首页 Cyclist route choice, traffic-related air pollution,

Cyclist route choice, traffic-related air pollution,

举报
开通vip

Cyclist route choice, traffic-related air pollution, 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. Envir...

Cyclist route choice, traffic-related air pollution,
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 http://www.ehjournal.net/content/12/1/14 (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 http://www.ehjournal.net/content/12/1/14 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 http://www.ehjournal.net/content/12/1/14 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
本文档为【Cyclist route choice, traffic-related air pollution,】,请使用软件OFFICE或WPS软件打开。作品中的文字与图均可以修改和编辑, 图片更改请在作品中右键图片并更换,文字修改请直接点击文字进行修改,也可以新增和删除文档中的内容。
该文档来自用户分享,如有侵权行为请发邮件ishare@vip.sina.com联系网站客服,我们会及时删除。
[版权声明] 本站所有资料为用户分享产生,若发现您的权利被侵害,请联系客服邮件isharekefu@iask.cn,我们尽快处理。
本作品所展示的图片、画像、字体、音乐的版权可能需版权方额外授权,请谨慎使用。
网站提供的党政主题相关内容(国旗、国徽、党徽..)目的在于配合国家政策宣传,仅限个人学习分享使用,禁止用于任何广告和商用目的。
下载需要: 免费 已有0 人下载
最新资料
资料动态
专题动态
is_311769
暂无简介~
格式:pdf
大小:2MB
软件:PDF阅读器
页数:0
分类:
上传时间:2013-11-06
浏览量:8