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自行车骑行者暴露于空气污染、噪声和绿化环境中的情况:一种基于人群水平的空间分析方法。

Cyclists' exposure to air pollution, noise, and greenery: a population-level spatial analysis approach.

机构信息

Digital Geography Lab, Faculty of Science, University of Helsinki, Helsinki, Finland.

Helsinki Institute of Sustainability Science, Institute of Urban and Regional Studies, University of Helsinki, Helsinki, Finland.

出版信息

Int J Health Geogr. 2023 Feb 10;22(1):5. doi: 10.1186/s12942-023-00326-7.

Abstract

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes' potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach's feasibility by analysing cyclists' air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.

摘要

城市出行使人们接触到各种对健康和福利有重大影响的环境质量。然而,人们对与出行相关的环境暴露的理解仍然有限。在这里,我们提出了一种新的方法,用于对主动出行的多种环境暴露进行人群水平评估。它能够分析(1)城市尺度暴露变化,(2)不同路线改善每种暴露类型暴露水平的潜力,以及(3)结合多种暴露。我们通过分析芬兰赫尔辛基的自行车骑行者的空气污染、噪音和绿化暴露,证明了该方法的可行性。我们应用了内部开发的路线规划和暴露评估软件,并将当地共享单车系统的 310 万次骑行纳入分析。我们表明,尤其是骑自行车时的噪音暴露超过了健康阈值,但骑车者可以通过选择路线来影响他们的暴露程度。该方法使规划者和个人能够从暴露的角度识别(不)健康的出行环境,并比较各个地区的环境质量对支持主动出行的程度。可转让的开放工具和数据进一步支持了该方法在其他城市的实施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9055/9921333/878119ced22a/12942_2023_326_Fig1_HTML.jpg

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