Spatial statistical analysis of the effects of urban form indicators on road-traffic noise exposure of a city in South Korea

Ryu, Hunjae; Park, In Kwon; Chun, Bumseok; Chang, Seo Il

doi:10.1016/j.apacoust.2016.08.025

Cited by 66 publications

(35 citation statements)

References 13 publications

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“…In addition, urban form influences the higher levels of sleep disturbance in the GMC with densely-located high-rise residential buildings near major roads such as beltways and highways. The characteristics of urban form is known to affect different patterns of nighttime human exposure between Eastern and Western cities [18,72]. This is also partially confirmed by another study for an Asian city.…”

Section: Discussionsupporting

confidence: 59%

The Public Health Impact of Road-Traffic Noise in a Highly-Populated City, Republic of Korea: Annoyance and Sleep Disturbance

et al. 2018

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Sustainable transportation is an essential part of a sustainable city; however, modern transportation systems with internal-combustion engines emits unacceptably high level of air-pollutants and noise. It is recognized widely that road-traffic noise has negative health impacts (such as annoyance and sleep disturbance) on exposed population in highly-populated cities. These harmful effects should be removed or at least reduced to guarantee the sustainability of modern cities. The estimation of pollutant levels at a specific location and the extent of the damage is therefore important for policy makers. This study presents a procedure to determine the levels of road-traffic noise at both day and night, and an assessment of the adverse health effects across Gwangju Metropolitan City (GMC), Republic of Korea (ROK). Road-traffic noise maps in 2-D and 3-D were generated, in order to find spatial distribution of noise levels across the city and noise level at the façade of a building-floor, respectively. The adoption of existing assessment models for the highly-annoyed (%HA) and highly-sleep-disturbed (%HSD) leads to building-based estimation of the affected population and spatial distribution of the road networks of the city. Very high noise levels were found to exist along major roads in the day and at night, with little difference between them. As a result, approximately 10% and 5% of the total population (n = 1,471,944) were estimated to experience high-level annoyance and sleep disturbance, respectively.

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Section: Discussionsupporting

confidence: 59%

The Public Health Impact of Road-Traffic Noise in a Highly-Populated City, Republic of Korea: Annoyance and Sleep Disturbance

et al. 2018

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“…In agreement with the predictor variables included in the spatial models underpinning the final predictive surface, recent models have identified road type and land use variables as important predictors of road traffic noise levels. [17,36,37] The lengths of nearby freeways, arterial roads, and collector roads, present as variables in each of the three cycle-specific spatial models, likely served as proxies for road traffic volume, a key determinant of road traffic noise levels. Nearby industrial area was also included in all three spatial models, indicating that although the noise measurements used to build the spatial models predominantly captured road traffic noise, a small proportion of the sampled noise was due to nontraffic sources such as industrial activities.…”

Section: Discussionmentioning

confidence: 99%

Exploring nighttime road traffic noise: A comprehensive predictive surface for Toronto, Canada

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Johnson

MacIntyre

et al. 2018

Journal of Occupational and Environmental Hygiene

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Road traffic noise can adversely impact the health of city residents, particularly when it occurs at night. The objective of this study was to evaluate nighttime traffic ambient noise in Toronto, Canada using measured and model-estimated noise levels. Road traffic noise was measured at 767 locations over 3 seasonal sampling campaigns between June 2012 and October 2013 to fully capture noise variability in Toronto. Temporal and campaign-specific spatial models, developed using the noise measurements, were used to build a final predictive surface. The surface was capable of estimating noise across the city over a 24-hr time frame. Measured and surface-estimated noise levels were compared with guidelines from the World Health Organization and the Province of Ontario to identify areas where noise may pose a health risk. Measured mean nighttime noise in Toronto exceeded World Health Organization (40 dBA) guidelines and mean daytime noise exceeded provincial (55 dBA) guidelines. The final predictive surface, incorporating spatial variables and daily cycles in noise levels, provides noise estimates geocoded for the entire study area. This tool could be used for epidemiological studies and to inform noise mitigation efforts. Based on surface-estimated noise levels during the quietest time of night (2 a.m.-2:30 a.m.), 100% of Toronto has nighttime noise exceeding 40 dBA (mean = 57 dBA, range = 49-110 dBA). A predictive surface was developed to estimate geocoded noise levels and facilitate further study of noise in Toronto. This tool can be used to assess road traffic noise, particularly at night, as an environmental health hazard.

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“…The main source of environmental noise in urban areas is road traffic, and most noise prediction non-LUR models have been developed based on factors such as estimates of traffic volumes, vehicle types, the mean vehicle speed and the distance from the traffic lanes [1]. Ryu et al [26] analysed indicators related to the urban form with road traffic noise, and found space and floor use, traffic related variables and industrial area are highly correlated. Zuo et al [32] collected two rounds of repeated measurements in a city and found that traffic related noise variability was predominantly spatial in nature, rather than temporal, and factors affecting noise were related to traffic and industrial areas.…”

Section: Predictors and Factors Affecting Noise Levelmentioning

confidence: 99%

“…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%

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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Spatial statistical analysis of the effects of urban form indicators on road-traffic noise exposure of a city in South Korea

Cited by 66 publications

References 13 publications

The Public Health Impact of Road-Traffic Noise in a Highly-Populated City, Republic of Korea: Annoyance and Sleep Disturbance

The Public Health Impact of Road-Traffic Noise in a Highly-Populated City, Republic of Korea: Annoyance and Sleep Disturbance

Exploring nighttime road traffic noise: A comprehensive predictive surface for Toronto, Canada

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

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