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乘客量超标暴露风险:评估共享单车骑行者 PM 健康暴露的新指标。

Ridership exceedance exposure risk: Novel indicators to assess PM health exposure of bike sharing riders.

机构信息

School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou, 510006, China; Guangdong Province Engineering Technology Research Center for Geographical Conditions Monitoring and Comprehensive Analysis, Guangzhou, 510006, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China.

School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou, 510006, China; Guangdong Province Engineering Technology Research Center for Geographical Conditions Monitoring and Comprehensive Analysis, Guangzhou, 510006, China; Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou, 510030, China; Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China.

出版信息

Environ Res. 2021 Jun;197:111020. doi: 10.1016/j.envres.2021.111020. Epub 2021 Mar 14.

Abstract

Identifying the fine particulate matter (PM) exposure risk for bicycle riders is crucial for promoting the development of theory and technology in transportation-related air pollution assessment as well as urban health planning. Previous studies have employed daily mean PM concentrations and designed routes to evaluate air pollution exposure risk. However, because the daily mean PM concentrations cannot fully illustrate the intra-day variations in PM, which are typically higher than daily mean values, the adverse effects of PM concentrations remain underestimated. Moreover, the quantity and representativeness of monitoring samples make large spatial-scale and multi-temporal-scale analysis challenging. By defining hourly exceedance PM concentration and sharing bicycle rider data, two novel indicators were proposed in our study: exceedance exposure risk of PM for sharing bicycle riders (EPSR) and accumulative exceedance exposure risk of PM for sharing bicycle riders (AEPSR). Standard deviation ellipse analysis was conducted to investigate the multi-temporal variation of ESPR and AEPSR. A geographically weighted regression model was applied to quantify the relationship between city function zones and exceedance PM exposure risk for sharing bicycle riders. Results revealed that the mean values of EPSR and AEPSR during morning peak periods ranged between 0.109 min μg/m and 1.27 min μg/m and 6.83 min μg/m and 43.41 min μg/m, respectively, whereas the mean values of EPSR and AEPSR during evening peak periods ranged between 0.19 min μg/m and 4.28 min μg/m and 14.67 min μg/m and 357.66 min μg/m, respectively. This implied that sharing bicycle riders were exposed to higher PM-related risks during the evening than in the morning. When considering the accumulative effects, the average centers of the AEPSR moved to the north side as compared to the average centers of the EPSR. Expanding areas of EPSR shrunk by 20.25 km. This indicated that accumulative effects aggregated spatial clusters of exceedance PM exposure risk for sharing bicycle riders more tightly to the north of the study areas. Spatiotemporal variation of EPSR and AEPSR led us to investigate the mechanism behind this phenomenon. Spatial associations between city function zones and EPSR and AEPSR showed that sharing bicycle riders experienced more severe exceedance PM exposure risk around financial/corporations and leisure service areas, with R values of 0.33 and 0.35, respectively. This spatial association tended to be more significant during the evening peak periods. By developing two novel indicators, the increasing health threats for bicycle riders caused by exceedance PM were investigated in this study. The mechanism results should be included for developing mitigation strategies to alleviate the adverse effects of air pollution for public rider participators and achieving the goal of eco-health cities.

摘要

识别自行车骑行者的细颗粒物 (PM) 暴露风险对于促进交通相关空气污染评估和城市健康规划理论和技术的发展至关重要。以前的研究使用日平均 PM 浓度和设计路线来评估空气污染暴露风险。然而,由于日平均 PM 浓度不能充分说明 PM 的日内变化,通常高于日平均值,因此 PM 浓度的不利影响仍然被低估。此外,监测样本的数量和代表性使得大空间尺度和多时间尺度分析具有挑战性。通过定义小时超标 PM 浓度并共享自行车骑手数据,本研究提出了两个新的指标:共享自行车骑手的超标 PM 暴露风险(EPSR)和共享自行车骑手的累积超标 PM 暴露风险(AEPSR)。标准差椭圆分析用于研究 EPSR 和 AEPSR 的多时间变化。应用地理加权回归模型量化城市功能区与共享自行车骑手超标 PM 暴露风险之间的关系。结果表明,早晚高峰期间 EPSR 和 AEPSR 的平均值分别在 0.109 min μg/m 和 1.27 min μg/m 至 6.83 min μg/m 和 43.41 min μg/m 之间,而早晚高峰期间 EPSR 和 AEPSR 的平均值分别在 0.19 min μg/m 和 4.28 min μg/m 至 14.67 min μg/m 和 357.66 min μg/m 之间。这意味着与早晨相比,共享自行车骑手在晚上暴露于更高的 PM 相关风险中。考虑到累积效应,AEPSR 的平均中心向北移动,而 EPSR 的平均中心则向南移动。EPSR 扩展区域缩小了 20.25 公里。这表明,累积效应将共享自行车骑手的超标 PM 暴露风险的空间聚类更紧密地聚集到研究区域的北部。EPSR 和 AEPSR 的时空变化促使我们研究这种现象背后的机制。城市功能区与 EPSR 和 AEPSR 之间的空间关联表明,金融/企业和休闲服务区周围的共享自行车骑手经历了更严重的超标 PM 暴露风险,R 值分别为 0.33 和 0.35。这种空间关联在晚上高峰期间往往更为显著。通过开发两个新的指标,本研究调查了超标 PM 对自行车骑手健康的威胁增加。应该考虑机制结果,以制定减轻空气污染对公众骑行者参与者的不利影响的缓解策略,并实现生态健康城市的目标。

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