RESEARCH
ic
y
such as bicycling would help reduce traffic volume and
related air pollution emissions. Short trips (under three
Despite its potential benefits, bicycling remains an
underutilized method of transportation. In the United
Jarjour et al. Environmental Health 2013, 12:14
http://www.ehjournal.net/content/12/1/14
inside vehicles than on bicycles [6,7], cyclists’ minute1University of California, Berkeley, 50 University Hall, Berkeley, CA 94720, USA
Full list of author information is available at the end of the article
miles) in particular have been identified as a good target
for this travel mode shift; reducing these vehicle miles
traveled in the United States by 0.8-1.8% would save an
estimated 20,000-46,000 tons/day of CO2 equivalent of ex-
haust emissions nation-wide (a 0.80-1.78 percent reduc-
tion) [1]. Such a shift may improve public health through
States, cycling accounts for less than 1% of trips [3].
The environmental and public health benefits of bicycle
commuting must be weighed against the associated risks
such as traffic accidents and air pollution exposure for the
cyclist. Vehicle traffic is associated with the emission of
multiple air pollutants and related health effects [4,5].
While measured concentrations of PM2.5, elemental car-
bon, and ultrafine particulate matter are similar or higher* Correspondence: sjarjour@berkeley.edu
Background: A travel mode shift to active transportation such as bicycling would help reduce traffic volume and
related air pollution emissions as well as promote increased physical activity level. Cyclists, however, are at risk for
exposure to vehicle-related air pollutants due to their proximity to vehicle traffic and elevated respiratory rates. To
promote safe bicycle commuting, the City of Berkeley, California, has designated a network of residential streets as
“Bicycle Boulevards.” We hypothesized that cyclist exposure to air pollution would be lower on these Bicycle
Boulevards when compared to busier roads and this elevated exposure may result in reduced lung function.
Methods: We recruited 15 healthy adults to cycle on two routes – a low-traffic Bicycle Boulevard route and a
high-traffic route. Each participant cycled on the low-traffic route once and the high-traffic route once. We
mounted pollutant monitors and a global positioning system (GPS) on the bicycles. The monitors were all synced
to GPS time so pollutant measurements could be spatially plotted. We measured lung function using spirometry
before and after each bike ride.
Results: We found that fine and ultrafine particulate matter, carbon monoxide, and black carbon were all elevated
on the high-traffic route compared to the low-traffic route. There were no corresponding changes in the lung
function of healthy non-asthmatic study subjects. We also found that wind-speed affected pollution concentrations.
Conclusions: These results suggest that by selecting low-traffic Bicycle Boulevards instead of heavily trafficked
roads, cyclists can reduce their exposure to vehicle-related air pollution. The lung function results indicate that
elevated pollutant exposure may not have acute negative effects on healthy cyclists, but further research is
necessary to determine long-term effects on a more diverse population. This study and broader field of research
have the potential to encourage policy-makers and city planners to expand infrastructure to promote safe and
healthy bicycle commuting.
Keywords: Bicycle boulevards, Active transportation, Air pollution, Lung function
Background
A shift from motor vehicle use to active transportation
increased physical activity, as bicycle commuting is also
inversely correlated with overweight and obesity [2].
Cyclist route choice, traff
and lung function: a scrip
Sarah Jarjour1*, Michael Jerrett1, Dane Westerdahl2, Audre
Jonah Lipsitt1 and John Balmes1,4
Abstract
© 2013 Jarjour et al.; licensee BioMed Central
Commons Attribution License (http://creativec
reproduction in any medium, provided the or
Open Access
-related air pollution,
ted exposure study
de Nazelle3, Cooper Hanning1, Laura Daly1,
Ltd. This is an Open Access article distributed under the terms of the Creative
ommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
iginal work is properly cited.
Jarjour et al. Environmental Health 2013, 12:14 Page 2 of 12
http://www.ehjournal.net/content/12/1/14
ventilation has been recorded at two to four times that of
car passengers, leading to overall higher inhaled doses of
pollutants for trips of the same length [6,8].
Few studies have examined whether these elevated
exposures relate to adverse health effects. A study in the
Netherlands that evaluated the relationships between ve-
hicle exhaust pollutant exposures during bicycle commut-
ing and respiratory health effects yielded inconclusive
results [9]. In contrast, a Canadian study of 42 bicyclists
found an inverse relationship between heart rate variabil-
ity (standard deviation of normal-to-normal interval) and
pollutant exposure (NO2 and O3 concentrations) as well
as an association between ultrafine particulate matter
(UFPM) and decreased high-frequency power [10].
Decreased heart rate variability is associated with morbid-
ity and mortality from cardiopulmonary disease [11], indi-
cating that pollutant exposure associated with bicycling
may have an adverse effect on cardiovascular health.
Our study builds on previous work by comparing the
pollutant exposures and associated lung function for
cyclists on high-traffic and low-traffic routes in the City
of Berkeley, California. Routes were chosen to compare
exposures on normal major roads to those on bicycle
boulevards. Berkeley’s bicycle boulevard system is a net-
work of low-volume residential streets designated as “bi-
cycle priority routes.” To our knowledge, this is the first
study to look at the difference between a study route
chosen to follow only cyclist-designated streets and a
study route on regular busy streets. This is significant
because bicycle boulevards can be designated without
the large capital investments often associated with bi-
cycle infrastructure and may therefore provide a low
cost means of promoting increased bicycling. Our study
aims to demonstrate that there is a potential health benefit
associated with choosing bicycle boulevards instead of
arterial streets. We hypothesized that cyclists would be
exposed to higher concentrations of particulate matter
and other vehicle exhaust pollutants on high-traffic routes
as compared to cyclists on low-traffic routes and that this
elevated exposure may result in reduced lung function.
Methods
Study site
We conducted this study in the City of Berkeley (popula-
tion ~112,580). Berkeley is within the San Francisco
Metropolitan Area (population ~7.3 million) in Northern
California (see Figure 1).
The City has a temperate Mediterranean climate, but
regularly experiences strong westerly winds from the Pacific
Ocean. As a result, the City has relatively low background
pollution, usually under the National Ambient Air Quality
Standards adopted by the U.S. Environmental Protection
Agency (EPA) [12] for ozone, carbon monoxide, sulfur
dioxide, particulate matter (10 μm diameter or PM10),
and fine particulate matter (2.5 μm diameter or PM2.5)
[13]. In 2000, the City implemented the Berkeley
Bicycle Plan, which established a network of seven
bicycle boulevards: low-volume residential streets with
signs, pavement markings, and traffic calming devices
designed to promote safe and convenient bicycle com-
muting and walking [14].
Low background air pollution, combined with an explicit
network of bicycle boulevards, makes Berkeley a good loca-
tion to conduct bicycle exposure studies. The University
of California, Berkeley, generates approximately 30,000
trips through the downtown area per weekday, 52% of
which are individuals driving alone and 11% of which are
carpools (the remaining 37% are divided between public
transportation, walking, and cycling) [15].
Routes
Data were collected on weekdays in April-June 2011 dur-
ing morning commute hours (8:00–10:00 AM). On each
study day, a pair of participants bicycled together on either
a low-traffic or high-traffic study route. The two routes
were similar in length (8–9.5 km) and elevation change
(~61 m). The high-traffic route followed busy streets with
more truck and bus traffic, while the low-traffic route
followed residential streets, all designated by the City of
Berkeley as bicycle boulevards (Figure 2). Traffic counts
on the high-traffic route range from ~10,000 vehicles per
day (vpd) on Dwight Way to ~26,000 vpd on San Pablo
Ave [16]. Traffic counts for the low-traffic route were not
available, but the Berkeley Bicycle Plan deemed streets
with low traffic volumes, defined as less than 3,000-4,000
vehicles per day, as appropriate for bicycle boulevards. In
many parts of the low-traffic route, counts are likely to be
less than 1,500 vpd [14].
We selected these routes to compare low and high traf-
fic exposures, so they do not necessarily model realistic
commuting scenarios. An actual commute would likely
follow a combination of residential and busy streets, de-
pending on the origin and destination of the cyclist and
personal preferences. Although differences in cyclist pace
and riding habits could not be completely controlled, par-
ticipants were asked to cycle at a normal commuting pace
and observe bicycle traffic rules (e.g., stopping at stop
signs and signaling turns).
Participants
We recruited 15 adults (age 23–48) by word of mouth
and departmental email lists from the UC Berkeley
School of Public Health. Subjects completed a prelimin-
ary screening survey prior to study enrollment. Exclu-
sion criteria included respiratory (including asthma),
cardiovascular, or other chronic conditions, and smoking
(current or recent). We only enrolled individuals who
were already regular cyclists in the City of Berkeley
Jarjour et al. Environmental Health 2013, 12:14 Page 3 of 12
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(defined as cycling more than once a week). These selec-
tion criteria were used to minimize risk of injury due to
unfamiliarity with Berkeley streets or cycling inexperi-
ence, or adverse acute health effects. Participants were
asked to refrain from alcohol and caffeine consumption
starting the evening before they were scheduled to ride
and were asked to avoid biking to the study site to
minimize potential pre-study cycling exposure. Partici-
pants primarily walked or drove to the study site, with
commute times of 10–20 minutes. The UC Berkeley
Center for Protection of Human Subjects approved this
study, and all participants provided written informed
consent.
Exposure measurements
We used a condensation particle counter (CPC) (TSI
Model 3007, Shoreview, MN) to measure ultrafine par-
ticulate matter (UFPM) (0.01 to 1.0 μm diameter) with a
logging interval of 10 seconds. Carbon monoxide (CO)
was recorded using a Q-Trak (TSI Model VelociCalc/
Figure 1 The City of Berkeley is in the San Francisco Bay Area in Nort
Q-Trak 7565, Shoreview, MN), also with a logging interval
of 10 seconds. A DustTrak (TSI Model 8520, Shoreview,
MN) fitted with a PM2.5 inlet was used to measure fine
particulate matter (PM2.5) (less than 2.5 μm diameter)
with a logging interval of 10 seconds, and a microaethel-
ometer (Magee Scientific Model AE-51, Berkeley, CA)
with a logging interval of 1 second was used to measure
black carbon (BC).
These devices were placed in a rear basket (40.5 × 33.5 ×
24.1 cm) of a bike. Together they weighed approximately
9.5 kg (21 pounds). One subject carried a GPS (GPSMAP
60CSx, Garmin, Olathe, KS) to track location. All monitor-
ing devices were synced to GPS time before each bike ride.
Data were collected on each machine’s internal memory.
After each bike ride, data were downloaded onto a com-
puter using the manufacturers’ software.
Meteorology
We downloaded meteorological information from
the downtown Berkeley weather station (weather.
hern California.
Jarjour et al. Environmental Health 2013, 12:14 Page 4 of 12
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berkeley.edu) for each study day. We calculated the
median of the wind speeds for all 19 study days. Days with
wind speeds above the median (6.83 mph = 3.05 m/s) were
classified as high-wind; days with wind speeds below the
median were classified as low-wind. The Q-Trak monitor
also recorded temperature and humidity.
Data processing
We adjusted PM2.5 measurements for humidity (RH, as
measured by the Q-Trak monitor) using the correction
factor (CF) [17]:
CF ¼ 1þ 0:25 RH
2
1� RHð Þ
Adjusted PM2.5 = Uncorrected PM2.5/CF.
We also calibrated PM2.5 measurements to account
for the lower precision of the DustTrak (as compared
to US EPA designated Federal Reference Method
Figure 2 Low and high-traffic routes in Berkeley, California. Both route
Berkeley campus.
measurements) using the equation developed in
Yanosky, et al. (2002) [18]:
y ¼ 0:33xþ 2:25
Where x equals recorded values and y equals corrected
values.
One-second BC data were processed to remove erro-
neous spikes that occurred when the microaethelometer
was exposed to sudden movement or vibration [19]. The
processed data were smoothed to a 29-second moving
average, and every tenth point was matched with the
other measurements taken in 10-second intervals. After
data processing, the few remaining negative BC and
PM2.5 values were assumed to be noise, again resulting
from equipment error, and set as missing measurements.
BC measurements below the minimum detection limit
(0.1 μg) were also removed.
As described in a previous vehicular emissions
study, the CPC (3007 model) undercounts UFPM
s start and end at University Hall on the west side of the UC
The mean age was 32 years, and compliant with the
exclusion criteria none of the participants reported
Table 1 Participant demographics and baseline lung
function
Characteristic
Female – n (%) 4 (26.7%)
Age – years
Mean ± SD 32.2 ± 6.67
Range 23 – 48
Height – meters 1.75 ± 0.09
BMI – kg/m2 22.03 ± 1.92
Predicted FVC – liters 4.70 ± 0.84
Baseline FVC – liters
Mean ± SD 4.95 ± 0.78
Range 3.45 – 6.75
Predicted FEV1 – liters 4.00 ± 0.74
Baseline FEV1 – liters
Mean ± SD 3.94 ± 0.64
Table 2 Average, standard deviation, range, and 5-95th
percentile of pollutant exposures for low and high-traffic
routes
Pollutant N* Mean ± SD Min Max 5th – 95th
UFPM – #/cm3
Low-traffic 9 14,311 ± 15,381 2,771 376,495 4,621-29,882
High-traffic 9 18,545 ± 42,482 1,900 1,033,188 4,148-51,265
CO – ppm
Low-traffic 8 0.79 ± 0.39 0.20 4.90 0.40-1.50
High-traffic 10 0.90 ± 0.64 0.10 10.60 0.40-1.90
PM2.5 – μg/m3
Low-traffic 6 4.88 ± 1.41 2.25 20.96 2.65-6.84
High-traffic 8 5.12 ± 1.86 2.25 27.40 2.94-7.10
BC – μg/m3
Low-traffic 9 1.76 ± 2.58 0.11 63.83 0.50-4.03
High-traffic 10 2.06 ± 3.23 0.1 53.53 0.37-5.06
Jarjour et al. Environmental Health 2013, 12:14 Page 5 of 12
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when concentrations are above 100,000 particles/cm3.
We used the correction equation:
y ¼ 38456e0:00001
Where x equals recorded values (over 100,000) and y
equals corrected values [20].
Exposure mapping
To map the spatial variation along each route, we aggre-
gated data points by 50 meter segments. The points
were added to a Berkeley base map (WGS_1984_UTM_-
Zone_10N) in ArcMap 10 (ArcGIS 10, Esri, Redlands,
CA). The bike route lines were buffered by 50 meters
and divided into 50 m × 50 m block segments (high-traf-
fic route 51.54 m × 50 m; low-traffic route 52.76 m ×
50 m), then the data points were assigned to block seg-
ments by spatial join and aggregated using the Dissolve
tool, which averaged the pollutant measurements of all
the points within a block segment. The low-traffic route
was broken down into 170 segments, and the high-
traffic route was 200 segments long. We calculated a
moving average of approximately 150 meters for each
block segment (i.e., a centered moving average of three
50 m block segments). These averages were only used
for qualitative purposes.
Statistical analysis
Exposure differences and percent differences between
high-traffic and low-traffic rides were calculated for each
subject. We compared the average high-traffic to low-
traffic exposures by subject using a pairwise t-test and
excluding subjects who were missing pollutant measure-
ments from one or both rides due to equipment mal-
functions. We used Stata (Version 10, StataCorp, College
Station, TX) for all statistical analyses.
Health outcome measurements
Each subject completed the high-traffic route and the low-
traffic route on separate days. Three spirometry sessions
per ride were recorded. Specifically, lung function was
evaluated before, immediately after, and about 4 hours
after each bike ride using an EasyOne Spirometer. Trained
research personnel coached each study subject through at
least three (up to eight) rounds of forceful inhalation and
exhalation following the performance criteria recom-
mended by the American Thoracic Society (ATS) and
European Respiratory Society (ERS) [21].
The spirometry data were reviewed by an experienced
pulmonologist. Six spirometry sessions did not meet
ATS/ERS standards for reproducibility and were omitted
from analysis. One of these sessions was a pre-ride base-
line measurement, so the corresponding post-ride and
4-hour follow-up measurements were also omitted.
Differences between baseline, post-ride, and 4-hour
follow-up measurements were calculated from the qual-
ity-assured dataset. We compared changes in lung func-
tion after the high-traffic and low-traffic routes using a
pairwise t-test (by subject).
Results
Participants and route completion
Of the 15 participants, four were female and 11 were
male (Table 1). All participants completed both routes.
Range 2.01 – 5.13
*N = 15 participants.
* N = number of sampling days with valid pollutant data (varied due to
equipment failures).
Table 3 Average high and low-traffic air pollution exposures by subject
UFPM (#/cm3) PM2.5 (μg/m
3) CO (ppm) BC(μg/m3)
Sub-
ject
High-
traffic
average Difference
(high-low)
% Diff.
(Diff/
Avg)*100
High-
traffic
average Difference
(high-low)
% Diff.
(diff/avg)
*100
High-
traffic
average Difference
(high-low)
% Diff.
(diff/
avg)
*100
High-
traffic
average Difference
(high-low)
% Diff.
(diff/
avg)
*100
Low-
traffic
average
Low-
traffic
average
Low-
traffic
average
Low-
traffic
average
1 8992.69 1554.56 18.9 3.69 −0.75 −18.5 0.79 0.13 18.5 1.59 0.39 27.8
7438.13 4.43 0.66 1.20
2 15163.01 −2836.17 −17.1 5.46 0.29 5.5 0.88 0.15 19.1 3.57 1.61 58.1
17999.18 5.17 0.73 1.96
3 23154.37 9140.15 49.2 3.24 1.19 0.43 43.6 1.77 0.16 9.6
14014.22 0.76 1.61
4 23154.37 9762.57 53.4 3.24 −2.69 −58.7 1.19 0.42 43.4 1.77 0.00 .18
13391.8 5.93 0.77 1.77
5 19443.05 12004.91 89.3 0.92 0.27 33.9 2.78 1.58 79.2
7438.13 4.43 0.66 1.20
6 4.32 1.07 28.2 0.79 1.99 0.35 19.5
14141.43 3.25 1.64
7 19443.05 6443.09 39.7 0.92 0.21 25.3 2.78 1.57 78.9
12999.95 4.89 0.72 1.21
8 13268.25 −745.98 −5.5 0.92 0.16 19.0 1.79 0.18 10.7
14014.22 0.76 1.61
9 15234.06 −2643.94 −16.0 6.07 0.94 −0.18 −18.0 1.69 −0.78 −37.5
17878.00 1.12 2.47
10 35506.35 22834.39 94.8 6.01 0.94 0.22 26.6 2.48 0.50 22.7
12671.96 0.72 1.97
11 35506.35 20343.34 84.5 6.01 0.94 0.22 26.6 2.48 0.50 22.7
12671.96 0.72 1.97
12 15234.06 1092.63 7.4 6.07 2.82 60.6 0.94 1.69 0.05 3.0
14141.43 3.25 1.64
13 18728.86 3134.31 18.3 5.89 0.34 5.9 0.72 −0.08 −11.1 1.99 0.05 2.5
15594.55 5.55 0.81 1.94
14 18203.17 2608.62 15.4 3.79 −1.76 −37.7 0.85 0.04 5.0 1.56 −0.38 −21.9
15594.55 5.55 0.81 1.94
15 18203.17 4811.37 30.5 3.79 −2.14 −44.0 0.85 0.08 10.3 1.56 −0.21 −12.6
13391.80 5.93 0.77 1.77
Table 4 Paired t-test by subject. Average pollutant exposure for each subject’s high-traffic ride vs. low-traffic ride
average
N* Mean Standard error of the mean p
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