Abstract
Dynamap, a co-financed project by the European Commission through the Life+ 2013 program, aims at developing a dynamic approach for noise mapping that is capable of updating environmental noise levels through a direct link with a limited number of noise monitoring terminals. Dynamap is based on the idea of finding a suitable set of roads that display similar traffic noise behavior (temporal noise profile over an entire day) so that one can group them together into a single noise map. Each map thus represents a group of road stretches whose traffic noise will be updated periodically, typically every five minutes during daily hours and every hour during night. The information regarding traffic noise will be taken continuously from a small number of monitoring stations (typically 24) appropriately distributed over the urban zone of interest. To achieve this goal, we have performed a detailed analysis of traffic noise data, recorded every second from 93 monitoring stations randomly distributed over the entire urban area of the City of Milan. Our results are presented for a restricted area, the urban Zone 9 of Milan. We have separated the entire set of (about 2000) stretches into six groups, each one represented by a noise map, and gave a prescription for the locations of the future 24 monitoring stations. From our analysis, it is estimated that the mean overall error for each group of stretches (noise map), averaged over the 24 h, is about 2 dB.
Highlights
In the last decade, distributed acoustic monitoring systems started to appear in urban contexts, due to lowering costs of electronic components and to cheaper and smaller hardware for data transfer.Triggered by the European Directive 2002/49/EC and connected to the assessment and management of environmental noise (END) [1], this interest has grown due to the awareness that noise maps represent a powerful tool for determining the population exposure to environmental noise
We have identified four processes whose fluctuations contribute to the total statistical error, σ2T, yielding the result σ2T = σ2pred + σ2stat + σ2comp + σ2sample corresponding to the intrinsic prediction error of the method, σ2pred, the statistical variance of the equivalent noise levels measured by the monitoring stations, σ2stat, the different cluster compositions for different time intervals, σ2comp, and the variance due to stratified sampling, σ2sample
We have described a method for predicting traffic noise in large urban environments
Summary
In the last decade, distributed acoustic monitoring systems started to appear in urban contexts, due to lowering costs of electronic components and to cheaper and smaller hardware for data transfer.Triggered by the European Directive 2002/49/EC and connected to the assessment and management of environmental noise (END) [1], this interest has grown due to the awareness that noise maps represent a powerful tool for determining the population exposure to environmental noise. Noise pollution continues to be a major health problem in Europe, with a host of health effects that can be summarized as follows: annoyance [3], sleep disorders with awakenings [4], learning impairment [5,6,7], and hypertension ischemic heart disease [8,9,10]. In this context, the END’s prescription for noise maps and action plans, Appl.
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