Environmental Health Perspectives • volume 118 | number 7 | July 2010 1021

Research | Children’s Health

The role of air pollution in the development of 

new-onset asthma remains controversial, and 

the contribution of this environmental risk factor to the pandemic remains unclear (Eder et al. 

2006; Sarnat and Holguin 2007). Although 

increasing evidence indicates that living near 

heavy traffic is associated with increased rates of 

asthma, some well-designed studies have found 

only weak or no associations (Heinrich and 

Wichmann 2004; Oftedal et al. 2009).

These inconsistencies may reflect incomplete assessment of exposures to traffic in the 

microenvironments in which children spend 

most of their time. Exposure at locations 

other than home, especially at school where 

children spend a large proportion of waking 

hours and may engage in physical activity that 

would increase the ventilation rate and dose of 

inhaled pollutants, may have strong influences 

on occurrence of asthma. However, few studies have examined the effect of traffic-related 

pollution at schools on asthma rates among 

children. Two cross-sectional Dutch studies 

that examined this question reported higher 

rates of respiratory symptoms among children 

in schools near roadways with heavy traffic, 

especially truck traffic (Janssen et al. 2003; 

van Vliet et al. 1997). A northern California 

study found that schools downwind from busy 

freeways had higher concentrations of oxides of 

nitrogen (NOx) and higher asthma prevalence 

rates (Kim et al. 2004). However, in a subsequent analysis of these same data, the effect of 

school exposure was attenuated and no longer 

significant after adjusting for modeled residential traffic-related exposure (Kim et al. 2008).

Nitrogen dioxide (NO2) is routinely 

measured at regulatory monitoring stations. 

However, these measurements reflect both 

background regional concentrations and local 

sources near the stations. Some studies have 

assessed local exposure to traffic-related pollution using measured NO2 (or NOx) as surrogate for the complex mixture of traffic-related 

pollutants that occurs in close proximity to 

roadways (Brauer et al. 2007; Gauderman 

et al. 2005; Jerrett et al. 2008; Kim et al. 2004; 

Kramer et al. 2000; Nordling et al. 2008). 

NO2 can feasibly be measured at a large number of locations, and it has been widely used as 

a proxy for the mixture of traffic-related pollutants that vary markedly depending on distance from roadways, season, wind speed, and 

wind direction. However, the mixture of pollutants in close proximity to roadways includes 

transition metals and organic aerosols, which 

are more plausible causes of asthma than NO2

(Li et al. 2003).

The Southern California Children’s Health 

Study (CHS) was designed to investigate the 

chronic effects of air pollution on respiratory 

health. Previous analyses have found associations of asthma with residential distance to 

major roads and modeled and measured pollutant markers for intracommunity variation 

in exposure to traffic (Gauderman et al. 2005; 

Jerrett et al. 2008; McConnell et al. 2006; 

Salam et al. 2007b) and with regional pollutants in susceptible children (Islam et al. 2008; 

McConnell et al. 2002). In the current analysis, a prospective, longitudinal evaluation of 

new cases of asthma has allowed assessment of 

exposure before the development of asthma in 

an ongoing cohort of school children recruited 

in kindergarten and first grade, an age at which 

physician-diagnosed asthma becomes reliable 

and valid (Martinez et al. 1995). Exposure to 

traffic-related pollutants has been estimated 

at participating schools and at residences. In 

addition, NO2, ozone (O3), and particulate 

matter < 10 μm (PM10) and < 2.5 μm in aerodynamic diameter (PM2.5) have been measured 

continuously during the lifetime of most study 

participants at a location in each community 

representative of exposure in the neighborhoods where children live (Kunzli et al. 2003; 

McConnell et al. 2006). This design allowed 

examination of the joint effects of traffic-related 

pollution exposure at school and home and of 

regional measured pollution at community 

Address correspondence to R. McConnell, Department 

of Preventive Medicine, USC Keck School of 

Medicine, 1540 Alcazar St., CHP 236, Los Angeles, 

CA 90033 USA. Telephone: (323) 442-1096. Fax: 

(323) 442-3272. E-mail: rmcconne@usc.edu

Supplemental Material is available online 

(doi:10.1289/ehp.0901232 via http://dx.doi.org/).

E. Rappaport and J. Manila managed data, and 

B. Penfold assigned exposures estimated from residential traffic. R. Ma, R.T. Burnett, and E. Hughes 

assisted with the development and implementation 

of the random effects program.

This study was supported by National Institutes 

of Health grants 5P30ES007048, 5P01ES009581, 

5 P01ES011627, 1R01ES016535, 5R01ES014447, 

5R03ES014046, and 5R01HL61768; U.S. 

Environmental Protection Agency grants R826708, 

RD831861, R831845, and R82735201; the South 

Coast Air Quality Management District, and the 

Hastings Foundation. 

F.L. is employed by Sonoma Technology, Inc, 

(Petaluma, CA). The other authors declare they have 

no actual or potential competing interests.

Received 22 July 2009; accepted 22 March 2010.

Childhood Incident Asthma and Traffic-Related Air Pollution at Home 

and School

Rob McConnell,1 Talat Islam,1 Ketan Shankardass,2 Michael Jerrett,3 Fred Lurmann,4 Frank Gilliland,1

Jim Gauderman,1 Ed Avol,1 Nino Künzli,5 Ling Yao,6 John Peters,1 and Kiros Berhane1

1University of Southern California, Los Angeles, California, USA; 2St. Michael’s Hospital, Toronto, Ontario, Canada; 3University of 

California, Berkeley, California, USA; 4Sonoma Technology, Inc, Petaluma, California, USA; 5Swiss Tropical and Public Health Institute, 

Basel and University of Basel, Switzerland; 6United Health Group, City of Hope Hospital Medical Center, Los Angeles, California, USA

Background: Traffic-related air pollution has been associated with adverse cardiorespiratory 

effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma.

Objectives: We evaluated the relationship of new-onset asthma with traffic-related pollution near 

homes and schools.

Methods: Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified 

during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were 

asthma- and wheezing-free at study entry into the Southern California Children’s Health Study. We 

assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, 

distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide 

(NO2), and particulate matter were measured continuously at one central site monitor in each of 13 

study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient 

central site pollutants and to the residential interquartile range for each traffic exposure metric.

Results: Asthma risk increased with modeled traffic-related pollution exposure from roadways near 

homes [HR 1.51; 95% confidence interval (CI), 1.25–1.82] and near schools (HR 1.45; 95% CI, 

1.06–1.98). Ambient NO2 measured at a central site in each community was also associated with 

increased risk (HR 2.18; 95% CI, 1.18–4.01). In models with both NO2 and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and 

home, whereas the estimate for NO2 was attenuated (HR 1.37; 95% CI, 0.69–2.71).

Conclusions: Traffic-related pollution exposure at school and homes may both contribute to the 

development of asthma.

Key words: air pollution, asthma, child, epidemiology, vehicular traffic. Environ Health Perspect

118:1021–1026 (2010). doi:10.1289/ehp.0901232 [Online 6 April 2010]
McConnell et al.

1022 volume 118 | number 7 | July 2010 • Environmental Health Perspectives

monitors. We hypothesized that exposure at 

school and home both contribute to the risk of 

new-onset asthma.

Methods

Study population. Characteristics of this cohort 

have been described previously (McConnell 

et al. 2006). Briefly, 5,349 children attending kindergarten and first grade were enrolled 

into a new CHS cohort during the 2002–2003

school year from 45 schools in 13 communities. 

Communities were selected to represent the 

range and mixture of regional particulate pollutants, NO2, and O3 in southern California. 

Parents provided informed consent and completed a baseline and yearly questionnaire with 

information about demographic characteristics, respiratory illness, and risk factors for 

asthma at study entry. Children with a history 

of physician-diagnosed asthma at study entry 

(n = 715, 13.4%) were excluded from this 

follow-up, because the focus was on risks for 

incident asthma. To ensure that the study population was free of any previously undiagnosed 

asthma, we then also excluded children with a 

history of wheeze (n = 868, 16.2%) and additional children with missing information about 

wheeze, or missing or a “don’t know” answer 

about asthma (n = 394, 7.4%). These exclusions resulted in a total of 3,372 children classified as disease free at baseline. We excluded 

another 340 children with no residential traffic 

information because home address could not 

be geocoded [disproportionately from a single 

community, as described in the Supplemental 

Material (doi:10.1289/ehp.0901232)] and 

another 535 children who did not participate 

in any of the annual follow-up questionnaires. 

The final sample included 2,497 children.

New-onset asthma and covariates. Children 

with physician-diagnosed asthma reported 

on a yearly questionnaire during 3 years of 

follow-up were defined to have new-onset 

asthma. Children missing questionnaires in 

any year continued to contribute person-time 

to the models until they answered yes (at which 

time they were censored) or were lost to follow-up. The date of new onset of asthma could 

not be precisely defined. Therefore, the date 

of onset was assigned to the midpoint of the 

interval between the date of the questionnaire 

when asthma diagnosis was first reported and 

the date of the previous questionnaire reporting 

asthma status, and this date was used for estimating follow-up time in all statistical analyses. 

Sociodemographic characteristics, exposure to 

cigarette and wildfire smoke, health insurance, 

housing characteristics, history of allergy, and 

parental asthma were assessed by questionnaire 

[see Supplemental Material (doi:10.1289/

ehp.0901232) for details].

Community ambient air pollution measurements. Ambient levels of O3, NO2, PM10,

and PM2.5 were measured continuously during follow-up at a central site monitor in each 

community, as described in the Supplemental 

Material (doi:10.1289/ehp.0901232) and in 

previous reports (Peters et al. 1999). Because 

there is marked diurnal variation in O3 and 

exposure occurs largely between 1000 and 

1800 hours, this average was used in all analyses. Average temperature and relative humidity were also obtained during follow-up from 

measurements at the monitoring stations.

Local traffic-related pollutant exposure.

Exposure to traffic-related pollution was estimated using methods described previously 

(McConnell et al. 2006), and several metrics 

Table 1. Characteristics of participants at study entry and incidence of asthma on follow-up.

Characteristic na (%) Cases IRb (95% CI)

Age at entry (years)

< 6 496 (20) 22 17.0 (11.2–25.9)

6 1,178 (47) 58 19.0 (14.7–24.6)

> 6 823 (33) 40 18.9 (13.9–25.8)

Race/ethnicity

Hispanic white 1,380 (55) 59 16.8 (13.0–21.7)

Non-Hispanic white 905 (36) 45 18.9 (14.0–25.2)

African American 77 (3.1) 6 33.9 (15.2–75.4)

Asian 97 (3.9) 8 30.8 (15.4–61.5)

Other/unknown 38 (1.5) 2 22.7 (5.7–90.8)

Sex

Female 1,307 (52) 62 18.2 (14.2–23.3)

Male 1,190 (48) 58 19.2 (14.8–24.8)

History of allergy

Yes 764 (34) 56 28.7 (22.1–37.4)

No 1,508 (66) 50 12.8 (6.7–24.4)

Play team sport

Yes 918 (39) 47 19.7 (14.8–26.2)

No 1,463 (61) 67 17.9 (9.2–34.5)

Parental history of asthma

Yes 389 (17) 31 31.3 (22.0–44.6)

No 1,899 (83) 76 15.4 (7.2–33.3)

Maternal smoking during pregnancy

Yes 156 (6.3) 11 27.9 (15.5–50.5)

No 2,308 (93.7) 107 18.0 (5.4–60.3)

Secondhand smoke

Yes 164 (6.7) 7 17.0 (8.1–35.6)

No 2,281 (93.3) 110 18.7 (4.1–84.0)

Mildew

Yes 532 (22) 29 21.1 (14.7–30.4)

No 1,911 (78) 85 17.3 (7.9–37.9)

Pests in home

Yes 1,603 (67) 75 18.1 (14.4–22.7)

No 777 (33) 36 18.2 (9.7–33.9)

Dogs in home

Yes 730 (30) 30 15.7 (11.0–22.4)

No 1,706 (70) 86 19.7 (9.1–42.8)

Cats in home

Yes 462 (19) 17 14.0 (8.7–22.5)

No 1,972 (81) 98 19.4 (7.2–52.2)

Indoor NO2 source

Yes 1,799 (73) 88 18.8 (15.3–23.2)

No 675 (27) 31 18.3 (9.9–33.9)

Wildfire exposurec

Yes 251 (12) 16 24.6 (15.1–40.1)

No 1,764 (88) 79 17.3 (6.2–48.2)

Health insurance

Yes 2,135 (88) 106 19.2 (15.9–23.3)

No 300 (12) 10 13.1 (5.7–30.3)

Household income (US$)

≤ 14,999 329 (14) 16 19.5 (11.9–31.8)

15,000–49,999 714 (31) 38 21.1 (15.4–29.0)

> 50,000 1,292 (55) 57 16.6 (12.8–21.6)

Parental education

Less than high school 508 (21) 23 17.9 (11.9–26.9)

At least high school 452 (19) 20 17.5 (11.3–27.1)

Some college 850 (36) 47 20.0 (14.9–26.9)

College and above 560 (24) 24 13.9 (9.1–21.3)

Total 2,497 120 18.7 (15.6–22.3)

aNumbers may not add up to 2,497 in some subgroups because of missing data. bCrude incidence (IR) rate per 1,000 

person-years. cOccurred in 2003 during first year of follow-up.
Childhood incident asthma and traffic-related air pollution

Environmental Health Perspectives • volume 118 | number 7 | July 2010 1023

of local traffic related exposure were compiled. 

Participant residence and school addresses were 

geocoded, and we estimated distances to the 

nearest freeway or other highways or arterial 

roads and traffic density within 150 m of each 

child’s residence and school. Concentrations 

of pollutants from local vehicle emissions at 

homes and schools were estimated separately 

from freeway and nonfreeway sources, using a 

line source dispersion model of the incremental 

contribution by these nearby sources to oxides 

of nitrogen above the regional background 

levels (Benson 1989). The modeled annual 

concentration estimates (parts per billion) were 

based on distance to roadways, vehicle counts, 

vehicle NOx emission rates, wind speed and 

direction, and height of the mixing layer in each 

community. However, these modeled exposures reflect the mixture of multiple pollutants 

from nearby traffic, and the high correlation of 

pollutants in the mixture precludes identifying 

the effect of any specific pollutant in the mixture, as described in the Supplemental Material 

(doi:10.1289/ehp.0901232). For brevity, these 

modeled exposures will be referred to as trafficrelated pollutants (TRP). An estimate of combined school and home exposure to traffic was 

made by weighting the estimates by approximate time at school (35 hr weekly, or 21%) 

and assigning the remaining 79% of time to the 

home. Health effect estimates based on models 

in which the contribution of school to the combined exposure was reduced to 16% to account 

for a lack of school exposure during summer 

recess were very similar (data not shown).

Statistical methods. We fitted a multilevel 

Cox proportional hazards model that allows 

for assessment of residual variation in time to 

asthma onset and also for clustering of children around schools and communities (Ma 

et al. 2003). Letting ui and uij denote community- and school (within community)-level 

random effects, with uij assumed to be positive 

and independent conditional on ui, we fit the 

following model:

hijl (t)= h0s(t) uij exp(βXijl + δTZijl), [1]

where, for the lth subject in the ith community, and jth school, hijl(t) is the hazard 

function at age t; h0s(t) is the baseline hazard 

function in stratum s (defined by age at study 

entry and sex), Xijl is a traffic pollution exposure metric, and Zijl are other individual covariates, such as secondhand smoke exposure, 

pets in the home, and other possible confounders. All models included race/ethnicity.

The model allowed for joint evaluation 

of the effects of exposure to traffic-related 

pollutants at homes and at schools and to 

ambient pollutants measured at community central sites, with effects scaled to the 

interquartile range (IQR) for each metric of 

residential exposure (e.g., for TRP from the 

line source dispersion model) and to the total 

range across the 13 communities, respectively. 

Traffic exposure at homes and school were 

correlated. Therefore, in models including 

both exposures, home traffic estimates were 

deviated from the corresponding school metric to minimize the chance of collinearity and 

allow for valid independent effect estimates 

(for example, r-square for school with deviated 

home TRP were < 0.38 for freeway, nonfreeway, and total TRP). Although the schoolspecific estimates of exposure were of primary 

interest, models including the average residential exposure corresponding to each school 

(and residential exposures deviated from this 

average) were also fitted [see Supplemental 

Material (doi:10.1289/ehp.0901232) for 

details]. Potential confounders were evaluated one at a time, based on whether the estimates for the pollutant associations changed 

by > 10%. We assessed heterogeneity of traffic pollution effects by level of community 

central site regional pollutant measurements 

by comparing nested models using a partial 

likelihood ratio test with and without interaction terms. We examined any potential 

nonlinearity in the exposure–response relationship using cubic spline terms, piecewise 

polynomials joined smoothly at a number of 

break points (Hastie and Tibshirani 1990), 

for the exposure terms and comparing the 

nested models using a partial likelihood ratio 

test. All analyses were conducted using software designed to run within R software (R 

Development Core Team, Vienna, Austria) 

for implementing random effects Cox proportional hazards models (Krewski et al. 2009; 

Ma et al. 2003). All hypotheses were tested 

assuming a 0.05 significance level and a twosided alternative hypothesis.

Results

Most of the 2,497 disease-free children included 

in this analysis were ≤ 6 years of age (range,

4.8–9.0 years) at study entry (Table 1). There 

were 120 new cases of asthma, resulting in an 

incidence rate of approximately 18.7 cases per 

1,000 person-years (based on 6,434 personyears of follow-up). Rates did not differ by age 

at study entry or sex. About half the children 

were Hispanic whites, and they had the lowest rates of asthma (16.8/1,000 person years), 

whereas African Americans had the highest rate 

(33.9/1,000 person-years), but this was based 

on only six cases. Substantially higher rates 

were also observed for children with history of 

allergy, parental history of asthma, and maternal smoking during pregnancy.

Both residential and school TRP metrics had a wide range with skewed distributions (Table 2). For example, the mean and 

median for nonfreeway residential TRP were 

7.3 and 6.1 ppb, respectively, and the IQR 

was 8.0 ppb, with a total range from 0.08 to 

a maximum of 55.1 ppb. A few homes and 

schools had very low freeway TRP (largely in a 

single community that has no freeways). There 

was ≥ 3-fold variation in the multiyear average 

NO2, PM10, and PM2.5 from the lowest to 

highest pollution community for measurements at the central site monitors. O3 levels 

varied by 2-fold.

The associations of new-onset asthma were 

strongest with nonfreeway TRP. The hazard ratio (HR) at homes was 1.51; 95% confidence interval (CI), 1.25–1.81; p < 0.001 

Table 2. Distribution of annual average residential traffic-modeled pollution and of community central site 

measurements.

Mean Median IQR Minimum Maximum Range

Residential traffic (ppb)

Nonfreeway TRP 7.3 6.1 8 0.08 55.1 55

Freeway TRP 11.1 7.3 13 < 0.0001 134.5 134.5

Total TRP 18.4 14.6 20.9 0.23 144.1 143.9

School traffic (ppb)

Nonfreeway TRP 6.1 5.9 5.9 0.3 19.7 19.4

Freeway TRP 10.9 8.2 15.2 < 0.0001 39.9 39.9

Total TRP 17 11.9 18.5 0.7 51.4 50.7

Central-site measurements

NO2 (ppb) 20.4 21.8 12.8 8.7 32.3 23.6

PM10 (μg/m3) 35.5 34 11.7 17.6 61.5 43.9

PM2.5 (μg/m3) 13.9 15.1 9.7 6.3 23.7 17.4

1000–1800 hours O3 (ppb) 44.6 43.6 11.1 29.5 59.8 30.3

Table 3. Association between new-onset asthma and modeled exposure at home and school.

Traffic-related exposurea Home HRb (95% CI) School HRb (95% CI) Combinedc HRb (95% CI)

Nonfreeway TRP 1.51 (1.25–1.81)* 1.45 (1.06–1.98) 1.61 (1.29–2.00)*

Freeway TRP 1.12 (0.95–1.31) 1.08 (0.86–1.34) 1.12 (0.94–1.35)

Total TRP 1.32 (1.08–1.61)** 1.20 (0.91–1.58) 1.34 (1.07–1.68)

aScaled to the IQR at homes for each metric (from Table 2). bHR (95% CI, adjusted for race/ethnicity and for baseline 

hazards strata of age at study entry and sex) with random effects for community and school. cCombined weighted for 

time at home and school. *p < 0.001. **p < 0.01.
McConnell et al.

1024 volume 118 | number 7 | July 2010 • Environmental Health Perspectives

(Table 3). The HR for exposure at schools 

was almost as large as for residential exposure 

(HR = 1.45; 95% CI, 1.06–1.98), and the 

combined average exposure weighted for time 

at school and home had a slightly stronger 

association (HR = 1.61; 95% CI, 1.29–2.00). 

These associations were not confounded by 

any of the covariates in Table 1 or by community relative humidity, which we have 

previously found to confound TRP exposure 

(Jerrett et al. 2008). There also were no significant interactions of either residential or 

school nonfreeway TRP with sex, allergy, or 

parental history of asthma, which we have 

previously shown modified effects of traffic 

exposure (McConnell et al. 2006). Similar 

effect sizes were observed in sensitivity analyses restricted to lifetime residents at the same 

address, analyses excluding children diagnosed 

in the first year of follow-up and excluding 

children who changed schools during the 

follow-up period. There was little evidence 

of nonlinearity in the exposure–response relationship based on sensitivity analyses comparing the fit of a smoothed cubic spline model 

of asthma with a linear model (p-value > 0.80) 

for the partial likelihood ratio test for models 

with 3 and 5 knots compared with the linear 

model. Weaker associations were observed 

with total TRP at homes (HR = 1.32; 95% 

CI, 1.08–1.61) and school (HR = 1.20; 95% 

CI, 0.91–1.58). There were weaker and nonsignificant associations with TRP modeled 

from freeways. Nonfreeway TRP was moderately to strongly correlated with freeway 

TRP at homes (R = 0.64) and at schools 

[R = 0.70; Supplemental Material, Table E-1, 

(doi:10.1289/ehp.0901232)]. However, in 

models co-adjusted for freeway and nonfreeway TRP, the nonfreeway effect estimates 

for school and home were similar to the 

unadjusted estimates.

Of the regional community central-site 

pollutants, NO2 was associated with more 

than double the risk of new-onset asthma (HR 

= 2.17; 95% CI, 1.18–4.00; p = 0.01) over 

the range of exposure (23.6 ppb) across the 13 

study communities (Table 4). In a model with 

NO2, school and residential nonfreeway TRP 

exposure, the estimate for NO2 was attenuated 

(HR = 1.37; 95% CI, 0.69–2.71; Table 5). 

However, the adjusted associations of asthma 

with nonfreeway TRP at homes (HR = 1.46) 

and schools (HR = 1.45) in Table 5 were 

very similar to the unadjusted effect estimates 

(HR = 1.51 and 1.45, respectively, shown in 

Table 3). In sensitivity analyses, a similar pattern of attenuation of regional NO2 effect estimates was observed in models weighted for 

time at school and home (results not shown), 

and in models adjusted for total (freeway plus 

nonfreeway) TRP exposure at school and 

home [Supplemental Material, Table E-2

(doi:10.1289/ehp.0901232)]. There were neither statistically significant interactions of central site pollutants with residential TRP nor 

any consistent patterns suggestive of different 

effects in high and low regional pollutant communities (results not shown). We have previously shown that O3 modified the association 

of team sports participation with new-onset

asthma in an older cohort (McConnell et al. 

2002), but there was neither a main effect of 

sports nor confounding or effect modification 

of effects of residential TRP or ambient central 

monitor pollutant exposure.

Distance to freeways and other major roads 

and traffic volume on those roads have been 

used in other studies as independent predictors 

of traffic exposure, so we examined the distribution [Supplemental Material, Table E-3 

(doi:10.1289/ehp.0901232)] and correlations of these metrics (Supplemental Material, 

Table E-1) and the association at school and 

home with asthma (Supplemental Material, 

Tables E-4 and E-5). In general, these exposure 

metrics were weakly to moderately correlated 

with the TRP metrics but were not consistently 

associated with asthma in our data set.

Discussion

The study is unique in its prospective assessment of the relationship of new-onset childhood asthma to community regional air 

pollution and near-source traffic-related 

exposure at home and in a large number of 

schools. The results indicate that associations 

of asthma with traffic-related pollution from 

nearby sources at schools were independent of 

estimated effects of exposures at homes, and 

these effects were of similar size over a range 

of exposure common in Southern California. 

Evidence of a school effect comparable with 

that associated with the much larger amount 

of time spent at home could potentially be 

explained by physical education and other 

exercise at school that may increase ventilation rate and dose of pollutants to the lungs 

and thereby increase the risk associated with 

exposure. Traffic-related pollutant levels may 

also be considerably higher during the morning hours, when children are arriving at school, 

especially during temperature inversions that 

occur largely in the winter months when children are attending school (Kim et al. 2002; 

Ning et al. 2007). It is possible that school 

exposure may have reflected time spent in other 

locations in the child’s neighborhood rather 

than school-specific exposure, and this possibility merits further investigation. However, time 

at school during the school year accounts for 

more than one-third of all waking hours, and 

in this age group it is likely that most of the 

child’s remaining nonschool time was spent 

at home (Xue et al. 2004). The increased risk 

associated with TRP could not be explained 

by confounders commonly associated with 

asthma or by ambient NO2 or other currently 

regulated regional pollutants measured at the 

central-site monitoring stations. These results 

strengthen an emerging body of evidence from 

both toxicologic and epidemiologic studies that 

air pollutants from nearby traffic contribute to 

the development of asthma (Salam et al. 2008).

NO2 measured at community monitoring stations has been associated with wheeze 

prevalence in an older CHS cohort (Peters 

et al. 1999) and with increased risk of asthma 

incidence in a Japanese cohort (Shima et al. 

2002). We have recently reported associations 

of both incident and prevalent asthma with 

ambient residential NO2 measured outside 

homes of a relatively small sample of children 

from another CHS cohort in many of the 

same communities, but NO2 in that study 

was a marker for intracommunity variation 

in the mixture of TRPs (Gauderman et al. 

2005; Jerrett et al. 2008). Other studies also 

indicate that associations of respiratory disease with intracommunity variation in NO2

reflect the effects of other TRP rather than 

NO2 (Kramer et al. 2000). A study that measured NO2 and NOx in central neighborhood 

Table 4. Association of new-onset asthma with 

community central site pollutant measurements.

Pollutant HRa (95% CI)

NO2 2.17 (1.18–4.00)

PM10 1.35 (0.64–2.85)

PM2.5 1.66 (0.91–3.05)

O3 0.76 (0.38–1.54)

aHR (95% CI) across the range of exposure in the 13 communities (23.6 ppb for NO2, 43.9 μg/m3 PM10, 17.4 μg/m3

PM2.5, and 30.3 ppb for 1000–1800 hours O3), adjusted for 

race/ethnicity and for baseline hazards strata of age at 

study entry and sex with random effects for community 

and school.

Table 5. Mutually adjusted associations of new-onset asthma with community central site pollutant 

measurements and nonfreeway TRP at home and school.a

Central site 

pollutant

HRb (95% CI) for ambient 

pollutant, adjusted for 

TRP at home and school

HRb (95% CI) for home TRP, 

adjusted for school TRP 

and ambient pollutant

HRb (95% CI) for school 

TRP, adjusted for home TRP 

and ambient pollutant

NO2 1.37 (0.69–2.71) 1.46 (1.16–1.84)* 1.45 (1.03–2.06)

PM10 1.40 (0.62–3.17) 1.46 (1.16–1.85)* 1.53 (1.10–2.12)*

PM2.5 1.30 (0.66–2.56) 1.48 (1.19–1.85)* 1.49 (1.07–2.08)

O3 1.01 (0.49–2.11) 1.50 (1.20–1.86)* 1.54 (1.10–2.14)*

aMutually adjusted across each row (i.e., effect of each community pollutant was examined separately in a model 

including both home and school TRP). bHR (95% CI) for central-site pollutants scaled across the range of exposure in the 

13 communities (23.6 ppb for NO2, 43.9 μg/m3 PM10, 17.4 μg/m3 PM2.5, and 30.3 ppb for 1000–1800 hours O3); household 

nonfreeway TRP was deviated from school, scaled to the IQR for home exposure (8 ppb from Table 2). *p < 0.01.
Childhood incident asthma and traffic-related air pollution

Environmental Health Perspectives • volume 118 | number 7 | July 2010 1025

locations found that associations with asthma 

were attenuated by adjustment for modeled 

residential traffic exposure (Kim et al. 2004, 

2008). In the current study, the attenuated 

association between asthma and NO2 continuously measured at the community monitor in models with adjustment for TRP also 

suggests that NO2 was not causally related to 

asthma. However, interpretation of this result 

is not clear because exposure measured at the 

community monitor may have misclassified 

exposures of children in parts of the community with significant local sources of NO2. 

We did not find evidence for main effects of 

regional O3 and PM with asthma, consistent 

with results from previous analyses in older 

CHS cohorts, although we have previously 

shown that O3 modified the effect of outdoor 

exercise in a genetically susceptible subgroup 

(Islam et al. 2009; McConnell et al. 2002).

Associations with asthma were significant 

for TRP exposure estimates modeled from 

local nonfreeway roadway proximity, traffic 

volume, and meteorology. There was little evidence for an effect of major roadway proximity alone, for traffic density, or for pollution 

from freeways. The absence of a freeway TRP 

effect suggests that causal pollutants may be 

highly reactive, resulting in steep spatial gradients from freeway sources and little exposure 

at the longer distances of homes and schools 

to freeways compared with distance to other 

major roads [Supplemental Material, Table E-3

(doi:10.1289/ehp.0901232)]. Several previous studies have found that the largest gradients in traffic-related pollutants occur within 

150 m from roadways (Gilbert et al. 2003; 

Zhu et al. 2002), and variability has often been 

best explained by traffic volume within 300 m 

(Briggs et al. 2000; Gilbert et al. 2003; Ross 

et al. 2006). High-volume traffic on secondary 

roads may also produce pollutants resulting 

from frequent stops and accelerations, which 

have been found to be associated with asthma 

symptoms (Ryan et al. 2005). However, in an 

older cohort from some of these same communities, we observed associations of freeway-modeled exposures and residential freeway proximity 

with respiratory outcomes (Gauderman et al. 

2005, 2007). Different traffic metric associations with respiratory health may have been due 

to variation in the distribution of freeway and 

nonfreeway traffic around homes in the different cohorts and the relatively small number 

of children living near freeways (< 7% within 

150 m in the current cohort). It is also possible 

that the accuracy of exposure models for the 

freeway and nonfreeway sources varied in the 

two cohorts or that the greater mobility of older 

children resulted in health effects associated 

with spending time in areas such as parks in 

close proximity to freeways near homes.

An important distinction between 

the TRP and simpler traffic metrics is the 

inclusion of meteorology (average annual 

wind speed and direction and height of the 

mixing layer) in addition to proximity and 

volume. Traffic density alone [Supplemental 

Material, Table E-4 (doi:10.1289/

ehp.0901232)] and proximity to a major 

roadway (Supplemental Material, Tables E-4 

and E-5) had weaker positive associations 

with incident asthma that were not statistically significant. Therefore, inconsistent findings in some of the (largely cross-sectional)

studies that have evaluated associations with 

asthma (Heinrich and Wichmann 2004) may 

reflect the failure to account for the impact of 

meteorology on exposure. We have recently 

measured NOx at > 900 locations in these 

communities. Approximately two thirds of 

the within-community variability in NOx

was explained by TRP, a substantially higher 

amount of variation than was explained by 

any other traffic metric (unpublished data). 

We have also previously observed that both 

TRP and residential traffic proximity were 

associated with increased asthma prevalence 

in this cohort (McConnell et al. 2006).

The prospective design is a strength of this 

study. In other prospective studies of birth 

cohorts in Sweden and the Netherlands, modeled traffic-related pollutants, including NOx

and/or PM2.5, were associated with incidence 

of wheeze and atopy (Nordling et al. 2008) 

and doctor-diagnosed asthma (Brauer et al. 

2007) in children < 5 years of age, when the 

diagnosis of asthma may be difficult to distinguish from transient wheeze not predictive of 

asthma. As these cohorts mature, the relationship to asthma is likely to become more clear. 

For example, associations between PM2.5 light 

absorption, roadway proximity, and asthmatic 

bronchitis and atopy were found in a study of 

two German birth cohorts that followed some 

children to 6 years of age (Morgenstern et al. 

2008). Associations with allergic symptoms 

in these cohorts were less robust at an earlier 

age (Gehring et al. 2002). Other recent results 

from prospective studies of birth cohorts 

(Gehring et al. 2009) and of children (Shima 

et al. 2002) followed to older ages have generally shown positive associations between markers of residential TRP and asthma, although a 

recent Norwegian study found no association 

with modeled exposure at the birth address of 

a large cohort of children (Oftedal et al. 2009). 

Genetic studies examining pathways likely to 

mediate effects of air pollution also strengthen 

the causal inference that traffic-related pollutants may cause asthma. We have shown elsewhere that the risk of asthma associated with 

traffic exposure and with traffic-related ambient PM was modified in a predictable way by 

functional gene variants in pathways associated with asthma, including inflammation and 

airway remodeling (Salam et al. 2007a) and 

oxidative stress (Islam et al. 2008; Salam et al. 

2007b). In another analysis of the data from 

this cohort, we have shown that the estimated 

effect of TRP was modified by socioeconomic 

status and psychosocial stress (Shankardass 

et al. 2009).

There are some limitations to these data. 

Our conservative estimates of TRP for the 

center of school buildings are likely to underestimate the exposure of students in buildings closer to the roadways, especially during 

dropoff and pickup of students at the beginning and end of the school day. The impact of 

this underestimate of exposure on the assessment of health effects is not clear, although in 

general it might be expected that this exposure 

misclassification would be nondifferential with 

respect to asthma and would result in underestimation of the effect of traffic exposure. Other 

limitations include the relatively short (3-year) 

period of follow-up and the retrospective questionnaire assessment of early-life risk factors, as 

this was not a pregnancy cohort. Our cohort 

excluded children who left the community 

before school entry and who had asthma or 

wheeze at study entry. However, our cohort 

allowed us to examine risk factors in school 

children, an age during which there has been 

little study of new-onset asthma.

Asthma is a clinical syndrome with no 

sensitive and specific diagnostic test available to confirm clinical assessments. Because 

of the clinical nature of the assessment, the 

reported physician diagnosis of asthma we used 

has been recommended and widely used as 

a method to classify asthma status in epidemiologic studies, although this approach has 

limitations (Asher et al. 1995; Burr 1992). 

If misclassification of true asthma based on 

this approach were random with respect to 

exposure to air pollution, then the observed 

associations would be likely to have underestimated the effect of exposure. Bias could 

explain our results if asthma misclassification 

were related also to exposure. This could occur 

if exposure were associated with access to care 

and differences in practice among physicians 

that have the potential to influence asthma 

diagnosis (Samet 1987). However, adjustment 

for factors that mediate access to care including 

family income, parental education, and having 

medical insurance did not alter our results, 

indicating that confounding due to differential access to care was unlikely to explain these 

findings. The validity of questionnaire-reported 

physician diagnosis has also been established 

in a subset of CHS participants (Salam et al. 

2007a). In addition, any child with a history 

of wheezing at study entry was excluded from 

the analysis, so it is unlikely that the exacerbation of undiagnosed prevalent asthma by traffic 

explained these associations. Loss to follow-up 

could also potentially result in bias because 

of selection. Sociodemographic characteristics 

varied between the analysis cohort and children 
McConnell et al.

1026 volume 118 | number 7 | July 2010 • Environmental Health Perspectives

not contributing to the analysis because of loss 

to follow-up or missing or poorly matched 

geocodes for addresses precluding estimation 

of residential exposure [Supplemental Material, 

Table E-6 (doi:10.1289/ehp.0901232)]. 

Hispanic children were less likely than nonHispanic white children to be included in the 

follow-up analysis, as were children without 

insurance and with lower parental education 

and income. However, adjusting for these factors had little impact on the pattern of observed 

effects. In addition, there was little difference 

between exposure to nonfreeway TRP in the 

study sample (7.3 ppb) and in the group not 

included because of loss to follow-up (7.6 ppb). 

Therefore, it is unlikely that selection bias due 

to loss to follow-up explained our results.

Conclusions

An estimated 6.2 million children have asthma 

in the United States, making it the most common chronic disease in childhood (Moorman 

et al. 2007), and rates have increased markedly in developed countries over the past several 

decades (Braman 2006). Morbidity results in 

impaired quality of life for the affected child 

and other family members, in increased use 

of health services, and in school absences that 

have large social and economic costs (Wang 

et al. 2005). Our results indicate that children 

exposed to higher levels of traffic-related air pollution at school and home are at increased risk 

of developing asthma. Almost 10% of public 

schools in California are located within 150 m 

of roadways with > 25,000 vehicles daily (Green 

et al. 2004). Students in urban areas in eastern 

U.S. cities are even more likely than children in 

Los Angeles to attend schools near major highways (Appatova et al. 2008). Therefore, exposure to TRP is potentially an important public 

health problem affecting large populations of 

children. Planning transportation and other 

urban development to limit population exposure to traffic exhaust, as well as more effective 

control of vehicular emissions, may result in 

substantial long-term public health benefits.

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