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Frontiers of Earth Science

Front. Earth Sci.    2017, Vol. 11 Issue (2) : 214-228     https://doi.org/10.1007/s11707-017-0615-6
RESEARCH ARTICLE |
GIS model for identifying urban areas vulnerable to noise pollution: case study
Ştefan BILAŞCO1,2(), Corina GOVOR1, Sanda ROŞCA1, Iuliu VESCAN1, Sorin FILIP1, Ioan FODOREAN1
1. "Babeş-Bolyai" University, Faculty of Geography, 400006 Cluj-Napoca, Romania
2. Romanian Academy, Cluj-Napoca Subsidiary Geography Section, 9, 400015 Cluj-Napoca, Romania
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Abstract

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.

Keywords GIS modelling      noise pollution      sound intensity      environmental impact      vulnerability     
Corresponding Authors: Ştefan BILAŞCO   
Just Accepted Date: 16 March 2017   Online First Date: 13 April 2017    Issue Date: 19 May 2017
 Cite this article:   
Ştefan BILAŞCO,Corina GOVOR,Sanda ROŞCA, et al. GIS model for identifying urban areas vulnerable to noise pollution: case study[J]. Front. Earth Sci., 2017, 11(2): 214-228.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-017-0615-6
http://journal.hep.com.cn/fesci/EN/Y2017/V11/I2/214
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Ştefan BILAŞCO
Corina GOVOR
Sanda ROŞCA
Iuliu VESCAN
Sorin FILIP
Ioan FODOREAN
Fig.1  Geographical position of the study area.
Fig.2  Methodological flow-chart.
DatabaseStructureAttributesType
LimitVector-line-Primary
Measurement pointsVector-pointMeasured value for each intervalPrimary
BuildingsVector-polygonZPrimary
RoadsVector-polygonNamePrimary
DEMRasterZDerived
Land useRasterLand use typePrimary
Sound propagation mapsRasterSound intensityModelled
Tab.1  Database structure
Fig.3  Map of measurement points.
No.LocationHourly intervals
6–812–1416–18
1Str. Constantin Brâncu?i565871
2Str. Constantin Brâncu?i696266
3Str. Constantin Brâncu?i586166
4Str. Constantin Brâncu?i596670
5Str. Constantin Brâncu?i576669
6Str. Minerilor565863
7Str. General Traian Mo?oiu ∩ Str. Aurel Suciu675569
8Str. Actorului ∩ Str. Bistri?ei485254
9Str. Aurel Suciu546669
10Aleea Băi?oara656961
11Aleea Slănic565365
12Aleea Valeriu Bologa555766
13Aleea Slănic ∩ Str. Alexandru Vaida Voievod677761
14Str. Baladei ∩ Str. Venus586563
15Str. General Traian Mo?oiu666575
16Aleea Herculane ∩ Aleea Borsec545058
17Aleea Herculane ∩ Aleea Snagov566259
18Bd Nicolae Titulescu ∩ Str. Liviu Rebreanu586264
19Str. Tache Ionescu ∩ Str. Liviu Rebreanu576364
20Str. Albac ∩ Str. Liviu Rebreanu576965
21Aleea Herculane ∩ Aleea Snagov566259
22Bd Nicolae Titulescu ∩ Str. Liviu Rebreanu586264
23Str. Tache Ionescu ∩ Str. Liviu Rebreanu576364
24Str. Albac ∩ Str. Liviu Rebreanu576965
25Str. Heltai Gaspar ∩ Str. Liviu Rebreanu566163
26Str. Teodor Mihali666868
27Str. Teodor Mihali656967
28Str. Alexandru Vaida Voievod606264
29Str. Unirii596162
30Str. Unirii586166
31Aleea Băi?oara ∩ Aleea Băi?a464950
32Aleea Băi?oara ∩ Aleea Scări?oara485055
33Aleea Azuga444855
34Str. Albac566668
35Str. Albac ∩ Str. Septimiu Albini616664
36Aleea Băi?oara535860
37Str. Constantin Brâncoveanu ∩ Str. Madach Imre545556
38Str. Axente Sever ∩ Str. Madach Imre515453
39Str. Tache Ionescu ∩ Str. Madach Imre525056
40Str. Secerătorilor ∩ Str. Mihai Veliciu474946
41Str. Arie?ului ∩ Str. Vasile Lupu475248
42Str. Arie?ului ∩ Str. Bu?teni505254
43Str. Tache Ionescu ∩ Str. Arie?ului484451
44Str. Vasile Lupu ∩ Str. Aron Densu?ianu505246
Tab.2  Measured values in each point
ParameterCharacteristic value
Air temperature19°C (66.2°F)
Wind speed13 km/h (8.08 mph)
Wind directionSE (120°)
Relative air humidity28%
General atmospheric conditionsClear sky, summer day
Hearing sensitivity1 kHz
Tab.3  General conditions and numerical databases used in the present work
Fig.4  Sound propagation model for the 6–8 time interval.
Fig.5  Sound propagation model for the 12–14 time interval.
Fig.6  Sound propagation model for the 16–18 time interval.
Fig.7  Difference in sound intensity between 12–14 and 6–8 time intervals.
Fig.8  Difference in sound intensity between 16–18 and 12–14 time intervals.
Valitation pointSound intensity (calculated)Sound intensity (measured)Difference
132.5 dB32.4 dB–0.1 dB (0.30 %)
210.3 dB10.5 dB0.2 dB (1.90 %)
336.6 dB36.7 dB0.1 dB (0.27 %)
426.1 dB26.3 dB0.2 dB (0.76 %)
Validation105.5 dB105.9 dB0.4 dB (96 %)
Tab.4  Sound intensity in the validation points
Fig.9  Position of validation points.
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