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Citation
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Lathouwers E, Dons E, Ampe T, Panis LI, Verstraelen M, de Geus B. J. Transp. Health 2021; 22: e101132. |
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Abstract
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Background
Differences in physical effort between cycling for transportation on an electric-assisted cycle (EAC) and a conventional cycle (CC) were previously studied. The effect of cycle type on respiratory ventilation and inhaled air pollution dose remains unclear.
Objective
The first aim was to predict respiratory ventilation while cycling on a conventional and electric-assisted cycle taking into account personal and route characteristics. The second aim was to predict the dose of inhaled pollutants while cycling on a conventional and electric-assisted cycle using the same independent variables.
Methods
Nineteen participants performed a maximal exercise test (lab test) and four cycling trips (field test): flat with CC and EAC, and hilly with CC and EAC. During each trip, heart rate, respiratory ventilation, oxygen uptake, and carbon dioxide production were measured continuously with a portable metabolic system. Cycling time, speed and distance as well as GPS coordinates were also recorded continuously. The ATMO-Street air pollution model was used to estimate black carbon (BC), nitrogen dioxide (NO2), particulate matter (PM2.5 and PM10) inhaled doses post hoc. Factors impacting respiratory ventilation and the dose of inhaled pollutants were estimated through linear mixed modelling including laboratory and field measurements.
Results
Mean respiratory ventilation was predicted based on sex (-7.78 L/min for women), cycle type (-17.61 L/min for EAC), height gain (+0.07 L/min) and speed (+1.30 L/min). Inhaled dose of pollutants both for BC dose/km and BC dose/min was primarily predicted by cycle type (-31.62% and -34.68% for EAC compared to CC, respectively).
RESULTS were similar for the other pollutants.
Conclusions
Cycle type, sex, speed and route topography contribute to the respiratory ventilation and the use of an EAC reduces the dose of inhaled pollutants by 33% compared to the CC. Future projects could develop an app that predicts the cleanest route in real-time based on physical effort and ambient air pollution to increase the health benefits of cycling.
Language: en |