Ambient environmental exposures while cycling on a vegetated trail versus the road

IF 6 2区 工程技术 Q1 ENVIRONMENTAL SCIENCES
Kevin Lanza , Brendan Allison , Baojiang Chen , Preston S. Wilson , Ethan T. Hunt , Kathryn G. Burford , Yuzi Zhang , Leigh Ann Ganzar , Timothy H. Keitt
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引用次数: 0

Abstract

Cycling can improve health, yet cyclists in cities may be exposed to hazardous conditions and have limited exposure to nature and its benefits. The purpose of this study was to measure and compare environmental exposures of urban cyclists on a vegetated, gravel trail route separated from cars and a fully paved route on local roads. In September 2021 in Austin, Texas, US, we cycled on the trail and road routes from 7:30–8:30 and 17:30–18:30 on one weekday and weekend day. While cycling, we wore sensors that measured fine particulate matter (PM2.5), total volatile organic compounds (VOCs), sounds, air temperature, relative humidity, light intensity, and geographic location. We used a neural network to distinguish anthropogenic and natural sounds. After time-matching all sensor data, we specified linear mixed effects models to test the association between route type and each environmental exposure, adjusting for afternoons and weekdays. We also used inverse distance weighting in GIS to map spatially continuous estimates of environmental exposures for each cycling trip. Compared to the road route, the trail was associated with higher levels of PM2.5, total VOCs, natural sounds, and relative humidity, and lower levels of anthropogenic sounds, temperature, and light intensity (p < 0.05). Mapping illustrated differences in environmental exposures within and between routes by time of day and day of week. Assessing exposures on existing and planned cycling routes may help inform the design of health interventions (e.g., tree planting along routes) in the face of increasing climate-related hazards.

Abstract Image

在植被覆盖的小路上骑行与在公路上骑行时的环境暴露
骑自行车可以改善健康,但城市里的骑自行车者可能会暴露在危险的环境中,与大自然及其好处的接触有限。这项研究的目的是测量和比较城市自行车骑行者在与汽车隔离的植被砾石小道和当地道路上完全铺设的路线上的环境暴露。2021年9月,在美国德克萨斯州奥斯汀,我们在一个工作日和周末的7:30-8:30和17:30-18:30在小路和公路上骑车。骑车时,我们佩戴了测量细颗粒物(PM2.5)、总挥发性有机化合物(VOCs)、声音、空气温度、相对湿度、光照强度和地理位置的传感器。我们使用神经网络来区分人为声音和自然声音。在对所有传感器数据进行时间匹配后,我们指定了线性混合效应模型来测试路线类型与每种环境暴露之间的关联,并对下午和工作日进行了调整。我们还在GIS中使用逆距离加权来绘制每次骑行的环境暴露的空间连续估计值。与公路路线相比,步道的PM2.5、总挥发性有机化合物、自然声音和相对湿度水平较高,而人为声音、温度和光照强度水平较低(p <;0.05)。地图显示了路线内和路线之间按一天和一周中不同时间的环境暴露差异。面对日益增加的与气候有关的危害,评估现有和规划的自行车路线上的暴露可能有助于为卫生干预措施(例如,在路线沿线植树)的设计提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Urban Climate
Urban Climate Social Sciences-Urban Studies
CiteScore
9.70
自引率
9.40%
发文量
286
期刊介绍: Urban Climate serves the scientific and decision making communities with the publication of research on theory, science and applications relevant to understanding urban climatic conditions and change in relation to their geography and to demographic, socioeconomic, institutional, technological and environmental dynamics and global change. Targeted towards both disciplinary and interdisciplinary audiences, this journal publishes original research papers, comprehensive review articles, book reviews, and short communications on topics including, but not limited to, the following: Urban meteorology and climate[...] Urban environmental pollution[...] Adaptation to global change[...] Urban economic and social issues[...] Research Approaches[...]
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