Bridging the Blind Spots of Urban Environments: a Crowdsensing Framework Enabled by Delivery Fleets
Bridging the Blind Spots of Urban Environments: a Crowdsensing Framework Enabled by Delivery Fleets
Haotuo Wu 1Junqi Wang 2Hanhui Yu 2Prashant Kumar 3Shi-Jie Cao2
作者信息
- 1. School of Architecture, Southeast University, 2 Sipailou, Nanjing 210096, China
- 2. School of Architecture, Southeast University, 2 Sipailou, Nanjing 210096, China; School of Architecture, Southeast University, 2 Sipailou, Nanjing 210096, China
- 3. School of Architecture, Southeast University, 2 Sipailou, Nanjing 210096, China;Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, 2 Sipailou, Nanjing 210096, China
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摘要
As urbanization intensifies, the synergistic stress of heat islands and air pollution poses significant challenges to urban sustainability, necessitating monitoring paradigms with higher spatiotemporal granularity. Conventional fixed-station networks, however, are constrained by their sparse distribution and inability to penetrate complex urban morphologies, leaving critical environmental ‘blind spots’. This study establishes a dynamic urban sensing framework by leveraging the pervasive mobility of food delivery riders-an emerging paradigm of opportunistic sensing. Specifically, a multi-modal data chain integrating portable sensors, GPS, and first-person visual systems is constructed to characterize compound environmental exposures within street canyons. Field data reveals that these microenvironments exhibit ambient temperatures ranging from 31 to 39 °C, with perceived temperatures frequently exceeding 40 °C, while PM2.5 concentrations display a distinct bimodal pattern peaking at 80 μg/m³. The Average Daily Dose (ADD) of PM2.5 during evening peaks is quantified at 0.32 times the Reference Dose (RfD), a health burden amplified 1.5-fold by the metabolic demands of high-intensity cycling. Furthermore, visual data facilitates the identification of ephemeral pollution sources, such as roadside barbecues, that typically evade stationary detection. Moving beyond diagnosis to intervention, an exposure risk map is constructed to inform adaptive governance strategies, including mandatory cooling intervals and shaded route prioritization. This study offers a scalable, cost-effective optimization of urban sensing systems and provides empirical support for human-centric decision-making in sustainable urban development.
Abstract
As urbanization intensifies, the synergistic stress of heat islands and air pollution poses significant challenges to urban sustainability, necessitating monitoring paradigms with higher spatiotemporal granularity. Conventional fixed-station networks, however, are constrained by their sparse distribution and inability to penetrate complex urban morphologies, leaving critical environmental blind spots. This study establishes a dynamic urban sensing framework by leveraging the pervasive mobility of food delivery riders-an emerging paradigm of opportunistic sensing. Specifically, a multi-modal data chain integrating portable sensors, GPS, and first-person visual systems is constructed to characterize compound environmental exposures within street canyons. Field data reveals that these microenvironments exhibit ambient temperatures ranging from 31 to 39 C, with perceived temperatures frequently exceeding 40 C, while PM2.5 concentrations display a distinct bimodal pattern peaking at 80g/m. The Average Daily Dose (ADD) of PM2.5 during evening peaks is quantified at 0.32 times the Reference Dose (RfD), a health burden amplified 1.5-fold by the metabolic demands of high-intensity cycling. Furthermore, visual data facilitates the identification of ephemeral pollution sources, such as roadside barbecues, that typically evade stationary detection. Moving beyond diagnosis to intervention, an exposure risk map is constructed to inform adaptive governance strategies, including mandatory cooling intervals and shaded route prioritization. This study offers a scalable, cost-effective optimization of urban sensing systems and provides empirical support for human-centric decision-making in sustainable urban development.关键词
Crowdsourced monitoring/Delivery riders/Occupational exposure/Sensing granularity/UN Sustainable development goalsKey words
Crowdsourced monitoring/Delivery riders/Occupational exposure/Sensing granularity/UN Sustainable development goals引用本文复制引用
Haotuo Wu,Junqi Wang,Hanhui Yu,Prashant Kumar,Shi-Jie Cao.Bridging the Blind Spots of Urban Environments: a Crowdsensing Framework Enabled by Delivery Fleets[EB/OL].(2026-01-10)[2026-01-12].https://chinaxiv.org/abs/202601.00108.学科分类
环境科学技术现状/环境污染、环境污染防治/社会与环境/环境管理/环境保护宣传、环境保护教育
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