Road traffic in urban areas is recognized to be associated with urban mobility and public health, and it is often the main source of noise pollution. Lately, noise maps have been considered a powerful tool to estimate the population exposure to environmental noise, but they need to be validated by measured noise data. The project Dynamic Acoustic Mapping (DYNAMAP), co-funded in the framework of the LIFE 2013 program, is aimed to develop a statistically based method to optimize the choice and the number of monitoring sites and to automate the noise mapping update using the data retrieved from a low-cost monitoring network. Indeed, the first objective should improve the spatial sampling based on the legislative road classification, as this classification is mainly based on the geometrical characteristics of the road, rather than its noise emission. The present paper describes the statistical approach of the methodology under development and the results of its preliminary application to a limited sample of roads in the city of Milan. The resulting categorization of roads, based on clustering the 24-h hourly L Aeqh , looks promising to optimize the spatial sampling of noise monitoring toward a description of the noise pollution due to complex urban road networks more efficient than that based on the legislative road classification.

Zambon, G., Benocci, R., & Brambilla, G. (2016). Cluster categorization of urban roads to optimize their noise monitoring. ENVIRONMENTAL MONITORING AND ASSESSMENT, 188(1), 1-11 [10.1007/s10661-015-4994-4].

Cluster categorization of urban roads to optimize their noise monitoring

ZAMBON, GIOVANNI
;
BENOCCI, ROBERTO;
2016

Abstract

Road traffic in urban areas is recognized to be associated with urban mobility and public health, and it is often the main source of noise pollution. Lately, noise maps have been considered a powerful tool to estimate the population exposure to environmental noise, but they need to be validated by measured noise data. The project Dynamic Acoustic Mapping (DYNAMAP), co-funded in the framework of the LIFE 2013 program, is aimed to develop a statistically based method to optimize the choice and the number of monitoring sites and to automate the noise mapping update using the data retrieved from a low-cost monitoring network. Indeed, the first objective should improve the spatial sampling based on the legislative road classification, as this classification is mainly based on the geometrical characteristics of the road, rather than its noise emission. The present paper describes the statistical approach of the methodology under development and the results of its preliminary application to a limited sample of roads in the city of Milan. The resulting categorization of roads, based on clustering the 24-h hourly L Aeqh , looks promising to optimize the spatial sampling of noise monitoring toward a description of the noise pollution due to complex urban road networks more efficient than that based on the legislative road classification.
Articolo in rivista - Articolo scientifico
Monitoring techniques; Road noise; Statistical analysis; Urban area;
Road traffic noise,·Noise monitoring,·Temporal sampling,·Accuracy,·Uncertainty
English
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11
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Zambon, G., Benocci, R., & Brambilla, G. (2016). Cluster categorization of urban roads to optimize their noise monitoring. ENVIRONMENTAL MONITORING AND ASSESSMENT, 188(1), 1-11 [10.1007/s10661-015-4994-4].
Zambon, G; Benocci, R; Brambilla, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/98429
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