Measurement and evaluation of the environmental noiselevels in the urban areas of the city of Nis (Serbia)

Momir R. Prascevic & Darko I. Mihajlov &

Dragan S. Cvetkovic

Received: 14 April 2013 /Accepted: 14 September 2013 /Published online: 3 October 2013# Springer Science+Business Media Dordrecht 2013

Abstract The environmental noise level represents oneof the key factors of life quality in urban areas of moderncities. A continuous monitoring of the noise levels andthe analysis of results have become a necessity when wediscuss a possible recovery of those areas with highlevels of noise pollution, and particularly, those zoneswhich were designed for specific activities, e.g., areasaround hospitals and schools. The city of Nis, Serbia,owing to the permanent long-term noise monitoring,possesses a database containing figures related to thenoise levels at relevant locations in the city, which canserve as a basis for an analysis of the change of condi-tions, their tendencies in the future, and recognizingfactors which influence the danger of noise pollution.The paper involves an analysis of the environmentalnoise level collected during the previous years.

Keywords Noise figure . Noise measurement . Trafficnoise . Noise pollution . Noise assessment .

Environmental noise

Introduction

Noise pollution represents a major problem in the envi-ronment of most urban areas. However, the problem ofnoise has not been approached properly so far, and notenough attention has been paid to it in spite of the fact thatit has a great impact on the quality of life of the endan-gered population. Reasons for such an approach could befound in the definition of noise as a subjective experienceof various external events, in its specific character, as wellas in the difficulties connected to relating the causes withthe effects it has on general health (Prascevic andCvetkovic 2005).

The results of medical studies (Langdon and Griffiths1981; Maschke et al. 1999; Skanberg and Ohrstrom2002) have shown that noise can have very adverseeffects on human health and thus have justified the needfor further explorations aiming at a better understandingand an improved control over noise.

Moreover, the level of environmental noise pollutionhas a great economic impact on real estate prices inresidential and business areas. Theebe (2004) has shownthat traffic noise cause a 5 % drop in real estate prices onthe average, and that the percentage can go up to 12% intimes of economic growth.

These facts stimulated researchers from all around theworld to dedicate more time to studying and defining theissue of traffic noise (Pandya 2001; Korfali andMassoud2003; Georgiadou et al. 2004; Jamrah et al. 2006;Omokhodion et al. 2008; Doygum and Gurun 2008).

The latest data related to the environmental noisepollution (Vos and Licitra 2012), collected from the

Environ Monit Assess (2014) 186:1157–1165DOI 10.1007/s10661-013-3446-2

M. R. Prascevic (*) :D. I. Mihajlov :D. S. CvetkovicFaculty of Occupational Safety, Department of Noiseand Vibration, University of Nis, Carnojevica 10a,18000 Nis, Serbiae-mail: momir.prascevic@znrfak.ni.ac.rs

D. I. Mihajlove-mail: darko.mihajlov@znrfak.ni.ac.rs

D. S. Cvetkovice-mail: dragan.cvetkovic@znrfak.ni.ac.rs

first round of strategic noise mapping for the EU ag-glomerations, indicate that 54 % of the population ofurban areas (56,001,200 inhabitants) is exposed today–evening–night noise levels above 55 dB(A),whereas 15% of the population (15,754,500 inhabitants)is exposed to day–evening–night noise levels above65 dB(A). Besides this, an additional 33,437,244 inhab-itants outside agglomerations live in areas where day–evening–night noise levels are above 55 dB(A) and7,657,083 live in areas where day–evening–night noiselevels are above 65 dB(A). Out of the total of 89,438,444inhabitants exposed to day–evening–night noise levelsabove 55 dB(A), almost 89million people are exposed tothe noise generated by traffic (road and railroad traffic, aswell as airplanes). The number of people exposed to thenoise generated by road traffic is close to 68 million,which designates it as the dominant source of noise andthe main cause of disturbance and anxiety in people.

If we take into consideration the facts that the noiselevel of above 55 dB(A) causes unpleasant feelings,aggressive behavior, and sleep disorders, and that per-manent exposure to the level noise of above 65 dB(A)can cause hypertension and that permanent exposure tothe noise level of above 75 dB(A) leads to higher stresslevels, increases the number of people with heart dis-orders, and can lead to hearing damage (Doygum andGurun 2008), it becomes clear that traffic planning andprotecting inhabitants of urban areas from traffic noiserequire a far more serious approach.

The conditions related to noise pollution in the cityof Nis are, in many ways, similar to conditions in otherurban environments. Collecting information on trafficcharacteristics and noise levels and updating it over alonger period has proven to be crucial to the evaluationand management of environmental noise. Furthermore,measurement and evaluation of traffic noise are

Fig. 1 Distribution of measurement spots for environmental noise monitoring in the city of Nis

1158 Environ Monit Assess (2014) 186:1157–1165

important activities which may result in the develop-ment of efficient methods for noise control.

Data on noise levels in the city of Nis have beensystematically collected and analyzed through the pro-ject of monitoring the noise level during a number ofyears starting from 1995 (Prascevic et al. 1995;Cvetkovic et al. 1997a; Mihajlov et al. 2008; Mihajlovet al. 2012). The obtained results give us an insight intothe current condition of the noise level at specificlocations, allowing us to compare them to previousmeasurement results and use this to evaluate tenden-cies related to possible changes in the future.

The elements of environmental noise levelmonitoring

Environmental noise level monitoring in Serbia isperformed in several cities and it is pursuant to thelaw of protection against environmental noise and theaccompanying regulations. Although these regulations

are in accordance with the national standards (SSI2010a, b), the methodology of noise monitoring variesin different cities. The issues which differ include asfollows: the number of measurement spots; the numberof daily, weekly, and monthly measurement intervals;the duration of measurement intervals; and measure-ment parameters and noise indicators used for noiseevaluation (Pljakic et al. 2012). Different measurementprocedures result from different city configurations,the traffic structure, the traffic flow, the arrangementof noise-sensitive objects, and different shares of noisesources.

The city of Nis belongs to the group of medium-sized cities, with around 300,000 inhabitants. Duringthe past decades, it has been growing and taking upmore territory, which has been followed by numerouschanges in regard to urbanization, industrialization,having a larger traffic network, and greater infrastruc-ture. Recently, the city has been particularly exposed toan increased frequency of road traffic, which, in thegiven circumstances of traffic infrastructure, represents

Table 1 Distribution of mea-surement spots per the land useclassification/zone (presented inTable 4)

Land use classification/zone No. of meas. spots

Rest and recreation/zone 1 3

Touristic areas/zone 2 2

School areas/zone 2 6

Residential areas/zone 3 7

Business-residential areas, commercial-residential areas/zone 4 8

City center, trade, commercial, administrative zones with dwellings/zone 5 3

Areas along the city roads/zone 5 15

40

45

50

55

60

65

70

75

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

No. of measurement spot

Ld

ay

[d

B(A

)]

2008

2009

2010

blackspots

Fig. 2 Yearly averaged values of Lday for all measurement spots

Environ Monit Assess (2014) 186:1157–1165 1159

a crucial factor in the increase of noise pollution. As itis generally assumed that the noise emission of vehiclesincrease with age, tear, and wear (Sandberg 2001), theaverage age of cars in Serbia (11 years old) and theaverage age of public transport buses (15 years old),give only a preliminary picture of the main sources ofcommunal noise on the territory of the city of Nis.Although, many investigations do not confirm that therewould be a significant noise increase in noise emissionof cars with age, the modern cars with more advancednoise reduction measures may be more sensitive to age-related impairment (Sandberg 2001). This effect is morepronounced for heavy vehicles (Sandberg 2001).

Conditions related to environmental noise at specif-ic spots in the city and their levels of noise pollution

depend on a number of factors, such as: the type ofvehicles taking part in traffic, passing frequency ofspecific vehicles on specific roads during referentialperiods, road characteristics (its width, the number oflanes, whether it is a one-way or a two-way road, aboulevard, the type and quality of the surface, itsslope), movement speed, and presence of specializedor natural sound barriers (greenery along the road,parks, etc.) between the road and the area of interest.

Environmental noise level monitoring in the city ofNis is performed by means of systematic and periodicalmeasurements, examination, and evaluation of noiseindicators—physical dimensions which describe noisein an environment and which are related to the adverseeffects of noise.

35

40

45

50

55

60

65

70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

No. of measurement spot

Ln

igh

t [d

B(A

)]

2008

2009

2010

blackspots

75

Fig. 3 Yearly averaged values of Lnight for all measurement spots

Table 2 The yearly percentageof the measurement spots withthe values of Lday and Lnightwithin the specific range

Noise level range[dB(A)]

Year

2008 2009 2010 2011

Noise descriptor

Lday Lnight Lday Lnight Lday Lnight Lday Lnight%

40÷45 – 3.85 – 2.27 – 2.27 – 2.27

46÷50 1.92 7.69 2.27 9.09 2.27 6.82 – –

51÷55 5.77 13.46 2.27 15.91 2.27 15.91 2.27 6.82

56÷60 15.38 42.31 20.45 38.64 20.45 45.45 4.55 27.27

61÷65 36.54 26.92 34.09 29.55 38.64 29.55 22.73 52.27

66÷70 32.69 3.85 38.64 4.55 34.05 – 59.09 11.36

>70 7.69 – 2.27 – 2.27 – 11.36 –

1160 Environ Monit Assess (2014) 186:1157–1165

The primary aims of environmental noise monitor-ing are to assess the environmental noise pollution dueto traffic noise, rate noise exposure in the differentareas of the city, to predict traffic noise levels byNAISS model and to compare predicted and measurednoise levels in order to validation of NAISS model.The NAISS model used for traffic noise prediction inthe city of Nis is described in detail by Prascevic et al.(1997) and Cvetkovic et al. (1997b).

For the purpose of the environmental noise levelmonitoring, continuous measurements of the soundpressure level are performed and they define its timedependencies at 44 measurement spots within all fivecity municipalities. There are the different methods forthe selection measurement spots (Doygum and Gurun2008). The choice of measurement spots was done inaccordance with population and residential location,characteristics of land uses, and road functions andstructure. The distribution of the measurements spotsis given in Fig. 1 and Table 1.

The environmental noise level monitoring is orga-nized on amonthly basis, for the reference time intervals:daytime (06:00÷22:00) and nighttime (22:00÷06:00).The procedure of continuous noise level monitoring lastsfor 12 months (the long-time interval).

The measurement time intervals were chosen insuch a way that they encompass the whole cycle ofnoise level changes during the reference time intervals.One measurement interval lasts for 15 min. The day-time measurement interval is divided into 16 one-hourperiods, whereas the nighttime measurement interval isdivided into 8 one-hour periods, which means thatduring 24 h, there are 24 measurement periods. At eachmeasurement spot, within 1 cycle/month, there is one15-min measurement in each of the 24 measurementperiods.

Yearly measurement dynamics involve definingthe time dependencies of the current noise levelsat 44 measurement spots within the defined mea-surement locations (Fig. 1), which means 1,056

Table 3 The yearly percentageof the measurement spots whereLday and Lnight exceeded theblack spots values

Exceeding range[dB(A)]

Year

2008 2009 2010 2011

Noise descriptor

Lday Lnight Lday Lnight Lday Lnight Lday Lnight%

NO 59.62 25.00 59.09 27.27 63.64 25.00 29.55 9.09

1÷5 32.69 42.31 38.64 38.64 34.09 45.45 59.09 27.27

6÷10 7.69 26.92 2.27 29.55 2.27 29.55 11.36 52.27

11÷15 – 3.85 – 4.55 – – – 11.36

Table 4 Limit values for outdoor noise indicators in Serbia

Zone Land use Noise level in dB (A)

Day and evening Night

1. For rest and recreation, hospitals and recoveryfacilities, cultural-historical locations, large parks

50 40

2. Touristic areas, camps and school zones 50 45

3. Residential areas 55 45

4. Business-residential areas, commercial-residential areas,and children's playgrounds

60 50

5. City center, trade, commercial, administrative zones withdwellings, areas along the motorways, main roads, and city roads

65 55

6. Industrial, storage, and servicing areas and transportterminals without dwellings

At this area borders, noise must not exceedthe limit value of the neighboring area

Environ Monit Assess (2014) 186:1157–1165 1161

measurements of the noise parameters, accompaniedby defining the traffic and road parameters. Theprocedure of noise level monitoring at each measure-ment spot is determined by the following measure-ment parameters:

& Noise parameters: equivalent noise level (Leq), per-centile noise level (L10, L50, and L90), noise profile,noise spectrum;

& Traffic parameters: passenger car frequency, lightand heavy truck frequency, bus and motorcyclefrequency;

& Road parameters: the type and width of the road,the type of surface, the height of buildings alongthe road.

The measurement equipment made based on thehand-held analyzer type Brüel & Kjær 2250, was usedfor environmental noise monitoring. This instrumenthas everything needed to perform high-precision, class1 measurement tasks in 4.2–22.4 kHz linear frequencyrange and in 16.6–140 dB A-weighted dynamic range.The hand-held analyzer was calibrated using calibratortype Brüel & Kjær 4230 giving a calibration level of94 dB(A). The A-weighted continuous equivalentnoise level Leq was measured.

The measuring equipment was placed 1.5 m abovethe ground and 7.5 m from the centerline of the traffic

lane (for the areas along the city roads) and 10–15 mfrom the centerline of the traffic lane (for other mea-surements spots).

Analysis and assessment of environmental noiselevels

The described procedure of environmental noise mon-itoring produced important quantity data related to thenoise levels in the urban areas of the city of Nis. On thebasis of these results, a long-term yearly average of theequivalent noise levels, Leq,LT, for all measurementintervals were calculated according to the followingequation:

Leq;LT ¼ 10log1

n

X

i¼1

n

100:1⋅Leq;i ð1Þ

where Leq,i is the equivalent noise level in the ith

sample and n is the number of samples of the measure-ment intervals (n=24).

It was necessary to create a time-average of thelong-term yearly average of the equivalent noise levelsin order to express the levels in one figure, separatelyfor the day-time period and the night-time period. Insuch a way, the day-time equivalent noise level (Lday)

Table 5 The values of NPT parameters and the maximal deviations (exceeding of the black spots values)

Period Year

2008 2009 2010 2011

NPT ΔLmax NPT ΔLmax NPT ΔLmax NPT ΔLmax

Percent (%) [dB(A)] Percent (%) [dB(A)] Percent (%) [dB(A)] Percent (%) [dB(A)]

Daytime 40.4 10 40.9 8 46.4 9 59.0 9

Nighttime 75 16 72.7 14 75.0 14 90.9 14

Table 6 The values of NEP parameters and the maximal deviations (exceeding of the black spots values)

Period Year

2008 2009 2010 2011

NEP ΔLmax NEP ΔLmax NEP ΔLmax NEP ΔLmax

Percent (%) [dB(A)] Percent (%) [dB(A)] Percent (%) [dB(A)] Percent (%) [dB(A)]

Daytime 59.0 18 59.0 17 59.0 18 70.5 19

Nighttime 93.0 20 93.0 18 93.0 18 93 22

1162 Environ Monit Assess (2014) 186:1157–1165

and the night-time equivalent noise level (Lnight) werecalculated according to the following equations:

Lday ¼ 10log1

16

X

i¼1

n

100:1⋅Leq;LT ;i ð2Þ

Lnight ¼ 10log1

8

X

i¼1

n

100:1⋅Leq;LT ;i ð3Þ

On the basis of the noise level monitoring results forthe years 2008, 2009, 2010, and 2011, it is possible tocreate an overview of the noise levels on the territory ofthe city of Nis in the previous period.

Figures 2 and 3 compare the average yearly noiselevels for the day-time and the night-time intervals,

respectively. The figure survey involves all 44 measure-ment spots and the data for 2008, 2009, and 2010. Thesefigures also indicate the highest allowed noise level valuesin open areas (black spots)

Yearly averaged values of Lday vary from 47 to75 dB(A) and yearly averaged values of Lnight variesfrom 40 to 71 dB(A).

An essential piece of information in the process ofevaluating noise pollution is the yearly percentage of themeasurement spots with the values of Lday and Lnightwithin the specific range (Table 2), followed by the yearlypercentage of the measurement spots where the values ofLday and Lnight exceeded the black spot values (Table 3).

By analysis of yearly averaged values of Lday andLnight, noise pollution territory parameter (NPT) pre-senting percentage of territory polluted with noise,where the values of Lday and Lnight are higher thanblack spots values and noise exposure people parame-ter (NEP) presenting percentage of population exposedto the noise, which levels are higher than limit noiselevels specified by Serbian Regulation on Noise Indi-cators, Limit Values, Assessment Methods for Indica-tors of Noise, Disturbance and Harmful Effects ofNoise in the Environment (SR 2010) were calculated.

The limit values of noise levels for six land-useclasses are summarized in Table 4. The six homoge-neous areas are characterized by different noise limitvalues on three temporal periods, referred to day period

86.7%

4.0%

1.3% 5.3%2.7%

Cars

Light-duty vehicles

Heavy-duty vehicles

Buses

Motorcycles

Fig. 4 Average vehicle structure in 2011

Table 7 The average daily ve-hicle volumes between the years2008 - 2011

Mark of meas. spot City street name Year

2008 2009 2010 2011

1.2 Bul. Nemanjica 21,152 21,458 22,589 26,371

1.4 Miljkovica str. 9,231 9,105 9,536 11,416

2.1 Sremska str. 9,953 10,123 10,850 12,273

2.3 Bul. Vizantijski 8,832 8,945 9,150 11,677

3.2 Bul. Djindjica 19,150 19,985 21,589 24,040

4.2 King Milan sqv. 20,125 19,589 20,985 23,944

5.1 Obilicev venac 10,125 10,998 10,896 11,364

6.1 Bul. 12 februar 17,501 17,952 18,952 22,591

6.4 Bul. N. Tesle 11,234 10,596 11,675 14,836

7.2 D. Tucovica str. 20,563 21,578 21,895 26,104

8.1 V. Gojka str. 9,253 9,879 9,578 11,895

9.1 Knjazevacka str. 17,976 17,258 18,941 21,564

9.4 Proleterska str. 14,325 14,986 15,432 16,389

10.3 Somborska str. 10,054 10,980 11,563 14,852

11.1 Bul. Konstantina 6,975 6,523 7,953 10,373

Environ Monit Assess (2014) 186:1157–1165 1163

(06–18), evening period (18–22), and night period(22–06). Limit values are equal for day and eveningtime. The limit values refer to overall noise from allsources in the considered area.

The value of NPT and NEP parameters are given inTables 5 and 6 as well as the maximal deviation of Ldayand Lnight values from black spots values and the limitnoise level specified by national regulations.

Percentage of population exposed to the excessivenoise was constant in the years 2008–2010. It is noticeda significant increase of percentage of population ex-posed to the excessive noise during day-time in 2011.This increase is the result of a significant increase in thenumber of motor vehicles in 2011 (especially cars),whose structure is given in Fig. 4. The average dailyvehicle volumes for the measurements spots in areasalong the city roads for the years 2008–2011 are givenin Table 7. The average daily vehicle volumes werecalculated based on traffic counting during the noiselevel monitoring procedure at each measurement spot.

Conclusion

The aim of this study was to analyze a large andreliable amount of data collected by environmentalnoise monitoring in the urban areas of the city of Nis.The results show that the noise levels obtained in thecity as a whole are quite high. It is due to commercialand communal activities, industry, and traffic. Howev-er, we can conclude that traffic noise pervades the cityof Nis more than any other type of noise.

Percentages of territory polluted with noise, whoseequivalent levels are higher than the black spot values,as well as the percentage of population exposed tonoise whose levels are higher than the permissiblenoise levels in the analyzed locations, are very high.The increase in these parameters, especially in 2011, iscaused by the increase in traffic flux.

The analysis of the environmental noise monitoringon the territory of the city of Nis for the previous 4-yearperiod (2008, 2009, 2010, and 2011) leads us towardsthe following conclusions:

& Motor vehicle traffic largely influenced the noiselevels;

& The share of cars in the structure of vehicle was aslarge as 86.7%;

& The violations of the limit noise levels were thegravest during the night-time measurement inter-val; one of the main reasons for this was the higheroverall speed due to the lower traffic intensity;

& The extent of exceeding the limit noise levels had atendency of growing with time, especially for thenight-time period in noise-sensitive areas (e.g.,around schools and hospitals);

& It is necessary to take serious mitigation measuresto reduce and control noise pollution at all loca-tions which are proven to be endangered, first of allby limiting vehicle speed, maintaining of road-surface, and implementing noise control measuresas barriers and silent asphalt.

Acknowledgments This research is part of the project “Devel-opment of methodology and means for noise protection fromurban areas” (No. TR-037020) and “Improvement of the moni-toring system and the assessment of a long-term populationexposure to pollutant substances in the environment using neuralnetworks” (No. III-43014). The authors gratefully acknowledgethe financial support of the Serbian Ministry for Education,Science and Technological Development for this work.

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