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 (PM_2.5 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.

中文翻译：

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沿着不同海拔剖面的路线骑传统自行车和电动自行车时的呼吸通风和吸入的空气污染剂量

背景

之前研究了电动助力自行车 (EAC) 和传统自行车 (CC) 上的自行车运输在体力方面的差异。循环类型对呼吸通气和吸入空气污染剂量的影响尚不清楚。

客观的

第一个目标是在考虑个人和路线特征的情况下预测在传统和电动自行车上骑行时的呼吸通气。第二个目标是使用相同的自变量预测在传统和电动辅助循环中骑行时吸入污染物的剂量。

方法

19 名参与者进行了最大运动测试（实验室测试）和四次自行车旅行（现场测试）：CC 和 EAC 平坦，CC 和 EAC 丘陵。在每次旅行中，使用便携式代谢系统连续测量心率、呼吸换气量、摄氧量和二氧化碳产生量。骑行时间、速度和距离以及 GPS 坐标也被连续记录。ATMO-Street 空气污染模型用于估计黑碳 (BC)、二氧化氮 (NO ₂ )、颗粒物 (PM _2.5和 PM ₁₀ ) 事后吸入剂量。通过包括实验室和现场测量在内的线性混合模型估计影响呼吸通气和吸入污染物剂量的因素。

结果

根据性别（女性为 -7.78 L/min）、周期类型（EAC 为 -17.61 L/min）、身高增加（+0.07 L/min）和速度（+1.30 L/min）预测平均呼吸通气量。BC 剂量/公里和 BC 剂量/分钟的污染物吸入剂量主要由循环类型预测（与 CC 相比，EAC 分别为 -31.62% 和 -34.68%）。其他污染物的结果相似。

结论

循环类型、性别、速度和路线地形有助于呼吸通风，与 CC 相比，EAC 的使用将吸入污染物的剂量减少了 33%。未来的项目可以开发一个应用程序，根据体力活动和环境空气污染实时预测最干净的路线，以增加骑自行车的健康益处。