Since the introduction of the Environmental Noise Directive, strategic noise mapping has been used as a tool for noise policy in many European countries. Although these strategic noise maps have their merits, they also have some shortcomings: accuracy in predicted noise levels in shielded or quiet areas is not very high, the maps fail to capture sounds that are less easy to predict, and above all the dynamics of the sound environment is not included. However, these dynamics might be important to evaluate sleep disturbance and noise annoyance. In this paper, a model to dynamically (every 15 minutes) update a noise map based on measurements is proposed. This model relies on reasonable good source and propagation models and a not-very-dense measurement network. The least mean squares method (LMS) is used for tuning model parameters. To avoid an under-determined system, the number of degrees of freedom is reduced by grouping the sources and propagation paths into different categories. Source strengths and propagation path attenuations in the same category are corrected by offsetting the same small values from their base levels. The map-based interpolation is performed jointly on L Aeq , L 10 and L 90 , and takes into account 1/3-octave band spectra. The efficiency of the proposed method was validated in a case study in the Katendrecht district of Rotterdam, the Netherlands. The results showed that more than 75% of the L Aeq predictions are closer to the measurement than the ab initio calculations based on traffic data. Values for L 10 and L 90 are closer to measurements for 55% and 90% of the observations, respectively.
SummarySound scattering due to atmospheric turbulence limits the noise reduction in shielded areas. An engineering model is presented, aimed to predict the scattered level for general noise mapping purposes including sound propagation between urban canyons. Energy based single scattering for homogeneous and isotropic turbulence following the Kolmogorov model is assumed as a starting point and a saturation based on the von Kármán model is used as a first-order multiple scattering approximation. For a single shielding obstacle the scattering model is used to calculate a large dataset as function of the effective height of the shielding obstacle and its distances to source and receiver. A parameterisation of the dataset is used when calculating the influence of single or double canyons, including standardised air attenuation rates as well as façade absorption and Fresnel weighting of the multiple façade reflections. Assuming a single point source, an averaging over three receiver positions and that each ground reflection causes energy doubling, the final engineering model is formulated as a scattered level for a case without canyon and a correction term for the effect of a single or a double canyon case, assuming a flat rooftop of the shielding building. Input parameters are, in addition to geometry and sound frequency, the strengths of velocity and temperature turbulence.
The effect of noise mitigation measures is generally expressed by noise levels only, neglecting the listener's perception. In this study, an auralization methodology is proposed that enables an auditive preview of noise abatement measures for road traffic noise, based on the direction dependent attenuation of a priori recordings made with a dedicated 32-channel spherical microphone array. This measurement-based auralization has the advantage that all non road traffic sounds that create the listening context are present. The potential of this auralization methodology is evaluated through the assessment of the effect of an L-shaped mound. The angular insertion loss of the mound is estimated by using the ISO 9613-2 propagation model, the Pierce barrier diffraction model and the Harmonoise point-to-point model. The realism of the auralization technique is evaluated by listening tests, indicating that listeners had great difficulty in differentiating between a posteriori recordings and auralized samples, which shows the validity of the followed approaches.
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