Green spaces have been proved to have a positive effect on traffic noise pollution in the local scale; however their effects have not been explored on the urban level. This paper investigates the effects of green space-related parameters from a land cover viewpoint on traffic noise pollution in order to understand to what extent greener cities can also be quieter. A triple level analysis was conducted in the agglomeration, urban and kernel level including various case study cities across Europe. The green space parameters were calculated based on land cover data available in a European scale, while traffic noise data were extracted from online noise maps and configured in noise indices. In the first level 25 agglomerations were investigated, six of which were further analyzed in the urban and kernel levels. It was found that the effect of green spaces on traffic noise pollution varies according to the scale of analysis. In the agglomeration level, there was no significant difference in the cluster of the higher green space index and the percentage of people exposed in the lowest (55-59 dB(A)) or the highest noise band of more than 70 dB(A). In the urban level it was found that lower noise levels can possibly be achieved in cities with a higher extent of porosity and green space coverage. Finally, in the kernel level a Geographically Weighted Regression (GWR) analysis was conducted for the identification of correlations between noise and green. Strong correlations were identified between 60% and 79%, while a further cluster analysis combined with land cover data revealed that lower noise levels were detected in the cluster with higher green space coverage. At last, all cities were ranked according to the calculated noise index.

Smart cities are required to engage with local communities by promoting a user-centred approach to deal with urban life issues and ultimately enhance people’s quality of life. Soundscape promotes a similar approach, based on individuals’ perception of acoustic environments. This paper aims to establish a model to implement soundscape maps for the monitoring and management of the acoustic environment and to demonstrate its feasibility. The final objective of the model is to generate visual maps related to perceptual attributes (e.g. ‘calm’, ‘pleasant’), starting from audio recordings of everyday acoustic environments. The proposed model relies on three main stages: (1) sound sources recognition and profiling, (2) prediction of the soundscape’s perceptual attributes and (3) implementation of soundscape maps. This research particularly explores the two latter phases, for which a set of sub-processes and methods is proposed and discussed. An accuracy analysiswas performed with satisfactory results: the prediction models of the second stage explained up to the 57.5% of the attributes’ variance; the cross-validation errors of the model were close to zero. These findings show that the proposed model is likely to produce representative maps of an individual’s sonic perception in a given environment.

Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract: The effect of greenery on traffic noise mitigation has been extensively studied on the level of single plants, green walls, berms and hedges, but not considering whole sample areas within the cities. Therefore, the aim of this paper is to investigate the relationship between features of urban morphology related to green spaces, roads or buildings and traffic noise distribution in urban areas. The analysis was applied in eight UK cities with different historical and architectural background, following two different settlement forms (radial, linear). In each city a 30 km 2 grid was defined and three different levels of approach were considered (macro-scale, meso-scale, micro-scale). The first level regarded the eight cities as single entities, while in the second one every single tile of the applied grid was investigated in two different cities. In the third level only the eight city centres were analyzed. Statistical analysis was used combined with GIS tools. In total 18 variables were constructed and tested for possible relationships with noise levels (L den ). It was found that in spite of the fact that each city has its own dynamic and form, features of urban morphology were related to traffic noise levels to a different extent at each scale. At the macro-scale, the green space pattern was related to the structure of the city as well as the traffic noise levels in combination with the rest of the morphological parameters. At the meso-scale, an increase in internal road connectivity contributed to higher traffic noise. Green space variables explained part of the variance in traffic prediction models. Finally, at the micro-scale, it was also proved that different areas can have the same building coverage but different noise levels. Therefore, these indexes could be profiled and used as an "a priori" tool for urban sound planning.

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