Effects of air pollution exposure on adult bicycle commuters: an investigation of respiratory health, motorised traffic proximity and the utility of commute re-routing

Doctor of Philosophy Thesis by Publication 2012

Thomas A. Cole-Hunter B. App. Sci. (Hons I) International Laboratory for Air Quality and Health, Institute of Health and Biomedical Innovation, Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Australia

TABLE of CONTENTS

LIST of PUBLICATIONS ......................................................................................................... 5  ABSTRACT ............................................................................................................................... 7  KEY WORDS ............................................................................................................................ 9  ABBREVIATIONS ................................................................................................................. 10  NOMENCLATURE ................................................................................................................ 10  STATEMENT of ORIGINAL AUTHORSHIP ....................................................................... 11  ACKNOWLEDGEMENT and DEDICATION....................................................................... 12  1. 

INTRODUCTION ........................................................................................................ 13 

1.1. 

Description of Research Problem Investigated ......................................................... 13 

1.2. 

Overall Objectives of the Study ................................................................................ 13 

1.3. 

Specific Aims of the Study........................................................................................ 14 

1.3.1. 

Project 1 ................................................................................................................. 14 

1.3.2. 

Project 2 ................................................................................................................. 14 

1.3.3. 

Project 3 ................................................................................................................. 15 

1.4. 

Specific Hypotheses of the Study.............................................................................. 16 

1.5. 

Account of Research Progress Linking the Research Papers .................................... 17 

2. 

LITERATURE REVIEW ............................................................................................. 18 

2.1. Introduction ................................................................................................................... 18  2.2. Ultrafine Particles (UFP) ............................................................................................... 19  2.3. In-transit UFP Exposure ................................................................................................ 20  2.4. UFP and Health Effects ................................................................................................. 26  2.5. Mechanisms of Cardiopulmonary Detriment ................................................................ 30  2.5.1. Measure of Dose: Surface Area vs. Number Concentration ...................................... 30  2.5.2. Effects of Chemical Composition .............................................................................. 31  2.5.3. Acute versus Chronic Exposure ................................................................................. 32 

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2.6. Knowledge Gaps ........................................................................................................... 34  3. 

GENERAL METHODS................................................................................................ 36 

3.1. 

Project Design (Project 1, 2 & 3) .............................................................................. 36 

3.2. 

Participant Recruitment (Project 1 & 3) .................................................................... 37 

3.3. 

Questionnaire (Project 1 & 3) ................................................................................... 37 

3.3.1. 

Perception Incidence and Magnitude .................................................................... 38 

3.3.2. 

Symptom Incidence and Severity .......................................................................... 38 

3.3.3. 

Risk Management Strategies ................................................................................. 39 

3.4. 

Ultrafine Particle Monitoring (Project 2 & 3) ........................................................... 39 

3.5. 

Ambient Pollution Data Monitoring (Project 1, 2 & 3) ............................................ 40 

3.6. 

Heart Rate and Estimated Ventilation Rate (Project 2 & 3) ..................................... 40 

3.7. 

Geographical Positioning (Project 2 & 3) ................................................................. 41 

3.8. 

Meteorological Data Monitoring (Project 2 & 3)...................................................... 41 

4. PROJECT ONE: A questionnaire-based investigation of perceptions, symptoms and risk management strategies for air pollution exposure and motorised traffic proximity of adult bicycle commuters................................................................................................................ 42  5. PROJECT TWO: Inhaled particle counts on bicycle commute routes of low and high proximity to motorised traffic .............................................................................................. 73  6. PROJECT THREE: The reduction of ultrafine particle exposure by utilising bicycle commute routes of low versus high proximity to major motorised traffic corridors ........... 76  7. 

GENERAL DISCUSSION ......................................................................................... 113 

7.1. Introduction and Summary .......................................................................................... 113  7.2. 

Air Quality............................................................................................................... 114 

7.3. 

Air Pollution Exposure Symptoms and Susceptibility ............................................ 115 

7.4. 

Air Pollution Exposure Perceptions and Risk Management ................................... 115 

7.5. 

Novel Method Use .................................................................................................. 116 

7.6. 

General Limitations ................................................................................................. 117 

8. 

CONCLUSIONS......................................................................................................... 118  3

9. 

FUTURE DIRECTIONS ............................................................................................ 120 

10. 

APPENDICES ............................................................................................................ 122 

A. 

Questionnaire [Complete (Project 1)] ..................................................................... 122 

B. 

Questionnaire [Amended (Project 3)] ..................................................................... 132 

C. 

Bikeway Maps with Cyclist Counts (Project 2) ...................................................... 135 

D. 

Media Releases (to assist participant recruitment for Projects 1 and 3) ................. 136 

E. 

Publication of Project 2 ........................................................................................... 141 

11. 

GENERAL REFERENCES ........................................................................................ 148 

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LIST of PUBLICATIONS

Journal Articles Knibbs L.D., Cole-Hunter T., Morawska L. (2011). A review of commuter exposure to ultrafine particles and its health effects. Atmospheric Environment, 45:2611-2622. Cole-Hunter, T., Morawska, L., Stewart, I., Jayaratne, R., Solomon, C. (2012). Inhaled particle counts on bicycle commute routes of low and high proximity to motorised traffic. Atmospheric Environment, 61:197-203. doi: 10.1016/j.atmosenv.2012.06.041 Cole-Hunter, T., Morawska, L., Solomon, C. (In Review). A questionnaire-based investigation of perceptions, symptoms, and risk management strategies for motorised traffic exposure of adult bicycle commuters. PLoS ONE. Cole-Hunter, T., Morawska, L., Stewart, I., Jayaratne, R., Solomon, C. (In Review). Utility of bicycle commute route alteration to lower exposure to motorised traffic-emitted ultrafine particles. Environmental Health.

International Conference Presentations Cole-Hunter, T., Morawska, L., Stewart, I., Jayaratne, R., Solomon, C. Utility of bicycle commute route alteration to lower exposure to motorised traffic-emitted ultrafine particles. European Respiratory Society Annual Congress, Vienna, Austria, September 2012. Cole-Hunter, T., Morawska, L., Solomon, C. Effects of Air Pollution on Lung Health and Function: A Direct Investigation with Brisbane City Commuter Cyclists. European Respiratory Society Annual Congress, Barcelona, Spain, September 2010.

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National Conference Presentations Cole-Hunter, T., Morawska, L., Solomon, C. Brisbane City Commuter Cyclists: Behaviour, Reasoning and Risk Management. Public Health Association Australia State Conference, Brisbane, QLD, March 2010. Cole-Hunter, T., Stewart, I., Solomon, C. Effect of Exercise Mode on 40-50 Year Old Female Triathletes. Sports Medicine Australia ‘Be Active’ National Conference, Brisbane, QLD, August 2009.

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ABSTRACT

Bicycle commuting has the potential to be an effective contributing solution to address some of modern society’s biggest issues, including cardiovascular disease, anthropogenic climate change and urban traffic congestion. However, individuals shifting from a passive to an active commute mode may be increasing their potential for air pollution exposure and the associated health risk. This project, consisting of three studies, was designed to investigate the health effects of bicycle commuters in relation to air pollution exposure, in a major city in Australia (Brisbane). The aims of the three studies were to: 1) examine the relationship of in-commute air pollution exposure perception, symptoms and risk management; 2) assess the efficacy of commute rerouting as a risk management strategy by determining the exposure potential profile of ultrafine particles along commute route alternatives of low and high proximity to motorised traffic; and, 3) evaluate the feasibility of implementing commute re-routing as a risk management strategy by monitoring ultrafine particle exposure and consequential physiological response from using commute route alternatives based on real-world circumstances; 3) investigate the potential of reducing exposure to ultrafine particles (UFP; < 0.1 µm) during bicycle commuting by lowering proximity to motorised traffic with real-time air pollution and acute inflammatory measurements in healthy individuals using their typical, and an alternative to their typical, bicycle commute route. The methods of the three studies included: 1) a questionnaire-based investigation with regular bicycle commuters in Brisbane, Australia. Participants (n = 153; age = 41 ± 11 yr; 28% female) reported the characteristics of their typical bicycle commute, along with exposure perception and acute respiratory symptoms, and amenability for using a respirator or rerouting their commute as risk management strategies; 2) inhaled particle counts measured along popular pre-identified bicycle commute route alterations of low (LOW) and high (HIGH) motorised traffic to the same inner-city destination at peak commute traffic times. During commute, real-time particle number concentration (PNC; mostly in the UFP range) and particle diameter (PD), heart and respiratory rate, geographical location, and meteorological variables were measured. To determine inhaled particle counts, ventilation rate was calculated from heart-rate-ventilation associations, produced from periodic exercise testing; 3) thirty-five healthy adults (mean ± SD: age = 39 ± 11 yr; 29% female) completed 7

two return trips of their typical route (HIGH) and a pre-determined altered route of lower proximity to motorised traffic (LOW; determined by the proportion of on-road cycle paths). Particle number concentration (PNC) and diameter (PD) were monitored in real-time incommute. Acute inflammatory indices of respiratory symptom incidence, lung function and spontaneous sputum (for inflammatory cell analyses) were collected immediately precommute, and one and three hours post-commute. The main results of the three studies are that: 1) healthy individuals reported a higher incidence of specific acute respiratory symptoms in- and post- (compared to pre-) commute (p < 0.05). The incidence of specific acute respiratory symptoms was significantly higher for participants with respiratory disorder history compared to healthy participants (p < 0.05). The incidence of in-commute offensive odour detection, and the perception of in-commute air pollution exposure, was significantly lower for participants with smoking history compared to healthy participants (p < 0.05). Females reported significantly higher incidence of incommute air pollution exposure perception and other specific acute respiratory symptoms, and were more amenable to commute re-routing, compared to males (p < 0.05). Healthy individuals have indicated a higher incidence of acute respiratory symptoms in- and post(compared to pre-) bicycle commuting, with female gender and respiratory disorder history indicating a comparably-higher susceptibility; 2) total mean PNC of LOW (compared to HIGH) was reduced (1.56 x e4 ± 0.38 x e4 versus 3.06 x e4 ± 0.53 x e4 ppcc; p = 0.012). Total estimated ventilation rate did not vary significantly between LOW and HIGH (43 ± 5 versus 46 ± 9 L•min; p = 0.136); however, due to total mean PNC, accumulated inhaled particle counts were 48% lower in LOW, compared to HIGH (7.6 x e8 ± 1.5 x e8 versus 14.6 x e8 ± 1.8 x e8; p = 0.003); 3) LOW resulted in a significant reduction in mean PNC (1.91 x e4 ± 0.93 x e4 ppcc vs. 2.95 x e4 ± 1.50 x e4 ppcc; p ≤ 0.001). Commute distance and duration were not significantly different between LOW and HIGH (12.8 ± 7.1 vs. 12.0 ± 6.9 km and 44 ± 17 vs. 42 ± 17 mins, respectively). Besides incidence of in-commute offensive odour detection (42 vs. 56 %; p = 0.019), incidence of dust and soot observation (33 vs. 47 %; p = 0.038) and nasopharyngeal irritation (31 vs. 41 %; p = 0.007), acute inflammatory indices were not significantly associated to in-commute PNC, nor were these indices reduced with LOW compared to HIGH. The main conclusions of the three studies are that: 1) the perception of air pollution exposure levels and the amenability to adopt exposure risk management strategies where applicable will aid the general population in shifting from passive, motorised transport modes to bicycle 8

commuting; 2) for bicycle commuting at peak morning commute times, inhaled particle counts and therefore cardiopulmonary health risk may be substantially reduced by decreasing exposure to motorised traffic, which should be considered by both bicycle commuters and urban planners; 3) exposure to PNC, and the incidence of offensive odour and nasopharyngeal irritation, can be significantly reduced when utilising a strategy of lowering proximity to motorised traffic whilst bicycle commuting, without significantly increasing commute distance or duration, which may bring important benefits for both healthy and susceptible individuals. In summary, the findings from this project suggests that bicycle commuters can significantly lower their exposure to ultrafine particle emissions by varying their commute route to reduce proximity to motorised traffic and associated combustion emissions without necessarily affecting their time of commute. While the health endpoints assessed with healthy individuals were not indicative of acute health detriment, individuals with pre-disposing physiologicalsusceptibility may benefit considerably from this risk management strategy – a necessary research focus with the contemporary increased popularity of both promotion and participation in bicycle commuting.

KEY WORDS

Air Pollution ; Ultrafine Particles ; Bicycle Commuting ; Inhaled Particle Count ; Perceptions ; Symptoms ; Lung Function ; Inflammatory Mediators ; Risk Assessment ; Risk Management

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ABBREVIATIONS

BPM

=

Beats per minute

BUG

=

Bicycle user group

CBD

=

Central business district

EXP

=

Estimate exposure potential

NOX

=

Nitrogen oxides

NO2

=

Nitrogen dioxide

PEF

=

Peak expiratory flow

PM

=

Particulate matter

PM2.5

=

PM 100 µg·m-3) [103]. However, short-term effects of exposure to diesel exhaust with significantly-higher PNC in mild or moderate asthmatics can lead to asymptomatic yet consistent reductions in lung function, and increases in inflammatory neutrophilic biomarkers as well as airway acidification [49].

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2.5.4.3. Cardiovascular Disease Acute cardiovascular effects in Type-2 Diabetics exposed to in-vehicle traffic-related pollution during highway trips include increases in low frequency heart-rate variability post-commute, and decreases in high frequency heart-rate variability the next day, associated with inter-quartile range increases in PNC [104]. Associations between daily PNC fluctuations, heart rate variability (representing cardiac autonomic control), and sympathovagal balance (representing the low-to-high frequency ratio) in elderly stable coronary artery disease patients have been shown during paced breathing [105]. UFP and PM2.5 cardiovascular effects are independent of each other and may be modified by individual and source exposure characteristics. Measuring HRV using ambulatory ECG recordings during exposure events is difficult but valid for large epidemiological studies [104].

2.6. Knowledge Gaps Along with epidemiological and toxicological investigation, it is apparent that further work is required to characterise personal exposure of bicycle commuters to real-world motorised traffic emissions of UFP, best done through field studies. The potential inhaled count or dose of UFP resulting from changes in physical effort and therefore pulmonary ventilation while bicycle commuting has not been investigated extensively; however, some studies have compared multiple modes and predicted ventilation rates. This thesis intended to address such a knowledge gap by accurately measuring in-commute, real-time ventilation rate while using popular bicycle commute routes of altered motorised traffic exposure and different directions in an urban setting. The exposure and consequential response of individuals to automotive emissions, particularly UFP, is a novel field of research (due to technological advancements of measurement devices) with many frontiers left to explore. Therefore, the strategies which could manage such an exposure response are yet to be addressed. The use of respirators for filtering inhaled air pollutants is not yet known to be effective for particulate matter in the ultrafine range, and so their efficacy in this regard warrants attention. Several studies have compared bicycle 34

commuters using routes of high and low exposure to motorised traffic. Such routes were defined by the study investigators to facilitate a coherent research question and protocol; however, as this definition was not reflecting the variance of real-world commute routes, the benefits of utilising these (or any) alternative routes are not applicable to the wider population. To address such a knowledge gap, this thesis intended to apply similar protocols to personally-identified and altered routes of high and low motorised traffic exposure, in realworld circumstances (for example, of typical commute departure time and speed or physical effort and therefore pulmonary ventilation rate). Ultimately, if certain circumstances are deemed to require strategies to be implemented to mitigate the exposure effects of air pollution on health, then such strategies should be guided by the needs and desires of the strategy beneficiary. For example, if a certain cohort of patients are particularly susceptible to certain pollutants, however would benefit from the frequent, moderate physical activity associated with bicycle commuting that would be otherwise difficult to obtain, then the use of a respirator or altered commute route may provide better odds for the cost-benefit ratio of such activity. To an extent, this thesis intended to provide insight on the desires of bicycle commutes when considering such risk management strategies as respirator use and more particularly commute re-routing. Accordingly, the proposed hypotheses of this thesis attempted to address the knowledge gaps identified. The first and third project intended to provide insight on the perception of incommute air pollution exposure and any related incidence of respiratory symptoms, along with preferences towards air pollution exposure risk management strategies. The second and third project then added to this knowledge base by investigating how effective and practical the risk management strategy of commute re-routing was for reducing in-commute exposure to newly-emitted motor vehicle exhaust (directly represented by PNC) in real-world conditions of commute characteristics and emission circumstances.

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3. GENERAL METHODS

3.1. Project Design (Project 1, 2 & 3) Project 1 was intended to help guide the design of Project 2 and 3. Accordingly, Project 1 was itself designed to characterise a large group (100+) of frequent bicycle commuters travelling into and/or through a one kilometre radius of the Brisbane CBD, and determine typical commute characteristics and attitudes to the risk management strategy of commute rerouting to be evaluated in Project 2 and 3. The questionnaire used in Project 1 was purposedesigned with review and input from fellow researchers and a sub-set of intended participants. With typical commute characteristics detailed, and a receptive attitude to commute re-routing recognised, Project 2 was designed to produce an air pollution exposure profile of popular bicycle commute routes from Brisbane suburbs to the CBD based on a single participant model. Pre-determined popular bicycle commute routes traversing Brisbane from the North, East, South and Western suburbs to the CBD were to be repeatedly monitored, with alterations of both high and low proximity to motorised traffic at morning peak traffic time, to quantify real-time and total mean PNC, heart and ventilation rates and to determine and compare PNC exposure and inhaled particle count. Project 3 was similar to Project 2, although designed to include an appropriately-sized participant group model (to satisfy statistical power needs). To replicate the aims of Project 2 under real-world circumstances while monitoring for a physiological inflammatory response related to in-commute PNC exposure levels, adults were to be recruited to perform their regular commute route (determined as high proximity to motorised traffic) and an alternative route of low proximity to motorised traffic. One return trip of low, and one of high, proximity to motorised traffic was to be performed while carrying geo-location, heart rate and ultrafine particle monitoring instruments. Further, symptom-experience reporting, peak flow metering and spontaneous sputum sampling was to be performed before and after commutes to relate in-commute exposure with post-commute physiological inflammatory response. In addition, route alteration preference (and features) was to be queried (between regular / ‘high’ and

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‘low’ route) to evaluate whether a conscious decision to lower proximity to motorised traffic (and therefore emissions) is feasible as an exposure risk management strategy.

3.2. Participant Recruitment (Project 1 & 3) Initially, contacts were established with major Brisbane bicycle user groups (BUGs) and dedicated bicycle commuter end-of-trip facilities in Brisbane city. Questionnaires were distributed both physically (by providing printed copies to BUG members via BUG administration, and attending active transport promotional events) and electronically (via BUG email lists and electronic newsletters). Further, a media campaign was engaged to reach a broader, non-BUG audience (incorporating a radio interview and several newspaper articles) reaching the greater south-east Queensland (SE QLD) region. Participants expressed informed-consent by returning the completed questionnaire. Further, participants were able to express interest and provide consent to be later contacted regarding subsequent research, such as the third study in this thesis. While the majority of previous participants were available for the third study, only 33 were deemed appropriate (according to their propensity for bicycle commuting proximal to motorised traffic). In addition, 120 new potential participants were gained through a second recruitment drive. Project 2 was a single participant model - that is, the principal investigator performed all tasks required and therefore participant recruitment was not required.

3.3. Questionnaire (Project 1 & 3) The questionnaire used in this investigation was novel and constructed with the help of review by fellow researchers and a sub-set of intended participants. The complete questionnaire used for Project 1 is included in the appendix (A) of this thesis. A total of 77 questions were delivered and based on a five-grade Likert scale (sub-totalling 43 questions with the scale of: 1 = “very low”; 2 = “low”; 3 = “moderate”; 4 = “high”; 5 = “very high”), or were of categorical (sub-totalling 5 questions), continuous (sub-totalling 12 questions) or nominal (sub-totalling 17 questions) format. A shortened version of the questionnaire used for Project 1 was supplied to participants of Project 3. This amended questionnaire included the same inquiry of pre-, in- and post-commute perceptions and symptoms, as well as the risk 37

management strategy features of importance for commute re-routing only. The amended questionnaire used for Project 3 is also included in the appendix (B) of this thesis. The qualitative Likert scale response data, concerning the incidence of in-commute air pollution exposure perception and symptoms, as well as the importance of factors concerning risk management strategy use, were converted to a group fractional mean between 1 and 5 (as: 1 = “very low”; 2 = “low”; 3 = “moderate”; 4 = “high”; 5 = “very high”). Determined attempts were not made to control for pre-disposing susceptibilities (e.g. health history, age and gender), however these factors were analysed and compared for increased incidence and severity of symptoms experienced both in- or post-commute and pre-commute (using the Likert scale ranking).

3.3.1. Perception Incidence and Magnitude Perception of air pollution exposure, defined as the conscious detection of poor air quality, was reported by the participant for incidence and magnitude (as “very low” to “very high”) for their typical bicycle commute. A participant was deemed to properly perceive air pollution when they reported the incidence and subsequent magnitude of perception as “moderate” or higher. Although the detection of offensive odour could be better defined as a sensory experience and the perception of poor air quality, it is considered a mild acute respiratory symptom by the American Thoracic Society [106].

3.3.2. Symptom Incidence and Severity The format of inquiry for acute respiratory symptoms attributable to air pollution exposure (including the detection of offensive odour) was based on recommendations by the American Thoracic Society [106] and previous research [107]. The incidence of acute respiratory symptoms of varying severity (including offensive odour detection, nasopharyngeal or ‘eye, nose and throat’ irritation, tussis or ‘coughing’, chest tightness and wheezing) were inquired of specifically for bicycle commuting (one hour pre-commute, in-commute, and one post-commute): frequency of incidence was ranked by participants from “very low” to “very high”. A participant was deemed 38

to experience an acute respiratory symptom when they ranked its incidence as of “moderate” or greater frequency.

3.3.3. Risk Management Strategies Air pollution risk management strategies, including commute re-routing and respirator use, had specific factors of importance for use ranked (from “very low” to “very high”) by participants to guide strategy implementation, if found appropriate and effective for susceptible individuals by future research. Brisbane bikeway maps [108]  were appended to the end of the complete questionnaire (used for Project 1) for participant reference of bicycle commute routing (see Appendix A).

3.4. Ultrafine Particle Monitoring (Project 2 & 3) To monitor exposure concentrations and inhaled particle count, a portable UFP recorder (Aerasense Nanotracer, Phillips, The Netherlands) was used for the second and third project. Logging the mean PNC and PD every 16 seconds (as the maximum frequency), an air quality index (of PNC and PD) was provided approximately every 5 metres (based on an average cycling speed of 20 km/hr, as indicated from Project 1). In-commute PNC and PD means, as well as minimum and maximum values, were calculated for comparison between identified commute routes of high and low proximity to motorised traffic. When PNC readings were below 100 ppcc, and when subsequent readings changed by more than a factor of 10, they were carefully considered and removed if deemed unrealistic [12, 109] – this happened only on rare occasion. Participants of the third project were instructed to maintain air quality (along with heart rate) monitoring for three hours postcommute, if practical, to observe any possible events affecting the three-hour-post-commute time-point testing – again, this only happened on rare occasion.

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3.5. Ambient Pollution Data Monitoring (Project 1, 2 & 3) Ambient hourly mean PNC of Brisbane CBD were recorded by a WCPC 3781 (TSI Inc., USA) and logged in a meta-database at the air monitoring research station of the Queensland University of Technology (QUT). The station is located on the sixth floor of a building in QUT’s south-eastern CBD campus and is of equivalent height to the Southeast Freeway approximately 100 m south-west of the station. The freeway, which experiences mild congestion during peak times, consists of four lanes each inbound and outbound. In-commute trip means (as an indication of localised concentrations) were referenced against ambient hourly means (as an indication of background concentrations) to evaluate commuteattributable UFP exposure.

3.6. Heart Rate and Estimated Ventilation Rate (Project 2 & 3) To help estimate in-commute ventilation rate (VE) and inhaled particle count, heart rate (FH) was recorded in real-time during bicycle commuting. Two different instruments were used for Project 2 (Equivital) and Project 3 (Polar) (with details to follow in relevant sections). Along with HR, in-commute breathing rate (FB) was recorded in the second project (although disregarded with the estimation of VE). Participants of the third project were instructed to maintain FH (along with air quality) monitoring for three hours post-commute, if practical, to observe any possible events affecting the three-hour-post-commute condition inflammatory response testing. FH data collected and logged during participant commute is applied to a standardised heart rate-ventilation curve, adjusted for age and gender. Data from previous laboratory studies indicate VE increases faster than FH when a participant performs upper body exercise compared with lower body exercise [110]. Using this method, as most cycling activities do not involve upper body exertion, it has been concluded that FH interpretation is a feasible approach to estimate VE in field studies [85].

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3.7. Geographical Positioning (Project 2 & 3) To localise air quality readings, a global positioning system device (BT-Q1000X, Qstarz, Taiwan) was used for the second and third project. Logging the longitude, latitude and altitude of a participants every 4 seconds, 20 metre lengths of a commute route could be allocated to a single mean PNC and PD. ArcGIS Desktop (Esri, USA) was used to graphically represent mean PNC and PD recorded along designated commute routes.

3.8. Meteorological Data Monitoring (Project 2 & 3) The Australian Bureau of Meteorology Climate Database [111] was accessed for hourly regional measures of temperature, humidity, wind direction and speed, air pressure, and precipitation. Wind direction was considered as either ‘downwind’ or ‘upwind’ of adjacent motorised traffic, along with low or high wind speeds. Meteorological variable data was collated and analysed to help explain any particle measurement anomalies.

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4. PROJECT ONE: A questionnaire-based investigation of perceptions, symptoms and risk management strategies for air pollution exposure and motorised traffic proximity of adult bicycle commuters

A questionnaire-based investigation of perceptions, symptoms and risk management strategies for air pollution exposure and motorised traffic proximity of adult bicycle commuters Tom Cole-Huntera,b, Lidia Morawskab, Colin Solomonc,d*

PLoS ONE, In Review.

a

Institute of Health and Biomedical Innovation, Queensland University of Technology, 60

Musk Avenue, QLD 4059, Australia. b

International Laboratory for Air Quality and Health, Queensland University of Technology,

2 George Street, QLD 4001, Australia. c

School of Life Sciences, Queensland University of Technology, 2 George Street, QLD 4001,

Australia. d

School of Health and Sport Sciences, University of the Sunshine Coast, Sippy Downs Drive,

QLD 4556, Australia.

*

Corresponding Author: Dr Colin Solomon, School of Health and Sport Sciences, University

of the Sunshine Coast, Sippy Downs Drive, Sippy Downs, QLD 4556, Australia. Telephone: +61754301128. E-mail: [email protected]

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ABSTRACT Bicycle commuting is encouraged in many cities around the world to improve public health, air quality, and traffic congestion. Information concerning the effects of air pollution, such as exposure perception, symptoms and risk management is necessary to responsibly advocate and sustain bicycle commuting participation. To determine reported air pollution exposure perceptions, symptoms and amenability for specific risk management strategies, and relate these to estimated levels of motorised traffic proximity, a questionnaire-based investigation was conducted with regular bicycle commuters in Brisbane, Australia. Participants (n = 153; age = 41 ± 11 yr; 28% female) reported the characteristics of their typical bicycle commute, along with exposure perception and acute respiratory symptoms, and amenability for using a respirator or re-routing their commute as risk management strategies. Healthy individuals reported a higher incidence of specific acute respiratory symptoms in- and post- (compared to pre-) commute (p < 0.05). The incidence of specific acute respiratory symptoms was significantly higher for participants with respiratory disorder history compared to healthy participants (p < 0.05). The incidence of in-commute offensive odour detection, and the perception of in-commute air pollution exposure, was significantly lower for participants with smoking history compared to healthy participants (p < 0.05). Females reported significantly higher incidence of in-commute air pollution exposure perception and other specific acute respiratory symptoms, and were more amenable to commute re-routing, compared to males (p < 0.05). Healthy individuals have indicated a higher incidence of acute respiratory symptoms in- and post- (compared to pre-) bicycle commuting, with female gender and respiratory disorder history indicating a comparably-higher susceptibility. The perception of air pollution exposure levels and the amenability to adopt exposure risk

44

management strategies where applicable will aid the general population in shifting from passive, motorised transport modes to bicycle commuting. Key words: air pollution; bicycle commuting; perception; symptom; risk management.

1.

Introduction

Bicycle commuting requires physical effort and is a fossil fuel-independent transport mode, therefore it is being increasingly promoted as a solution to help alleviate physical-inactivityrelated cardiovascular disease and anthropogenic climate change, as well as intra-urban motorised traffic congestion [3-5, 25]. However, barriers (either physical or psychological) may exist for individuals choosing to shift from passive, motorised transport to bicycle commuting such as the greater perceived or actual risk of exposure to air pollution [6, 19]. It is known that elevated air pollution exposure is a health risk which can be increased with heightened pulmonary ventilation [10-13] and proximity to motorised traffic emissions [11, 14-19]; however, the overall benefits associated with improved air quality and public health from a major uptake in bicycle commuting have been shown to negate such risks [6, 25, 26]. Regionally-monitored air pollutants of potential interest for bicycle commuting in urban environments include particulate matter (PM) and nitrogen dioxide (NO2), which are emitted with motorised traffic exhaust, can be consciously detected (i.e. perceived) and elicit acute respiratory symptoms upon exposure [6]. Generally, nasopharyngeal irritation, airway inflammation and bronchoconstriction (manifesting as cough and phlegm production, and chest tightness or wheezing) are acute (i.e. rapid-onset and short-lived) symptoms which may arise in an individual exposed to such pollutants at elevated concentrations, especially if a pre-disposing respiratory disorder such as asthma exists [27]. Further, chronic exposure to elevated PM and NO2 can suppress airway immune defences and consequently increase the 45

incidence and severity of (sometimes debilitating) upper respiratory tract infections [30, 31]. The reporting of such symptoms has been used previously to assess air pollution exposure in healthy and asthmatic children [32-34], as well as healthy and asthmatic adults [35, 36], the elderly [37], and the general community [38, 39]. Further, symptom questionnaires have been used successfully with adults to investigate multi-modal air pollution exposure perceptions for work-related commuting [40]. Investigation of perceived and actual exposure risk is warranted for an informed transition from passive, motorised transport modes to bicycle commuting, especially for individuals with a physiological pre-disposition to exposure effects. Further, if risk management strategies (such as commute re-routing or respirator use) are deemed appropriate and effective, the successful adoption of these strategies will rely on the features meeting the needs and desires of the potential user. The aims of this project were to investigate: air pollution exposure perceptions, symptoms and amenability for specific risk management strategies of frequent adult bicycle commuters, and to relate these with participant history of respiratory disorder or smoking and also estimated proximity to motorised traffic; if this perception is influenced by factors of typical bicycle commute or personal characteristics, and; if perception of exposure can facilitate self-managed exposure risk strategies. This study did not intend to compare estimated and perceived exposures to actual exposures, but to inform subsequent studies for which personal exposure measurements are taken. Accordingly, it was hypothesised that in adult bicycle commuters: 1) the incidence and severity of acute respiratory symptoms will be greater in-commute compared to pre- and post-commute, and with individuals of respiratory disorder history or female gender, and will be positively-associated with estimated proximity to motorised traffic; 2) the perceived exposure levels of air pollution by adult bicycle commuters will be consistent with estimated motorised traffic proximity levels; and, 3) the amenability to adopt air pollution exposure risk

46

management strategies will be positively-associated with perceived exposure levels and incidence of, or physiological pre-disposition to, acute respiratory symptoms.

2.

Methods

2.1.

Project Design

A questionnaire-based investigation was performed with the aim to: 1) evaluate the incidence and severity of acute respiratory symptoms associated with estimated proximity to motorised traffic, and determine if pre-disposing factors of age, gender and respiratory disorder history are associated with the incidence and severity of these acute respiratory symptoms; 2) assess if participants’ reported in-commute perception of air pollution exposure is consistent with estimated proximity to motorised traffic (derived from commute duration, frequency and proximity to motorised traffic); and, 3) evaluate if the amenability of participants to adopt commute re-routing or respirator use as air pollution exposure risk management strategies (along with the importance of specific strategy features) depends on individual characteristics, exposure perception levels, incidence of (and physiological-susceptibility to) acute respiratory symptoms. 2.2.

Questionnaire Design

The questionnaire used in this investigation was purpose-designed with review and input from researchers and a sub-set of intended participants. Further, the format of assessment for acute respiratory signs and symptoms attributable to air pollution exposure was based on recommendations by the American Thoracic Society [106] and previous research [107]. The complete questionnaire is available online from the journal as an additional electronic file. The questions (total of 77) used a qualitative five-grade scale of equal variance (43 questions: 47

1 = “very low”; 2 = “low”; 3 = “moderate”; 4 = “high”; 5 = “very high”), or were of categorical (5 questions), continuous (12 questions) or nominal (17 questions) format. The incidence of acute respiratory signs and symptoms (including offensive odour detection, eye, nose and throat or ‘nasopharyngeal’ irritation, tussis or coughing, chest tightness and wheezing) of varying severity were assessed specifically for bicycle commuting at conditions of one hour pre-commute, in-commute, and one hour post-commute: the frequency of incidence was ranked by participants from “very low” / ‘1’ to “very high” / ‘5’. Perception of in-commute air pollution exposure (defined as the reporting and therefore conscious detection of moderately-poor air quality), was reported by participants for incidence (“yes”/ “no”) and level (“very low” / ‘1’ to “very high” / ‘5’) of their typical bicycle commute. To address individual response subjectivity, a participant was considered to perceive air pollution exposure only when they reported the level of perception as “moderate” / ‘3’ or higher. As well as an indicator of perception, the detection of offensive odour is considered an acute respiratory sign [106] and could aid the self-management of air pollution exposure incidence and level. An inquiry of upper respiratory tract infection (URTI; either mild or debilitating and interfering with normal daily duties) along with commute history was made to elucidate a possible effect of chronic exposure to motorised traffic emissions [30, 31]. To address individual response subjectivity, a participant was considered to experience an acute respiratory symptom only when they reported its’ incidence as of “moderate” / ‘3’ or greater frequency. Air pollution risk management strategies, including commute re-routing and respirator use, had the importance of specific strategy features rated (from “very low” / ‘1’ to “very high” / ‘5’) by participants to guide future implementation of such strategies, if found to be appropriate and effective by future research. The intention of this inquiry is to indicate which strategy might warrant the most attention for future research, and which properties of each 48

strategy might require the most attention for developers. Brisbane bikeway maps [108] were appended to the end of the questionnaire for participant reference of bicycle commute routing. 2.3.

Participant Recruitment and Sample

Potential participants were initially contacted through the university and major Brisbane bicycle user groups (BUGs), or through newspaper and radio segments (noting the research and recruitment contact details) to reach a larger group of potential participants. Questionnaires were distributed as a paper copy (with a reply paid envelope; via BUG facilities administration, mail-out, and attending an active transport promotional event) or an electronic copy (via return E-mail). Eligible participants were adults and regular bicycle commuters of the Brisbane inner-city region (defined as completing two or more return trips in a five week-day period to a destination within a one kilometre radius of Brisbane’s Central Business District). Potential participants with a current smoking status, or smoking cessation of less than twenty-four months prior to the study, were not eligible for participation. Eligible participants were supplied with the questionnaire and instructed to provide responses which most accurately and completely recollected the cumulative experience of their own typical bicycle commuting. The project was conducted with a protocol approved by the Queensland University of Technology. Participants indicated informed consent by returning the completed questionnaire. See Table 1 for recruited participant characteristics. The mean frequency of return bicycle commutes per week and the mean historical duration of indicated bicycle commute route suggests the participants of this study were regular bicycle commuters with a variety of commuting experience (Table 1). Participants were divided into groups according to history of respiratory disorder or smoking, as well as gender, for comparative analysis of responses (Table 2, 3, 4 and 5).

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INSERT TABLE 1 HERE

2.4.

Data Calculation and Statistical Analyses

The qualitative five-grade scale data, concerning frequency of in-commute air pollution exposure perception and symptoms, as well as importance of the features evaluated for the use of risk minimisation strategies, were converted from an interval scale of measurement (of ‘very low’ to ‘very high’) to an ordinal scale (of 1.0 to 5.0) as: 1.0 = “very low”; 2.0 = “low”; 3.0 = “moderate”; 4.0 = “high”; 5.0 = “very high” to allow group mean values for collation and statistical analysis. Attempts were not made to control for pre-dispositions of respiratory disorder history or smoking history, or gender, but participants with these characteristics were grouped, analysed and compared against each other (eg. female versus male gender) for incidence (using ordinal scale data) of symptoms assessed between either in-commute or one hour post-commute and one hour pre-commute conditions. To provide an objective estimation of in-commute proximity to motorised traffic (PROX), a ranking was calculated for individual participants as the product of their bicycle commute duration, frequency (per week) and use of motorised traffic corridors (being the proportion of route ‘on-road’, or route shared with motorised traffic) for their typical bicycle commute. The proportion of on-road paths taken for a typical bicycle commute was converted to a fraction from 0.1 to 0.9, with 0-10% on-road use ranked as the minimum (‘0.1’), ~50% on-road use ranked as the median (‘0.5’), and 90-100% on-road use ranked as the maximum (‘0.9’), etcetera. The on-road proportional fraction was then multiplied by the duration (as minutes) and the frequency (as number of return trips per week) for a total time of motorised traffic

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proximity whilst bicycle commuting per week. The product of this process allocated participants a rank of either 1 (“very low”; n = 30), 2 (“low”; n = 32), 3 (“moderate”; n = 30), 4 (“high”; n = 32) or 5 (“very high”; n = 32) to represent the level of estimated proximity to motorised traffic (as a proportion of total commute adjacent to motorised traffic corridors) and associated exposure to air pollution emissions. The interval value (of approximately 25) for each PROX rank allowed near-equal numbers of participants per rank for better control of covariates when performing statistical analyses. The questionnaire responses were analysed using predictive analytics software (PASW Statistics Data Editor, V18.0; IBM Corporation, USA). One-way analysis of variance (ANOVA) were performed to identify differences between group mean responses (of perception and symptom incidence) at pre-, in- and post-commute conditions, along with group mean differences between participant characteristic groups (of respiratory disorder or smoking history and gender), and commute behaviour (including PROX). Subsequently, Tukey HSD Post Hoc comparisons were performed with these ANOVA to identify specific pair-wise differences. Further, Fisher’s Exact Test and Pearson’s Chi Square Test were performed to signify the effect of participant and commute characteristics (of PROX, respiratory disorder or smoking history, gender and historical duration of commute) on participant responses, and the associated incidence of one participant’s response to another response (within an individual). Statistical significance was indicated at the 95% confidence interval (i.e. p < 0.05), which was not adjusted for repeated measures.

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3.

Results

3.1.

Participant recruitment and response rate

The estimated target population size, according to BUG membership of targeted organisations during recruitment, was 500. Approximately 200 potentially eligible individuals expressed interest to participate in this study. Of these, 160 were confirmed as eligible and therefore supplied with their preferred choice of a physical or electronic copy of the questionnaire: 60 of 61 (98%) electronic copies and 93 of 99 (94%) printed copies supplied to eligible participants were returned within a three month period from March to June of 2010. 3.2.

Symptom incidence and exposure perception reporting

Healthy participants reported significantly higher incidence of specific acute respiratory symptoms in- and post-commute compared to pre-commute (p < 0.05; Table 2). The incidence of in-commute offensive odour detection was positively-associated with incidence of in-commute nasopharyngeal irritation [F(29,99) = 11.22, p < 0.001], tussis [F(18,151) = 4.50, p = 0.002], chest tightness [F(10,87) = 4.39, p = 0.002] and wheezing [F(6,79) = 2.82, p = 0.027]; and, post-commute nasopharyngeal irritation [F(13,178) = 2.84, p = 0.026], tussis [F(8,107) = 2.97, p = 0.022] and chest tightness [F(3,51) = 2.50, p = 0.045] of healthy individuals. The majority of participants (80%) reported in-commute perception of exposure to moderate or higher levels of air pollution (Table 3), which was positively-associated with the incidence of in-commute offensive odour detection [F(43,136) = 48.25, p < 0.001], nasopharyngeal irritation [F(8,120) = 10.63, p = 0.001] and chest wheeze [F(2,82) = 4.59, p = 0.034]. Additionally, in-commute air pollution exposure perception was positively-associated with 52

the number of weekly return trips performed [F(9,256) = 5.50, p = 0.020], and general concern for Brisbane’s ambient air pollution levels [F(3,28) = 18.59, p = 0.001].

3.3.

Factors of physiological susceptibility

3.3.1. History of respiratory disorder or smoking The incidence of acute respiratory symptoms (nasopharyngeal irritation, tussis, chest tightness and wheezing) was significantly higher for participants with respiratory disorder history compared to healthy participants (p < 0.05; Table 4). The incidence of upper respiratory tract infection (URTI) was significantly higher for participants with respiratory disorder history compared to healthy participants, both mild URTI (p < 0.001) and debilitating URTI (p = 0.002). The incidence of in-commute offensive odour detection, and the perception of in-commute air pollution exposure level (of moderate or above), was significantly lower for participants with smoking history compared to healthy participants (p < 0.05; Table 4 and 5).

3.3.2. Gender Females reported significantly higher incidence of in-commute nasopharyngeal irritation (p = 0.009) and chest wheeze (p = 0.046), and post-commute nasopharyngeal irritation (p = 0.006), as well as in-commute air pollution exposure perception (p = 0.039), compared to males (Table 2 and 3).

INSERT TABLE 2 HERE

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INSERT TABLE 3 HERE

INSERT TABLE 4 HERE

INSERT TABLE 5 HERE

3.4.

Historical duration of bicycle commute

There was a positive-association between historical duration of bicycle commute and PROX [F(17976,144390) = 2.26, p = 0.027]. The incidence of debilitating URTI was nearsignificantly associated with longer historical duration of bicycle commute [F(22,53) = 1.51, p = 0.058], and debilitating URTI incidence was positively-associated with in-commute nasopharyngeal irritation [F(7,120) = 2.98, p = 0.033] and tussis [F(9,158) = 2.84, p = 0.040], and post-commute tussis [F(7,108) = 3.03, p = 0.031]. Amenability to utilise a higher proportion of off-road paths as an air pollution exposure risk-management strategy, if found to be appropriate and effective, was negatively-associated with historical duration of bicycle commute [F(11,23) = 1.69, p = 0.021].

3.5.

Estimated in-commute proximity to motorised traffic

The total group mean proportion of estimated in-commute proximity to motorised traffic (PROX; the product of commute duration, frequency and proportional fraction of commute 54

using motorised traffic corridors) was 51.8 ± 2.8 % (Table 3). There was no significant difference between groups according to gender, smoking history and respiratory disorder history (Table 3 and 5). PROX was positively-associated with in-commute air pollution exposure perception [F(3,22) = 2.31, p = 0.023] and incidence of in-commute offensive odour detection [F(18,160) = 2.08, p = 0.041]. However, PROX was not associated with the incidence of any acute respiratory symptoms, either in- (p ≥ 0.113) or post-commute (p ≥ 0.095). A higher general concern for Brisbane’s ambient air pollution level was not associated with a lower PROX (p = 0.42).

3.6.

Air pollution exposure risk management strategies

3.6.1. Commute re-route Most participants (68%) reported that they were amenable to re-route their commute to reduce proximity to motorised traffic as an exposure risk management strategy, if proven to be appropriate and effective, which varied with gender (Table 3) and health status (Table 5). Females, compared to males, were significantly more amenable to commute re-routing (p < 0.05; Table 3). The group mean importance of strategy features (out of 5.0) for the adoption of commute re-routing were, from highest to lowest, “safety” (4.0), “time” (3.8), “fitness” (2.9), “health” (2.9) and “social” (1.5). Further, a participant’s PROX level was negativelyassociated with the importance for commute re-routing strategy features of “health” [F(34,196) = 3.25, p = 0.002] and “safety” [F(31,182) = 3.10, p = 0.003]. 3.6.2. Respirator use Zero participants currently used a respirator during their bicycle commute; however, approximately one fifth of participants (21%) had previously considered such use, and this 55

consideration was positively-associated with in-commute air pollution exposure perception [F(1,23) = 6.33, p = 0.013] and offensive odour detection [F(3,22) = 4.52, p = 0.002]. The majority of participants (75%) indicated that they would use a respirator as an exposure risk management strategy if proven to be appropriate and effective, which (similar to commute rerouting) varied with gender (Table 3) and health status (Table 5). The group mean importance of strategy features (out of 5.0) for using a respirator while bicycle commuting were, from highest to lowest, “breathing impedance” (3.9), “wear comfort” (3.7), “appearance” (2.9) and “expense” (2.5).

4.

Discussion

The major results of this study suggest that in healthy individuals, the incidence of specific acute respiratory symptoms is higher in- and post-commute compared to pre-commute conditions. Further, the incidence of acute respiratory symptoms in association with bicycle commuting is higher with respiratory disorder history (compared to healthy) and female (compared to male) gender participant cohorts. A significant positive-association exists between the perceived level of in-commute air pollution exposure and the estimated level of in-commute proximity to motorised traffic (PROX; a product of commute duration, frequency and proportion of commute on-road) by healthy participants. However, PROX was not associated with the reported incidence of acute respiratory symptoms in or post typical bicycle commuting in healthy participants. The majority of participants indicated that they were amenable to the risk management strategies of commute re-routing and respirator use if these strategies were shown to be necessary and effective by future research and they were developed with specified practical features.

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The detection of offensive odours, associated with vapour gases such as nitrogen dioxide (NO2), has previously been positively-associated with perceived health risk and prevalence of acute respiratory symptom reporting [112]. Populations living near environmental odour sources have reported consistent patterns of subjective symptoms, including exacerbation of underlying medical conditions and stress-induced illness from offensive odour exposure [38, 113, 114]. However, air pollution exposure limits may not coincide with odour detection or irritation, as some pollution constituents can cause irritation or harm below perceivable limits [115]. Previously, exposed individuals have been shown to be capable of both under- and over-estimation of exposure according to self-reported perception and symptoms compared to direct air quality measurements [116, 117]. Therefore, communication of accurate air pollution levels and consequential exposure risk could help to effectively facilitate selfmanaged exposure risk strategies for elevated air pollution events, unfavourable meteorological conditions and physiologically-susceptible individuals, if found to be appropriate and effective by future research. The increased incidence of acute respiratory symptoms in participants with physiological susceptibility has also been observed in past research [32, 35] and other questionnaire-based studies [32, 34-39, 118, 119]. Participants in the current study with a history of respiratory disorder were more susceptible to acute respiratory symptoms (and chronic URTI): a history of respiratory disorder increased , and a history of smoking decreased, the incidence of perception to moderate or higher levels of incommute air pollution, compared to healthy participants, which has been observed elsewhere [120]. Particulate matter (PM) can trigger inflammation in the airways, exacerbate respiratory disorders, and suppress airway antimicrobial defences [31, 121]. During the study period, south-east Queensland (SE QLD; surrounding Brisbane, Australia) ambient PM10 and PM2.5 (particulate matter with diameters of 10 and 2.5 micrometres, respectively) maximum daily 57

mean particle mass concentrations were 37 and 19 μg/m3, respectively [122]. Previous recordings of roadside PM10 showed one-hour mean concentrations of 25 ± 13 μg/m3 and a maximum of 90 μg/m3, associated with high traffic counts and large proportions of heavy duty vehicles [123]. PM2.5 one-hour mean concentrations of 21 ± 11 μg/m3 and a maximum of 195 μg/m3 were also shown, with the highest values on week-days (Monday to Friday) believed to be due to greater traffic counts and proportion of heavy duty vehicles [123]. Short-term exposure to PM10 and PM2.5 at regionalised outdoor mass concentrations of 14 ± 7 and 11 ± 5 μg/m3, respectively, has not been associated with detrimental health effects or detectable systemic inflammation in young, healthy participants performing exercise [124]. As roadside PM concentrations in this project were higher than that previously shown to be non-detrimental, in-commute PM exposure could be the cause of acute respiratory symptoms in physiologically-susceptible individuals in this study; however, the greatest concern regarding PM is considered to be the particle number concentration (that is, particle count) rather than particle mass concentration [90]. For a specific mass concentration, ultrafine particles (UFPs; < 0.1 μm diameter) are the main diameter range of motorised traffic particulate emissions [59]. As UFPs are not routinely monitored in SE QLD, it is difficult to consider the effects on bicycle commuters indirectly. Similar to PM, NO2 can trigger inflammation in the lower airways, exacerbate asthma and chronic bronchitis, and suppress upper respiratory tract antimicrobial defences such as macrophage function [31, 121]. During the study period, Brisbane’s ambient NO2 annual mean was 7 parts per billion (ppb), with a daily peak 1-hour mean of 37 ppb (DERM, 2011). At such levels, acute respiratory symptoms in healthy adults have not been shown. However, as NO2 is a major emission component of motorised traffic, exposure concentrations are expected to be much higher when adjacent to major traffic corridors. Brisbane’s roadside mean NO2 concentrations have been recorded between 18 and 34 ppb with peaks of 58

approximately 60 ppb positively-correlated with morning (7.00-8.00 AM) and afternoon (4.00-6.00 PM) commute traffic flow rates, indicating traffic emissions as a dominant emission source [123]. Asthmatic adults are twice as sensitive as non-asthmatics to shortterm exposures of NO2, however significantly increased airway resistance (due to inflammation) has not been observed below 500 ppb [30]. Further, acute exposure of very high concentrations (~5,000 - 10,000 ppb) are necessary to elicit symptoms due to inflammation such as nasopharyngeal irritation, dyspnoea and tussis in healthy adults [30]. Historical duration of commute and the incidence and severity of URTI were not significantly linked in this study, nor were acute symptom incidence (such as nasopharyngeal irritation, dyspnoea and tussis) and estimated exposure level, most likely due to the relatively low regional traffic counts and associated emissions compared to previously studied regions [30, 125, 126]. Future research involving direct investigation of bike-path and roadside air quality is warranted to advise the appropriateness and efficacy of implementing risk management strategies such as commute re-routing. Preferences of participants for air pollution exposure risk management strategy features were evaluated to highlight which features of a commute route or respirator are necessary or desirable to help strategy adoption, if shown to be appropriate and effective. Currently, less than half of the participant cohort used the highest proportion of off-road paths and nil used a respirator. It may be the case that some bicycle commute routes do not have a high proportion of off-road paths available for use, or that there is limited knowledge of respirator availability for bicycle commuters. The feature of “time (defined to the participant as “more convenient / quickest route”) was ranked as more important than the feature of “health” (defined to the participant as “to avoid air pollution”) by participants when choosing a commute route, which should be considered when new bike paths are being developed. The most important factors to be addressed for respirator use by participants in this study were “breathing impedance” 59

and “wear comfort”. There are commercially-available respirators that are recommended for use by urban bicycle commuters due to their design accommodating increased heat production, perspiration and ventilation rate associated with moderate physical activity (e.g. ‘City Mask’ by Respro Ltd, UK). To support the desired increased participation of bicycle commuting as a response to public health and infrastructure concerns, desirable properties of self-managed risk strategies such as direct and safe bicycle paths, or comfortable and functional respirators, may need to be implemented in some circumstances. Commuters using different travel modes in SE QLD have previously indicated that they thought of air pollution exposure as a substantial health concern [40]. Regardless, the participants of this previous study did not consider air pollution exposure to be a major barrier to participating in active transport (such as bicycle commuting) [40]. However, a limitation highlighted by the authors of this previous study was the relatively small sub-set of active commuters (n = 64 of 745 / 9%) surveyed. The current study (with a cohort of twice that number) contributes with the suggestion that healthy bicycle commuters can perceive incommute air pollution exposure levels consistent with estimated proximity to motorised traffic, and are generally amenable to reduce their exposure to air pollution by adopting risk management strategies, if known to be appropriate and effective. Limitations of the current study include the design of a unique questionnaire - to the authors’ knowledge, a precedent model was not available for reference - however, this design process was rigorously performed with the review and input of respiratory scientists, epidemiological statisticians and a sample of the intended participant cohort. Bias for participation of potential respondents is possible due to the nature of the questionnaire (i.e. bringing focus to a subject which may discourage the act of bicycle commuting by highlighting associated dangers), however potential participants spoken-to expressed a positive attitude towards the issue and hence recruitment did not prove difficult. As the symptoms were self-reported, 60

misunderstandings could have arisen by question misinterpretation; however, again, due to the review process of questionnaire design, this was believed to be minimised. Finally, the specifics of human exposure and the associated biological responses (reported as symptom experience) to ambient air pollution are difficult to assess due to atmospheric mixing effects and meteorological influence [127], which were beyond the scope of this study. The merit of responsibly encouraging increased participation of bicycle commuting, and a strength of this study, is indicated by the fact that the mean one-way commute duration of participants was approximately 30 minutes, which coincides with daily physical-activity recommendations to reduce the risk of cardiopulmonary disease development [20-22]. Further, participants typically performed this activity twice a day on four days per week. Eligibility for participation only required two or more return trips per week (to satisfy the definition of a regular bicycle commuter) but the group mean was four trips per week, suggesting that this study represented a dedicated and experienced population of bicycle commuters. However, seasonal variation of bicycle commuting participation was not investigated.

5. Conclusions Healthy individuals have indicated a higher incidence of acute respiratory symptoms in- and post- (compared to pre-) bicycle commute, with respiratory disorder history and female gender indicating a comparably-higher susceptibility to air pollution exposure, in this study. The perception of air pollution exposure levels, combined with an amenability of susceptible populations to adopt exposure risk management strategies, will aid the informed transition

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from passive, motorised transport modes to bicycle commuting required for contemporary urban growth and the increasing popularity of bicycle commuting. Abbreviations: PROX (estimation ranking of in-commute proximity to motorised traffic); ppb (parts per billion); SE QLD (South East Queensland); URTI (upper respiratory tract infection).

ACKNOWLEDGEMENTS The authors declare they have no actual or potential competing personal or financial interests. The project was conducted with a protocol approved (#1000001175) by the University Human Research Ethics Committee (UHREC: +61 7 3138 5123 / [email protected]), Queensland University of Technology. TCH performed all data acquisition, analyses and manuscript preparation under the supervision of CS and LM. TCH is supported by an Australian Postgraduate Award (Department of Innovation, Industry, Science and Research, Australian Government) scholarship. CS and LM hold academic positions at the University of the Sunshine Coast and the Queensland University of Technology, respectively. The authors acknowledge and sincerely thank Dr Ian Stewart (Institute of Health and Biomedical Innovation) and Dr Rohan Jayaratne (International Laboratory for Air Quality and Health) of Queensland University of Technology for research consultation, along with Andrew Onley (Cycle2City Centre, Brisbane, Australia), Sabrina von Bayer (Active Transport Unit, Brisbane City Council, Australia), and Jan Bell (Department of Environment and Resource Management, Queensland Government, Australia) for research participant recruitment. Finally, we sincerely thank all research participants for their vital role in this study.

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34. Shusterman D (1992) Community health and odor pollution regulation. American Journal of Public Health 82: 1566. 35. Paustenbach DJ, Gaffney SH (2006) The role of odor and irritation, as well as risk perception, in the setting of occupational exposure limits. International Archives of Occupational and Environmental Health 79: 339-342. 36. Dalton P (1999) Cognitive influences on health symptoms from acute chemical exposure. Health Psychology 18: 579-590. 37. Righi E, Aggazzotti G, Fantuzzi G, Ciccarese V, Predieri G (2002) Air quality and wellbeing perception in subjects attending university libraries in Modena (Italy). Science of the Total Environment 286: 41-50. 38. Hirsch T, Weiland SK, Von Mutius E, Safeca AF, Gräfe H, et al. (1999) Inner city air pollution and respiratory health and atopy in children. European Respiratory Journal 14: 669677. 39. Simoni M, Annesi-Maesano I, Sigsgaard T, Norback D, Wieslander G, et al. (2010) School air quality related to dry cough, rhinitis and nasal patency in children. European Respiratory Journal 35: 742. 40. Nikolopoulou M, Kleissl J, Linden P, Lykoudis S (2011) Pedestrians' perception of environmental stimuli through field surveys: Focus on particulate pollution. The Science of the total environment 409: 2493-2502. 41. Nel AE, Diaz-Sanchez D, Ng D, Hiura T, Saxon A (1998) Enhancement of allergic inflammation by the interaction between diesel exhaust particles and the immune system. Journal of Allergy and Clinical Immunology 102: 539-554. 42. Management) DDoEaR (2011) Queensland 2010 Air Monitoring Report. 43. Ayoko G, Jamriska M, Jayaratne R, Morawska L. Air pollution levels measured at traffic hot spots: Brisbane urban corridor study; 2005. CASANZ (Clean Air Society of Australia & New Zealand). pp. 1-7. 44. Bräuner EV, Møller P, Barregard L, Dragsted LO, Glasius M, et al. (2008) Exposure to ambient concentrations of particulate air pollution does not influence vascular function or inflammatory pathways in young healthy individuals. Particle and Fibre Toxicology 5: 13. 66

45. Oberdörster G, Oberdörster E, Oberdörster J (2005) Nanotoxicology: an emerging discipline evolving from studies of ultrafine particles. Environmental Health Perspectives 113: 823. 46. Knibbs LD, Cole-Hunter T, Morawska L (2011) A review of commuter exposure to ultrafine particles and its health effects. Atmospheric Environment 45: 2611-2622. 47. Belanger E, Kielb C, Lin S (2006) Asthma Hospitalization Rates Among Children, and School Building Conditions, by New York State School Districts, 1991 2001. Journal of School Health 76: 408-413. 48. Pribyl CR, Racca J (1996) Toxic gas exposures in ice arenas. Clinical Journal of Sport Medicine 6: 232. 49. Blair SN, Morris JN (2009) Healthy hearts--and the universal benefits of being physically active: Physical activity and health. Annals of Epidemiology 19: 253-256. 50. Shiroma EJ, Lee I (2010) Physical activity and cardiovascular health: lessons learned from epidemiological studies across age, gender, and race/ethnicity. Circulation 122: 743. 51. Warburton DER, Nicol CW, Bredin SSD (2006) Health benefits of physical activity: the evidence. Canadian Medical Association Journal 174: 801. 52. Brunekreef B, Holgate ST (2002) Air pollution and health. The Lancet 360: 1233-1242.

67

TABLES Table 1 - Characteristics of Bicycle Commuter Participants CHARACTERISTIC

MEAN

SD

MIN / MAX

Gender (% female)

28.2

--

--

Age (years)

41

11

19 / 64

Single Trip Distance (km)

11

7

4 / 32

Single Trip Duration (min)

31

15

10 / 65

Return Trips (per week)

4

1

2/5

Commute History (month)

27

32

6 / 180

Inbound Start Time (24 hr)

07:15

0:56

05:30 / 10:00

Outbound Start Time (24 hr)

17:10

1:00

15:30 / 19:00

68

Table 3 - Perceptions and preferences of regular bicycle commuters for the total group and gender groups RESPONSE

TOTAL

GENDER

Female

Male

[n = 153 (100%)]

[n = 43 (28%)]

[n = 110 (72%)]

Ambient Air Concern (%Yes)

71

80

68

Perceived Exposure (%Yes)

80

91*

76

Re-route Amenable (%Yes)

68

80*

63

Respirator Amenable (%Yes)

75

77

74

Estimated Exposure (%Commute)

52 ± 2.8

46 ± 3.4

54 ± 2.6

‘%Yes’ = proportion of positive response from Total / Gender group. ‘%Commute’ = group mean proportional time in proximity to motorised traffic of Total / Gender group; values are group mean ± standard deviation (SD). * p < 0.05, higher positive response than Male. Participants [n = participant number (percentage of total cohort)] reported their general concern for ambient air pollution, their perception of in-commute air pollution exposure, their amenability to use risk management strategies of commute re-routing or respirator use. Estimated exposure is given as a percentage of time bicycle commuting adjacent to motorised traffic corridors. All other percentages are group total of positive responses. One-way analysis of variance (ANOVA) were performed between groups of female and male participants to highlight any significant differences of Gender group mean responses.

69

Table 2 – Acute respiratory symptoms of regular bicycle commuters for the total group and gender groups RESPONSE

Offensive Odour

Nasopharyngeal Irritation

Cough and/or Phlegm

Chest Tightness

Chest Wheeze

TOTAL

GENDER

Female

Male

[n = 153 (100%)]

[n = 43 (28%)]

[n = 110 (72%)]

Pre

In

Post

Pre

In

Post

Pre

In

Post

1.38

2.70

1.44

1.45

2.77

1.50

1.35

2.67

1.41

±

±

±

±

±

±

±

±

±

0.08

0.12

0.08

0.12

0.14

0.11

0.06

0.11

0.07

1.39

2.05

1.72

1.50

2.27** 2.11** 1.35

1.96

1.57

±

±

±

±

±

±

±

±

±

0.07

0.10

0.12

0.11

0.13

0.25

0.06

0.09

0.07

1.35

1.96

1.66

1.39

2.00

1.75

1.33

1.94

1.63

±

±

±

±

±

±

±

±

±

0.07

0.11

0.09

0.11

0.15

0.13

0.06

0.10

0.08

1.21

1.48

1.30

1.27

1.52

1.34

1.18

1.47

1.28

±

±

±

±

±

±

±

±

±

0.05

0.09

0.07

0.09

0.12

0.11

0.04

0.08

0.05

1.18

1.45

1.30

1.30

1.64*

1.39

1.14

1.38

1.27

±

±

±

±

±

±

±

±

±

0.05

0.08

0.05

0.09

0.12

0.01

0.04

0.07

0.06

Group mean ± standard deviation (SD) * p < 0.05, ** p < 0.01, higher incidence than Male. Participants [n = participant number (percentage of total cohort)] reported the incidence of air pollution perception and acute respiratory symptoms of increasing severity one hour before (Pre), during (In) and one hour after (Post) their standard bicycle commute, using an ordinal scale of 1.00 to 5.00 converted from the group mean reported interval scale (as: 1 = Very Low; 2 = Low; 3 = Moderate; 4 = High; 5 = Very High). One-way analysis of variance (ANOVA) were performed between groups of female and male participants to highlight any significant differences of Gender group mean responses.

70

Table 5 – Perceptions and preferences for regular bicycle commuters of health status groups RESPONSE

HEALTH STATUS

Healthy

Smoking History

Disorder History

[n = 93 (62%)] [n = 24 (15%)] 

[n = 36 (23%)] 

Ambient Air Concern (% Yes)

74

64

65

Perceived Exposure (% Yes)

82

68*

72

Re-route Amenable (% Yes)

68

59

65

Respirator Amenable (% Yes)

75

71

73

44 ± 5.2

45

Estimated Exposure (% Commute) 55 ± 2.6

‘%Yes’ = proportion of positive response from Health Status groups. ‘%Commute’ = group mean proportional time in proximity to motorised traffic of Health Status groups; values are group mean ± standard deviation (SD). * p < 0.05, lower positive response than Healthy. Participants [n = participant number (percentage of total cohort)] reported their general concern for ambient air pollution, their perception of in-commute air pollution exposure, their amenability to use risk management strategies of commute re-routing or respirator use. Estimated exposure is given as a percentage of time bicycle commuting adjacent to motorised traffic corridors. All other percentages are group total of positive responses. Smoking History is defined as a participant who ceased habitual smoking greater than 24 months previously but is otherwise healthy. Disorder History is defined as a participant who reported any history of a respiratory disorder. One-way analysis of variance (ANOVA) were performed between groups of Healthy, Smoking History and Disorder History participants to highlight any significant differences of Health Status group mean responses.

71

Table 4 – Acute respiratory symptoms of regular bicycle commuters (according to health status) RESPONSE

HEALTH STATUS

Healthy

Smoking History

Disorder History

[n = 93 (62%)]

[n = 24 (15%)]

[n = 36 (23%)]

Post

Pre

In

Post

Pre

In

Post

1.38

1.45

2.40+

1.45

1.53

2.76

1.51

±

±

±

±

±

±

±

0.07

0.14

0.22

0.14

0.15

0.19

0.13

1.63*

1.60

1.95*

1.70

1.44

2.24##

1.97#

±

±

±

±

±

±

±

0.12

0.21

0.21

0.16

0.10

0.16

0.17

1.47*

1.45

1.95*

1.90*

1.47

2.41##

2.05##

±

±

±

±

±

±

±

0.07

0.15

0.25

0.25

0.14

0.21

0.16

1.18

1.25

1.55*

1.40

1.28

1.84##

1.51#

±

±

±

±

±

±

±

0.05

0.10

0.15

0.13

0.09

0.18

0.11

1.18

1.20

1.45

1.40

1.28

1.76##

1.57#

±

±

±

±

±

±

±

0.05

0.09

0.15

0.15

0.09

0.16

0.13

Pre Offensive Odour

1.31 ± 0.06

Nasopharyngeal Irritation

1.34 ± 0.06

Cough and/or Phlegm

1.28 ± 0.06

Chest Tightness

1.15 ± 0.04

Chest Wheeze

1.14 ± 0.05

In 2.67 ± 0.11

1.96

*

± 0.09

1.80* ± 0.09

1.35 ± 0.06

1.36 ± 0.07

Group mean ± standard deviation (SD) *p < 0.05, higher symptom incidence than pre-commute (‘Pre’). #p < 0.05,

##

p < 0.01, higher incidence than Healthy.

+

p < 0.05, lower incidence than Healthy. Participants [n =

participant number (percentage of total cohort)] reported the incidence of air pollution perception and acute respiratory symptoms of increasing severity one hour before (Pre), during (In) and one hour after (Post) their standard bicycle commute, using an ordinal scale of 1.00 to 5.00 converted from the group mean reported interval scale (as: 1 = Very Low; 2 = Low; 3 = Moderate; 4 = High; 5 = Very High). Smoking History is defined as a participant who ceased habitual smoking greater than 24 months previously but is otherwise healthy. Disorder History is defined as a participant who reported any history of a respiratory disorder. One-way analysis of variance (ANOVA) were performed between groups of healthy, smoking history and disorder history participants to highlight any significant differences of Health Status group mean responses.

72

5. PROJECT TWO: Inhaled particle counts on bicycle commute routes of low and high proximity to motorised traffic

Inhaled particle counts on bicycle commute routes of low and high proximity to motorised traffic

Tom Cole-Huntera,b, Lidia Morawskab, Ian Stewarta, Rohan Jayaratneb, Colin Solomonc, d*

Atmospheric Environment, (2012) 61:197-203. doi: 10.1016/j.atmosenv.2012.06.041

a

Institute of Health and Biomedical Innovation, Queensland University of Technology, 60

Musk Avenue, QLD 4059, Australia. b

International Laboratory for Air Quality and Health, Queensland University of Technology,

2 George Street, QLD 4001, Australia. c

School of Life Sciences, Queensland University of Technology, 2 George Street, QLD 4001,

Australia. d

School of Health and Sport Sciences, University of the Sunshine Coast, Sippy Downs Drive,

QLD 4556, Australia.

*

Corresponding Author: Dr Colin Solomon, School of Health and Sport Sciences, University

of the Sunshine Coast, Sippy Downs Drive, Sippy Downs, QLD 4556, Australia. Telephone: +61 7 54301128. E-mail: [email protected]

73

74

SEE APPENDIX E FOR PUBLICATION

75

6. PROJECT THREE: The reduction of ultrafine particle exposure by utilising bicycle commute routes of low versus high proximity to major motorised traffic corridors

The reduction of ultrafine particle exposure by utilising bicycle commute routes of low versus high proximity to major motorised traffic corridors

Tom Cole-Hunter1,2, Lidia Morawska2, Ian Stewart1, Rohan Jayaratne2, Matthew Hadaway1, Colin Solomon3,4,*

Environmental Health, In Review.

1

Institute of Health and Biomedical Innovation, Queensland University of Technology, 60

Musk Avenue, QLD 4059, Australia. 2

International Laboratory for Air Quality and Health, Queensland University of

Technology, 2 George Street, QLD 4001, Australia. 3

School of Life Sciences, Queensland University of Technology, 2 George Street, QLD 4001,

Australia. 4

School of Health and Sport Sciences, University of the Sunshine Coast, Sippy Downs Drive,

QLD 4556, Australia.

*

Corresponding Author: Dr Colin Solomon, School of Health and Sport Sciences, University

of the Sunshine Coast, Sippy Downs Drive, Sippy Downs, QLD 4556, Australia. Telephone: +61754301128. E-mail: [email protected]

76

77

Abstract Background Bicycle commuting in an urban environment of high levels of air pollution is known as a potential health risk, especially for susceptible individuals. While risk management strategies aimed to reduce motorised traffic emissions exposure have been suggested, limited studies have assessed the utility of such strategies in real-world circumstances. Objectives The potential of lowering exposure to ultrafine particles (UFP; < 0.1 µm) during bicycle commuting by reducing proximity to major motorised traffic corridors (without significantly affecting commute distance or duration) was investigated using continuous UFP and periodic respiratory symptom and inflammation measurements in healthy individuals using their typical, and an alternative purposely-designed, bicycle commute route. Methods Thirty-five healthy adults (mean ± SD: age = 39 ± 11 yr; 29 % female) each completed two return trips of their typical route (HIGH) and a purposely-designed route alteration of lower proximity to major motorised traffic corridors (LOW). Particle number concentration (PNC) and diameter (PD) were monitored continuously in-commute. Acute inflammatory indices of respiratory symptom occurrence, pulmonary function and spontaneous sputum (for inflammatory cell analyses) were collected immediately pre-commute, and one and three hours post-commute. Results In-commute mean PNC was significantly reduced in LOW compared to HIGH [mean ± SD: 1.91 x e4 ± 0.93 x e4 parts per cubic centimetre (ppcc) vs. 2.95 x e4 ± 1.50 x e4 ppcc; p ≤ 0.001]. Commute distance and duration were not significantly different between LOW and HIGH (12.8 ± 7.1 vs. 12.0 ± 6.9 km, p = 0.325; 44 ± 17 vs. 42 ± 17 mins, p = 0.196; 78

respectively). Self-reported occurrences of acute health-associated signs and symptoms were reduced in LOW compared to HIGH, including in-commute offensive odour detection (42 vs. 56 %; p = 0.019), dust and soot observation (33 vs. 47 %; p = 0.038) and nasopharyngeal irritation (31 vs. 41 %; p = 0.007) . Conclusions Exposure level of UFP PNC (and the occurrence of offensive odour and nasopharyngeal irritation) can be significantly reduced (without significantly affecting commute distance or duration) if an individual uses a purposely-designed route of reduced proximity to major motorised traffic corridors whilst bicycle commuting, which will provide health benefits for both healthy and susceptible individuals.

Key words Air pollution, bicycle commuting, route alteration, ultrafine particle, respiratory symptom, peak expiratory flow, inflammatory cell

Introduction The health benefits of physical activity associated with active transport are well-established [1-3], as are the negative health effects associated with elevated levels of air pollution exposure [4-6]. Subsequently, there has been investigation of approaches to reduce the degree of air pollution exposure, along with mechanisms of health effects, whilst actively commuting [7-11]. Risk management strategies for reducing air pollution exposure whilst actively commuting can include reducing proximity to motorised traffic by avoiding major motorised traffic corridors at peak traffic times [12]. The majority of projects on this topic have utilised micro-environments of designated off-road and on-road bicycle paths, and have determined that the former generally facilitates a significantly lower potential for exposure to 79

air pollution, mainly from motorised traffic emissions such as ultrafine particles (UFP) [1317]. Health indices, including acute respiratory symptoms, impaired pulmonary function and inflammation-associated cell distribution have been used to investigate the physiological response to components of air pollution including particle number concentration (PNC; which is dominated by UFP) [18-20]. Questionnaires that assess the influence of exposure on the respiratory system have been used previously [29, 30], including specific symptoms attributable to acute air pollution exposure recommended by the American Thoracic Society [31]. For instance, airway narrowing due to inflammation and excessive mucous secretion (as an immune response to airway irritation by pollutants) can induce coughing and chest tightness or wheezing, as well as reduce pulmonary function indicated by lowered peak expiratory flow rates [32]. Further, an increase in the number of leukocytes, and specifically neutrophils, found in the airways and systemic circulation can indicate an inflammatory response to exposure from pollutants such as ultrafine particles [33, 34]. Yet to be investigated is the feasibility to reduce exposure to elevated PNC levels (and thereby decrease any associated negative health effects) with bicycle commuters using a purposely-designed alteration of their typical route to avoid major motorised traffic corridors. Bicycle commuters may not have the amenity of a route which allows complete use of offroad bicycle paths. Therefore, it is not expected to be practical for a bicycle commuter to completely alter their commute route and altogether avoid exposure to motorised traffic emissions, particularly due to factors such as road crossings which dissect off-road paths; however, it is feasible to decrease exposure to UFP by selecting a route which has reduced proximity to major motorised traffic corridors. A previous study by the current investigators, using a single participant model to determine inhaled particle counts along popular bicycle commute routes of high and low proximity to major motorised traffic corridors, has shown 80

that air quality (indicated by PNC) can be significantly improved by taking such a purposelydesigned route alteration [49]. For the current project, it was hypothesised that: 1) a bicycle commute route alteration purposely-designed to be of reduced proximity to major motorised traffic corridors will significantly reduce exposure to combustion emissions [represented by the dominant ultrafine particle (UFP; < 0.1 µm) number concentration (PNC)], compared to a higher proximity route; 2) health outcomes (as occurrence and severity of acute respiratory symptoms, peak flow rate, and cell distribution in sputum) will be improved with the use of a route of reduced proximity to major motorised traffic corridors, compared to a route of high proximity; 3) the difference in the estimated inhaled UFP count between the two routes will be attributable to the difference in PNC, rather than any differences in physical effort (represented by heart rate and reflecting ventilation rate).

Methods Project Design This project was intended to determine whether the use of a bicycle commute route purposely-designed to reduce proximity to major motorised traffic corridors (and therefore exposure to associated emissions) is practical as an air pollution exposure reduction and risk management strategy. Thirty-five healthy adults were recruited to perform their typical workday commute along both their typical route (selected as being of high proximity to major motorised traffic corridors; labelled ‘HIGH’) and an altered route (designed to be of reduced proximity to major motorised traffic corridors; labelled ‘LOW’). The participants and their bicycles were instrumented to measure real-time exposure variables of geolocation, heart rate, and particle number concentration and size while in-commute. Participants performed symptom-occurrence reporting, peak expiratory flow (PEF) metering and spontaneous 81

sputum sampling immediately pre-commute, and immediately and three hours post-commute. Data collection occurred in Brisbane, Australia between Autumn and Spring (April to September) of 2011, on consecutive days if practical for participants and not including the weekend. Participants The participants of this project were healthy adults (N: 35; 29% female. Mean ± SD: age = 39 ± 11 yr; PFR, female = 447± 66, male = 584 ± 89, |total = 558 ± 105| L·min) with no history of cardiopulmonary disease and no recent history of smoking (cessation > twentyfour months prior) or respiratory infection (symptoms > two weeks prior). Participants were required to be frequent bicycle commuters of the Brisbane inner-city region [defined as completing two or more return trips in a five day period to a destination within a 1 km radius of the Brisbane Central Business District (CBD)] and have a typical commute route of high proximity to major motorised traffic corridors. Recruitment was conducted from participants who provided consent as part of a previous unpublished study, and eligible respondents of a regional media release. Participants were requested to avoid any air pollution sources where possible, such as second-hand smoke and traffic congestion during the one hour pre-commute and three hour post-commute monitoring period. This request may have affected a participant’s typical daily exposure, however was included to minimise any confounding of an acute inflammatory response from non-commute exposure and in-commute exposure. Project Locality The current project was performed in Brisbane, which is the state capital of Queensland and the third largest city in Australia. The Brisbane CBD is located at 27º3´ South, 153º9´ East, approximately 20 km inland from the Pacific Ocean. A large river runs through the city of Brisbane (located within a low-lying floodplain) with several large hills of 82

up to 300 metres in height within the area, bordered to the west by a coastal mountain range. The regional climate is sub-tropical, being cool and dry in winter (June to August), and humid and wet in summer (December to February) [23]. The city of Brisbane has a population of approximately two million, which has been increasing for the last two decades by approximately two percent annually [24]. Motorised traffic volume (along with population growth) is rapidly increasing, particularly due to outer-city residential development [24]. The number of motor vehicles registered within Brisbane in 2011 was approximately 1 million, however the greater region of South-East Queensland includes a total of 2.8 million motor vehicles [25]. Industrial air pollution sources include a major airport, seaport, and oil refineries (approximately 15 km north-east of the CBD), a coal power station (approximately 30 km south-west of the CBD), and various manufacturing companies in the outer suburbs. Brisbane is in compliance with the standards and goal of the Ambient Air Quality National Environment Protection Measure (AAQ NEPM). The Queensland air monitoring report 2011 [70] confirms compliance of the South-East Queensland Air-shed (containing Brisbane) for CO (daily AAQ NEPM standard upper limit ≥ 9.0 ppm; number of days exceeding = 0 days), NO2 (≥ 0.12 ppm; 0 days), PM10 (≥ 50 µg/m3; 1 day), PM2.5 (≥ 25 µg/m3; 1 day), SO2 (≥ 0.20 ppm; 0 days) in 2011. Routes of High and Low Proximity to Major Motorised Traffic Corridors Participants, in consultation with the primary investigator, designed an altered route of reduced proximity to major motorised traffic corridors (LOW) based on their typical bicycle commute route (HIGH). Each participant rode a return trip [inbound (morning) and outbound (evening)] of HIGH and LOW on consecutive days. An equal number of participants performed either HIGH or LOW first, to counter-balance and negate any influence of the order of the route alteration. Therefore, a total of 140 trips were performed as

83

a result of 35 participants each completing an inbound-HIGH, outbound-HIGH, inboundLOW and outbound-LOW trip. In-commute Particle Concentration and Diameter To measure and record real-time particle number concentration (PNC) and particle diameter (PD) in-commute, an Aerasense NanoTracer (Philips, The Netherlands) with a 16second logging frequency was carried by each participant; three units were used to process three participants simultaneously, with one unit assigned to each participant. The sampling tube of the NanoTracer was attached to the participant in their immediate breathing zone, for example on their shirt collar or upper backpack strap. The NanoTracer is a compact and portable device capable of measuring PNC [0 – e6 particles per cubic centimetre (ppcc)] and PD (0.01 – 0.3 µm) in real-time via diffusion charging [26, 65]; thus, tilt errors that may be experienced with fluid-reliant instruments (such as a condensation particle counter) during vigorous use (such as active transport monitoring) are avoided with the NanoTracer. Correction factors for the NanoTracer are regularly determined by laboratory calibration testing at the International Laboratory for Air Quality and Health (ILAQH) against a waterbased condensation particle counter (WCPC 3781; TSI Inc., USA) and a scanning mobility particle sizer (SMPS 3934, TSI Inc., USA) in atmospheric air and at outdoor locations for 4 hours to derive a ratio of the two average values. The WCPC and SMPS are themselves regularly calibrated in the laboratory using standard aerosols of known size and concentration. For the current project, correction factors (of PNC ± 500 ppcc and PD ± 0.01 µm) were applied to raw particle measurement data prior to statistical analyses; the three units generally have a close correlation (r2 = 0.94). In-commute PNC and PD means, medians and range were calculated with NanoReporter software (Philips, The Netherlands) for comparison between HIGH and LOW. PNC or PD 84

16-second-means below 100 ppcc or 0.01 µm, respectively, have been previously considered as unrealistic and thus were removed prior to analyses [10, 27]. In-commute Heart Rate Heart rate (FH) was monitored in-commute using a telemetry unit (Polar Electro, Finland) logging at five second intervals. In-commute FH was compared between HIGH and LOW to determine if there is a difference in physical effort when performing the two routes and therefore to indicate if an inhaled particle count for the two routes is due primarily to variation in air quality or ventilation rate. As an individual’s FH and ventilation rate are associated [28], a higher mean trip FH would produce a higher mean trip ventilation rate and therefore a higher total number of inhaled particles at any PNC. Meteorology The Australian Bureau of Meteorology Climate Database [23] was accessed for hourly regional measures of temperature, humidity, wind direction and speed, air pressure, and precipitation. Meteorological data was collated and analysed to determine any particle measurement differences between commute monitoring days due to changing atmospheric conditions. Physiological Inflammatory Responses The participants performed three self-administered tests to assess an acute biological inflammatory response attributable to air pollution exposure. A verbal demonstration and written explanation for the performance of each test was provided to participants at an induction meeting, either one day before or on the day of monitoring commencement. Symptom experience and peak flow rates were self-administered immediately pre- and postcommute, and three hours post-commute either at the participant’s home or work location. 85

Sputum samples were collected immediately pre-commute and three hours post-commute only. Symptom Experience Questionnaire Participants were supplied with a questionnaire to report the occurrence of specific signs and symptoms of differing severity, including offensive odour, dust or soot detection (such as the smell or sight of motor vehicle exhaust or roadway dust), nasopharyngeal or “eye, nose and throat” irritation, tussis or “cough”, chest tightness and/or wheezing, on a five-grade scale (1 = ‘Very Low’, 2 = ‘Low’, 3 = ‘Moderate’, 4 = ‘High’, and 5 = ‘Very High’). The questionnaire used in this investigation was purpose-designed with review and input from researchers and a sub-set of intended participants. Further, the format of assessment for acute respiratory signs and symptoms attributable to air pollution exposure was based on recommendations by the American Thoracic Society [31] and previous research [64]. The same questions were used for each time period of one hour precommute, in-commute, and three hours post-commute to attribute symptom occurrence to air pollution data of each monitored trip. See appendix for symptom questionnaire and written instructions provided to participants. Peak Expiratory Flow Rates To obtain an indication of airway diameter and therefore pulmonary function, participants were supplied with peak expiratory flow meters (MicroPeak, CareFusion, UK). Participants were instructed to perform and record three peak expiratory flow tests (to obtain a best value) immediately pre-commute, immediately post-commute, and three hours post-commute to relate airway

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diameter changes to UFP data of each monitored trip. See appendix for written instructions provided to participants. Sputum Cell Counts To collect sputum samples, participants were supplied with plastic (Falcon) tubes (15 mL) containing 2 mL of RNAlater (Ambion, USA), as well as the relevant RNAlater material safety data sheet and instructions for spontaneous sputum production. Approximately 2 mL of sputum was collected in the RNAlater and immediately refrigerated (at approximately 4 ºC) by participants, and then frozen at -80 ºC within 24 hours by investigators for later analysis. Total and differential cell counts were performed (double-blinded) via haemocytometry (Olympus light microscope), using 20 µL of cell suspension (cell pellet plus 2 mL PBS). Cell count reference values in induced sputum of healthy adults were consulted from a previous study [35]. See appendix for written instructions provided to participants for sputum production. Total Cell Counts Samples were removed from the -80°C freezer and thawed, then centrifuged for 15 mins at 500 x G at 25°C. The supernatant was removed and the cell pellet maintained, adding 2 mL of PBS to suspend the cells and then briefly vortexed. The cell suspension was aliquoted (2 x 10 µL) to a haemocytometer and then viewed under a 40X lens and the cells counted by a blinded investigator. The proportion of squamous epithelial cells (SEC) was determined to indicate validity or saliva contamination of each sputum sample (as ≤ 400 SEC per 100 leukocytes).

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Differential Cell Counts 200 µL of cell suspension was cytospun (for 5 mins at 100 x G at 25°C), fixed with methanol, stained (Diff-Quick) and mounted (Permount). Satisfactory differential cell counts (from 2 slides with > 50% viability and < 25% squamous cells, 100 non-squamous cells counted per slide) were conducted by a blinded investigator. Statistical Data Analysis Due to the expected variation of typical commute characteristics (including commute time, distance and duration) within the participant group, the in-commute trip variable means and medians of the four different individual data sets (i.e. both inbound and outbound trips of HIGH and LOW) of a single participant were initially compared. Subsequently, group means or medians of both inbound and outbound trips of HIGH and LOW were compared within an individual to determine if lower exposure to motorised traffic-emitted UFP occurred because of an individual’s purposely-designed route alteration; however, no attempts were made to compare in-commute exposure between individual participants. All analyses were performed with predictive analytics software (PASW v18.0; IBM, New York). Estimated marginal means of personal and commute exposure factors, along with descriptive values, were produced. Pearson bivariate correlations were performed for particle measurements (PNC, PD) with independent variables of meteorology, and symptom occurrence with participant characteristics such as age. Pearson bivariate correlations were also performed between CBD proximity (indicative of motorised traffic level) and HIGH or LOW values of PNC, PD and FH. Multivariate repeated measure ANOVA was performed with the mean and median of the dependent variables of PNC and PD for both inbound and outbound HIGH and LOW to signify intra-individual variability. One-way ANOVA (plus 88

Tukey Post-Hoc, where applicable) were performed with PNC, PD (now as independent variables), gender and the dependent variable of symptom reporting at the three different time-points. Mixed Effects Models analysis was performed with PNC, PD and participant symptom reporting, peak flow and cell counts to determine the effect of air quality on a physiological inflammatory response between inbound and outbound HIGH and LOW. Further, this analysis was performed to signify the difference across the three commuterelated time-points (i.e. one-hour pre-commute, one-hour post-commute and three-hours postcommute) in relation to in-commute PNC and PD. Statistical significance was accepted at a confidence interval of 95% (i.e. p < 0.05).

Results Bicycle Commute Characteristics Due to the local and regional location of the bicycle paths, it was not practically possible to produce exactly the same proportion of off-road paths; therefore, as expected, there was a range in the distribution of path type within HIGH and LOW. For example, popular South and West LOW routes ran adjacent (but physically-separated from) two different major motorised traffic corridors and therefore had lower proportions of off-road paths. Conversely, popular North and East LOW routes ran adjacent to parklands and a major river, respectively, allowing higher proportions of off-road paths. In-commute Particle Measurements The group mean commute particle number concentration (PNC) of HIGH was significantly higher than in LOW [F-statistic (degrees of freedom) and p-value: F(1,35) = 21.079 and p ≤ 0.001], and the group mean commute PNC in HIGH was significantly higher 89

with inbound compared to outbound trips [F(1,35) = 8.441; p = 0.007]. See Table 1. Additionally, the group median commute PNC in HIGH was significantly higher than in LOW [F(1,35) = 14.025; p = 0.001], however there was no significant difference between inbound and outbound trips in HIGH [F(1,35) = 23.154; p = 0.085] or LOW [F(1,35) = 19.237; p = 0.122]. See Table 1. The group median commute particle diameter (PD) was not significantly different between HIGH and LOW, nor between inbound and outbound trips of either HIGH or LOW [F(1,35) = 4.843; p = 0.083]. However, group mean PNC and PD were significantly negativelycorrelated (r = -0.645; p = 0.048). See Table 1. Commute Speed and Heart Rate The group mean commute distance and duration, and therefore commute speed, for HIGH and LOW, were not significantly different (12.0 ± 6.9 vs. 12.8 ± 7.1 km and 42 ± 17 vs. 44 ± 17 mins, respectively). The group mean commute heart rate between HIGH and LOW (136 ± 11 vs. 133 ± 9 bpm), or between inbound and outbound trips in either HIGH or LOW, was not significantly different (See Table 1). Meteorology All meteorological variables were not significantly different between HIGH and LOW. Due to natural diurnal variation, the group mean inbound (morning) commute temperature was significantly lower [F(1,35) = 47.085; p ≤ 0.001] and the humidity significantly higher [F(1,35) = 54.114; p ≤ 0.001], compared to the outbound (afternoon) trip. See Table 1. Group mean regional commute temperature was negatively-correlated with PNC (r = -0.83; p = 0.005) and positively-correlated with PD (r = 0.79; p = 0.014). However, group mean 90

regional commute humidity was not significantly correlated with mean PNC or PD. While regional wind direction was not correlated to particle measurements, general wind speed was negatively-correlated to PNC (r = -0.77; p = 0.018) and PD (r = -0.74; p = 0.021). Inflammatory Response Air Quality Detection and Symptom Experience Offensive odour detection occurrence in HIGH was significantly greater than in LOW [F(1,406) = 5.515; p = 0.019], as was dust and soot detection [F(1,140) = 4.340; p = 0.038], nasal irritation [F(1,140) = 7.266; p = 0.007] and throat irritation [F(1,140) = 8.876; p = 0.003]. All other specific acute respiratory symptoms reported were not significantly different between HIGH and LOW. See Table 2. The occurrence of offensive odour, and dust or soot, detection was significantly higher in-commute, compared to pre-commute and post-commute, for both HIGH and LOW [F(1,406) = 4.165 ; p = 0.031]; however, the occurrence of nasal and throat irritation was significantly higher in-commute, compared to pre-commute and postcommute, only for HIGH [F(1,140) = 7.545; p = 0.006]. The group mean total occurrence of symptoms in-commute, compared to precommute and post-commute, was significantly higher for offensive odour detection (p ≤ 0.001), dust or soot detection (p ≤ 0.001), eye irritation (p ≤ 0.001), nasal irritation (p ≤ 0.001); throat irritation (p ≤ 0.001); phlegm production (p ≤ 0.001); and, chest tightness (p = 0.003). Tussis and chest wheeze were significantly higher in occurrence for the in-commute time period (p = 0.012 and p = 0.017), but not the post-commute time period (p = 0.070 and p = 0.176), compared to the pre-commute time period.

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Age was positively-correlated with the in-commute occurrence of throat irritation (r = 0.78, p = 0.049) and phlegm production (r = 0.83, p = 0.024). Further, female participants, compared to males, reported significantly higher in-commute occurrence of throat irritation (1.57 ± 0.88 versus 1.33 ± 0.68; p ≤ 0.001) and headache (1.14 ± 0.49 versus 1.06 ± 0.35; p = 0.005). Peak Flow Rate As an indication of airway diameter, peak flow rate was not significantly different from pre-commute to one or three hours post-commute in HIGH or LOW. Further, there was no significant difference between post-commute HIGH and LOW measurements. Female, compared to male, group mean baseline peak flow rate was significantly lower (447 ± 66 versus 584 ± 89 L·min; p ≤ 0.001); however, the percentage change from pre-commute and post-commute measures were not significantly different when females were compared to males (1.105 ± 0.189 versus 1.103 ± 0.197 %Δ; p = 0.394). Intra-individual PFR reproduction variability as a group mean was 20.3 ± 11.3 L·min. See Table 2. Sputum Cell Counts Total and differential cell counts of valid participant sample sets (when compared to previously established reference values of healthy adults [35] as stated in the Methods, 22 out of 35, or 63%) were not significantly different between pre-commute in either HIGH or LOW and post-commute in either HIGH or LOW (p > 0.07). Further, cell counts were not significantly different between post-commute sampling in HIGH and post-commute sampling in LOW (p = 0.09). There was no correlation between the percent change of pre-commute to post-commute cell count group means and in-commute PNC group means (r = 0.54, p = 0.08). See Table 3. 92

Discussion Within the scope of this project, the results suggest that a purposely-designed bicycle commute route alteration designed to reduce proximity to major motorised traffic corridors can significantly reduce exposure to combustion UFP PNC without necessarily affecting factors of route utility such as commute distance or duration. Subjective symptoms of nasal and throat irritation occurrence were reduced in LOW compared to HIGH; however, an objectively-measured physiological inflammatory response (represented by the reduction in peak expiratory flow and an increase of inflammatory cells within sputum) was not seen in these healthy individuals. Due to the group mean heart rate not being significantly different between HIGH and LOW, it is suggested that an inhaled UFP count would be typically determined by a variation in air quality (that is, PNC) rather than a difference in physical effort (and thus ventilation rate) of alternative bicycle commute routes. While off-road routes allow for reduced proximity to major motorised traffic corridors and thus reduced air pollution exposure, these routes can be less direct and increase commute duration; however, this was not the case in the current project, as commute distance and duration were not significantly affected by a commute route alteration to reduce proximity to major motorised traffic corridors. Published work by another research group of the same region found that while local commuters recognised air pollution exposure as a health risk, it did not deter them from bicycle commuting [29]. Regardless, the development of appropriate infrastructure (separating traffic types) and educational schemes (indicating best air quality routes) would be desirable to sustain the increased popularity of bicycle commuting, and to assist an individual in managing their own air pollution exposure risk.

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In-commute Particle Measurements The strategy of lowering proximity to major motorised traffic corridors while bicycle commuting to improve air quality has been supported as feasible, with mean and median PNC significantly reduced in LOW compared to HIGH, in agreement with similar previous research [6, 10, 15]. Further, this mean reduction was most marked for the inbound, compared to outbound, commute in HIGH, which is also in agreement with previous urban measurement studies and reflects the expectation that morning peak hour traffic is more timeconcentrated than the afternoon peak [36-38]. The median reduction of PNC being smaller in magnitude than the mean reduction (however still significant) indicates the influence peak PNC events (such as road crossing with traffic control lights) have on total commute PNC exposure. There was no significant difference between LOW inbound and outbound trips, indicating the influence of proximity to motorised traffic on PNC. Previously, a mean PNC of 7.4 x e3 ppcc and a median PD of 40 nm (a diameter stronglyassociated with motor vehicle emissions) have been shown in Brisbane [39]. More recently, PNC in Brisbane has been shown to have marginally increased to a mean of 10.0 x e3 ppcc, and PD slightly decreased to a median of 38 nm. However, these PNC levels are relativelylow compared with other studied cities worldwide [40, 66] and generally would not reflect incommute exposure [41, 42]. A meta-analysis performed with 71 UFP studies of different environments showed typical mean PNC of 7.3 x e3 ppcc for urban background and 42.1 x e3 ppcc for roadside measurements, and indicated that greater proximity to major motorised traffic corridors is positively-associated with PNC [43]. In the current project, the group median particle diameter (PD) was not significantly different between HIGH and LOW or between inbound and outbound trips of either HIGH or LOW, suggesting that the detection of

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fresh petrol emissions from either more (HIGH) or less (LOW) motorised traffic was not necessarily occurring. A recent meta-analytical review of UFP exposure in-transit across different modes of transport indicated that cyclists are generally exposed to the lowest PNC mean values of any mode [44]. Studies specifically comparing bicycle commute routes with high and low proximity to major motorised traffic corridors have indicated a PNC mean of 3.5 x e4 and 2.6 x e4 ppcc, respectively [44]. In comparison to other commute modes, motor vehicle passengers can be exposed to PNC means of 1.3 times greater than that of cyclists [45, 46]. While interest in particulate matter (such as PNC) for in-commute air pollution exposure studies is increasing, further investigation of the dynamic, heterogenous mixture (which was beyond the scope of this study) is warranted; for example, PNC may be negatively-correlated with, or be adsorbed with, other toxic components or reaction products of motorised traffic emissions [67]. It has been shown that ozone and proximity to major motorised traffic corridors are negatively-correlated and so conditions of low proximity to major motorised traffic corridors may facilitate low UFP but high ozone exposure [68].

Heart Rate and Physical Effort As bicycle commuting requires physical exercise, pulmonary ventilation rates of participants can be an important factor when determining the inhaled dose of UFP and therefore a toxic biological interaction. Ventilation rates of cyclists observed during epidemiological studies have been approximately 2 to 4 times greater than motor vehicle passengers, though this rate is believed to be conservative [46, 47, 69]. An experimental study showed that particle deposition can be 4.5 times higher during moderate bicycling exercise compared to rest in healthy individuals [48]. Further, it has been shown that inhaled mean PNC dose can be halved by using a pre-determined route alteration of low, compared to 95

high, proximity to major motorised traffic corridors [49], and also suggested by the current project according to mean PNC exposure concentrations. Importantly, the group mean distance and duration were not significantly increased from the alteration of LOW from HIGH, therefore not increasing overall exposure to motorised traffic emissions due to an increased exposure time. Further, as commute distance or duration is not increased, the utility of an altered bicycle commute route to reduce proximity to motorised traffic emissions has been demonstrated as practical for individuals rating time as an important feature of a commute route. The correlation between heart rate and pulmonary ventilation rate during exercise is high and, while it varies between individuals, a predictable association can be made for an individual once a heart-rate ventilation association equation has been produced [58, 59]. The current project did not include exercise testing to provide values for input to such an equation; however, the intention was to make intra-individual comparison of heart rates between route alterations and suggest if inhaled particle count was determined by PNC rather than pulmonary ventilation level. As heart rates did not significantly differ between HIGH and LOW, it could be inferred that any potential difference in inhaled particle count would be attributable to differences in PNC rather than physical exercise, and therefore the ventilation rate, required to use HIGH or LOW. A previous study in the same geographical region by the current research group showed that estimated ventilation rates (via heart rate-ventilation associated curves produced with exercise testing) did not significantly differ between popular bicycle commute routes of low and high proximity to major motorised traffic corridors [49].

Meteorological Variation In the current project, the measured meteorological variables were not significantly different between HIGH and LOW; however, the diurnal variation of climate facilitated a 96

lower mean temperature and higher mean humidity for the inbound (morning), compared to the outbound (afternoon), commute. The influence that these differences in temperature and humidity have when comparing air quality measures of HIGH and LOW is negligible as inbound and outbound trips were performed in equal measure for HIGH and LOW; however, the significantly-higher PNC in HIGH inbound compared to HIGH outbound could be attributable to nucleation inhibition which can occur in the circumstance of higher temperature and lower humidity [60]. Previous study conditions using lower temperature, and higher relative humidity, have been shown to facilitate secondary particle production and an increase in PNC [61-63], such as the circumstances in the HIGH inbound (morning) commute in the current project.

Physiological Inflammatory Response Despite significantly higher PNC in HIGH compared to LOW, the occurrence of acute respiratory symptoms (beyond offensive odour detection and nasal irritation) was not increased in-commute or post-commute. While personal NOX exposure was not monitored, it is likely that concentrations were substantially higher in HIGH compared to LOW due to the strong association of NOX and PNC to motorised traffic emissions [51]. An increased occurrence of offensive odour detection in HIGH compared to LOW, acute respiratory symptoms associated with elevated NOX exposure concentrations (including nasopharyngeal irritation, dyspnoea and tussis) seen in previous research [52] were not observed postcommute or reduced in LOW compared to HIGH. Further, there was no significant change in peak expiratory flow rate or neutrophil counts, either pre-commute to post-commute, or in LOW compared to HIGH. Previously, healthy and asthmatic adults exposed to a mean PNC of 1.45 x e5 ppcc during 2 hours of intermittent exercise did not exhibit significant differences in sputum neutrophil counts immediately and four hours post-exposure [33]. Similarly,

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healthy and asthmatic adults exposed to a mean PNC of 4.77 x e6 ppcc during rest and exercise did not exhibit significant differences in respiratory symptoms or sputum neutrophil counts, however there was a decrease in maximal mid-expiratory flow rate (not measured in the current project) twenty-one hours post-exposure [4]. Sputum neutrophils, obtained from the lower airways, have been used previously as a biomarker of airway inflammation but can have a low association with pulmonary function and respiratory symptom reporting [20]. However, the utility of repeated sputum induction on cell counts over a 24-hour period has been questioned [55]. Some research has shown no significant changes in sputum cell differential counts of healthy individuals in response to PNC exposure during rest and exercise (≤ 6.9 x e6 ppcc, 120 mins). However, in asthmatics following a similar protocol, PNC was associated with a significant increase in alveolar macrophage percentage of 11% compared to filtered air [4]. Similar to acute respiratory symptoms and pulmonary function, a physiological inflammatory response was not indicated by a significant change in neutrophil count in this study. Significant effects of UFP exposure on symptoms, pulmonary function, and markers of airway or systemic inflammation are not yet confirmed [56]. While the mechanisms of these effects for inhaled UFP are not yet known, these particles have been shown to have significantly greater pulmonary inflammatory effects compared to coarser particles at equal mass dose [7, 44, 57]. While this study did not indicate any acute health implications from the variables measured, the PNC exposure levels surpass previous levels observed to increase systemic markers of inflammation in healthy individuals exercising intermittently for a longer duration (1.1 x e4 ppcc, 120 mins) [53]. Further, exposure at higher levels and longer durations than the current project has produced increases in lower airway inflammatory mediators [54] and systemic markers of inflammation [34], oxidative DNA damage [42], and decreased airway diameter

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[6]. In summary, it is suggested that mean PNC exposure levels or exposure durations in HIGH and LOW were too low or too short, respectively, to significantly affect the acute health-associated variables measured in the current project. Limitations The acute inflammatory tests used in this study were purposefully simple and performed soon (one to three hours) after exposure, with this limited protocol being necessary to not influence participant commute behaviour such as by the logistics of laboratory testing or the over-lapping of inbound (morning) and outbound (afternoon) commute monitoring. Also, while PNC exposure levels and commute duration were realistic, they may not have been of a sufficient level or duration, respectively, to allow observable effects of the healthassociated variables measured. Further, this study was based on the available portable Philips NanoTracer which measures PNC, a key component of motor vehicle exhaust, although not the perfect marker. The design of a unique questionnaire (such as that used for sign and symptom reporting in the current project) without a precedent model available for reference will have a factor of unknown validity and reliability. Additionally, as the questionnaire was self-administered, respondent misunderstandings could have arisen due to question misinterpretation. Further, questionnaire response bias may have resulted as participants could not be made blind to the routes of HIGH or LOW; however, the limitation of respondent misunderstanding is believed to have been minimised due to the review process of questionnaire design (see Methods). Similar to symptom reporting, the performance of peak flow measurement and spontaneous sputum sampling were reliant on participant competence. While verbal and written instructions were provided to participants at induction, field performance was not supervised and therefore cannot be validated. 99

The use of a new personal UFP monitor for field research was novel and therefore precedent reference was not available. However, the majority of data collected was deemed valid and the measurement accuracy was calibrated in controlled conditions against an accepted device (see Methods). It is possible that the instrument may have not detected the smallest diameter range (approaching 10 nm) of particles typically associated with traffic exhaust emissions and thus explaining the low PNC means reported in the current project, relative to other studies of on-road environments. The lower range cut-off of the device is 10 nm, while some particles between 10 and 20 nm are also not detected.

Conclusions Exposure to ultrafine particles, typically associated with combustion emissions from motorised traffic, can be significantly reduced by lowering proximity to major motorised traffic corridors without necessarily increasing commute distance or duration whilst bicycle commuting. Governing authorities encouraging bicycle commuting participation should educate participants in air quality and risk management, but also give proper consideration to creating bicycle commuting routes, to reduce proximity to major motorised traffic corridors and therefore minimise any health risk as a consequence of frequent in-commute exposure to motorised traffic emissions.

List of abbreviations bpm; beats per minute. CBD; central business district. PEF; peak expiratory flow. PM; particulate matter. ppcc; particles per cubic centimetre. PNC; particle number concentration. UFP; ultrafine particle. 100

Competing interests The authors declare that they have no real or perceived competing interests.

Authors’ contributions TCH managed participants, data collection, data analyses and manuscript preparation; LM assisted with project supervision and manuscript revision; IS assisted with project supervision and manuscript revision; RJ assisted with project supervision and manuscript revision; MH assisted with laboratory procedures; CS assisted with project supervision and manuscript revision .

Acknowledgements We thank the participants for their enthusiasm an involvement in this project. Further, we thank Camilla Tuttle for detailed guidance with laboratory procedures, and Dr Dimitrios Vagenas for his patient help with statistical analyses.

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109

Tables Table 1. Commute variables for routes of high (HIGH) and low (LOW) proximity to major motorised traffic corridors both Inbound and Outbound

Condition

HIGH

LOW

Inbound

Outbound

Inbound

Outbound

8:20 ± 0:22

16:39 ± 0:23

8:04 ± 0:22

16:33 ± 0:23

12.3 ± 6.9

11.7 ± 6.9

12.9 ± 7.2

12.6 ± 7.0

42 ± 18

41 ± 15

45 ± 17

43 ± 16

Speed (km·hr )

17.3 ± 4.3

16.7 ± 4.8

17.1 ± 4.6

17.1 ± 4.7

Heart Rate (bpm)

137 ± 11

135 ± 11

134 ± 9

131 ± 9

17.9 ± 3.5

21.1 ± 3.0##

17.7 ± 3.5

21.5 ± 3.2##

Humidity (%)

61 ± 14

48 ± 19##

62 ± 13

49 ± 19##

Wind Speed (km·hr-1)

5.8 ± 3.2

9.5 ± 4.8

7.1 ± 3.2

8.5 ± 4.4

1019 ± 6

1016 ± 5

1019 ± 6

1016 ± 5

Precipitation (mL·day)

0.33 ± 0.01

0.25 ± 0.01

0.31 ± 0.01

0.26 ± 0.02

PNC Mean ( x e4; ppcc)

3.30 ± 1.57

2.60 ± 1.35##

1.99 ± 1.02**

1.84 ± 0.84**

PNC Median ( x e4; ppcc)

2.20 ± 1.02

1.77 ± 1.08

1.34 ± 0.79**

1.38 ± 0.67**

10.82 ± 2.14

10.57 ± 2.06

5.70 ± 1.44

4.20 ± 1.26

1.11 ± 0.98

0.80 ± 0.77

0.71 ± 0.84

0.73 ± 0.95

PD Median (nm)

47 ± 8

49 ± 10

50 ± 11

52 ± 11

PD Min (nm)

30 ± 6

35 ± 7

31 ± 4

34 ± 5

67 ± 5

69 ± 5

79 ± 4

83 ± 7

Time of Day (24:00) Distance (km) Duration (min) -1

Temperature (ºC)

Air Pressure (hPa)

PNC Max ( x e4; ppcc) PNC Min ( x e4; ppcc)

PD Max (nm)

Values are Means (or Median, and Ranges, where indicated) ± Standard Deviation. Significance [from multivariate repeated measure ANOVA]: *p < 0.05,

**

p < 0.01 compared to HIGH; #p < 0.05,

compared to Inbound. PNC (ppcc) = particle number concentration (particles per cubic centimetre).

110

##

p < 0.01

Table 2. Time point comparison of health-associated variables for routes of high (HIGH) and low (LOW) proximity to major motorised traffic corridors

HIGH

Condition Timepoint

Inbound

LOW Outbound

Inbound

Outbound

Pre

In

Post

Pre

In

Post

Pre

In

Post

Pre

In

Post

Offensive Odour

1.09 ± 0.12

2.71*,## ± 0.73

1.21 ± 0.15

1.12 ± 0.13

2.88*,## ± 0.83

1.06 ± 0.11

1.18 ± 0.13

2.06## ± 0.42

1.09 ± 0.15

1.18 ± 0.14

2.18## ± 0.48

1.09 ± 0.11

Dust, Soot

1.06 ± 0.11

2.35*,## ± 0.55

1.06 ± 0.11

1.06 ± 0.11

2.21*,## ± 0.49

1.03 ± 0.12

1.18 ± 0.14

1.65*,## ± 0.27

1.06 ± 0.11

1.15 ± 0.13

1.65*,## ± 0.27

1.09 ± 0.21

Eye Irritation

1.06 ± 0.11

1.56 ± 0.24

1.06 ± 0.11

1.18 ± 0.14

1.65 ± 0.27

1.06 ± 0.10

1.18 ± 0.10

1.26 ± 0.16

1.09 ± 0.12

1.18 ± 0.14

1.35 ± 0.18

1.06 ± 0.12

Nose Irritation

1.38 ± 0.19

1.82** ± 0.33

1.24 ± 0.15

1.24 ± 0.16

1.74 ± 0.30

1.12 ± 0.13

1.24 ± 0.15

1.53 ± 0.23

1.12 ± 0.13

1.09 ± 0.12

1.38 ± 0.19

1.12 ± 0.13

Throat Irritation

1.56 ± 0.24

2.00** ± 0.40

1.41 ± 0.19

1.35 ± 0.18

2.09 ± 0.44

1.26 ± 0.16

1.38 ± 0.19

1.56 ± 0.24

1.26 ± 0.16

1.24 ± 0.15

1.56 ± 0.25

1.35 ± 0.18

PFR (%∆)

0.00 ± 0.00

1.28 ± 0.16

1.18 ± 0.14

0.00 ± 0.00

1.65 ± 0.27

1.63 ± 0.27

0.00 ± 0.00

1.76 ± 0.31

1.41 ± 0.20

0.00 ± 0.00

2.38 ± 0.57

2.14 ± 0.46

Values are Group Mean ± Standard Deviation. Significance [from Linear Mixed Models]: * p < 0.05, ** p < 0.01 compared to LOW; # p < 0.05, ## p < 0.01 compared to pre-commute / ‘Pre’. Values presented are the selfreported group means, on a frequency scale from 1 (very low) to 5 (very high). Timepoint is the period relative to bicycle commute trip performance: immediately pre-commute = Pre; in-commute = In; three hours postcommute = Post. Peak flow rate (PFR) is expressed as the percentage change from pre-commute / ‘Pre’ values.

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Table 3. Group Means of Total and Differential Cell Counts: Time Point Comparison for Routes of High (HIGH) and Low (LOW) Proximity to major motorised traffic corridors HIGH Condition

Inbound

LOW Outbound

Inbound

Outbound

Timepoint

Pre

Post

Pre

Post

Pre

Post

Pre

Post

Leukocyte (x e6 cells·g-1)

1.36 ± 0.42 1.16 ± 0.30 0.58 ± 0.18 0.78 ± 0.30 59 ± 18 1.4 ± 0.4 39 ± 12 0.6 ± 0.2

1.38 ± 0.43 1.19 ± 0.31 0.59 ± 0.18 0.79 ± 0.31 58 ± 18 1.5 ± 0.5 40 ± 12 0.5 ± 0.1

1.23 ± 0.38 1.05 ± 0.27 0.53 ± 0.16 0.70 ± 0.27 59 ± 18 0.8 ± 0.3 40 ± 12 0.2 ± 0.1

1.28 ± 0.39 1.10 ± 0.28 0.55 ± 0.17 0.73 ± 0.28 59 ± 18 0.9 ± 0.3 40 ± 12 0.1 ± 0.1

1.40 ± 0.43 1.20 ± 0.31 0.60 ± 0.19 0.80 ± 0.31 59 ± 18 0.8 ± 0.2 40 ± 12 0.2 ± 0.1

1.37 ± 0.42 1.17 ± 0.30 0.59 ± 0.15 0.78 ± 0.20 59 ± 20 1.3 ± 0.5 39 ± 10 0.7 ± 0.2

1.44 ± 0.45 1.23 ± 0.32 0.62 ± 0.22 0.82 ± 0.29 58 ± 17 1.0 ± 0.3 41 ± 11 0.1 ± 0.1

1.44 ± 0.45 1.23 ± 0.30 0.62 ± 0.21 0.82 ± 0.30 59 ± 18 1.0 ± 0.4 40 ± 11 0.1 ± 0.1

Epithelial (x e6 cells·g-1) Columnar (x e6 cells·g-1) Squamous (x e6 cells·g-1) Macrophage (%) Lymphocyte (%) Neutrophil (%) Eosinophil (%)

Values are Group Mean ± Standard Deviation. Significance [from Linear Mixed Models]: * p < 0.05, ** p < 0.01 compared to LOW, # p < 0.05, ## p < 0.01 of three hours post-commute (Post) compared to immediately pre-commute (Pre). Total cell count values (of leukocyte – squamous) are presented as number of cells per gram of spontaneous sputum sample. Differential cell count values (of macrophage – eosinophil) are given as percentage of the total leukocyte cell count. Timepoint is relative to bicycle commute trip performance: Pre = sampled immediately pre-commute; Post = sampled three hours post-commute.

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7. GENERAL DISCUSSION

7.1. Introduction and Summary The proposed risk management strategy of bicycle commute re-routing to lower proximity to motorised traffic and thus reduce exposure to elevated roadside air pollution emissions for participants has been shown effective and feasible by this research project, although most appropriately for susceptible individuals. The results of Project 1 and 3 suggest that air pollution exposure levels may be adequately perceived by healthy individuals, although not reflected by the incidence of the assessed acute physiological inflammatory response during or after typical bicycle commuting in healthy participants. However, as identified with Project 1 (concurrent with previous literature), the incidence of such symptoms were increased with personal characteristics of female gender and respiratory disorder history [128]. The findings of Project 2 and 3 suggest that using an informed bicycle commute route alteration to lower proximity to motorised traffic (determined by commute duration, frequency and proximity to motorised traffic) will facilitate a significant reduction in particle number concentration (PNC; dominated by particles in the ultrafine diameter range, UFP), which is concurrent with previous literature [128]. Further, the potential UFP inhaled particle count (and thus dose) from proximity to motorised traffic could be attributable to variation in air quality rather than variation in physical effort (and thus ventilation rate) of an alternative route. However, as identified by Project 3, a physiological inflammatory response (represented by the incidence and severity of acute respiratory symptoms, the change in lung function, and the presence of inflammatory mediators in sputum) is not associated with current recorded PNC levels in healthy individuals – while this finding is concurrent with some previous literature [128], it is not in agreement with other studies using higher exposure levels [128], longer exposure periods [60, 88], or more sensitive detection methods [51, 60]. Regardless, Project 1 and 3 have highlighted that the majority of participants are amenable to adopting risk management strategies, particularly commute re-routing, as deemed potentially effective by Project 2 and 3. Commute re-routing as a risk management strategy could be complemented

with

improved

bicycle

commuting

113

infrastructure,

including

more

considerately planned off-road paths, and community education schemes, in addition to realtime roadside air quality or motorised traffic congestion broadcasts.

7.2. Air Quality The quality of air adjacent to major traffic corridors, attributable to automotive emissions, has previously been indicated as poorer than regional background levels [129]. However, roadside air quality in the current study is favourable when compared with that of other cities studied previously by different research groups [128], possibly attributable to the current regions relatively small (although quickly growing) motor vehicle fleet. As identified by Project 1, previously measured ambient and roadside levels of nitrogen dioxide (NO2; which is a pollutant strongly-associated with motor vehicle emissions) was not high enough to elicit the specific symptoms inquired by the supplied questionnaire; however, particulate matter was. As discussed by Project 1, the greatest concern when considering particulate matter (PM) is PNC, which is strongly-correlated to proximity to motorised traffic [59]. As PNC, dominated by particles in the ultrafine range (UFP; < 0.1 μm diameter), is not regionally-monitored, Project 2 and 3 aimed to both profile the exposure and inhaled particle count of UFP and any consequential physiological inflammatory response between popular bicycle commute routes of high and low motorised traffic proximity. In general, an association of measured air quality and the incidence and severity of specific acute respiratory symptoms was not shown, despite a significant reduction of PNC exposure levels from lowering proximity to motorised traffic practically (that is, conserving the values of commute distance and duration for viability as a risk management strategy, being applicable to commuters desiring direct or short-duration routes). An improvement of health endpoints was not exhibited in Project 3, although importantly, only healthy individuals were monitored; susceptible individuals such as those with a history of respiratory disorder may present differently under the same circumstances.

114

7.3. Air Pollution Exposure Symptoms and Susceptibility In general, healthy individuals participating in frequent bicycle commuting activity are not indicated to be acutely affected from in-commute exposure to particulate pollution. However, results from susceptible individuals participating in Project 1 suggest they possess higher sensitivity to exposure effects. Participants with a history of cardiopulmonary disorder or of female gender indicated a higher incidence of mild and more severe respiratory symptoms from in-commute exposure, which is in agreement with past research [32, 35] and other questionnaire-based studies [128]. In the current study, females (compared to males) were more likely to report the incidence of moderate air pollution exposure during their typical bicycle commute, as well as chest wheeze in-commute with nasopharyngeal irritation in- and post-commute. Individuals with a history of respiratory disorder (compared to healthy individuals) reported a higher incidence of acute respiratory symptoms during and a few hours after their typical bicycle commute (along with chronic upper respiratory tract infection), but also had a compromised perception of exposure compared to estimated levels. A history of respiratory disease increased (and a history of smoking decreased) the incidence of exposure perception to moderate or higher levels of in-commute air pollution, compared to healthy respondents, which has been observed elsewhere [130].

7.4. Air Pollution Exposure Perceptions and Risk Management The perception of in-commute air quality is suggested to be reasonably-accurate compared to estimated and measured air pollution exposure levels. In effect, susceptible individuals identified from current and previous research, and other individuals stressed by their frequent exposure to elevated air pollution levels, could sensibly manage their exposure risk. Two risk management strategies were addressed in Project 1, including commute re-routing and respirator use. The majority of participants perceived moderate in-commute air pollution exposure, however less than half of the participants used commute routes of low proximity to motorised traffic and nil used a respirator. Due to resource constraints, Project 2 and 3 focused on the strategy of commute re-routing to minimise proximity to motorised traffic.

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In Project 1, the willingness of a participant to use a respirator or re-route was addressed to determine what factors would have to be present in a risk management strategy for it to be adopted, if further research indicates frequent in-commute air pollution exposure to be a significant health risk. The most important factors to be addressed for respirator use by participants, being “breathing impedance” and “wear comfort”, were both functional and not financial or psychological. The respirators recommended for adoption by urban bicycle commuters, if deemed appropriate and effective, are designed to accommodate moderate physical activity with neoprene material and two ventilation valves. When rating the importance of factors for choosing a commute route in Project 1, “time (e.g. more convenient, quickest route)” was ranked as more important than the factor of “health (e.g. to avoid air pollution)” by participants. For Project 3, when participants were asked if they preferred their original or LOW route alteration and the reasons for this, “time (e.g. more convenient, quickest route)” and “safety (e.g. greater riding space, visibility)” were of the highest importance. Infrastructure which allows direct (that is, time efficient) yet safe (that is, adequate riding space and visibility of other traffic) commuting. The factors of time and safety can be antagonistic, however they are both necessary for bicycle commuters and public health advocates to accommodate greater participation rates and lower proximity to motorised traffic and associated air pollution emissions.

7.5. Novel Method Use This project was possible because of the use of two novel instruments. The first, which was used in both Project 1 and 3, is a questionnaire inquiring of perceptions, symptoms and preference of risk management strategies associated with in-commute air pollution exposure. While novel in application, the design of the questionnaire was guided by recommendations [106] and previous research [107]. Further, the question format used was rigorously reviewed by respiratory scientists, epidemiological statisticians and a sample of the intended respondent cohort before provision to research participants. Secondly, to produce an exposure profile of popular bicycle commute routes (Project 2) and to associate questionnaire responses with actual in-commute air quality (Project 3), a compact, light-weight and portable device (Philips Nanotracer) capable of measuring particles of very high concentrations and at very low diameters [131] was used. To the 116

authors’ knowledge, the use of this instrument in field research such as the current study is novel; however, the authors’ hope that the current study will serve as an example of the practicality for such an instrument in future field research.

7.6. General Limitations The use of novel methods will present certain limitations. For example, the design of a unique questionnaire (used in Project 1 and 3) without a complete precedent model available for reference has a factor of unknown reliability. Additionally, as the questionnaires were self-administered, respondent misunderstandings could have arisen due to question misinterpretation. However, due to the review process of questionnaire design (noted in General Methods), these limitations are believed to be minimised. In Project 2, a single participant model was used to estimate inhaled particle count. Therefore, broader application of findings to the general public is limited; however, commute behaviour such as route, distance, speed, and departure times were informed by the realworld results of Project 1. Additionally, Project 2 monitored only morning peak commute times; however, this represented what is expected to be more consistent commute performance times and capitalised on more reliable meteorological conditions. The performance of spontaneous sputum sampling and peak flow metering in Project 3 were reliant on participant competence. While training was given to participants, performance was not supervised and therefore is hard to validate. Total cell counts of sputum were performed to indicate validity of spontaneous sampling. Similarly, the standard deviations of peak flow rate performance were used to indicate reproducibility of tests. A lesser limitation was with the use of a novel particle measurement device, and its’ application to field research. Precedent reference of reliability in field research had not yet been established; however, the majority of data collected in this study was deemed valid, with measurement accuracy calibrated in controlled conditions against a benchmark device. On the contrary, tilt errors experienced with fluid-reliant instruments (being the previous technology of condensation particle counters) during vigorous use were not an issue with this novel device.

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8. CONCLUSIONS

This project has shown that bicycle commute route alterations of low proximity to motorised traffic facilitate improves air quality (represented by particle number concentrations, typically emitted from petrol-powered motorised traffic) compared to routes high proximity to motorised traffic. The consistent perception of estimated in-commute air pollution exposure levels and the willingness to reduce proximity to motorised traffic during frequent, inner-city commuting has been indicated in this project; therefore, a self-managed commute re-routing strategy could be effective at reducing acute respiratory symptoms in more susceptible groups such as females and respiratory health-compromised individuals. It is recommended that frequent bicycle commuters with physiological-susceptibility to air pollution exposure, more prone to health detriment, consider reducing their proximity to motorised traffic for their frequent bicycle commute. While healthy individuals were observed to be frequently exposed to elevated in-commute air pollution concentrations, significant incidence or severity of acute respiratory symptoms has not been indicated; however, recurrent assessment of this may be necessary during contemporary regional urban growth and increasing popularity of active transport such as bicycle commuting. Appropriate governmental bodies wanting to increase bicycle commuting participation rates should both educate susceptible participants about air quality and risk management and apply similar knowledge when creating bicycle commuting infrastructure. The body of work contained within this thesis contributes multiple findings to current knowledge. It has been made apparent that individuals who bicycle commute frequently to and from the city centre can reasonably assess their own exposure to air pollution and are willing to alter their commute route if deemed appropriate. Altering a commute route to lower proximity to motorised traffic has been shown to be effective at significantly reducing PNC exposure, as a function of significantly improving air quality rather than an increase in pulmonary ventilation rate (and therefore potential inhaled particle count or dose), without significantly increasing commute distance or duration. If possible, authorities involved with planning new bicycle infrastructure (as being done in many places across the world) are urged to consider the proximity of bicycle paths to major motorised traffic corridors. Further, appropriate education to assist self-managed risk strategies against elevated air pollution exposure, such as what constitutes a sign or symptom of elevated air pollution exposure, 118

would be appropriate as indicated by this thesis. Particularly, individuals with susceptibilities to air pollution exposure (such as asthmatics) and/or individuals who wish to continue (or do not have an alternative to) using paths proximal to major motorised traffic corridors could be assisted by such education in the alleviation of any side-effects of exposure associated with frequent bicycle commuting.

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9. FUTURE DIRECTIONS

In this project, healthy individuals have not exhibited acute health detriment from frequent, elevated in-commute PNC (such as with bicycle commuting alongside motorised traffic), and therefore an alleviation of such detriment by lowering proximity to a primary PNC emission source (such as motorised traffic) has not been found as appropriate for a healthy population. Therefore, future research should include individuals representing pre-disposed and physiologically-susceptible populations, such as asthmatics [35, 36] who are especially prevalent in the region of SE QLD [132]. Such research direction is further supported by the fact that Project 1 included a sub-population of 36 asthmatic individuals (as 23% of all participants), suggesting that asthmatics are a significant component of regional frequent bicycle commuters and deserve the appropriate attention. Further still, Project 1 showed that asthmatics may experience a higher incidence of acute respiratory symptoms compared to healthy participants. The efficacy of respirators (for which amenability of use, found to be high, with certain features was evaluated by participants of Project 1) has not been investigated with PNC. A commercially-available unit is currently used by fire services personnel; while the unit is accommodating of increased perspiration and ventilation rates, it has only been shown to be effective with larger particles and certain vapour gases [133]. In-commute use of a respirator was slightly more popular than commute re-routing as an air pollution exposure risk management strategy, with 75% compared to 68% of participants willing to adopt either strategy, respectively. Further, 21% of participants had already considered using a respirator for bicycle commuting. The use of a respirator might be a more viable option to individuals who identify time as important when choosing a commute route, or who are commuting from directions that have a low proportion of designated off-road paths (i.e. low proximity to motorised traffic) available for use. A line of investigation not taken in this thesis, although worthy of future research, is extrapolation of the real-time PNC, heart rate and geographical location for the third project (as considered with the second project) plotted using geospatial visualisation (in software such as ArcGIS). The benefit of such analysis would be the provision of additional information concerning the levels and timing of high and low in-commute motorised traffic emission exposures, and therefore the highlighting of any exposure ‘hot-spots’ that may 120

warrant attention by the exposed individual (e.g. to avoid by re-routing) or by the individuals responsible for infrastructure development (e.g. to mitigate by constructing isolation barriers). However, such plotting of individual data sets would prove challenging – instead, a network of locations commonly encountered by multiple participants may give a broader overview of regional hot-spots. The second project of this thesis could succeed in such plotting as the routes were defined with repeated measurements by the one participant, without significant deviation/alteration of route. In summary, the exploration of other air pollution risk management strategies to support the desire of individuals, and especially the needs of susceptible populations, participating in bicycle commuting (as a method to improve their own health and that of the public and the environment) should be supported with future research into alternative, appropriate and effective risk management strategies - such an example would be the encouragement of respirator use by asthmatics. Additionally, more in-depth analysis and visualisation of realtime data of real-world bicycle commute routes (collected in the third project) would help to identify hot-spots of poor air quality which could be useful information for both route users and developers.

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10. APPENDICES A. Questionnaire [Complete (Project 1)]

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(Example of Bikeway Maps Appended to Questionnaire. Bikeway Map #5 of a total 12)

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B. Questionnaire [Amended (Project 3)]

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C. Bikeway Maps with Cyclist Counts (Project 2) Popular Bicycle Commute Routes of Brisbane, Australia [134]

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D. Media Releases (to assist participant recruitment for Projects 1 and 3)

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E. Publication of Project 2

Due to copyright restrictions, the published version of this journal article is not available here. Please view the published version online at: http://dx.doi.org/10.1016/j.atmosenv.2012.06.041

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