Development and application of practical criteria for the recognition of potential Health Restoration Soundscapes (HeReS) in urban greenspaces.

Urban greenspace soundscapes can contribute to the restorative effects that nature provides for the psychological and emotional health of people when certain conditions are met. The main objective of this paper is to propose practical criteria to help planners and managers in the design, development and preservation of urban greenspaces whose soundscapes may contribute to the renewal of health. Systematic literature review informed a conceptual potential Health Restoration Soundscapes (HeReS) model, based on five conditions: (1) Naturalness, (2) Sound Levels, (3) Perceived Sound Sources, (4) Soundscape Assessment, and (5) Sensescape Coherence and proposed Health Restoration Soundscapes Criteria (HeReS-C), for HeReS evaluation in urban green areas. The HeReS-C were applied in 21 sites in Argentina, Sweden, and Chile. General results are provided for all 21 sites, along with three in-depth profiles of HeReS-C applications that provide case studies across a range of resulting HeReS-C scenarios, including sites that meet the HeReS-C criteria, those that do not, and sites that could qualify in the future, if appropriate management measures are taken. HeReS-C showed to be a promising tool for the recognition of potential health-restoring soundscapes in urban greenspaces; informing their design and management to favor the well-being and health of the population.

A Green Soundscape Index (GSI): The potential of assessing the perceived balance between natural sound and traffic noise.

Urban soundscapes are dynamic and complex multivariable environmental systems. Soundscapes can be organized into three main entities containing the multiple variables: Experienced Environment (EE), Acoustic Environment (AE), and Extra-Acoustic Environment (XE). This work applies a multidimensional and synchronic data-collecting methodology at eight urban environments in the city of Córdoba, Argentina. The EE was assessed by means of surveys, the AE by acoustic measurements and audio recordings, and the XE by photos, video, and complementary sources. In total, 39 measurement locations were considered, where data corresponding to 61 AE and 203 EE were collected. Multivariate analysis and GIS techniques were used for data processing. The types of sound sources perceived, and their extents make up part of the collected variables that belong to the EE, i.e. traffic, people, natural sounds, and others. Sources explaining most of the variance were traffic noise and natural sounds. Thus, a Green Soundscape Index (GSI) is defined here as the ratio of the perceived extents of natural sounds to traffic noise. Collected data were divided into three ranges according to GSI value: 1) perceptual predominance of traffic noise, 2) balanced perception, and 3) perceptual predominance of natural sounds. For each group, three additional variables from the EE and three from the AE were applied, which reported significant differences, especially between ranges 1 and 2 with 3. These results confirm the key role of perceiving natural sounds in a town environment and also support the proposal of a GSI as a valuable indicator to classify urban soundscapes. In addition, the collected GSI-related data significantly helps to assess the overall soundscape. It is noted that this proposed simple perceptual index not only allows one to assess and classify urban soundscapes but also contributes greatly toward a technique for separating environmental sound sources.