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将环境传感集成到货运自行车中,用于最后一英里配送中的污染感知物流。

Integrating Environmental Sensing into Cargo Bikes for Pollution-Aware Logistics in Last-Mile Deliveries.

作者信息

Cameli Leonardo, Pazzini Margherita, Ceriani Riccardo, Vignali Valeria, Simone Andrea, Lantieri Claudio

机构信息

Department of Civil, Environmental and Material (DICAM) Engineering, University of Bologna, 40136 Bologna, Italy.

出版信息

Sensors (Basel). 2025 Aug 7;25(15):4874. doi: 10.3390/s25154874.

Abstract

Cycling represents a significant share of urban transportation, especially in terms of last-mile delivery. It has clear benefits for delivery times, as well as for environmental issues related to freight distribution. Furthermore, the increasing accessibility of low-cost environmental sensors (LCSs) provides an opportunity for urban monitoring in any situation. Moving in this direction, this research aims to investigate the use of LCSs to monitor particulate PM2.5 and PM10 levels and map them over delivery ride paths. The calibration process took 49 days of measurements into account, spanning different seasonal conditions (from May 2024 to November 2024). The employment of multiple linear regression and robust regression revealed a strong correlation between pollutant levels from two sources and other factors such as temperature and humidity. Subsequently, a one-month trial was carried out in the city of Faenza (Italy). In this study, a commercially available LCS was mounted on a cargo bike for measurement during delivery processes. This approach was adopted to reveal biker exposure to air pollutants. In this way, the operator's route would be designed to select the best route in terms of delivery timing and polluting factors in the future. Furthermore, the integration of environmental monitoring to map urban environments has the potential to enhance the accuracy of local pollution mapping, thereby supporting municipal efforts to inform citizens and develop targeted air quality strategies.

摘要

骑行在城市交通中占很大比例,尤其是在最后一英里配送方面。它在配送时间以及与货物配送相关的环境问题上都有明显优势。此外,低成本环境传感器(LCS)的可及性不断提高,为在任何情况下进行城市监测提供了机会。朝着这个方向发展,本研究旨在调查利用LCS监测细颗粒物PM2.5和PM10水平并在配送骑行路径上绘制其分布图。校准过程考虑了49天的测量数据,涵盖不同季节条件(从2024年5月到2024年11月)。多元线性回归和稳健回归的应用揭示了来自两个来源的污染物水平与温度和湿度等其他因素之间的强相关性。随后,在意大利法恩扎市进行了为期一个月的试验。在本研究中,将一个商用LCS安装在一辆货运自行车上,用于在配送过程中进行测量。采用这种方法来揭示骑行者接触空气污染物的情况。通过这种方式,未来操作员的路线将被设计为在配送时间和污染因素方面选择最佳路线。此外,将环境监测整合到城市环境地图绘制中,有可能提高局部污染地图的准确性,从而支持市政当局向市民提供信息并制定针对性空气质量策略的努力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5959/12349590/58c638535fdb/sensors-25-04874-g001.jpg

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