- Email: [email protected]
Investigating the link between cyclist volumes and air pollution along bicycle facilities in a dense urban core
Transportation Research Part D 17 (2012) 619–625
Contents lists available at SciVerse ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Investigating the link between cyclist volumes and air pollution along bicycle facilities in a dense urban core Jillian Strauss a, Luis Miranda-Moreno a, Dan Crouse b, Mark S. Goldberg c, Nancy A. Ross d, Marianne Hatzopoulou a,⇑ a
Department of Civil Engineering and Applied Mechanics, McGill University, Montréal, QC, Canada H3A 2K6 Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada K1A 0K9 c Division of Clinical Epidemiology, Department of Medicine, McGill University Health Centre, Royal Victoria Hospital, Montréal, QC, Canada H3A 1A1 d Department of Geography, McGill University, Montréal, QC, Canada H3A 2K6 b
a r t i c l e Keywords: Bicycling Air pollution Bicycle facilities Cyclist exposure
i n f o
a b s t r a c t In this paper we explore the air pollution levels along types of bicycle facilities using a NO2 land use model previously developed for Montreal. We explore potential associations between bicycle volumes through signalized intersections and pollution levels at those intersections. We further investigate this relationship through the comparison of over thirty cycling corridors as well as an evaluation of the potential exposure of cyclists to air pollution along five routes. We observe NO2 concentrations to be positively correlated with bicycle flows at the intersection level. We also observe that corridors with either a bicycle path or cycle track generally rank higher in terms of bicycle volume and also have higher NO2 concentrations than corridors without bicycle facilities. This indicates that intersections and bicycle facilities with a large number of cyclists are also those characterized with the highest air pollution levels. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Recently, a strong association between the built environment, physical activity and obesity has been demonstrated, suggesting that compact, mixed-use urban areas contribute to decreased automobile dependency and increased walking and cycling (Frank et al., 2007). Indeed, many North American cities are investing in bicycle infrastructure, bicycle sharing programs and large campaigns to promote cycling as an affordable and environmentally friendly alternative mode of transportation. Yet, while compact urban areas promote active transportation, they also trap pollutants in street canyons, thus concentrating vehicle-induced air pollution. Cyclists are at risk of being exposed to higher concentrations of air pollution as compared to other road users due to their proximity to traffic, high respiration rates and longer journeys. Research into cyclist’s exposure to air pollution has focused on measuring actual exposure levels of samples of cyclists on pre-selected routes using personal samplers (Zuurbier et al., 2011) or inferring personal exposure from measurements of street-level pollution using mobile laboratories or fixed-site ambient air monitoring stations (Int Panis et al., 2010). Many studies have also attempted to correlate specific physiological responses with cyclist’s exposure to air pollution and found evidence that adverse health effects can result from short-term exposure (Weichenthal et al., 2011). Rather than directly measuring cyclist exposure, the purpose of this paper is to explore potential associations between the volume of cyclists along roads and intersections and the concentrations of traffic-related air pollution. We hypothesize that
⇑ Corresponding author. Tel.: +1 514 398 6935; fax: +1 514 398 7361. E-mail address: [email protected] (M. Hatzopoulou). 1361-9209/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trd.2012.07.007
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625
bicycle activity is correlated with air quality around the city and cyclists are therefore often found on streets characterized by high levels of air pollution. Unlike previous studies which attempt to quantify the level of cyclist exposure on a trip or daily basis, we are concerned with evaluating the relationship between the numbers of cyclists attracted towards intersections and bicycle facilities and the air quality levels characterizing them. Our study is set in Montreal, Canada and has the objectives of: investigating the association between bicycle flow through signalized intersections and air pollution concentrations, comparing specific cycling corridors according to estimated levels of traffic-related pollution, bicycle activity and presence of bicycle paths or track, and evaluating the average exposure of a cyclist to air pollution. 2. Materials and methods Concentrations of nitrogen dioxide (NO2) were estimated across the island of Montreal at signalized intersections for which bicycle flow data are available. Concentrations of NO2 were also estimated along specific cycling corridors. The latter data were used to estimate cyclist exposure along cycling routes. Estimates of the annual average concentrations of NO2 were obtained through the application of a land use model developed specifically for the island of Montreal. These data provide an excellent marker of traffic-related air pollution. Briefly, a series of dense sampling campaigns were conducted in 2005 and 2006 to estimate integrated two-week NO2 concentrations at individual points throughout the entire island. Concentrations of NO2 were measured using two-sided Ogawa passive samplers (Ogawa and Co., USA) during: November/December 2005, April/May 2006, and August 2006. The samplers were installed at a height of 2.5 metres near the sidewalk at 133 locations across Montreal. A set of variables describing specific land use and road density characteristics measured at radial distances away from the samplers (100, 300, 500 and 700 m) was then generated. With these variables, a land use model was estimated and used to predict concentrations of NO2 where measurements were not taken. The resulting model was able to predict 80% of the variability in NO2 levels (Crouse et al., 2009). Fig. 1 presents NO2 concentrations across Montreal; the highest concentrations, identified by the darkest grey shade on the map, occur along highways and major urban streets. The city of Montreal carried out manual bicycle counts at a large sample of signalized intersections. This study focuses on 753 signalized intersections that meet the following criteria: recent cyclist and traffic counts are available for 2008 and 2009, and counts were taken during the cycling season, when seasonal bicycle facilities are open (April–November). These intersections were not randomly selected, however they do represent over 30% of all the intersections on the island as well as provide a representative sample of the built environments in the City. Bicycle counts were conducted over an 8 h period
Fig. 1. Concentration of NO2 (in parts per billion) across the island overlaid by signalized intersections at which cyclist counts were conducted.
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625 Table 1 Characteristics of selected routes cycled by research assistants to evaluate the average exposure along these routes.
a
Route
Distance travelled (km)a
Signalized intersections (intersections with data)
Average light cycle length (s)
Bicycle facility
% Of route with bicycle facility (%)
1 2 3 4 5
4.2 4.9 4.4 4.6 4.3
20 24 19 18 35
81 80 80 90 82
Cycle track Cycle track None None Cycle track
80 13 0 0 28
(16) (18) (13) (16) (30)
Approximate distance of one-way trip.
during the same day (6:00 am–9:00 am; 11:00 am–1:00 pm; 3:30 pm–6:30 pm) at each intersection. The city of Montreal has also installed permanent bicycle counters along five of Montreal’s bicycle facilities. Using these automatic hourly bicycle count data, hourly, daily and monthly expansion factors were developed. These factors were then applied to the 8 h counts to convert them to average annual daily values (Strauss and Miranda-Moreno, 2011). The locations of the signalized intersections at which bicycle counts were conducted were overlaid onto the NO2 map (Fig. 1) and NO2 concentrations were extracted at each intersection. To investigate whether a relationship exists between bicycle flow through intersections, NO2 concentrations, and other variables, a correlation matrix was constructed focusing on: bicycle flow at the intersection, concentration of NO2 at the intersection, distance of the intersection to the downtown area as a proxy for traffic flows which are greater downtown and the type of intersection based on the classes of the intersecting streets; intersections where all approaches are arterials and where all approaches are collectors. A correlation matrix was preferred over more sophisticated tests because it captures the main effect that we are trying to unveil; namely the association between cyclist volumes and air pollution. Linear and non-linear associations were tested. In addition, a box plot showing the NO2 concentrations for ranges of bicycle flows was generated to illustrate the same effect. Cycling corridors were constructed by selecting a set of consecutive intersections running along Montreal streets. Thirtythree corridors were constructed for which bicycle flows were available for a minimum of eight intersections. The length of these corridors ranges from 2.3 km to 31 km, the number of intersections ranges from 8 to 65. Some have a bicycle path or a cycle track while others have no bicycle facilities. Overall, 28 corridors run along arterial streets and five run along collectors. The purpose of the corridor analysis is to identify whether corridors in certain locations, with specific attributes and with or without bicycle facilities, are characterized by higher concentrations of air pollution. The average NO2 level along each corridor was calculated by averaging the NO2 levels over all the intersections forming the corridor. Attributes of each corridor such as street typology (arterial, collector or local), number of signalized intersections and length were extracted using ESRI’s ArcMap, geographic information systems. The type of bicycle facility (bicycle path or cycle track) along the corridor was also identified. In this paper, bicycle path is the term used to describe any bicycle facility that is not separated from the roadway by a physical barrier and often just has painted lines. Cycle tracks often have concrete medians or bollards separating them from car traffic. Bicycle facilities slightly offset from the road are also considered cycle tracks. The corridors were then ranked based on the number of cyclists that travel along them and their air pollution levels. Using these rankings, the Spearman’s Rank Correlation Coefficient was computed. A simple correlation matrix looking at the associations between the NO2 concentrations along the corridors, number of cyclists and the road types characterizing the corridor was also developed. Again simple analyses are used rather than more sophisticated statistical techniques since the goal of this paper is to explore the link between NO2 levels and cyclist flows rather than to quantify the strength of factors affecting cyclist’s exposure which would be impossible with the current data. To derive the concentration of NO2 potentially accumulated by cyclists throughout sample trips in Montreal and ‘‘simulate’’ a personal exposure study without actually recruiting cyclists and providing them with personal exposure devices, the NO2 map was used as a proxy for ambient air quality concentrations. Research assistants were asked to cycle along specified routes while carrying a GPS unit (only) and hence recording their cycling speed. We define a route as a set of consecutive road segments and intersections linking a trip origin and a destination. Five popular routes of roughly the same length were identified. These routes are known to be heavily used by cyclists as they link common origins and destinations in Montreal. The characteristics of the five routes are presented in Table 1. Two research assistants equipped with a GPS unit cycled along the selected routes whereby in the ‘‘inbound’’ direction research assistants cycled towards downtown and in the ‘‘outbound’’ direction where they cycled away from downtown. Each assistant cycled along the same route only once, carrying a GPS (BT-Q1000ex unit developed by QStarz International Co. Ltd.) set at a frequency of 1 Hertz providing second-by-second positions. The position data were then overlaid with the NO2 map and the NO2 level associated with each position was extracted based on the nearest NO2 data point. Cumulative concentrations were computed to obtain the NO2 exposure potentially accumulated by cyclists.1
1 This exercise is not meant to replace a personal exposure study of cyclists but to help shed light on the routes that are the most polluted taking into account that the NO2 land use model provides yearly average concentration levels and hence a good idea of the spatial distribution of NO2 levels around the city.
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625
Fig. 2. Box plot illustrating NO2 concentrations for ranges of bicycle flows (red dots represent mean values). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 2 Correlation matrix illustrating the association between NO2 levels and bicycle flows at intersections. Variables
NO2 concentration
Concentration of NO2 at intersection Natural logarithm of bicycle flow All approaches arterials All approaches collectors Distance to downtown
1 0.266* 0.137* 0.083** 0.516*
Ln bicycle flow
All approaches arterials
All approaches collectors
Distance to downtown
1 0.049 0.017 0.597*
1 0.086** 0.034
1 0.023
1
*
Significant at 1%. ** Significant at 5%.
3. Results The intersections were split into eight groups based on bicycle flows. Mean NO2 concentrations were found to be higher for the intersections with higher bicycle flows (Fig. 2). Table 2 presents the correlation between the concentration of NO2 at each intersection, bicycle flow, distance of the intersection to downtown and the type of intersecting roads. Since the distribution of bicycle flow has a positive skew, its natural logarithm was used. A significant correlation is observed between NO2 concentrations and bicycle flows at the intersection level. In addition, intersections where all approaches are either arterials or collectors are significantly associated with NO2 concentrations. The distance to downtown is negatively correlated with NO2 levels and bicycle activity. NO2 levels along all bicycle facilities on the island of Montreal are presented in Fig. 3. This figure identifies the facilities in the central neighbourhoods as having greater NO2 levels compared to the east and west ends of the Island. Also, the paths running along the boundary of the Island are associated with the lowest NO2 levels. On an annual basis, in 2006 Montreal did not surpass the standard for NO2 (53 ppb USEPA and 54.7 ppb Quebec) and the highest recorded value was 38 ppb.2 Average NO2 levels were computed along each of the 33 corridors; summary statistics are provided in Table 3. Splitting the corridors into those with and without bicycle facilities reveals that the average NO2 concentration is greater along corridors with facilities by 1.35 ppb, and the number of cyclists is more than two times higher. This indicates that a larger number of cyclists are exposed to higher NO2 levels. Table 4 illustrates the correlation between NO2 levels, cyclist flows and facility types showing the non-linearity between NO2 levels and cyclist flows. This non-linearity is also illustrated in Fig. 4. The Spearman’s rank correlation coefficient was also computed yielding a value of 0.68 for the correlation between the number of cyclists on the corridor and the NO2 level along the corridor. The five routes located in boroughs of Montreal were selected as illustrated in Fig. 5. The average NO2 level accumulated by the research assistants who volunteered to carry a GPS along each route is also presented. Table 5 shows that the average exposure across the routes varies significantly. Routes requiring cyclists to cross many intersections have higher exposure to
2 Despite not exceeding the standard, studies have found that even low levels of NO2 are associated with specific health responses (Weichenthal et al., 2011; Strak et al., 2010).
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Fig. 3. NO2 levels along bicycle facilities in Montreal.
Table 3 Summary of NO2 levels and bicycle flows along selected corridors. Corridor classification
Without bicycle facility
With bicycle facility
Min NO2 (ppb) Max NO2 (ppb) Average NO2 (ppb) 25th Percentile NO2 (ppb) 50th Percentile NO2 (ppb) 75th Percentile NO2 (ppb) Observations
9.52 20.78 13.19 11.55 12.99 14.54 20
10.86 18.51 14.54 12.64 14.55 15.57 13
Table 4 Correlation matrix between NO2 levels, bicycle flows and facility types along selected corridors.
* **
Variable
Average NO2 concentration
Average daily cyclists
Natural log of average daily cyclists
NO2 concentration Average daily cyclists Natural log of average daily cyclists Facility
1 0.444** 0.599*
1 0.893*
1
0.253
0.413**
0.434**
Significant at 1%. Significant at 5%.
Facility
1
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625
Fig. 4. Trend in NO2 levels (in ppb) and average daily number of cyclists along selected corridors.
Fig. 5. NO2 levels (ppb) along selected bicycle routes overlaying the map of NO2 levels (ppb) in Montreal (shades of grey); numbers in yellow boxes indicate averages across each route. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625 Table 5 Exposure and travel time along selected routes. Route
1 2 3 4 5
Biking inbound
Biking outbound
Travel time (min)
Average exposure (ppb)
Travel time (min)
Average exposure (ppb)
14 16 26 20 15
12.8 12.6 10.1 12.4 17.2
18 17 16 13 15
12.7 12.6 9.8 12.2 17.7
Intersections
Average annual daily cyclists
20 24 19 18 35
1317 82 178 49 1199
NO2 and overall, the routes with more cyclists also have greater NO2 levels. The average exposure is greatest on the fifth route and is much greater than the exposure along the other routes; this route passes through a busy and congested part of Montreal. Routes 1, 2 and 4 have very similar exposure values while the exposure along route 3 is by far the lowest. 4. Conclusion This paper presents a method of combining NO2 pollution data with bicycle flow counts. The output of an NO2 land use model was used to determine the NO2 levels at signalized intersections and along bicycle facilities in Montreal providing an initial glimpse into the link between where cyclists are riding and where air pollution levels are highest. The downtown area and some boroughs of Montreal are strongly and positively associated with bicycle flows and NO2 levels. Higher air pollution levels were found to be along corridors with bicycle facilities (bicycle paths and cycle tracks) and with higher numbers of cyclists. This latter finding has significant policy repercussions. While corridors with bicycle facilities are safer with respect to the risk of cyclist injury, this study shows that they are also associated with higher air pollution levels due to their location along busy arterial roads. References Crouse, D.L., Goldberg, M.S., Ross, N.A., 2009. A prediction-based approach to modelling temporal and spatial variability of traffic-related air pollution in Montreal, Canada. Atmospheric Environment 43, 5075–5084. Frank, L.D., Saelens, B.E., Powell, K.E., Chapman, J.E., 2007. Stepping towards causation: do built environments or neighbourhood and travel preferences explain physical activity, driving and obesity? Social Science and Medicine 65, 1898–1914. Int Panis, L., de Geus, B., Vandenbulcke, G., Willems, H., Degraeuwe, B., Bleux, N., Mishra, V., Thomas, I., Meeusen, R., 2010. Exposure to particulate matter in traffic: a comparison of cyclists and car passengers. Atmospheric Environment 44, 2263–2270. Strak, M., Boogaard, H., Meliefste, K., Oldenwening, M., Zuurbier, M., Brunekreef, B., Hoek, G., 2010. Respiratory health effects of ultrafine and fine particle exposure in cyclists. Occupational Environmental Medicine 67, 118–124. Strauss, J., Miranda-Moreno, L., 2011. Spatial modeling of bicycle activity at signalized intersections. In Proceedings of the first ‘‘World Symposium on Transport and Land Use Research (WSTLUR)’’, Whistler, British Columbia, July 27–30, 2011. Weichenthal, S., Kulka, R., Dubeau, A., Martin, C., Wang, D., Dales, R., 2011. Traffic-related air pollution and acute changes in heart rate variability and respiratory function in urban cyclists. Environmental Health Perspectives 119, 1373–1378. Zuurbier, M., Hoek, G., Oldenwening, M., Meliefste, K., Van den Hazel, P., Bruneekreef, B., 2011. Respiratory effects of commuter’s exposure to air pollution in traffic. Epidemiology 22, 219–227.
Contents lists available at SciVerse ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Investigating the link between cyclist volumes and air pollution along bicycle facilities in a dense urban core Jillian Strauss a, Luis Miranda-Moreno a, Dan Crouse b, Mark S. Goldberg c, Nancy A. Ross d, Marianne Hatzopoulou a,⇑ a
Department of Civil Engineering and Applied Mechanics, McGill University, Montréal, QC, Canada H3A 2K6 Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada K1A 0K9 c Division of Clinical Epidemiology, Department of Medicine, McGill University Health Centre, Royal Victoria Hospital, Montréal, QC, Canada H3A 1A1 d Department of Geography, McGill University, Montréal, QC, Canada H3A 2K6 b
a r t i c l e Keywords: Bicycling Air pollution Bicycle facilities Cyclist exposure
i n f o
a b s t r a c t In this paper we explore the air pollution levels along types of bicycle facilities using a NO2 land use model previously developed for Montreal. We explore potential associations between bicycle volumes through signalized intersections and pollution levels at those intersections. We further investigate this relationship through the comparison of over thirty cycling corridors as well as an evaluation of the potential exposure of cyclists to air pollution along five routes. We observe NO2 concentrations to be positively correlated with bicycle flows at the intersection level. We also observe that corridors with either a bicycle path or cycle track generally rank higher in terms of bicycle volume and also have higher NO2 concentrations than corridors without bicycle facilities. This indicates that intersections and bicycle facilities with a large number of cyclists are also those characterized with the highest air pollution levels. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Recently, a strong association between the built environment, physical activity and obesity has been demonstrated, suggesting that compact, mixed-use urban areas contribute to decreased automobile dependency and increased walking and cycling (Frank et al., 2007). Indeed, many North American cities are investing in bicycle infrastructure, bicycle sharing programs and large campaigns to promote cycling as an affordable and environmentally friendly alternative mode of transportation. Yet, while compact urban areas promote active transportation, they also trap pollutants in street canyons, thus concentrating vehicle-induced air pollution. Cyclists are at risk of being exposed to higher concentrations of air pollution as compared to other road users due to their proximity to traffic, high respiration rates and longer journeys. Research into cyclist’s exposure to air pollution has focused on measuring actual exposure levels of samples of cyclists on pre-selected routes using personal samplers (Zuurbier et al., 2011) or inferring personal exposure from measurements of street-level pollution using mobile laboratories or fixed-site ambient air monitoring stations (Int Panis et al., 2010). Many studies have also attempted to correlate specific physiological responses with cyclist’s exposure to air pollution and found evidence that adverse health effects can result from short-term exposure (Weichenthal et al., 2011). Rather than directly measuring cyclist exposure, the purpose of this paper is to explore potential associations between the volume of cyclists along roads and intersections and the concentrations of traffic-related air pollution. We hypothesize that
⇑ Corresponding author. Tel.: +1 514 398 6935; fax: +1 514 398 7361. E-mail address: [email protected] (M. Hatzopoulou). 1361-9209/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trd.2012.07.007
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625
bicycle activity is correlated with air quality around the city and cyclists are therefore often found on streets characterized by high levels of air pollution. Unlike previous studies which attempt to quantify the level of cyclist exposure on a trip or daily basis, we are concerned with evaluating the relationship between the numbers of cyclists attracted towards intersections and bicycle facilities and the air quality levels characterizing them. Our study is set in Montreal, Canada and has the objectives of: investigating the association between bicycle flow through signalized intersections and air pollution concentrations, comparing specific cycling corridors according to estimated levels of traffic-related pollution, bicycle activity and presence of bicycle paths or track, and evaluating the average exposure of a cyclist to air pollution. 2. Materials and methods Concentrations of nitrogen dioxide (NO2) were estimated across the island of Montreal at signalized intersections for which bicycle flow data are available. Concentrations of NO2 were also estimated along specific cycling corridors. The latter data were used to estimate cyclist exposure along cycling routes. Estimates of the annual average concentrations of NO2 were obtained through the application of a land use model developed specifically for the island of Montreal. These data provide an excellent marker of traffic-related air pollution. Briefly, a series of dense sampling campaigns were conducted in 2005 and 2006 to estimate integrated two-week NO2 concentrations at individual points throughout the entire island. Concentrations of NO2 were measured using two-sided Ogawa passive samplers (Ogawa and Co., USA) during: November/December 2005, April/May 2006, and August 2006. The samplers were installed at a height of 2.5 metres near the sidewalk at 133 locations across Montreal. A set of variables describing specific land use and road density characteristics measured at radial distances away from the samplers (100, 300, 500 and 700 m) was then generated. With these variables, a land use model was estimated and used to predict concentrations of NO2 where measurements were not taken. The resulting model was able to predict 80% of the variability in NO2 levels (Crouse et al., 2009). Fig. 1 presents NO2 concentrations across Montreal; the highest concentrations, identified by the darkest grey shade on the map, occur along highways and major urban streets. The city of Montreal carried out manual bicycle counts at a large sample of signalized intersections. This study focuses on 753 signalized intersections that meet the following criteria: recent cyclist and traffic counts are available for 2008 and 2009, and counts were taken during the cycling season, when seasonal bicycle facilities are open (April–November). These intersections were not randomly selected, however they do represent over 30% of all the intersections on the island as well as provide a representative sample of the built environments in the City. Bicycle counts were conducted over an 8 h period
Fig. 1. Concentration of NO2 (in parts per billion) across the island overlaid by signalized intersections at which cyclist counts were conducted.
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625 Table 1 Characteristics of selected routes cycled by research assistants to evaluate the average exposure along these routes.
a
Route
Distance travelled (km)a
Signalized intersections (intersections with data)
Average light cycle length (s)
Bicycle facility
% Of route with bicycle facility (%)
1 2 3 4 5
4.2 4.9 4.4 4.6 4.3
20 24 19 18 35
81 80 80 90 82
Cycle track Cycle track None None Cycle track
80 13 0 0 28
(16) (18) (13) (16) (30)
Approximate distance of one-way trip.
during the same day (6:00 am–9:00 am; 11:00 am–1:00 pm; 3:30 pm–6:30 pm) at each intersection. The city of Montreal has also installed permanent bicycle counters along five of Montreal’s bicycle facilities. Using these automatic hourly bicycle count data, hourly, daily and monthly expansion factors were developed. These factors were then applied to the 8 h counts to convert them to average annual daily values (Strauss and Miranda-Moreno, 2011). The locations of the signalized intersections at which bicycle counts were conducted were overlaid onto the NO2 map (Fig. 1) and NO2 concentrations were extracted at each intersection. To investigate whether a relationship exists between bicycle flow through intersections, NO2 concentrations, and other variables, a correlation matrix was constructed focusing on: bicycle flow at the intersection, concentration of NO2 at the intersection, distance of the intersection to the downtown area as a proxy for traffic flows which are greater downtown and the type of intersection based on the classes of the intersecting streets; intersections where all approaches are arterials and where all approaches are collectors. A correlation matrix was preferred over more sophisticated tests because it captures the main effect that we are trying to unveil; namely the association between cyclist volumes and air pollution. Linear and non-linear associations were tested. In addition, a box plot showing the NO2 concentrations for ranges of bicycle flows was generated to illustrate the same effect. Cycling corridors were constructed by selecting a set of consecutive intersections running along Montreal streets. Thirtythree corridors were constructed for which bicycle flows were available for a minimum of eight intersections. The length of these corridors ranges from 2.3 km to 31 km, the number of intersections ranges from 8 to 65. Some have a bicycle path or a cycle track while others have no bicycle facilities. Overall, 28 corridors run along arterial streets and five run along collectors. The purpose of the corridor analysis is to identify whether corridors in certain locations, with specific attributes and with or without bicycle facilities, are characterized by higher concentrations of air pollution. The average NO2 level along each corridor was calculated by averaging the NO2 levels over all the intersections forming the corridor. Attributes of each corridor such as street typology (arterial, collector or local), number of signalized intersections and length were extracted using ESRI’s ArcMap, geographic information systems. The type of bicycle facility (bicycle path or cycle track) along the corridor was also identified. In this paper, bicycle path is the term used to describe any bicycle facility that is not separated from the roadway by a physical barrier and often just has painted lines. Cycle tracks often have concrete medians or bollards separating them from car traffic. Bicycle facilities slightly offset from the road are also considered cycle tracks. The corridors were then ranked based on the number of cyclists that travel along them and their air pollution levels. Using these rankings, the Spearman’s Rank Correlation Coefficient was computed. A simple correlation matrix looking at the associations between the NO2 concentrations along the corridors, number of cyclists and the road types characterizing the corridor was also developed. Again simple analyses are used rather than more sophisticated statistical techniques since the goal of this paper is to explore the link between NO2 levels and cyclist flows rather than to quantify the strength of factors affecting cyclist’s exposure which would be impossible with the current data. To derive the concentration of NO2 potentially accumulated by cyclists throughout sample trips in Montreal and ‘‘simulate’’ a personal exposure study without actually recruiting cyclists and providing them with personal exposure devices, the NO2 map was used as a proxy for ambient air quality concentrations. Research assistants were asked to cycle along specified routes while carrying a GPS unit (only) and hence recording their cycling speed. We define a route as a set of consecutive road segments and intersections linking a trip origin and a destination. Five popular routes of roughly the same length were identified. These routes are known to be heavily used by cyclists as they link common origins and destinations in Montreal. The characteristics of the five routes are presented in Table 1. Two research assistants equipped with a GPS unit cycled along the selected routes whereby in the ‘‘inbound’’ direction research assistants cycled towards downtown and in the ‘‘outbound’’ direction where they cycled away from downtown. Each assistant cycled along the same route only once, carrying a GPS (BT-Q1000ex unit developed by QStarz International Co. Ltd.) set at a frequency of 1 Hertz providing second-by-second positions. The position data were then overlaid with the NO2 map and the NO2 level associated with each position was extracted based on the nearest NO2 data point. Cumulative concentrations were computed to obtain the NO2 exposure potentially accumulated by cyclists.1
1 This exercise is not meant to replace a personal exposure study of cyclists but to help shed light on the routes that are the most polluted taking into account that the NO2 land use model provides yearly average concentration levels and hence a good idea of the spatial distribution of NO2 levels around the city.
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J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625
Fig. 2. Box plot illustrating NO2 concentrations for ranges of bicycle flows (red dots represent mean values). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Table 2 Correlation matrix illustrating the association between NO2 levels and bicycle flows at intersections. Variables
NO2 concentration
Concentration of NO2 at intersection Natural logarithm of bicycle flow All approaches arterials All approaches collectors Distance to downtown
1 0.266* 0.137* 0.083** 0.516*
Ln bicycle flow
All approaches arterials
All approaches collectors
Distance to downtown
1 0.049 0.017 0.597*
1 0.086** 0.034
1 0.023
1
*
Significant at 1%. ** Significant at 5%.
3. Results The intersections were split into eight groups based on bicycle flows. Mean NO2 concentrations were found to be higher for the intersections with higher bicycle flows (Fig. 2). Table 2 presents the correlation between the concentration of NO2 at each intersection, bicycle flow, distance of the intersection to downtown and the type of intersecting roads. Since the distribution of bicycle flow has a positive skew, its natural logarithm was used. A significant correlation is observed between NO2 concentrations and bicycle flows at the intersection level. In addition, intersections where all approaches are either arterials or collectors are significantly associated with NO2 concentrations. The distance to downtown is negatively correlated with NO2 levels and bicycle activity. NO2 levels along all bicycle facilities on the island of Montreal are presented in Fig. 3. This figure identifies the facilities in the central neighbourhoods as having greater NO2 levels compared to the east and west ends of the Island. Also, the paths running along the boundary of the Island are associated with the lowest NO2 levels. On an annual basis, in 2006 Montreal did not surpass the standard for NO2 (53 ppb USEPA and 54.7 ppb Quebec) and the highest recorded value was 38 ppb.2 Average NO2 levels were computed along each of the 33 corridors; summary statistics are provided in Table 3. Splitting the corridors into those with and without bicycle facilities reveals that the average NO2 concentration is greater along corridors with facilities by 1.35 ppb, and the number of cyclists is more than two times higher. This indicates that a larger number of cyclists are exposed to higher NO2 levels. Table 4 illustrates the correlation between NO2 levels, cyclist flows and facility types showing the non-linearity between NO2 levels and cyclist flows. This non-linearity is also illustrated in Fig. 4. The Spearman’s rank correlation coefficient was also computed yielding a value of 0.68 for the correlation between the number of cyclists on the corridor and the NO2 level along the corridor. The five routes located in boroughs of Montreal were selected as illustrated in Fig. 5. The average NO2 level accumulated by the research assistants who volunteered to carry a GPS along each route is also presented. Table 5 shows that the average exposure across the routes varies significantly. Routes requiring cyclists to cross many intersections have higher exposure to
2 Despite not exceeding the standard, studies have found that even low levels of NO2 are associated with specific health responses (Weichenthal et al., 2011; Strak et al., 2010).
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Fig. 3. NO2 levels along bicycle facilities in Montreal.
Table 3 Summary of NO2 levels and bicycle flows along selected corridors. Corridor classification
Without bicycle facility
With bicycle facility
Min NO2 (ppb) Max NO2 (ppb) Average NO2 (ppb) 25th Percentile NO2 (ppb) 50th Percentile NO2 (ppb) 75th Percentile NO2 (ppb) Observations
9.52 20.78 13.19 11.55 12.99 14.54 20
10.86 18.51 14.54 12.64 14.55 15.57 13
Table 4 Correlation matrix between NO2 levels, bicycle flows and facility types along selected corridors.
* **
Variable
Average NO2 concentration
Average daily cyclists
Natural log of average daily cyclists
NO2 concentration Average daily cyclists Natural log of average daily cyclists Facility
1 0.444** 0.599*
1 0.893*
1
0.253
0.413**
0.434**
Significant at 1%. Significant at 5%.
Facility
1
624
J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625
Fig. 4. Trend in NO2 levels (in ppb) and average daily number of cyclists along selected corridors.
Fig. 5. NO2 levels (ppb) along selected bicycle routes overlaying the map of NO2 levels (ppb) in Montreal (shades of grey); numbers in yellow boxes indicate averages across each route. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
625
J. Strauss et al. / Transportation Research Part D 17 (2012) 619–625 Table 5 Exposure and travel time along selected routes. Route
1 2 3 4 5
Biking inbound
Biking outbound
Travel time (min)
Average exposure (ppb)
Travel time (min)
Average exposure (ppb)
14 16 26 20 15
12.8 12.6 10.1 12.4 17.2
18 17 16 13 15
12.7 12.6 9.8 12.2 17.7
Intersections
Average annual daily cyclists
20 24 19 18 35
1317 82 178 49 1199
NO2 and overall, the routes with more cyclists also have greater NO2 levels. The average exposure is greatest on the fifth route and is much greater than the exposure along the other routes; this route passes through a busy and congested part of Montreal. Routes 1, 2 and 4 have very similar exposure values while the exposure along route 3 is by far the lowest. 4. Conclusion This paper presents a method of combining NO2 pollution data with bicycle flow counts. The output of an NO2 land use model was used to determine the NO2 levels at signalized intersections and along bicycle facilities in Montreal providing an initial glimpse into the link between where cyclists are riding and where air pollution levels are highest. The downtown area and some boroughs of Montreal are strongly and positively associated with bicycle flows and NO2 levels. Higher air pollution levels were found to be along corridors with bicycle facilities (bicycle paths and cycle tracks) and with higher numbers of cyclists. This latter finding has significant policy repercussions. While corridors with bicycle facilities are safer with respect to the risk of cyclist injury, this study shows that they are also associated with higher air pollution levels due to their location along busy arterial roads. References Crouse, D.L., Goldberg, M.S., Ross, N.A., 2009. A prediction-based approach to modelling temporal and spatial variability of traffic-related air pollution in Montreal, Canada. Atmospheric Environment 43, 5075–5084. Frank, L.D., Saelens, B.E., Powell, K.E., Chapman, J.E., 2007. Stepping towards causation: do built environments or neighbourhood and travel preferences explain physical activity, driving and obesity? Social Science and Medicine 65, 1898–1914. Int Panis, L., de Geus, B., Vandenbulcke, G., Willems, H., Degraeuwe, B., Bleux, N., Mishra, V., Thomas, I., Meeusen, R., 2010. Exposure to particulate matter in traffic: a comparison of cyclists and car passengers. Atmospheric Environment 44, 2263–2270. Strak, M., Boogaard, H., Meliefste, K., Oldenwening, M., Zuurbier, M., Brunekreef, B., Hoek, G., 2010. Respiratory health effects of ultrafine and fine particle exposure in cyclists. Occupational Environmental Medicine 67, 118–124. Strauss, J., Miranda-Moreno, L., 2011. Spatial modeling of bicycle activity at signalized intersections. In Proceedings of the first ‘‘World Symposium on Transport and Land Use Research (WSTLUR)’’, Whistler, British Columbia, July 27–30, 2011. Weichenthal, S., Kulka, R., Dubeau, A., Martin, C., Wang, D., Dales, R., 2011. Traffic-related air pollution and acute changes in heart rate variability and respiratory function in urban cyclists. Environmental Health Perspectives 119, 1373–1378. Zuurbier, M., Hoek, G., Oldenwening, M., Meliefste, K., Van den Hazel, P., Bruneekreef, B., 2011. Respiratory effects of commuter’s exposure to air pollution in traffic. Epidemiology 22, 219–227.