The study of the impact of acoustical noise on human activities is a very important issue in cities and areas in which relevant noise sources are present. The effect on health, both auditory and non-auditory is largely documented in literature and several models have been developed to take care of this problem. Since almost all the national regulations fix a maximum acoustical level, according to the area and to the kind of buildings and activities that occur in that, a model based on threshold exceedances study is suitable. In this paper, a non-homogeneous Poisson model is presented and applied to a large dataset of noise measurements. The parameters probability distributions estimation, based on Monte Carlo Markov Chains and Gibbs algorithm, will be described. The posterior distributions of the parameters will be shown and their mean values will be used to plot the cumulative mean function. This function, that represents the number of surpassings of the threshold as a function of the time, can be compared with the observed exceedances.

Analysis of Noise Level Exceedances by Exponential Rate Function in Non-Homogeneous Poisson Model

GUARNACCIA, CLAUDIO;QUARTIERI, Joseph;TEPEDINO, CARMINE
2015

Abstract

The study of the impact of acoustical noise on human activities is a very important issue in cities and areas in which relevant noise sources are present. The effect on health, both auditory and non-auditory is largely documented in literature and several models have been developed to take care of this problem. Since almost all the national regulations fix a maximum acoustical level, according to the area and to the kind of buildings and activities that occur in that, a model based on threshold exceedances study is suitable. In this paper, a non-homogeneous Poisson model is presented and applied to a large dataset of noise measurements. The parameters probability distributions estimation, based on Monte Carlo Markov Chains and Gibbs algorithm, will be described. The posterior distributions of the parameters will be shown and their mean values will be used to plot the cumulative mean function. This function, that represents the number of surpassings of the threshold as a function of the time, can be compared with the observed exceedances.
978-1-61804-324-5
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4663769
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