Source:Environmental Research 

Authors: Erica D. Walkera, Jaime E. Harta, Petros Koutrakisa, Jennifer M. Cavallaria, Trang VoPhamb, Marcos Lunad, Francine Ladena

Introduction

Urban environments worldwide play host to a wide variety of sounds and for many urban dwellers, the environmental soundscape has traditionally been viewed as the sacrifice they must accept in exchange for the convenience associated with living in close proximity to urban activities (Anthrop, 1970). In recent years, however, epidemiological studies have begun to shed light on the extent this sacrifice may have on human health. Exposure to urban environmental noise has been shown to be associated with a wide range of stress and cardiovascular related responses, such as elevated cortisol (Selander et al., 2009); blood pressure (Haralabidis et al., 2008); hypertension (Bluhm et al., 2007; Bodin et al., 2009 ;  Babisch et al., 2005); myocardial infarction (Babisch et al., 2005 ;  Selander et al., 2009); antihypertensive, anxiolytic, and antacid medication use (Floud et al., 2011); cardiovascular related hospital admissions (Hansell et al., 2013; Correia et al., 2013), and mortality (Hansell et al., 2013).

In these epidemiological studies, primarily based in Europe, sound exposure has traditionally been focused on sounds emanating from transportation sources such as road, rail, and aircraft traffic. Sound exposure levels have typically been ascertained using sound maps derived from sophisticated prediction models developed by municipal authorities or federal agencies. As data inputs needed for sound models becomes more freely available many researchers are utilizing less computationally and financially costly—yet powerful—methods to construct models to predict sound levels. (Gulliver et al., 2015; Torija and Ruiz, 2015; Aguilera et al., 2015; Goudreau et al., 2014; Zuo et al., 2014; Xie et al., 2011 ;  Seto et al., 2007). Such methods show promise as they tend to correlate strongly with gold-standard propagation models, allow for variable inputs unique to a given geographical area, allow for consideration of community noise overall and not just sounds from transportation sources, and can be built to match as closely as possible to models of common confounders such as air pollution.

The typical sound exposures modeled are metrics focused on a sound’s loudness, typically the A-weighted decibel (dB(A)). The A-weighting system emphasizes the frequency range where the human ear is most sensitive, while discounting both higher and lower frequencies (Alves-Pereira and Castelo Branco, 2007). However, a sound’s frequency is the second component necessary to fully characterize sound. Existing epidemiological and occupational research suggests that in addition to a sound’s loudness, its frequency profile is also an important characteristic to consider when assessing the relationship between noise and health outcomes (Persson Waye et al., 2003; Kumar et al., 2008; Chang et al., 2014; Walker et al., 2016).

The goal of our study was to develop spatial-temporal models across the sound spectrum for the City of Boston that may be potentially used in future epidemiologic studies. Specifically, our goal was to collect representative measurements of low, mid, and high frequency noise and A-weighted sound throughout Boston; build models to predict low frequency, mid frequency, and high frequency sound and A-weighted sound throughout the city; and compare each of the frequency-specific models to the models for the commonly used A-weighted sound.