Annual fluctuation of atmospheric absorption of sound in various world regions

Okada, Yasuaki; Yoshihisa, Koichi; Tatsuda, Kenji

doi:10.1250/ast.37.66

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…We found that atmospheric pressure, humidity, and wind speed contributed to predicting traffic noise. Previous studies found that air temperature, relative humidity, and atmospheric pressure can affect the attenuation of sound by atmospheric absorption . In fact, noise attenuation is controlled by a complex nonlinear relationship between these factors.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies found that air temperature, relative humidity, and atmospheric pressure can affect the attenuation of sound by atmospheric absorption. 53 In fact, noise attenuation is controlled by a complex nonlinear relationship between these factors. Increasing atmospheric pressure causes a reduction in sound attenuation 54 and when relative humidity decreased from 50 to 20%, noise decreased to 1.0 dB.…”

Section: ■ Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

Yin

Fallah-Shorshani

McConnell

et al. 2020

Environ. Sci. Technol.

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Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which produces fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, California and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network, to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorological conditions, and land use type. Among all machine learning models, extreme gradient boosting had the best results in validation tests (leave-one-route-out R 2 = 0.71, root mean square error (RMSE) of 4.54 dB; 5-fold R 2 = 0.96, RMSE of 1.8 dB). Local traffic volume was the most important predictor of noise; road features, land use, and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in traffic-related noise over a mixed-use urban area.

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Section: Discussionmentioning

confidence: 99%

Section: ■ Discussionmentioning

confidence: 99%

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

Yin

Fallah-Shorshani

McConnell

et al. 2020

Environ. Sci. Technol.

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“…Andreas (1988) showed that the Arctic climate has very complex impacts on the optical index of refraction; however, impacts on acoustic index of refraction and attenuation have not been closely examined. Some insight into the behavior of acoustic attenuation in very cold air is provided by studies in northern temperate climates (Wilson and Thomson 1991;Okada et al 2016), which found that attenuation during the winter is substantially higher and varies dramatically with specific humidity.…”

Section: Wide-angle Parabolic Equationmentioning

confidence: 99%

Infrasound propagation in the Arctic

Wilson¹,

Ostashev²,

Shaw³

et al. 2021

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This report summarizes results of the basic research project “Infrasound Propagation in the Arctic.” The scientific objective of this project was to provide a baseline understanding of the characteristic horizontal propagation distances, frequency dependencies, and conditions leading to enhanced propagation of infrasound in the Arctic region. The approach emphasized theory and numerical modeling as an initial step toward improving understanding of the basic phenomenology, and thus lay the foundation for productive experiments in the future. The modeling approach combined mesoscale numerical weather forecasts from the Polar Weather Research and Forecasting model with advanced acoustic propagation calculations. The project produced significant advances with regard to parabolic equation modeling of sound propagation in a windy atmosphere. For the polar low, interesting interactions with the stratosphere were found, which could possibly be used to provide early warning of strong stratospheric warming events (i.e., the polar vortex). The katabatic wind resulted in a very strong low-level duct, which, when combined with a highly reflective icy ground surface, leads to efficient long-distance propagation. This information is useful in devising strategies for positioning sensors to monitor environmental phenomena and human activities.

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“…Similarly, low-level wind jets, which tend to occur above such temperature inversions, and katabatic winds common in parts of the Arctic (Bromwich, 1989;Bromwich et al, 2001;Jakobson et al, 2013) could create stronger wind shear and directionally dependent ducting than normally present at temperate latitudes. Acoustic absorption coefficients can furthermore increase and vary dramatically at very low temperature and specific humidity (Wilson and Thomson, 1991;Okada et al, 2016). Prevailing circumpolar wind patterns in the stratosphere and their intermittent reversals can cause directional asymmetries in longrange infrasound propagation (Rind and Donn, 1978;Evers and Siegmund, 2009).…”

Section: Introductionmentioning

confidence: 99%

Numerical modeling of mesoscale infrasound propagation in the Arctic

Wilson¹,

Shaw²,

Ostashev³

et al. 2022

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The impacts of characteristic weather events and seasonal patterns on infrasound propagation in the Arctic region are simulated numerically. The methodology utilizes wide-angle parabolic equation methods for a windy atmosphere with inputs provided by radiosonde observations and a high-resolution reanalysis of Arctic weather. The calculations involve horizontal distances up to 200 km for which interactions with the troposphere and lower stratosphere dominate. Among the events examined are two sudden stratospheric warmings, which are found to weaken upward refraction by temperature gradients while creating strongly asymmetric refraction from disturbances to the circumpolar winds. Also examined are polar low events, which are found to enhance negative temperature gradients in the troposphere and thus lead to strong upward refraction. Smaller-scale and topographically driven phenomena, such as low-level jets, katabatic winds, and surface-based temperature inversions, are found to create frequent surface-based ducting out to 100 km. The simulations suggest that horizontal variations in the atmospheric profiles, in response to changing topography and surface property transitions, such as ice boundaries, play an important role in the propagation.

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Annual fluctuation of atmospheric absorption of sound in various world regions

Cited by 8 publications

References 6 publications

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning

Infrasound propagation in the Arctic

Numerical modeling of mesoscale infrasound propagation in the Arctic

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