### 1168 volume 117 | number 7 | July 2009

• 

### Environmental Health Perspectives 

# Research 

# | Children’s Health

Fine particles [particles ≤ 2.5 μ m diam

eter (PM 2.5 

### )] originate from both local and regional pollution sources. A large fraction of

PM 2.5 

### mass is associated with the combus

tion of fuels such as gasoline, diesel, coal, oil,

and wood. PM 2.5 

### 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 regu

lators identify sources of particle pollution. These techniques have been used to investigate associations between sources of particle pol

lution 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 vis

its (Sarnat et al. 2008), and adult and pediat

ric 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).

PM 2.5 

### mass concentration has been asso

ciated 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 popula

tion such as children with asthma.

Annual PM 2.5 

### 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 μ g/m 3 

### (U.S. EPA 2009). We examined associations between daily exposure

to PM 2.5 

### components and sources identified through factor analysis and source apportion

ment, 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 enter

prises, power plants, and a harbor handling ships that deliver heating oil for New England (U.S. EPA 2008). New Haven County, pop

ulation 824,008, covers an area of 2,233 km 2


### (863 mi 2 

### ) (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 col

lect 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. Leaderer 1

1 Center for Perinatal, Pediatric and Environmental Epidemiology, Dep artment of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut, USA; 2 Harvard University School of Public Health, Boston, Massachusetts, USA



### B ackground 

### : Exposure to ambient fine particles [particulate matter ≤ 2.5 μm diameter (PM 2.5 

### )] 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 spe

cific components.

O b jective 

### : We examined associations between daily exposure to fine particle components and sources, and symptoms and medication use in children with asthma.

M ethods 

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

R

esults 

### : More than half of mean PM 2.5 

### 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/m 3 

### increase in particles from motor vehicles to a 28%

increased likelihood of shortness of breath for increases in road dust. Neither the other sources iden

tified nor PM 2.5 

### alone was associated with increased health outcome risks.

C onclusions 

### : 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.

K ey words 

### : childhood asthma, fine particle pollution, PM 2.5 

### , 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.

PM 2.5 

### filter samples ( n = 1,181) were col

lected 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 (avail

able 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 tech

nique (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 com

ponent 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 concentra

tions in micrograms per cubic meter by regress

ing total PM 2.5 

### 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 PM 2.5 

### 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 ele

ments 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 ele

ments 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 esti

mating 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 uncorre

lated 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 O 3 

### , which is mon

itored only in warm months. Health effects of elements included in the factor analysis, all six sources, and PM 2.5 

### 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 partici

pants 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 symp

toms 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 dur

ing the year-long follow-up study (Table 2). All subjects resided 0.9–27 km from the air

**Table 1.** Personal characteristics for children par

ticipating 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)

a The 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)

a A 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 PM 2.5 

### during the study was

17.0 ± 9.8 μ g/m 3 

### , which is above the U.S. annual standard for fine particles of 15 μ g/m 3


### (U.S. EPA 2009). The contributions from each source to PM 2.5 

### mass and to elemental com

ponents of PM 2.5 

### 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 con

centration – measured concentration|/mea

sured concentration). Elemental components of

PM 2.5 

### 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 1 554.8 ng/m 3 

### ) and 77% of the mean Zn mass (22.3 of 29.0 ng/m 3 

### ). On average, over the 4 years of the study, over half of daily PM 2.5 

### 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 PM 2.5 

### came from regional sulfur sources.

The associations between PM 2.5 

### elemental components and daily symptoms or medica

tion 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 originat

ing from the motor vehicle, road dust, bio

mass burning, and oil sources are associated with symptoms and/or medication use. For example, a significantly increased likelihood of wheeze, shortness of breath, chest tight

ness, or short-acting inhaler use is associated with each 1,000

- ng/m 3 

### increase in EC. The strongest associations were found for the 3

- day averaged exposures to the elemental constitu

ents of road dust. No associations were found

between individual elements associated with sulfur or sea salt sources and symptoms or

medication use. Total PM 2.5 

### was not signifi

cantly associated with any symptoms or medi

cation use [see Supplemental Material, Table 3 (available online at http://www.ehponline.org/ members/2009/0800335/suppl.pdf)]. Daily source contributions to PM 2.5 

### are illustrated for the 4 years of the study in Figure 1C–1H. Results of logistic regression models examining associations between health

outcomes and PM 2.5 

### 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 ‐μ g/m 3 

### increase in PM 2.5 

### 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 associ

ated with 5 ‐μ g/m 3 

### 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 (NO 2 

### , CO, SO 2 

### , or O 3 

### ) to the source exposure **Figure 1.** Daily prevalence of wheeze ( A ) and short-acting inhaler use ( B ). Daily PM 2.5 source concentra

tions 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/m** **3 )** **Concentration (μg/m 3** **) Concentration (μg/m** **3 )** **Concentration (μg/m 3** **) Concentration (μg/m** **3 )** **Concentration (μg/m 3** **) 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 wheez e

Motor vehicles

Sulfur

Oil Sea sal t Biomass burning

Road dust Daily short-acting inhaler us e

× 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 addi

tion 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 NO 2 

### (another marker for traffic), each 20-ppb

increase in NO 2 

### increases the likelihood of wheeze by 8% ( p

= 0.09). No significant asso

ciations 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 PM 2.5 

### can be attributed to traffic sources motor vehi

cles 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 mod

els that include all six PM 2.5 

### sources as well as one gaseous co-pollutant. Of co-pollut

ants added to the model one at a time, NO 2 

### , 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 con

sidered 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 PM 2.5 

### , 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 ele

ments 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/m 3 

### 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 attribut

able 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 O 3 

### and respiratory health in southern New England (Gent et al. 2003). In that analysis, we found O 3 

### exposure to be a risk for increased respiratory symptoms, but only among children taking asthma mainte

nance medication (e.g., corticosteroids). The

model with PM 2.5 

### sources and O 3 

### shown in

Table 6 shows O 3 

### 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 mainte

nance medication status. Reanalysis stratified by maintenance medication use showed that every 50-ppb increase in O 3 

### (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 O 3 

### : For children on maintenance medication ( n = 130), the likeli

hood of wheeze increases by 35% (OR = 1.35; 95% CI, 1.11–1.65) (Gent et al. 2003). Although exposure to total PM 2.5 

### mass has itself been shown to be a significant risk fac

tor 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 par

ticles is geologic in origin (i.e., rock dust), and that annual levels of PM mass are low,

6 μg/m 3 

### (95% CI, 0.5–7.0). The authors found no association between exposure to PM 2.5 

### mass

**Table 3.** Source contributions a to PM 2.5 mass (μg/m 3 ) and elemental (ng/m 3 ) 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/m 3 )

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

Zn 22.3 b 2.6 0.8 0.5 1.7 1.0 29.0 23.0 26.3

Pb 3.4 b 0.5 0.6 0.5

— 0.1 5.1 4.9 4.0

Cu 2.1 b 1.3 0.5 1.8 0.2

— 6.0 6.3 5.5
Se 0.4 b –0.1 0.2

— 0.0 0.0 0.5 0.5 3.5

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

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

Al –7.0 54.8 b 7.0 5.8

— 1.6 62.0 67.5 8.1

Ca 17.8 29.1 b

— 1.5 1.1 2.0 51.6 51.2 0.7

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

Ti 1.2 4.2 b 0.5 0.9

— — 6.9 7.4 6.3

S 210.4 166.3 906.7 b 75.4 18.7 26.0 1403.4 1451.8 3.3

P 14.0 6.6 36.8 b 2.6 1.5

— 61.5 64.8 5.2

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

V 0.6 1.1 0.8 0.2 6.0 b 0.2 8.9 9.9 9.4

Ni 1.9 0.4 0.2 0.1 2.6 b 0.2 5.6 5.2 6.9

Na 20.9 29.3 37.0 4.4

— 47.0 b 140.1 180.8 22.5

Cl 20.5

— –11.2

— 6.9 32.0 b 48.7 29.6 64.4

PM 2.5 ( μ g/m 3 ) 6.6 b 2.3 5.5 0.9 0.8 0.5 16.6 17.0 2.4 Daily source contribution to PM 2.5 (%)

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.

a Mean concentration for 1,181 days between August 2000 and January 2004. b Maximum 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, sig

nificant effects were seen between increases

in PM 10 

### mass concentrations (PM with aero

dynamic diameter < 10 μ m) and increased visits for asthma and prescriptions for inhal

ers (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 PM 2.5 

### mass and respiratory health. However, using traffic as the exposure metric (calculated as traffic density in 50-m residential buffers) revealed a signifi

cant 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 respi

ratory health. Large studies of schoolchil

dren 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 asso

ciated with increased risk of asthma symp

toms. 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 associa

tions were found between black smoke (a proxy for diesel exhaust), measured at sub

jects’ schools, and respiratory outcomes. No significant associations were found between respiratory symptoms and volume of auto

mobile 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 prox

imity to roads or traffic density, we used factor analysis and source apportionment techniques to examine exposures to potentially toxic por

tions of particle mass and the health of chil

dren residing in an area of significant mobile
source activity.

**Table 4.** ORs from separate repeated measures logistic regression analyses a of associations between daily respiratory symptoms and each daily elemental con

centration of PM 2.5

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

Source/element Lag (ng/m 3 ) 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.

a Separate 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 longi

tudinal 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 expo

sure to environmental tobacco smoke were

also included varied little from those shown in

Table 6. For example, each 5 ‐μ g/m 3 

### 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 com

bination 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 ownresults 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 reli

ance 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 prob

ably 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’ expo

sures 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 PM 2.5 

### 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 PM 2.5 

### considers only mass and not composition (U.S. EPA 2009). However, health impacts associated with exposures to specific particle components or sources, especially for vulner

able 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 asso

ciations. For children with asthma living in an area of noncompliance with PM 2.5 

### standards, increased risk of daily symptoms and medi

cation use was associated with daily trafficrelated fine particle sources.

**R efe**  **r ences** 

Andersen ZJ, Wahlin P, Raaschou-Nielsen O, Scheike T,

Loft S. 2007. Ambient particle source apportionment and
daily hospital admissions among children and elderly in Copenhagen. J Exp Sci Environ Epidemiol 17(7):625–636.
Belanger K, Beckett W, Triche E, Bracken MB, Holford T,

Ren P, et al. 2003. Symptoms of wheeze and persistent
cough in the first year of life: associations with indoor
allergens, air contaminants and maternal history of
asthma. Am J Epidemiol 158:195–202. **Table 5.** ORs (95% CIs) from repeated measures logistic regression models of respiratory symptoms and rescue medication use and daily source concentrations of PM 2.5

.

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 PM 2.5 sources listed, either as same-day concen

trations (L0), or the concentration averaged over the same day and previous 2 days (L02), as well as variables control

ling for season, day of week, and date. Logistic regressions were performed using GEE. All ORs are given for a 5-μg/m 3

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 NO 2 CO SO 2 O 3

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 regression analyses using GEE were performed for each health outcome. Each model includes the six particle sources (same-day exposure) as well as season, day of week, and date. Gaseous co-pollutants were added to the source expo

sure model one at a time. ORs for sources are given for a 5-μg/m 3 increase in source concentration; ORs for co-pollutants are given for each 20 ppb (NO 2 , ozone), 0.5 ppm (CO), or 5 ppb (SO 2 ). Pearson correlations between PM 2.5 sources and criteria pollutants were < 0.20 except for motor vehicles with NO 2 ( r = 0.49), CO ( r = 0.59), SO 2 ( r = 0.45); road dust with NO 2 ( r = 0.37), O 3 ( r = 0.58); and oil with CO ( r = 0.22), SO 2 ( r = 0.32).

### Gent et al.



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• 

### Environmental Health Perspectives Brown SG, Frankel A, Raffuse SM, Roberts PT, Hafner HR, Anderson DJ. 2007. Source apportionment of fine particu

late matter in Phoenix, AZ, using positive matrix factoriza

tion. J Air Waste Manag Assoc 57:741–752.
Brunekreef B, Janssen NAH, de Hartog JJ, Harssema H,
Knape M, Van Vliet P. 1997. Air pollution from truck traf

fic and lung function in children living near motorways.
Epidemiology 8:298–303. Chardon B, Lefranc A, Granados D, Gremy I. 2007. Air pollu

tion and doctors’ house calls for respiratory diseases in the Greater Paris area (2000–2003). Occup Environ Med

64:320–324. Chimonas M-AR, Gessner BD. 2007. Airborne particulate matter
from primarily geologic, non-industrial sources at levels below National Ambient Air Quality Standards is associ

ated with outpatient visits for asthma and quick-relief
medication prescriptions among children less than 20
years old enrolled in Medicaid in Anchorage, Alaska.

Environ Research 103:397–404. Cyrys J, Heinrich J, Hoek G, Meliefste K, Lewne M, Gehring U,

et al. 2003. Comparison between different traffic-related particle indicators: elemental carbon (EC), PM 2.5 mass, and absorbance. J Exp Anal Environ Epidemiol 13:134–143. Delfino RJ, Quintana PJE, Floro J, Gastanaga VM, Samimi BS,

Kleinman MT, et al. 2004. Association of FEV 1 in asthmatic
children with personal and microenvironmental exposure
to airborne particulate matter. Environ Health Perspect

112:932–941. Franklin M, Koutrakis P, Schwartz J. 2008. The role of particle
composition on the association between PM 2.5 and mor

tality. Epidemiology 19:680–689.
Gent JF, Koutrakis P, Leaderer BP. 2008. Childhood asthma: asso

ciation of exposure to fine particle sources with respiratory symptoms [Abstract]. Am J Respir Crit Care Med 177:A186.
Gent JF, Triche EW, Holford TR, Belanger K, Bracken MB,
Beckett WS, et al. 2003. Association of low-level ozone
and fine particles with respiratory symptoms in children

with asthma. JAMA 290:1859–1867. Grieshop AP, Lipsky EM, Pekney NJ, Takahama S, Robinson AL.
2006. Fine particle emission factors from vehicles in a
highway tunnel: effects of fleet composition and season.

Atmos Environ 40:S287–S298. Hertz-Picciotto I, Baker RJ, Yap P-S, Dostal M, Joad JP, Lipsett M,

et al. 2007. Early childhood lower respiratory illness and air
pollution. Environ Health Perspect 115:1510–1518. Holguin F, Flores S, Ross Z, Corez M, Molina M, Molina L, et al.
2007. Traffic-related exposures, airway function, inflam

mation, and respiratory symptoms in children. Am J Resp
Crit Care Med 176(12):1236–1242.
Hong Y-C, Hwang S-S, Kim JH, Lee K-H, Lee H-J, Lee K-H,

et al. 2007. Metals in particulate pollutants affect peak
expiratory flow of schoolchildren. Environ Health Perspect

115:430–434.
Janssen NAH, Brunekreef B, Van Vliet P, Aarts F, Meliefste K,
Harssema H, et al. 2003. The relationship between air pollu

tion from heavy traffic and allergic sensitization, bronchial
hyperresponsiveness, and respiratory symptoms in Dutch
schoolchildren. Environ Health Perspect 111:1512–1518.
Janssen NAH, van Vliet PHN, Aarts F, Harssema H,

Brunekreef B. 2001. Assessment of exposure to traffic
related air pollution of children attending schools near
motorways. Atmos Environ 35:3875–3884. Johnston FH, Webby RJ, Pilotto LS, Bailie RS, Parry DL,
Halpin SJ. 2006. Vegetation fires, particulate air pollution
and asthma: a panel study in the Australian monsoon trop

ics. Int J Environ Health Res 16(6):391–404.
Kavouras IG, Koutrakis P, Cereceda-Balic F, Oyola P. 2001. Source apportionment of PM 10 and PM 2.5 in five Chilean
cities using factor analysis. J Air Waste Manag Assoc

51:451–464. Kinney PL, Aggarwal M, Northridge ME, Hanssen NAH, Shepard P. 2000. Airborne concentrations of PM 2.5 and
diesel exhaust particles on Harlem sidewalks: a communi

ty-based pilot study. Environ Health Perspect 108:213–218. Laden F, Neas LM, Dockery DW, Schwartz J. 2000. Association
of fine particulate matter from different sources with daily mortality in six U.S. cities. Environ Health Perspect

108:941–947. Lewis TC, Robins TG, Dvonch JT, Keeler GJ, Yip FY, Mentz GB,

et al. 2005. Air pollution-associated changes in lung func

tion among asthmatic children in Detroit. Environ Health
Perspect 113:1068–1075. Mar TF, Larson TV, Stier RA, Claiborn C, Koenig JQ. 2004. An
analysis of the association between respiratory symptoms in subjects with asthma and daily air pollution in Spokane,
Washington. Inhal Toxicol 16:809–815. Mar TF, Norris GA, Koenig JQ, Larson TV. 2000. Associations
between air pollution and mortality in Phoenix, 1995–1997.
Environ Health Perspect 108:347–353. Moreno T, Querol X, Alastuey A, Minguillon M, Pey J,
Rodriguez S, et al. 2007. Recreational atmospheric pol

lution episodes: Inhalable metalliferous particles from
firework displays. Atmos Environ 41:913–922.
Moshammer H, Hutter H-P, Hauck H, Neuberger M. 2006. Low
levels of air pollution induce changes of lung function in a panel of schoolchildren. Eur Respir J 27(6):1138–1143.
Nicolai T, Carr D, Weiland SK, Duhme H, von Ehrenstein O, Wagner C, et al. 2003. Urban traffic and pollutant expo

sure related to respiratory outcomes and atopy in a large
sample of children. Eur Respir J 21:956–963. Ning Z, Polidori A, Schauer JJ, Sioutas C. 2008. Emission fac

tors of PM species based on freeway measurements and
comparison with tunnel and dynamoter studies. Atmos

Environ 42:3099–3114. Ozkaynak H, Thurston GD. 1987. Associations between 1980 U.S. mortality rates and alternative measures of airborne particle concentration. Risk Anal 7(4):449–461.

Penttinen P, Vallius M, Tiittanen P, Ruuskanen J, Pekkanen J. 2006. Source-specific fine particles in urban air and respi

ratory function among adult asthmatics. Inhal Toxicol

18:191–198. Pino P, Walter T, Oyarzun M, Villegas R, Romieu I. 2004. Fine
particulate matter and wheezing illnesses in the first year of life. Epidemiology 15(6):702–708.
Rabinovitch N, Strand M, Gelfand EW. 2006. Particulate lev

els are associated with early asthma worsening in chil

dren with persistent disease. Am J Resp Crit Care Med
173(10):1098–1105.
Ranzi A, Gambini M, Spattini A, Galassi C, Sesti D, Bedeschi M,

et al. 2004. Air pollution and respiratory status in asthmatic
children: hints for a locally based preventive strategy. AIRE study. Eur J Epidemiol 19(6):567–576.
Rodriguez C, Tonkin R, Heyworth J, Kusel M, DeKlerk N, Sly P,

et al. 2007. The relationship between outdoor air quality and respiratory symptoms in young children. Int J Environ
Health Res 17(5):351–360.
Ryan PH, LeMasters G, Biagini J, Bernstein D, Grinshpun SA,
Shukla R, et al. 2005. Is it traffic type, volume, or distance?
Wheezing in infants living near truck and bus traffic.

J Allerg Clin Immunol 116:279–284.
Sarnat JA, Marmur A, Klein M, Kim E, Russell AG, Sarnat SE,

et al. 2008. Fine particle sources and cardiorespiratory
morbidity: an application of chemical mass balance and
factor analytical source-apportionment methods. Environ
Health Perspect 116:459–466. Sigler JM, Lee X, Munger W. 2003. Emission and long-range transport of gaseous mercury from a large-scale Canadian
boreal forest fire. Environ Sci Tech 37(19):4343–4347.
Slaughter JC, Lumley T, Sheppard L, Koenig JQ, Shapiro GG.
2003. Effects of ambient air pollution on symptom severity
and medication use in children with asthma. Ann Allergy
Asthma Immunol 91(4):346–353.
Taino M, Tuomisto JT, Hanninen O, Aarnio P, Koistinen KJ,

Jantunen MJ, et al. 2005. Health effects caused by primary fine particulate matter (PM 2.5 ) emitted from buses in the Helsinki metropolitan area, Finland. Risk Anal 25(1):151–160.
Thurston GD, Spengler JD. 1985. A quantitative assessment of
source contributions to inhalable particulate matter pollu

tion in metropolitan Boston. Atmos Environ 19(1):9–25.
Trenga CA, Sullivan JH, Schildcrout JS, Shepherd KP, Shapiro GG, Liu LJS, et al. 2006. Effect of particulate air
pollution on lung function in adult and pediatric subjects in a Seattle panel study. Chest 129(6):1614–1622.

U.S. Census Bureau. 2000. United States Census 2000.
Available: http://www.census.gov/main/www/cen2000. html [accessed 1 April 2008].
U.S. Department of Health and Human Services. 2002. Global Initiative for Asthma, Global Strategy for Asthma
Management and Prevention 02-3659. Washington, DC:National Institutes of Health, National Heart, Lung, and

Blood Institute. U.S. EPA (U.S. Environmental Protection Agency). 2008. National Emissions Inventory (NEI). Available: http://www. epa.gov/ttn/chief/software/speciate/index.html [accessed
1 June 2009]. U.S. EPA (U.S. Environmental Protection Agency). 2009. National Ambient Air Quality Standards (NAAQS) for Particulate Matter (PM). Available: http://www.epa.gov/ ttn/naaqs/standards/pm/s_pm_index.html [accessed 1 January 2009].
Van Vliet P, Knape M, de Hartog J, Janssen N, Harssema H,

Brunekreef B. 1997. Motor vehicle exhaust and chronic
respiratory symptoms in children living near freeways.

Environ Res 74:122–132. Venn AJ, Lewis SA, Cooper M, Hubbard R, Britton J. 2001.
Living near a main road and the risk of wheezing illness in children. Am J Respir Crit Care Med 164:2177–2180. Watson JG, Chow JC, Frazier CA. 1999. X-ray fluorescence
analysis of ambient air samples. In: Elemental Analysis of Airborne Particles (Landsberger S, Creatchman M,
eds). Amsterdam: Gordon and Breach Science Publishers,

67–96. Watson JG, Zhu T, Chow JC, Engelbrecht J, Fujita EM,

Wilson WE. 2002. Receptor modeling application frame

work for particle source apportionment. Chemosphere

49:1093–1136.