Mapping the Sound Environment of Andorra and Escaldes-Engordany by Means of a 3D City Model Platform

• Main Authors: Rosa Ma Alsina-Pagès, Marc Vilella, Marc Pons, Robert Garcia Almazan
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Urban Science
• Subjects: acoustic source; sound mapping; smart city; 3D model platform; acoustic monitoring; LAeq; SNR
• Online Access: https://www.mdpi.com/2413-8851/3/3/89

In the new paradigm of the smart cities world, public opinion is one of the most important issues in the new conception of urban space and its corresponding regulations. The data collection in terms of environmental noise cannot only be related to the value of the equivalent noise level <inline-formula> <math display="inline"> <semantics> <msub> <mi>L</mi> <mrow> <mi>A</mi> <mi>e</mi> <mi>q</mi> </mrow> </msub> </semantics> </math> </inline-formula> of the places of interest. According to WHO reports, the different types of noise (traffic, anthropomorphic, industrial, and others) have different effects on citizens; the focus of this study is to use the identification of noise sources and their single impacts on background urban noise to develop a visualization tool that can represent all this information in real time. This work used a 3D model platform to visualize the acoustic measurements recorded at three strategic positions over the country by means of a sound map. This was a pilot project in terms of noise source identification. The visualization method presented in this work supports the understanding of the data collected and helps the space-time interpretation of the events. In the study of soundscape, it is essential not only to have the information of the events that have occurred, but also to have the relations established between them and their location. The platform visualizes the measured noise and differentiates four types of noise, the equivalent acoustic level measured and the salience of the event with respect to background noise by means of the calculation of SNR (Signal-to-Noise), providing better data both in terms of quantity and quality and allowing policy-makers to make better-informed decisions on how to minimize the impact of environmental noise on people.