In order to manage noise pollution and reduce its environmental impact and health outcomes, several regulations have been issued in the last few decades, defining acoustic indicators and their thresholds. However, the acoustic environment can be considered a resource, focusing on people’s subjective perception of sounds in accordance with the soundscape approach. The integration of the tools, already applied by the legislation, and the soundscape technique produces a more thorough and comprehensive evaluation of the environmental noise that is necessary for its management. Starting from the best practice of the soundscape in urban planning, this paper presents an application of this approach at the Fisciano campus of the University of Salerno (Italy). The overarching goal is the comparison between the physical parameters, obtained by measuring the sound pressure level, and the psychoacoustic ones, derived by questionnaires given to a group of local experts during a soundwalk. The results will show, for example, some areas characterized by high sound pressure levels and a good perception of the soundscape. As a consequence, the application would seem to have discrepancies between the results of the two methods, but a deeper analysis can reveal further information to the traditional measurements that allow a more accurate knowledge of the acoustic environment.

The assessment and control of acoustic noise in every place in which humans live is usually performed measuring the sound pressure levels and comparing these results with the thresholds defined by regulations. The latest approaches include the possibility to consider the subjective perception of sounds, using the so-called “soundscape” approach. In this paper the authors present a practical application of this approach performed in the Campus of Fisciano, University of Salerno (Italy), with the aim to compare the physical parameters, obtained by measuring the sound level, and the psychoacoustics one, acquired by administering questionnaires to a group of students during a soundwalk. Results will show that the higher sound pressure levels will not always correspond to the more annoying places. In particular, the main park of the University campus will present a positive soundscape, even though noise from the nearby highway will be highly present. Similar conditions will occur in vibrant areas of the campus, in which the interviews will highlight a good perception of the soundscape, even with quite high sound pressure levels.

The assessment of road traffic noise is very important for the health of people living in urban areas. Noise is usually assessed by field measurements, and predictive models play an important role when experimental data are not available. Nevertheless, when they are based on regression techniques, predictive models suffer from the drawback of strong dependence on the calibration data. In this paper, the authors present a regressive model calibrated on computed noise levels without the need for field measurements. The independence from field measurements makes the model flexible and adjustable for any road traffic condition possible. A multilinear regression technique is applied to establish the correlation between the computed equivalent noise levels and several independent variables, including, among others, traffic flow and distance. The model is then validated on a large field measurement database to check its efficiency in terms of prediction accuracy. The validation is performed both via error distribution analysis and using different error metrics. The results are encouraging, showing that the model provides good results in terms of the average error (less than 2 dBA) and is not susceptible to the presence of outliers in the input data that correspond to unconventional conditions of the traffic flow.

Estimation of road traffic noise is fundamental for the health of people living in urban areas, and it is usually assessed based on field-measured data. Real data may not always be available, anyway, and for this reason, predictive models play an important role in the evaluation and controlling of the noise impact. In this contribution, the authors present a multilinear regressive model calibrated on simulated noise levels instead that on real measured ones, correlating percentile noise levels to independent traffic variables. The model efficiency is then evaluated on two field measurement datasets by analyzing data statistics and error metrics. Results show that the model provides good results in terms of mean error (less than 1 dBA on average) even if slight underestimations and overestimations are present. The presented model, then, can be used to assess the impact of road traffic noise anytime field measurements are not available, or even predict it when designing new road infrastructures.

Motorsport races significantly affect, on a local scale, noise pollution even if they do not represent the majority of its contribution, which is a prerogative of road transportation, railways, airports, and industries. Nevertheless, such noise emissions surely affect the well-being of inhabitants in the surrounding area of the circuit. In fact, during a motor race event, vehicles produce high noise emissions while on tests, qualifying, and race sessions. Since noise indicators commonly used in national regulations are computed over fixed times, it is challenging to properly assess the total noise emission and immission at the receivers during such events. Moreover, in literature, only a few works can be found assessing this specific issue, and consequently, there’s also a lack of appropriate methods to properly measure the global noise emission of each event. In this contribution, the authors report the characterization of noise emission during motor race events by using two new acoustic indicators, namely LEL (Lap Equivalent Level) and REL (Race Equivalent Level) starting from noise data collected on different points along a racing circuit. Measurements show that the REL tends to stabilize its value during a race, suggesting that its modelling can be achieved only based on the average LEL and the number of vehicles participating in a race. These indicators will allow predicting the total noise emission at a certain receiver of a motor race event by knowing the number and type of cars involved, without using the duration of the race itself.