Decision trees and labeling of low noise pavements as support for noise action plans.

Low noise pavements (LNPs) are a market driven trend to mitigate the high road traffic noise exposure levels. Their improvement towards acoustic efficiency and durability over time is a challenge since these factors can conflict with road primary functions, such as safety. LNPs are not always the most cost-effective solution in health effects prevention. Whilst Green Public Procurement (GPP) highlighted the importance of reducing rolling noise emissions by introducing new regulations for new-layed LNPs, the fixed minimum requirements are not exhaustive. Generally, limits are set following the Close ProXimity method, which is only source oriented. This method does not consider real traffic flows and it is not aimed at evaluating citizens' disturbance. This work presents strategy tools that could assist policymakers in choosing LNPs, when truly effective, over other mitigations. The approach includes a variety of indicators that would allow for comparing different facets of noise assessment. The proposed methodology does not require additional efforts from stakeholders because the measurements required for the estimation of the indicators must already be carried out for both verification of legal limits and GPP. The strategy tools are a decisional tree to support the evaluation of the applicability of a LNP before its approval, and an evaluation flowchart applicable after its laying to evaluate its efficiency. Finally, a first LNP labeling approach, based on the same set of indicators, is proposed. As a case study, these tools are applied to measurements performed before and after the laying of twelve LNPs part of the LIFE NEREiDE project.

Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment.

Low-noise surfaces have become a common mitigation action in the last decade, so much so that different methods for feature extraction have been established to evaluate their efficacy. Among these, the Close Proximity Index (CPX) evaluates the noise emissions by means of multiple runs at different speeds performed with a vehicle equipped with a reference tire and with acoustic sensors close to the wheel. However, signals acquired with CPX make it source oriented, and the analysis does not consider the real traffic flow of the studied site for a receiver-oriented approach. These aspects are remedied by Statistical Pass-By (SPB), a method based on sensor feature extraction with live detection of events; noise and speed acquisitions are performed at the roadside in real case scenarios. Unfortunately, the specific SPB requirements for its measurement setup do not allow an evaluation in urban context unless a special setup is used, but this may alter the acoustical context in which the measurement was performed. The present paper illustrates the testing and validation of a method named Urban Pass-By (U-SPB), developed during the LIFE NEREiDE project. U-SPB originates from standard SPB, exploits unattended measurements and develops an in-lab feature detection and extraction procedure. The U-SPB extends the evaluation in terms of before/after data comparison of the efficiency of low-noise laying in an urban context while combining the estimation of long-term noise levels and traffic parameters for other environmental noise purposes, such as noise mapping and action planning.

Annoyance Judgment and Measurements of Environmental Noise: A Focus on Italian Secondary Schools.

The effects of noise on students' health, well-being, and learning are of growing concern among both the general public and policy-makers in Europe. Several studies have highlighted the consequences of noise on children's learning and performance at school. This study investigates the relationship between noise judgment in school goers aged 11-18 and noise measurements aimed at evaluating their exposure at school. For this purpose, a questionnaire was administered to 521 individuals in 28 classrooms in eight schools of four cities in Italy, with different environmental characteristics. Using a Likert-type scale, a selected set of responses related to noise generated an Annoyance Index (AI) score for each student and a classroom median score (MAI). From the noise data acquired, a global noise score (GNS) was assigned to each classroom. A higher AI was found in industrialized areas and among younger students. No significant differences in noise judgment were found by gender. A significant inverse correlation was described between MAI and GNS, thus the better the acoustic quality of the classrooms, the less the perceived noise and annoyance. The results show that noise perception and consequent disturbance are highly correlated with classroom acoustics, and confirm that annoyance represents the most widespread subjective response to noise.

Low frequency noise impact from road traffic according to different noise prediction methods.

The European Noise Directive 2002/49/EC requires to draw up noise action plans. Most of the implemented solutions consist in using barriers, even if some studies evidenced that annoyance could increase after their installation. This action dumps the high frequencies, decreasing the masking effect on low ones. Therefore, people annoyance and complaints may increase despite the mitigation. This can happen even in pedestrian zones near main roads due to the screening effect of first buildings row. In this paper, the authors analyze the post-operam screening effects in terms of low frequency noise. The difference between C- and A-weighted levels is calculated as annoyance indicator (LC-A). Different methods able to map noise with octave bands detail are tested in order to establish differences in the estimates of annoyance exposure. In particular, a comparison is carried out between data from interim method NMPB 96, its updated version 2008, NORD 2000 and those provided by a customized procedure through ISO 9613 propagation and Statistical Pass By measurements. Test sites are simulated in order to validate each model results through measurements. Results are discussed for real locations in Pisa city center and virtual scenarios in a rising scale of complexity.

Gden: An indicator for European noise maps comparison and to support action plans.

Ten years after the approval of the Environmental Noise Directive 2002/49/EC (END) a large experience have been acquired to develop noise maps and action plans: the Noise Observation and Information Service for Europe maintained by the European Environment Agency (EEA) on behalf of the European Commission contains all data delivered in accordance with the END by Members States within the first round of implementation of the END. This large database should be useful to evaluate the pollution of Europe and to guide policy makers to establish best practices. However, local procedures and national methods do not permit a direct comparison of data reported. A comparison within agglomerations in EU is here carried out in order to find suitable indicators to identify most polluted cities despite different methods used. Critical and quiet areas have been assessed in action plans, but national laws and requirements are various, as different indicators used for their identification. The analysis was performed on noise exposure classes distribution, grouping them together using Gden and Gnight indicators to offer a new tool for presenting noise maps of the cities to the public permitting their comparison and for drawing detailed action plans. Strong relationship between these indicators and highly annoyed and highly sleep-disturbed people percentages are obtained. Furthermore, a comparison between Gden and Qcity Noise Scoring for local hot spot identification is carried out for the agglomeration of Pisa, where different transportation noise sources are present. The final goal is to define faster methods for suitable indicators calculation in hot spot identifications.