Exposure to High-Frequency Sound and Ultrasound in Public Places: Examples from Zurich, Switzerland

Sensory unpleasantness of very-high frequency sound and audible ultrasound

Audible very-high frequency sound (VHFS) and ultrasound (US) have been rated more unpleasant than lower frequency sounds when presented to listeners at similar sensation levels (SLs). In this study, 17 participants rated the sensory unpleasantness of 14-, 16-, and 18-kHz tones and a 1-kHz reference tone. Tones were presented at equal subjective loudness levels for each individual, corresponding to levels of 10, 20, and 30 dB SL measured at 1 kHz. Participants were categorized as either "symptomatic" or "asymptomatic" based on self-reported previous symptoms that they attributed to exposure to VHFS/US. In both groups, subjective loudness increased more rapidly with sound pressure level for VHFS/US than for the 1-kHz reference tone, which is consistent with a reduced dynamic range at the higher frequencies. For loudness-matched tones, participants rated VHFS/US as more unpleasant than that for the 1-kHz reference. These results suggest that increased sensory unpleasantness and reduced dynamic range at high frequencies should be considered when designing or deploying equipment which emits VHFS/US that could be audible to exposed people.

Acoustic recording tags provide insight into the springtime acoustic behavior of sei whales in Massachusetts Bay

The acoustic ecology of sei whales (Balaenoptera borealis) is poorly understood due to limited direct observation of the behavioral context of sound production and individual behavior. Suction cup-attached acoustic recording tags were deployed on sei whales to unambiguously assign call types and explore the acoustic behavior of this endangered species. Twelve tag deployments resulted in ∼173 h of acoustic data and 1030 calls. Sound types included downsweeps and three previously undescribed call types. Knocks were short duration (<1 s), with an average peak frequency of 330 Hz. Pulse type 1 and pulse type 2 calls, typically produced in sequences, were short in duration (0.08 and 0.28 s) and low in average peak frequency (50 and 26 Hz), with relatively high received levels. Average call rates for all call types combined were three calls per hour, but increased during twilight. Sex differences in call type usage included a higher use of pulses by females and knocks by males. Calls were almost exclusively produced at depths <10 m, although whales rarely dove deeper in this study. These data provide a more comprehensive picture of the acoustic and behavioral ecology of sei whales than previously possible, which can inform future conservation efforts for this endangered species.

A non-destructive technique using digital holographic vibrometry and Lamb waves for quality determination of polymer-metal laminates

We used digital holographic vibrometry (DHV) as a non-destructive method to detect debonding areas in laminates made of aluminum and polymer (polylactide, polyvinylidene fluoride or polycarbonate). At low frequencies (up to 30 kHz) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A_0$$\end{document}A0 Lamb waves were excited and the amplitude and the phase patterns of the vibration of the sample were simultaneously registered for metal and polymer side of the laminate. Based on these patterns debonding areas in laminates were localized. The transmission properties at low frequencies were also studied in terms of: the frequency range for which regular Lamb waves have been observed, Lamb wave amplitudes and Lamb wave propagation velocity depending on the frequency. We have shown that these properties also change when a defect occures in the laminate. Even when we could not localize the defect it was still possible to detect if a sample was damaged based on the behaviour of the Lamb waves.

Mobile surveys and machine learning can improve urban noise mapping: Beyond A-weighted measurements of exposure

Urban noise pollution is a major environmental issue, second only to fine particulate matter in its impacts on physical and mental health. To identify who is affected and where to prioritise actions, noise maps derived from traffic flows and propagation algorithms are widely used. These may not reflect true levels of exposure because they fail to consider noise from all sources and may leave gaps where roads or traffic data are absent. We present an improved approach to overcome these limitations. Using walking surveys, we recorded 52,366 audio clips of 10 s each along 733 km of routes throughout the port city of Southampton. We extracted power levels in low (11 to 177 Hz), mid (177 Hz to 5.68 kHz), high (5.68 to 22.72 kHz) and A-weighted frequencies and then built machine-learning (ML) models to predict noise levels at 30 m resolution across the entire city, driven by urban form. Model performance (r²) ranged from 0.41 (low frequencies) to 0.61 (mid frequencies) with mean absolute errors of 4.05 to 4.75 dB. The main predictors of noise were related to modes of transport (road, air, rail and water) but for low frequencies, port activities were also important. When mapped to the city scale, A-weighted frequencies produced a similar spatial pattern to mid-frequencies, but did not capture the major sources of low frequency noise from the port or scattered hotspots of high frequencies. We question whether A-weighted noise mapping is adequate for health and wellbeing impact assessments. We conclude that mobile surveys combined with ML offer an alternative way to map noise from all sources and at fine resolution across entire cities that may more accurately reflect true exposures. Our approach is suitable for noise data gathered by citizen scientists, or from a network of sensors, as well as from structured surveys.