1168 volume 117 | number 7 | July 2009 • Environmental Health Perspectives

Research | Children’s Health

Fine particles [particles ≤ 2.5 μm diameter (PM2.5)] originate from both local and 

regional pollution sources. A large fraction of 

PM2.5 mass is associated with the combustion of fuels such as gasoline, diesel, coal, oil, 

and wood. PM2.5 mass also includes crustal 

material (road dust and soil particles), sea salt, 

and biological particles. Factor analysis and 

source apportionment techniques [Brown 

et al. 2007; U.S. Environmental Protection 

Agency (EPA) 2008; Watson et al. 2002] have 

been used for many years to help policy regulators identify sources of particle pollution. 

These techniques have been used to investigate 

associations between sources of particle pollution and health effects including studies of 

adult mortality (Laden et al. 2000; Mar et al. 

2000; Ozkaynak and Thurston 1987; Taino 

et al. 2005), adult emergency department visits (Sarnat et al. 2008), and adult and pediatric hospital admissions (Andersen et al. 2007). 

Recently, one study associated specific sources 

of fine particles with respiratory morbidity in 

adults with asthma (Penttinen et al. 2006).

PM2.5 mass concentration has been associated with adverse health effects in children, 

particularly children with asthma: increased 

respiratory morbidity and/or medication use 

(Gent et al. 2003; Johnston et al. 2006; Mar 

et al. 2004; Rabinovitch et al. 2006; Ranzi 

et al. 2004; Rodriguez et al. 2007; Slaughter 

et al. 2003); increased number of doctor visits 

for respiratory illness (Chardon et al. 2007; 

Hertz-Picciotto et al. 2007; Pino et al. 2004); 

and decreased lung function (Delfino et al. 

2004; Hong et al. 2007; Lewis et al. 2005; 

Moshammer et al. 2006; Trenga et al. 2006). 

Linking respiratory health effects to specific 

particle pollution sources is critical to efforts 

to develop cost-effective strategies to protect 

public health, particularly the respiratory 

health of vulnerable members of the population such as children with asthma.

Annual PM2.5 levels at a monitoring site in 

New Haven, Connecticut, where subjects in the 

present study live, exceed the U.S. EPA annual 

standard of 15 μg/m3 (U.S. EPA 2009). We 

examined associations between daily exposure 

to PM2.5 components and sources identified 

through factor analysis and source apportionment, and daily symptoms and medication use 

in children with asthma. Some of the results of 

this study have been previously reported in the 

form of an abstract (Gent et al. 2008).

Methods

Subjects. Subjects were drawn from a larger 

study population 4–12 years of age when 

enrolled between 2000 and 2003. Eligibility 

criteria were physician-diagnosed asthma and 

symptoms or medication use in the previous 

12 months. Subjects in the present analysis 

(n = 149) were restricted to those residing in 

New Haven County within 30 km of the air 

quality monitor. Study participants included 

siblings with asthma (one per family; n =88, 

59%) of infants enrolled in a birth cohort to 

study the development of asthma (Belanger 

et al. 2003), patients recruited from the Yale 

Pediatric Asthma Clinic (n =43, 29%), and 

children with asthma identified through a 

survey on childhood asthma conducted in the 

New Haven public schools (n = 18, 12%). 

In addition to abundant mobile sources of 

air pollution (e.g., interstate highways 95, 

91, 84), New Haven is home to stationary 

sources including small manufacturing enterprises, power plants, and a harbor handling 

ships that deliver heating oil for New England 

(U.S. EPA 2008). New Haven County, population 824,008, covers an area of 2,233 km2

(863 mi2) (U.S. Census Bureau 2000). Yale 

University Human Investigation Committee 

approved the study, and all respondents 

(mothers of study subjects) gave informed 

consent prior to participation.

Data collection. During the enrollment 

visit, a research assistant administered a 

questionnaire to the child’s mother to collect demographic, medical history, and home 

environment information. Mothers recorded 

daily symptoms [wheeze, persistent cough 

(defined as coughing throughout the day), 

shortness of breath, chest tightness, night 

symptoms] and medication use (rescue 

medication including bronchodilators, and 

maintenance medication including steroids, 

cromolyn sodium, and leukotriene inhibitors) 

on study diaries and reported this information 

during monthly telephone interviews. Dates 

away from New Haven during the follow-up 

Address correspondence to J.F. Gent, Yale Center for 

Perinatal, Pediatric and Environmental Epidemiology, 

One Church Street, 6th Floor, New Haven, CT 

06510 USA. Telephone: (203) 764-9375. Fax: (203) 

764-9378. E-mail: janneane.gent@yale.edu

Supplemental Material is available online at http://

www.ehponline.org/members/2009/0800335/suppl.pdf

This study was funded by National Institutes of 

Health grants ES011013 and ES05410.

The authors declare they have no competing 

financial interests.

Received 29 October 2008; accepted 31 March 2009.

Symptoms and Medication Use in Children with Asthma and Traffic-Related 

Sources of Fine Particle Pollution

Janneane F. Gent,1 Petros Koutrakis,2 Kathleen Belanger,1 Elizabeth Triche,1 Theodore R. Holford,1

Michael B. Bracken,1 and Brian P. Leaderer1

1Center for Perinatal, Pediatric and Environmental Epidemiology, Department of Epidemiology and Public Health, Yale University School 

of Medicine, New Haven, Connecticut, USA; 2Harvard University School of Public Health, Boston, Massachusetts, USA

Background: Exposure to ambient fine particles [particulate matter ≤ 2.5 μm diameter (PM2.5)] 

is a potential factor in the exacerbation of asthma. National air quality particle standards consider 

total mass, not composition or sources, and may not protect against health impacts related to specific components.

Objective: We examined associations between daily exposure to fine particle components and 

sources, and symptoms and medication use in children with asthma. 

Methods: Children with asthma (n = 149) 4–12 years of age were enrolled in a year-long study. 

We analyzed particle samples for trace elements (X-ray fluorescence) and elemental carbon (light 

reflectance). Using factor analysis/source apportionment, we identified particle sources (e.g., motor 

vehicle emissions) and quantified daily contributions. Symptoms and medication use were recorded 

on study diaries. Repeated measures logistic regression models examined associations between 

health outcomes and particle exposures as elemental concentrations and source contributions. 

Results: More than half of mean PM2.5 was attributed to traffic-related sources motor vehicles 

(42%) and road dust (12%). Increased likelihood of symptoms and inhaler use was largest for 3-day 

averaged exposures to traffic-related sources or their elemental constituents and ranged from a 10% 

increased likelihood of wheeze for each 5-μg/m3 increase in particles from motor vehicles to a 28% 

increased likelihood of shortness of breath for increases in road dust. Neither the other sources identified nor PM2.5 alone was associated with increased health outcome risks. 

Conclusions: Linking respiratory health effects to specific particle pollution composition or 

sources is critical to efforts to protect public health. We associated increased risk of symptoms and 

inhaler use in children with asthma with exposure to traffic-related fine particles.

Key words: childhood asthma, fine particle pollution, PM2.5, respiratory morbidity, source 

apportionment, traffic pollution. Environ Health Perspect 117:1168–1174 (2009). doi:10.1289/

ehp.0800335 available via http://dx.doi.org/ [Online 31 March 2009]
Pediatric asthma and particle pollution

Environmental Health Perspectives • volume 117 | number 7 | July 2009 1169

period were reported at an exit interview and 

were not used in the analysis.

Exposure measurements. Filter analysis. 

PM2.5 filter samples (n = 1,181) were collected between 1 August 2000 and 3 February 

2004 from the Stiles Street monitoring site 

(98% of the filters) or the State Street site 

(within 3 km). Filters were analyzed by X-ray 

fluorescence (XRF) (Desert Research Institute, 

Reno, Nevada) (Watson et al. 1999). A list 

of the 51 elements analyzed is presented in 

the Supplemental Material, Table 1 (available online at http://www.ehponline.org/

members/2009/0800335/suppl.pdf). 

Light reflectance analysis. Before XRF, 

elemental carbon (EC) concentrations were 

determined using an optical reflectance technique (Cyrys et al. 2003; Janssen et al. 2001; 

Kinney et al. 2000). See Supplemental Material 

for further details (available online at http://

www.ehponline.org/members/2009/0800335/

suppl.pdf). 

Source apportionment. We analyzed daily 

concentrations of 17 elements [see Supplemental 

Material, Table 1 (available online at http://

www.ehponline.org/members/2009/0800335/

suppl.pdf)] as well as EC using principal component analysis with an orthogonal (varimax) 

rotation in SAS (version 9.1; SAS Institute Inc., 

Cary, NC) (Kavouras et al. 2001; Thurston 

and Spengler 1985). We calculated daily scores 

for each factor by multiplying the normalized 

elemental concentrations by the respective 

standardized elemental scoring coefficient and 

factor, then summing across all component 

products. Scores were rescaled to concentrations in micrograms per cubic meter by regressing total PM2.5 concentration on factor scores 

and obtaining the product of each factor score 

with its regression coefficient. Rescaled factor 

scores, or daily source contributions, were used 

as exposures in the health effects analyses. 

Six sources of PM2.5 were identified based 

on loadings (or correlations) of elements on 

each of the six factors determined by factor 

analysis. The factor with high loadings for 

EC and trace elements zinc, lead, and copper 

was attributed to motor vehicle emissions. 

The factor road dust was associated with elements of terrestrial origin such as silicon, iron, 

aluminum, and calcium as well as barium, 

which is a tracer for tire wear particles. The 

sulfur factor represents particles from regional 

sources and was highly correlated with the 

elements sulfur and phosphorus. The factor 

with a high potassium loading was attributed 

to biomass burning. The oil factor represents 

oil combustion emissions from power plants 

and home heating and was characterized by 

the presence of vanadium and nickel. Finally, 

the sea salt factor was associated with the elements sodium and chlorine.

Data analysis. The association between 

daily elemental concentrations or source 

contributions and daily respiratory symptoms 

and rescue medication use was examined with 

repeated measures logistic regression (SAS 

Institute Inc.). Models used generalized estimating equations (GEE) specifying a 1-day lag 

autoregressive structure (AR1) for the health 

outcomes correlation matrices. All models 

were adjusted for season using sine and cosine 

functions of date to capture annual cycles [i.e., 

cos((date/365) × 2π) and sin((date/365) × 2π)], 

day of week (for weekly cycles), and date (to 

control for time trends over the 4-year course 

of the study). Because sources were uncorrelated by design (orthogonal rotation of factors), 

all six sources were included simultaneously in 

health effects models. Gaseous co-pollutants 

(nitrogen dioxide, carbon monoxide, sulfur 

dioxide, ozone) were examined separately 

in models that also included all six sources. 

Models included year-round data except for 

the co-pollutant model with O3, which is monitored only in warm months. Health effects of 

elements included in the factor analysis, all six 

sources, and PM2.5 were examined for sameday exposures [lag0 (L0)] as well as exposures 

lagged by 1 or 2 days (L1, L2) or the mean of 

lagged days 0–2 (L02). Tests for goodness of fit 

were performed using the Hosmer–Lemeshow 

statistic for logistic regression.

Results

Demographic characteristics of study participants are shown in Table 1. Most subjects were 

male (60%), ≥ 8 years of age (53%) (mean ± SD 

= 8 ± 2 years), and self-reported as white (56%). 

More than one-third of the adult respondents 

had a college degree (36%). Environmental 

tobacco smoke exposure was reported in 6% of 

the homes. Subjects participated for a mean of 

313 ± 92 days [median 366; interquartile range 

(IQR) 54 days]. The distribution of daily symptoms and medication use is shown in Table 2. 

The most frequently experienced symptom was 

persistent cough (reported by 86%), followed 

by wheeze (76%), which was experienced by 

subjects for a median of 19 days (IQR 29) or 

8 days (IQR 24), respectively, of the year of 

follow-up. Subjects experienced shortness of 

breath or chest tightness (reported by two-thirds 

of the subjects) less frequently for a median of 

4 (IQR 14) days each, and used short-acting 

inhalers (reported by 80%) for a median of 18 

(IQR 47) days. Nearly one-third of the subjects 

had moderate to severe persistent asthma during the year-long follow-up study (Table 2). 

All subjects resided 0.9–27 km from the air 

Table 1. Personal characteristics for children participating in a year-long prospective study (n = 149), 

New Haven, Connecticut, August 2000–January 

2004.

Enrollment characteristic No. (%)

Sex

Male 89 (59.7)

Female 60 (40.3)

Ethnicity

White 84 (56.4)

Black 36 (24.2)

Hispanic 27 (18.1)

Other 2 (1.3)

Age at enrollment (years)

< 8 70 (47.0)

≥ 8 79 (53.0)

Mother’s education (years)a

< 12 13 (8.7)

12–15 83 (55.7)

> 15 53 (36.0)

Environmental exposure to tobacco smoke

No 140 (94.0)

Yes 9 (6.0)

aThe adult respondent was the subject’s mother in all but 

two homes.

Table 2. Distribution of daily symptoms, daily 

inhaler use, and mean asthma severity during a 

year-long prospective study (n = 149), New Haven, 

Connecticut, August 2000–January 2004.

Variable No. (%)

Symptoms and inhaler use during 

12-month study period (days)

Wheeze

None 35 (23.5)

1–7 36 (24.2)

8–14 22 (14.8)

15–21 15 (10.1)

22–29 11 (7.4)

≥ 30 30 (20.1)

Persistent cough

None 20 (13.5)

1–7 31 (21.0)

8–14 12 (8.1)

15–21 17 (11.5)

22–29 21 (14.2)

≥ 30 47 (31.8)

Shortness of breath

None 46 (30.9)

1–7 43 (28.9)

8–14 23 (15.4)

15–21 9 (6.0)

22–29 9 (6.0)

≥ 30 19 (12.8)

Chest tightness

None 49 (32.9)

1–7 44 (29.5)

8–14 20 (13.4)

15–21 9 (6.0)

22–29 7 (4.7)

≥ 30 20 (13.4)

Short-acting inhaler use

None 29 (19.5)

1–7 28 (18.8)

8–14 14 (9.4)

15–21 11 (7.4)

22–29 11 (7.4)

≥ 30 56 (37.6)

Asthma severity (GINA score)a

0 No symptoms/medication 5 (3.4)

1 Intermittent 66 (44.9)

2 Mild persistent 29 (19.7)

3 Moderate persistent 30 (20.4)

4 Severe persistent 17 (11.6)

aA five-point asthma severity score [from 0 (no symptoms 

or medication use) to 4 (severe persistent)], based on the 

Global Initiative for Asthma guidelines (U.S. Department of 

Health and Human Services 2002) was calculated for each 

month of the study. The mean severity was the arithmetic 

mean of the 12 monthly severity scores.
Gent et al.

1170 volume 117 | number 7 | July 2009 • Environmental Health Perspectives

pollution monitor site (mean ± SD = 10.2 ± 

8.4 km). The mean number of subjects under 

observation each day was 36 ± 8 and ranged 

from 10 to 49. The mean number of subjects 

experiencing symptoms or using short-acting 

inhalers each day was as follows: shortness of 

breath or chest tightness 3 ± 3%; wheeze 5 ± 

4%; persistent cough 8 ± 5%; and inhaler use 

12 ± 6% (Figure 1A,B). 

Mean level of PM2.5 during the study was 

17.0 ± 9.8 μg/m3, which is above the U.S. 

annual standard for fine particles of 15 μg/m3

(U.S. EPA 2009). The contributions from each 

source to PM2.5 mass and to elemental components of PM2.5 are given in Table 3. The 

mean estimated and measured mass are given 

as well as the percent root mean square error 

(%RMSE), defined as 100 × (|estimated concentration – measured concentration|/measured concentration). Elemental components of 

PM2.5 are grouped according to the source with 

which they were most strongly associated. For 

example, the motor vehicle source contributed 

62% of the estimated mean EC mass (961.3 

of 1554.8 ng/m3) and 77% of the mean Zn 

mass (22.3 of 29.0 ng/m3). On average, over 

the 4 years of the study, over half of daily PM2.5

came from the two traffic-related sources motor 

vehicles [median (IQR) 42.5% (26.7%)] and 

road dust [12.3% (13.8%)] (bottom of Table 2). 

An additional 28.7% (30.3%) of daily PM2.5

came from regional sulfur sources. 

The associations between PM2.5 elemental 

components and daily symptoms or medication use are presented in Table 4. Individual 

elements in fine particle mass may come 

from more than one source but are listed in 

Table 4 according to the source with which 

they were most strongly associated (Table 3). 

Associations for same-day exposure (L0) and 

exposures averaged over the same day and 

previous 2 days (L02) are shown in Table 4. 

Exposures lagged by 1 or 2 days (L1, L2) 

are included in Table 2 in the Supplemental 

Material (available online at http://www.

ehponline.org/members/2009/0800335/

suppl.pdf). In general, trace elements originating from the motor vehicle, road dust, biomass burning, and oil sources are associated 

with symptoms and/or medication use. For 

example, a significantly increased likelihood 

of wheeze, shortness of breath, chest tightness, or short-acting inhaler use is associated 

with each 1,000-ng/m3 increase in EC. The 

strongest associations were found for the 3-day 

averaged exposures to the elemental constituents of road dust. No associations were found 

between individual elements associated with 

sulfur or sea salt sources and symptoms or 

medication use. Total PM2.5 was not significantly associated with any symptoms or medication use [see Supplemental Material, Table 3 

(available online at http://www.ehponline.org/

members/2009/0800335/suppl.pdf)].

Daily source contributions to PM2.5

are illustrated for the 4 years of the study in 

Figure 1C–1H. Results of logistic regression 

models examining associations between health 

outcomes and PM2.5 sources are shown in 

Table 5 for same-day exposures (Table 5, L0 

model) and exposures averaged over the same 

day and previous 2 days (Table 5, L02 model). 

Source exposures lagged by 1 or 2 days 

(L1, L2) are included in the Supplemental 

Material, Table 3 (available online at http://

www.ehponline.org/members/2009/0800335/

suppl.pdf). For same-day or 3-day averaged 

exposures, each 5‐μg/m3 increase in PM2.5

mass concentration coming from either motor 

vehicles or road dust is consistently associated 

with increases in the likelihood of respiratory 

symptoms or inhaler use (Table 5). Reduced 

likelihood of wheeze or inhaler use is associated with 5‐μg/m3 increases in the same day, 

but not for lagged exposures to the sulfur 

source. Significant reductions in the likelihood 

of wheeze were also associated with exposures 

to the biomass burning source. 

The effect of adding a gaseous co-pollutant 

(NO2, CO, SO2, or O3) to the source exposure 

Figure 1. Daily prevalence of wheeze (A) and short-acting inhaler use (B). Daily PM2.5 source concentrations of motor vehicle (C), road dust (D), sulfur (E), biomass burning (F), oil (G), and sea salt (H). Dotted line 

at left of x-axis indicates warmer months (April–September). n = 149 children, New Haven, Connecticut, 

August 2000–January 2004. 

30

20

10

0

30

20

10

0

40

30

20

10

0

40

30

20

10

0

40

30

20

10

0

40

30

20

10

0

40

30

20

10

0

40

30

20

10

0

Prevalence Concentration (μg/m3

)

Concentration (μg/m3

)

Concentration (μg/m3

)

Concentration (μg/m3

)

Concentration (μg/m3

)

Concentration (μg/m3

) Prevalence

1 April 1 Jul 30 Sep 30 Dec 31 Mar 1 April 1 Jul 30 Sep 30 Dec 31 Mar

1 April 1 Jul 30 Sep 30 Dec 31 Mar 1 April 1 Jul 30 Sep 30 Dec 31 Mar

1 April 1 Jul 30 Sep 30 Dec 31 Mar 1 April 1 Jul 30 Sep 30 Dec 31 Mar

1 April 1 Jul 30 Sep 30 Dec 31 Mar 1 April 1 Jul 30 Sep 30 Dec 31 Mar

Daily wheeze

Motor vehicles

Sulfur

Oil Sea salt

Biomass burning

Road dust

Daily short-acting inhaler use

×

2000–2001

2001–2002

2002–2003

2003–2004
Pediatric asthma and particle pollution

Environmental Health Perspectives • volume 117 | number 7 | July 2009 1171

model is shown for wheeze and short-acting 

inhaler use in Table 6. For example, the addition of any of the gaseous co-pollutants to the 

source exposure model reduces the effect of the 

motor vehicle source on wheeze. Interestingly, 

in the source model that also includes NO2

(another marker for traffic), each 20-ppb 

increase in NO2 increases the likelihood of 

wheeze by 8% (p = 0.09). No significant associations were seen in co-pollutant models 

between exposure and likelihood of persistent 

cough or chest tightness.

Discussion

Results of the source apportionment analysis 

suggest that over half of the mean PM2.5 can 

be attributed to traffic sources motor vehicles and road dust, and that daily exposure to 

these sources is associated with increased risk 

of respiratory symptoms and inhaler use in 

children with asthma. Risks remain in models that include all six PM2.5 sources as well 

as one gaseous co-pollutant. Of co-pollutants added to the model one at a time, NO2, 

which is a marker for traffic (Grieshop et al. 

2006), was also found to be an independent 

risk factor for increased wheeze (Table 6). 

In single-element analyses, EC, also considered a marker for traffic (Grieshop et al. 

2006; Sarnat et al. 2008), is itself associated 

with increased symptoms (wheeze, shortness 

of breath, chest tightness) and medication 

use (Table 4). Of the EC measured in PM2.5, 

88% was contributed by the traffic-related 

sources: 62% from motor vehicles and 26% 

from road dust (Table 3). Among elements 

associated with the road dust source, Ca 

appears to have the strongest association with 

symptoms (Table 4). Numerous studies have 

found that the composition of Ca in road 

dust is higher than that in the earth’s crust 

(Grieshop et al. 2006). Results from tunnel 

and chassis dynamometer studies have shown 

that Ca is emitted from light and heavy-duty 

vehicles and is possibly associated with the 

combustion of motor oil (Ning et al. 2008). 

The stronger associations with fine-particle 

Ca compared with other major terrestrial elements such as Al and Si suggests that it may 

be a better tracer of urban road dust.

A negative association was observed 

between biomass burning and wheeze 

(Table 5). Potassium, the tracer element for 

biomass burning source and an element used 

in firework propellant mixture, was present 

at high concentrations when Fourth of July 

displays took place (Moreno et al. 2007). 

When the analysis was rerun without the days 

with the five highest potassium values [all 

> 1,000 ng/m3 and all occurring in July (4 

July 2001, 2002, 2003; 5 July 2003; 7 July 

2002) (Figure 1F)], the negative association 

seen previously for wheeze remains but is no 

longer significant [odds ratio (OR) 0.85; p = 

0.29]. Of note is the high level of potassium 

measured on 7 July 2002, which is attributable not to fireworks but to a smoke plume 

from a massive Canadian forest fire (Sigler 

et al. 2003). For our subjects, symptoms and 

medication use are at their lowest in summer 

and highest in winter (Figure 1A,B), and if 

these children with asthma were kept away 

from fireworks displays and/or kept indoors 

during the smoke plume days, this may have 

resulted in the apparent protective association 

with potassium levels.

Subjects who were in follow-up during 

the summer of 2001 also contributed data to 

an earlier analysis of O3 and respiratory health 

in southern New England (Gent et al. 2003). 

In that analysis, we found O3 exposure to be 

a risk for increased respiratory symptoms, but 

only among children taking asthma maintenance medication (e.g., corticosteroids). The 

model with PM2.5 sources and O3 shown in 

Table 6 shows O3 to be a risk factor increasing 

the likelihood of wheeze by 8% (p = 0.08), 

but this model includes data from all children 

living in New Haven regardless of maintenance medication status. Reanalysis stratified 

by maintenance medication use showed that 

every 50-ppb increase in O3 (8-hr average) 

resulted in a larger risk of wheeze for users of 

maintenance medication (n =76) [OR = 1.29; 

95% confidence interval (CI), 0.99–1.68; p = 

0.06] than for nonusers (n =63) (OR = 1.10; 

95% CI, 0.77–1.57; p = 0.60). Comparable 

results from the earlier report are given for a 

50-ppb increase in 1-hr O3: For children on 

maintenance medication (n = 130), the likelihood of wheeze increases by 35% (OR = 1.35; 

95% CI, 1.11–1.65) (Gent et al. 2003). 

Although exposure to total PM2.5 mass has 

itself been shown to be a significant risk factor for adverse respiratory outcomes among 

children (Johnston et al. 2006; Mar et al. 

2004; Rabinovitch et al. 2006; Ranzi et al. 

2004; Rodriguez et al. 2007; Slaughter et al. 

2003), this is not always the case (Chimonas 

and Gessner 2007; Holguin et al. 2007). In 

a study of Medicaid claims for doctor visits 

for asthma in Anchorage, Alaska, the authors 

noted that the major constituent of their particles is geologic in origin (i.e., rock dust), 

and that annual levels of PM mass are low, 

6 μg/m3 (95% CI, 0.5–7.0). The authors found 

no association between exposure to PM2.5 mass 

Table 3. Source contributionsa to PM2.5 mass (μg/m3) and elemental (ng/m3) concentrations in New Haven, Connecticut, August 2000 – January 2004. 

Source Mass

Source Motor vehicles Road dust Sulfur Biomass burning Oil Sea salt Estimated Measured %RMSE

Element (ng/m3)

EC 961.3b 403.4 118.8 22.1 68.1 –19.0 1554.8 1894.5 17.9

Zn 22.3b 2.6 0.8 0.5 1.7 1.0 29.0 23.0 26.3

Pb 3.4b 0.5 0.6 0.5 — 0.1 5.1 4.9 4.0

Cu 2.1b 1.3 0.5 1.8 0.2 — 6.0 6.3 5.5

Se 0.4b –0.1 0.2 — 0.0 0.0 0.5 0.5 3.5

Si –4.5 115.7b 5.7 4.8 2.0 2.2 125.9 117.0 7.6

Fe 84.7 101.1b 3.0 11.6 12.2 –1.7 210.9 219.0 3.7

Al –7.0 54.8b 7.0 5.8 — 1.6 62.0 67.5 8.1

Ca 17.8 29.1b — 1.5 1.1 2.0 51.6 51.2 0.7

Ba 2.9 4.9b 0.5 4.1 0.8 –0.4 12.7 12.6 0.4

Ti 1.2 4.2b 0.5 0.9 — — 6.9 7.4 6.3

S 210.4 166.3 906.7b 75.4 18.7 26.0 1403.4 1451.8 3.3

P 14.0 6.6 36.8b 2.6 1.5 — 61.5 64.8 5.2

K –8.0 13.8 12.4 60.5b –2.7 3.7 79.6 63.2 25.8

V 0.6 1.1 0.8 0.2 6.0b 0.2 8.9 9.9 9.4

Ni 1.9 0.4 0.2 0.1 2.6b 0.2 5.6 5.2 6.9

Na 20.9 29.3 37.0 4.4 — 47.0b 140.1 180.8 22.5

Cl 20.5 — –11.2 — 6.9 32.0b 48.7 29.6 64.4

PM2.5 (μg/m3) 6.6b 2.3 5.5 0.9 0.8 0.5 16.6 17.0 2.4

Daily source contribution to PM2.5 (%) 

Median 42.5 12.3 28.7 4.4 2.2 1.2

IQR 26.7 13.8 30.3 5.6 7.7 7.2 

—, nonsignificant contribution.

aMean concentration for 1,181 days between August 2000 and January 2004. bMaximum contribution from a particular source for each element. 
Gent et al.

1172 volume 117 | number 7 | July 2009 • Environmental Health Perspectives

and health outcomes. On the other hand, significant effects were seen between increases 

in PM10 mass concentrations (PM with aerodynamic diameter < 10 μm) and increased 

visits for asthma and prescriptions for inhalers (Chimonas and Gessner 2007). In a study 

of asthmatic children and respiratory health 

in a busy, traffic-filled Mexican border town, 

Holguin et al. (2007) found no significant 

association between level of PM2.5 mass and 

respiratory health. However, using traffic as the 

exposure metric (calculated as traffic density 

in 50-m residential buffers) revealed a significant risk for increased respiratory symptoms 

with increasing traffic (OR = 1.58; 95% CI, 

1.05–2.38) (Holguin et al. 2007).

Studies using proximity to traffic as 

an exposure metric suggest that children 

living near busy roads have impaired respiratory health. Large studies of schoolchildren in Munich (Nicolai et al. 2003) and 

Nottingham, United Kingdom (Venn et al. 

2001), have found that proximity of residence 

(< 50–90 m) to heavily traveled roads is associated with increased risk of asthma symptoms. Studies of schoolchildren conducted 

in the Netherlands found increased health 

risks associated with proximity specifically to 

truck traffic and wheeze (Janssen et al. 2003), 

chronic respiratory symptoms (Van Vliet 

et al. 1997), and lung function (Brunekreef 

et al. 1997). Similar increased risk associations were found between black smoke (a 

proxy for diesel exhaust), measured at subjects’ schools, and respiratory outcomes. No 

significant associations were found between 

respiratory symptoms and volume of automobile traffic. The strongest associations were 

found for children living within 300 m of a 

highway (Brunekreef et al. 1997). A smaller 

study in Cincinnati, Ohio (Ryan et al. 2005), 

found an increased risk of wheezing in infants 

associated with stop-and-go bus and truck 

traffic within 100 m of the residence. All of 

these studies suggest that proximity to traffic, 

in particular high-volume truck traffic, has a 

negative impact on the respiratory health of 

children. Rather than a metric based on proximity to roads or traffic density, we used factor 

analysis and source apportionment techniques 

to examine exposures to potentially toxic portions of particle mass and the health of children residing in an area of significant mobile 

source activity. 

Table 4. ORs from separate repeated measures logistic regression analysesa of associations between daily respiratory symptoms and each daily elemental concentration of PM2.5. 

Unit increase Wheeze Persistent cough Shortness of breath Chest tightness Inhaler short-acting

Source/element Lag (ng/m3) OR p-Value OR p-Value OR p-Value OR p-Value OR p-Value

Motor vehicles

EC L0 1,000 1.04 0.04 1.01 0.42 1.06 0.001 1.03 0.20 1.01 0.15

L02 1,000 1.07 0.06 1.03 0.23 1.12 0.01 1.10 0.04 1.02 0.40

Zn L0 10 1.00 0.69 1.00 0.60 1.02 0.001 1.00 0.72 1.00 0.41

L02 10 1.00 0.98 1.00 0.94 1.04 0.06 1.03 0.13 1.01 0.53

Pb L0 5 1.02 0.31 1.02 0.25 1.03 0.11 1.02 0.31 1.01 0.06

L02 5 1.07 0.13 1.05 0.12 1.12 0.01 1.10 0.02 1.04 0.10

Cu L0 5 1.01 0.59 1.02 0.13 1.06 0.01 1.03 0.23 1.01 0.22

L02 5 1.02 0.67 1.05 0.04 1.06 0.21 1.04 0.39 1.01 0.46

Se L0 1 1.00 0.97 1.00 0.84 1.02 0.40 1.00 0.79 0.99 0.20

L02 1 1.02 0.71 0.98 0.43 1.02 0.67 0.98 0.61 0.99 0.75

Road dust

Si L0 100 1.03 0.03 1.02 0.01 1.04 0.01 1.02 0.20 1.02 0.004

L02 100 1.07 0.04 1.05 0.02 1.08 0.02 1.06 0.10 1.03 0.09

Fe L0 100 1.04 0.02 1.02 0.06 1.06 0.002 1.01 0.47 1.02 0.004

L02 100 1.07 0.05 1.04 0.04 1.08 0.04 1.05 0.21 1.03 0.08

Al L0 50 1.02 0.17 1.03 0.002 1.05 0.002 1.02 0.21 1.02 0.02

L02 50 1.07 0.03 1.06 0.01 1.09 0.004 1.07 0.04 1.02 0.11

Ca L0 50 1.07 0.02 1.05 0.01 1.10 0.002 1.04 0.26 1.04 0.01

L02 50 1.14 0.04 1.09 0.03 1.18 0.01 1.14 0.07 1.04 0.17

Ba L0 10 0.99 0.57 1.00 0.83 1.04 0.02 1.01 0.63 1.01 0.08

L02 10 0.99 0.81 1.00 0.81 1.03 0.38 1.02 0.51 1.01 0.36

Ti L0 5 1.00 0.59 1.00 0.57 1.01 0.01 1.00 0.34 1.00 0.72

L02 5 1.01 0.56 1.01 0.29 1.03 0.05 1.01 0.52 1.00 0.66

Sulfur

S L0 1,000 0.98 0.43 1.00 0.84 1.01 0.63 0.99 0.80 0.99 0.13

L02 1,000 1.00 0.99 1.02 0.27 1.01 0.79 1.02 0.68 1.00 0.81

P L0 50 0.98 0.39 1.00 0.75 1.01 0.61 1.00 0.88 0.98 0.15

L02 50 0.99 0.89 1.03 0.30 1.01 0.78 1.02 0.67 1.00 0.99

Biomass burning

K L0 50 0.98 0.06 1.00 0.64 1.01 0.01 1.01 0.02 1.00 0.68

L02 50 0.96 0.04 1.00 0.86 1.00 0.79 0.99 0.67 0.99 0.28

Oil

V L0 10 0.99 0.73 1.01 0.56 1.01 0.46 0.99 0.71 0.98 0.12

L02 10 0.93 0.04 0.96 0.05 0.98 0.58 0.94 0.12 0.96 0.03

Ni L0 5 1.01 0.59 1.01 0.21 1.04 0.05 1.01 0.58 1.01 0.48

L02 5 0.99 0.72 1.00 0.99 1.04 0.32 1.01 0.84 1.01 0.48

Sea salt

Na L0 100 0.98 0.23 1.00 0.58 1.00 0.94 0.99 0.43 0.99 0.35

L02 100 0.97 0.29 0.98 0.21 0.99 0.74 0.98 0.61 0.99 0.37

Cl L0 10 1.00 0.89 1.00 0.31 1.00 0.89 1.00 0.24 1.00 0.69

L02 10 1.00 0.81 1.00 0.06 1.00 0.80 1.00 0.65 1.00 0.83

Associations are shown for same-day exposure (L0) and averaged over the same day and previous 2 days (L02). n = 149 children with asthma, New Haven, Connecticut, August 

2000–January 2004.

aSeparate analyses were performed for each element and each respiratory symptom and medication use. Models were adjusted for season, day of week, and date. ORs are given for the 

unit increase given in the second column for each element (and EC). Individual elements in fine particle mass may come from more than one source but are listed here according to their 

major source. See Table 3 for the contribution of each source to each element’s total mass. Results from analyses using elemental exposures lagged by 1 or 2 days (L1, L2) are included 

in Supplemental Material, Table 2 (available online at http://www.ehponline.org/members/2009/0800335/suppl.pdf).
Pediatric asthma and particle pollution

Environmental Health Perspectives • volume 117 | number 7 | July 2009 1173

One strength of this study is its longitudinal design. More than 46,000 subject-days 

of data were available for analyses (resulting 

in small CIs) where each subject served as his 

or her own control for potential confounders. 

Effect estimates from analyses where sex, age, 

ethnicity, parental education level, and exposure to environmental tobacco smoke were 

also included varied little from those shown in 

Table 6. For example, each 5‐μg/m3 increase in 

exposure to motor vehicle source was associated 

with identical increased risks of wheeze and 

shortness of breath and inhaler use. Likewise, 

the risk estimates associated with exposure to 

road dust source were identical for persistent 

cough and inhaler use and slightly decreased 

for wheeze (OR = 1.09; 95% CI, 1.01–1.19) 

and shortness of breath (OR = 1.11; 95% CI, 

1.02–1.21) (for comparison, see Table 5). 

Goodness-of-fit tests suggest that the models 

for wheeze and chest tightness are reasonably 

good fits to the data (Table 5). There were no 

systematic patterns to the lack of fit for models 

for persistent cough, shortness of breath, or 

short-acting inhaler use. However, because of 

the repeated measurements, observations were 

not independent in any of the models, which 

may affect the interpretation of the Hosmer–

Lemeshow statistic. It is possible that the more 

frequently reported events of persistent cough 

and short-acting inhaler use (Table 2) may be 

associated with ambient air pollution in combination with other factors (e.g., activity level) 

not included in the current study.

One limitation of our study is the lack 

of co-located EC calibration measurements. 

Previous studies have used thermal optical 

reflectance (TOR) to measure EC collected on 

quartz filters in co-located monitors. TOR, a 

relatively costly but accurate measure of EC, 

is used to calibrate EC estimated by the much 

less expensive light reflectance measures on 

Teflon filters. We assumed a factor of 1 based 

on calibration factors reported in other studies 

examining air pollution in urban settings (Cyrys 

et al. 2003; Kinney et al. 2000) and on our own 

results of TOR EC measurements on 30, colocated quartz filters [see Supplemental Material 

(available online at http://www.ehponline.org/

members/2009/0800335/suppl.pdf)]. 

Another limitation to our study is our reliance on a central site monitor. It is likely that 

measurements from this site, located on an 

on-ramp to I-95 at the southern edge of New 

Haven County, represent the highest end of 

the exposure distribution. Exposure error for 

regional sources, for example, sulfur, is probably low because of its homogeneity within 

the study area. On the other hand, local traffic 

exposures vary across the study area, which may 

have led to an overestimate of subjects’ exposures to motor vehicle and road dust sources 

and an underestimate of health effects; that is, 

it is possible that levels lower than those used 

have significant adverse effects on respiratory 

health of children with asthma in our region. 

Determination of spatial variability of PM2.5

away from central site monitors will be needed 

to improve assessment of exposures actually 

experienced by vulnerable populations.

Conclusions

The composition of ambient fine particles 

is complex, because it depends on emission 

sources and atmospheric processes. The U.S. 

EPA national air quality standard for PM2.5

considers only mass and not composition 

(U.S. EPA 2009). However, health impacts 

associated with exposures to specific particle 

components or sources, especially for vulnerable populations, may be missed with this 

aggregate characterization (Franklin et al. 

2008; Sarnat et al. 2008). Apportioning 

fine particle mass according to contributing 

sources may help unmask source-specific associations. For children with asthma living in an 

area of noncompliance with PM2.5 standards, 

increased risk of daily symptoms and medication use was associated with daily trafficrelated fine particle sources.

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Table 5. ORs (95% CIs) from repeated measures logistic regression models of respiratory symptoms and 

rescue medication use and daily source concentrations of PM2.5. 

Shortness Inhaler

Source Wheeze Persistent cough of breath Chest tightness short-acting

L0 model

Motor vehicles 1.05 (0.99–1.10) 1.02 (0.99–1.04) 1.06 (1.01–1.11) 1.02 (0.97–1.08) 1.02 (1.00–1.05)

Road dust 1.10 (1.01–1.19) 1.06 (1.01–1.11) 1.12 (1.02–1.22) 1.04 (0.95–1.15) 1.06 (1.02–1.11)

Sulfur 0.97 (0.94–1.00) 1.00 (0.98–1.01) 0.98 (0.94–1.02) 0.99 (0.94–1.03) 0.98 (0.97–1.00)

Biomass burning 0.80 (0.66–0.98) 0.97 (0.92–1.03) 1.05 (0.95–1.17) 1.06 (0.95–1.18) 1.00 (0.96–1.03)

Oil 1.02 (0.86–1.20) 1.02 (0.95–1.10) 1.07 (0.92–1.26) 0.99 (0.82–1.18) 0.98 (0.91–1.05)

Sea salt 0.96 (0.86–1.07) 0.99 (0.92–1.07) 1.01 (0.92–1.12) 0.95 (0.84–1.08) 0.99 (0.94–1.04)

L02 model

Motor vehicles 1.10 (1.01–1.19) 1.03 (0.98–1.09) 1.12 (1.01–1.24) 1.08 (0.98–1.20) 1.03 (0.98–1.08)

Road dust 1.26 (1.05–1.51) 1.16 (1.02–1.32) 1.28 (1.05–1.55) 1.20 (0.97–1.49) 1.09 (1.00–1.19)

Sulfur 0.98 (0.92–1.04) 1.01 (0.98–1.05) 0.97 (0.90–1.04) 1.00 (0.92–1.08) 1.00 (0.97–1.03)

Biomass burning 0.64 (0.46–0.88) 0.93 (0.81–1.06) 0.78 (0.52–1.18) 0.87 (0.62–1.22) 0.95 (0.87–1.04)

Oil 0.80 (0.56–1.08) 0.84 (0.71–1.00) 0.94 (0.69–1.29) 0.80 (0.58–1.10) 0.92 (0.81–1.05)

Sea salt 0.91 (0.82–1.16) 0.88 (0.77–1.01) 1.01 (0.79–1.29) 0.95 (0.71–1.27) 0.97 (0.88–1.07)

n = 149 children with asthma, New Haven, Connecticut, August 2000–January 2004. Separate logistic regression analyses 

were performed for each health outcome. Each model included the six PM2.5 sources listed, either as same-day concentrations (L0), or the concentration averaged over the same day and previous 2 days (L02), as well as variables controlling for season, day of week, and date. Logistic regressions were performed using GEE. All ORs are given for a 5-μg/m3

increase in source concentration. 

Table 6. Effect of adding a gaseous co-pollutant to the source exposure model [OR (95% CI)].

Co-pollutants

Outcome/source Sources alone NO2 CO SO2 O3

Wheeze

Motor vehicles 1.05 (0.99–1.10) 1.03 (0.98–1.08) 1.05 (0.99–1.11) 1.04 (0.99–1.09) 1.06 (0.97–1.16)

Road dust 1.10 (1.01–1.19) 1.11 (1.02–1.20) 1.10 (1.01–1.19) 1.10 (1.01–1.19) 1.11 (1.01–1.23)

Sulfur 0.97 (0.94–1.00) 0.96 (0.92–0.99) 0.97 (0.94–1.01) 0.97 (0.93–1.00) 0.95 (0.91–1.00)

Biomass burning 0.80 (0.66–0.98) 0.79 (0.65–0.98) 0.80 (0.66–0.98) 0.79 (0.64–0.98) 0.74 (0.57–0.97)

Oil 1.02 (0.86–1.20) 1.02 (0.87–1.21) 1.02 (0.86–1.20) 1.01 (0.86–1.19) 0.92 (0.62–1.39)

Sea salt 0.96 (0.86–1.07) 0.96 (0.85–1.07) 0.96 (0.86–1.08) 0.95 (0.85–1.07) 1.01 (0.72–1.40)

Co-pollutant 1.08 (0.99–1.18) 1.00 (0.94–1.07) 1.02 (0.96–1.09) 1.08 (0.99–1.18)

Short-acting inhaler use

Motor vehicles 1.02 (1.00–1.05) 1.02 (0.99–1.04) 1.02 (0.99–1.05) 1.02 (0.99–1.04) 1.02 (0.98–1.07)

Road dust 1.06 (1.02–1.11) 1.06 (1.02–1.10) 1.06 (1.02–1.11) 1.06 (1.02–1.11) 1.06 (1.00–1.13)

Sulfur 0.98 (0.97–1.00) 0.98 (0.96–1.00) 0.98 (0.96–1.00) 0.98 (0.96–1.00) 0.97 (0.95–1.00)

Biomass burning 1.00 (0.96–1.03) 1.00 (0.96–1.03) 0.99 (0.96–1.03) 0.99 (0.96–1.03) 0.99 (0.95–1.03)

Oil 0.98 (0.91–1.05) 0.98 (0.91–1.05) 0.97 (0.91–1.04) 0.97 (0.91–1.04) 1.03 (0.88–1.22)

Sea salt 0.99 (0.94–1.04) 0.99 (0.94–1.04) 0.99 (0.94–1.04) 0.99 (0.94–1.04) 1.01 (0.88–1.15)

Co-pollutant 1.01 (0.97–1.06) 1.02 (0.98–1.05) 1.01 (0.99–1.04) 1.01 (0.97–1.05)

n = 149 children with asthma, New Haven, Connecticut, August 2000–January 2004. Separate repeated measures logistic 

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(same-day exposure) as well as season, day of week, and date. Gaseous co-pollutants were added to the source exposure model one at a time. ORs for sources are given for a 5-μg/m3

 increase in source concentration; ORs for co-pollutants 

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