Heat Risk Assessment and Impact-Based Early Warning Modeling for Vulnerable Populations

Climate change and the urban heat island (UHI) effect have exacerbated the health impacts of extreme heatwaves on aging populations. However, a significant gap exists between outdoor meteorological forecasts and the actual indoor heat stress experienced by vulnerable groups. Therefore, this study develops and validates a transdisciplinary, impact-based indoor thermal environment early warning model, providing a scientific basis for urban climate adaptation and public health responses.

The methodology of this study includes both the urban and indoor residential scales. First, at the urban scale, a spatial heat risk model is constructed using Geographic Information Systems (GIS). Data including the elderly ratio, low-income household density, medical accessibility, and impervious surface fraction are standardized using Z-scores. A comprehensive heat risk index is then calculated by Principal Component Analysis (PCA) to delineate urban heat hotspots urgently requiring social resource intervention. Second, to accurately assess the actual living environments of the elderly, the research shifts to the building scale, employing the EnergyPlus building energy model for dynamic indoor thermal simulations. The physical boundary conditions of the model are set using local meteorological data, while occupancy profiles and air-conditioning operational schedules are configured based on field surveys of 30 solitary elderly households in high-risk districts. This model accurately reflects the target population's high physiological vulnerability and low frequency of air conditioning use.

The results indicate that the cross-scale integration of spatial risk mapping and energy simulation for indoor temperatures achieves a more accurate assessment of heat hazards. Field surveys reveal that over 80% of the elderly suffer from temperature-sensitive chronic diseases. Under this extremely vulnerable scenario, the EnergyPlus model demonstrated the thermal delay effect caused by the heat storage of building envelopes. It also accurately predicts the time when the interior reaches critical heat stress, exhibiting high reliability in short-term temperature prediction.

By integrating urban-scale and building-scale analytical methods, this study establishes an accurate and operational climate assessment model. When the predicted indoor temperature exceeds 32°C, the system triggers an alert, providing social workers and government authorities with a 3-to-6 hour lead time for response. This model provides a data-driven strategy that helps to accurately deploy care visits and effectively mitigate the risk of mortality caused by high temperatures in an aging society.