# ENVIRONMENTAL HEALTH PERSPECTIVES ENVIRONMENTAL HEALTH PERSPECTIVES

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# 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 

# ehp1

Residential Traffic-Related Pollution Exposures and Exhaled Nitric Oxide

in the Children's Health Study

Sandrah P. Eckel 1 , Kiros Berhane 1 , Muhammad T. Salam 1 , Edward B. Rappaport 1 , William S.
Linn 1 , Theresa M. Bastain 1 , Yue Zhang 1 , Frederick Lurmann 2 , Edward L. Avol 1 , Frank D. Gilliland 1

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

2 Sonoma Technology Inc., Petaluma, CA, USA
Correspondence to: Sandrah P. Eckel, PhD, 1540 Alca zar Street, CHP-220, Department of Preventive Medicine, Keck School of Medicine, Unive rsity 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 resp iratory health, Exhaled nitric oxide, Traffic
Acknowledgements: This work was supported by the National Heart, Lun g 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 Healt h Center (grants 5P01ES009581, R826708- 01 and RD831861-01) funded by the National Institut e of Environmental Health Sciences and Page 1 of 292

the Environmental Protection Agency; the National I nstitute of Environmental Health Sciences (grant 5P01ES011627); and the Hastings Foundation ( Pasadena, CA).

Abbreviations: CALINE4—California Line Source Dispersion Model, CH S—Southern California Children’s Health Study, CI—Confidence I nterval, FeNO—fractional concentration of exhaled nitric oxide, FRC—functional road class, GAM—Generalized Additive Model, ICS—inhaled corticosteroids, ICV—CHS Intra-Communit y Variation study, NO—nitrogen oxide, NO 2 —nitrogen dioxide, NO x —nitrogen oxides, O 3 —ozone, PM—Particulate Matter,
R 2 —coefficient of determination, SD—Standard Deviatio n, TRP—traffic-related pollution
Declaration of competing financial interests: F.L. is employed by Sonoma Technology, Inc, (Petalu ma, CA). The other authors declare they have no actual or potential competing interests. Page 2 of 293

ABSTRACT

BACKGROUND: The fractional concentration of nitric oxide in exh aled air (FeNO) potentially detects airway inflammation related to air pollutio n 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 residentia l 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) t o five classes of metrics of residential TRP: distances to freeways and major roads; length of al l and local roads within circular buffers around the home; traffic densities within buffers; annual average line source dispersion modeled
NO x from freeways and non-freeway roads; and predicted annual average NO, NO 2 , and NO x from a model based on intra-community sampling in t he CHS.

RESULTS: In children with asthma, length of roads was posit ively 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 (R 2 = 0.004).

CONCLUSION: Length of road was the only indicator of residentia l TRP exposure associated with airway inflammation in children with asthma, a s measured by FeNO. Page 3 of 294

INTRODUCTION
The effects of air pollution on children’s respirat ory 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 pathophysiolog ic mechanism by which air pollution is thought to affect respiratory health is airway infl ammation. The fractional concentration of nitric oxide in exhaled air (FeNO) is a non-invasive marke r of aspects of airway inflammation (Baraldi and de Jongste 2002; Kharitonov and Barnes 2002; Pi acentini 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 po llutants (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 (CH S) is an ongoing prospective cohort study designed to study the chronic effects of air pollut ion 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 pro ximity to freeways was associated with substantial deficits in lung function growth, indep endent of regional pollutant effects (Gauderman et al. 2007); residential proximity to major roads and line-source dispersion modeled pollutants were associated with increased r isk of asthma and wheeze (McConnell et al. 2006); annual average line-source dispersion modele d 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 associa ted with an increased odds of severe Page 4 of 295

wheezing amongst children with asthma (McConnell et al. 2010b). In the CHS, short-term increases in community-level ambient particulate ma tter (PM) of 2.5 and 10 microns or less in aerodynamic diameter (PM 2.5 and PM 10 , respectively), and ozone (O 3 ) were associated with elevated FeNO (Berhane et al. 2011) and elevated Fe NO has been associated with increased risk for incident asthma (Bastain et al. 2010).
The objective of this study was to evaluate the ass ociation of FeNO with five classes of metrics of residential TRP exposure in a large cohort of ch ildren.

METHODS
Study population . Study participants were children from a CHS cohor t enrolled from kindergarten or first grade classrooms in 2002-2003 in 12 communities in southern California (a
13 th community, Lake Gregory, was excluded due to lack of information on TRP), using a protocol approved by the University of Southern Cal ifornia Institutional Review Board. Informed consent was obtained from a parent/guardia n, who completed baseline and annual follow-up written questionnaires, and informed asse nt was obtained from each child. More information on the study design is available elsewh ere (McConnell et al. 2006).

FeNO assessment . Detailed descriptions of FeNO collection in the C HS 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 th e 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 p eaks in ambient NO and recent food intake. Page 5 of 296

Participants with acute respiratory infection in th e prior 3 days were rescheduled or excluded. To differentiate seasonal and spatial effects, each CH S community was visited at least twice in different seasons. In subsequent study years, onlin e FeNO collection, which allows real-time flow monitoring and which is not subject to NO meas urement errors related to analysis delay or bagged sample contamination, became feasible in a l arge study population. A pilot study with collection of offline (100 ml/sec flow) FeNO and me asurement of online (50 ml/sec flow, collected according to ATS/ERS guidelines) FeNO wit hin 4 hours of each other in 2006-2007 was used to develop a model that reliably predicted online values of FeNO (R 2 =0.94) using measured offline FeNO, concurrent ambient NO, and o ffline 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; Berha ne et al. 2011)—from 2005-2006. This year had information on FeNO for the largest number of c hildren and FeNO collection occurred in conjunction with a large exposure measurement campa ign designed to quantify intra-community variation of local ambient pollutants (Franklin et al., unpublished data).
Residential TRP exposures . We characterized exposure to residential TRP usin g five classes of metrics. Distances in meters (m) to the nearest fre eway and to the nearest non-freeway major road were obtained by geocoding residential address es, as described previously (McConnell et al. 2006). The total length of roads (m) within circula r buffers with radii of 50m, 100m, and 200m centered at the participants’ residences were calcu lated using TeleAtlas Multi-net road class data. Local roads lengths were obtained using data only f rom 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 3 00m falloff radii of the participants’ residences were calculated using additional informa tion on average annual daily traffic assigned Page 6 of 297

to TeleAtlas Multi-Net roadway links as described p reviously (Gauderman et al. 2005). Predicted annual average NO x (in parts per billion (ppb)) from freeway and non-freeway roads at participant residence locations were obtained via t he CALINE4 line-source air quality dispersion model using information on roadway geometry, traffi c volumes, wind speed and direction, atmospheric stability, mixing heights and vehicle e mission rates, as described elsewhere (Benson 1989; McConnell et al. 2006). The Intra-Community V ariability (ICV) study sampled NO and
NO 2 at 942 CHS participant resident locations, schools and central sites across 12 CHS communities for 2 weeks in the summer and 2 weeks i n 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 R 2 = 0.75), NO 2 (adjusted R 2 = 0.67), and
NO x (adjusted R 2 = 0.75) at CHS participants’ homes, with the follo wing information as model inputs: CALINE4 NO x estimates from freeways and non-freeways, distance s to freeways and non-freeway major roads, population density, elevat ion, and whether the community was inside the Los Angeles basin.
Covariate information . Parent/guardian responses to a written questionna ire during the 2005- 2006 school year provided information on race/ethni city, highest attained parental education, physician diagnosis of asthma, rhinitis, asthma med ication use in the past 12 months, and exposure to second hand smoke. Height and weight me asured on the day of the FeNO test were used to calculate age- and sex-specific body mass i ndex (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 o n 2,709 participants who provided questionnaire data. We excluded 52 partici pants whose addresses could not be Page 7 of 298

geocoded with the highest-quality match code and 33 1participants without information on all TRP exposure metrics. Because inhaled corticosteroi d (ICS) medication is known to acutely affect FeNO levels (Beck-Ripp et al. 2002), we addi tionally 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 analysi s data set included 2,143 participants.
Statistical analysis We performed exploratory and descriptive data analy ses to summarize the characteristics of the study population and the distributions of the TRP e xposure metrics (henceforth referred to as exposures). We calculated within-community correlat ions of the exposures by subtracting community-specific means from each exposure and the n calculating the Pearson’s correlation of the resultant deviations from community-specific me ans. 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, se x, asthma status, use of asthma medication (controller and/or rescue) in the past 12 months, r hinitis 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 comm unity such as regional air pollution). We investigated potential effect modification by asthm a 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 singl e pollutant models. Page 8 of 299

To investigate possible nonlinear exposure-response relationships, we fit Generalized Additive Models (GAMs) (Hastie and Tibshirani 1990) to asses s the functional relationship of each exposure metric with ln(FeNO), using a procedure th at estimates the degrees of freedom of the smooth relationship as part of the model fitting pr ocess (Wood 2006). The adjustment variables were the same as in the linear regression.
We applied an indicator variable approach to addres s the small proportion of missing data on the adjustment covariates (5.6% of study participants w ere missing data on at least one covariate). Our results were not sensitive to this approach, si nce 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 rece nt pollution: ambient NO at the time of test at the testing location, daily community-specific cent ral site 24-hr cumulative lagged average of
PM 2.5 (over 1-8 days)(Berhane et al. 2011), or central s ite ambient O 3 , NO 2 , or PM 2.5 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) restricti ng 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 Supplementa l Material, Power Simulation) using information from this study on sample size, the dis tribution 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 significanc e level. Page 9 of 2910

RESULTS Participant characteristics. The children were between 7 and 11 years old, slig htly 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 high er FeNO, more often were male, and more often had current respiratory allergy (Table 1). Fe NO levels varied significantly between CHS communities (p<0.001), with the highest geometric m ean 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 len gth of road and the ICV prediction of annual average NO 2 , which were more symmetrically distributed (Table 2). Approximately 22% of the children had 50 m 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 r oads contributed most to the total length of roads, particularly in smaller buffers (see Supplem ental 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 o f freeway and non-freeway NO x had low correlation (0.05), and ICV predictions of NO, NO 2 , and NO x were highly correlated (0.91 to 0.98) (Table 3). Length of road generally had low c orrelation (<0.38) with the other TRP metrics. Page 10 of 2911

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 compa red to all roads (Figure 1(a)). A large proportion of the variability in FeNO remained unex plained by our models. A model fit on data from all children with only the adjustment covariat es had an R 2 of 0.127 (or 0.233 for a model fit only on children with asthma). The maximum R 2 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 bu ffer (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 F eNO 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 fun ction, 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 ass ume the relationship is linear over the range Page 11 of 2912

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 effe cts 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 mo ve since November of 2004 (data not shown) and not sensitive to adjustment for recent p ollution, 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(F eNO) (R 2 =0.004), after controlling for the adjustment covariates. The minimum detectable effec t sizes for the given exposure contrasts ranged from a percent difference in FeNO of 1.15 fo r a 5 ppb increase in CALINE4 estimated annual average freeway NO x to 7.66 for a 100m increase in the length of local roads in a 50m buffer (see Supplemental Material, Table 1). As exp ected (Piantadosi 2005), the estimated TRP effects in this study are generally smaller than th e 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 buf fers for children with asthma, replicating findings in other studies. Sensitivity analyses res tricting the analysis to children who had not moved in the prior year or additionally adjusting f or short-term ambient pollution yielded similar Page 12 of 2913

results, confirming the findings. To the best of ou r 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 modele d annual averages of NO 2 , SO 2 , coarse PM,
PM 2.5 , and black smoke at the residential postal code; ( 2) distance to a single major truck transportation route; and (3) length of all or loca l roadways within 200m of the residence (Dales et al. 2008). FeNO was associated with length of al l (p < 0.01) and local (p < 0.05) roadways. In our study, we had no metric analogous to distance t o 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 NO 2 , but comparable distributions of lengths of roads in a 200m buffer. The estimated percent difference in FeNO associated wit h 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 l ength of all roads in a 200m buffer was statistically significantly associated with FeNO. W e 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, 10 0m, 200m, 300m, 400m, 500m, and 750m buffers around schools and homes (Holguin et al. 20 07). Statistically significant associations of FeNO with residential length of road were found onl y for children with asthma. The associations were strongest in the smallest buffers. Results fro m our study were qualitatively similar, with tighter confidence intervals as expected from our l arger sample size (see Supplemental Material, Figure 2). For example, in a residential 50m buffer , the percent difference in FeNO associated Page 13 of 2914

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 poss ibly 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 of fline FeNO to be statistically significantly associated with recent PM 10 (0-3 days before the test), but not with distance from a motorway or with traffic counts, although a larger positive ass ociation (not significant) was observed for children with asthma (Graveland et al. 2010). Two s maller studies of children found short-term increases in ambient NO x (N=19) (Murata et al. 2007) and personal NO 2 (N=45) (Delfino et al. 2006) to be associated with elevated FeNO, while a third study found no association with shortterm ambient NO 2 in 182 children with asthma (Liu et al. 2009). Fou rteen 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, on going, 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 road ways and may be indicators of short- or longterm exposure. CALINE4 predictions accounted for ke y factors that determine exposure, such as wind speed and direction, and the CHS Intra-Communi ty Variation study predictions offered a Page 14 of 2915

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 un able 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 fo od intake or exercise was not available, but we adjusted for time of day of collection. We adjus ted for parent education, but since socioeconomic status (SES) may be related to TRP exposur e, there is a potential for residual confounding by SES. However, results were similar w hen 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 potent ially important confounding variables. Asthma is an important susceptibility factor, and o ur 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 pattern s. The potential for resultant exposure misclassification may be reduced by the long-term c haracterization 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 w ith FeNO in our study population. For other metrics, we can compare minimum detectable effect s izes based on our study design to effect Page 15 of 2916

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 NO 2 . Our study design had 80% power to detect an assoc iation of similar magnitude (a 4.1% difference in FeNO per 10 ppb inc rease in ICV predicted annual average
NO 2 ), assuming similarity in these metrics across stud ies. Yet, in our study—with larger sample size and greater exposure contrast—we observed an e ffect that was smaller and negative (-1.2, 95% CI: -8.6, 6.9). This result, along with our oth er findings, is consistent with the null hypotheses that: local, long-term average NO 2 , NO, NO x exposures; local traffic densities; and distance to freeway and major road are not associat ed with FeNO in our study population. Similarly, in a previous study, we found evidence o f short-term but not long-term effects of community-level ambient PM 2.5 on FeNO (Berhane et al. 2011).
Length of road had little correlation with the othe r TRP metrics, potentially signifying that it captures information on a different, relevant featu re of TRP. It would be useful scientifically and from a public health perspective to identify this f eature. Length of road predicts soil lead levels in Los Angeles (Wu et al. 2010) and ambient polluti on in land use regression models (Hoek et al. 2008), and is associated with acute respiratory ill ness requiring a hospital visit in children with asthma (Chang et al. 2009). Length of road may bett er represent exposure to TRP than traffic density or dispersion models because of limited inf ormation on traffic counts on smaller local roads (Dales et al. 2008; Holguin et al. 2007). Als o, a dense network of local roads may imply proximity to intersections with potentially sharp e xposure gradients of combustion products associated with acceleration and break wear emissio ns associated with stopping. Page 16 of 2917

CONCLUSIONS Length of road was the only indicator of residentia l TRP exposure associated with airway inflammation in children with asthma, as measured b y FeNO. This finding is robust and replicates previous studies, warranting further inv estigation to identify the attributes of on-road activity driving this association. Page 17 of 2918

REFERENCES
ATS. 1999. Recommendations for standardized procedu res for the on-line and off-line measurement of exhaled lower respiratory nitric oxi de and nasal nitric oxide in adults and children-1999. This official statement of the Ameri can Thoracic Society was adopted by the ATS Board of Directors, July 1999. Am J Respir Crit Car e Med 160(6):2104-2117.

ATS/ERS. 2005. ATS/ERS recommendations for standard ized procedures for the online and offline measurement of exhaled lower respiratory ni tric oxide and nasal nitric oxide, 2005. Am J Respir Crit Care Med 171(8):912-930.
Baraldi E, de Jongste JC. 2002. Measurement of exha led nitric oxide in children, 2001. Eur Respir J 20(1):223-237.
Bastain TM, Islam T, Berhane KT, McConnell RS, Rapp aport EB, Salam MT, et al. 2010. Exhaled nitric oxide, susceptibility and new-onset asthma in the children's health study. Eur Respir J.
Beck-Ripp J, Griese M, Arenz S, Koring C, Pasqualon i B, Bufler P. 2002. Changes of exhaled nitric oxide during steroid treatment of childhood asthma. Eur Respir J 19(6):1015-1019.
Benson P. 1989. CALINE-4. A dispersion model for pr edicting air pollution concentrations near roadways. Final Report.FHWA/CA/TL-84/15. California Department of Transportation, Sacramento, CA.
Berhane K, Zhang Y, Linn WS, Rappaport EB, Bastain TM, Salam MT, et al. 2011. The effect of ambient air pollution on exhaled nitric oxide in the Children's Health Study. European Respiratory Journal 37(5):1029-1036.
Brunekreef B, Holgate ST. 2002. Air pollution and h ealth. Lancet 360(9341):1233-1242.
Burr ML. 1992. Diagnosing asthma by questionnaire i n epidemiological surveys. Clin Exp Allergy 22(5):509-510.
CDC (Centers for Disease Control and Prevention). 2 009. A SAS program for the CDC growth charts. Available: http://www.cdc.gov/nccdphp/dnpao /growthcharts/resources/sas.htm [accessed 30 March 2011]. Page 18 of 2919

Chang J, Delfino RJ, Gillen D, Tjoa T, Nickerson B, Cooper D. 2009. Repeated respiratory hospital encounters among children with asthma and residential proximity to traffic. Occup Environ Med 66(2):90-98.
Dales R, Wheeler A, Mahmud M, Frescura AM, Smith-Do iron M, Nethery E, et al. 2008. The influence of living near roadways on spirometry and exhaled nitric oxide in elementary schoolchildren. Environ Health Perspect 116(10):142 3-1427.
Delfino RJ, Staimer N, Gillen D, Tjoa T, Sioutas C, Fung K, et al. 2006. Personal and ambient air pollution is associated with increased exhaled nitric oxide in children with asthma. Environ Health Perspect 114(11):1736-1743.
Fischer PH, Steerenberg PA, Snelder JD, van Loveren H, van Amsterdam JG. 2002. Association between exhaled nitric oxide, ambient air pollution and respiratory health in school children. Int Arch Occup Environ Health 75(5):348-353.
Gauderman WJ, Avol E, Lurmann F, Kuenzli N, Gillila nd F, Peters J, et al. 2005. Childhood asthma and exposure to traffic and nitrogen dioxide . Epidemiology 16(6):737-743.
Gauderman WJ, McConnell R, Gilliland F, London S, T homas D, Avol E, et al. 2000. Association between air pollution and lung function growth in southern California children. Am J Respir Crit Care Med 162(4 Pt 1):1383-1390.
Gauderman WJ, Vora H, McConnell R, Berhane K, Gilli land F, Thomas D, et al. 2007. Effect of exposure to traffic on lung development from 10 to 18 years of age: a cohort study. Lancet 369(9561):571-577.
Graveland H, Van Roosbroeck SA, Rensen WM, Brunekre ef B, Gehring U. 2010. Air pollution and exhaled nitric oxide in Dutch schoolchildren. O ccup Environ Med.
Hastie T, Tibshirani R. 1990. Generalized additive models: Chapman & Hall/CRC.
Hoek G, Beelen R, de Hoogh K, Vienneau D, Gulliver J, Fischer P, et al. 2008. A review of land-use regression models to assess spatial variat ion of outdoor air pollution. Atmospheric environment 42(33):7561-7578. Page 19 of 2920

Holguin F, Flores S, Ross Z, Cortez M, Molina M, Mo lina L, et al. 2007. Traffic-related exposures, airway function, inflammation, and respi ratory symptoms in children. Am J Respir Crit Care Med 176(12):1236-1242.
Kharitonov SA, Barnes PJ. 2002. Biomarkers of some pulmonary diseases in exhaled breath. Biomarkers 7(1):1-32.
Koenig JQ, Jansen K, Mar TF, Lumley T, Kaufman J, T renga CA, et al. 2003. Measurement of offline exhaled nitric oxide in a study of communit y exposure to air pollution. Environ Health Perspect 111(13):1625-1629.
Larsson BM, Grunewald J, Skold CM, Lundin A, Sandst rom T, Eklund A, et al. 2010. Limited airway effects in mild asthmatics after exposure to air pollution in a road tunnel. Respir Med.
Linn WS, Berhane KT, Rappaport EB, Bastain TM, Avol EL, Gilliland FD. 2009a. Relationships of online exhaled, offline exhaled, and ambient nit ric oxide in an epidemiologic survey of schoolchildren. J Expo Sci Environ Epidemiol 19(7): 674-681.
Linn WS, Rappaport EB, Berhane KT, Bastain TM, Avol EL, Gilliland FD. 2009b. Exhaled nitric oxide in a population-based study of souther n California schoolchildren. Respir Res 10:28.
Liu L, Poon R, Chen L, Frescura AM, Montuschi P, Ci abattoni G, et al. 2009. Acute effects of air pollution on pulmonary function, airway inflamm ation, and oxidative stress in asthmatic children. Environ Health Perspect 117(4):668-674.
McConnell R, Berhane K, Yao L, Jerrett M, Lurmann F , Gilliland F, et al. 2006. Traffic, susceptibility, and childhood asthma. Environ Healt h Perspect 114(5):766-772.
McConnell R, Islam T, Shankardass K, Jerrett M, Lur mann F, Gilliland F, et al. 2010a. Childhood incident asthma and traffic-related air p ollution at home and school. Environ Health Perspect 118(7):1021-1026.
McConnell R, Liu F, Wu J, Lurmann F, Peters J, Berh ane K. 2010b. Asthma and school commuting time. J Occup Environ Med 52(8):827-828.
Murata A, Kida K, Hasunuma H, Kanegae H, Ishimaru Y , Motegi T, et al. 2007. Environmental influence on the measurement of exhaled nitric oxid e concentration in school children: special reference to methodology. J Nippon Med Sch 74(1):30 -36. Page 20 of 2921

Piacentini GL, Bodini A, Costella S, Vicentini L, M azzi P, Sperandio S, et al. 1999. Exhaled nitric oxide and sputum eosinophil markers of infla mmation in asthmatic children. Eur Respir J 13(6):1386-1390.
Piantadosi S. 2005. Clinical trials: a methodologic perspective: Wiley-Blackwell.
Steerenberg PA, Nierkens S, Fischer PH, van Loveren H, Opperhuizen A, Vos JG, et al. 2001. Traffic-related air pollution affects peak expirato ry flow, exhaled nitric oxide, and inflammatory nasal markers. Arch Environ Health 56(2):167-174.

Wood S. 2006. Generalized additive models: an intro duction with R: CRC Press.
Wu J, Edwards R, He XE, Liu Z, Kleinman M. 2010. Sp atial analysis of bioavailable soil lead concentrations in Los Angeles, California. Environ Res 110(4):309-317. Page 21 of 2922

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 % <12 th grade 20.4 15.3 % Completed 12 th grade 17.1 16.7 % >12 th 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). Page 22 of 2923Table 2. Distribution summaries for the traffic-related pollution exposure metricsa.Percentile--------------------------------------------------Exposure Mean SD Min5th25th50th75th95thMax-------------------------------------------------------------------------------------------------------------------------------------------------Distance: Freeway 1469.7 1200.5 23.8 131.6 483.9 1167.2 2191.4 3723.0 8567.4Distance: Major Road 462.1 533.13.0 7.8 140.0 294.1 585.9 1541.0 5642.3Length all roads: 50m buffer 126.848.7 0.0 75.3 99.3 106.5 153.6204.8 408.0Length local roads: 50m buffer 114.4 47.90.0 0.0 98.2 99.9 145.1 190.9 302.4Length all roads: 100m buffer 406.4162.0 0.0 196.8 299.6 393.5 507.3688.0 1072.0Length local roads: 100m buffer 356.8153.3 0.0 120.4 247.7 356.2 460.9607.9 971.7Length all roads: 200m buffer 1552.0565.7 0.0 572.7 1175.2 1582.2 1928.0 2454.1 3311.0Length local roads: 200m buffer 1313.9 511.8 0.0 421.5 971.5 1356.5 1682.4 2086.0 3090.7Density: 150m buffer 9199.1 19562.20.0 0.0 0.0 2285.5 10429.3 34902.3195515.8Density: 300m buffer 16435.5 28963.60.0 0.0 870.7 6348.1 18716.4 71089.3 224321.4CALINE4 NOx: Freeway 13.3 15.4 0.0 1.1 3.3 9.0 17.8 38.9 197.0CALINE4 NOx: Non-Freeway 6.7 5.9 0.0 0.92.8 5.2 9.3 16.949.8Predicted NO 17.9 14.4 0.4 2.77.2 13.3 23.8 47.9 78.3Predicted NO219.4 8.8 2.9 4.9 11.1 20.7 26.7 31.6 41.4Predicted NOx37.4 21.8 4.0 8.4 19.2 34.0 51.6 77.3 109.0-------------------------------------------------------------------------------------------------------------------------------------------------aUnits of measurement: meters for distances and length of roads; vehicles/day for traffic densities; parts per billion for CALINE4 NOxand intra-community predictions of NO, NO2, and NOx.Page 23 of 2924Table 3. Within community correlations of the traffic-related pollution exposure metrics.Distance Length: 50m Length:100mLength: 200m Density CALINE4PredictedExposure Fwy M. Road All Local All Local All Local 150m 300m Fwy Non-Fwy NO NO2NOxDistance: Freeway 1.00Distance: Major Road 0.42 1.00Length all roads: 50m buffer -0.11 -0.14 1.00Length local roads: 50m buffer -0.04 0.07 0.61 1.00Length all roads: 100m buffer -0.17 -0.20 0.62 0.40 1.00Length local roads: 100m buffer -0.06 0.030.38 0.69 0.72 1.00Length all roads: 200m buffer -0.26 -0.30 0.40 0.29 0.73 0.55 1.00Length local roads: 200m buffer -0.08 -0.010.28 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 1.00Density: 300m buffer -0.30 -0.16 0.11 -0.02 0.23 -0.05 0.35 -0.09 0.90 1.00CALINE4 NOx: Freeway -0.48 -0.15 0.040.03 0.13 0.01 0.22 -0.01 0.590.70 1.00CALINE4 NOx: Non-Freeway -0.10 -0.25 0.38 -0.170.25 -0.15 0.22 -0.07 0.26 0.180.05 1.00Predicted NO -0.44 -0.31 0.29 -0.100.26 -0.08 0.32 -0.04 0.49 0.510.57 0.69 1.00Predicted 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 1.00Predicted 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 0.98 1.00Page 24 of 2925

Table 4. Estimated percent difference in FeNO a associated with an increase b 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 NO x : Freeway -0.32 (-1.38, 0.76) 0.56 CALINE4 NO x : Non-Freeway -0.63 (-3.39, 2.21) 0.66 Predicted NO -2.59 (-6.88, 1.90) 0.25 Predicted NO 2 -1.17 (-8.60, 6.87) 0.77 Predicted NO x -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 smo ke, 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 CALIN E4 predicted NO x ; and 10 ppb for intracommunity predictions of NO, NO 2 , and NO x . Page 25 of 2926

Figure Legends
Figure 1. Estimated percent difference in FeNO a and 95% confidence interval associated with an increase b in each length of road metric: (a) adjusting for a sthma 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 toba cco smoke, parental education, month and hour of FeNO collection, outdoor testing, and commu nity of residence.

b Exposure contrasts: 100m, 300m, and 1,000m for len gth of roads in a 50m, 100m, and 200m buffer, respectively.

Figure 2. Smooth association of length of local roads in a 50 m buffer with FeNO for children with asthma a (the black solid line is the smooth fitted value f or 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 associati on of the smooth with FeNO are displayed b

. The fitted value from a linear regression with a si ngle knot at 100m is displayed in grey and the corresponding intercept is plotted as a dotted blac k 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 medicati on, rhinitis history, age at collection, BMI percentile, second hand tobacco smoke, parental edu cation, month and hour of FeNO collection, outdoor testing, and community of residence. Page 26 of 2927

b The EDF=1 when the association is linear. Page 27 of 29215x279mm (600 x 600 DPI) Page 28 of 29

### 0 50 100 150 200 250 

## Length (m) of local roads in 50m bufferFeNO (ppb)5102050100 

### p−value: 0.004 EDF: 1.9 

## Smooth fit Single knot fit Intercept Page 29 of 29