Assessing the Spatiotemporal Impact of SLODs in Urban Square, Considering User’s Exposure and Vulnerability

Slow-onset disasters (SLODs), such as increasing temperatures and air pollution, impact the microclimate, health and habits of users in the built environment (BE), especially in outdoor spaces such as squares. While numerous risk assessment methodologies are available for the BE at a macroscopic level, methodologies focused on local analysis of mesoscale elements, are still limited. These spaces play a crucial role in the public life of cities, influencing the activities and behaviours of inhabitants. The SLODs risk in the squares depends primarily on the interaction of various factors, including specific hazards, square characteristics (e.g., morphology, activities, and type of activities and services available), exposure and user characteristics (e.g., health, age, mobility). There is a requirement for methodologies to effectively incorporate all these factors, specifically to enhance the implementation of risk reduction strategies. This work introduces an innovative approach to formulate a user-oriented risk index using a Risk Matrix (RMA), which combines the different factors involved. The proposed method ensures rapid applicability while integrating quantitative analyses (from large datasets, accessible online) and qualitative assessments (from experience and knowledge). It allows for the representation of assessed risk levels through mesoscale maps that show the risk variation as specific local conditions change. The simplicity and versatility of the method facilitate its use by non-experts and local authorities to obtain a quick risk assessment and support the definition of targeted mitigation strategies. The study assessed and mapped local risks in both a real scenario and a project scenario where green areas are implemented, and traffic is reduced. Results highlight the influence of localized features, such as greenery and specific attractions on risk levels (e.g., shops, universities, railway stations).