Numerous epidemiologic studies have reported adverse effects of short- and long-term traffic-related air pollution on pulmonary function in children. The majority of previous studies has focused on regularly monitored air pollutants (carbon monoxide, nitrogen oxide, nitrogen dioxide, sulfur dioxide, ozone, and particulate matter (PM) with aerodynamic diameter <10 and <2.5 μm)^1–4 and to a lesser extent proximity to traffic.^5,6 To our knowledge, only one previous study has examined the relationship between exposure to polycyclic aromatic hydrocarbons (PAHs) and pulmonary function in children.⁷

PAHs are a class of chemicals defined by two or more fused aromatic rings that are products of incomplete combustion of fossil fuels, wood, coal, and tobacco. Major sources of ambient PAHs are vehicular emissions, home heating, wildland fires, agricultural burning, and power plants.⁸ PAHs are important components of PM_2.5 and PM₁₀, both of which have been linked to respiratory health.⁹ PAHs have received particular attention because of their potential to cause oxidative stress and related cytotoxicity.¹⁰

Exposure to PAHs has been linked to several adverse outcomes in children, including asthma symptoms,^11,12 biomarkers of asthma,^13,14 respiratory health,^15,16 and cognitive development.^17,18 PAHs have also been associated with lower pulmonary function and wheezing in adults.¹⁹ One study of community-level air pollution compared the average pulmonary function across two communities with different concentrations of ambient air pollutants, including PAHs,⁷ but no previous study of individual PAH exposure and pulmonary function in children has been reported.

Estimates of exposures to ambient air pollutants are typically based on measurements from single fixed-site monitors that can capture temporal variability, but not often spatial variability.²⁰ For PAHs, this poses two problems. First, ambient concentrations of PAHs are not routinely measured at air quality monitoring sites.²¹ Second, PAHs are highly dependent on local sources that have a high degree of spatial variability within an urban environment.^21–24

Exposure assessment for spatially heterogeneous air pollutants is best performed with a model of exposure that accounts for both the temporal and spatial distributions. Land-use regression models can estimate pollution exposure by exploiting the relationship between the measurement site and local environmental variables, although they are typically cross-sectional and thought to represent long-term concentration gradients.²⁵ This relationship, formalized with regression equations, can incorporate small area variation and be used to assign estimated exposures for all participants in a cohort study. When repeated air pollution measures are available, mixed-effects regression can be used to model shorter time periods by accounting for both short-term temporal variability and spatial variability.^22,23

Fresno is located in California’s San Joaquin Valley and is one of the fastest-growing areas of California.^26–28 During the years 2005 to 2007, the population of Fresno was exposed to an annual average PM_2.5 concentration that often exceeded the federal annual standard by over 40%.^26,29–31 Motor vehicles account for one-third of PAH emissions in the United States,³² and motor vehicles and residential wood combustion are the major sources of air pollution in Fresno.^33–36 The burden of asthma is also very high in Fresno. The 2009 lifetime prevalence of asthma in children 5 to 17 years in Fresno County was 20% (95% confidence interval (CI): 13–27) compared with 16% (95% CI: 15–18) for the state of California.³⁷ For these reasons, Fresno was selected for a study of the differential effects of air pollution on the respiratory health of asthmatic and non-asthmatic children.

The current study examines the relationship between estimated personal exposures to the sum of PAHs with 4, 5, or 6 rings (PAH456) and pre- and postbronchodilator pulmonary function tests in asthmatic and non-asthmatic children in Fresno, California.

The entire study population consists of 467 children, of which 309 had acceptable postbronchodilator FEV₁ and/or FEF_25–75. We were able to estimate PAH456 exposure for 297 of those children (96% of those with acceptable postbronchodilator spirometry).

The ages of the children ranged from 9 to 18 years (Table 1). The majority of the population is Hispanic and about half of the study population lives in family-owned homes. Asthmatic children were more likely to be African American and have slightly more parental education, but otherwise there was little difference in demographic characteristics between asthmatic and non-asthmatic participants.

The distributions of exposures for each of the averaging times and by asthmatic status are presented in Table 2a. The correlation matrix of the exposure periods is in Table 2b.

Tables 3a and b show the distribution of FEV₁ and FEF_25–75 values stratified by asthmatic status. The tables include the raw values by sex and the percent predicted values using NHANES reference equations.⁴⁰ As expected, girls had lower pulmonary function compared with boys and, in general, asthmatic children had lower pulmonary function than non-asthmatic children with one exception among boys for prebronchodilator FEV₁. The tables are split by prebronchodilator (Table 3a) and postbronchodilator (Table 3b) values.

Tables 4a and b present the main results of the final analysis of the adjusted association of PAH456 during each of the averaging periods with maximum pre- and postbronchodilator FEV₁ and FEF_25–75, respectively, stratified by asthma status. For the prebronchodilator pulmonary function tests, the estimates were in mixed directions and only one estimate was statistically significant. A 1 ng/m³ increase in 1-week PAH456 was associated with a 0.29 l increase in FEV₁ (95% CI: 0.07, 0.51) among asthmatics (Table 4a). As expected, after adjusting for season for this short-term exposure, the result was no longer statistically significant (data not shown).

For the postbronchodilator pulmonary function tests that represent a more chronic status of lung function, the associations between PAH and pulmonary function were more consistent. Among non-asthmatics, there was a statistically significant association between PAH during the previous 3 months, 6 months and 1 year and FEV₁. The magnitude of the association increased with the length of the averaging period.

Among non-asthmatic children, a 1 ng/m³ increase in PAH456 was associated with a 0.11 l (110 ml) decrease in FEV₁ (95% CI: −0.20, −0.01) (Table 4b).

After adjustment for season, the postbronchodilator estimates were essentially unchanged and were borderline significant. The results adjusted for secondhand smoke were not considerably different among non-asthmatics. The effect estimates were larger among asthmatics compared with the primary analysis, although not statistically significant (Appendix Tables A1a and b). When the analysis was stratified by sex, no significant differences between boys and girls were found (data not shown). The results stratified by use of controller medication is presented in the (Appendix Tables A2a and b) for both pre- and postbronchodilator pulmonary function tests by PAH exposure periods. As with the other results, they are largely null, especially among those on controller medications. There are subtle, but not convincing signals among those without controller medication use, but in opposite directions for FEV₁ and FEF_25–75.

We found a statistically significant association between exposure to PAH456 during the previous 3 months, 6 months and 1 year and a decrease in FEV₁ among non-asthmatic children. Among asthmatic children, there was little evidence for an effect of exposure to PAH456 on FEV₁. To our knowledge, this is the first study of the effect of individual-level PAH exposures on lung function in children.

Relatively few studies have evaluated the effects of short- or long-term exposure to PAHs on respiratory health, despite the potential biological impacts of this class of organic compounds.⁴¹ In a study of longer-term exposure to air pollutants in Southern California, variants of the PAH-metabolizing enzyme, microsomal epoxide hydrolase, were associated with an increased risk of asthma and, in addition, the risk increased with proximity of residences to freeways.⁴² Children living in a town in the Czech Republic with two- to threefold higher air pollution levels (including PAHs) had lower pulmonary function tests compared with children living in a town with lower air pollution.⁷

An additional study found prenatal exposure to PAHs, as measured by personal monitors during the third trimester of pregnancy, was not associated with respiratory symptoms of children at age 12 and 24 months; however, an interaction of PAH exposure with secondhand tobacco smoke to increase risk of respiratory symptoms was found.¹¹

We showed in a previous analysis of the FACES that short-term increases in PAH456 were associated with risk of wheeze. Odds ratios ranged from 1.01 (95% CI, 1.00–1.02) to 1.10 (95% CI, 1.04–1.17) for multiple lags and moving averages out to 9 days.¹² In a second study that included a subset of 71 FACES participants, we demonstrated an association between PAH exposure and suppression of regulatory T-cell function through methylation of the FoxP3 gene that regulates the function of these cells.¹⁴ The suppression of regulatory T cells also was significantly associated with enhancement of the Th-2 phenotype, asthma symptoms, and lower FEV₁ in the 71 FACES children. The finding that PAH exposure was associated with methylation of FoxP3 is consistent with the observation that exposure to traffic-related air pollution can lead to DNA methylation.⁴³ The association of PAH with FoxP3 methylation is also consistent with the known effect of a common PAH, phenanthrene, to enhance IgE synthesis.⁴⁴ In another more recent analysis of 256 CHAPS subjects, results demonstrated that increased ambient PAH exposure was associated with impaired systemic immunity and epigenetic modifications in two key genes involved in atopy: FoxP3 and IFNγ, with a higher impact on atopic children.⁴⁵ These FACES and CHAPS data, taken together with the other studies described above, provide evidence that PAH exposure may contribute to asthma morbidity.

Somewhat surprisingly, we found a statistically significant effect of PAH exposure on lung function in non-asthmatic children, but not in asthmatic children in the current study. One possible explanation for these results is that lung function is more variable with asthma such that a relatively small effect of PAH exposure could not be detected against the background variability. Thus, we might not have had the statistical power to detect a difference in pulmonary function among asthmatic participants. Another potential explanation may lie in the results of controlled exposure studies of diesel exhaust (containing multiple PAHs), which show that subjects with asthma have less acute inflammatory responses than those without asthma.⁴⁶ The results stratified by controller medication use among asthmatics did not provide any clear explanation to our results; however, there was little power to detect such a difference.

The role of season in the study of air pollution and pulmonary function is complex. The adjustment for season is more reasonably justified for the shorter averaging periods, but less so for the longer ones, particularly the 1-year averages, during which all the seasons are represented. In our study, adjustment for season did not substantially change the effect estimates.

Our study has several strengths. It is the first study of pulmonary function in children to employ individual daily estimates of PAH exposure. The exposure assessment is based on a well-developed regression model of PAH exposure that captures spatial variability in a region of the United States with high concentrations of PAHs. An additional strength is the phenotypically well-characterized study population with individual covariate data and careful quality control of spirometry.

Several limitations need to be considered, however, in the interpretation of these data. Because the spirometric data were cross-sectional, we were not able to assess change in pulmonary function over time and, therefore, could not examine the association between PAHs and growth of lung function.

We did not estimate individual-level exposures for other pollutants, so we did not evaluate other components of PM, secondhand smoke, or gaseous pollutants that are correlated with PAHs. (Although we explored family and home smoking, they were not associated with pulmonary function.) Thus, we cannot estimate the degree to which the associations we report here are independent of other pollutants. On the basis of the correlation structure of the other pollutants collected at the central site, the PAHs in this analysis are correlated with PM_2.5, carbon monoxide, nitrogen dioxide, and elemental carbon. These strong correlations suggest that the PAHs we measured in Fresno are likely from traffic emissions primarily.

The spatiotemporal regression model accounts for some of the spatial variability in the data, but the health effects associated with model estimates of PAH exposure are likely attenuated and biased toward the null. The PAH exposure estimates contain both classical and Berkson’s errors. However, we did not incorporate errors attached to the spatiotemporal model.²³

In conclusion, this study identifies an association between PAH456 and pulmonary function in children without asthma. Additional studies are needed to further explore the association between exposure to PAHs and pulmonary function, especially differential effects between asthmatic and non-asthmatic children. Future studies would benefit from improved individual-level assessment of PAH exposures and longitudinal pulmonary function follow-up.