Relationship between green space-related morphology and noise pollution

Abstract

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.

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

“…GWR is a local spatial statistical method for evaluating how the relationships between a dependent variable and one or more explanatory variables change spatially. As one of the useful tools to explore the spatial local heterogeneity, GWR has been widely used in many fields in recent years, For example, the geographic variation and impact factors of urban public green space availability [46], peri-urban agriculture [47], noise pollution [48], population [49] and resident recreation demand [50] have been investigated with GWR. The GWR method was usually compared with global spatial statistical methods, such as the ordinary least squares (OLS) regression, regression kriging, or co-kriging and the comparisons showed the advantages of GWR in improving mapping quality and exploring spatially varying local relationships [47][48][49][50][51].…”

Modelamiento kriging para mapas acústicos de las festividades culturales de la región de Puno

“…Resultados similares revelaron el comportamiento cotidiano del decibelio para el LAeq (69,16 dB(A), atribuido al tráfico rodado en la propia ciudad de Puno (Marín, Marín & Argota, 2017). Otro estudio en 25 nú-cleos urbanos de ciudades europeas, la exposición a la banda de ruido fue más baja que 70 dB(A), cuyas exposiciones presentan un riesgo elevado (Margaritis & Kang, 2017).…”

Spatial Variation in Sound Frequency Components Across an Urban Area Derived from Mobile Surveys

“…Approximately 20% of the European population is exposed to unacceptable noise levels (WHO 2011, EEA 2014a. Road traffic is the main cause of noise pollution in urban settings (Sørensen et al 2012, EEA 2014a, Brown 2015, Margaritis and Kang 2017 followed by rail and air traffic and industry (EEA 2014a). Beyond Europe the situation may be even worse since in those countries noise pollution is not always considered an environmental problem (Murphy and King 2014).…”