Cyclists' personal exposure to traffic-related air pollution and its influence on bikeability

被引:37
|
作者
Tran, Phuong T. M. [1 ,2 ]
Zhao, Mushu [3 ]
Yamamoto, Kohei [3 ]
Minet, Laura [4 ]
Teron Nguyen [2 ]
Balasubramanian, Rajasekhar [1 ]
机构
[1] Natl Univ Singapore, Dept Civil & Environm Engn, Singapore 117576, Singapore
[2] Univ Sci & Technol, Univ Danang, 54 Nguyen Luong Bang St, Danang City, Vietnam
[3] Natl Univ Singapore, Dept Geog, Singapore 117570, Singapore
[4] Univ Toronto, Dept Civil & Mineral Engn, 35 St George St, Toronto, ON M5S 1A4, Canada
基金
新加坡国家研究基金会;
关键词
Cycling index; PM2.5; Black carbon; Land use regression model; Deep neural network; Singapore; USE REGRESSION-MODELS; BUILT ENVIRONMENT; BLACK CARBON; STREET WALKABILITY; TRAVEL BEHAVIOR; URBAN; BICYCLE; IMPACT; TRANSPORT; WALKING;
D O I
10.1016/j.trd.2020.102563
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Previous studies on bikeability/cycling index have explored factors that influence cycling in cities, and developed indicators to characterize a bicycle-friendly environment. However, despite its strong influence on cycling behavior, cyclists' exposure to traffic-related air pollution has been often disregarded. To close this knowledge gap, we propose a comprehensive bikeability index that comprises four sub-indices: accessibility, suitability, perceptibility, and prevailing air quality in the vicinity of cycling routes. We evaluate cyclists' exposure to fine particulate matter and black carbon, and used open-source data, land-use regression models, deep neural networks and spatial analysis. The application of the proposed bikeability framework reveals that the inclusion of air quality makes a significant difference when calculating bikeability index in Singapore and hence it merits serious consideration. We believe that the newly developed framework will convince city planners to consider the importance of assessing cyclists' exposure to airborne particles when planning cycling infrastructure.
引用
收藏
页数:21
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