GIS model for identifying urban areas vulnerable to noise pollution： case study

• Published in: Frontiers of earth science 2017-06, Vol.11 (2), p.214-228
• Main Authors: BILA CO, tefan, GOVOR, Corina, RO CA, Sanda, VESCAN, Iuliu, FILIP, Sorin, FODOREAN, Ioan
• Format: Article
• Language: English
• Subjects: Case studies; Earth and Environmental Science; Earth Sciences; Economic development; Economic growth; Economics; Environmental impact; Geographical information systems; GIS modelling; gis模型; Information processing; Mathematical models; Noise; Noise pollution; Numerical data bases; Pollution; Pollution dispersion; Pollution studies; Public administration; Research Article; Sound; Sound intensity; Sound propagation; Spatial analysis; Traffic; Transport; Transportation noise; Urban areas; vulnerability; Wind direction; 交通基础设施; 噪声污染; 城市中心; 污染地区; 矢量数据库; 空间分析模型
• ISSN: 2095-0195; 2095-0209
• DOI: 10.1007/s11707-017-0615-6

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.