Assessing the Impact of Heat Mitigation Strategies in Urban Ecosystems using ASLUM-Py

Urban heat islands (UHIs) exacerbate the adverse effects of extreme temperatures in metropolitan areas, necessitating effective mitigation strategies to improve urban livability. This study integrates a state-of-the-art urban canopy model, viz. the Arizona Single-Layer Urban Canopy Model into the Python platform (ASLUM-Py). The newly developed modeling framework is then used to assess and enhance heat mitigation interventions in urban ecosystems. Our primary objective is to evaluate the effectiveness of three key strategies: white roofs, urban irrigation, and shade trees. Using the ASLUM framework, we simulate urban microclimate dynamics in response to different heat mitigation strategies, to identify the most effective greening configurations for specific urban settings. This study focuses on Phoenix, Arizona, known for its extreme temperatures and rapid urbanization, providing a robust case study for our methodology. The ASLUM-Py framework facilitates a comprehensive assessment of the urban ecosystem dynamics and will enable data-driven, machine-learning-based protocols for greening initiatives. This study contributes to the growing body of knowledge on urban climate resilience for future urban planning and sustainable development, which underscores the importance of strategic urban greening initiatives in combating the adverse effects of UHIs and promoting ecological well-being.

Keywords: Greening scenarios; Heat mitigation strategies; Predictive accuracy; ASLUM-Py; Sustainable urban development