Wu Tzong-Gang, Chen Yan-Da, Chen Bang-Hua, Harada Kouji H, Lee Kiyoung, Deng Furong, Rood Mark J, Chen Chu-Chih, Tran Cong-Thanh, Chien Kuo-Liong, Wen Tzai-Hung, Wu Chang-Fu
Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd, Taipei, 10055, Taiwan; Innovation and Policy Center for Population Health and Sustainable Environment, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd, Taipei, 10055, Taiwan.
Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd, Taipei, 10055, Taiwan; Department of Health and Environmental Sciences, Kyoto University Graduate School of Medicine, Kyoto University, Yoshida-konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan.
Environ Pollut. 2022 Feb 1;294:118597. doi: 10.1016/j.envpol.2021.118597. Epub 2021 Nov 27.
Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.
由于在城市通勤期间骑行在道路上或靠近道路，骑自行车的人很容易接触到与交通相关的污染物。颗粒物已被确定为车辆排放的主要污染物之一，并与心肺疾病和呼吸道疾病相关。由于有人建议通过规划路线来减少骑自行车者的接触量，因此在本研究中，在通勤期间使用低成本传感器对颗粒物进行监测，以建立模型来识别三个亚洲城市（台北、大阪和首尔）中的低暴露路线。通过采用两阶段建模方法中的随机森林算法，开发了城市中颗粒物的映射模型。用于解释颗粒物空间变化的土地利用特征是从开源土地利用数据库OpenStreetMap中获取的。各城市监测路线的总长度在101.36至148.22公里之间，平均颗粒物浓度在13.51至15.40微克/立方米之间。两阶段模型在台北、大阪和首尔的标准k折交叉验证（CV）R分别为0.93、0.74和0.84。为了解决空间自相关问题，采用了一种空间交叉验证方法，该方法对模型训练和测试数据之间的距离限制为100米。从而防止了对预测的过度乐观估计，在台北、大阪和首尔的模型CV-R分别为0.91、0.67和0.78。最短距离路线和最低暴露路线之间的比较表明，随着距离增加37.8%，平均颗粒物暴露减少的最大百分比可达32.1%。鉴于本研究的结果，应鼓励规划路线行为。随着日常通勤行程的增加，随着时间的推移，累积效应可能会对慢性暴露产生显著影响。因此，应开发一种减少暴露的路线规划工具并向公众推广。
