Authors: Erica D. Walkera, Jaime E. Harta, Petros Koutrakisa, Jennifer M. Cavallaria, Trang VoPhamb, Marcos Lunad, Francine Ladena
- An examination of a sound’s frequency is necessary to fully capture the environmental soundscape.
- Spatial-temporal models were built to characterize sound frequency using an elastic net approach.
- The model of a sound’s loudness using A-weighting was similar to previously constructed models.
- Frequency-specific models included variables not traditionally captured when only describing loudness.
- The elastic net approach may be a promising tool to be used in constructing sound models.
Urban sound levels are a ubiquitous environmental stressor and have been shown to be associated with a wide variety of health outcomes. While much is known about the predictors of A-weighted sound pressure levels in the urban environment, far less is known about other frequencies.
To develop a series of spatial-temporal sound models to predict A-weighted sound pressure levels, low, mid, and high frequency sound for Boston, Massachusetts.
Short-term sound levels were gathered at n = 400 sites from February 2015 – February 2016. Spatial and meteorological attributes at or near the sound monitoring site were obtained using publicly available data and a portable weather station. An elastic net variable selection technique was used to select predictors of A-weighted, low, mid, and high frequency sound.
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.