Quantifying the value of a clean ride: How far would you bicycle to avoid exposure to traffic-related air pollution?

Anowar, Sabreena; Eluru, Naveen; Hatzopoulou, Marianne

doi:10.1016/j.tra.2017.08.017

Cited by 35 publications

(40 citation statements)

References 57 publications

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“…Studies have found that several factors influence bicycle uptake in high-income countries, including attitudes [4,5], weather [6][7][8][9][10], social norms and identities [11][12][13]; individual socio-demographic characteristics [14][15][16], comfort and safety [17], environmental awareness [18], air quality [19], integration with public transport [20,21], gender [22], and purpose of cycling [23,24]. A recent study by Iwińska et al [25] in Warsaw found that a car-oriented culture and fear of injury were the greatest impediments to cycling uptake.…”

Section: Introductionmentioning

confidence: 99%

Why Choose to Cycle in a Low-Income Country?

Øverland

Sabyrbekov

2020

Sustainability

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Research on the adoption of the bicycle as a means of transport has been booming in high-income countries. However, little is known about bicycle adoption in lower-income countries where air pollution is high and cycling infrastructure is poor. Understanding the drivers of cycling adoption in developing economies can increase the efficiency of transport policies while reducing local air pollution, improving health, and cutting greenhouse gas emissions. The objective of this study is to identify the factors affecting cycling uptake in a low-income country using the city of Bishkek in Kyrgyzstan as a case study. The analysis is based on the Theory of Planned Behavior, a questionnaire-based survey of 900 respondents, factor analysis, and a logit model. In contrast to studies carried out in developed countries, this study finds that students are less likely to adopt cycling than other population groups. Other findings suggest that support for public transport, a desire for regular exercise and perceptions of the environmental benefits of cycling increase the probability of the use of cycling as a mode of transport in a low-income country. The paper also identifies positive and negative perceptions of cycling among cyclists and non-cyclists.

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Section: Introductionmentioning

confidence: 99%

Why Choose to Cycle in a Low-Income Country?

Øverland

Sabyrbekov

2020

Sustainability

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“…In the comparison between different routes with different distances, a cleanest route algorithm should preferably also take into account travel time. In fact, walking significantly longer to avoid polluted roads may result in a higher total uptake of pollutants; moreover, it is necessary to also consider the willingness of people to take longer routes (Anowar et al, 2017) [40].…”

Section: Discussionmentioning

confidence: 99%

Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

et al. 2019

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Land Use Regression (LUR) modeling is a widely used technique to model the spatial variability of air pollutants in epidemiology. In this study, we explore whether a LUR model can predict home-to-school commuting exposure to black carbon (BC). During January and February 2019, 43 children walking to school were involved in a personal monitoring campaign measuring exposure to BC and tracking their home-to-school routes. At the same time, a previously developed LUR model for the study area was applied to estimate BC exposure on points along the route. Personal BC exposure varied widely with mean ± SD of 9003 ± 4864 ng/m3. The comparison between the two methods showed good agreement (Pearson's r = 0.74, Lin's Concordance Correlation Coefficient = 0.6), suggesting that LUR estimates are capable of catching differences among routes and predicting the cleanest route. However, the model tends to underestimate absolute concentrations by 29% on average. A LUR model can be useful in predicting personal exposure and can help urban planners in Milan to build a healthier city for schoolchildren by promoting less polluted home-to-school routes.

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“…Of these, seven articles were excluded. The reasons for exclusion were that two articles were narrative review articles instead of original studies (Li et al 2015;Lü et al 2015), two articles were not based on LMICs (Anowar et al 2017;Di 2010), one article was a simulation study instead of a field study (de Sá et al 2017), one article was a comment instead of an original study (Wang 2016), and one article did not report any outcome pertaining to health behaviors (Braniš and Větvička 2010). In total, ten articles were included in the review (An and Yu 2018;Chen and Lin 2016;Hu et al 2017;Li andKamargianni 2017, 2018;Yang and Zacharias 2016;Yu et al 2017a;Yu et al 2017b;Zhang and An 2018;Zhao et al 2018).…”

Section: Study Quality Assessmentmentioning

confidence: 99%

Impact of ambient air pollution on physical activity and sedentary behavior in China: A systematic review

Shen

Ying

et al. 2019

Environmental Research

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This study systematically reviewed scientific evidence linking ambient air pollution to physical activity and sedentary behavior in China. A keyword and reference search was conducted in PubMed, Web of Science, and the Cochrane Library. Predetermined selection criteria included-study designs: interventions or experiments, retrospective or prospective cohort studies, cross-sectional studies, and case-control studies; subjects: people of all ages; exposures: specific air pollutants and/or overall air quality; outcomes: physical activity and/or sedentary behavior; and country/area: mainland China. Ten studies met the selection criteria and were included in the review. Six adopted a cross-sectional design and the remaining four adopted a prospective cohort design. Four studies assessed a specific air pollutant namely particulate matter with diameter < 2.5 µg/m 3 (PM2.5), whereas the remaining six focused on overall air quality, defined using air quality indexes. Decline in overall air quality and increase in PM2.5 concentration were found to be associated with reduced daily/weekly duration of outdoor leisure-time and/or transportation-related physical activity such as walking but increased duration of daytime/nighttime sleeping among Chinese residents. In contrast, evidence linking overall air quality and PM2.5 concentration to sedentary behavior remains mixed and inconclusive. In conclusion, preliminary evidence indicates that ambient air pollution impacts Chinese residents' daily physical activity-related behaviors. Future studies adopting objective measures of physical activity and a longitudinal or experimental study design are warranted to examine the impact of air pollution on sensitive sub-3 populations such as children, older adults and people with pre-existing conditions, and in locations outside China.

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Quantifying the value of a clean ride: How far would you bicycle to avoid exposure to traffic-related air pollution?

Cited by 35 publications

References 57 publications

Why Choose to Cycle in a Low-Income Country?

Why Choose to Cycle in a Low-Income Country?

Is a Land Use Regression Model Capable of Predicting the Cleanest Route to School?

Impact of ambient air pollution on physical activity and sedentary behavior in China: A systematic review

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