GIS model for identifying urban areas vulnerable to noise pollution: case study Ștefan BILA ȘCO (✉)1,2, Corina GOVOR1, Sanda RO ȘCA1, Iuliu VESCAN1,… [627763]

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offprintRESEARCH ARTICLE
GIS model for identifying urban areas vulnerable to noise
pollution: case study
Ștefan BILA ȘCO (✉)1,2, Corina GOVOR1, Sanda RO ȘCA1, Iuliu VESCAN1, Sorin FILIP1,
Ioan FODOREAN1
1 "Babe ș-Bolyai" University, Faculty of Geography, 400006 Cluj-Napoca, Romania
2 Romanian Academy, Cluj-Napoca Subsidiary Geography Section, 9, 400015 Cluj-Napoca, Romania
© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017
Abstract The unprecedented expansion of the national
car ownership over the last few years has been determinedby economic growth and the need for the population andeconomic agents to reduce travel time in progressivelyexpanding large urban centres. This has led to an increasein the level of road noise and a stronger impact on thequality of the environment. Noise pollution generated bymeans 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, tolerablelimits of sound intensity for the comfort of inhabitants havebeen determined worldwide and the generation of soundmaps has been made compulsory in order to identify thevulnerable zones and to make recommendations how todecrease 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 GISmodel is based on the analysis of all the componentsinfluencing sound propagation, represented as vector
databases (points of sound intensity measurements,buildings, lands use, transport infrastructure), rasterdatabases (DEM), and numerical databases (wind directionand speed, sound intensity). Secondly, the hourly changes
(for representative hours) were analysed to identify the
hotspots characterised by major traf ficflows speci fict o
rush hours. The validated results of the model arerepresented by GIS databases and useful maps for thelocal public administration to use as a source ofinformation and in the process of making decisions.
Keywords GIS modelling, noise pollution, sound inten-
sity, environmental impact, vulnerability1 Introduction and study area
1.1 Introduction
In the present conditions of civilisation, one of the negativefactors in fluencing the daily life of inhabitants is the
increased sound intensity which determines the presence ofa continuous sound environment. From a physiological
point of view, a sound is a sensation affecting the auditory
organ, which is produced by the material vibrations ofobjects and is transmitted via acoustic waves. The humanear generally perceives sounds between the frequencies of16 Hz and 20 kHz (Olson, 1967), higher levels beinguncomfortable (Job, 1999; Pandya, 2003; Baloye andPalamuleni, 2015).
According to various studies, the noise pollution
generated by road traf fic plays an important role among
the environmental factors which lead to illnesses (Teens-berg, 1999; Passchier-Vermeer and Passchier, 2000;Babisch, 2004; Bluhm et al., 2004; Babisch, 2006;Maschke and Hecht, 2007; Belojevic et al., 2008;Mihăiescu and Mih ăiescu, 2009; European Environment
Agency, 2010; Joint Research Centre, 2011; Stansfeld andClark, 2011; Swain and Goswami, 2013; Dzhambov and
Dimitrova, 2014; Quamrul et al., 2015). Speci fic norms
and standards have been determined in most countries inorder to monitor and evaluate sound. The European Unionadopted the 2002/49/CE Directive of the EuropeanParliament and Council on the 25th of June 2002concerning the evaluation and mitigation of environmentalnoise and imposing the existence of noise maps for largecities to use when devising action plans.
In Europe, the main noise sourceis represented by
road traf fic. Vehicle propulsion noise is the result of
the combination given by engine noise and the tyrenoise output. (Sternberg, 1999; European Federation for
Received June 13, 2016; accepted September 13, 2016
E-mail: sbilasco@geogra fie.ubbcluj.roFront. Earth Sci. 2017, 11(2): 214 –228
DOI 10.1007/s11707-017-0615-6
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offprintTransport and Environment, 2008; European Environment
Agency, 2014).
The intensity of traf fic noise is in fluenced by car speed,
traffic volume, the number of heavy vehicles, and the
material of road infrastructure (European Federation forTransport and Environment, 2008).
In the category of negative effects determined by
prolonged exposure to noise pollution one can include:
decrease of auditory sensitivity, partial or total deafness,
symptoms of general tiredness, sleep disturbance, mentalhealth disturbance, high blood pressure, modi fied breath-
ing and heart rates, and increased risk of heart attack(Konecni, 1975; Mathews and Cannon, 1975; Berglundand Lindvall, 1995; Carter, 1996; Stansfeld and Matheson,2003; Ising and Kruppa, 2004; Babisch et al., 2005;Stansfeld and Clark, 2011; Anees, et al., 2013; Méline et
al., 2013; Dzhambov et al., 2014; Weber et al., 2014;
Jazani et al., 2015). In Cluj-Napoca and in the other largecities of Romania, one of the most common effects ofexcessive noise is auditory tiredness. Among the noiseeffects on the nervous system, sleep disturbance is the mostencountered symptom, as well as irritability. The latter ischaracterised by a temporary increase of the threshold ofauditory perception due to the exposure to the action of an
intense noise (Darabont et al., 1983).
In 1999 the World Health Organisation made public a
series of recommendations referring to environmentalnoise (Berglund et al., 1999): to its measurement, its effectson health, threshold values of sound intensity, as well asmeasures of reducing its negative effects. Protectionmeasures against noise for people working in of fice
buildings or living in residential zones with high noise
pollution and prolonged exposure to noise pollution risk
were eventually determined and included soundproo fing,
wall reinforcement with sound-absorbing materials, soundinsulation, etc.
As a consequence, the identi fication of acceptable sound
thresholds for different urban zones becomes veryimportant: 60 dB for the city centre (where most of thesocial and cultural facilities are concentrated); 55 dB for
the neighbourhood centres; 50 dB for the residential zones
with frequently used social and cultural buildings andfacilities such as schools, commercial spaces, medical carefacilities; 45 dB for zones of relaxation and resting,protected social and cultural facilities like hospitals,sanatoriums, elder houses, libraries. (European Environ-mental Protection Agency, 1974; Darabont et al., 1983;Romanian Government Decree, no. 321/2005).
By analysing the threshold values of sound intensity theconcept of noise was used as a standard for identifying
vulnerability to noise pollution. Environmental noise isdefined as “the unwanted or harmful exterior sound
generated by human activities, including the soundproduced by means of transport, road, rail and air traf fic,
and industrial establishments. ”(European Environment
Agency, 2014).
According to the 2002/49/CE directive of the European
Parliament regarding the assessment and management of
environmental noise, one of the main environmentalproblems in Europe is represented by noise pollution.This directive proposes the establishment of a commonmethod for noise determination as well as threshold values.The noise indicators being used are: Lden –the A-
weighted average level of sound pressure for the sum ofday time periods over a year being used for the assessment
of discomfort during daytime; Lnight- the A-weighted
average level of sound pressure for the sum of periods ofnight time over a year, being used for the assessment ofsound disturbance. The two indicators express an averagelevel of sound pressure. Lzsn is the noise indicator for theday, evening and night and it is associated with the generaldiscomfort over 24 hours. The measurements account forthe following principles: the daytime has 12 hours, the
evening has 4 hours and the night has 8 hours. Thus, the
three periods are correspondent to the following timeintervals: 7 –19, 19 –23 and 23 –7. The applied methods of
calculation are included in the standards ISO 1996/2-08:1995
1)and ISO 9613-22).
Through this directive, the members of the European
Union must create noise maps for: cities with more than250,000 inhabitants, for the major roads with larger traf fic
values than 6 million vehicles a year, for railroads with
traffic values over 60,000 trains a year, and for major
airports. These maps need to be updated every 5 years. Incase the limit values are surpassed, the member states mustalso make action plans including: a description of theagglomeration of the noise sources (roads, railroads,airports), the limit values, the synthesis of the resultsgenerated by noise mapping, the assessment of the
approximate number of people being exposed to noise,
identi fication of problems and aspects requiring improve-
ment, noise reduction measures, and a long-term strategyassessment.
According the Ministry of the Environment, Water and
Forest, the Romanian legislation includes several lawsabout noise: Romanian Government Decree, no. 152/558/119/532-2008
3), Romanian Government Decree, no. 321
from April 14th, 20054), Romanian Government Decree,
1) ISO 1996/2-08: standards for the description of noise outdoors in community environments.
2) ISO 9613-2: standards for calculating the attenuation of sound during propagation outdoors in order to predict the levels of environmental noise a t a distance
from a variety of sources.
3) Romanian Government (2008). 152/558/119/532 Decree no. 152/558/119/532, on the approval of the Guide concerning maximal values for noise in
Romanian.
4) Romanian Government, Decree no. 321/2005 for evaluation and environmental noise management, in Romanian.Ștefan BILA ȘCO et al. GIS model for identifying urban areas vulnerable to noise pollution 215
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offprintno.678/1344/915/1397 from 20061). Romanian Govern-
ment Decree no. 1830/20072)for the approval of the Guide
for generating, analyzing, and assessing strategic noisemap, as well as other similar laws, has as its main purposeand avoidance, prevention, or decrease of negative effectscaused by environmental noise, by creating noise maps andtaking speci fic action plans.
Thus, the present work includes maps identifying areas
vulnerable to noise pollution which were created by
modelling road traf fic noise. The study aims at highlighting
both the areas with high sound intensity and quiet onesfrom the centre of Cluj-Napoca city, where the traf fic and
its corresponding environmental noise are progressivelyincreasing. At the same time, the GIS analysis highlightsthe differences between the noise pollution being mappedat different representative hours, as well as the integrative
analysis of the results based on the cause-effect relation-
ship.
1.2 Study area
The increased territorial and demographic development ofCluj-Napoca Municipality, due to its establishment as themain centre of commercial, cultural, educational and
economical attraction, has led to a directly proportional
increase in the number of vehicles owned by residents andpassing through the city. One of the busiest traf fic areas is
represented by the Gheorgheni neighbourhood (Fig. 1).
The test area for the present study focused on the
Gheorgheni neighbourhood where busy boulevards con-trast with green areas. The Gheorgheni neighbourhood islocated in the east of Cluj-Napoca Municipality and was
built in the 1960s following an urban planning which
included many green areas, parks, and recreation zones.The built-up area is represented both by blocks of flats
(with 4, 8, or 10 storeys) and houses, and ConstantinBrâncu și and Nicolae Titulescu streets represent the main
axes of the neighbourhood.
The high traf fic intensity of the neighbourhood is the
main reason for selecting it as a study area. This intensity is
determined by a high number of inhabitants commuting to
their workplaces, usually into the surrounding neighbour-hoods, by the major arterial roads used for city transit aswell as by the presence of schools which act as convergingcentres of the traf ficflows at speci fic hours.
2 GIS sound modelling, database and
methodology
Geographic informational systems prove themselves of
great importance in the application of the methodologicaland scienti fic endeavour as they optimise the work flow
and automate speci fic calculations, facilitating the user's
work.
There are many data types being used for sound
modelling, some authors using factors such as the traf fic
conditions, the con figuration of the road network, the
category of the road network and the main sound sources(Sheng and Tang, 2011). The utility of the geographic
informational systems derives from the fact that they allow
the acquisition, the management, the analysis, the model-ling, and the highly accurate mapping of the results (Shengand Tang, 2011).
2.1 GIS sound modelling
There is a variety of software simulating sound propaga-
tion using various physical factors which in fluence it
(SoundPlan, LimA, SPreAD-GIS for ArcGIS, Noise-M@ap, etc). The results are highly accurate and allowcomplex processing as well as export in various forms.
Among the advantages, one must notice the possibility
of populating the georeferenced database with attributes,the introduction of noise sources, and, last but not least, thepossibility of applying spatial analysis (Kucas et al., 2007;
Farca șand Sivertun, 2009; Tsai et al., 2009; Haq et al.
2012; Garg and Maji, 2014).
For sound monitoring and mapping, the GIS technology
enables the generation of a spatial database encompassingthe required information in different data types: buildingsand their elevation, roads, DEM, sound measurementpoints etc. Each type of database is organised on thematiclayers accompanied by information in the form of attribute
tables which is relevant for the process of spatial analysis.
The various databases are integrated into correspondingspatial analysis types and the results are represented asmaps and databases, both being useful for further analysesby using specialised software or can be disseminated in thevirtual environment (digital databases). Visual and inter-pretative analyses can also be performed for public andinformative use (cartographic materials) (Van Blokland
and Peeters, 2009; Kephalopoulos and Paviotti, 2013;
Licitra, 2013; Licitra and Ascari, 2013).
The evolution of technology has led to an improvement
in GIS sound modelling, moving it closer to reality as thegeneration of different noise reduction scenarios and costanalyses became possible.
2.2 Database and methodology
The digital mapping of areas vulnerable to noise pollutionusing GIS analysis was performed using the SpreAD-Gisextension (Reed et al., 2012) which operates in ArcGIS.
1) Romanian Government (2006). Decree no. 678/1344/915/1397, on the approval of the Guide concerning noise calculus methods of the industrial, road and
ferries and aerial traf fic in the airports proximity in Romanian.
2) Romanian Government (2007). Decree no. 1830, on the approval of the Guide concerning analizys and assessment of strategic noise maps in Romanian.216 Front. Earth Sci. 2017, 11(2): 214 –228
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Fig. 1 Geographical position of the study area.Ștefan BILA ȘCO et al. GIS model for identifying urban areas vulnerable to noise pollution 217
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offprintThis extension was selected for the study case because it is
free to use by all owners of ArcGIS licenses (Romanianpublic administration) and it enables operations with amultitude of databases both spatial and numeric (measurednumerical values of sound intensity, wind speed, anddirection for a speci fic area, general methodological
conditions at a speci fic time, spatial location of sound
intensity measurements, etc.) which determine effects on
the modelling of sound propagation and the mapping of its
specific intervals.
The use of GIS software through spatial modelling for
implementing the mapping methodology of the areasvulnerable to sound propagation requires the creation of aspecific database and the use of a method which is based on
the software and its auxiliary components. The databasewhich was used in the present work includes two main GIS
database categories (Fig. 2): numerical databases asso-
ciated with spatial databases for identi fication and point
measurement of noise level (sound values measured in dB,wind speed, and direction), databases including vector
datasets (measurement points, limit of study area, 2-Dbuilding contour, land use, traf fic systems) as well as
databases including raster datasets (digital elevationmodel, derived digital elevation model) (Table 1).
The sound intensity databases (Table 2) were created
through direct point measurements using an SL 5868digital sound pressure noise level meter device. For
enhanced accuracy three measurements were performed
per interval in each point. The measurements were done innormal environmental noise conditions at approximately1.5 m above ground due to the great variety of vehicles(cars, buses, trucks, etc.) which pass through the measure-ment points.
The choice of the 44 fixed measurement points was
made considering their representative position on the road
networks and their speci fic traf ficflows, but also due to
their statistical dispersion in the analysed territory (Fig. 3).
The time intervals selected for measurements (Table 2)
Fig. 2 Methodological flow-chart.218 Front. Earth Sci. 2017, 11(2): 214 –228
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offprintwere: 6 –8 (corresponding to morning time and capturing
the period in which the inhabitants commute to their workplace and/or the time interval in which students go to
schools), 12 –14 (a representative time interval for two
reasons: economical activities are in full progress, reducingnoise pollution in residential areas, road traf fic is at low
levels, and it is a time interval in which students returnfrom school and the schedule of public transportintensi fies) and 16 –18, which captures the moment in
which the inhabitants return to the residential areas orprepare for other activities, thus the increased traf fic
intensity. Therefore all the three intervals de fine importantmoments during a day in which the traf fic from Cluj-
Napoca and Gheorgheni residential neighbourhood inten-sifies.
The database representing the built-up area (buildings
with different functions) was created as a vector databaseusing recent orthophotoplans. Considering the fact that theheight of the building plays an important part in soundpropagation and in the modelling process, the spatialdatabase was associated with numerical databases repre-senting the height of each building, varying between 2 mand 35 m.
The land use information was integrated into the modelTable 1 Database structure
Database Structure Attributes Type
Limit Vector-line – PrimaryMeasurement points Vector-point Measured value for each interval PrimaryBuildings Vector-polygon Z Primary
Roads Vector-polygon Name Primary
DEM Raster Z DerivedLand use Raster Land use type PrimarySound propagation maps Raster Sound intensity Modelled
Fig. 3 Map of measurement points.Ștefan BILA ȘCO et al. GIS model for identifying urban areas vulnerable to noise pollution 219
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offprintTable 2 Measured values in each point
No. LocationHourly intervals
6–81 2 –14 16 –18
1 Str. Constantin Brâncu și5 6 5 8 7 1
2 Str. Constantin Brâncu și6 9 6 2 6 6
3 Str. Constantin Brâncu și5 8 6 1 6 6
4 Str. Constantin Brâncu și5 9 6 6 7 0
5 Str. Constantin Brâncu și5 7 6 6 6 9
6 Str. Minerilor 56 58 63
7 Str. General Traian Mo șoiu∩Str. Aurel Suciu 67 55 69
8 Str. Actorului ∩Str. Bistri ței 48 52 54
9 Str. Aurel Suciu 54 66 6910 Aleea B ăișoara 65 69 61
11 Aleea Sl ănic 56 53 65
12 Aleea Valeriu Bologa 55 57 66
13 Aleea Sl ănic∩Str. Alexandru Vaida Voievod 67 77 61
14 Str. Baladei ∩Str. Venus 58 65 63
15 Str. General Traian Mo șoiu 66 65 75
16 Aleea Herculane ∩Aleea Borsec 54 50 58
17 Aleea Herculane ∩Aleea Snagov 56 62 59
18 Bd Nicolae Titulescu ∩Str. Liviu Rebreanu 58 62 64
19 Str. Tache Ionescu ∩Str. Liviu Rebreanu 57 63 64
20 Str. Albac ∩Str. Liviu Rebreanu 57 69 65
21 Aleea Herculane ∩Aleea Snagov 56 62 59
22 Bd Nicolae Titulescu ∩Str. Liviu Rebreanu 58 62 64
23 Str. Tache Ionescu ∩Str. Liviu Rebreanu 57 63 64
24 Str. Albac ∩Str. Liviu Rebreanu 57 69 65
25 Str. Heltai Gaspar ∩Str. Liviu Rebreanu 56 61 63
26 Str. Teodor Mihali 66 68 68
27 Str. Teodor Mihali 65 69 67
28 Str. Alexandru Vaida Voievod 60 62 6429 Str. Unirii 59 61 6230 Str. Unirii 58 61 6631 Aleea B ăișoara∩Aleea B ăița4 6 4 9 5 0
32 Aleea B ăi
șoara∩Aleea Sc ărișoara 48 50 55
33 Aleea Azuga 44 48 5534 Str. Albac 56 66 6835 Str. Albac ∩Str. Septimiu Albini 61 66 64
36 Aleea B ăișoara 53 58 60
37 Str. Constantin Brâncoveanu ∩Str. Madach Imre 54 55 56
38 Str. Axente Sever ∩Str. Madach Imre 51 54 53
39 Str. Tache Ionescu ∩Str. Madach Imre 52 50 56
40 Str. Secer ătorilor ∩Str. Mihai Veliciu 47 49 46
41 Str. Arie șului∩Str. Vasile Lupu 47 52 48
42 Str. Arie șului∩Str. Bu șteni 50 52 54
43 Str. Tache Ionescu ∩Str. Arie șului 48 44 51
44 Str. Vasile Lupu ∩Str. Aron Densu șianu 50 52 46220 Front. Earth Sci. 2017, 11(2): 214 –228
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offprintas a spatial database using as criteria the environmental
conditions which in fluence the propagation, absorption or
intensi fication of sound. The database was created mainly
based on speci fic levels represented by materials used in
the building and insulation of construction (brick, concrete,polyester) and green areas (parks or green areas withbushes and lawns).
The environmental conditions can be modelled by using
a derived GIS model based on the spatial vector land use
data. Thus, every land use class received a set of speci fic
attributes depending on its type and its in fluence on sound
propagation. Consequently, for the present work and thefrequency of 1000 Hz (a common frequency for an averagehearing), speci fic values have been associated with
different types of land use: 40 dB for concrete buildings,35 dB for brick buildings, 20 dB for parks or green areas
with bushes and buildings insulated with polyester, and 24
dB for herbaceous plants or grassland.
Through its con figuration, topography plays an impor-
tant role in blocking the sound propagation by in fluencing
wind speed and direction. As the application used in thepresent study does not take into consideration the height ofbuildings as a blocking factor in sound propagation, adigital elevation model was derived from topography and
building heights in order to capture this particular aspect.
In order to highlight the blocking effect of buildings for
sound propagation a derived DEM was generated with a10 m resolution which considers the area betweenbuildings to be flat (0 m elevation) and each cell of the
built-up area to have a speci fic elevation. Thus the DEM
will represent in the spatial analysis model a doubleblocking role: a topography barrier and a cumulated
topography-building elevation barrier where buildings are
present.
In order to complete the spatial analysis model with the
extension previously presented, it is necessary to includeadditional numerical data which in fluences sound propa-
gation: air temperature, wind speed and direction, thedegree of air humidity, hearing sensitivity, and generalatmospheric conditions (Table 3).
The digital mapping of the areas with different soundintensity generated by road traf fic was performed by using
two functions of the SpreAD-GIS toolbox (Reed et al,2010): Calculate noise propagation for one point con-
sidering the environmental conditions, in order to makeindividual maps for sound intensity corresponding to eachsound measurement point and every temporal interval; theresult is represented by 144 maps represented by rasterdatabases. The function sum noise propagation for multi-
ple points enables the integration of the calculated
databases for each individual point in a unitary databasefor the whole study area, resulting three maps (for speci fic
hours) represented by raster databases (Figs. 3 –6).
3 Results, discussion, model validation and
conclusions
The analysis of results focused both on the spatial
distribution of sound intensity areas in order to highlight
the effect of traf fic at representative time intervals and on
the change in sound intensity between speci fic hours in
order to identify possible causes and issue recommenda-tions for noise pollution mitigation measures.
3.1 Results and discussion
All the resulting rasters were classi fied so that the intervals
corresponded to the intensity of known phenomena, inorder to facilitate an interpretation of the resulting levels(according to STAS 10009-88
1), modi fied): 0 –10 dB
corresponding to the sound of swishing leaves; 10.1 –12
dB–whispered conversation; 12.1 –20 dB –clock ticking;
20.1–30 dB –birds singing; 30.1 –40 dB –quiet of fice;
40.1–50 dB –quiet conversation; 50.1 –60 dB –busy of fice.
The sound intensity values modelled for the 6 –8 time
interval range from 0 to 49.94 dB (Fig. 4), highlighting twoareas with large differences in modelled sound intensity:thefirst area with values ranging up to 12 dB is located in
the space between Bistri ței and Arie șului streets. This area
is characterised by short buildings with elder inhabitantswhich keeps the morning traf fic at low levels due to the
presence of few residents in these zones and the vicinity of
the public transportation stations which are located on the
main road artery (Nicolae Titulescu Street crosses theneighbourhood).
The quietest zone is located in the north of the
neighbourhood. This is due to the presence of tall buildingssurrounding the area and acting as a barrier against thenoise on the neighbouring boulevards. The second zone ischaracterised by high levels of sound intensity and is
spatially located at the crossroads of the main road arteries.
Such hotspots are at the crossroad between CaleaDoroban ților and Teodor Mihali streets, which is crossed
by the traf ficflow from the central zone towards theTable 3 General conditions and numerical databases used in the
present work
Parameter Characteristic value
Air temperature 19°C (66.2°F)Wind speed 13 km/h (8.08 mph)Wind direction SE (120°)Relative air humidity 28 %
General atmospheric conditions Clear sky, summer day
Hearing sensitivity 1 kHz
1) STAS 10009-88: Building acoustics. Urban zone acoustics. Maximal noise level.Ștefan BILA ȘCO et al. GIS model for identifying urban areas vulnerable to noise pollution 221
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offprintMărăști working neighbourhood as well as by the traf fic
flow from Gheorgheni neighbourhood towards M ărăști
neighbourhood and the eastern city exit. Another important
hotspot in fluenced by traf ficflows was identi fied by the
modelling process at the crossroad of the main road arteriespassing through the neighbourhood: Nicolae TitulescuBoulevard, Unirii Street, Liviu Rebreanu Street and AleeaSlănic Street. Another cause for this hotspot is the presence
of the Interservisan Clinic, which attracts a large number ofpeople. One also must notice Constanti Brâncu și Street as a
transit road artery between neighbourhoods negatively
influencing the noise pollution of the surrounding areas as
it carries a great traf ficflow from the new residential
neighbourhoods from the southern part of the city towardsthe city centre. The modelling of high levels of soundintensity and the identi fication of hotspots negatively
influencing the environment is also determined by the
presence of administrative buildings of great interest forthe population: on Traian Mo șoiu Street there is the
Military Regional Hospital where the modelled sound
values were higher than 30 dB, on Teodor Mihali Streetclose to the crossroad with Sl ănic Street where there is the
largest building of the Babe șBolyai University and where
most of the students study during the day. In the closevicinity there are also the student dorms and the of fice
buildings where people go every morning to work.
A large number of people going to work and producing
noise pollution is what highlights the area of Aleea
Herculane and Septimiu Albini streets, the former beingcharacterised only by a large crowding of people, while thelatter is also in fluenced by the existence of main road
junctions used as exits from or entrances into theneighbourhood.
In the 12 –14 time interval, the sound intensity values
vary between 0 –58.22 dB (Fig. 5). The maximum values of
sound intensity can be identi fied in almost the same areas
exposed to noise pollution during the morning hours but ata lower intensity. One can thus identify a hotspot at thecrossroads between Septimiu Albini Street and NicolaeTitulescu Boulevard, which is justi fied through the
intensifying of the traf ficflows from the areas close to
schools towards the residential zones of the neighbourhoodand the city centre.
The vulnerable areas with values of sound intensity over
30 dB are spatially limited: the crossroad between NicolaeTitulescu Boulevard and Septimiu Albini Street, which isusually a crowded point; Unirii Street; the crossroadbetween Nicolae Titulescu Boulevard and Aleea Sl ănic
Fig. 4 Sound propagation model for the 6 –8 time interval.222 Front. Earth Sci. 2017, 11(2): 214 –228
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offprintStreet, as well as Liviu Rebreanu Street. In the daytime, the
predominant values range between 20 –30 dB. The quiet
zone is located in the same area as in the first time interval,
however, it has a smaller extension.
An increase in sound intensity can be noticed in the 16 –
18 time interval up to a maximum value of 54.16 dB,which is also the noisiest period during the day.
The quiet areas shrink even more from the previously
analysed time interval or the morning interval, a large partof the quiet areas from the north of the neighbourhoodbeing included in the vulnerable category of high levels of
noise. Values ranging between 30 –40 dB are predominant.
What is different from the first two time intervals is the fact
that the highest values (40 –50 dB) are found only in small
areas, in the access zones towards M ărăști neighbourhood
(Calea Doroban ților Street, General Traian Mo șoiu Street).
In this context, another hotspot can be identi fied in the
Iulius Mall area which is closely located to the tall blocksofflats from Aleea Herculane Street and the surrounding
space but also to the of fice buildings. One must notice that
the hotspot which was previously identi fied in the area of
the UBB building is no longer present in the class of highvulnerability and is included instead in the low vulner-ability class with values ranging between 0 and 20 dB. Atthe same time, the quiet area from the north of the
neighbourhood migrates to the east as well, close to TeodorMihali Street, a fact which indicates the reorientation of the
trafficflows from this artery to other road arteries (mainly
Nicolae Titulescu Boulevard and Unirii Street) whenpeople return from their work places.
The blocking effect of buildings in sound propagation is
clearly re flected by the results. Although the major traf fic
flows are concentrated on the main arteries which are
flanked by tall blocks of flats (8 –10 storeys), the sound
intensity behind them is included in the category of low
vulnerability, while on other segments of the same road
arteries which are flanked by short buildings sound travels
farther and at a higher intensity. Between tall blocks offlats, there are areas where sound intensity values tend to
concentrate, as a result one can identify several groupedsmall areas with high values (Fig. 6).
In order to highlight the differences between the sound
values in the three day time intervals, speci fic equations of
GIS spatial analysis were used based on mathematical
operators implemented in ArcGIS software.
When comparing the first two intervals (12 –14, 6 –8) one
notices that the areas which are usually crowded in themorning when the inhabitants go to work are characterised
Fig. 5 Sound propagation model for the 12 –14 time interval.Ștefan BILA ȘCO et al. GIS model for identifying urban areas vulnerable to noise pollution 223
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offprintby low values at noon. Such situations are encountered at
the main crossroads and the access point from ColoniaBorhanci. General Traian Mo șoiu and Valeriu Brani ște
streets, which are usually used as shortcuts towards the
centre or towards Calea Doroban ților Street have lower
values. In the last time interval, when people leave work,the values rise again. Hence, these areas get crowdedmainly at rush hours.
In the time interval 12 –14 (Fig. 7), in the area around
Colegiul Energetic the noise is at lower levels thanbetween 6 and 8 o'clock and in the 16 –18 time interval
(Fig. 8). The main reason for this is that the transport flows
are directed towards the school in the morning for thestudents to get to their courses and in the evening thetransport flow brings the students home from school and
their parents from their work places to their residentialzones.
An important case from one of the hotspots was
identi fied in the first two time intervals and is represented
by Albac Street and the crossroad between Albac Street
and Diaconu Coresii Street, where there is the Road PoliceStation. When analysing the changes in sound intensity,one can notice an important decrease in sound intensitybetween the 16 –18 and 12 –14 time intervals, as the activityof the station ends and with it the transport flows directed
towards it during the day, the remaining noise beinggenerated only by the normal residential traf fic.
3.2 Model validation
The validation of the mapping and prediction model for the
vulnerable areas to noise pollution was performed usingthe direct comparison method of the modelling results withsamples of manually measured values. For this, four soundintensity measuring stations were placed in four points
differently in fluenced by environmental factors which
determine sound propagation (Fig. 9).
Thefirst point of direct measurement was located behind
tall blocks of flats which block sound propagation, the
second station was placed in the quiet area of the northernpart of the neighbourhood, the third point of directmeasurements was placed in the vicinity of the maincrossroad where traf ficflows have high values and the
fourth station was placed in the block of flats area close to
Septimiu Albini Street, which was modelled as having anaverage to high sound intensity.
The direct comparison produced the following results
(Table 4).
Fig. 6 Sound propagation model for the 16 –18 time interval.224 Front. Earth Sci. 2017, 11(2): 214 –228
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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.Ștefan BILA ȘCO et al. GIS model for identifying urban areas vulnerable to noise pollution 225
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offprintBy analysing the direct comparison of the sound
intensity, the measured and the modelled values are almostequal, thus the model is validated for the study area. As aresult, the model can be successfully applied to urban areastaking into consideration the fact that it fits into the
accepted error range ( >95%) and the point values are
included in the 0 %–5%interval of prediction both for the
overestimation and underestimation of the values.
3.3 Conclusions
Environmental noise has become a daily problem in bigcities with road traf fic as its main cause. Cluj-Napoca
Municipality has a progressively growing population andan increasingly intense traf fic with many traf fic jams and a
reduced flow at rush hours. Therefore, it is important to
know the current situation of noise in the city and to
d e v e l o pa na c t i o np l a ni nt h ea r e a sw h e r es p e c i fic
thresholds are surpassed. Among the possible measureswhich could be implemented there are: the limitation ofvehicle access on certain streets, reducing the speed limit,and repaving the streets where the surface of the roadpavement is of low quality. The Gheorgheni neighbour-hood is considered a quiet area of Cluj. However, it hasbecome more of a city zone with many green areas. The
building of Iulius Mall shopping centre, the Faculty ofEconomical Sciences, clinics and of fice buildings as well
as the placement of buildings belonging to the local publicadministration have led to the increase of road traf fic in this
neighbourhood.
Traffic jams and areas with high noise pollution can be
frequently identi fied, especially on the main arteries and at
the main crossroads: Nicolae Titulescu Boulevard, Unirii
Street, Aleea Sl ănic Street, Teodor Mihali Street.
The implementation of a GIS model in the monitoring
and modelling of sound propagation can facilitate theprocessing and analysis of data and the creation ofscenarios with the main purpose of offering solutions forlowering the vulnerability and the risk to noise pollution.
The software and the extensions used for digital
mapping and modelling of sound offer impressive results
and are characterised by high accuracy, being compatiblewith various database formats. As a result, they becomeuseful for the local administration and allow the reductionof analysis and decision time.
As possible solutions for reducing noise and its negative
effects on humans the present work includes: encourage-ment of the inhabitants to use public transportation orTable 4 Sound intensity in the validation points
Valitation point Sound intensity (calculated) Sound intensity (measured) Difference
1 32.5 dB 32.4 dB –0.1 dB (0.30 %)
2 10.3 dB 10.5 dB 0.2 dB (1.90 %)
3 36.6 dB 36.7 dB 0.1 dB (0.27 %)
4 26.1 dB 26.3 dB 0.2 dB (0.76 %)
Validation 105.5 dB 105.9 dB 0.4 dB (96 %)
Fig. 9 Position of validation points.226 Front. Earth Sci. 2017, 11(2): 214 –228
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offprintbicycles in order to reduce traf fic, and increasing the
number of spaces for parks and green areas which absorband diffuse sound waves. These actions would have anadditional positive effect, reducing both noise and airpollution.
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