Noise pollution is a critical factor and it has an important impact on public health, with the relationship between road traffic noise (RTN) and several illnesses in urban areas of particular concern. Andorra is currently developing a national strategy regarding noise pollution in their urban environments. The Ministry of Environment, Agriculture and Sustainability is trying to to identify, monitor, map and model the effects of noise pollution and design mitigation policies to reduce the impact in certain priority areas. This analysis should take into account the existence of different types of anomalous noise events (ANEs) present in the street, e.g., horns, people talking, music, and other events that coexist with RTN, to characterize the soundscape of each of the locations. This paper presents a preliminary analysis considering both the Signal-to-Noise Ratio (SNR) and the duration of the ANEs to evaluate their presence in urban areas in the three different locations in Andorra la Vella and Escaldes-Engordany. The experiments conducted required a 10-h recording campaign distributed in the three locations under study, which was evaluated on two different days, one during the week and the other on the weekend. Afterwards, the data were carefully labeled and the SNR of each event was evaluated to determine the potential impact of the four categories under study: vehicles, works, city life and people.
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 L A e q 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.
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