Daniel J. Mennitt

A geospatial model of ambient sound pressure levels in the contiguous United States.

This paper presents a model that predicts measured sound pressure levels using geospatial features such as topography, climate, hydrology, and anthropogenic activity. The model utilizes random forest, a tree-based machine learning algorithm, which does not incorporate a priori knowledge of source characteristics or propagation mechanics. The response data encompasses 270 000 h of acoustical measurements from 190 sites located in National Parks across the contiguous United States. The explanatory variables were derived from national geospatial data layers and cross validation procedures were used to evaluate model performance and identify variables with predictive power. Using the model, the effects of individual explanatory variables on sound pressure level were isolated and quantified to reveal systematic trends across environmental gradients. Model performance varies by the acoustical metric of interest; the seasonal L50 can be predicted with a median absolute deviation of approximately 3 dB. The primary application for this model is to generalize point measurements to maps expressing spatial variation in ambient sound levels. An example of this mapping capability is presented for Zion National Park and Cedar Breaks National Monument in southwestern Utah.

Multiple-array passive acoustic source localization in urban environments

In many situations of interest, obstacles to acoustic wave propagation such as terrain or buildings exist that provide unique challenges to localization. These obstacles introduce multiple propagation paths, reflections, and diffraction into the propagation. In this paper, matched field processing is proposed as an effective method of acoustic localization in a two dimensional scattering environment. Numerical techniques can be used to model complex propagation in a space where analytical solutions are not feasible. Realistically, there is always some uncertainty in model parameters that in turn can adversely affect localization ability. In particular, uncertainty in array location, sound speed, and various parameters affecting inter-array coherence only are investigated. A spatially distributed, multiarray network is shown to mitigate the effects of uncertainty. Multiarray inverse filter processing techniques are evaluated through perturbation of uncertain model parameters. These techniques are more accurate and flexible to implement than other matched field processing methods such as time reversal.

Race/ethnicity, socioeconomic status, residential segregation, and spatial variation in noise exposure in the contiguous United States

Background: Prior research has reported disparities in environmental exposures in the United States, but, to our knowledge, no nationwide studies have assessed inequality in noise pollution. Objectives: We aimed to a) assess racial/ethnic and socioeconomic inequalities in noise pollution in the contiguous United States; and b) consider the modifying role of metropolitan level racial residential segregation. Methods: We used a geospatial sound model to estimate census block group–level median (L50) nighttime and daytime noise exposure and 90th percentile (L10) daytime noise exposure. Block group variables from the 2006–2010 American Community Survey (ACS) included race/ethnicity, education, income, poverty, unemployment, homeownership, and linguistic isolation. We estimated associations using polynomial terms in spatial error models adjusted for total population and population density. We also evaluated the relationship between race/ethnicity and noise, stratified by levels of metropolitan area racial residential segregation, classified using a multigroup dissimilarity index. Results: Generally, estimated nighttime and daytime noise levels were higher for census block groups with higher proportions of nonwhite and lower-socioeconomic status (SES) residents. For example, estimated nighttime noise levels in urban block groups with 75% vs. 0% black residents were 46.3 A-weighted decibels (dBA) [interquartile range (IQR): 44.3–47.8 dBA] and 42.3 dBA (IQR: 40.4–45.5 dBA), respectively. In urban block groups with 50% vs. 0% of residents living below poverty, estimated nighttime noise levels were 46.9 dBA (IQR: 44.7–48.5 dBA) and 44.0 dBA (IQR: 42.2–45.5 dBA), respectively. Block groups with the highest metropolitan area segregation had the highest estimated noise exposures, regardless of racial composition. Results were generally consistent between urban and suburban/rural census block groups, and for daytime and nighttime noise and robust to different spatial weight and neighbor definitions. Conclusions: We found evidence of racial/ethnic and socioeconomic differences in model-based estimates of noise exposure throughout the United States. Additional research is needed to determine if differences in noise exposure may contribute to health disparities in the United States. https://doi.org/10.1289/EHP898

Influence factors and spatiotemporal patterns of environmental sound levels in the contiguous United States

Environmental sound levels represent the cumulative contributions of many types - and possibly an uncountable number - of sound sources. This recommends a statistical approach to modeling. Using 1.5 million hours of acoustical data from hundreds of sites, regression models were built to predict sound levels across the contiguous United States. These models discern often nonlinear and interacting relationships between measured sound levels and nonacoustic environmental summaries extracted from nationwide geospatial data layers. Tens of potential explanatory factors were examined including climate, topography, human activity, and time of day and year. Mapping sound levels at landscape scales and diagnostic tools, like partial dependence functions, can reveal the effects of influential factors on measured sound levels. These results illustrate the foundations of many spatiotemporal patterns, provide tools for understanding current acoustical conditions and demonstrate the potential consequences of shifts in environmental conditions.

Acoustic environments matter: Synergistic benefits to humans and ecological communities

Protected areas are critical locations worldwide for biodiversity preservation and offer important opportunities for increasingly urbanized humans to experience nature. However, biodiversity preservation and visitor access are often at odds and creative solutions are needed to safeguard protected area natural resources in the face of high visitor use. Managing human impacts to natural soundscapes could serve as a powerful tool for resolving these conflicting objectives. Here, we review emerging research that demonstrates that the acoustic environment is critical to wildlife and that sounds shape the quality of nature-based experiences for humans. Human-made noise is known to affect animal behavior, distributions and reproductive success, and the organization of ecological communities. Additionally, new research suggests that interactions with nature, including natural sounds, confer benefits to human welfare termed psychological ecosystem services. In areas influenced by noise, elevated human-made noise not only limits the variety and abundance of organisms accessible to outdoor recreationists, but also impairs their capacity to perceive the wildlife that remains. Thus soundscape changes can degrade, and potentially limit the benefits derived from experiences with nature via indirect and direct mechanisms. We discuss the effects of noise on wildlife and visitors through the concept of listening area and demonstrate how the perceptual worlds of both birds and humans are reduced by noise. Finally, we discuss how management of soundscapes in protected areas may be an innovative solution to safeguarding both and recommend several key questions and research directions to stimulate new research.

A spatially explicit estimate of environmental noise exposure in the contiguous United States

Environmental noise is widespread across the United States, the spatial patterns of which are dependent on a complex linkage of environmental and socioeconomic factors. Chronic exposure brings with it adverse consequences to terrestrial organisms; effects on human health and wellbeing include hypertension, cardiovascular disease, sleep disturbance, cognitive impairment, and annoyance. Assessments of noise exposure are essential to understand the extent of impact as well as inform land use planning and noise abatement strategies. Using extensive empirical data and a geospatial framework, we modeled the day-night average sound level (Ldn). The dominant factors driving sound levels are land use, climate, population, and proximity to traffic corridors. Model predictions were mapped to reveal the spatial distribution of expected sound pressure levels across the contiguous United States at a resolution of 270 m. The expected Ldn was compared with localized population density to estimate the number of inhabitant...

Spatial variation of natural ambient sound pressure levels in Rocky Mountain National Park.

The soundscape is a critical component of an ecological community, and knowledge of natural ambient sound pressure levels is crucial to assessing impacts of noise. However, the spatial correlation of natural ambient sound pressure levels is largely unknown and a given measurement may not be representative of the locale. Much anthropogenic noise can be considered point or line sources; the spatial correlation of the resulting sound pressure levels can be completely described by directivity and the inverse square law in an isotropic medium. However, most natural sources (wind, birdsong, rain, insects, etc.) may be better described in aggregate as an irregular source with stochastic characteristics. Recently, a 9‐day long study collected continuous audio data at 18 locations over a roughly 3.5 s km1 area in Rocky Mountain National Park. Analysis seeks to investigate the spatial variation of natural ambient sound pressure level measurements and the degree to which they are correlated across space. Results wil...

Gain and directivity of exponential horn receivers

The self-noise of acoustical sensors limits their capacity to monitor extremely quiet environments and measure the subtle, adventitious cues that animals routinely rely upon. Although primarily used in sound production, horns also can amplify sound prior to transduction by a microphone. Given the small size of microelectromechanical microphones, substantial gain can be achieved at wavelengths larger than the horn. An analytical model of an exponential horn embedded in a rigid spherical housing was formulated to describe the gain relative to a free-field receiver as a function of frequency and angle of arrival. Through comparison with experiment and numerical models, the directivity of the horn receiver is shown to be adequately described by the analytical model up to a critical wavelength, beyond which physical assumptions are violated to some degree. Numerical models, based on the equivalent source method, describe the acoustic scattering within and around the horn and provide a means for identifying the...

Benefits and challenges of using consumer audio equipment for unattended acoustical monitoring

The quality of an acoustical measurement impacts the accuracy of all inferences that rely on the resulting data. While standard sound level meters are well suited for noise studies requiring high precision, their cost, power consumption, and capabilities constrain the scope of application. Alternatively, the wide variety of consumer audio equipment offers many options for acoustical monitoring. The ability to make high resolution, multichannel audio recordings with packages that are relatively small, inexpensive, and low power is especially attractive for long-term acoustical monitoring in remote areas and large-scale spatial surveys that require many devices. These recordings are more valuable when they are calibrated and processed to yield sound level data. Despite the promise of consumer audio equipment, there are several drawbacks and unknowns. Within the framework of the signal chain, this talk will discuss some of the benefits, challenges, and unknowns of using consumer audio equipment for unattende...

Characterization of gain and directivity of exponential horn receiversa)

It is difficult and expensive to match the sensitivity of the most sensitive vertebrate ears with off-the-shelf microphones due to the self-noise of the sensor. The extremely small apertures of microelectromechanical microphones create options to use horn waveguides to amplify sound prior to transduction without resulting in an unacceptably narrow directivity. Substantial gain can be achieved at wavelengths larger than the horn. An analytical model of an exponential horn embedded in a rigid spherical housing was formulated to describe the gain relative to a free-field receiver as a function of frequency and angle of arrival. For waves incident on-axis, the analytical model provided an accurate estimate of gain at high frequencies as validated by experimental measurement. Numerical models, using the equivalent source method, can account for higher order modes and comprehensively describe the acoustic scattering within and around the horn for waves arriving from any direction. Results show the directivity of horn receivers were adequately described by the analytical model up to a critical wavelength, and the mechanisms of deviation in gain at high frequencies and large angles of arrival were identified.