2. Universidad Cardenal Herrera-CEU

Accurate predictions of environmental noise levels are necessary to implement noise reduction strategies in urban areas. In this paper, a stochastic model is introduced to describe and predict the L(den), L(day), L(evening), and L(night) levels. A Gaussian Ornstein-Uhlenbeck model is used to represent the dynamics of the noise levels, where the mean-reversion properties and seasonal volatility for each day of the week are studied separately.

This paper summarises 5 years of continuous noise measurements carried out at one of the most important squares in Valencia (Spain). The chosen square is a clear hotspot for traffic noise in a large city. The aim of this study is to determine the appropriate measuring time in order to obtain a 24-h noise level suitable to represent the annual equivalent level. Our findings allow us to reach a number of conclusions in terms of the most suitable urban traffic noise measurement techniques. A random day strategy for sampling is found to give a more accurate representation than a consecutive days strategy. If the sampling strategy involves measurements on randomly-chosen days, then at least 6 days should be used.

This work summarizes several years of measurements of the main noise sources present in large populations, specially the noise produced by the traffic in the city of Valencia. The study includes one-week monitoring in the period between the years 2012 and 2014 in three different types of scenarios (heavy, medium and low traffic). The aim of this work is firstly to determine the most representative measurement day under several approaches related to the type of day (working day, non-working day or full week), and to the different periods throughout the day (night, day and evening). This permits to reach conclusions about the optimization of measurement techniques of the overall urban traffic noise, and will facilitate the measurement tasks in specific locations of the city of Valencia, characterized by specific traffic conditions.