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. Author manuscript; available in PMC: 2014 Feb 1.
Published in final edited form as: Environ Res. 2012 Nov 23;121:71–78. doi: 10.1016/j.envres.2012.10.012

Traffic-related air pollutants and exhaled markers of airway inflammation and oxidative stress in New York City adolescents

Molini M Patel a, Steven N Chillrud b, KC Deepti c, James M Ross b, Patrick L Kinney a
PMCID: PMC3577992  NIHMSID: NIHMS421632  PMID: 23177171

Abstract

Exposures to ambient diesel exhaust particles have been associated with respiratory symptoms and asthma exacerbations in children; however, epidemiologic evidence linking short-term exposure to ambient diesel exhaust particles with airway inflammation is limited. We conducted a panel study with asthmatic and nonasthmatic adolescents to characterize associations between ambient diesel exhaust particle exposures and exhaled biological markers of airway inflammation and oxidative stress.

Over four weeks, exhaled breath condensate was collected twice a week from 18 asthmatics and 18 nonasthmatics (ages 14–19 years) attending two New York City schools and analyzed for pH and 8-isoprostane as indicators of airway inflammation and oxidative stress, respectively. Air concentrations of black carbon, a diesel exhaust particle indicator, were measured outside schools. Air measurements of nitrogen dioxide, ozone, and fine particulate matter were obtained for the closest central monitoring sites. Relationships between ambient pollutants and exhaled biomarkers were characterized using mixed effects models.

Among all subjects, increases in 1- to 5-day averages of black carbon were associated with decreases in exhaled breath condensate pH, indicating increased airway inflammation, and increases in 8-isoprostane, indicating increased oxidative stress. Increases in 1- to 5-day averages of nitrogen dioxide were associated with increases in 8-isoprostane. Ozone and fine particulate matter were inconsistently associated with exhaled biomarkers. Associations did not differ between asthmatics and nonasthmatics. The findings indicate that short-term exposure to traffic-related air pollutants may increase airway inflammation and/or oxidative stress in urban youth and provide mechanistic support for associations documented between traffic-related pollutant exposures and respiratory morbidity.

Keywords: air pollution, exhaled breath condensate, inflammation, oxidative stress, traffic

1. Introduction

Asthma prevalence and morbidity are higher in New York City communities such as Harlem and South Bronx compared with other neighborhoods and surrounding suburbs (Garg et al., 2003; New York State Department of Health, 2007). These New York City communities also contain numerous diesel emissions sources including major trucking routes, bus depots, and waste transfer stations; and higher levels of diesel exhaust particles have been measured in New York City communities with higher traffic volumes (Maciejczyk et al., 2004; Patel et al., 2009).

Several epidemiologic studies have demonstrated associations between short-term increases in ambient concentrations of elemental or black carbon, widely-used indicators of diesel exhaust particles, and increases in respiratory hospital admissions or symptoms (Bell et al., 2009; Gent et al., 2009; Patel et al., 2010; Spira-Cohen et al., 2011). Specifically, daily black carbon concentrations have been associated with daily respiratory symptoms among New York City children and adolescents, including those residing in Harlem and South Bronx (Patel et al., 2010; Spira-Cohen et al., 2011). Mechanistic support has been provided by controlled human exposure studies that found diesel exhaust particle exposures to induce transient increases in inflammatory cell counts and/or cytokine concentrations in airways (Behndig et al., 2006; Kongerud et al., 2006). Epidemiologic studies increasingly are finding that short-term increases in ambient pollutants such as fine particulate matter (PM2.5) and nitrogen dioxide (NO2) are associated with increases in airway inflammation in children and adults (Adamkiewicz et al., 2004; Barraza-Villarreal et al., 2008; Delfino et al., 2006; Delfino et al., 2010; Koenig et al., 2003; Liu et al., 2009; McCreanor et al., 2007; Romieu et al., 2008; Zhang et al., 2009). Investigation of associations between diesel exhaust particle exposures and airway inflammation in children (Delfino et al., 2006), particularly healthy children, is limited, and investigation in adults has focused largely on road-side exposures (McCreanor et al., 2007; Zhang et al., 2009). Hence, the changes in airway inflammation associated with short-term exposure to ambient diesel particles and the potential effects in healthy individuals have not been well characterized.

Several indicators of airway inflammation and oxidative stress, including pH, 8-isoprostane, and cytokines have been measured in exhaled breath condensate. These indicators differ between asthmatics and nonasthmatics and change acutely upon asthma exacerbation or treatment with asthma medication (Antus et al., 2010; Baraldi et al., 2003; Hunt et al., 2000; Montuschi et al., 2006). As biological markers of lung responses, analysis of these biomarkers may provide insight into diesel exhaust particle mechanisms of action and serve to link findings between experimental and epidemiologic studies.

To improve understanding of the association between short-term exposure to ambient diesel exhaust particles and respiratory morbidity, we conducted a panel study involving continuous monitoring of black carbon outside two New York City high schools and measurement of pH and 8-isoprostane in exhaled breath condensate collected twice a week from students with and without asthma. Observations that ambient black carbon concentrations are associated with diesel exhaust particle concentrations and volume of diesel traffic but not car traffic support the measurement of black carbon as an indicator of diesel exhaust particles (Patel et al., 2009; Wu et al., 2007). We hypothesized that short-term increases in ambient black carbon would be associated with decreases in exhaled breath condensate pH, reflecting increases in airway inflammation, and with increases in 8-isoprostane, reflecting increases in airway oxidative stress. Further, we hypothesized that the associations between black carbon and exhaled biomarkers would differ between asthmatic and nonasthmatic adolescents.

2. Materials and methods

2.1. Subject recruitment and data collection

Students from two New York City high schools were recruited into the four-week panel study. The study protocol was approved by the Columbia University Medical Center Institutional Review Board, which is responsible for ensuring the ethical conduct of human research studies. The New York City Board of Education granted permission with the requirement that school identities remain confidential. School 1 was selected based on its participation in a larger study examining respiratory symptoms and the participation of a large number of asthmatic subjects (Patel et al., 2010). School 2 was selected because its curriculum was focused on a similar subject matter, and it was located in an area with similar traffic characteristics, specifically, on a two-lane roadway with stop-and-go traffic and near (0.2–0.3 km) a highway that prohibits commercial vehicles, including most diesel trucks. The two schools were located less than 15 km apart. The annual average daily traffic counts on the closest cross-street and highway were for School 1, 31,950 and 73,693, respectively, and for School 2, 23,294 and 179,891, respectively (New York State Department of Transportation, 2011).

Written informed consent was obtained from subjects and their parents/guardians before the start of the study. School 1 subjects were recruited from the larger pool of 70 participants of the symptom study (90% participation rate based on number of signed consent forms returned) who were recruited through various means, including classroom presentations, hallway advertisements, and school nurses. For the exhaled breath condensate study, 20 School 1 students returned signed consent forms, and 18 (90%) participated by providing at least two exhaled breath condensate samples. School 2 subjects were recruited through classroom presentations, during which study information and consent forms were distributed to interested students. Consent forms also were left with the teacher. Of the 21 signed consent forms returned at School 2, 18 (86%) subjects participated by providing at least two exhaled breath condensate samples. All subjects who provided written informed consent met the inclusion criteria of being enrolled in one of the two study schools, completing a questionnaire at baseline, and providing at least two exhaled breath condensate samples during the study period. No other inclusion or exclusion criteria were applied.

Data on demographic characteristics and respiratory health history were obtained from questionnaires administered at baseline. Current asthma was defined as self-report of physician-diagnosed asthma plus respiratory symptoms in the previous 12 months. Data on daily incidence of respiratory symptoms and use of medication for asthma were obtained from questionnaires administered each day after exhaled breath condensate collection.

Study subjects were followed for four weeks between 11 May and 14 June 2005, during which two samples of exhaled breath condensate were collected a week from each subject. A maximum of eight exhaled breath condensate samples was collected per subject. At School 1, exhaled breath condensate was collected mostly on Tuesdays and Thursdays and on one Friday because of a conflict with a school event. At School 2, samples were collected on Wednesdays and either Monday or Friday. All subjects within a school were observed on the same day. To control for diurnal variation in biological markers, exhaled breath condensate was collected from each subject at the same time of day on each collection day, either during a morning session between 10:00 and 11:00 or during an afternoon session between 15:00 and 16:00. Two collection periods were provided in order to accommodate subjects’ class schedules. Subjects were not observed during 20–30 May because of conflicts with end of the year school events and a holiday.

2.2. Ambient pollutant measurements

At each school, ambient black carbon in the PM2.5-size range was monitored using aethalometers (Magee Scientific, Berkeley, CA, U.S.A.) as described (Patel et al., 2009). The aethalometers were placed with air inlets located outside classroom windows and facing the adjacent roadway. Data on ambient PM2.5 concentrations were obtained from the U.S. Environmental Protection Agency’s Air Quality Systems database (U.S. Environmental Protection Agency, 2005) for a monitoring site located within 10 km from study schools that had daily measurements available. Data on ambient NO2 and ozone (O3) concentrations were obtained from a different site in the city located within 14 km from study schools that had data available for the entire study period. Pollutant concentrations were highly correlated between sites over the study period (Spearman r = 0.99 for PM2.5, 0.85 for NO2, and 0.95 for O3), indicating that pollutant concentrations at the two sites similarly represented day-to-day variations in city-wide ambient concentrations. Data were analyzed as daily average concentrations of black carbon, NO2, and PM2.5 and daily maximum 8-hour moving average concentrations of O3.

2.3. Exhaled breath condensate collection and analysis

Exhaled breath condensate was collected by having subjects breathe orally with normal frequency and effort through an R-Tube (Respiratory Research Inc., Charlottesville, VA, U.S.A.) for 7 min. After collection, R-tubes were stored on dry ice and transported to the laboratory at the end of each school day. On the same day as collection, in the laboratory, samples were divided into aliquots and stored at −70°C to minimize artifactual changes in concentrations of biological markers. Biomarkers were measured in all samples after the final day of collection to avoid batch-related measurement error. All samples were analyzed within 6 weeks of collection and in a random order to the date of collection. The mean (range) time between collection and analysis was 25 days (9–42 days). Exhaled breath condensate 8-isoprostane concentrations were not related to the number of days samples were stored (Appendix, Figure A.1).

Before pH analysis, argon gas was bubbled through exhaled breath condensate samples for 10 min to remove carbon dioxide, which may artificially lower pH of samples. pH of exhaled breath condensate was measured with a pH meter (Cole-Parmer Instrument Co, Vernon Hills, IL, U.S.A.). 8-isoprostane concentrations were quantified using enzyme-linked immunosorbent assays (Assay Designs, Ann Arbor, MI, U.S.A.). Concentrations were quantified using standard curves generated from analyzing 6–100,000 pg/ml standard concentrations processed simultaneously with subject samples on the same plate.

2.4. Statistical Analysis

As did similar panel studies (Barraza-Villarreal et al., 2008; Delfino et al., 2006; Koenig et al., 2003; Liu et al., 2009), we used mixed effects regression models (SAS 9.2, SAS Institute, Cary, NC, U.S.A.) to characterize associations between ambient pollutants and exhaled biomarkers in data pooled from the two schools. Mixed effects models also were used to characterize within- and between-subject variability in exhaled breath condensate pH and 8-isoprostane. The repeated outcome measures within subjects were modeled with subject-specific random intercepts. We specified an unstructured covariance structure to account for the varying correlation among pairs of serially collected exhaled biomarkers (Appendix, Tables A.1 and A.2). Exhaled breath condensate 8-isoprostane data were natural log transformed before analysis to obtain a normal distribution of residuals from regression models. Analysis of model residuals of pH and natural log transformed 8-isoprostane indicated equal distribution around the mean values.

We adjusted results for school to account for differences in daily ambient black carbon concentrations and respiratory symptom incidence. Consistent with other repeated measures analyses of short-term air pollution exposures and health effects, we considered confounding by several time-varying factors that have been shown to have similar temporal patterns as ambient pollutant concentrations and respiratory symptoms, including day of the week, meteorological factors (National Oceanic and Atmospheric Administration, 2006), and other ambient air pollutants. It was not necessary to adjust for season or long-term temporal trends because the study was conducted over a single season. We excluded daily average humidity and dummy variables for day of week from the final models because they were not associated with respiratory symptoms and did not change pollutant effect estimates by more than 10%. Rain was recorded only on 3 of 16 sampling days and was not included in final models. Final models for black carbon, PM2.5, and NO2 included covariates for school, concurrent daily average temperature, and concurrent 8-hour maximum O3 concentration, and final models for O3 additionally were adjusted for concurrent daily average black carbon. These covariates were associated with respiratory symptoms or changed the pollutant effect estimates by more than 10%. Temperature was modeled as a linear term since model fit, as assessed with Akaike’s Information Criteria, did not improve by using a nonlinear term. Other co-pollutant models were not analyzed because the high correlation observed among some pollutants (range of Pearson r = 0.62–0.80) could have produced biased effect estimates. Heterogeneity of effect by school or by current asthma was examined by modeling pollutant by school or pollutant by asthma interaction terms, respectively.

We examined associations with various delayed and cumulative lags of pollutant exposure, including same-day (lag 0), previous-day (lag 1), and 2- to 5-day average pollutant concentrations. We also analyzed the average black carbon from the 1 hour preceding exhaled breath condensate collection and 8-hour school-day average. To facilitate comparisons among different pollutants, effect estimates are reported as change in outcome and 95% confidence interval (CI) per interquartile range (i.e., difference in the 25th to 75th percentile) increase in same-day average pollutant concentration using the full range of concentrations in the dataset.

3. Results

3.1. Study Population Characteristics

The study population comprised 36 predominantly nonwhite (94%) adolescents, ages 14–19 years, although the distributions of several characteristics differed between the two schools (Table 1). Eighteen subjects (50%) reported current asthma. Information related to asthma severity was limited to prevalence of daily symptoms and asthma medication use. Among subjects with current asthma, 69% of subjects reported symptoms on at least one of the eight exhaled breath condensate collection days; 50% of asthmatics reported any use of medication for asthma; and 17% of asthmatics reported taking asthma medication every day that exhaled breath condensate was collected. Asthma and symptom prevalence were higher among School 1 subjects than School 2 subjects. Compared with subjects who participated in the larger symptom study, School 1 subjects in the present study included larger proportions of Black/African American (19% versus 28%), Hispanic (36% versus 56%), and asthmatic subjects (19% versus 63%). School 2 subjects also comprised mostly Hispanic subjects (83%). School 2 did not participate in the earlier study, and no additional data on school demographic characteristics were available. U.S. Census 2000 data indicate that the general population of New York City differed with respect to the distribution of specific race/ethnicity groups; however, it similarly comprised large proportions of nonwhite populations (25% Black/African American and 27% Hispanic) (U.S. Census Bureau, 2000). The specific racial and ethnic distribution in the present study may have differed from those of the schools’ enrolled populations and the target population of New York City adolescents; however, the aim of the recruitment was to oversample students with asthma.

Table 1.

Characteristics of study population

Characteristic School 1 School 2
Number of subjects 18 18
Age [median (range) in years] 16 (14–19) 15 (14–18)
Sex
 Male 17% 44%
 Female 83% 56%
Race/Ethnicity
 White, non-Hispanic 11% 0%
 Black, non-Hispanic 28% 17%
 Hispanic 56% 83%
 Other 6% 0%
Father’s education level
 Less than high school 28% 6%
 High school graduate 39% 33%
 College graduate or more 17% 11%
 Not known 16% 50%
Current asthma 63% 33%
Daily prevalence of respiratory outcomes [median (range)]a
 Wheeze 19.6% (6.3–36.4) 2.5% (0–7.1)
 Persistent cough 22.5% (7.7–36.4) 8.9% (0–18.2)
 Shortness of breath 35.5% (13.6–54.5) 8.7% (5.9–14.3)
 Chest tightness 17.8% (0–36.4) 15.4% (6.3–23.5)
 Use of medication for asthma 22.2% (12.5–30.8) 6.2% (0–14.3)
Exhaled breath condensate pH
 Overall median (range of within-subject medians) 7.9 (5.8–8.1) 8.1 (7.4–8.2)
 Total variance due to between-subject variance 35.6% 19.4%
Exhaled breath condensate 8-isoprostane (pg/ml)
 Overall median (range of within-subject medians) 42.3 (25.1–67.2) 54.8 (22.1–72.9)
 Total variance due to between-subject variance 6.5% 15.5%
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a

On a given day during the study period, the number of subjects reporting presence of a symptom or use of medication for asthma divided by all subjects providing symptom or medication use data.

3.2. Ambient pollutant concentrations

During the four-week study period, daily average ambient black carbon concentrations were higher at School 1 than at School 2, and concentrations were moderately correlated (Table 2). The median (interquartile range) black carbon concentrations were, for School 1, 1.53 (1.00) μg/m3 and for School 2, 0.96 (0.44) μg/m3. At each school, daily average black carbon concentrations were positively correlated with daily average PM2.5 and NO2 concentrations measured at the central site, with higher correlations found for School 1 (Table 2 and Appendix, Figure A.2). Daily average black carbon and daily maximum 8-hour average O3 were weakly correlated.

Table 2.

Ambient air measurements for study period

Environmental variable Median (interquartile range) Pearson correlation (95% CI)a with School 1 black carbon Pearson correlation (95% CI)a with School 2 black carbon
School 1 daily average black carbon (μg/m3) 1.53 (1.00)
School 2 daily average black carbon (μg/m3) 0.96 (0.44) 0.70 (0.43, 0.86)
Daily average PM2.5 (μg/m3)b 10.0 (9.3) 0.72 (0.47, 0.87) 0.40 (0.01, 0.68)
Daily average NO2 (ppb)c 23.3 (8.6) 0.80 (0.63, 0.89) 0.62 (0.36, 0.79)
Daily maximum 8- hour average O3 (ppb)c 38.8 (15.5) 0.13 (−0.21, 0.44) 0.08 (−0.26, 0.40)
Daily average temperature (°C)d 17.5 (8.6) 0.26 (−0.14, 0.59) 0.31 (−0.09, 0.62)
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BC = black carbon, PM2.5 = fine particulate matter, NO2 = nitrogen dioxide, O3 = ozone

a

95% CIs were calculated from a Fisher transformation of Pearson correlation.

b

Measured at a central monitoring site less than 10 km from schools.

c

Measured at a central monitoring site less than 14 km from schools.

d

Measured at Central Park, New York City.

3.3. Exhaled breath condensate pH and 8-isoprostane

Subjects provided a total of 217 exhaled breath condensate samples. The mean number (range) of samples collected per day was 13 (6–17), and 25/36 subjects (69%) provided at least 6 samples during the study period. Among nonasthmatics, the range in daily exhaled breath condensate pH was 5.2–8.3 with a group median (interquartile range) of 8.0 (0.61). Among asthmatics, the range in daily exhaled breath condensate pH was 4.7–8.4 with a group median (interquartile range) of 7.7 (0.48). The difference in exhaled breath condensate pH between asthmatics and nonasthmatics was small (mean: 5%, 95% CI: −27, 53%) with adjustment for within-subject correlation. Variability in exhaled pH was observed across sampling days as illustrated in Appendix Figure A.3. Between-subject variance accounted for a small percentage of the total variance in exhaled breath condensate pH (Table 1). Other studies have reported mean exhaled breath condensate pH values of 7.4 to 7.8 in groups with and without asthma (Barraza-Villarreal et al., 2008; Hunt et al., 2000; Kostikas et al., 2002; McCreanor et al., 2007), and in children with asthma, Hunt et al. (2000) found a range of pH values of 4.5–8.5 that shifted to lower values in children with acute asthma. Because daily median and subject median pH values in the present study were in the range of plausible values reported in other studies, no observations were excluded from analyses with ambient pollutants.

Two out of 217 samples had 8-isoprostane concentrations below the 4 pg/ml limit of detection. 8-isoprostane concentrations varied across sampling days as illustrated in Appendix Figure A.4. Similar to pH, between-subject variance accounted for a small percentage of total variance in 8-isoprostane concentrations (Table 1). Among nonasthmatics, the range in daily exhaled breath condensate 8-isoprostane was 3.8–123.6 pg/ml with a median (interquartile range) of 49.6 (29.6) pg/ml. The range of daily values among asthmatic subjects was 7.2–132.9 pg/ml with a median (interquartile range) of 55.3 (27.3) pg/ml. There was a mean 21% (95% CI: −13, 68%) difference in exhaled 8-isoprostane concentrations between nonasthmatics and asthmatics. In other studies, concentrations of 8-isoprostane in exhaled breath condensate have ranged from approximately 4 pg/ml in healthy children to greater than 100 pg/ml in subjects with respiratory disease (Antczak et al., 2012; Baraldi et al., 2003; Brussino et al., 2010; Hasan et al., 2012; Hasan et al., 2011; Kostikas et al., 2002; Liu et al., 2009; Samitas et al., 2009; Shahid et al., 2005). Because 8-isoprostane values in the present study were in the range of plausible values reported in other studies, no observations were excluded from analyses with ambient pollutants.

Lower exhaled breath condensate pH was associated with higher but imprecise odds of wheeze, shortness of breath, and use of medication for asthma (Appendix, Table A.3). Higher exhaled 8-isoprostane generally was associated with lower but imprecise odds of respiratory symptoms. Inclusion of respiratory symptoms or medication use in models did not alter associations of exhaled biomarkers with respiratory symptoms (data not shown).

3.4. Associations of ambient pollutants with exhaled breath condensate pH and 8-isoprostane

In models pooling data from both schools, increases in ambient black carbon concentrations at all evaluated lags of exposure were associated with decreases in exhaled breath condensate pH and increases in 8-isoprostane, with adjustment for school, maximum 8-hour average O3, and daily average temperature (Table 3). An interquartile range (0.81 μg/m3) increase in same-day average (lag 0) black carbon was associated with a 0.18 unit (95% CI: 0.03, 0.34) decrease in exhaled breath condensate pH. Increases in 2- to 5-day average black carbon concentrations were associated with similar magnitudes of decrease in exhaled breath condensate pH. A 0.81 μg/m3 increase in lag 0 black carbon was associated with a 0.31 unit (95% CI: 0.02, 0.61) increase in natural log-transformed exhaled breath condensate 8-isoprostane. The 3-day average black carbon was associated with the largest increase in 8-isoprostane. Shorter averaging times of black carbon (1- or 8-hour averages) were not associated with exhaled biomarkers (data not shown). Associations of exhaled biomarkers with PM2.5, as a whole, were inconsistent; across the various lags evaluated, increases in PM2.5 were associated with increases and decreases in each biomarker (Table 3).

Table 3.

Unit change in exhaled breath condensate pH and 8-isoprostane (95% CI) per interquartile rangea increase in pollutant concentration at various lags of exposure

Pollutant pH 8-isoprostane (log-transformed)
Person-days of data available 217 217
Black carbon (daily average)b
 Lag 0 −0.18 (−0.34, −0.03) 0.31 (0.02, 0.61)
 Lag 1 −0.12 (−0.26, 0.02) 0.31 (0.14, 0.38)
 2-day average −0.23 (−0.40, −0.07) 0.46 (0.21, 0.71)
 3-day average −0.23 (−0.43, −0.04) 0.77 (0.48, 1.1)
 4-day average −0.22 (−0.47, 0.02) 0.44 (0.11, 0.77)
 5-day average −0.17 (−0.44, 0.09) 0.09 (−0.24, 0.41)
PM2.5 (daily average)b
 Lag 0 0.06 (−0.07, 0.18) 0.05 (−0.15, 0.25)
 Lag 1 −0.15 (−0.28, −0.02) 0.14 (−0.03, 0.31)
 2-day average −0.03 (−0.20, 0.14) 0.15 (−0.05, 0.34)
 3-day average −0.16 (−0.44, 0.13) 0.38 (0.11, 0.65)
 4-day average 0.15 (−0.37, 0.66) −0.10 (−0.53, 0.33)
 5-day average 0.52 (0.04, 0.99) −0.58 (−0.98, −0.19)
NO2 (daily average)b
 Lag 0 −0.01 (−0.15, 0.13) 0.32 (0.12, 0.52)
 Lag 1 −0.01 (−0.16, 0.14) 0.35 (0.13, 0.57)
 2-day average −0.06 (−0.22, 0.09) 0.49 (0.24, 0.83)
 3-day average −0.04 (−0.21, 0.23) 0.59 (0.33, 0.86)
 4-day average −0.02 (−0.23, 0.20) 0.59 (0.25, 0.94)
 5-day average 0.09 (−0.18, 0.36) 0.40 (−0.03, 0.83)
O3 (8-hour maximum)c
 Lag 0 −0.08 (−0.19, 0.03) −0.24 (−0.42, −0.06)
 Lag 1 0 (−0.16, 0.16) −0.51 (−0.76, −0.26)
 2-day average −0.08 (−0.22, 0.06) −0.40 (−0.62, −0.18)
 3-day average −0.04 (−0.21, 0.12) −0.50 (−0.75, −0.25)
 4-day average −0.06 (−0.24, 0.12) −0.69 (−0.98, −0.39)
 5-day average −0.08 (−0.30, 0.14) −0.70 (−1.1, −0.33)
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PM2.5 = fine particulate matter, NO2 = nitrogen dioxide, O3 = ozone.

a

Interquartile ranges of pollutants are 0.81 μg/m3 for black carbon, 9.9 μg/m3 for PM2.5, 3.8 ppb for NO2, and 11.6 ppb for O3.

b

Models combine data from both schools and adjust for school, daily maximum 8-hour average O3, and daily average temperature.

c

Models combine data from both schools and adjust for school, daily average black carbon, and daily average temperature.

Associations of ambient NO2 with exhaled breath condensate pH were near null (Table 3). All evaluated lags of NO2 exposure were associated with increases in exhaled breath condensate 8-isoprostane, with the largest increases found for increases in 3- and 4-day average concentrations (Table 3). A 3.8 ppb (interquartile range) increase in lag 0 NO2 was associated with a 0.32 unit (95% CI: 0.12, 0.52) increase in natural log-transformed 8-isoprostane, whereas a 3.8 ppb increase in 4-day average NO2 was associated with a 0.59 unit increase (95% CI: 0.25, 0.94).

Increases in daily maximum 8-hour average O3 were associated mostly with small, imprecise decreases in exhaled breath condensate pH, in the range of those found for NO2 (Table 3). Ozone was associated with decreases in exhaled breath condensate 8-isoprostane at all evaluated lags of exposure, with the largest decrease observed in association with 4- and 5-day average O3 concentrations. Product terms representing interactions of pollutant concentrations with school or asthma all had wide 95% CIs that included the null value (data not shown). For example, the difference in exhaled breath condensate pH per interquartile range increase in black carbon by school and by asthma status were 0.11 units (95% CI: −0.35, 0.57) and −0.04 (95% CI: −0.21, 0.13), respectively. Because of the imprecise differences in associations of the examined pollutants with exhaled breath condensate biomarkers between schools or between asthmatic and nonasthmatic subjects, we did not analyze associations stratified by school or asthma status.

4. Discussion

In the present panel study, we demonstrated that short-term increases in ambient black carbon (an indicator of diesel exhaust particles) and NO2 (a general indicator of vehicle emissions) were associated with increases in airway inflammation and oxidative stress, as reflected by decreases in pH and increases in 8-isoprostane, respectively, in exhaled breath condensate. PM2.5, as a whole, was associated with changes in exhaled biomarkers that were inconsistent in direction and smaller than those found for black carbon. Together, the results indicate that exposure to pollutants from traffic, including specific particle components from diesel traffic (i.e., elemental and organic carbon), may affect airway inflammation and respiratory morbidity independently of a heterogeneous mixture of particles from multiple sources. The present findings are in concordance with our previous observations in a population of New York City adolescents that included subjects from School 1 showing that daily ambient black carbon and NO2 were more strongly associated with daily respiratory symptoms than was PM2.5 (Patel et al., 2010).

We collected exhaled breath condensate with the aim of sampling the respiratory tract lining fluid, which is a target for inhaled pollutants. The relevance of exhaled breath condensate markers to serve as indicators of airway inflammation is supported by many observations. A decrease in exhaled breath condensate pH has been associated with acute asthma exacerbations (Hunt et al., 2000; Kostikas et al., 2002). 8-isoprostane, produced from oxidation of cell membrane phospholipids (Dworski, 2000; Janssen, 2001), consistently is higher in asthmatics than in nonasthmatics and increases during asthma exacerbations (Baraldi et al., 2003; Robroeks et al., 2007; Zanconato et al., 2004). Acidic pH can enhance oxidative stress, which can increase the activities of redox-sensitive transcription factors, which in turn, increase the expression of pro-inflammatory cytokines (Adcock et al., 1994; Bellocq et al., 1998; Haddad et al., 2000). Controlled human exposure studies have shown that acute exposure to diesel exhaust particles can transiently increase airway inflammation (Behndig et al., 2006; Bosson et al., 2008; Kongerud et al., 2006). The present study provides evidence that airway inflammation may increase with exposure to diesel exhaust particles in the ambient environment. Further, the present findings may provide mechanistic explanation for associations observed of ambient diesel exhaust particle concentrations with respiratory symptoms and asthma exacerbations in children (Bell et al., 2009; Gent et al., 2009; Patel et al., 2010; Spira-Cohen et al., 2011).

Our results are consistent with those of another panel study that found increases in airway inflammation and oxidative stress in children in association with increases in ambient concentrations of elemental carbon (Delfino et al., 2006). A common strength of these studies is that the repeated measurement of biological markers over time allows each subject to serve as his/her own control, thus minimizing confounding from time-invariant between-subject factors. Despite the relatively small number of subjects, the large number of samples collected from each subject at short intervals of time enhanced the power of this study to characterize acute pulmonary-specific responses to ambient black carbon exposures. Furthermore, both ambient black carbon monitoring and exhaled breath condensate collection took place at subjects’ schools where they spent 6–7 hours per weekday, which may have improved assessment of black carbon exposure. Interestingly, same-day and multiday average black carbon exposures were associated with exhaled breath condensate pH and 8-isoprostane, whereas shorter averaging times (1- and 8-hour) were not. These findings may indicate that exposures accumulated over the course of a day or multiple days may have greater influence on airway inflammation and oxidative stress or may indicate that the use of central air conditioning in schools results in greater error in the measurement of shorter duration black carbon exposures. We did not measure personal daily black carbon exposures or black carbon in locations other than outside the schools. Because students spend only a small portion of the day at school and live at varying distances from the school in areas that may vary in roadway proximity and traffic volume, exposure measurement error is likely. However, daily school-based and personal elemental carbon exposures among New York City students have been reported to be highly correlated over time (Spira-Cohen et al., 2010). Thus, in repeated measures studies, exposure assignment based on school-based fixed site monitoring may produce nondifferential measurement error but reasonably capture temporal changes in personal exposures of urban youth to ambient diesel exhaust particle indicators.

In contrast with other studies, (Barraza-Villarreal et al., 2008; McCreanor et al., 2007; Romieu et al., 2008; Zhang et al., 2009), we did not find ambient O3 or PM2.5 to be associated with consistent changes in exhaled breath condensate biomarkers. One possible explanation is that in high-traffic urban neighborhoods, traffic-related pollutants may exert greater effects on airway inflammation than do other pollutants. Additionally, this study was conducted before the peak O3 summer season, and the associated health effects may vary seasonally with the seasonal variation in the mix of air pollutants. In the present study, although maximum 8-hour O3 concentrations were weakly correlated with daily average black carbon and NO2, peaks in black carbon and NO2 often corresponded with drops in O3 and vice versa. Thus, the contrasting temporal trends and the positive associations observed between black carbon and NO2 and exhaled breath condensate 8-isoprostane may explain the negative associations observed for O3.

Alternatively, the lack of consistent association of exhaled breath condensate biomarkers with O3 and PM2.5 in the present study may be attributable to greater error in the measurement of their exposures. Despite the closer proximity of School 2 to the central site, central-site pollutants were less correlated with black carbon at School 2 than with black carbon at School 1. These results suggest that School 2 may be located in an area with unique source contributions and/or physical features that result in a distinct distribution of airborne pollutants. Consequently, the use of central site PM2.5 and O3 measurements may have biased their associations with exhaled breath condensate pH and 8-isoprostane to the null. We did not find large differences in association by school, but we may have lacked sufficient power to identify such differences. Measurement error in NO2 may explain the larger effect estimates observed for multiday NO2 average concentrations, in which measurement errors in single-day lags are smoothed.

The exhaled breath condensate 8-isoprostane concentrations among asthmatic (mean: 55.3 pg/ml) and nonasthmatic (mean: 49.6 pg/ml) subjects in this study were higher than those reported in other studies. Other studies have reported mean 8-isoprostane concentrations of 1.4–77 pg/ml in asthmatics and 2.6–16 pg/ml in nonasthmatics (Antczak et al., 2012; Baraldi et al., 2003; Brussino et al., 2010; Hasan et al., 2012; Hasan et al., 2011; Kostikas et al., 2002; Liu et al., 2009; Samitas et al., 2009; Shahid et al., 2005). 8-isoprostane concentrations in biological samples have been reported to increase with increasing storage time. We stored exhaled breath condensate samples at −70°C, which is considered to provide the best storage life. In the present study, samples were analyzed within 6 weeks (9 to 42 days) of collection and in an order random to the date of collection. Kostikas et al. (2002) reported changes in 8-isoprostane concentration in exhaled breath condensate samples stored 1–3 weeks; however, in the present study, 8-isoprostane concentration was not related to the number of days samples were stored. Thus, the data do not indicate that an increase in lipid oxidation related to the time samples were stored biased the results in the present study to produce associations where there were none. The differences in 8-isoprostane concentrations in the present and other studies may have been related to the immunoassays used to quantify 8-isoprostane concentrations. pH of exhaled breath condensate has been found to be stable up to two years (Vaughan et al., 2003), longer than the maximum storage time of samples in the present study.

Both black carbon and NO2 are indicators of traffic emissions, with black carbon serving as a more specific indicator of diesel emissions. In the present study, both black carbon and NO2 were associated with exhaled breath condensate 8-isoprostane. Because of the high correlation between black carbon and NO2, it was difficult to distinguish between the effects of particulate and gaseous traffic-related pollutants. In previous investigations of New York City schools, we found that diesel but not car traffic volume was associated with ambient black carbon (Patel et al., 2009), which provides support for the independent effects of diesel-related emissions on increasing airway inflammation and oxidative stress. Alternatively, black carbon and NO2 may be serving as indicators of other highly correlated individual traffic- or combustion-related pollutants or mixtures that are causally associated with airway inflammation.

In the present study, exhaled breath condensate pH and 8-isoprostane were not strongly associated with reports of respiratory symptoms in the 24 hours before exhaled breath condensate collection, which may indicate that black carbon exposure leads to transient and subclinical changes in airway inflammation. Romieu et al. (2008) found that exhaled breath condensate malondialdehyde, an indicator of oxidative stress, was positively associated with wheeze with a 3-day lag, indicating that changes in exhaled biomarkers may precede respiratory symptom incidence. In the present study, we only collected data on respiratory symptoms occurring in the previous 24 hours; thus, we could not assess associations with exhaled breath condensate markers that may occur with a different lag period.

Contrary to our hypothesis, we did not observe precise differences in association between pollutants and exhaled biomarkers by asthma status. Asthmatics and nonasthmatics did not have large differences in exhaled pH or 8-isoprostane concentrations. The findings indicate that exposure to traffic-related air pollution may not only increase airway inflammation and oxidative stress in asthmatic children, who have a greater susceptibility to airway inflammation, but also may exert effects in healthy children. Epidemiologic studies have not consistently found asthmatics to be more susceptible to ambient air pollution-associated airway inflammation and oxidative stress (Barraza-Villarreal et al., 2008; Liu et al., 2009; McCreanor et al., 2007), and in a controlled human exposure study, 2-hour diesel exhaust particle exposure increased airway inflammation in healthy adult subjects but not in subjects with mild to moderate asthma (Behndig et al., 2011). In the present study, because of the potential heterogeneity in asthma severity due to differences in frequency of asthma medication use, we may have had insufficient numbers of asthmatics and serial measurements of exhaled breath condensate to detect differences between asthmatics and nonasthmatics.

The different proportions of asthmatics between schools also may have contributed to the lack of strong effect modification by asthma status. Importantly, in this repeated measures study examining temporal associations, between-school differences in asthma and symptom prevalence, demographic characteristics, and ambient air pollution composition likely were not sources of confounding and did not artifactually produce associations; however, they may limit the generalizability of results to the schools’ enrolled populations or to New York City adolescents overall. However, we did not aim to obtain representative populations but rather aimed to oversample asthmatics and examine differences in susceptibility between asthmatics and nonasthmatics.

5. Conclusions

Results from the present study increase understanding of the respiratory effects associated with exposure to airborne particles from diesel sources and traffic-related gaseous pollutants by demonstrating that increases in ambient concentrations of black carbon and NO2, respectively, were associated with increases in exhaled indicators of airway inflammation and oxidative stress in New York City adolescents. Observations of short-term temporal variability in exhaled breath condensate pH and 8-isoprostane and associations with ambient air pollutant concentrations provide support for the use of exhaled breath condensate biomarkers to monitor respiratory responses to ambient air pollution exposure. The ability to collect repeated samples of exhaled breath condensate from asthmatics and nonasthmatics and measure a diverse set of biological markers may improve the characterization of airway inflammation in response to air pollution exposures, potential differences in susceptibility between asthmatics and nonasthmatics, and the mechanisms underlying air pollution-related respiratory health effects.

Supplementary Material

01

Highlights.

  • We measured airway inflammation in urban youth with and without asthma for 4 weeks

  • We measured daily ambient air black carbon as an indicator of diesel air pollution

  • Black carbon was associated with airway inflammation and oxidative stress over time

  • Ambient air nitrogen dioxide was associated with airway oxidative stress over time

  • Traffic emissions may increase airway inflammation in youth with and without asthma

Acknowledgments

We thank the study subjects, school staff, and the New York City Board of Education for their participation and Drs. Hari Bhat and Ginger Chew for laboratory support. Funding for this study was provided by the National Institute of Environmental Health Sciences [grants ES11379, P30ES009089, and T32ES007322].

Role of the Funding Source

The funding sources had no involvement in the study design; collection, analysis, and interpretation of data; writing of the manuscript; or decision to submit the paper for publication.

Sources of funding

Funding for this study was provided by the National Institute of Environmental Health Sciences [grants ES11379, P30ES009089, and T32ES007322]. The study protocol and consent procedures were approved by the Columbia University Medical Center Institutional Review Board (protocol number: IRB-AAAA5579).

Footnotes

Disclosure Statement

D.K. was affiliated with West Harlem Environmental Action, Inc., which is involved in environmental justice and advocacy activities in the study area. The other authors have no actual or potential financial or nonfinancial conflicts of interest to disclose.

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Contributor Information

Molini M. Patel, Email: patel.molini@epa.gov.

Steven N. Chillrud, Email: chilli@ldeo.columbia.edu.

KC Deepti, Email: deeptikc@gmail.com.

James M. Ross, Email: jross@ldeo.columbia.edu.

Patrick L. Kinney, Email: plk3@columbia.edu.

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