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This research aims to design a community-focused indoor heat emergency alert system for vulnerable populations during extreme heat events. The impacts of extreme heat events are becoming more severe, especially for vulnerable populations. Current warning systems lack information about indoor conditions, and many people do not realize the heat risk, appropriate actions, or available cooling resources. The CommHEAT application is designed based on the Fogg Behavior Model to promote people’s heat-protective behaviors during extreme heat events. It has three key features: customized indoor heat risk prediction, community monitoring and support, and location-based resources. A multi-mode concept testing (exploratory interaction, task-based scenarios, and semi-structured interviews) was conducted with nine heat-vulnerable residents and three stakeholders to evaluate the CommHEAT application prototype. The result shows that this smartphone-based app prototype is promising as an intervention for protecting people from extreme heat. However, the results also identified challenges like trust issues and privacy concerns. Future work will focus on improving the application and assessing its impact on residents’ behavior change during extreme heat events. 

Typical Meteorological Year (TMY) datasets, widely used in building energy modeling, overlook Urban Heat Island (UHI) effects and future climate trends by relying on long-term data from rural stations such as airports. This study addresses this limitation by integrating Urban Weather Generator (UWG) simulations with CCWorldWeatherGen projections to produce microclimate-adjusted and future weather scenarios. These datasets were then incorporated into an Urban Building Energy Modeling (UBEM) framework using Urban Modeling Interface (UMI) to evaluate energy performance across a low-income residential neighborhood in Des Moines, Iowa. Results show that UHI intensity will rise from an annual average of 0.55 °C under current conditions to 0.60 °C by 2050 and 0.63 °C by 2080, with peak intensities in summer. The UHI elevates cooling Energy Use Intensity (EUI) by 7% today, with projections indicating a sharp increase—91% by 2050 and 154% by 2080. The UHI will further amplify cooling demand by 2.3% and 6.2% in 2050 and 2080, respectively. Conversely, heating EUI will decline by 20.0% by 2050 and 40.1% by 2080, with the UHI slightly reducing heating demand. Insulation mitigates cooling loads but becomes less effective for heating demand over time. These findings highlight the need for climate-adaptive policies, building retrofits, and UHI mitigation to manage future cooling demand. 

Urban areas often experience higher air temperatures than their surrounding rural counterparts, a phenomenon known as the urban heat island (UHI) effect. This significant human-induced alteration of urban microclimates has notable consequences, especially on urban energy consumption and resulting economic implications. This study presents an in-depth analysis of the UHI effect on urban building energy consumption in a US Midwest neighbourhood. Utilizing a three-phase methodology, the research first simulated UHI intensities with current and future Typical Meteorological Year (TMY) data, integrated with the Local Climate Zone (LCZ) classification system and the Urban Weather Generator (UWG) model. The second phase employed the urban modelling interface (umi) for building energy simulation, capturing the UHI impact on both residential and commercial buildings. The third phase demonstrates that UHI effects lead to reduced heating demand but increased cooling requirements in the future, with residential areas being more affected. The study's findings reveal critical challenges for urban planners and policymakers, emphasizing the need for sustainable designs to address fluctuating heating and cooling demands in changing climates. 

Extreme heat is the cause of most US weather-related deaths and is a serious risk for older adults, children, and people with medical issues living in vulnerable neighborhoods. This study explored how extreme heat events affect vulnerable communities, focusing on people’s perceptions, adaptive behaviors, and social network support. Mobile ecological momentary assessment through Short Message Service was employed to collect real-time data from residents in a socioeconomically challenged urban neighborhood in Iowa. Participants responded to text messages with survey links twice daily during heat events over the summer, in which they were asked about their perceptions and behaviors. The results show the need for targeted, inclusive, and effective heat warnings and social network support to reduce heat risks and help vulnerable communities better cope with extreme heat.