Potential of Nature-Based Solutions to Diminish Urban Heat Island Effects and Improve Outdoor Thermal Comfort in Summer: Case Study of Matadero Madrid

Abstract

Urban heat island effects and climate change are climatic phenomena responsible for periods of extreme heat in summer which severely impact citizens’ well-being and health. In this alarming context which questions the livability of our cities, Nature-Based Solutions (NBSs) are considered an unavoidable component of the complex strategy in diminishing urban temperatures. The present work aims to show the relevance of NBSs in urban temperature regulation through the estimation of their potential to improve outdoor thermal comfort of the heritage site Matadero Madrid. To this end, this article evaluates the effects of a scenario combining different solutions including NBSs and identifies which solutions are the most effective. The results show that this scenario has an impact on direct solar radiation and wind speed but does not affect air temperature and relative humidity. Furthermore, even if this scenario combining a fabric canopy, tree canopy, and green area significantly improves thermal conditions, it does not allow us to reach an optimal level of thermal comfort for visitors. To consider the implementation of more meaningful interventions, existing legal, administrative, and cultural limitations of the case study should be omitted. This would enable us to identify which restrictions could be adapted, thus unlocking the adaptation potential of Matadero Madrid.

Numerical Investigation of Interventions to Mitigate Heat Stress: A Case Study in Dubai

“…The proposed approach suggests that numerical simulations like CFD can utilize geometric shape descriptors, yet its effectiveness may vary across different scenarios, potentially impacting model accuracy [43,44]. Olivieri et al reported that the air temperature error was approximately 10%, meeting acceptable thresholds for numerical simulations [45,46]. Additionally, Wilkinson et al hypothesized that the surface pressure distribution around tall buildings can be predicted with less than 20% error, indicating potential reliability for practical use [44].…”