Analysis of Methods to Evaluate the Noise Reduction due to Acoustic Barriers Installation

Transportation infrastructures represent a relevant noise source in residential areas and have to be carefully taken into account in urban planning. Road traffic is commonly assumed to be the most relevant transportation mean in developed countries. For this reason, road traffic noise can be considered as the most important source of annoyance. The extremely random nature of road traffic makes very difficult to model the phenomenon and give reliable predictions. In the infrastructure design phase, a proper acoustic modelling can be helpful to minimize the noise impact. If the road is already present in the area, it is important to design effective mitigation actions. In this paper, the installation of noise barriers is simulated in a case study. This location, in south Italy, is characterized by several buildings placed in proximity of a motorway. In particular, a new building set has been built just in front of the motorway, without providing any noise mitigation action. In this paper, once the noise map of the area is obtained with a predictive software, the effects of the barriers, measured in terms of noise level reduction, are evaluated by means of literature, regulation and software approaches. The comparison between these approaches will be discussed and will show that, in order to obtain a reliable estimation of the noise reduction, diffraction, reflection and other relevant parameters cannot be neglected

Acoustical Noise Analysis and Prediction by means of Multiple Seasonality Time Series Model

Physical polluting agents are a relevant problem in urban areas. The need for monitoring and prediction of their time evolution is very useful to assess the impact to human health and activities. Considering their effects on health, the most hazardous agents to be considered are air pollution, acoustical noise and electromagnetic fields. Regarding acoustical noise, the complexity of predicting its slope is strongly correlated to its intrinsic randomness, related to the great variability of the sources. Sometimes, in some special areas, the predominant sources are stationary or have a periodic behaviour. In these cases, a time series analysis approach can be adopted, considering that a general trend and a local periodicity can be highlighted and used to build a predictive model. In particular, in this paper, the model is built composing three parts: the trend, that is the long term behaviour, the seasonality, that is the periodic component, and the irregularity, that includes the random variations. Applying such a model to a traffic noise levels dataset, obtained from a site in the city of Messina, Italy, a multiple seasonality is evidenced, resulting in two seasonal coefficients introduction (low frequency and high frequency). The validation of the presented model will be performed on a 44 days dataset, not used in the calibration. Results will be encouraging and will show a very good prediction performances of the model, especially in terms of difference between observed and simulated values (error). The error distributions will be analyzed and discussed by means of statistical indexes, plots and tests

Statistical and semi-dynamical road traffic noise models comparison with field measurements

The need for road traffic noise monitoring is growing in urban areas due to the growth of vehicles number and to the consequent increase of risk for human health. Noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption. For this reasons, Road Traffic Noise predictive Models (RTNMs) can be implemented to estimate the noise levels at any distance, knowing certain parameters needed as input of the RTNM. In this paper, the main statistical RTNMs are presented, together with the implementation of two innovative and advanced models: the EU suggested model (CNOSSOS-EU) and a research model presented by Quartieri et al. (2010). These models will be compared with noise measurements performed in different sites and with different traffic conditions, in order to avoid bias from geometry or other features of the area under study. The main conclusion is that the application of innovative models and the inclusion of dynamical information about traffic flow, will lead to better results with respect to statistical models

Modelling and Simulation in Environmental Data Analysis

Environmental data analysis is one of the most important issue for assessing the impact of polluting agents in areas in which human activities have altered the standard natural development and evolution of air, water and ground. In particular, physical and chemical polluting agents need to be monitored and controlled, in order to reduce the dangers to human health due both to long term and to short term, but high level, exposures. In this paper, the attention will be mainly focused on the assessment and prediction of acoustical noise, and partially to air pollution data analysis. A review of some of the literature methods will be presented and two recent approaches will be sketched in the central section. The merging and intersection of more than one pollutant analysis will be finally discussed. In the author’s opinion, these techniques should be encouraged and represent the future perspectives of environmental data analysis. In fact, the possibility of performing a complex field measurement campaign, able to record more than one pollutant data, can help in building reliable models, based on advanced mathematical and physical methods

Wind Farm Noise Maps in a Country Side Area

The need for alternative and sustainable energy sources is of high importance nowadays, due to the growth in energy demand and, subsequently, the rise of pollution issues. One of the most relevant source of sustainable energy is the wind. Even if wind turbines represent a clean power source, the acoustical and visual impacts have to be taken into account. The installation of wind turbine, in fact, can lead to annoyance related to the noise emissions, even in rural areas. In this paper, the monitoring of noise emissions by a wind farm is performed with the aid of a predictive software. Different noise emission scenarios are sketched, related to the wind condition. The introduction of directivity in the source represents a novelty that can improve the prediction of noise maps, with respect to the pointlike source approximatio

On the Improvement of Statistical Traffic Noise Prediction Tools

In this paper the authors propose a new expression to perform a traffic noise prediction by the collection of the acoustical energy sent to the receiver. The equivalent level, in fact, depends on the acoustical energy emitted by each passing vehicle, which can be directly related to its speed by some experimental relations. The main advantage of this procedure is that no measurements in situ are required in order to estimate the global noise level, with a consequent gain in easiness and time respect to other expression present in literature

A Hybrid Predictive Model for Acoustic Noise in Urban Areas Based on Time Series Analysis and Artificial Neural Network

The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to “learn” the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN on the residuals. This hybrid model will exploit interesting features and results, with a significant variation related to the number of steps forward in the prediction. It will be shown that the best results, in terms of prediction, are achieved predicting one step ahead in the future. Anyway, a 7 days prediction can be performed, with a slightly greater error, but offering a larger range of prediction, with respect to the single day ahead predictive model

Noise Fundamental Diagram deduced by Traffic Dynamics

Road traffic noise is a relevant issue in environmental impact studies. Many models have been developed to predict vehicles behaviour on one side, and noise produced by traffic on the other side. In this paper the authors propose the integration between statistical Traffic Noise predictive Models (TNMs) and traffic flow dynamics models. In particular, the starting point is a parametric formula that relates the hourly equivalent level to vehicle flow and speed. Thus, the speed-flow-density relationship is obtained with a "car-following" model approach and integrated in the noise prediction formula. The final result of this procedure is the dependence of the equivalent level from vehicle density and speed, in three different dynamics postulates assumed in the "car-following" model theory. This is the first step to the postulation of the Traffic Noise Fundamental Diagram, in analogy to the Fundamental Diagram, i.e. the set of graphs that exploits the speed-flow-density relationship