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Particulate matter exposure and health impacts of urban cyclists: a randomized crossover study Cole, Christie A; Carlsten, Christopher; Koehle, Michael; Brauer, Michael Nov 14, 2018

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RESEARCH Open AccessParticulate matter exposure and healthimpacts of urban cyclists: a randomizedcrossover studyChristie A. Cole1, Christopher Carlsten2, Michael Koehle3 and Michael Brauer1*AbstractBackground: Cycling and other forms of active transportation provide health benefits via increased physicalactivity. However, direct evidence of the extent to which these benefits may be offset by exposure and intake oftraffic-related air pollution is limited. The purpose of this study is to measure changes in endothelial function,measures of oxidative stress and inflammation, and lung function in healthy participants before and after cyclingalong a high- and low- traffic route.Methods: Participants (n = 38) bicycled for 1 h along a Downtown and a Residential designated bicycle route in arandomized crossover trial. Heart rate, power output, particulate matter air pollution (PM10, PM2.5, and PM1) andparticle number concentration (PNC) were measured. Lung function, endothelial function (reactive hyperemia index,RHI), C-reactive protein, interleukin-6, and 8-hydroxy-2′-deoxyguanosine were assessed within one hour pre- andpost-trial.Results: Geometric mean PNC exposures and intakes were higher along the Downtown (exposure = 16,226particles/cm3; intake = 4.54 × 1010 particles) compared to the Residential route (exposure = 9367 particles/cm3;intake = 3.13 × 1010 particles). RHI decreased following cycling along the Downtown route and increased on theResidential route; in mixed linear regression models, the (post-pre) change in RHI was 21% lower following cycling onthe Downtown versus the Residential route (−0.43, 95% CI: -0.79, −0.079) but RHI decreases were not associated withmeasured exposure or intake of air pollutants. The differences in RHI by route were larger amongst females and olderparticipants. No consistent associations were observed for any of the other outcome measures.Conclusions: Although PNC exposures and intakes were higher along the Downtown route, the lack of associationbetween air pollutant exposure or intake with RHI and other measures suggests other exposures related to cycling onthe Downtown route may have been influential in the observed differences between routes in RHI.Trial registration: ClinicalTrials.gov, NCT01708356. Registered 16 October 2012.Keywords: Cycling, Endothelial function, Oxidative stress, Inflammation, Lung function, Air pollution, Particulate matterBackgroundThe World Health Organization (WHO) identifies seden-tary lifestyles as a major risk factor for global mortalityand chronic disease [1]. Only 15% of Canadian adults [2]meet WHO and Canadian Physical Activity Guidelines of150 min of moderate-to-vigorous physical activity perweek in bouts of 10 min or more [1, 3]. Cycling is a formof active transportation that it is relatively accessible toall socio-economic classes and addresses typical utili-tarian transportation distances of < 8 km [4, 5]. Inaddition to its potential as a strategy to reduce physicalinactivity [6], cycling provides further societal benefitas a form of transportation that does not produceharmful emissions [7–10].Despite the potential benefits of cycling, there are con-cerns regarding adverse health impacts to cyclists due toincreased inhalation of traffic-related air pollution espe-cially in urban areas where cyclists often travel in close* Correspondence: michael.brauer@ubc.ca1School of Population and Public Health, University of British Columbia, 2206East Mall, Vancouver, BC V6T 1Z3, CanadaFull list of author information is available at the end of the article© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Cole et al. Environmental Health           (2018) 17:78 https://doi.org/10.1186/s12940-018-0424-8proximity to road traffic [11–13]. Studies have consist-ently shown the physiological impacts of acute exposureto particulate matter (PM), linking it with changes invascular tone [14–16], cardiovascular morbidity andmortality [17, 18], oxidative stress [19], pulmonary in-flammation [20], and stimulation of pulmonary irritantreceptors [21, 22]. However, these studies only serve todemonstrate the acute health effects of PM exposure inthe home and laboratory, environments that lack elementspresent during a cycling trip that may further impactthe effect of PM exposure on health. For instance, in-creased respiration rates due to physical effort involvedwhile cycling leads to increased inhalation of pollutedair [23, 24].While several studies have documented unfavorablephysiological changes, such as to lung function andheart rate variability [25–27], as well as increases in in-flammatory biomarkers [28, 29] amongst cyclists ex-posed to air pollution, few such studies have attemptedto quantify exposure and intake levels of air pollutantsand the resulting effect on acute health measurements.There is also limited understanding of the health impactsdue to variations in pollution concentrations along cyclingroutes. Therefore, this study aims to assess the acutehealth impacts of cycling along a low versus a hightraffic route using a randomized crossover design.This study compares exposure to, and intake of, PMwhile cycling, using a number of biochemical and clin-ical parameters measured before and after travelingalong two routes. Specific measurements were selectedto represent a set of well validated biochemicalmarkers of inflammation (interleukin-6 [IL-6] andC-reactive protein [CRP]) and oxidative stress(8-hydroxy-2′-deoxyguanosine [8-OHdG]), along withclinical vascular (reactive hyperemia by EndoPAT) andpulmonary function measures.MethodsParticipants and study designHealthy adult (ages 19–39) participants were recruitedusing advertisements posted to local cycling and univer-sity bulletin boards and along cycling routes in the cityof Vancouver, Canada. Participants were eligible if theywere non-smokers, not diagnosed with or taking a medi-cation for any respiratory or cardiovascular condition,not exposed to environmental tobacco smoke in thehome or otherwise exposed to significant respiratory ex-posures in the workplace. Participants with seasonal al-lergies were asked to participate at a time of the yearwhen they were asymptomatic. Females were tested dur-ing days 1–8 of their follicular phase, while those usingmonocyclic oral contraceptives were tested on dayswhere they took an active pill to ensure consistent hor-monal status. Written informed consent was obtainedfor each participant after a session given to familiarizeeach person with the equipment and protocols, prior tocommencing the first cycling trial. The Health Canada(certificate #2011–0009) and University of BritishColumbia Clinical Research (#H10–00902) Ethics Boardsapproved this study. The study was registered withClinicalTrials.gov (NCT01708356).All cycling trials began and ended at the VancouverGeneral Hospital in central Vancouver, British Columbiaduring May– November 2010 and May– November 2011.Metro Vancouver has an annual mean particle numberconcentration of 18,200 particles/cm3 (pt/cm³) (standarddeviation = 15,900 pt/cm3) (from 2009 and 2010 data)[30], an annual mean PM2.5 concentration of 4.3 μg/m3 atthe T2:Vancouver- Kitsilano station, and an annual meanPM10 concentration of 10.5 μg/m3 at the nearest monitor-ing location (T24: Burnaby North ) [31]. Trials were con-ducted between 700 and 1600 h, and each participantcompleted both trials at the same time of day (+/− 1 h),scheduled 2 to 6 weeks apart. Two routes were selectedusing a recent study that obtained particulate matter ex-posure measurements from designated bicycle routes inVancouver [13], with consideration of nearby land-use cat-egories in order to select one predominantly residentialuse (“Residential”) route and one predominantly commer-cial or higher density residential (“Downtown”) route. Themid-point of the Downtown ride (Dunsmuir Street atRichards Street, 2011 through-traffic counts) had trafficcounts between 7.8 to 9.9 times that of the traffic on atypical section (Ontario Street at 36th Street, based on2006 traffic counts) along the Residential Route. Routeorder was assigned randomly. The Downtown routewas a 9.7 km loop, and was always traveled in acounter-clockwise direction, with a section of protectedbike lane that could be repeated if the cyclist was a fas-ter rider. Most participants repeated the protected bikelane section, resulting in a total elevation gain of 127 mover three major uphill segments (with the most signifi-cant segments being 29 m, 26 m, and 26 m in elevationgain) [32]. The Residential route was a 12.0 km loopthat was traveled in a clockwise direction for some tri-als (120 m of elevation gain, from 2 major uphill seg-ments of 49 m and 26 m), or as an out-and-back ride ina counter-clockwise direction, to the far south-east cor-ner of the route before reversing direction (172 m ofelevation gain, over 3 major uphill segments of 41 m,36 m, and 18 m) [32]. A trailing research assistant (alsoon bike) provided wayfinding and timing directionswith the aim of facilitating a 60-min ride, rather thancompleting a specific distance. Questionnaires prior tothe beginning of each trial were used to screen for al-lergy or cold symptoms, to confirm that each partici-pant had limited alcohol and caffeine consumption, andto confirm that each participant consumed the sameCole et al. Environmental Health           (2018) 17:78 Page 2 of 14meals prior to the trials. Participants were also asked totravel to the study site using the same method of trans-portation on both trial days.Exercise and exposure monitoringTwo bicycles of the same model (KHS, Flite 250, LosAngeles, USA), one each of small and large frame size,were used in all trials. A PowerTap Comp (PowerTap,Madison, WI, USA) wiring set which automatically re-corded at 3-s intervals during the ride, was installed onthe handlebars of each bicycle to measure power output,cadence, and heart rate from a chest-worn heart rate strap.A single PowerTap hub was installed on a wheel that couldbe transferred between the two bicycles. A P-trak (UltrafineParticle Counter 8525, TSI Inc., Shoreview, MN, USA),with the tilt sensor removed was mounted into a wire pan-nier (Swagman Fat Folding Basket, Swagman Racks, Pentic-ton, BC, CAN) by placing a horizontal bar through thehandle of the monitor and stabilized using elastic cords andmetal bearings, thus allowing the P-trak to remain horizon-tal despite changing inclines. The P-trak recorded particlenumber (0.02–1 μm) concentration (PNC) at one-secondintervals. A GRIMM Dust Monitor (Model 1.108, GRIMMTechnologies, Inc. Douglasville, Georgia, USA) placed intoa separate rear pannier measured PM10, PM2.5, and PM1concentrations at 6-s intervals. Sampling inlets for bothmonitors were secured along the top tube of the bicycle, ex-tending to the centre of the handlebars. A GPS logger(DG-100 GPS DataLogger, GlobalSat WorldCom Corpor-ation, New Taipei City, Taiwan) recorded the location ofthe bicycle at 6-s intervals. To align measurements from allinstruments, data collected at intervals longer than 1 s wasapplied to the closest one second time point until a newdata point was available, carried for a maximum of 6 s.Physiological measurementsAll measurements were completed within one hour priorto the beginning of each bicycle ride, and were repeatedapproximately 15 min after the cyclists’ return and com-pleted within 90 min of ride termination. Tests were ad-ministered in uniform order (endothelial function,followed by spirometry, followed by bloodwork) beforeand after each ride, with exceptions noted and replicatedfor a given subject when possible. For a given individual,tests for the second trial were performed within onehour of the same time of day as they were performed inthe first trial. Endothelial function was measured usingthe EndoPAT 2000 device (Itamar Medical Ltd., Caesarea,Israel) to measure RHI, following the five-minute occlusionprocedure recommended in the user manual. Lung func-tion was measured with a KoKo spirometer (nSpire Health,Longmont, Colorado, USA), following American ThoracicSociety standards [33]. Five milliliters of blood were col-lected, centrifuged, and frozen prior to analysis for CRP(Dimension Vista® System Flex® reagent cartridge forhigh sensitivity CRP, Siemens Healthcare DiagnosticsProducts GmbH 2009), IL-6 (Quantikine® ELISA Hu-man IL-6 Immunoassay D6050, R&D Systems, Inc.Minneapolis, MN, USA) and 8-OHdG (Highly Sensitive8-OHdG Check ELISA method, Japan Institute for theControl of Aging, Nikken Seil Co. Ltd., Fukuroi, Shizu-oka, Japan). Increased RHI indicates improved endothe-lial function and increases in spirometric measuresindicate improved lung function, whereas increases inany of the blood measures indicate increased oxidativestress and/or inflammation.The first 15 participants completed an abbreviated in-door cycling test after the one-hour ride. Minute ventila-tion was recorded while cycling indoors at the meanheart rate recorded during the ride along each outdoorroute. All indoor cycling tests were completed on an ad-justable Velotron Dynafit Pro cycle ergometer (RacermateInc., Seattle WA). The remaining 23 participants com-pleted a step-wise submaximal exercise test, in incrementsof 20 watts every two minutes for females and 30 wattsevery two minutes for males. Heart rate using a Polar HRsensor strap (Polar s810i, Polar Electro, Finland), and mi-nute ventilation ( _VE ) using a respirometer (Spirolab II,Medical International Research, Rome, Italy) were re-corded during the second minute at every resistance levelthroughout the heart rate range experienced by each par-ticipant. Heart rate data from each participant’s submaxi-mal test results were used to estimate a subject-specificHR- _VE relationship, which was then used to estimate in-stantaneous and mean _VE for each outdoor trial for eachparticipant; _VE was estimated at each data point duringoutdoor cycling trials to calculate the total volume of in-haled air, and therefore PM intake values.Statistical analysisOnly participants who completed trials on both routeswere included in the data analysis. Out of 76 exposuretrials, 6 trials had missing exposure data. Health mea-sures were analyzed in the group results only whencomplete pairs of pre and post measurements wereavailable; incomplete pairs were excluded. Data wereanalyzed by paired t-tests comparing the two trialsfor each participant, and in mixed effects regressionmodels. Fixed variables (e.g. bivariate variables comparingthe Residential and Downtown routes, or continuous vari-ables such as air pollution exposure or intake values), andrandom variables (participants) were modeled in R [34]using the lme4 package [35] to predict changes in clinicalmeasures. Both exposure and pollutant intake were con-sidered in analyses.Intake was estimated for each participant by summingthe product of each 1-s pollutant concentration (in pt/cm3Cole et al. Environmental Health           (2018) 17:78 Page 3 of 14or μg/m3) by the volume of air inhaled each second de-rived from the heart rate data (the mean _VE of the entiretrial, converted to L/s). As high correlations were mea-sured between the PM10, PM2.5, and PM1 measurements,(Pearson product-moment correlation coefficients of 0.96to 0.99) only results for the models that include PM2.5 andPNC concentrations are presented. Pollutant concentra-tion distributions within each ride were right-skewed andsummarized by the geometric mean (GM). Fourmixed-effects models (with participant included as a ran-dom effect) were built in order to evaluate the effect ofroute differences (model 1), the effects of pollutant expos-ure (model 2) or intake (model 3) and the effect of pollu-tant intake while including route in the same model(model 4):1) Change in clinical measurement = ß Route + participant.2) Change in clinical measurement = ß GM exposure +participant.3) Change in clinical measurement = ß Pollutant intake +participant.4) Change in clinical measurement = ß Route +ß Pollutant intake + participantEffect modification was explored by modeling bodymass index (BMI: lower and upper half, stratified at themedian), age (younger half and older half, stratified atthe median), and sex (female or male) variables with theRoute variable scaled to the Residential route.ResultsMean age of the participants (n = 28 male, 10 female)was 29 ± 6 years (range 20–39 years), and mean BMIwas 22.8 ± 2.0 kg/m2 (Table 1). Most participants (87%)used the same method of transportation to arrive at thestudy site on both testing days. Three participants re-ported taking prescribed thyroid or anxiety medicationsbefore completing each of their trials.Overall mean ride time for all trials along both routeswas 63.9 and 62.9 min on the Downtown and Residentialroutes, respectively (range = 56–73 min). Mean concen-trations of PNC were significantly higher along theDowntown route (16,870 pt/cm³) compared to the Resi-dential route (10,840 pt/cm³, Table 2). Higher concentra-tions of other particle measures were also observed intrials along the Downtown compared to the Residentialroute (Table 2).Male participants experienced mean _VE of 47.9(SD = 15.0) L/min during all trials, while female par-ticipants experienced mean _VE of 40.4 (SD = 11.0) L/min. Measurements from a subset of 22 cyclists per-mitted the comparison of estimated _VE ratio duringa cycling trial compared to at rest (Table 3). Theoverall mean _VE ratio (cycling: rest) for the groupwas 3.5. Cyclists had a higher _VE ratio (3.8) whencycling along the Residential route compared to theDowntown route (3.3). The mean volume of air esti-mated to be inhaled by participants during a singlecycling trial was 2900 L, and ranged from 820 to4700 L.Power output was lower along the Downtown route com-pared to the Residential route (mean difference = 9.9 watts,95% CI: 18, 1.8 watts). Mean heart rate was also slightlylower on the Downtown route (mean difference = 4.5 beatsper minute, 95% CI: 8.6, 0.3 beats per minute). TheseTable 1 Descriptive data of participants and summary of physiological baseline measurementsVariable Baseline mean [SD] or total RangeTotal participants (male, female) 38 (28, 10) –Age (years) Overall 29 [5.6]; median = 29Male mean = 29Female mean = 3120–39BMI (kg/m2) 22.8 [2.0];median = 22.818.3–28.0Systolic BP (mmHg) 117 [9] 90–139Diastolic BP (mmHg) 67 [6] 52–83Reactive Hyperemia Index (RHI) 2.02 [0.64] 1.29–4.28CRP (mg/dL) 0.85 [1.2] 0.11–7.7IL-6 (pg/mL) 3.6 [4.3] 0.023–168-OHdG (ng/mL) 0.20 [0.12] 0.012–0.73# participants whose first trial was alongthe Downtown route17 –Morning test (session end by 12:30 pm) 23 –Cold Questionnaire score (≥ 3 probable viral infections) 0.4 [0.9] 0–3Cole et al. Environmental Health           (2018) 17:78 Page 4 of 14Table2Airpollutionexposuremeasurements,ascalculatedfrommeansofeachtrialPollutantaDowntownrouteResidentialrouteRatioofdowntown:residentialrouteb95%CIofthedowntown–residentialdifferenceMean[SD]Median;range(min,max)GM[GSD]Mean[SD]Median;range(min,max)GM[GSD]PNC(pt/cm³)16,870[4838]15,740(9597,29,060)16,226[1.33]10,840[5159]10,280(977,22,130)9367[1.86]1.533695,8376PM1(μg/m3 )5.0[4.2]3.7(1.2,20)3.8[2.0]3.8[2.9]2.9(0.48,12)2.9[2.1]1.3−0.44,2.9PM2.5(μg/m3 )7.3[5.3]5.5(2.4,24)6.0[1.9]5.8[3.7]4.4(1.1,15)4.7[1.9]1.3−0.58,3.6PM10(μg/m3 )13[7.3]11(4.3,33)11[1.8]9.9[5.6]8.8(2.2,29)8.4[1.8]1.2−0.14,5.9a CompletePMdatawasmissingfor6trialswithPNCdatamissingfor3trialsandPMdatamissingfor3trialsbRatioofthemedianoftheDowntownRoutecomparedtothemedianoftheResidentialRouteCole et al. Environmental Health           (2018) 17:78 Page 5 of 14Table3Airintakeparametersbysexandbyroute.VentilationaverageswithpollutionconcentrationateachtimepointdeterminedestimatedvaluesParticipantgroupmeanorroutemeanRidetime(minutes)[sd]Mean_ V EinL/minatrest[sd]Mean_ V EinL/minduringridebyHR[sd]EstimatedintakeofPNCduringtrial(particles)[sd]Estimatedvolumeofairinhaledatrestduringequivalentridetime(Litres)(resting_ V Exridetime)[sd]Estimatedvolumeofairinhaledduringride(Litres)(ridemean_ V Exridetime)[sd]Ratioof_ V Eridingcomparedtoatrest[sd]Males(n=28)63.6[2.69]15.8[5.16]a47.9[15.0]3.86×1010[1.72×1010]1107[317]3000[961]3.5[2.8]aFemales(n=10)63.1[3.35]10.4[1.76]a40.4[10.9]3.86×1010[2.21×1010]827[157]2540[946]3.4[1.1]aDowntown63.9[3.42]–44.8[14.0]4.54×1010[1.79×1010]–2840[688]3.3[2.2]aResidential62.9[3.57]–47.8[14.3]3.13×1010[1.61×1010]–3020[859]3.8[2.9]aOverall63.414.6[5.13]a46.1[14.5]3.86×1010[1.95×1010]10462890[922]3.5[3.5]aa Meanscalculatedusingcompletepairsonly;_ V EinL/minatrestwasonlyavailablefor22participants(17males,5females)Cole et al. Environmental Health           (2018) 17:78 Page 6 of 14differences could be attributed to the higher number ofstops and intersections along the Downtown route.Figure 1 displays the relationship between the spatialpatterns in exposure (Fig. 1a), ventilation (Fig. 1b) andintake (Fig. 1c) for the 22 participants (as quintiles ofeach variable within each trial, normalized across all par-ticipants) with complete heart rate and minute ventilationprofiles. The highest exposures occur at the northwestcorner of the Downtown route and along the northernportion of the Residential route. For ventilation, however,the highest levels occur when cycling uphill, in a numberof locations (for example, when cycling over the western-most bridge leading away from the central business dis-trict and in the southeast segment of the Residentialroute). Intake values are highest, as expected, in locationswith both high PNC and high ventilation, for example atthe northern section of the westernmost bridge and in themiddle of the northern segment of the Residential route.Given that the overall mean _VE ratio (cycling: rest) for thegroup was 3.5 and can therefore lead to 3.5-fold variationin intake for the same level of exposure, these resultsillustrate the importance of considering both _VE andexposure.Paired t-tests indicated no changes in lung function aftercycling along either route, with the exception of the forcedexpiratory flow (FEF25-75) measurement, which increasedafter cycling along the Downtown route.Endothelial function increased by mean RHI of 0.25after cycling along the Residential route, and decreasedby mean RHI of 0.18 after cycling along the Downtownroute. There was a difference in the change (post - pre) inRHI between the Residential compared to the Downtownroute (−0.39, 95% CI: -0.77, −0.017, Table 4), suggesting aroute-dependent impact on the level of improvement inendothelial function following cycling. Minor changes toblood biomarkers were observed when comparing post-pre measures along both routes, with small net increasesin IL-6 and 8-OHdG after cycling the Downtown routecompared to the Residential route (Table 4).There were some indications of small increases in IL-6associated with exposure to PNC and PM2.5 while cyc-ling (Table 5), although confidence intervals crossedzero. Similarly, there were indications of associations be-tween PNC exposure and a small decrease in FVC(Table 5). Exposure to PM2.5 appeared to contribute to areduction in FEV1, but was not seen in association withPNC exposure (Table 5). RHI decreases were associatedwith cycling along the downtown route in models whereonly route was considered (Table 6, model 1) as well asin models including pollutant intake (Table 6, model 4),whereas estimates for pollutant intake showed no evi-dence of association with RHI (Table 6, model 3 andmodel 4). This suggests that the observed associationsbetween route and RHI were independent of pollutantexposure or intake. The decrease of 0.43 units for cyc-ling on the Downtown route was equivalent to a 21% de-crease (based on mean pre-cycling RHI of 2.0 across allparticipants) in RHI. Other endpoints showed no associ-ations with pollutant intake or route (Table 6).Effect modification for RHI change by route (model 1)was analyzed by sex and age, dichotomized at the me-dian (≤ 28 versus > 29 years), shown in Fig. 2. The ef-fect of route on RHI change was larger amongstfemales (ß = −0.81, 95% CI: -1.6, −0.0048) comparedto males (ß = −0.29, 95% CI: -0.66, 0.081). Similarly,the effect of route was larger amongst the older halfof the participants (ß = −0.75, 95% CI: -1.3, −0.18) com-pared to younger half of the participants (ß = −0.11, 95%CI: -0.50, 0.28). There was no evidence of effect modifica-tion by BMI.DiscussionThis study compared exposure and intake levels of airpollutants while cycling along low versus high trafficroutes, and measured acute health impacts of cyclingalong these routes. Mean exposure and intake levels ofair pollutants were observed to be higher along the hightraffic (Downtown) route. Improvements in endothelialfunction were observed after cycling along the low trafficResidential route, while cycling along the Downtownroute led to a mean decrease in endothelial functionmeasures. These decreases were larger amongst femalesand older participants. No consistent changes in lungfunction or in blood biomarkers of systemic inflamma-tion and oxidative stress were observed after cycling ei-ther route in this study.The lack of agreement between measures of endothe-lial function and blood biomarkers of oxidative stressand inflammation in this study, despite the higher mea-sured levels of air pollutants along the high traffic route,suggests that there are other factors besides air pollutionwhich may have affected endothelial function. Few stud-ies have evaluated the effects of air pollution on endo-thelial function and blood biomarkers concurrently, andthese few studies have reported conflicting results on theeffect of air pollutant exposure on endothelial function[36–41]. Consistent with the findings of this study, mostof these studies found non-significant changes to thebiomarkers of CRP [36, 42], IL-6 [28, 29, 36, 41, 42], and8-OHdG [43] at different levels of PM exposure.It is possible that the observed improvement in endo-thelial function when cycling the Residential route wasdue to higher physical activity intensity as indicated bythe higher measured power output for this route. Cy-clists encountered more frequent stopping conditions(e.g. traffic lights, intersections) along the Downtownroute, leading to lower mean workloads and opportunitiesCole et al. Environmental Health           (2018) 17:78 Page 7 of 14Fig. 1 Quintiles for each individual cycling trial (of the 22 participants with complete heart rate and minute ventilation profiles), normalizedacross all participants, of the locations of highest and lowest a PNC levels, b Ventilation, c Intake (PNC x Ventilation). The start location isindicated by a diamond and arrows indicate the direction of travel (the Residential route was travelled in both directions)Cole et al. Environmental Health           (2018) 17:78 Page 8 of 14for heart rates to decrease during these intermittent restperiods. The total ride time was nearly the same for bothroutes, differing by only one minute, for an overall meanride time of 63.4 min.Other potential influencing factors may include thedifferences in noise levels and cyclist stress or anxietylevels along these two routes. Noise has been shown toimpact measures of cardiovascular disease. For instance,mean daytime sound pressure levels in excess of 60 dbAslightly increased the risk for ischemic heart diseases[44], while we have reported an association between longterm exposure to primarily traffic-related noise and cor-onary heart disease mortality in Vancouver [45]. The lowtraffic environment and higher green space exposure ofthe Residential route may have impacted stress and anx-iety levels [46], and measures of blood pressure whilecycling this route [46]. A study conducted in Londoncomparing the effects on respiratory function after walk-ing along high vs. low traffic routes concluded that ex-posure to higher pollution levels negated any potentialhealth benefits from walking [47]. Future researchshould explore how route characteristics such as thesemay play a role in affecting measured health variables. Inaddition, it would be valuable to understand how longthe acute effects of these exposures may persist and toevaluate the potential for more longer-term impacts ofrepeatedly traveling on high versus low traffic routes oncyclists’ health. Such research may help inform bothurban design more generally (e.g. a potential need toseparate cyclist infrastructure and routes from sourcesof air pollution and noise) as well as the design and loca-tion of cycling routes specifically (e.g. a potential benefitof greenness).The overall magnitude of exposures encountered alongthe two routes in our study (Downtown route mean =16,870 pt/cm³; Residential route mean = 10,840 pt/cm³)were somewhat lower than measured on other studies ofcyclist exposures, although the contrast between theroutes was of similar relative magnitude. Jarjour et al.found no changes in lung function after comparing cyc-ling along high (mean concentration = 19,945 pt/cm³)and low (13,517 pt/cm³) traffic routes in Berkeley, USA[48]. Strak et al. measured respiratory symptoms, exhalednitric oxide and lung function changes after cycling high(44,090 pt/cm³) and low (27,813 pt/cm³) traffic routes inUtrecht, The Netherlands. PNC levels were associatedwith post-pre ride decreases in peak expiratory flow (PEF),but not with any of the other measures [26]. The overallTable 4 Clinical measurement summary by route (Downtown and Residential) of post- and pre- cycling clinical measurementsVariable Downtown route Residential route Δ Downtown - Δ ResidentialChange (post-pre)Mean [Δ Downtown](95% CI)Change (post-pre)Mean [Δ Residential](95% CI)Mean difference (95% CI)Endothelial Function- EndoPAT™RHI −0.18 (−0.46, 0.11) 0.25 (0.03, 0.47) −0.39 (−0.77, −0.017)Spirometry (unit)FVC (mL) 46 (−3.8, 97) 21 (−84, 130) 28 (−77, 134)FEV1 (mL) 46 (6.6, 86) 49 (−11, 110) −0.81 (−62, 64)FEF25–75 (mL/s) 110 (23, 190) 82 (−59, 220) 0.024 (−0.13, 0.18)Blood Measures (unit)CRP (μg/dL) 8.8 (−12, 29) 6.6 (−30, 43) 2.4 (−44, 49)IL-6 (pg/ml) 0.55 (−0.59, 1.68) −0.61 (−1.8, 0.57) 1.13 (−0.82, 3.1)8-OHdG (ng/ml) −0.00045 (−0.040, 0.040) −0.031 (−0.071, 0.0082) 0.029 (−0.023, 0.081)Paired spirometry data was missing for 2 participants and paired endothelial function data missing for 4Table 5 Mixed effects (model 2) coefficients of subclinical health measure, modeled using the GM concentration of PNC or PM2.5exposures for each trialOutcome measurement GM of PNC ß-coefficient 95% CI GM of PM2.5 ß-coefficient 95% CIRHI 0.066 −0.22, 0.35 −0.051 −0.25, 0.15FEV1 (mL) −4.1 −53, 45 −32 −66, 3.0FVC (mL) −63 − 145, 19 −41 −102, 19CRP (μg/dL) 8.7 −14, 32 2.2 −14, 19IL-6 (pg/mL) 0.78 −0.45, 2.0 0.64 −0.20, 1.58-OHdG (ng/mL) 0.027 −0.013, 0.067 0.010 −0.019, 0.039ß-coefficient values are presented for an interquartile range change in PNC (7637 pt/cm³) or PM2.5 (4.7 μg/m3) exposureCole et al. Environmental Health           (2018) 17:78 Page 9 of 14Table6EffectsestimatesperIQRchangeinPNCintake,PM2.5intakeandroute(Downtown,withResidentialasthereference)modeledforclinicalmeasuresClinicalmeasure[Model3]PNCintakeß-coefficient(95%CI)[Model4]PNCintakeß-coefficientinmodeladjustingforroute(95%CI)Downtownrouteß-coefficient(95%CI)[Model3]PM2.5intakeß-coefficient(95%CI)[Model4]PM2.5intakeß-coefficientinmodeladjustingforroute(95%CI)Downtownrouteß-coefficient(95%CI)[Model1]Downtownrouteß-coefficient(95%CI)RHI0.050(−0.20,0.30)0.15(−0.10,0.40)−0.50(−0.90,−0.10)0.0010(−0.091,0.093)0.017(−0.073,0.11)−0.46(−0.83,−0.089)−0.43(−0.79,−0.079)FEV 1(mL)20(−26,66)21(−29,70)−3.3(−74,67)−0.64(−17,16)−0.20(−17,16)−13(−80,55)0.81(−60,62)FVC(mL)−33(−109,43)−46(−130,34)55(−60,170)−0.79(−29,27)−1.5(−30,27)20(−91,130)28(−74,130)CRP(μg/dL)1.7(−26,30)1.2(−29,31)2.1(−44,49)−0.072(−7.7,7.6)−0.77(−8.5,7.0)19(−13,50)2.3(−39,43)IL-6(pg/mL)0.55(−0.53,1.6)0.36(−0.76,1.5)1.1(−0.62,2.8)0.17(−0.22,0.57)0.13(−0.26,0.53)1.2(−0.52,2.8)1.2(−0.43,2.7)8-OHdG(ng/mL)15(−25,54)2.8(−41,47)32(−25,88)2.2(−11,15)−0.037(−13,13)39(−12,90)30(−17,78)ß-coefficientvaluesarepresentedforaninterquartilerangechangeinPNC(2.5×1010particles)orPM2.5(15.0μg)intakeCole et al. Environmental Health           (2018) 17:78 Page 10 of 14absence of impacts on respiratory health endpoints wasconsistent with our findings, especially considering theoverall higher exposure levels and larger differencesbetween routes in the Dutch study. Similarly, Zuurbier etal., in a similar route comparison study (high traffic:48,939 pt/cm³, low traffic: 39,576 pt/cm³) in Arnhem, TheNetherlands reported an association between PNC expos-ure with decreased PEF and increased airway resistance,but no changes in other lung function parameters or ex-haled nitric oxide [49]. In some cases these differences inparticle counts may be due to different particle size limitsof the instruments used to measure PNC; while we usedthe P-trak 8525 (range of 20 nm to 1 μm), other studiesused models of mobile condensation particle counterswith a broader range of 10 nm to > 1 μm [49].Although we did not identify other cyclist studies withmeasures of endothelial function, in the study conductedin Arnhem, a weak positive association was observed be-tween PNC and CRP levels but results for other bio-markers (IL-6/8/10, tumor necrosis factor-alpha, Claracell protein 16, blood cell counts and blood coagulationmarkers) were null [50]. Weichenthal et al., reported as-sociations between PNC exposures during cycling withFEF25–75 and exhaled nitric oxide, although the com-parisons included a clean indoor (1162 pt/cm³) cyclingtrial in addition to cycling on high (19,747 pt/cm³) andlow (10,882 pt/cm³) traffic routes in Ottawa, Canada[27]. This study also reported associations betweenPNC exposure with measures of heart rate variability,similar to a study in Dublin, Ireland where cyclist ex-posures were compared to those of other commutingmodes [25].This study included participants of a variety of fitnesslevels, which may have resulted in the inconsistent mag-nitude or direction of the blood biomarker results. Thereis evidence that stress-associated biomarkers may re-spond in dissimilar ways due to effect modification byfitness level [51, 52]. Study limitations include the useof a small sample size, which limits power to detect anydifferences. Because we did not measure the maximalaerobic capacity (V˙O2max) of the participants, wewere unable to objectively quantify the fitness level ofparticipants, which prevented us from adjusting for thisvariable. Furthermore, it is possible that only individ-uals who are frequent cyclists with interests in the topicof air pollution chose to participate in this study, limit-ing generalizability of the results to people who cycleless frequently or for leisure, or to cyclists outside thestudy age group of 19–39 years including those thatmay live with chronic health conditions or that takemedications excluded by this study protocol. Inaddition, RHI is a surrogate measure of vascular functionand while generally consistent with other predictive mea-sures of cardiovascular risk [53], it is susceptible to effectsof increased sympathetic activation due to environmentaldiscomfort and not directly comparable to other measuresof vascular function such as flow-mediated dilation [54,55]. Further, RHI was measured immediately following ex-ercise and we were therefore not able to assess how expos-ure impacts may have affected the various measures in thehours following the conclusion of our testing and it is un-certain how long the effects we observed with RHI wouldcontinue to persist following exposures and cyclingactivity.Fig. 2 Effect modification of RHI by variables including sex, BMI, and age. BMI and age were stratified by those above and below the medianlevel (BMI: 22.8 kg/m2, age: 29 years)Cole et al. Environmental Health           (2018) 17:78 Page 11 of 14ConclusionsGiven the individual benefit of improving physical fitnessand the societal reward of reducing healthcare costs as-sociated with improved fitness and air quality [8, 56], theestimated benefits due to cycling outweigh the associ-ated risks [57–59]. From this perspective it is advisableto support cycling in the conditions described along ei-ther of these route types. Cycling either route providesthe benefit of physical activity, and neither route wasfound to conclusively lead to adverse measures of thesurrogate health endpoints that were measured in thisstudy. With regard to heterogeneity between route types,there may be advantages to cycling along routes that re-quire additional effort or that have lower air pollutionlevels. A better understanding of other potential healthinfluencing factors while cycling is needed to informpublic health messages to cyclists in selecting routeswith the most beneficial features, as well as to informplanning and policy when designing cycling routes.Abbreviations8-OHdG: 8-hydroxy-2′-deoxyguanosine; BMI: Body mass index; CRP: C-reactiveprotein; GM: Geometric mean; IL-6: Interleukin 6; PEF: Peak expiratory flow;PM: Particulate matter; PNC: Particle number concentration; RHI: ReactiveHyperemia Index; SD: Standard deviation; _VE: Minute ventilation;; _VO2 max: Maximal aerobic capacity; WHO: World Health OrganizationAcknowledgementsWe thank Yen Li Chu for assistance in preparing the manuscript and RebeccaAbernethy, Luisa Giles, Majid Kajbafzadeh, Barbara Karlen, Tracy Kirkham, AlistairScott, Catherine Steer and Angela White for assistance with field and laboratorywork. Thanks also to Nima Hazar for developing software to synchronize datafrom multiple instruments. Further, we thank all of the participants whodonated their time to provide measurements for the study.FundingFunding for this work was provided in part by Health Canada (AgreementH1008–111481/001/XSB).Availability of data and materialsThe datasets used and/or analyzed during the current study are availablefrom the corresponding author on reasonable request.Authors’ contributionsCAC analyzed and interpreted data. CAC led drafting of the manuscript withMB. All authors contributed to writing and editing, and read and approvedthe final manuscript.Ethics approval and consent to participateWritten informed consent was obtained for all participants. The HealthCanada (certificate #2011–0009) and University of British Columbia ClinicalResearch (#H10–00902) Ethics Boards approved this study.Consent for publicationNot applicableCompeting interestsThe authors declare that they have no competing interests.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.Author details1School of Population and Public Health, University of British Columbia, 2206East Mall, Vancouver, BC V6T 1Z3, Canada. 2Air Pollution Exposure Lab,Department of Medicine, University of British Columbia, 2775 Laurel Street7th Floor, Vancouver, BC V5Z 1M9, Canada. 3School of Kinesiology andDivision of Sport & Exercise Medicine, University of British Columbia, 2176Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.Received: 7 June 2018 Accepted: 30 October 2018References1. World Health Organization. WHO | Global recommendations on physicalactivity for health. Geneva; 2015. [cited 22 Nov 2017]. Available from: http://www.who.int/dietphysicalactivity/factsheet_recommendations/en/.2. Colley RC, Garriguet D, Janssen I, Craig CL, Clarke J, Tremblay MS. Physicalactivity of Canadian adults: accelerometer results from the 2007 to 2009Canadian health measures survey. Health Rep. 2011;22:7–14. https://doi.org/10.1016/j.yspm.2011.03.006.3. Canadian Society for Exercise Physiology. Canadian Physical ActivityGuidelines for Adults 18-64 years. Ottawa; 2011. [cited 22 Nov 2017].Available from: http://www.csep.ca/view.asp?ccid=580.4. Cragg S, Cameron C, Craig C.L. 2004 National Transportation Survey. Ottawa:Canadian Fitness and Lifestyle Research Institute. 2006. [cited 22 Nov 2017].Available from: https://www.cflri.ca/sites/default/files/node/593/files/National%20Transportation%20survey%20final%20report%202006%20formatted%20june%202.pdf.5. Statistics Canada. 2006 Census of Population: Place of work and commutingto work [Internet]. 97-561-XCB2006010. 2006. [cited 27 Nov 2013]. Availablefrom: https://www150.statcan.gc.ca/n1/en/catalogue/97-561-X2006010.6. Larouche R, Faulkner G, Tremblay MS. Active travel and adults’ health: the2007-to-2011 Canadian Health Measures Surveys. Health Rep. 2016;27:10–8.7. Oja P, Titze S, Bauman A, de Geus B, Krenn P, Reger-Nash B, et al. Healthbenefits of cycling: a systematic review. Scand J Med Sci Sports. 2011;21:496–509. https://doi.org/10.1111/j.1600-0838.2011.01299.x.8. Grabow ML, Spak SN, Holloway T, Stone B, Mednick AC, Patz JA. Air qualityand exercise-related health benefits from reduced car travel in themidwestern United States. Environ Health Perspect. 2012;120:68–76. https://doi.org/10.1289/ehp.1103440.9. Jarrett J, Woodcock J, Griffiths UK, Chalabi Z, Edwards P, Roberts I, et al.Effect of increasing active travel in urban England and Wales on costs tothe National Health Service. Lancet. 2012;379:2198–205. https://doi.org/10.1016/S0140-6736(12)60766-1.10. Maizlish N, Woodcock J, Co S, Ostro B, Fanai A, Fairley D. Health cobenefitsand transportation-related reductions in greenhouse gas emissions in theSan Francisco Bay Area. Am J Public Health. 2013;103:703–9. https://doi.org/10.2105/AJPH.2012.300939.11. Hatzopoulou M, Weichenthal S, Dugum H, Pickett G, Miranda-Moreno L,Kulka R, et al. The impact of traffic volume, composition, and road geometryon personal air pollution exposures among cyclists in Montreal, Canada. J ExpoSci Environ Epidemiol. 2013;23:46–51. https://doi.org/10.1038/jes.2012.85.12. MacNaughton P, Melly S, Vallarino J, Adamkiewicz G, Spengler JD. Impact ofbicycle route type on exposure to traffic-related air pollution. Sci TotalEnviron. 2014;490:37–43. https://doi.org/10.1016/j.scitotenv.2014.04.111.13. Thai A, McKendry I, Brauer M. Particulate matter exposure along designatedbicycle routes in Vancouver, British Columbia. Sci Total Environ. 2008;405:26–35. https://doi.org/10.1016/j.scitotenv.2008.06.035.14. Mills NL, Tornqvist H, Robinson SD, Gonzalez M, Darnley K, MacNee W, et al.Diesel exhaust inhalation causes vascular dysfunction and impairedendogenous fibrinolysis. Circulation. 2005;112:3930–6. https://doi.org/10.1161/CIRCULATIONAHA.105.588962.15. Peretz A, Sullivan JH, Leotta DF, Trenga CA, Sands FN, Allen J, et al. Dieselexhaust inhalation elicits acute vasoconstriction in vivo. Environ HealthPerspect. 2008;116:937–42. https://doi.org/10.1289/ehp.11027.16. Zanobetti A, Canner MJ, Stone PH, Schwartz J, Sher D, Eagan-Bengston E, etal. Ambient pollution and blood pressure in cardiac rehabilitation patients.Circulation. 2004;110:2184–9. https://doi.org/10.1161/01.CIR.0000143831.33243.D8.17. Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV, etal. Particulate matter air pollution and cardiovascular disease: an update toCole et al. Environmental Health           (2018) 17:78 Page 12 of 14the scientific statement from the american heart association. Circulation.2010;121:2331–78. https://doi.org/10.1161/CIR.0b013e3181dbece1.18. Dockery DW, Pope CA, Xu X, Spengler JD, Ware JH, Fay ME, et al. Anassociation between air pollution and mortality in six U.S. cities. N Engl JMed. 1993;329:1753–9. https://doi.org/10.1056/NEJM199312093292401.19. Araujo JA, Nel AE. Particulate matter and atherosclerosis: role of particle size,composition and oxidative stress. Part Fibre Toxicol. 2009;6:24. https://doi.org/10.1186/1743-8977-6-24.20. McCreanor J, Cullinan P, Nieuwenhuijsen MJ, Stewart-Evans J, Malliarou E,Jarup L, et al. Respiratory effects of exposure to diesel traffic in persons withasthma. N Engl J Med. 2007;357:2348–58.21. Deering-Rice CE, Shapiro D, Romero EG, Stockmann C, Bevans TS, Phan QM,et al. Activation of transient receptor potential ankyrin-1 by insolubleparticulate material and association with asthma. Am J Respir Cell Mol Biol.2015;53:893–901. https://doi.org/10.1165/rcmb.2015-0086OC.22. Kile ML, Coker ES, Smit E, Sudakin D, Molitor J, Harding AK. A cross-sectionalstudy of the association between ventilation of gas stoves and chronicrespiratory illness in U.S. children enrolled in NHANESIII. Environ Health.2014;13:71. https://doi.org/10.1186/1476-069X-13-71.23. Bigazzi AY, Figliozzi MA. Review of urban bicyclists’ intake and uptake oftraffic-related air pollution. Transp Rev. 2014;34:221–45. https://doi.org/10.1080/01441647.2014.897772.24. Knibbs LD, Cole-Hunter T, Morawska L. A review of commuter exposure toultrafine particles and its health effects. Atmos Environ. 2011;45:2611–22.https://doi.org/10.1016/j.atmosenv.2011.02.065.25. Nyhan M, McNabola A, Misstear B. Comparison of particulate matter doseand acute heart rate variability response in cyclists, pedestrians, bus andtrain passengers. Sci Total Environ. 2013;468–469:821–31. https://doi.org/10.1016/j.scitotenv.2013.08.096.26. Strak M, Boogaard H, Meliefste K, Oldenwening M, Zuurbier M, Brunekreef B,et al. Respiratory health effects of ultrafine and fine particle exposure in cyclists.Occup Environ Med. 2010;67:118–24. https://doi.org/10.1136/oem.2009.046847.27. Weichenthal S, Kulka R, Dubeau A, Martin C, Wang D, Dales R. Traffic-relatedair pollution and acute changes in heart rate variability and respiratoryfunction in urban cyclists. Environ Health Perspect. 2011;119:1373–8. https://doi.org/10.1289/ehp.1003321.28. Jacobs L, Nawrot TS, de Geus B, Meeusen R, Degraeuwe B, Bernard A, et al.Subclinical responses in healthy cyclists briefly exposed to traffic-related airpollution: an intervention study. Environ Health. 2010;9:64. https://doi.org/10.1186/1476-069X-9-64.29. Nwokoro C, Ewin C, Harrison C, Ibrahim M, Dundas I, Dickson I, et al. Cyclingto work in London and inhaled dose of black carbon. Eur Respir J. 2012;40:1091–7. https://doi.org/10.1183/09031936.00195711.30. Abernethy RC, Allen RW, McKendry IG, Brauer M. A land use regressionmodel for ultrafine particles in Vancouver, Canada. Environ Sci Technol.2013;47(10):5217–25.31. Metro Vancouver. 2011 Lower Fraser Valley Air Quality Monitoring Report[online] Air Quality Policy and Management Division of Metro Vancouver. 2013.http://www.metrovancouver.org/services/air-quality/AirQualityPublications/2011LFVAQMonitoringFullReport.pdf.32. Strava. Strava Route Builder (Beta). 2018. [cited 21 Oct 2018]. https://www.strava.com/routes.33. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al.Standardisation of spirometry. Eur Respir J. 2005;26:319–38. https://doi.org/10.1183/09031936.05.00034805.34. R Development CT. R: A Language and Environment for StatisticalComputing. 2011. [cited 22 Nov 2017]. http://www.r-project.org.35. Bates D, Maechler M, Bolker BM. lme4: linear mixed-effects models usingEigen and S4. J Stat Softw. 2012. [cited 22 Nov 2017]; http://r-forge.r-project.org/projects/lme4/%5Cnpapers2://publication/uuid/09CD5C30-FC06-4C2C-B283-51E78FD144A3.36. Allen RW, Carlsten C, Karlen B, Leckie S, van Eeden S, Vedal S, et al. An airfilter intervention study of endothelial function among healthy adults in awoodsmoke-impacted community. Am J Respir Crit Care Med. 2011;183:1222–30. https://doi.org/10.1164/rccm.201010-1572OC.37. Bräuner EV, Forchhammer L, Møller P, Barregard L, Gunnarsen L, Afshari A,et al. Indoor particles affect vascular function in the aged: an air filtration-based intervention study. Am J Respir Crit Care Med. 2008;177:419–25.https://doi.org/10.1164/rccm.200704-632OC.38. Forchhammer L, Moller P, Riddervold IS, Bonlokke J, Massling A, Sigsgaard T,et al. Controlled human wood smoke exposure: oxidative stress,inflammation and microvascular function. Part Fibre Toxicol. 2012;9:7.https://doi.org/10.1186/1743-8977-9-7.39. Karottki DG, Spilak M, Frederiksen M, Gunnarsen L, Brauner EV, Kolarik B, etal. An indoor air filtration study in homes of elderly: cardiovascular andrespiratory effects of exposure to particulate matter. Environ Health. 2013;12:116. https://doi.org/10.1186/1476-069X-12-116.40. Stieb DM, Shutt R, Kauri L, Mason S, Chen L, Szyszkowicz M, et al. Cardio-respiratory effects of air pollution in a panel study of outdoor physicalactivity and health in rural older adults. J Occup Environ Med. 2017;59:356–64. https://doi.org/10.1097/JOM.0000000000000954.41. Kajbafzadeh M, Brauer M, Karlen B, Carlsten C, van Eeden S, Allen RW. Theimpacts of traffic-related and woodsmoke particulate matter on measuresof cardiovascular health: a HEPA filter intervention study. Occup EnvironMed. 2015;72:394–400. https://doi.org/10.1136/oemed-2014-102696.42. Bräuner EV, Møller P, Barregard L, Dragsted LO, Glasius M, Wåhlin P, et al.Exposure to ambient concentrations of particulate air pollution doesnot influence vascular function or inflammatory pathways in younghealthy individuals. Part Fibre Toxicol. 2008;5:13. https://doi.org/10.1186/1743-8977-5-13.43. Chuang KJ, Chan CC, Su TC, Te Lee C, Tang CS. The effect of urban airpollution on inflammation, oxidative stress, coagulation, and autonomicdysfunction in young adults. Am J Respir Crit Care Med. 2007;176:370–6.https://doi.org/10.1164/rccm.200611-1627OC.44. Fritschi L, Brown AL, Kim R, Schwela D, Kephalopoulos S. Burden of diseasefrom environmental noise: quantification of healthy life years lost in Europe.Geneva: World Health Organization; 2011.45. Gan WQ, Davies HW, Koehoorn M, Brauer M. Association of long-termexposure to community noise and traffic-related air pollution with coronaryheart disease mortality. Am J Epidemiol. 2012;175:898–906.46. Horiuchi M, Endo J, Akatsuka S, Uno T, Hasegawa T. Influence of forestwalking on blood pressure, profile of mood states and stress markers fromthe viewpoint of aging. J Aging Gerontol. 2013;1:9–17. https://doi.org/10.12974/2309-6128.2013. Sinharay R, Gong J, Barratt B, Ohman-Strickland P, Ernst S, Kelly FJ, et al.Respiratory and cardiovascular responses to walking down a traffic-pollutedroad compared with walking in a traffic-free area in participants aged 60years and older with chronic lung or heart disease and age-matchedhealthy controls: a randomised, crossover study. Lancet. 2018;391:339–49.https://doi.org/10.1016/S0140-6736(17)32643-0.48. Jarjour S, Jerrett M, Westerdahl D, de Nazelle A, Hanning C, Daly L, et al.Cyclist route choice, traffic-related air pollution, and lung function: ascripted exposure study. Environ Health. 2013;12:14. https://doi.org/10.1186/1476-069X-12-14.49. Zuurbier M, Hoek G, Oldenwening M, Meliefste K, Van Den Hazel P,Brunekreef B. Respiratory effects of commuters’exposure to air pollution intraffic. Epidemiology. 2011;22:219–27.50. Zuurbier M, Hoek G, Oldenwening M, Meliefste K, Krop E, van den Hazel P, et al.In-traffic air pollution exposure and CC16, blood coagulation, and inflammationmarkers in healthy adults. Environ Health Perspect. 2011;119(10)1384–9.51. Hamer M, Steptoe A. Association between physical fitness, parasympatheticcontrol, and proinflammatory responses to mental stress. Psychosom Med.2007;69:660–6. https://doi.org/10.1097/PSY.0b013e318148c4c0.52. Huang CJ, Webb HE, Zourdos MC, Acevedo EO. Cardiovascular reactivity, stress,and physical activity. Front Physiol. 2013;4:314. https://doi.org/10.3389/fphys.2013.00314.53. Kuvin JT, Mammen A, Mooney P, Alsheikh-Ali AA, Karas RH. Assessment ofperipheral vascular endothelial function in the ambulatory setting. VascMed. 2007;12:13–6. https://doi.org/10.1177/1358863X06076227.54. Hijmering ML, Stroes ESG, Olijhoek J, Hutten BA, Blankestijn PJ, Rabelink TJ.Sympathetic activation markedly reduces endothelium-dependent, flow-mediated vasodilation. J Am Coll Cardiol. 2002;39:683–8. https://doi.org/10.1016/S0735-1097(01)01786-7.55. Hedetoft M, Olsen NV. Evaluation of endothelial function by peripheralarterial tonometry and relation with the nitric oxide pathway. Nitric Oxide.2014;42 Supplement C:1–8. https://doi.org/10.1016/j.niox.2014.07.003.56. Rojas-Rueda D, de Nazelle A, Tainio M, Nieuwenhuijsen MJ. The health risks andbenefits of cycling in urban environments compared with car use: health impactassessment study. BMJ. 2011;343:d4521. https://doi.org/10.1136/bmj.d4521.57. De Hartog JJ, Boogaard H, Nijland H, Hoek G. Do the health benefits ofcycling outweigh the safety risks? Environ Health Perspect. 2010;118:1109–16.https://doi.org/10.1289/ehp.0901747.Cole et al. Environmental Health           (2018) 17:78 Page 13 of 1458. Mueller N, Rojas-Rueda D, Cole-Hunter T, de Nazelle A, Dons E, Gerike R, et al.Health impact assessment of active transportation: a systematic review. PrevMed. 2015;76:103–14. https://doi.org/10.1016/j.ypmed.2015.04.010.59. Tainio M, de Nazelle AJ, Götschi T, Kahlmeier S, Rojas-Rueda D,Nieuwenhuijsen MJ, et al. Can air pollution negate the health benefits ofcycling and walking? Prev Med. 2016;87 Supplement C:233–6. https://doi.org/10.1016/j.ypmed.2016.02.002.Cole et al. Environmental Health           (2018) 17:78 Page 14 of 14


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