Numerous epidemiologic studies have linked

particulate air pollution to increased morbidity

and mortality. Observed increases in acute

exacerbations of pulmonary and cardiovascular

disease are thought to be partly the result of an

inflammatory cascade that is triggered by

oxidative stress to the lungs (Seaton et al.

1995). Existing research supports this hypothesized mechanism with associations documented between particulate exposures and

increases in oxidative radicals in the lung

(Gurgueira et al. 2002), enhanced production

of proinflammatory cytokines by alveolar

macrophages (van Eeden et al. 2001), and

influxes of inflammatory cells into the lungs

(Ghio et al. 2000). Particulate levels have also

been linked to clinical effects such as respiratory-related symptoms (Ward and Ayres

2004), medical visits (Schwartz et al. 1993),

and death (Clancy et al. 2002; Samet et al.

2000). 

Adverse health effects have been documented for pollution of nonspecific origin as

well as pollution from mobile sources.

Findings with traffic-related pollutants have

been reasonably consistent despite the fact that

most investigations used relatively crude

measures of traffic, such as proximity to traffic

(Garshick et al. 2003; Janssen et al. 2003;

Nitta et al. 1993; van Vliet et al. 1997; Venn

et al. 2005), traffic density (Behrens et al.

2004; Ciccone et al. 1998; Janssen et al. 2003;

Nicolai et al. 2003; van Vliet et al. 1997; Wjst

et al. 1993), and estimated and measured levels

of ambient traffic-related pollutants (Brauer

et al. 2002; Hwang et al. 2005; Janssen et al.

2003; Kim et al. 2004; Migliaretti et al. 2005;

van Vliet et al. 1997). Most of these investigations have been limited to children, however,

with few investigations specifically examining

the effects of traffic-related pollutants in the

elderly, a population believed to have enhanced

susceptibility with respect to air pollution

(Bateson and Schwartz 2004; Goldberg et al.

2001; Katsouyanni et al. 2001). 

For this article, we examined pulmonary

inflammation in an elderly cohort as a result

of direct exposures to traffic-related pollution.

Specifically, we measured fractional concentrations of exhaled nitric oxide (FENO) because

this is generally considered to be an effective

noninvasive marker for assessing subclinical

pulmonary inflammation (American Thoracic

Society 2006; van Amsterdam et al. 2000).

Data for this investigation come from a study

designed specifically to enhance the magnitude and variability of exposures to traffic.

Our design involved transporting senior citizens from their suburban living facilities into

urban areas through field trips aboard a dieselpowered bus. Using the resulting enhanced

exposures, we investigated the associations

between particulate air pollution and FENO in

samples collected before and after the group

trips. We hypothesized that increasing levels

of particulate air pollution would be associated with increasing levels of FENO for all samples. We further hypothesized that stronger

associations would be seen in samples collected after the trip than samples collected

before the trip, because of either enhanced

toxicity of traffic-related particles and/or

decreased measurement error. 

Methods

Study population. Data were collected from 44

nonsmoking seniors (≥ 60 years of age) under

the supervision of the Harvard School of

Public Health Human Subjects Committee.

All participants were independently mobile

and lived in one of four independent senior

residences in suburban St. Louis, Missouri.

Because data on heart rate variability also were

collected in this study, individuals with atrial

flutter, atrial fibrillation, or a paced rhythm

were excluded from participation. Similarly,

individuals with left bundle branch blocks

were selected only if their heart rate variability

Environmental Health Perspectives • VOLUME 115 | NUMBER 4 | April 2007 507

Research

Address correspondence to S.D. Adar, Department

of Environmental and Occupational Health

Sciences, University of Washington, 4225 Roosevelt

Way NE, Suite 302, Box Number: 354965, Seattle,

WA 98105-8123 USA. Telephone: (206) 221-5932.

Fax: (206) 685-3872. E-mail: dubowsky@

u.washington.edu

We are grateful for the input of D. Dockery,

A. Zanobetti, and E. Rimm and appreciate the vital

contributions of M. Davey, J. Turner, S. Forrester,

C. Peter, our field staff, and study subjects. 

This work was funded by the National Institute of

Environmental Health Sciences (ES09825/ES00002),

U.S. Environmental Protection Agency (R827353),

and Electric Power Research Institute (EPRI)

(W09207). 

H.S. received grant support from EPRI but certifies that her freedom to design, conduct, interpret,

and publish research is not compromised as a result

of this sponsorship. The other authors declare they

have no competing financial interests.

Received 30 May 2006; accepted 4 December 2006.

Ambient and Microenvironmental Particles and Exhaled Nitric Oxide Before

and After a Group Bus Trip

Sara Dubowsky Adar,1 Gary Adamkiewicz,2 Diane R. Gold,2,3 Joel Schwartz,2,3,4 Brent A. Coull,5 and Helen Suh2

1Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA; 2Department of

Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; 3Channing Laboratory, Department of Medicine,

Brigham and Women’s Hospital, Boston, Massachusetts, USA; 4Department of Epidemiology, and 5Department of Biostatistics, Harvard

School of Public Health, Boston, Massachusetts, USA

OBJECTIVES: Airborne particles have been linked to pulmonary oxidative stress and inflammation.

Because these effects may be particularly great for traffic-related particles, we examined associations

between particle exposures and exhaled nitric oxide (FENO) in a study of 44 senior citizens, which

involved repeated trips aboard a diesel bus. 

METHODS: Samples of FENO collected before and after the trips were regressed against microenvironmental and ambient particle concentrations using mixed models controlling for subject, day,

trip, vitamins, collection device, mold, pollen, room air nitric oxide, apparent temperature, and

time to analysis. Although ambient concentrations were collected at a fixed location, continuous

group-level personal samples characterized microenvironmental exposures throughout facility and

trip periods.

RESULTS: In pre-trip samples, both microenvironmental and ambient exposures to fine particles

were positively associated with FENO. For example, an interquartile increase of 4 μg/m3 in the daily

microenvironmental PM2.5 concentration was associated with a 13% [95% confidence interval

(CI), 2–24%) increase in FENO. After the trips, however, FENO concentrations were associated predominantly with microenvironmental exposures, with significant associations for concentrations

measured throughout the whole day. Associations with exposures during the trip also were strong

and statistically significant with a 24% (95% CI, 15–34%) increase in FENO predicted per

interquartile increase of 9 μg/m3 in PM2.5. Although pre-trip findings were generally robust, our

post-trip findings were sensitive to several influential days. 

CONCLUSIONS: Fine particle exposures resulted in increased levels of FENO in elderly adults, suggestive of increased airway inflammation. These associations were best assessed by microenvironmental

exposure measurements during periods of high personal particle exposures. 

KEY WORDS: air pollution, exhaled nitric oxide, inflammation, particulate matter, traffic. Environ

Health Perspect 115:507–512 (2007). doi:10.1289/ehp.9386 available via http://dx.doi.org/ [Online

4 December 2006]
could be ascertained. Persons with unstable

angina also were excluded. All participants

provided informed written consent.

Study design. All study subjects were asked

to participate in four group trips into downtown St. Louis between March and June

2002. Each subject was scheduled to participate approximately once per month in an outing that included one activity, lunch, and two

standardized 1-hr periods aboard a diesel shuttle bus. Activities used in this study included a

theater performance, Omni movie, outdoor

band concert, and a Mississippi River boat

cruise. All trips typically begin around 0930

and concluded by 1600 hr. 

Samples of FENO were collected between

0800 and 0900 hr on the mornings before and

after each trip. In the hours surrounding these

samples, group-level measurements of particle

concentrations also were collected using several continuous instruments installed on two

portable carts. These carts were first positioned

in a central location inside the participants’

living facilities 24 hr before each trip. The

carts remained at the facilities until it was time

for the trips, at which point they followed the

participants from the health testing room,

onto the bus, to the group activity, and to

lunch. After the trip home aboard the bus, the

carts were returned to the central location in

the living facility where they remained until

the conclusion of the health testing on the following morning. Continuous measurements

of ambient particles and gases also were collected from a central monitoring station in

East St. Louis, Illinois.

Health measurements. Exhaled breath

samples were collected on the mornings before

and after each trip in accordance with the

American Thoracic Society guidelines for

offline measurements of FENO (American

Thoracic Society 2006). To remove any nitric

oxide in inspiratory air, all participants began

FENO sampling by first tidal breathing through

a nitric oxide scrubber for a minimum of 30 sec.

Immediately following this breathing exercise,

participants inspired to total lung capacity and

exhaled without delay into a Mylar bag at a

constant pressure of 12.5 mmHg. This

oropharyngeal pressure was used to minimize

sampling variability by preventing nasal nitric

oxide interference and normalizing the expiratory flow rates to approximately 350 mL/sec

(American Thoracic Society 2006). Participants

also wore nose clips and were instructed to

form a tight seal around the mouthpiece to

prevent any inadvertent contamination by

room air. Subjects were coached by trained

technicians throughout each maneuver to

ensure accurate FENO collection. To counteract any subtle deviations from the collection

protocol, three sequential samples were collected per person per session. Room air samples also were collected during each sampling

period to assess the level of ambient nitric

oxide levels at the time of breath collection.

Similar samples were taken in the laboratory

where FENO was analyzed within 24 hr of collection using a model 42 Chemiluminescence

Analyzer (Thermo Electron Corporation,

Franklin, MA). In our analyzis we used the

median value of each set of three balloons as

the measure of central tendency that was least

sensitive to outlying values. Samples with

insufficient volume to obtain a stable chemical

measurement were excluded from this analysis.

Immediately preceding the collection of

FENO samples, information regarding health

status and medication use was ascertained

using daily technician-administered questionnaires. In addition, data regarding the participants’ overall health status were collected at

baseline before enrollment in the study.

Exposure measurements. Two portable

carts containing continuous air pollution

monitors were used to measure group-level

micro-environmental exposures to trafficrelated pollutants, including fine particulate

mass (< 2.5 μm aerodynamic diameter;

PM2.5), black carbon, and size-specific particle

counts. PM2.5 concentrations were measured

continuously using a DustTrak aerosol monitor model 8520 (TSI, Shoreview, MN) with a

Nafion diffusion dryer (Perma Pure LLC,

Toms River, NJ). Integrated samples of PM2.5

mass also were collected using a Harvard

Impactor (Air Diagnostics and Engineering

Inc., Harrison, ME) for daily calibration of

the trip and facility periods. Continuous black

carbon concentrations were measured using a

portable aethalometer (Magee Scientific,

Berkeley, CA) with a 2.5-μm impaction inlet.

Particle counts were measured using a model

CI500 optical particle counter (Climet

Instruments Company, Redlands, CA) with a

modified flow rate of 0.1 cubic feet per

minute. Before analysis, data from the Climet

were aggregated by aerodynamic diameter and

expressed as fine (0.3–2.5 μm) and coarse

(2.5–10 μm) particle count concentrations.

Temperature and relative humidity were

recorded with a Telaire model 7001 carbon

dioxide monitor (General Electric Sensing,

Goleta, CA) and used to calculate apparent

temperature, a biological weather stress index

that incorporates both temperature and relative humidity (O’Neill et al. 2003). 

Ambient particle data were obtained from

a U.S. Environmental Protection Agency–

(EPA)–sponsored Supersite. This Supersite is

located in an industrial area adjacent to several

interstate highways and just across the

Mississippi River from downtown St. Louis.

From its location approximately 10–15 miles

from our facilities, concentrations of ambient

PM2.5 were recorded using a continuous ambient mass monitor (Andersen Instruments,

Smyrna, Georgia) with a Nafion diffusion

dryer. Ambient black carbon concentrations

were reported using an aethalometer (McGee

Scientific). Criteria gas data were obtained

from the Missouri Department of Natural

Resources and Illinois EPA monitoring station, located immediately adjacent to the

Supersite (http://www.epa.gov/ttn/airs/airsaqs/detaildata/downloadaqsdata.htm). These

measurements were collected using a model

48 carbon monoxide analyzer (Thermo

Electron Corporation), a model 2000A nitrogen dioxide analyzer (API, San Diego,

California), a model 4108 sulfur dioxide analyzer (Dasibi Environmental Corporation,

Glendale, California), and a model 1008RS

ozone analyzer (Dasibi Environmental

Corporation). Daily pollen and mold data

also were obtained from the county health

department and examined as total counts. 

We calculated mean concentrations for all

exposure metrics over the 6 and 24 hr preceding each FENO sample. These averaging periods

were selected to maximize data completeness

and were based on the findings of past investigations, which have indicated that the

strongest associations between exhaled nitric

oxide and particles occur within the first 24 hr

(Adamkiewicz et al. 2004; Fischer et al. 2002;

Steerenberg et al. 2001). In addition, associations between exposures measured during the

trip periods were examined with post-trip

FENO levels. 

Statistical analysis. Before statistical modeling, all measures of FENO were log-transformed because the data were highly skewed.

We then ran mixed models containing random subject effects using SAS (version 8.02;

SAS Institute, Cary, NC) to examine the

impact of various personal characteristics on

FENO. We calculated empirical (sandwich)

standard errors to ensure conclusions were

robust to choice of covariance model. We

modeled absolute differences between FENO

levels on days before and after the trip using a

mixed model with random intercepts; and we

used an analysis of variance model with

autoregressive terms to distinguish between

the mean exposure levels on pre-trip and

post-trip days. Spearman correlations also

were calculated to investigate the relationships

between the exposure metrics. 

We examined associations between FENO

and air pollution using mixed models with

random subject effects in SAS. Time-varying

parameters evaluated as potential confounders

included the categorical variables of day of

week, trip type, FENO collection device, current illness, as well as the use of vitamins, antihistamines, statins, steroids, and asthma

medications. We also investigated continuous

predictors of apparent temperature, pollen,

mold, nitric oxide levels in the testing room,

and the time between sample collection and

analysis. Of these covariates, day of week, trip,

Adar et al.

508 VOLUME 115 | NUMBER 4 | April 2007 • Environmental Health Perspectives
collection device, vitamin use, pollen, mold,

apparent temperature during the current hour,

time to analysis, and nitric oxide concentrations in the testing room were selected for

inclusion in our models. These covariates were

selected based on their significant relationship

with FENO in either pre-trip or post-trip samples at the 0.2 level among the unexposed

(lower 50th percentile by exposure) (Mickey

and Greenland 1989). All confounders were

treated as categorical or linear except for

apparent temperature, which was modeled as a

linear spline with one knot. This more flexible

form allowed for the nonlinear trend observed

with apparent temperature using LOESS

smoothing plots from S-Plus2000 (MathSoft,

Cambridge, MA). This form was considered

reasonable based on our belief that temperatures above or below some ideal level may produce unfavorable physiologic consequences.

All pollutants were assumed to have a linear

relationship with FENO.

Our models first examined the single-pollutant associations with FENO samples collected before the trips and then with FENO

samples collected after the trips. Pre- and

post-trip samples were analyzed separately

(i.e., change analyses were not performed)

based on the anticipated reduction in statistical power due to increased missing data and

numbers of confounders required in a single

model (i.e., 2 days of confounders per change

score). Change score models also would not

allow us to examine differences in effect sizes

between days with and without bus trips. 

Our main models examined associations

between FENO levels and the 6- and 24-hr

averages of pollution concentrations preceding

the health measurements. In each of these

analyses, we compared the associations for

microenvironmental and ambient particles to

provide insight about the level of measurement error introduced through the use of

ambient concentrations for personal exposures. Models also were created to examine the

impact of the fine particle exposures for hours

during which the trips occurred. Finally, we

conducted sensitivity analyses for all models to

test the impact of influential points on our

analyses. All effect estimates (β) and their 95%

confidence intervals (CI) were transformed

into percent changes and reported per

interquartile range (IQR) of a pollutant using

the formula (e(β*IQR)-1) × 100. 

Results

Study participants and levels of FENO. A total

of 25 trips were conducted over the duration of

the study, with most subjects (35 of 44) participating in four trips. From our 158 completed

person-trips, 299 of the 316 possible samples

had sufficient exhaled breath volume for analysis. Summary statistics for these samples are

presented by subject characteristics in Table 1.

The median FENO level was 11.1 ppb with a

range of 3.7–43.8 ppb in our predominantly

Caucasian female population. Although

approximately 10% of the subjects reported a

doctor diagnosis of asthma and a different

10% reported a diagnosis of chronic obstructive pulmonary disease, tests of differences

between these subgroups did not detect any

significant differences in FENO levels at the

95% confidence level. 

Comparison of FENO and pollution levels

before and after trip. Concentrations of

PM2.5, black carbon, fine particle counts, and

coarse particle counts were systematically

higher aboard the bus and during the trips

than during periods spent at the living facilities. Black carbon, a common indicator for

traffic, was most strongly enhanced by the bus

trips, with a 9-fold increase in the mean concentration during bus periods. Concentrations

were similarly elevated 2- to 3-fold during

activity and lunch periods in the urban environment. PM2.5, fine particle count, and

coarse particle count concentrations also were

elevated aboard the bus and during the

remainder of the trips, with 2- to 10-fold

increases over concentrations measured at the

living facilities. Further detail regarding the

influence of the bus trips is presented elsewhere (Adar et al. 2007). 

Elevated particle levels during the trips

also affected the daily mean concentrations

measured in the microenvironment, resulting

in statistically significant increases in black

carbon and coarse particle count concentrations and nonsignificant increases in PM2.5

and fine particle count concentrations

(Table 2). Despite the increased particle concentrations, levels of FENO were statistically

lower in samples collected the morning after

Exhaled nitric oxide and air pollution

Environmental Health Perspectives • VOLUME 115 | NUMBER 4 | April 2007 509

Table 1. FENO levels by participant characteristics.

Characteristic Subjects [n (%)] Samples [n (%)] Median (ppb) Range (ppb)

All participants 44 (100) 299 (100) 11.1 3.7–43.8

Sex

Female 36 (82) 245 (82) 11.6 3.7–43.8

Male 8 (18) 54 (18) 9.9 3.7–24.9

Race/ethnicity

White 41 (93) 280 (94) 11.5 3.7–43.8

African American 3 (7) 19 (6) 8 4.4–37.4

Age (years)

62–79 20 (45) 134 (45) 11.4 3.7–43.8

80–94 24 (55) 165 (55) 11 3.7–37.4

Cigarette smokinga

Never 23 (52) 157 (53) 10.8 3.7–43.8

Former 21 (48) 142 (47) 11.6 3.7–37.1

Asthmab

Yes 4 (9) 25 (8) 10.8 6.2–37.4

No 40 (91) 274 (92) 11.2 3.7–43.8

COPDb

Yes 5 (11) 38 (13) 10.4 3.7–43.8

No 39 (89) 261 (87) 11.2 3.7–37.4

COPD, chronic obstructive pulmonary disease.

aAll subjects were current nonsmokers. bAs defined by report of doctor diagnosis by participant.

Table 2. FENO and 24-hr mean environmental levels preceding health measurements—before (pre) and

after (post) trips.

Pre-trip Post-trip Pre vs. post

Median IQR Median IQR p-valuea

FENO (ppb) 12.1 9.0–15.8 10.2 7.8–13.9 0.0002

Microenvironmental measures

PM2.5 (μg/m3) 7.6 6.4–9.8 9.2 7.2–11.3 0.14

Black carbon (ng/m3) 283 194–361 681 484–969 < 0.0001

Fine particle counts (pt/cm3) 41 32–65 56 32–71 0.36

Coarse particle counts (pt/cm3) 0.02 0.01–0.04 0.09 0.07–0.1 < 0.0001

Room air nitric oxide (ppb) 12.4 6.7–21.9 11.3 6.4–30.4 0.71

Apparent temperature (°C) 21.5 20.5–22.0 20.7 19.8–22.7 0.89

Ambient measures

PM2.5 (μg/m3) 14.8 11.2–20.1 16.5 11.7–21.6 0.94

Black carbon (ng/m3) 795 678–988 925 729–1,127 0.59

Carbon monoxide (ppm) 0.4 0.3–0.5 0.4 0.4–0.5 0.85

Nitrogen dioxide (ppb) 15.5 12.9–19.4 18.3 14.7–21.2 0.30

Sulfur dioxide (ppb) 3.6 2.8–6.1 2.9 2.1 –6.0 0.52

Ozone (ppb) 24.3 17.5–31.5 25.3 16.9–29.7 0.84

Pollen counts (pt/m3

) 92.5 42–390 102 48–346 0.82

Mold counts (pt/m3) 7,599 2,720–23,070 11,047 3,600–19,970 0.57

aDifferences between pre-trip and post-trip FENO values were calculated using a crude mixed-model with random subject

effects. Differences between exposure and meteorologic parameters were determined using a crude regression model

that controlled for autoregressive terms.
the trip than in samples collected before the

trips. No significant differences were found

between average ambient particle or gas concentrations between pre- and post-trip days. 

Correlations were high between all fine

particle measures in the microenvironment,

with nearly perfect correlation (r = 0.98)

between the 24-hr mean PM2.5 and fine particle count concentrations on days before and

after the trips. Correlations with microenvironmental black carbon were slightly lower

but remained strong, with correlations of

0.74 and 0.81 for PM2.5 and fine particle

counts, respectively. Coarse particles, on the

other hand, were weakly correlated with all

measures of microenvironmental fine particle

concentrations except for black carbon on the

day before the trip (r = 0.60). Similar to

microenvironmental samples, concentrations

of PM2.5 and black carbon in ambient air

were strongly correlated (r = 0.74). Weak correlations were observed between ambient particles and ambient gases, but moderate

correlation was seen between ambient and

microenvironmental fine particles. Ambient

gases generally had low to moderate correlations with microenvironmental particles.

Associations between pollution and FENO

in pre-trip samples. Significant positive associations were consistently found between pre-trip

samples of FENO and levels of microenvironmental fine particulate matter (Table 3).

These associations were strongest for PM2.5

and fine particle count concentrations, with

an approximate 20% increase in FENO per 4.2

μg/m3 or 45 pt/cm3 increase in the preceding

6-hr mean pollution level, respectively.

Positive associations also were observed with

the microenvironmental black carbon whereas

generally null associations were found with

microenvironmental coarse particle counts.

Ambient PM2.5 concentrations also exhibited

strong positive associations with FENO in pretrip samples with a 15% (95% CI, 3–28%)

increase predicted per 12.7-μg/m3 increase in

the 6-hr average and a 22% (95% CI,

7–39%) increase predicted per 9.8-μg/m3

increase in the 24-hr average. No associations

were found for ambient black carbon or

ambient gases (results not shown) in FENO

samples collected before the trips.

Associations between pollution and FENO

in post-trip samples. On the mornings after

the trips, statistically significant associations

were again found between measured concentrations of FENO and all fine particle concentrations measured in the microenvironment

(Table 3). Unlike the day before the trip, however, these associations were found with the

24-hr moving averages but not the 6-hr moving averages. In addition, FENO concentrations

were now most strongly associated with

microenvironmental black carbon concentrations with a 25% (95% CI, 8–44%) increase

in FENO predicted per increase of 451 ng/m3.

Associations with the daily micro-environmental PM2.5, black carbon, and fine particle

count concentrations remained similar in

magnitude to those before the trip, but coarse

particle count concentrations became significantly and negatively associated with post-trip

FENO. These inverse associations were unaffected by control for PM2.5 in a two-pollutant

model. Associations between ambient PM2.5

and FENO collected after the trips also differed

substantially from pre-trip associations.

Despite strong and significant associations

with pre-trip FENO, associations with post-trip

FENO were virtually nonexistent. Again, ambient gases did not predict levels of FENO

(results not shown).

Further investigation of post-trip FENO

indicated that exposures measured during the

trip periods were strongly and significantly

associated with FENO levels (Table 4). These

associations were larger than those reported in

Table 3 for the 24-hr average as a result of

elevated interquartile ranges during the trip

periods although the unadjusted effect estimates were similar in magnitude. 

Sensitivity of results. Associations between

particulate exposures and FENO collected

before the trip were not the result of a few

influential points. On the other hand, associations between microenvironmental particulate

pollution and FENO in samples collected after

the bus trips were found to be sensitive to the

highest and lowest pollution days. Removing

the lowest and highest two trips by exposure

(approximately 15% of the data) resulted in

nonsignificant relationships between all particles and FENO on the day after the trip. No

explanatory factors were found for the observations at the highest or lowest levels of pollution despite consideration of many factors

such as susceptible individuals, weather, wind

direction, activity during the trips, facility,

and gaseous co-pollutants. 

Discussion

In this investigation we found that fine particulate matter was positively associated with

FENO in samples collected before and after

group trips aboard a diesel bus. Although

similar effect estimates for microenvironmental exposures before and after the bus trip did

not provide evidence of enhanced toxicity for

fresh traffic-related particles, our findings do

suggest that exposures to traffic increase the

levels of FENO and possibly pulmonary

inflammation. This is indicated by our study

design with its enhanced exposures to trafficrelated particles as well as the strong post-trip

associations with concentrations of the trafficrelated pollutant black carbon and exposures

during the trips. Further support is provided

by the fact that 6-hr moving averages of

microenvironmental pollution, which do not

include the contribution of the bus, were not

associated with post-trip FENO. Similarly,

ambient pollution was predominantly predictive of FENO in samples collected pre-trip,

Adar et al.

510 VOLUME 115 | NUMBER 4 | April 2007 • Environmental Health Perspectives

Table 3. Percent change (95% CI) in FENO per interquartile change in microenvironmental and ambient

particles.

Averaging Mean Pre-trip Post-trip 

period (hr) IQR percent change percent change

Microenvironmental particles

PM2.5 (μg/m3

) 6 4.2 17.0 (6.7 to 28.2)** –1.7 (–13.8 to 12.2)

24 4.1 12.5 (2.1 to 24.0)* 13.0 (5.4 to 21.0)**

Black carbon (ng/m3

) 6 251 6.9 (0.3 to 13.9)* –7.6 (–18.0 to 4.2)

24 451 24.9 (–0.05 to 56.1) 24.8 (8.0 to 44.4)**

Fine particle counts (pt/cm3

) 6 45 20.2 (6.2 to 36.0)** 3.5 (–7.3 to 15.6)

24 39 15.5 (3.7 to 28.7)* 12.9 (5.3 to 21.0)**

Coarse particle counts (pt/cm3

) 6 0.03 –4.5 (–14.4 to 6.4) –3.9 (–12.0 to 5.0)

24 0.07 0.7 (–21.8 to 29.8) –25.8 (–33.7 to –16.8)**

Ambient particles

PM2.5 (μg/m3

) 6 12.7 14.9 (3.1 to 28.0)* –0.5 (–10.3 to 10.4)

24 9.8 21.9 (6.7 to 39.4)** –4.7 (–17.1 to 9.6)

Black carbon (ng/m3

) 6 529 4.0 (–4.5 to 13.3) 13.9 (2.5 to 26.5)*

24 350 –2.1 (–13.6 to 10.8) 6.3 (–5.3 to 19.3)

*Statistical significance at the 95% confidence level. **Statistical significance at the 99% confidence level. All models

were controlled for random subject effects, trip type, day of week, collection device, vitamin use, pollen, mold, apparent

temperature in the current hour, time between collection and analysis, and room air nitric oxide. 

Table 4. Percent change (95% CI) in post-trip levels of FENO per interquartile change in microenvironmental

concentrations measured during the trip. 

IQR Post-trip percent change

PM2.5 (μg/m3

) 8.7 24.4 (15.1 to 34.4)*

Black carbon (ng/m3) 1,245 26.5 (7.0 to 49.6)*

Fine particle counts (pt/cm3) 60 18.3 (10.4 to 26.9)*

Coarse particle counts (pt/cm3) 0.15 –16.0 (–21.5 to –10.1)*

All models were controlled for random subject effects, trip type, day of week, collection device, vitamin use, pollen, mold,

apparent temperature in the current hour, time between collection and analysis, and room air nitric oxide. 

*Statistical significance at the 99% confidence level. 
indicating that direct exposures to traffic can

substantially modify the predictive power of

pollution measured at a central location. 

Although the clinical implications of these

results are uncertain, the findings of this investigation provide further evidence of the proposed inflammatory mechanism through

which air pollution may affect health.

Evidence of an inflammatory response in

human lungs is important partly because it has

been hypothesized that pulmonary inflammation may lead to a systemic cascade that can

ultimately affect the cardiovascular system.

Despite the complexity of roles played by

nitric oxide in the body, we selected FENO as a

marker of pulmonary inflammation because it

is known to be expressed by macrophages,

neutrophils, endothelial cells, and muscle cells

as a result of induction by proinflammatory

cytokines (Ricciardolo 2003). Elevated FENO

has also been documented for inflammatory

diseases such as asthma, rhinitis, viral respiratory tract infections, systemic lupus erythematosis, and acute lung allograft rejections

(American Thoracic Society 2006).

Past experimental investigations have

demonstrated such effects following direct

exposures to diesel exhaust particles. One

study found increased constitutive nitric

oxide synthase in the airway epithelium and

increased inducible nitric oxide synthase in

macrophages of mice exposed to diesel

exhaust particles compared with control mice

(Lim et al. 1998). In addition, L-arginine, an

inhibitor of inducible nitric oxide synthase,

was found to reduce airway inflammation following exposure to diesel exhaust particles

(Lim et al. 1998; Takano et al. 1999). 

Human studies also have demonstrated

positive associations between exposures to

ambient particulate pollution and FENO in

both adults (Adamkiewicz et al. 2004; Jansen

et al. 2005; van Amsterdam et al. 1999) and

children (Fischer et al. 2002; Koenig et al.

2003; Steerenberg et al. 2001, 2003), with

several publications suggesting an increased

importance of mobile source pollution. For

example, several studies from the Netherlands

have reported that traffic-related pollutants

such as black smoke, carbon monoxide, nitric

oxide, and nitrogen dioxide have associations

of similar and sometimes larger magnitude

with FENO than those of PM10 (Fischer et al.

2002; Steerenberg et al. 2001; van Amsterdam

et al. 1999). The magnitudes of these associations with traffic-related pollutants seem to be

generally consistent across this and other studies, suggesting that these associations may be

similar in older populations and children. One

study did imply, however, that proximity to

traffic might be an effect modifier of the associations with FENO: Children living in urban

areas had a more profound response to these

pollutants than those in suburban areas

(Steerenberg et al. 2001). We did not find,

however, that bus trips resulted in obvious

increases in toxicity for PM2.5 and fine particle

counts. 

In this investigation, positive associations

with FENO were consistently found for fine

particle concentrations. Negative associations

were generally found with coarse particle concentrations and no associations were found

for ambient gas concentrations. Although no

other observational studies examined associations with coarse particles, our findings are

supported by one experimental study in which

humans exposed to concentrated coarse particles did not elicit a response with respect to

FENO (Gong et al. 2004). On the other hand,

several investigations have found associations

between FENO and ambient gases (Fischer et al.

2002; Nickmilder et al. 2003; Steerenberg

et al. 2001, 2003; van Amsterdam et al.

1999). Our lack of associations may be attributable to the poor correlation in this data set

between ambient gases and microenvironmental particles. Although most other investigators did not report the correlations between

particles and gases, it is possible that correlations were higher for those studies such that

confounding might exist. In the only investigation that used multipollutant models,

ambient nitric oxide was found to lose its

significance following control for PM2.5,

although the effect estimate for PM2.5

remained strong and significant (Adamkiewicz

et al. 2004). Similarly, no associations were

found between controlled ozone exposures in

humans and FENO in an experimental study

design (Olin et al. 2001). Nevertheless, gases

may have a causal connection to pulmonary

inflammation; respiratory outcomes have

been documented in humans following controlled exposures to nitrogen dioxide and sulfur dioxide (Tunnicliffe et al. 2003). 

In several previous investigations, associations between air pollution and FENO appeared

to be limited to specific susceptible subgroups,

such as asthmatics. Therefore, it is also possible

that characteristics of our study population

may explain differences between this and previous studies with respect to gaseous exposures.

For example, associations between air pollution

and FENO in Steubenville, Ohio, were limited

predominantly to senior adults with chronic

obstructive pulmonary disease (Adamkiewicz

et al. 2004). In Seattle, Washington, associations were limited to asthmatic children who

were not using inhaled corticosteroids (Koenig

et al. 2003). In our investigation, associations

between fine particles and inflammation were

relatively homogeneous across participants

without evidence of effect modification by respiratory disease or medication use. Similarly,

we found no enhanced susceptibility for individuals with diabetes, obesity, or elevated circulating inflammatory markers despite the fact

that such susceptibility was previously found in

this population for systemic inflammation

(Dubowsky et al. 2006). Despite these apparent differences, our and other investigations all

had few potentially susceptible individuals (<

10), making it difficult to draw conclusions

regarding true effect modification. 

The relatively low numbers of study subjects (44) and total number of trips conducted

(25) can be considered a general limitation of

this study. For example, although strong and

statistically significant associations between

fine particulate pollution and FENO were

found post-trip, two influential points were

found to drive these associations. Although

investigation of these 2 sampling days did not

reveal any obvious reason for exclusion, it is

possible that some unidentified aspect of the

bus trip, such as social activity or exercise, positively affected our subjects, because the crude

levels of FENO generally decreased after the

trips. The generalizability of these results also

may be somewhat limited by the predominance of Caucasian women, though the proportion of elderly women does exceed the

proportion of elderly men in the general U.S.

population. Despite these potential limitations, robust significant associations found

pre-trip, consistency between measures of

microenvironmental pollution on the 2 days,

and similarity to past studies support the overall findings of this investigation.

In summary, this study links increasing

levels of fine particulate matter to increasing

FENO in older adults, which is suggestive of

elevated airway inflammation. Although associations with both microenvironmental and

ambient exposures were evident in samples

collected after routine exposures at home,

samples collected after periods spent aboard a

diesel bus exhibited associations only with

measures of microenvironmental exposure.

Strong associations were also observed for

exposures during trip periods. Associations

following the trip may have been sensitive to

2 influential days, but associations before the

trip seemed to be reasonably robust. 

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