Cyclist route choice, traffic-related air pollution, and lung function: a scripted exposure study
© Jarjour et al; licensee BioMed Central Ltd. 2013
Received: 3 July 2012
Accepted: 4 February 2013
Published: 7 February 2013
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.
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.
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.
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.
KeywordsBicycle boulevards Active transportation Air pollution Lung function
A shift from motor vehicle use to active transportation such as bicycling would help reduce traffic volume and related air pollution emissions. Short trips (under three 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 exhaust emissions nation-wide (a 0.80-1.78 percent reduction) . Such a shift may improve public health through increased physical activity, as bicycle commuting is also inversely correlated with overweight and obesity . Despite its potential benefits, bicycling remains an underutilized method of transportation. In the United States, cycling accounts for less than 1% of trips .
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 carbon, and ultrafine particulate matter are similar or higher inside vehicles than on bicycles [6, 7], cyclists’ minute 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 vehicle exhaust pollutant exposures during bicycle commuting and respiratory health effects yielded inconclusive results . In contrast, a Canadian study of 42 bicyclists found an inverse relationship between heart rate variability (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 . Decreased heart rate variability is associated with morbidity and mortality from cardiopulmonary disease , indicating 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 network of low-volume residential streets designated as “bicycle 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 bicycle 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.
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)  for ozone, carbon monoxide, sulfur dioxide, particulate matter (10 μm diameter or PM10), and fine particulate matter (2.5 μm diameter or PM2.5) . 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 commuting and walking .
Low background air pollution, combined with an explicit network of bicycle boulevards, makes Berkeley a good location 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) .
We selected these routes to compare low and high traffic exposures, so they do not necessarily model realistic commuting scenarios. An actual commute would likely follow a combination of residential and busy streets, depending on the origin and destination of the cyclist and personal preferences. Although differences in cyclist pace and riding habits could not be completely controlled, participants were asked to cycle at a normal commuting pace and observe bicycle traffic rules (e.g., stopping at stop signs and signaling turns).
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 preliminary screening survey prior to study enrollment. Exclusion 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 (defined as cycling more than once a week). These selection criteria were used to minimize risk of injury due to unfamiliarity with Berkeley streets or cycling inexperience, 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. Participants 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.
We used a condensation particle counter (CPC) (TSI Model 3007, Shoreview, MN) to measure ultrafine particulate 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/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 microaethelometer (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 monitoring 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 computer using the manufacturers’ software.
We downloaded meteorological information from the downtown Berkeley weather station (weather.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.
Adjusted PM2.5 = Uncorrected PM2.5/CF.
Where x equals recorded values and y equals corrected values.
One-second BC data were processed to remove erroneous spikes that occurred when the microaethelometer was exposed to sudden movement or vibration . 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.
Where x equals recorded values (over 100,000) and y equals corrected values .
To map the spatial variation along each route, we aggregated 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-traffic route 51.54 m × 50 m; low-traffic route 52.76 m × 50 m), then the data points were assigned to block segments 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.
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 measurements from one or both rides due to equipment malfunctions. 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 recommended by the American Thoracic Society (ATS) and European Respiratory Society (ERS) .
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 baseline 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 quality-assured dataset. We compared changes in lung function after the high-traffic and low-traffic routes using a pairwise t-test (by subject).
Participants and route completion
Participant demographics and baseline lung function
Female – n (%)
Age – years
Mean ± SD
32.2 ± 6.67
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
3.45 – 6.75
Predicted FEV1 – liters
4.00 ± 0.74
Baseline FEV1 – liters
Mean ± SD
3.94 ± 0.64
2.01 – 5.13
Air pollutant exposure
Black carbon measurements were recorded on all study days. Ultrafine particulate matter and carbon monoxide measurements were obtained for all but one day. Due to equipment failure, fine particulate matter measurements were missing for five study days. Two days of measurement were excluded due to rain and a flat tire.
Average, standard deviation, range, and 5-95 th percentile of pollutant exposures for low and high-traffic routes
Mean ± SD
5th – 95th
UFPM – #/cm3
14,311 ± 15,381
18,545 ± 42,482
CO – ppm
0.79 ± 0.39
0.90 ± 0.64
PM2.5 – μg/m3
4.88 ± 1.41
5.12 ± 1.86
BC – μg/m3
1.76 ± 2.58
2.06 ± 3.23
Average high and low-traffic air pollution exposures by subject
% Diff. (Diff/Avg)*100
% Diff. (diff/avg)*100
% Diff. (diff/avg)*100
% Diff. (diff/avg)*100
Paired t -test by subject. Average pollutant exposure for each subject’s high-traffic ride vs. low-traffic ride average
Standard error of the mean
95% CI of difference
Average measurements and changes in lung function
Baseline ± SD
4.90 ± 0.71
5.01 ± 0.83
Post-ride (Δ from baseline)
4-hour (Δ from baseline)
FEV 1 (liters)
Baseline ± SD
3.91 ± 0.60
3.95 ± 0.62
Post-ride (Δ from baseline)
4-hour (Δ from baseline)
FEV 1 /FVC (liters)
Baseline ± SD
0.81 ± 0.07
0.79 ± 0.06
Post-ride (Δ from baseline)
4-hour (Δ from baseline)
FEF 25-75% (liters)
Baseline ± SD
3.87 ± 0.94
3.61 ± 0.91
Post-ride (Δ from baseline)
4-hour (Δ from baseline)
Air pollution measurements
We found significantly elevated concentrations of ultrafine particulate matter and carbon monoxide, and borderline significant differences for black carbon on the high-traffic route relative to the low-traffic route. When we spatially plotted the pollutant measurements using GPS coordinates, we also found that spikes in pollutant concentrations occurred mainly at intersections and busy streets or truck routes. This supports our hypothesis that traffic volume is directly related to outdoor pollutant concentrations and suggests that route choice can affect commuter exposure to pollutants associated with vehicle exhaust. These findings are consistent with previous European and Canadian studies comparing pollution exposure levels on routes of different traffic levels [9, 10, 22], and our measured exposure difference between high-traffic and low-traffic routes was comparable to these previous studies. For example, the mean ultrafine particular matter (in particles per cubic centimeter, also referred to as particle number) measurements in our study were: 14,311 (low-traffic) and 18,545 (high-traffic). Strak, et al. reported means of 27,813 (low) and 44,090 (high); Weichenthal, et al. 10,882 (low) and 19,747 (high); and McCreanor, et al. 18,300 (low) and 63,700 (high) [9, 10, 23]. The high to low traffic ratios are: 1.29 (this work), 1.62, 1.81, and 3.48, respectively. Zuurbier, et al. calculated 1.6-2.5 times higher particle number on their high-traffic route . Our maximum value was 1,033,188 particles per cubic centimeter, indicating the potential for extraordinarily high exposures over 10-second intervals. Potential health effects of such spikes are unknown, but may merit attention in future research.
We also found that wind speed was associated with significant differences in average measured pollutant concentrations, suggesting that weather conditions also impact the pollution exposure levels of bicycle commuters. Time of day is another important factor – avoiding rush-hour periods significantly reduces cyclists’ pollution exposure .
Health effects of pollutant exposure
Although we did not find significant changes in pulmonary function after cycling on either route, this is not surprising given that our subjects were healthy and screened to not have asthma. A previous study demonstrated reduced lung function and elevated levels of biomarkers of inflammation associated with increased pollutant concentrations in asthmatic subjects who walked along an inner city roadway with high-volume diesel-powered traffic , though these findings were not reproduced in a study with non-asthmatic study subjects bicycling on routes with different traffic levels . In studies looking at other pulmonary and cardiovascular outcomes in healthy subjects, findings have been inconclusive. Zuurbier, et al. (2011) did not find consistent associations between commuting-related air pollution exposure and observed biomarkers including Clara cell protein 16, blood cell count, coagulation markers, and inflammation markers . Similarly, in a comparison of cyclist exposure on a busy road versus a laboratory with filtered air, Jacobs, et al. (2010) did not observe a significant difference in Clara cell protein 16, exhaled nitric oxide (NO), plasma IL-6, platelet function, and total blood leukocytes; however a small increase in inflammatory blood cells was observed .
A major limitation of this study was variable wind speed and other meteorological conditions, which affected measured concentrations independent of road traffic volume. Equipment failure also reduced the number of viable pollutant measurements. Participants did not cycle to the study site, but pre-study exposure and potential exposure between the post-ride and 4-hour follow-up spirometry measurements were not otherwise controlled. In particular, we recognize that allowing participants to drive to the study site may have influenced their pre-exposure to vehicle-related air pollutants. Finally, the exposure time on the routes may not have been long enough to elicit measurable physiological changes.
This study contributes to a small but growing body of literature that investigates the network of relationships among active transport, air pollutant exposure, and health. More research in this field is still needed to more conclusively determine the long-term health effects of bicycle commuting in variable traffic conditions. Our results indicate that choosing low-traffic routes can decrease the exposure of bicyclists to air pollutants, potentially reducing associated detrimental health effects. In particular, this study demonstrates that even simple infrastructure modifications, such as designating a particular network of streets as “bicycle boulevards,” have the potential to mitigate exposure to pollution. Implementing a system of bicycle boulevards does not require the construction of new streets or bike paths, nor does it necessitate the narrowing of existing lanes of traffic, which are often major barriers to implementing effective bicycling infrastructure. Lower pollutant exposures on bicycle boulevards could increase the number of cyclist commuters, especially in subpopulations more vulnerable to the health effects of air pollution, such as asthmatics. An increase in bike commuting could in turn decrease the risk of vehicle-bicycle collision, as studies have shown that higher numbers of cyclists raise awareness and lower the rate of accidents, i.e. the “safety in numbers” effect [26, 27]. Lower pollutant exposures associated with low-traffic routes and bicycle boulevards may support future changes to the built environment (city, building, and street design) that allow for healthier and safer routes for active transportation.
Global positioning system
- CO2 :
- PM2.5 :
Fine particulate matter (diameter ≤ 2.5 micrometers)
- NO2 :
- O3 :
Environmental Protection Agency
Vehicle miles traveled
Condensation particle counter
American Thoracic Society
European Respiratory Society
Forced vital capacity
- FEV1 :
Forced expiratory volume in 1 second
Ratio of FVC over FEV1
- FEF25-75 :
Forced expiratory flow rate between 25-75% of vital capacity.
We are grateful to our study subjects for their participation. We also thank Caitlin Kontgis for her assistance with data collection.
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