healthier, might be less resilient to NO 2 . This is consistent with the finding from other race/ethnicity health disparities studies (Breslau et al. 2006). We also found higher mortality risks among women compared with men, which is at odds with the results reported by Crouse et al. (2015); however, too few studies have investigated differential effects of NO 2 -associated mortality risk by sex; thus, further investigation is warranted. Last, the effect of modification of age was not apparent, which was similar to the study of NO 2 and mortality in three Canadian cities (Chen et al. 2013).
Long-term exposure to NO 2 has been associated with acute and chronic respiratory diseases (Abbey et al. 1993), such as increased bronchial hyperresponsiveness (Jammes et al. 1998), increased respiratory infection (Liang et al. 2020), and decreased lung function (Nori-Sarma et al. 2021). Biological evidence has been reported for plausible mechanisms regarding the health effects of NO 2 . One critical review suggests that NO 2 inhalation can induce lung function changes, accelerate pulmonary infections, and aggravate existing lung diseases by triggering a proinflammatory response, which is an innate immune response (Hesterberg et al. 2009). Moreover, an in vitro study found that NO 2 can enhance oxidative stress and lead to the generation of reactive oxygen and nitrogen species (Ayyagari et al. 2007), and another study found NO 2 could deteriorate the cardiovascular and immune systems in mice (Bevelander et al. 2007).
To the best of our knowledge, few studies have restricted ambient NO 2 exposure below current annual guidelines to investigate the exposure-response relationship between NO 2 and mortality in a large-scale population-based study (Chen et al. 2013;Sanyal et al. 2018;Yorifuji and Kashima 2020). Our study includes all Medicare beneficiaries in the southeastern United States, which includes all residents exposed to low-level NO 2 in both rural and urban areas. Our large, representative sample size provides ample statistical power to characterize complex spatiotemporal patterns among populations exposed to low-level pollution concentrations. Taken together, our results may provide a more confident characterization of the independent mortality effects of NO 2 through the use of single-, bi-, and multipollutant modeling and a rigorous statistical approach for deriving confidence intervals through an m-of-n bootstrapping approach (as a comparison, Table S7 shows the standard errors before and after bootstrapping).
Several limitations of this study should be acknowledged. First, as with any exposure assessment at an ecologic scale, the potential for exposure misclassification is of particular concern. The use of ZIP codes to estimate long-term exposure to NO 2 concentrations may not correlate well with individual-level exposure. Although the comparison of major roadways ( Figure S1), 1-km 2 NO 2 concentrations ( Figure S2), and ZIP code-scale NO 2 concentrations ( Figure  1) suggests that even though ZIP code-level NO 2 may serve as a good indicator of traffic pollution at the larger scale, large differences in NO 2 could still occur within a major source area, for example, at locations near major roadways. As such, a 1-km 2 scale of NO 2 exposure may still be too coarse a resolution given the decay gradient of NO 2 , which limits the ability to capture local or small-area variations in traffic-related pollution and proximity to roads. Second, the Medicare data do not provide the underlying cause of death necessary for understanding possible causal pathways. Third, given the use of administrative data, we cannot exclude the possibility of outcome misclassification due to coding errors or residual confounding bias on account of individual-level risk factors for mortality, such as smoking, alcohol consumption, and physical activity, which were not ascertained in this study. However, this was a semi-individual study because of the exposure aggregation, and these behaviors have been shown in personal exposure studies (Weisskopf and Webster 2017) to be uncorrelated with outdoor exposure levels; they are correlated only through neighborhood-level SES. Therefore, controlling for neighborhood SES and, secondarily, for neighborhood obesity and smoking, is appropriate for confounding adjustment. That said, we must admit that our neighborhood  Figure 3. The hazard ratios of mortality associated with a 10-ppb increase in NO 2 concentrations for study subgroups. Density Q1-Q4 stand for low population density, low-medium population density, medium-high population density, and high population density, respectively. The numeric data for these measures of associations are provided in the Table S3. Note: NO 2 , nitrogen dioxide; Q, quartile.
smoking and obesity information is not ideal, because we have only the information gathered on a county level from the BRFSS, and residual confounding remains a concern. Fourth, our findings may not be generalized to younger age groups or represent the vast differences across the United States, where the pollution composition and demographic characteristics vary significantly. Furthermore, having controlled for O 3 and PM 2:5 , we cannot rule out the possibility that NO 2 may be an indicator of other traffic-related air pollutants, such as ultrafine particles, soot, and trace metals or other potential noiserelated confounding factors (Beckerman et al. 2008;Moshammer et al. 2020).
In conclusion, we found an association between long-term exposure to NO 2 and all-cause mortality, independent of PM 2:5 and O 3 exposure. Our findings contribute to the evidence base of the increased risk of mortality associated with traffic-related air pollution. Nevertheless, our results should be taken as part of a growing, although insufficiently studied, area of air pollution epidemiology. Further research is needed to study the association between long-term NO 2 exposure and mortality, particularly at low levels, with improved methods and measurements of exposure (e.g., improved with increasing spatial monitoring density). Reconsidering both national and international NO 2 emissions guidelines may yield significant health benefits.