ENVIRONMENTAL

HEALTH 

PERSPECTIVES

ENVIRONMENTAL

HEALTH 

PERSPECTIVES

National Institutes of Health

U.S. Department of Health and Human Services 

Residential Traffic-Related Pollution Exposures and

Exhaled Nitric Oxide in the Children's Health Study

Sandrah P. Eckel, Kiros Berhane, Muhammad T. Salam,

Edward B. Rappaport, William S. Linn, Theresa M. Bastain,

Yue Zhang, Frederick Lurmann, Edward L. Avol, Frank D. Gilliland

http://dx.doi.org/10.1289/ehp.1103516

Online 27 June 2011

ehponline.org

ehp
1 

Residential Traffic-Related Pollution Exposures and Exhaled Nitric Oxide 

in the Children's Health Study 

Sandrah P. Eckel1

, Kiros Berhane1, Muhammad T. Salam1, Edward B. Rappaport1, William S. 

Linn1, Theresa M. Bastain1, Yue Zhang1, Frederick Lurmann2, Edward L. Avol1, Frank D. 

Gilliland1

1University of Southern California, Los Angeles, CA, USA; 

2

 Sonoma Technology Inc., Petaluma, CA, USA

Correspondence to: Sandrah P. Eckel, PhD, 1540 Alcazar Street, CHP-220, Department of 

Preventive Medicine, Keck School of Medicine, University of Southern California 

Los Angeles, CA 90089-9011, E-mail: eckel@usc.edu; Tel.: 323-442-2030; Fax: 323-442-2349 

Running title: Traffic-Related Pollutants and FeNO 

Key words: Air pollution, Airway inflammation, Children's respiratory health, Exhaled nitric 

oxide, Traffic

Acknowledgements: This work was supported by the National Heart, Lung and Blood Institute 

(grants 5R01HL61768 and 5R01HL76647); the Southern California Environmental Health 

Sciences Center (grant 5P30ES007048) funded by the National Institute of Environmental 

Health Sciences; the Children’s Environmental Health Center (grants 5P01ES009581, R826708-

01 and RD831861-01) funded by the National Institute of Environmental Health Sciences and 

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the Environmental Protection Agency; the National Institute of Environmental Health Sciences 

(grant 5P01ES011627); and the Hastings Foundation (Pasadena, CA). 

Abbreviations: CALINE4—California Line Source Dispersion Model, CHS—Southern 

California Children’s Health Study, CI—Confidence Interval, FeNO—fractional concentration 

of exhaled nitric oxide, FRC—functional road class, GAM—Generalized Additive Model, 

ICS—inhaled corticosteroids, ICV—CHS Intra-Community Variation study, NO—nitrogen 

oxide, NO2—nitrogen dioxide, NOx—nitrogen oxides, O3—ozone, PM—Particulate Matter, 

R

2—coefficient of determination, SD—Standard Deviation, TRP—traffic-related pollution 

Declaration of competing financial interests: 

F.L. is employed by Sonoma Technology, Inc, (Petaluma, CA). The other authors declare they 

have no actual or potential competing interests.

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ABSTRACT 

BACKGROUND: The fractional concentration of nitric oxide in exhaled air (FeNO) potentially 

detects airway inflammation related to air pollution exposure. Existing studies have not yet 

provided conclusive evidence on FeNO’s association with traffic-related pollution (TRP). 

OBJECTIVES: To evaluate the association of FeNO with residential TRP exposure in a large 

cohort of children. 

METHODS: We related FeNO measured on 2,143 children (ages 7-11) who participated in the 

Southern California Children’s Health Study (CHS) to five classes of metrics of residential TRP: 

distances to freeways and major roads; length of all and local roads within circular buffers 

around the home; traffic densities within buffers; annual average line source dispersion modeled 

NOx from freeways and non-freeway roads; and predicted annual average NO, NO2, and NOx

from a model based on intra-community sampling in the CHS. 

RESULTS: In children with asthma, length of roads was positively associated with FeNO, with 

stronger associations in smaller buffers (46.7% (95% confidence interval (CI): 14.3, 88.4), 

12.4% (95% CI: -8.8, 38.4), and 4.1% (95% CI: -14.6, 26.8) higher FeNO for 100-, 300-, and 

1000-m increases in the length of all roads in 50-, 100-, and 200-m buffers, respectively). Other 

TRP metrics were not significantly associated with FeNO, even though the study design was 

powered to detect exposures explaining as little as 0.4% of the variation in natural log 

transformed FeNO (R2

 = 0.004). 

CONCLUSION: Length of road was the only indicator of residential TRP exposure associated 

with airway inflammation in children with asthma, as measured by FeNO. 

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INTRODUCTION 

The effects of air pollution on children’s respiratory health (Brunekreef and Holgate 2002) are 

important because reduced lung function growth and asthma early in life may have life-long 

effects (Gauderman et al. 2000). One pathophysiologic mechanism by which air pollution is 

thought to affect respiratory health is airway inflammation. The fractional concentration of nitric 

oxide in exhaled air (FeNO) is a non-invasive marker of aspects of airway inflammation (Baraldi 

and de Jongste 2002; Kharitonov and Barnes 2002; Piacentini et al. 1999) that has been 

associated with air pollution exposure (Fischer et al. 2002; Koenig et al. 2003). Several studies 

have examined the association of traffic-related pollutants (TRP) with FeNO in children (Delfino 

et al. 2006; Holguin et al. 2007; Steerenberg et al. 2001), but results have not been conclusive. 

Many studies use different TRP metrics and only one involves a large number of children (Dales 

et al. 2008). 

The Southern California Children’s Health Study (CHS) is an ongoing prospective cohort study 

designed to study the chronic effects of air pollution on children’s respiratory health. Traffic 

plays an important role in southern California air pollution. TRP has been previously associated 

with respiratory health in the CHS. Residential proximity to freeways was associated with 

substantial deficits in lung function growth, independent of regional pollutant effects 

(Gauderman et al. 2007); residential proximity to major roads and line-source dispersion 

modeled pollutants were associated with increased risk of asthma and wheeze (McConnell et al. 

2006); annual average line-source dispersion modeled pollutants at homes and at schools were 

associated with increased risk of asthma (McConnell et al. 2010a); and longer school commute 

time (as a marker for on-road exposure) was associated with an increased odds of severe 

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wheezing amongst children with asthma (McConnell et al. 2010b). In the CHS, short-term 

increases in community-level ambient particulate matter (PM) of 2.5 and 10 microns or less in 

aerodynamic diameter (PM2.5 and PM10, respectively), and ozone (O3) were associated with 

elevated FeNO (Berhane et al. 2011) and elevated FeNO has been associated with increased risk 

for incident asthma (Bastain et al. 2010). 

The objective of this study was to evaluate the association of FeNO with five classes of metrics 

of residential TRP exposure in a large cohort of children. 

METHODS 

Study population. Study participants were children from a CHS cohort enrolled from 

kindergarten or first grade classrooms in 2002-2003 in 12 communities in southern California (a 

13th community, Lake Gregory, was excluded due to lack of information on TRP), using a 

protocol approved by the University of Southern California Institutional Review Board. 

Informed consent was obtained from a parent/guardian, who completed baseline and annual 

follow-up written questionnaires, and informed assent was obtained from each child. More 

information on the study design is available elsewhere (McConnell et al. 2006). 

FeNO assessment. Detailed descriptions of FeNO collection in the CHS have been reported 

previously (Bastain et al. 2010; Linn et al. 2009a; Linn et al. 2009b). Briefly, FeNO was 

collected at schools from October to June during the 2005-2006 school year using an offline 

breath collection technique, according to American Thoracic Society (ATS) guidelines (ATS 

1999; ATS/ERS 2005). Collection occurred primarily in the mid to late morning to minimize 

possible effects of early morning traffic-related peaks in ambient NO and recent food intake. 

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Participants with acute respiratory infection in the prior 3 days were rescheduled or excluded. To 

differentiate seasonal and spatial effects, each CHS community was visited at least twice in 

different seasons. In subsequent study years, online FeNO collection, which allows real-time 

flow monitoring and which is not subject to NO measurement errors related to analysis delay or 

bagged sample contamination, became feasible in a large study population. A pilot study with 

collection of offline (100 ml/sec flow) FeNO and measurement of online (50 ml/sec flow, 

collected according to ATS/ERS guidelines) FeNO within 4 hours of each other in 2006-2007 

was used to develop a model that reliably predicted online values of FeNO (R2=0.94) using 

measured offline FeNO, concurrent ambient NO, and offline sample analysis interval (Linn et al. 

2009a). In this study, we used the predicted values for online FeNO at 50 ml/sec flow—similar 

to previous CHS studies (Bastain et al. 2010; Berhane et al. 2011)—from 2005-2006. This year 

had information on FeNO for the largest number of children and FeNO collection occurred in 

conjunction with a large exposure measurement campaign designed to quantify intra-community 

variation of local ambient pollutants (Franklin et al., unpublished data). 

Residential TRP exposures. We characterized exposure to residential TRP using five classes of 

metrics. Distances in meters (m) to the nearest freeway and to the nearest non-freeway major 

road were obtained by geocoding residential addresses, as described previously (McConnell et al. 

2006). The total length of roads (m) within circular buffers with radii of 50m, 100m, and 200m 

centered at the participants’ residences were calculated using TeleAtlas Multi-net road class data. 

Local roads lengths were obtained using data only from the roads classified as major or minor 

collectors corresponding to functional road class (FRC) 5 or FRC6, respectively. Traffic density 

(distance-decayed vehicles per day) with 150m and 300m falloff radii of the participants’ 

residences were calculated using additional information on average annual daily traffic assigned 

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to TeleAtlas Multi-Net roadway links as described previously (Gauderman et al. 2005). Predicted 

annual average NOx (in parts per billion (ppb)) from freeway and non-freeway roads at 

participant residence locations were obtained via the CALINE4 line-source air quality dispersion 

model using information on roadway geometry, traffic volumes, wind speed and direction, 

atmospheric stability, mixing heights and vehicle emission rates, as described elsewhere (Benson 

1989; McConnell et al. 2006). The Intra-Community Variability (ICV) study sampled NO and 

NO2 at 942 CHS participant resident locations, schools and central sites across 12 CHS 

communities for 2 weeks in the summer and 2 weeks in the winter of 2005 (Franklin et al., 

unpublished data). As part of the ICV study, these measurements were used to develop a 

prediction model for annual average NO (adjusted R2 = 0.75), NO2 (adjusted R2 = 0.67), and 

NOx (adjusted R2

 = 0.75) at CHS participants’ homes, with the following information as model 

inputs: CALINE4 NOx estimates from freeways and non-freeways, distances to freeways and 

non-freeway major roads, population density, elevation, and whether the community was inside 

the Los Angeles basin. 

Covariate information. Parent/guardian responses to a written questionnaire during the 2005-

2006 school year provided information on race/ethnicity, highest attained parental education, 

physician diagnosis of asthma, rhinitis, asthma medication use in the past 12 months, and 

exposure to second hand smoke. Height and weight measured on the day of the FeNO test were 

used to calculate age- and sex-specific body mass index (BMI) percentiles from Centers for 

Disease Control and Prevention (CDC) growth charts (CDC 2009). 

Exclusion criteria. In the 2005-2006 school year, FeNO was measured on 2,709 participants who 

provided questionnaire data. We excluded 52 participants whose addresses could not be 

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geocoded with the highest-quality match code and 331participants without information on all 

TRP exposure metrics. Because inhaled corticosteroid (ICS) medication is known to acutely 

affect FeNO levels (Beck-Ripp et al. 2002), we additionally excluded 90 participants who 

reported taking ICS medication within the previous 12 months and 93 participants who provided 

no information on medication use. The final analysis data set included 2,143 participants. 

Statistical analysis 

We performed exploratory and descriptive data analyses to summarize the characteristics of the 

study population and the distributions of the TRP exposure metrics (henceforth referred to as 

exposures). We calculated within-community correlations of the exposures by subtracting 

community-specific means from each exposure and then calculating the Pearson’s correlation of 

the resultant deviations from community-specific means. We used multiple linear regression 

models to relate natural log (ln) transformed FeNO to exposures because FeNO has a rightskewed distribution. After careful consideration of potential confounders and effect modifiers, all 

models were adjusted for child’s race/ethnicity, sex, asthma status, use of asthma medication 

(controller and/or rescue) in the past 12 months, rhinitis history (never, not current, or current), 

age at FeNO collection, BMI percentile, second hand tobacco smoke, parental education, month 

and hour of FeNO collection, whether the FeNO test was performed outdoors, and community of 

residence (to control for factors that vary by community such as regional air pollution). We 

investigated potential effect modification by asthma status by fitting models with an appropriate 

interaction term and by fitting separate models for children with and without asthma. Because 

many of the exposures were correlated, we fit single pollutant models. 

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To investigate possible nonlinear exposure-response relationships, we fit Generalized Additive 

Models (GAMs) (Hastie and Tibshirani 1990) to assess the functional relationship of each 

exposure metric with ln(FeNO), using a procedure that estimates the degrees of freedom of the 

smooth relationship as part of the model fitting process (Wood 2006). The adjustment variables 

were the same as in the linear regression. 

We applied an indicator variable approach to address the small proportion of missing data on the 

adjustment covariates (5.6% of study participants were missing data on at least one covariate). 

Our results were not sensitive to this approach, since complete case or multiple imputation 

analyses produced similar results (data not shown).

We performed additional sensitivity analyses by: (1) testing for heterogeneity in TRP effects by 

race/ethnicity, (2) additionally adjusting for recent pollution: ambient NO at the time of test at 

the testing location, daily community-specific central site 24-hr cumulative lagged average of 

PM2.5 (over 1-8 days)(Berhane et al. 2011), or central site ambient O3, NO2, or PM2.5 on the day 

of or 1 or 2 days prior to the test or the average of the 2 days prior to the test, and (3) restricting 

the analysis to the subset of children reporting no change of residence since November 2004. 

To determine the lower bound of effects detectable at 80% power with our study design, we 

performed a power simulation study (see Supplemental Material, Power Simulation) using 

information from this study on sample size, the distribution of FeNO and its association with 

adjustment covariates, as well as the distributions of the TRP variables. 

Analyses and simulations were performed using the R statistical software. All hypothesis tests 

used a two-sided alternative and a 0.05 significance level. 

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RESULTS 

Participant characteristics. The children were between 7 and 11 years old, slightly more than 

half were female, a majority reported an ethnicity of Hispanic, and 5.0% were exposed to 

secondhand smoke. FeNO was right skewed and ranged from 2.8 to 176.3 parts per billion (ppb), 

with a geometric mean and standard deviation of 13.3 and 1.9 ppb, respectively. Compared to 

children without asthma, those with asthma had higher FeNO, more often were male, and more 

often had current respiratory allergy (Table 1). FeNO levels varied significantly between CHS 

communities (p<0.001), with the highest geometric mean observed in Long Beach (16.4 ppb) 

and the lowest in Glendora (10.6 ppb). 

TRP distributions and correlations. Most TRP metrics were right-skewed, except for length of 

road and the ICV prediction of annual average NO2, which were more symmetrically distributed 

(Table 2). Approximately 22% of the children had 50m buffer length of road values of 99-100m 

due to the unique geometry of having a single road set back 10m from the residence location at 

the center of the small buffer. On average, local roads contributed most to the total length of 

roads, particularly in smaller buffers (see Supplemental Material, Figure 1). Within communities, 

distances to freeway and major road were moderately correlated (0.42), traffic densities within 

150m and 300m buffers were highly correlated (0.90), the lengths of all and local roads had 

correlations of 0.61 to 0.72, CALINE4 predictions of freeway and non-freeway NOx had low 

correlation (0.05), and ICV predictions of NO, NO2, and NOx were highly correlated (0.91 to 

0.98) (Table 3). Length of road generally had low correlation (<0.38) with the other TRP 

metrics. 

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TRP-FeNO associations. Length of road was the only class of TRP exposure metric that had any 

statistically significant associations with FeNO in all children (Table 4), with slightly stronger 

positive associations for local roads only as compared to all roads (Figure 1(a)). A large 

proportion of the variability in FeNO remained unexplained by our models. A model fit on data 

from all children with only the adjustment covariates had an R2 of 0.127 (or 0.233 for a model fit 

only on children with asthma). The maximum R2 of a model that included an additional linear 

effect of a single TRP metric was 0.130 (0.273 for children with asthma). For children with 

asthma, there was a positive association of length of road with FeNO, with a stronger and 

statistically significant association in the 50m buffer (Figure 1(b)). For children without asthma, 

there was no statistically significant association of any length of road metric with FeNO. 

Specifically, the estimated percent difference in FeNO associated with a 100m, 300m, and 

1000m increase in the length of all roads in a 50m, 100m, and 200m buffer was 46.7 (95% CI: 

14.3, 88.4), 12.4 (95% CI: -8.8, 38.4), and 4.1 (95% CI: -14.6, 26.8), respectively, for children 

with asthma and -0.2 (95% CI: -5.5, 5.3), 4.6 (95% CI: -0.6, 10.0), and 4.7 (95% CI: -0.8, 10.4), 

respectively, for children without asthma. 

Non-linear TRP-FeNO associations. For children with asthma, FeNO had a non-linear 

association with the length of local roads in a 50m buffer. There was no evidence for an 

association of length of road with FeNO for shorter lengths of road, but a strong positive 

association when the length of road was longer than 100m (Figure 2). Using a linear spline with 

a single knot at 100m to approximate the smooth function, we estimated that, for children with 

asthma who have more than 100m of local roadways in a 50m buffer, a 50m increase in local 

road length is associated with a 38.3% increase in FeNO (95% CI: 16.1, 64.8), more than twice 

the estimated effect for a 50m increase when we assume the relationship is linear over the range 

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of the data (18.7, 95% CI: 5.9, 33.1). There was no evidence of other biologically relevant nonlinear exposure-response relationships between the other TRP metrics and FeNO (data not 

shown). 

Sensitivity analyses. There was no evidence of heterogeneity in TRP effects by race/ethnicity or 

community. The associations of length of road with FeNO were generally stronger in the 

subgroup of 1937 children who had not reported a move since November of 2004 (data not 

shown) and not sensitive to adjustment for recent pollution, including ambient NO at the time of 

the test. 

Power simulation study. A study with our design has 80% power to detect an exposure that 

explains as little as 0.4% of the variation in ln(FeNO) (R2

=0.004), after controlling for the 

adjustment covariates. The minimum detectable effect sizes for the given exposure contrasts 

ranged from a percent difference in FeNO of 1.15 for a 5 ppb increase in CALINE4 estimated 

annual average freeway NOx to 7.66 for a 100m increase in the length of local roads in a 50m 

buffer (see Supplemental Material, Table 1). As expected (Piantadosi 2005), the estimated TRP 

effects in this study are generally smaller than the minimum detectable effect sizes, except for 

the length of road metrics. 

DISCUSSION

Length of road was the only residential TRP metric associated with FeNO. The strongest 

significant associations were observed in small buffers for children with asthma, replicating 

findings in other studies. Sensitivity analyses restricting the analysis to children who had not 

moved in the prior year or additionally adjusting for short-term ambient pollution yielded similar 

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results, confirming the findings. To the best of our knowledge, this study of many indicators of 

TRP and FeNO in children is the largest to date. 

The largest comparable study related FeNO in 1,613 children in the single community of 

Windsor, Ontario to: (1) land use regression modeled annual averages of NO2, SO2, coarse PM, 

PM2.5, and black smoke at the residential postal code; (2) distance to a single major truck 

transportation route; and (3) length of all or local roadways within 200m of the residence (Dales 

et al. 2008). FeNO was associated with length of all (p < 0.01) and local (p < 0.05) roadways. In 

our study, we had no metric analogous to distance to truck route, but FeNO was highest in Long 

Beach, a community with more truck traffic. We had finer spatial resolution and a wider range 

for annual average NO2, but comparable distributions of lengths of roads in a 200m buffer. The 

estimated percent difference in FeNO associated with a 1000m increase in the length of local

roads in a 200m buffer in our study (6.8, 95% CI: 1.1, 12.9) was similar to the estimate in 

Windsor (6.8, 95% CI: 0.2, 13.9). In Windsor, the length of all roads in a 200m buffer was 

statistically significantly associated with FeNO. We observed smaller, non-significant 

associations (see Supplemental Material, Figure 2).

A study of 200 children in Mexico, half with asthma, examined the association of FeNO with 

length of road and traffic densities within 50m, 100m, 200m, 300m, 400m, 500m, and 750m 

buffers around schools and homes (Holguin et al. 2007). Statistically significant associations of 

FeNO with residential length of road were found only for children with asthma. The associations 

were strongest in the smallest buffers. Results from our study were qualitatively similar, with 

tighter confidence intervals as expected from our larger sample size (see Supplemental Material, 

Figure 2). For example, in a residential 50m buffer, the percent difference in FeNO associated 

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with a 100m increase in the length of all roads for children with asthma was 46.7 (95% CI: 14.3, 

88.4) in our study and 47.9 (95% CI: 5.0, 108.2) in Mexico. 

A study of 82 children found offline FeNO to be 8.8% higher (95% CI: -7, 58) in urban children 

as compared to suburban children, a difference possibly related to TRP exposure, but potentially 

confounded by the lack of adjustment for ambient NO at the time of the test (Steerenberg et al. 

2001). A study of 812 Dutch schoolchildren found offline FeNO to be statistically significantly 

associated with recent PM10 (0-3 days before the test), but not with distance from a motorway or 

with traffic counts, although a larger positive association (not significant) was observed for 

children with asthma (Graveland et al. 2010). Two smaller studies of children found short-term 

increases in ambient NOx (N=19) (Murata et al. 2007) and personal NO2 (N=45) (Delfino et al. 

2006) to be associated with elevated FeNO, while a third study found no association with shortterm ambient NO2 in 182 children with asthma (Liu et al. 2009). Fourteen non-smoking adults 

with mild asthma had no significant differences in FeNO after exposure to rush-hour traffic in a 

tunnel (Larsson et al. 2010). 

This study has several strengths. It is a large, ongoing, prospective cohort study that included 

ethnically diverse children—with and without asthma—in 12 communities in southern 

California, an area with a uniquely broad range of air pollution exposures in which TRP plays an 

important role. Multiple metrics were available to measure different features of TRP exposure. 

Distance, total length of road, and traffic density offered straightforward, though somewhat 

crude, measures of the effects of proximity to roadways and may be indicators of short- or longterm exposure. CALINE4 predictions accounted for key factors that determine exposure, such as 

wind speed and direction, and the CHS Intra-Community Variation study predictions offered a 

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further refinement of the exposure surface. However, both predictions focused only on annual 

averages of specific TRP components that have been considered as representative surrogates for 

products of traffic-related combustion (Brunekreef and Holgate 2002) and did not, for example, 

model ultra-fine particles. 

This study also has several limitations. We were unable to disentangle the effects of asthma 

medication use on the TRP-FeNO association. We had information only on parent report of 

asthma medication use in the prior 12 months, so we excluded participants taking inhaled 

corticosteroid medication. Information on recent food intake or exercise was not available, but 

we adjusted for time of day of collection. We adjusted for parent education, but since socioeconomic status (SES) may be related to TRP exposure, there is a potential for residual 

confounding by SES. However, results were similar when we additionally adjusted for household 

income and whether the child had health insurance. We conducted thorough exploration during 

model building and sensitivity analyses, but as in any analysis of observational data, we may 

have lacked data on or been unaware of other potentially important confounding variables. 

Asthma is an important susceptibility factor, and our determination of asthma status by parent 

report of doctor diagnosis has limitations, but is widely used in epidemiologic studies (Burr 

1992). We had limited data on time-activity patterns. The potential for resultant exposure 

misclassification may be reduced by the long-term characterization of many of the TRP metrics. 

Future work improving exposure assignment would be beneficial (see Supplementary Material 

for a discussion on length of road). 

Length of road was the only TRP metric associated with FeNO in our study population. For other 

metrics, we can compare minimum detectable effect sizes based on our study design to effect 

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sizes observed in other studies. For example, Dales et al. (2008) estimated a 4.0% (95% CI: -

10.2, 20.6) difference in FeNO associated with a 10 ppb increase in land-use regression modeled 

annual average NO2. Our study design had 80% power to detect an association of similar 

magnitude (a 4.1% difference in FeNO per 10 ppb increase in ICV predicted annual average 

NO2), assuming similarity in these metrics across studies. Yet, in our study—with larger sample 

size and greater exposure contrast—we observed an effect that was smaller and negative (-1.2, 

95% CI: -8.6, 6.9). This result, along with our other findings, is consistent with the null 

hypotheses that: local, long-term average NO2, NO, NOx exposures; local traffic densities; and 

distance to freeway and major road are not associated with FeNO in our study population. 

Similarly, in a previous study, we found evidence of short-term but not long-term effects of 

community-level ambient PM2.5 on FeNO (Berhane et al. 2011). 

Length of road had little correlation with the other TRP metrics, potentially signifying that it 

captures information on a different, relevant feature of TRP. It would be useful scientifically and 

from a public health perspective to identify this feature. Length of road predicts soil lead levels 

in Los Angeles (Wu et al. 2010) and ambient pollution in land use regression models (Hoek et al. 

2008), and is associated with acute respiratory illness requiring a hospital visit in children with 

asthma (Chang et al. 2009). Length of road may better represent exposure to TRP than traffic 

density or dispersion models because of limited information on traffic counts on smaller local 

roads (Dales et al. 2008; Holguin et al. 2007). Also, a dense network of local roads may imply 

proximity to intersections with potentially sharp exposure gradients of combustion products 

associated with acceleration and break wear emissions associated with stopping. 

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CONCLUSIONS 

Length of road was the only indicator of residential TRP exposure associated with airway 

inflammation in children with asthma, as measured by FeNO. This finding is robust and 

replicates previous studies, warranting further investigation to identify the attributes of on-road 

activity driving this association. 

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Table 1. Demographic characteristics and potential confounders by parent report of doctor 

diagnosed asthma. 

Without asthma With asthma 

(N=1934) (N=209) 

Characteristics Mean (SD) or % Mean (SD) or %

FeNO (ppb)* 13.1 (1.8) 16.2 (2.1) 

Age (years) 9.3 (0.6) 9.3 (0.6) 

% Male 47.1 55.5 

Body mass index percentile 66.0 (29.2) 69.7 (28.7) 

 % Missing 0.6 0.5 

Race/ethnicity 

 % White/non-Hispanic 33.7 32.1 

 % Hispanic 56.8 55.0 

 % Black 1.8 2.9 

 % Asian/Hawaiian/Pacific Islander 3.1 3.3

 % Other 4.6 6.7 

 % Missing 0.1 0.0 

Parent education 

 % <12th grade 20.4 15.3 

 % Completed 12th grade 17.1 16.7 

 % >12th grade 58.0 64.6 

 % Missing 4.5 3.3 

Respiratory allergy 

 % Never 47.6 17.7 

 % No current 28.3 29.7 

 % Current 24.0 52.6 

 % Missing 0.1 0.0 

Asthma medication 

 % None 97.4 62.7 

 % Rescue only 2.2 28.2 

 % Control only 0.2 3.3 

 % Rescue and control 0.2 5.7 

% Exposed to secondhand smoke 5.1 3.8 

 % Missing 0.6 1.4 

Time of FeNO collection 

 % 8:00–8:59 6.3 5.3 

 % 9:00–11:59 85.7 81.8 

 % 12:00–13:59 7.4 12.0 

 % 14:00–15:59 0.7 1.0 

% Outdoor test 3.2 2.4 

--------------------------------------------------------------------------------------------------- 

* Geometric mean and standard deviation (SD).

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Table 2. Distribution summaries for the traffic-related pollution exposure metricsa. 

Percentile 

 -------------------------------------------------- Exposure Mean SD Min 5th 25th 50th 75th 95th 

----------------------------------------------------------------------------------------------------------------------------------------

Distance: Freeway 1469.7 1200.5 23.8 131.6 483.9 1167.2 2191.4 3723.0 

Distance: Major Road 462.1 533.1 3.0 7.8 140.0 294.1 585.9 1541.0 Length all roads: 50m buffer 126.8 48.7 0.0 75.3 99.3 106.5 153.6 204.8 

Length local roads: 50m buffer 114.4 47.9 0.0 0.0 98.2 99.9 145.1 190.9 Length all roads: 100m buffer 406.4 162.0 0.0 196.8 299.6 393.5 507.3 688.0 Length local roads: 100m buffer 356.8 153.3 0.0 120.4 247.7 356.2 460.9 607.9 Length all roads: 200m buffer 1552.0 565.7 0.0 572.7 1175.2 1582.2 1928.0 2454.1 Length local roads: 200m buffer 1313.9 511.8 0.0 421.5 971.5 1356.5 1682.4 2086.0 

Density: 150m buffer 9199.1 19562.2 0.0 0.0 0.0 2285.5 10429.3 34902.3 1

Density: 300m buffer 16435.5 28963.6 0.0 0.0 870.7 6348.1 18716.4 71089.3 2

CALINE4 NOx: Freeway 13.3 15.4 0.0 1.1 3.3 9.0 17.8 38.9 

CALINE4 NOx: Non-Freeway 6.7 5.9 0.0 0.9 2.8 5.2 9.3 16.9 Predicted NO 17.9 14.4 0.4 2.7 7.2 13.3 23.8 47.9 

Predicted NO2 19.4 8.8 2.9 4.9 11.1 20.7 26.7 31.6 

Predicted NOx 37.4 21.8 4.0 8.4 19.2 34.0 51.6 77.3 

----------------------------------------------------------------------------------------------------------------------------------------

a Units of measurement: meters for distances and length of roads; vehicles/day for traffic densities; parts per billi

and intra-community predictions of NO, NO2, and NOx.

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Table 3. Within community correlations of the traffic-related pollution exposure metrics. 

Distance Length: 50m Length:100m Length: 200m Density CALINE4 Pr

Exposure Fwy M. Road All Local All Local All Local 150m 300m Fwy Non-Fwy NO 

Distance: Freeway 1.00 Distance: Major Road 0.42 1.00 Length all roads: 50m buffer -0.11 -0.14 1.00Length local roads: 50m buffer -0.04 0.07 0.61 1.00 Length all roads: 100m buffer -0.17 -0.20 0.62 0.40 1.00 Length local roads: 100m buffer -0.06 0.03 0.38 0.69 0.72 1.00 Length all roads: 200m buffer -0.26 -0.30 0.40 0.29 0.73 0.55 1.00 Length local roads: 200m buffer -0.08 -0.01 0.28 0.49 0.54 0.77 0.72 1.00Density: 150m buffer -0.22 -0.14 0.15 -0.07 0.29 -0.08 0.31 -0.09 1.00 Density: 300m buffer -0.30 -0.16 0.11 -0.02 0.23 -0.05 0.35 -0.09 0.90 1.00 CALINE4 NOx: Freeway -0.48 -0.15 0.04 0.03 0.13 0.01 0.22 -0.01 0.59 0.70 1.00 

CALINE4 NOx: Non-Freeway -0.10 -0.25 0.38 -0.17 0.25 -0.15 0.22 -0.07 0.26 0.18 0.05 1.00 

Predicted NO -0.44 -0.31 0.29 -0.10 0.26 -0.08 0.32 -0.04 0.49 0.51 0.57 0.69 1.00 

Predicted NO2 -0.50 -0.41 0.26 -0.05 0.25 -0.03 0.34 0.03 0.38 0.42 0.54 0.67 0.91 

Predicted NOx -0.49 -0.37 0.27 -0.07 0.25 -0.05 0.34 -0.01 0.45 0.49 0.59 0.69 0.97 
25 

Table 4. Estimated percent difference in FeNOa associated with an increaseb in each trafficrelated pollution exposure metric. 

 Percent 

Exposure Difference (95% CI) p-value 

------------------------------------------------------------------------------------------ 

Distance: Freeway -0.15 (-1.48, 1.21) 0.83 

Distance: Major Road -0.72 (-1.82, 0.39) 0.20 

Length all roads: 50m buffer 2.84 (-2.45, 8.42) 0.30 

Length local roads:50m buffer 4.75 (-0.68,10.49) 0.09 

Length all roads:100m buffer 5.07 (0.03,10.37) 0.05 

Length local roads:100m buffer 6.02 (0.72,11.60) 0.03 

Length all roads: 200m buffer 4.80 (-0.42, 10.29) 0.07 

Length local roads: 200m buffer 6.84 (1.10, 12.90) 0.02 

Density: 150m buffer 0.36 (-1.00, 1.73) 0.61 

Density: 300m buffer 0.06 (-0.88, 1.01) 0.90 

CALINE4 NOx: Freeway -0.32 (-1.38, 0.76) 0.56 

CALINE4 NOx: Non-Freeway -0.63 (-3.39, 2.21) 0.66 

Predicted NO -2.59 (-6.88, 1.90) 0.25 

Predicted NO2 -1.17 (-8.60, 6.87) 0.77 

Predicted NOx -1.08 (-3.98, 1.90) 0.48 

------------------------------------------------------------------------------------------ 

a

 Adjusted for: race/ethnicity, sex, asthma status, asthma medication, rhinitis history, age at 

collection, BMI percentile, second hand tobacco smoke, parental education, month and hour of 

FeNO collection, outdoor testing, and community of residence. 

b

 Exposure contrasts: 500m for distance to freeway; 200m for distance to major road; 100m, 

300m, and 1,000m for length of roads in a 50m, 100m, and 200m buffer, respectively; 10,000 

vehicles/day for traffic densities; 5 ppb for CALINE4 predicted NOx; and 10 ppb for intracommunity predictions of NO, NO2, and NOx.

Page 25 of 29
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Figure Legends 

Figure 1. Estimated percent difference in FeNOa and 95% confidence interval associated with 

an increaseb in each length of road metric: (a) adjusting for asthma status and (b) fitting separate 

models by asthma status. 

a

 Adjusted for: race/ethnicity, sex, asthma status (plot (a)), asthma medication, rhinitis history, 

age at collection, BMI percentile, second hand tobacco smoke, parental education, month and 

hour of FeNO collection, outdoor testing, and community of residence. 

b

 Exposure contrasts: 100m, 300m, and 1,000m for length of roads in a 50m, 100m, and 200m 

buffer, respectively. 

Figure 2. Smooth association of length of local roads in a 50m buffer with FeNO for children 

with asthmaa (the black solid line is the smooth fitted value for a child with average values for 

the adjustment covariates and the black dashed line is the 95% CI). The estimated degrees of 

freedom (EDF) and the p-value testing the association of the smooth with FeNO are displayed b. 

The fitted value from a linear regression with a single knot at 100m is displayed in grey and the 

corresponding intercept is plotted as a dotted black reference line. On the y-axis, values are 

plotted on the natural log scale and labeled on the original scale. 

a

 Adjusted for: race/ethnicity, sex, asthma medication, rhinitis history, age at collection, BMI 

percentile, second hand tobacco smoke, parental education, month and hour of FeNO collection, 

outdoor testing, and community of residence. 

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27 

b

 The EDF=1 when the association is linear.

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215x279mm (600 x 600 DPI) 

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0 50 100 150 200 250

Length (m) of local roads in 50m buffer

FeNO (ppb)

5 10 20 50 100

p−value: 0.004

EDF: 1.9

Smooth fit

Single knot fit

Intercept

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