Flexible Workflow for Determining Critical Hazard and Exposure Scenarios for Assessing SLODs Risk in Urban Built Environments

Juan Diego Blanco Cadena,Michele Lucesoli,Marco D’Orazio,Enrico Quagliarini,Graziano Salvalai

doi:10.3390/su13084538

Juan Diego Blanco Cadena, Michele Lucesoli + Show 3 more

Open Access

https://doi.org/10.3390/su13084538

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Abstract

Urban Built Environments (UBE) are increasingly prone to SLow-Onset Disasters (SLODs) such as air pollution and heatwaves. The effectiveness of sustainable risk-mitigation solutions for the exposed individuals’ health should be defined by considering the effective scenarios in which emergency conditions can appear. Combining environmental (including climatic) conditions and exposed users’ presence and behaviors is a paramount task to support decision-makers in risk assessment. A clear definition of input scenarios and related critical conditions to be analyzed is needed, especially while applying simulation-based approaches. This work provides a methodology to fill this gap, based on hazard and exposure peaks identification. Quick and remote data-collection is adopted to speed up the process and promote the method application by low-trained specialists. Results firstly trace critical conditions by overlapping air pollution and heatwaves occurrence in the UBE. Exposure peaks (identified by remote analyses on the intended use of UBEs) are then merged to retrieve critical conditions due to the presence of the individuals over time and UBE spaces. The application to a significant case study (UBE in Milan, Italy) demonstrates the approach capabilities to identify key input scenarios for future human behavior simulation activities from a user-centered approach.

Highlights

Published: 19 April 2021It is common to confuse a rapid-onset disaster with a SLow-Onset Disaster (SLOD) or to misinterpret the evidence of SLODs with the actual disaster
It is necessary to acknowledge the SLODs characteristics that differentiate them from any other disaster type, as described by Siegele [1], based on their temporal scale, intensity and frequency: Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
This procedure follows the methodology proposed by Blanco Cadena et al [26], which, in brief, comprises: (1) identification of possible, and/or accessible, weather and air quality data sources from the site, or in its proximity; (2) hourly data processing to understand the arousal of critical heat stress and poor air quality excess; (3) superposition of heat stress and poor air quality levels to recognize the moments of coexisting peaks; and, (4) averaging the number of hours of TRUE parallel arousal of SLODs peak intensity for the desired analysis period, to delineate a daily profile

Summary

Introduction

It is common to confuse a rapid-onset disaster with a SLow-Onset Disaster (SLOD) or to misinterpret the evidence of SLODs with the actual disaster. It is necessary to acknowledge the SLODs characteristics that differentiate them from any other disaster type, as described by Siegele [1], based on their temporal scale, intensity and frequency: Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. These disasters vary in temporal scale; while rapid-onset disasters unfold “almost instantly”, slow-onset disasters can be predicted much further in advance and unfold over months or even years. Slow-onset disasters are strongly related to the effects of the man-made climate change dynamics; SLODs vary in impact type. Rapid-onset disasters tend to create their disruption through the immediate/short-term physical impacts, whereas slow-onset disasters do not emerge from a single and distinct event but emerges gradually over time and can typically create crises through the economic and social impacts of the disaster

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