Road traffic noise in Montreal and environmental equity: What is the situation for the most vulnerable population groups?

Abstract

Road traffic noise is one of the most detrimental environmental nuisances for the population. Prolonged exposure to high road noise levels can lead to various problems in people's health and well-being. The objective of this article is to determine whether the groups most vulnerable to road noise, that is, children under 15 years old, people 65 years old and over, and the groups most likely to experience high nuisance levels, visible minorities and low-income individuals, are affected by an environmental inequity related to this nuisance. The method of estimating this nuisance employed in the study is based on a combination of several elements: that is, average traffic flows, road geometries, normal atmospheric conditions, and the characteristics of the urban environment. All of these parameters were considered for the 14 boroughs that make up the central portion of the Island of Montreal. Modelling was used to calculate the maximum daily road noise, based in part on the LimA software predictive model and according to the XPS 31-133 computation method. The results obtained from three different statistical tests and spatial regression analyses show that, on the one hand, the groups chosen on the basis of age are not affected by any environmental inequity. On the other hand, low-income individuals and visible minorities live in city blocks marked by road traffic noise levels that are slightly higher than those experienced by the rest of the population.

Exploring nighttime road traffic noise: A comprehensive predictive surface for Toronto, Canada

“…It can be used to assign noise level estimates to populations (e.g., linking based on postal code or residential address), enabling research that characterizes the association between noise and adverse health effects as well as the identification of environmental inequities. [34] In addition, the predictive surface can identify spatial and temporal patterns, highlight excessive noise hotspots, and permit the specific examination of noise sensitive areas (e.g., hospitals), informing city-wide efforts (e.g., policy development and urban planning) to reduce or prevent exposure to harmful levels of noise. Furthermore, given the frequent co-occurrence of noise and air pollution, noise level estimates derived from the predictive surface may have some utility as a proxy for air pollution exposure [35] or could be used to address the role of noise as a potential confounding variable in air pollution studies.…”

Application of a Global Environmental Equity Index in Montreal: Diagnostic and Further Implications

“…The dependent variables in this model refer to each of the indicators of the urban environment and to their cumulative effect, whereas the independent variables refer to the proportions of each of the four population groups studied. Spatial regression models are often used in environmental equity studies, especially in regard to air pollution (Carrier et al 2014;Chakraborty 2009), road traffic noise (Carrier, Apparicio, and Séguin 2016;Nega et al 2013), and vegetation Pham et al 2012). The spatial regression analyses were computed in R by using the spdep library (Bivand 2013).…”

L'identification des zones calmes et un diagnostic d’équité environnementale à Montréal

“…Par conséquent, ils sont susceptibles de s'installer dans des zones géographiques moins attractives, plus polluées et bruyantes, où la qualité du milieu bâti est moindre (Braubach et Fairburn ; Cesaroni et al ). De fait, plusieurs études ont démontré qu'ils subissent des iniquités en termes d'exposition au bruit, notamment à Birmingham (Brainard et al ), à Hongkong (Lam et al ), à Minneapolis (Nega et al ) ou encore à Montréal (Carrier, Apparicio et Séguin ). Toutefois, il est important de noter que cette observation n'est pas valable dans tous les contextes géographiques.…”