Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities (original) (raw)
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Estimation of spatial variations in urban noise levels with a land use regression model. Environ
2016
Background: Outdoor noise is a source of annoyance and health problems in cities worldwide. Objective: We developed a land-use regression using a GAM Model to estimate the spatial variation of noise levels in Montreal. Methods: Noise levels were measured over a two week period during the summer of 2010 at 87 sites and during the winter of 2011 at 62 sites. A land use regression model was produced for both seasons to estimate noise levels as LAeq24h (resolution of 20 m). A leave one out cross-validation (LOOCV) was performed. Results: LAeq24h measured range from 53.4 to 73.7 dBA for the summer and from 54.1 to 77.7 dBA for the winter. The land use regression models explained 64 % of spatial variability for the summer and 40 % for the winter. The main predictors are the Normalized Difference Vegetation Index; the length of vehicular arteries, highways, and bus lines; and the proximity to an international airport. The Root mean square error from the LOOCV was 3.3 and 4.5 dBA for the su...
Journal of Exposure Science and Environmental Epidemiology, 2016
The availability of noise maps to assess exposure to noise is often limited, especially in North American cities. We developed land use regression (LUR) models for LA eq24h , L night , and L den to assess the long-term spatial variability of environmental noise levels in Montreal, Canada, considering various transportation noise sources (road, rail, and air). To explore the effects of sampling duration, we compared our LA eq24h levels that were computed over at least five complete contiguous days of measurements to shorter sampling periods (20 min and 24 h). LUR models were built with General Additive Models using continuous 2-min noise measurements from 204 sites. Model performance (adjusted R 2) was 0.68, 0.59, and 0.69 for LA eq24h , L night , and L den , respectively. Main predictors of measured noise levels were road-traffic and vegetation variables. Twenty-minute non-rush hour measurements corresponded well with LA eq24h levels computed over 5 days at road-traffic sites (bias: − 0.7 dB(A)), but not at rail (−2.1 dB(A)) nor at air (−2.2 dB(A)) sites. Our study provides important insights into the spatial variation of environmental noise levels in a Canadian city. To assess long-term noise levels, sampling strategies should be stratified by noise sources and preferably should include 1 week of measurements at locations exposed to rail and aircraft noise.
The Effect of Land-Use Categories on Traffic Noise Annoyance
International Journal of Environmental Research and Public Health
Land-use categories are often used to define the exposure limits of national environmental noise policies. Often different guideline values for noise are applied for purely residential areas versus residential areas with mixed-use. Mixed-use includes living plus limited activities through crafts, commerce, trade, agriculture, and forestry activities. This differentiation especially when rating noise from road, railway, and air traffic might be argued by different expectations and therefore noise annoyance in those two categories while scientific evidence is missing. It should be tested on empirically derived data. Surveys from two studies in the state of Tyrol in urban and rural areas were retrospectively matched with spatial data to analyze the potential different influences on noise effects. Using non-parametric tests, the correlation between land-use category on self-reported noise sensitivity and noise annoyance was investigated. Exposure–response for the two analyzed land-use c...
The Use of GIS in Studying and Modeling Traffic Noise and its Relationship to Land Uses in Riyadh
This study aims to monitor, analyze and model spatial relationships of traffic noise and its intensity in major streets of Riyadh and surrounding residential areas. We shall adopt a mathematical model to obtain noise intensity level from the average number of passing vehicles in the road, their average speed and the percentage of trucks. By using methods of spatial data insertion in GIS system, it was possible to prepare noise maps (continuous area) which shows the nature and pattern of noise distribution in roads and surrounding areas, one set throughout the day and another through peak hours. These maps are useful for delineating areas with high noise level and areas of low noise levels. This information, in turn, is useful for selection of sites for land uses that require low level of noise, such as health facilities, schools, public libraries, etc. By analyzing the relationship between noise and land use of areas surrounding roads, it was possible to know the relationship between the activities and land uses of areas adjoining road, and the level of noise in the roads. The spatial analysis was also useful in understanding the factors that lead to the high noise levels in some roads. The conclusion contains recommendations and planning criteria that may help planners and decision makers to reduce noise levels down to allowable levels, to preserve people's health and comfort and conserve the environment.
Applied Acoustics, 2017
The purpose of this study is to present a statistical model which can predict the noise level of road-traffic in urban area. A spatial statistical model which can take into account spatial dependency on geographically neighboring areas is constructed from a noise map of a city in South Korea. A system of 250 m  250 m grid cells is placed on the city of Cheongju, South Korea, and the noise level and urban form indicators are averaged over each cell. The population-weighted mean of the noise level is subsequently regressed on the average urban form by adopting the spatial autoregressive model (SAR) and the spatial error model (SEM), as well as an ordinary least squares (OLS) model. Direct and indirect impacts are analyzed for a valid interpretation of the spatial statistical models. Factors such as GSI, FSI, traffic volume, traffic speed, road area density, and the fraction of industrial area turn out to have significant impacts on the noise level.
GIS model for identifying urban areas vulnerable to noise pollution: case study
Frontiers of Earth Science, 2017
The unprecedented expansion of the national car ownership over the last few years has been determined by economic growth and the need for the population and economic agents to reduce travel time in progressively expanding large urban centres. This has led to an increase in the level of road noise and a stronger impact on the quality of the environment. Noise pollution generated by means of transport represents one of the most important types of pollution with negative effects on a population's health in large urban areas. As a consequence, tolerable limits of sound intensity for the comfort of inhabitants have been determined worldwide and the generation of sound maps has been made compulsory in order to identify the vulnerable zones and to make recommendations how to decrease the negative impact on humans. In this context, the present study aims at presenting a GIS spatial analysis model-based methodology for identifying and mapping zones vulnerable to noise pollution. The developed GIS model is based on the analysis of all the components influencing sound propagation, represented as vector databases (points of sound intensity measurements, buildings, lands use, transport infrastructure), raster databases (DEM), and numerical databases (wind direction and speed, sound intensity). Secondly, the hourly changes (for representative hours) were analysed to identify the hotspots characterised by major traffic flows specific to rush hours. The validated results of the model are represented by GIS databases and useful maps for the local public administration to use as a source of information and in the process of making decisions.
People exposed to traffic noise in european agglomerations from noise maps. A critical review
Noise Mapping, 2014
Two of the main objectives of the European Directive on environmental noise are, firstly, to unify acoustic indices for assessing environmental noise and, secondly, to standardize assessment methodologies. The ultimate goal is to objectively and comparably manage the impact and evolution of environmental noise caused both by urban agglomerations and by traffic infrastructures (roads, rails and airports). The use of common indices and methodologies (together with five-year plan assessment required by the authorities in charge) should show how noise pollution levels are evolving plus the effectiveness of corrective measures implemented in the action plans. In this paper, available results from numerous European agglomerations (with particular emphasis on Spanish agglomerations) are compared and analysed. The impact and its evolution are based on the percentage of people exposed to noise. More specifically, it demonstrates the impact caused by road traffic, which proves to be the main noise source in all agglomerations. In many cases, the results are extremely remarkable. In some case, the results are illogical. For such cases, it can be concluded that either assessment methodologies have been significantly amended or the input variables to the calculation programs have been remarkably changed. The uncertainty associated with the results is such that, in our opinion, no conclusions can be drawn concerning the effectiveness of remedial measures designed within the action plans after the Directive's first implementation Phase.
2001
Traffic noise is a significant effect of transport in urban areas and a priority issue in the EU. Noise as an environmental factor needs to be considered more closely when transport policies and relevant measures are examined and planned to tackle transport related problems. There is a need for including impacts on noise both at the macro and the micro level. Existing official traffic noise calculation methods mainly focus on the former. By using suitable methods for both these levels in combination with transport simulation models and GIS technology, it is possible to approach the whole issue in a more comprehensive way. Visualisation of problem areas and spots with extreme effects as well as other traffic parameters is easily achieved with the use of GIS functionality.
Noise mapping and gis: optimising quality, accuracy and efficiency of noise studies
2000
Noise caused by industry and infrastructure is a major source of dissatisfaction with the environment in residential areas. Policies on noise control have been developed in most European countries. Noise effect studies are carried out to support these policies. Since important decisions are based on the results of noise effect studies, it is not only important to quantify noise effects, but also to have information on the quality and the reliability of the results. However the need for this information is often discarded. The quality of the results of noise effect studies depends on the quality of the data and models used. The integration of Geographical Information Systems (GIS) and noise models makes it possible to increase the quality of noise effect studies by automating the modelling process, by dealing with uncertainties and by applying standardised methods to study and quantify noise effects.