EAGER: A Sensor Cloud-based Community-Centric Approach for Analyzing and Mitigating Urban Heat Hazards

EAGER：一种基于传感器云、以社区为中心的方法，用于分析和减轻城市热危害

• 批准号: 1637277
• 负责人: Deepak Mishra
• 金额: $ 23.84万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637277&HistoricalAwards=false
• 关键词: EAGER; Sensor; Cloud; based; Community

This project will analyze how smart and pervasive devices including human and vehicle-borne sensors can be harnessed to effectively map and identify urban heat islands (UHIs), and mitigate UHI associated risks on various communities. Excessive generation and retention of heat in urban areas by the built environment results in UHIs. Driven by climate change, extreme heat events are increasingly posing a major health hazard to many urban communities in U.S. and around the world. Studies analyzing the impact of UHIs on communities have primarily focused on generating coarse grained heat maps of cities using satellite or weather station data, and correlating heat events with human mortality and morbidity data. This exploratory project will develop and test a prototype community-centric approach to urban heat vulnerability research. Focusing on heat stress risks of individuals and communities in fine-granular geographical areas will radically transform UHI research and efforts to mitigate them. The findings from this study will be extremely useful for understanding the heat exposure vulnerabilities of individual communities such as people living in poorly-planned neighborhoods, poor and elderly, city and municipal outdoor workers, construction workers, bus commuters, and mail delivery personnel. Furthermore, this study will lay the foundation for city/local government officials and business leaders to devise targeted and more efficacious heat hazard mitigation efforts such as increasing greenspace and developing better heat-safety policies for their workers. This research will build a scalable and robust smart-sensor-cloud framework for leveraging variety of human and vehicle-borne smart sensors (e.g., smartphones, environmental micro data loggers) in conjunction with traditional data sources (e.g., satellites and weather stations) for gathering, and analyzing accurate and fine-grained temperature information for urban areas as well as specific urban communities. In this context several important questions will be addressed including: (1) How to effectively harness and integrate heterogeneous data from multiple devices such as smartphones, Unmanned Aerial System (UAS) sensors, micro data loggers, and other modern sensing technologies to create UHI maps for individuals and communities? (2) What are the spatial and temporal differences and variability between satellite, UAS and smart-device derived UHI maps, and what is the optimum granularity required to develop a standardized UHI mapping protocol? and (3) What are the differences in heat exposure levels within a community based on socio-economic factors such as demographics, occupation, and residence location? The temperature maps will be generated using multiple smart devices such as UAS mounted thermal sensors, micro temperature sensors (e.g., Kestrel drops), and iPhone and Android mobile phone based applications. Various field experiments and simulations will be performed to develop temperature conversion calibration coefficients in order to enhance the accuracy of the maps. The temperature maps will be compared with coincident UAS and satellite derived heat maps to analyze the loss of spatial variability of UHIs within an urban area. This project will expand beyond the limits of conventional UHI research by developing hyperlocal and community-centric heat hazard models which will allow the assessment of a community's or an individual's heat stress risk, a tangible step toward a personalized heat warning system.

该项目将分析如何利用包括人类和车载传感器在内的智能和普及设备来有效地绘制和识别城市热岛（UHI），并减轻各种社区的UHI相关风险。在城市地区，建筑环境过度产生和保留热量会导致UHI。在气候变化的推动下，极端高温事件越来越多地对美国和世界各地的许多城市社区构成重大健康危害。分析UHIs对社区影响的研究主要集中在使用卫星或气象站数据生成粗粒度的城市热图，并将热事件与人类死亡率和发病率数据相关联。这一探索性项目将开发和测试以社区为中心的城市热脆弱性研究原型方法。关注细粒度地理区域中个人和社区的热应激风险将从根本上改变城市热岛研究和减轻这些风险的努力。这项研究的结果对于了解单个社区的热暴露脆弱性非常有用，例如居住在规划不良的社区的人，穷人和老人，城市和市政户外工作者，建筑工人，公共汽车通勤者和邮件递送人员。此外，这项研究将为城市/地方政府官员和企业领导人制定有针对性和更有效的热危害缓解措施奠定基础，例如增加绿地和为工人制定更好的热安全政策。这项研究将建立一个可扩展的和强大的智能传感器云框架，以利用各种人类和车载智能传感器（例如，智能手机、环境微数据记录器）与传统数据源（例如，卫星和气象站），用于收集和分析城市地区以及特定城市社区的精确和精细的温度信息。在这种情况下，几个重要的问题将得到解决，包括：（1）如何有效地利用和集成异构数据从多种设备，如智能手机，无人机系统（UAS）传感器，微型数据记录器，和其他现代传感技术，以创建城市热岛地图的个人和社区？(2)卫星、无人机系统和智能设备衍生的城市热岛地图之间的时空差异和可变性是什么？开发标准化城市热岛地图协议所需的最佳粒度是什么？以及（3）基于人口统计学、职业和居住地点等社会经济因素，社区内热暴露水平的差异是什么？温度图将使用多个智能设备生成，例如安装在UAS上的热传感器、微型温度传感器（例如，Kestrel滴），以及iPhone和Android移动的手机为基础的应用程序。将进行各种实地实验和模拟，以确定温度转换校准系数，从而提高地图的准确性。温度图将与UAS和卫星导出的热图进行比较，以分析城市区域内UHI空间变异性的损失。该项目将通过开发超本地和以社区为中心的热危害模型来扩展传统的UHI研究的局限性，该模型将允许评估社区或个人的热应激风险，这是迈向个性化热预警系统的切实步骤。

项目成果

Urban ambient air temperature estimation using hyperlocal data from smart vehicle-borne sensors

使用智能车载传感器的超本地数据估算城市环境空气温度

• 发表时间: 2020
• 期刊: Computers environment and urban systems
• 影响因子: 6.8
• 作者: Yin, Y;Hashemi, N;Grundstein, A;Mishra, D. R;Ramaswamy, L;Dowd, J
• 通讯作者: Dowd, J