SCC-IRG Track 2: A data-driven approach to designing a community-focused indoor heat emergency alert system for vulnerable residents (CommHEAT)

极端高温是致命的，对老年人和低收入社区居民的影响不成比例。未来几年，极端高温和潮湿事件将会增加。缺乏空调和城市热岛效应造成了危险的室内条件。在低收入地区，高达60%的老旧、建筑质量差的房屋没有空调。将机器学习(SciML)和基于代理的模型(ABM)中的居民行为和建筑特征相结合，将识别居民何时暴露在家里的过热风险中，并将他们与资源联系起来，以缓解危险条件。利用爱荷华州立大学、得梅因市、波尔克县、社区组织以及UNI和UTA的合作者之间的合作，该团队将使用经验数据和参与性过程来开发新型混合社会和物理感知模型，以提高与极端高温相关的室内条件的可预测性。以社区为中心的小气候信息室内高温应急警报(CommHEAT)系统将使社区与高温相关的应急管理能力个性化。这将通过改进对家庭室内条件的预测来适应极端高温，从而提供社会效益。社会-生物物理模型具有广泛的可转移性，以促进气候适应和改善与极端高温相关的公共健康。项目的完成将为社区提供一个实时了解小气候的高温警报框架。研究结果将支持当地的热健康行动计划，以减少紧急呼叫、与中暑相关的住院和室内热暴露造成的死亡率。这项研究将通过适应/反应室内极端高温的社会和热物理模型来解决知识差距。数据将把对极端高温的社会/行为反应与物理约束的模型相结合，并制定跨越空间和时间尺度的反应策略。智能优势包括开发新的数据驱动建模，将经过验证的ABM和物理约束的建筑特征与建筑物内/附近的人类行为的SciML模型相结合。该项目为三个科学进步做出了贡献：(1)基于人类的选择描述人类在极端事件期间的行为；(2)使用实时参数创建局部物理约束的室内条件模型；以及(3)使用可移植的框架将模型集成到应用程序中，以随时间预测条件。通过与研究地区弱势居民的参与式设计，ABM将成为经验有效的社区模型，能够在高温警报系统中预测在气候情景下对不同APP启用的降温策略的反应，从而改善健康结果。可转移的SciML将解释将当地条件与建筑热性能联系在一起的潜在物理因素。CommHEAT应用程序将可视化警报缓解场景，以指导多个级别的决策适应。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Extreme heat is deadly and disproportionately affects the elderly and residents of low-income neighborhoods. Extreme heat and humidity events will increase in coming years. Lack of air-conditioning and the urban heat island effect create dangerous indoor conditions. Up to 60% of older, poorly built homes in low-income areas lack AC. Combining residents’ behavior and building characteristics in machine learning (SciML) and agent-based models (ABM) will identify when residents are exposed to overheating risks in their home and connect them with resources to mitigate dangerous conditions. Leveraging collaboration between Iowa State University, the City of Des Moines, Polk County, community organizations, and collaborators at UNI and UTA, this team will use empirical data and participatory processes to develop novel hybrid social- and physics-aware models to increase predictability of extreme heat-related indoor conditions. A community-focused microclimate-informed indoor heat emergency alert (CommHEAT) system will personalize community heat-related emergency management capacity. This will provide societal benefits via improved prediction of indoor conditions in homes for adaptation to extreme heat. The social-biophysical models are broadly transferable to facilitate climate adaptation and improve public health associated with extreme heat. Project completion will provide communities with a framework for microclimate-informed heat alerts in real time. Outcomes will support local heat health action plans to reduce emergency calls, heat illness-related hospitalizations, and mortality from indoor heat exposure.This research will address knowledge gaps through social and thermal-physical models of adaptation/response to extreme heat indoors. Data will integrate social/behavioral responses to extreme heat with physics-constrained models and develop response strategies across spatial and temporal scales. Intellectual merit includes development of novel data-driven modeling combining validated ABM and physics-constrained SciML models of building features with human behavior within/near buildings. This project contributes to three scientific advances: (1) describing human behavior during extreme events based on human choices; (2) creating localized physics-constrained indoor condition models with real-time parameters; and (3) integrating models in an app using a transferable framework to predict conditions over time. Through participatory design with vulnerable residents in the study area the ABM will be an empirically valid model of the community, enabling prediction of responses to different app-enabled heat mitigation strategies under climate scenarios in a heat alert system that will improve health outcomes. Transferable SciML will account for underlying physics that tie local conditions to building thermal properties. The CommHEAT app will visualize alert mitigation scenarios to guide decision-making at multiple scales for adaptation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.