Temporal and spatial variations in road traffic noise for different frequency components in metropolitan Taichung, Taiwan

Wang, Ven-Shing; Lo, Ei-Wen; Liang, Chih-Hsiang; Chao, Keh-Ping; Bao, Bo-Ying; Chang, Ta-Yuan

doi:10.1016/j.envpol.2016.10.055

Cited by 44 publications

(28 citation statements)

References 26 publications

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“…Temporal variation represented by hours, days and seasons were found very influential for the hourly model, H. This finding is different from that of the of Wang et al [25]. The dataset in this study may be the reason, which was temporally rich to capture the variation of the noise level.…”

Section: Discussioncontrasting

confidence: 87%

“…The dataset in this study may be the reason, which was temporally rich to capture the variation of the noise level. In addition, meteorological variables, which were not found correlated by [25] were also found, correlated in this study (e.g. wind and rain).…”

Section: Discussioncontrasting

confidence: 64%

“…Various forms of noise models were available in the literature, such as noise source modelling and sound propagation algorithms [15], Artificial Neural Networks [24], regression models to explain the variation in the measured data or previously mapped noise data [25,26], etc. Researchers from China were pioneer in applying LUR to model urban noise mapping and predict the effects of future planning decisions on noise levels [17].…”

Section: Lur As a Tool For Modellingmentioning

confidence: 99%

“…road traffic, rail traffic, industry, etc.) which can be captured by LUR based mod- [25] recently analysed road traffic noise sources using a regression analysis and found traffic and landuse are predominantly related, however no seasonal effect and meteorological variables except temperature were found to correlate with noise data. A repeated measurement of three times in a year may be too small to capture the temporal variation and impact of meteorological variables.…”

Section: Predictors and Factors Affecting Noise Levelmentioning

confidence: 99%

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Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

et al. 2017

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Abstract:The impact of temporal aspects of noise data on model development and intra-urban variability on environmental noise levels are often ignored in the development of models used to predict its spatiotemporal variation within a city. Using a Land Use Regression approach, this study develops a framework which uses routine noise monitors to model the prevailing ambient noise, and to develop a noise variability map showing the variation within a city caused by land-use setting. The impact of data resolution on model development and the impact of meteorological variables on the noise level which are often ignored were also assessed. Six models were developed based on monthly, daily and hourly resolutions of both the noise and predictor data. Cross validation highlighted that only the hourly resolution model having 59% explanatory power of the observed data (adjusted R 2 ) and a potential of explaining at least 0.47% variation of any independent dataset (cross validation R 2 ), was a suitable candidate among all the developed models for explaining intraurban variability of noise. In the hourly model, regions with roads of high traffic volumes, with higher concentrations of heavy goods vehicles, and being close to activity centres were found to have more impact on the prevailing ambient noise. Road lengths were found to be the most influential predictors and identified as having an impact on the ambient noise monitors.

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Section: Discussioncontrasting

confidence: 87%

Section: Discussioncontrasting

confidence: 64%

Section: Lur As a Tool For Modellingmentioning

confidence: 99%

Section: Predictors and Factors Affecting Noise Levelmentioning

confidence: 99%

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Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

et al. 2017

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“…Long term exposure can lead to annoyance, stress, sleep disturbance and daytime sleepiness, affect cognitive performance in schoolchildren, and the performance of staff and patient outcomes in hospitals (Ramirez et al 2004, EEA 2014b, Kraus et al 2015, Leighton 2016. Apart from causing hearing loss and tinnitus, exposure to noise is also linked to the occurrence of hypertension and an increased risk of heart attack, stroke, ischemia and other cardiovascular diseases (Beutel et al 2016, Fecht et al 2016, Wang et al 2016.…”

Section: Introductionmentioning

confidence: 99%

Spatial Variation in Sound Frequency Components Across an Urban Area Derived from Mobile Surveys

Alvares-Sanches

Osborne

White

et al. 2019

Future Cities and Environment

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Continuous exposure to noise can lead to premature hearing loss, reduced cognitive performance, insomnia, stress, hypertension, cardiovascular diseases and stroke. Road noise affects the health of >125 million people in the European Union and Member States are required to map major noise hotspots. These strategic noise maps are usually derived from traffic counts and propagation models because large-scale measurement of the acoustic environment using conventional methods is infeasible. In this study, the authors surveyed the entire city of Southampton, UK using a mobile survey technique, capturing spatial variations in street-level sound characteristics across multiple frequencies from all sound sources. Over 52,000 calibrated and georeferenced sound clips covering 11 Hz to 22.7 kHz are analysed here to investigate variations in sound frequency composition across urban space and then applied to two issues: the definition of naturalness in the acoustic environment; and perceptions of social inequity in sound exposure. Clusters of acoustic characteristics were identified and mapped using spectral clustering and principal components analysis based on octave bands, ecoacoustic indices and dBA. We found independent patterns in low, mid and high frequencies, and the ecoacoustic indices that related to land use. Ecoacoustic indices partially mapped onto greenspace, identifying naturalness, but not uniquely, probably because urban anthropogenic sounds occur at higher frequencies than in the natural areas where such indices were developed. There was some evidence of inequity in sound exposure according to social deprivation and ethnicity, and results differed according to frequency bands. The consequences of these findings and the benefits of city-wide sound surveys for urban planning are discussed.

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A waterborne polyurethane‐based noise‐reducing material and factors influencing its damping and noise‐reducing performance

Wu,

Cao,

Miao

et al. 2024

J of Applied Polymer Sci

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In this study, damping coatings were used to dissipate low‐ and medium‐frequency (200–1600 Hz) traffic noise. A waterborne polyurethane/polyacrylate (WPUA) emulsion with superior damping and mechanical properties was prepared by blending polyurethane with commercial waterborne polyacrylate (SETAQUA 6754). The emulsion was combined with sound‐absorbing polyester fibers (PFs) to develop a PF/WPUA damping coating. SETAQUA 6754 improved the loss factor and mechanical properties of the waterborne resin. The composition ratio, molecular chain length, and crosslinking degree improved the damping properties of WPUA by modulating its glass transition temperature, molecular chain friction, hydrogen bonding, and molecular chain free volume. When the thickness of the waterborne damping coating was 1 cm, PF/WPUA reduced the noise by 7 dB at 200 Hz and 23 dB at 1600 Hz, indicating the effectiveness of the PF/WPUA coating in reducing reduce low‐ and medium‐frequency traffic noise.

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Temporal and spatial variations in road traffic noise for different frequency components in metropolitan Taichung, Taiwan

Cited by 44 publications

References 26 publications

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Spatial Variation in Sound Frequency Components Across an Urban Area Derived from Mobile Surveys

A waterborne polyurethane‐based noise‐reducing material and factors influencing its damping and noise‐reducing performance

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