Journal
5TH INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT RESEARCH (ICEER 2018)
Volume 153, Issue -, Pages 334-341Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.egypro.2018.10.072
Keywords
Quiet areas; sustainability; urban acoustics; urban noise
Categories
Funding
- FCT - Fundacao Ciencia e Tecnologia, Program COMPETE e QREN - Quadro de Referencia Estrategico Nacional
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About 0.62 million DALYs are lost yearly among the urban population in EU countries owing to the occurrence of noise annoyance. One of the appointed causes deals with the fact that urban areas are focus of sustainability strategies not considering the noise factor. As a matter of fact, natural resources, energy, economy, air quality among others, are usual environmental factors associated to sustainability studies but not noise. Because of the evolution of humanity, people are driven to live together in big (very big) urban spaces, where urban noise is usually the scope of acoustic studies in its physical aspects but rarely involving people reaction. It is the case of the reaction of users of Quiet Areas evaluated by noise maps and statistical parameters. The aim of this work is focused in the relationship between urban noise in quiet areas and human reaction as a beacon for urban sustainability. It is proposed the quiet area reaction to noise- ANN concept to promote areas in urban landscape where not only sound is related with nature, but also fauna and flora are representative of a men low impact intervention. International rules recommend that any citizen should not be farther from a quiet area than 30 walking min. ANN is proposed as an annoyance reaction correlation with Equivalent Noise Level [Lden, Ln]. The proposed concept is applied at Porto in two different cases of Quiet Areas, City Park and Covelo Farm, from where data is presented as an illustration of the concept. Conclusions allow understanding that noise maps based on Equivalent Noise Levels [Leq, Ln] are not suitable to identify people reaction in Quiet Areas. The proposed model is quite accurate for none and highly annoyance environments and further research is needed do better identify moderate and little annoyance. (C) 2018 The Authors. Published by Elsevier Ltd.
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