evaluation of traffic noise pollution in amman, jordan

Environmental Monitoring and Assessment (2006) 120: 499–525

DOI: 10.1007/s10661-005-9077-5 c© Springer 2006

EVALUATION OF TRAFFIC NOISE POLLUTION IN AMMAN, JORDAN

AHMAD JAMRAH1,∗, ABBAS AL-OMARI2 and REEM SHARABI1

1Department of Civil Engineering and 2Water and Environment Research and Study Center,University of Jordan, Amman 11942, Jordan

(∗author for correspondence, e-mails: [email protected]; [email protected])

(Received 22 July 2005; accepted 7 October 2005)

Abstract. The City of Amman, Jordan, has been subjected to persistent increase in road traffic due to

overall increase in prosperity, fast development and expansion of economy, travel and tourism. This

study investigates traffic noise pollution in Amman. Road traffic noise index L10(1 h) was measured

at 28 locations that cover most of the City of Amman. Noise measurements were carried out at these

28 locations two times a day for a period of one hour during the early morning and early evening

rush hours, in the presence and absence of a barrier. The Calculation of Road Traffic Noise (CRTN)

prediction model was employed to predict noise levels at the locations chosen for the study. Data

required for the model include traffic volume, speed, percentage of heavy vehicles, road surface,

gradient, obstructions, distance, noise path, intervening ground, effect of shielding, and angle of view.

The results of the investigation showed that the minimum and the maximum noise levels are 46 dB(A)

and 81 dB(A) during day-time and 58 dB(A) and 71 dB(A) during night-time. The measured noise

level exceeded the 62 dB(A) acceptable limit at most of the locations. The CTRN prediction model

was successful in predicting noise levels at most of the locations chosen for this investigation, with

more accurate predictions for night-time measurements.

Keywords: noise pollution, road traffic noise, CRTN prediction model

Introduction

Noise pollution is a significant environmental problem in many urban areas. Thisproblem has not been properly recognized despite the fact that it is steadily growingin developing countries (Barboza et al., 1995). Davis and Masten (2004) stated threevalid reasons as to why widespread recognition of noise pollution problem has notmaterialized in a similar fashion as have air and water pollution problems. Thesereasons are summarized in the definition and perception of noise as a subjectiveexperience, short decay time, and difficulty to associate cause with effect when itcomes to health impacts.

Existing evidence indicating that noise pollution may have negative impacts onhuman health has justified research in order to provide better understanding of noisepollution problems and control (Georgiadou et al., 2004). Noise pollution has beenstated as a serious health hazard (Bies and Hansen, 1996), with noise-related damageto humans ranging from annoyance to insanity and death (Mato and Mufuruki,1999). Maschke (1999) treated the impact of noise as a stress inductor, and statedthat induced stress by noise has a psychosocial component. Nelson (1987) reported

500 A. JAMRAH ET AL.

that long term exposure to high occupational noise can result in permanent hearingloss. Additionally, commonly experienced noise effects may include annoyance,deterioration of sleep quality, and stress-related ischaemic heart disease (NHC,1997; Morrell et al., 1997).

Adverse effects due to exposure to noise may include interference with speechcommunication and decreasing children’s learning skills (Mato and Mufuruki,1999). More recently, attempts have been made to estimate health and economic im-pacts due to noise pollution. Franssen et al. (2002) showed that significant portionof hypertension is attributed to aircraft noise when they presented a comprehensiveapproach for assessing health consequences in environmental impact assessmentdue to the noise resulting from the operation of Schiphol airport in Amsterdam.Moreover, there have been some attempts to financially quantify the cost of dam-ages to residential areas and environment due to noise pollution (Levinson andGillen, 1997; Theebe, 2004).

Traffic is the dominating source of noise (Skanberg and Ohrstrom, 2002) and isthe major source of nuisance and annoyance as cited in social surveys (Pandya,2003). Additionally, traffic noise has significant economical impacts on houseprices. Theebe (2004) reported that, in a rising market, the impacts of traffic noiseon house prices reached a maximum of 12 percent with an average of about 5 per-cent. This has lead researchers in many countries to investigate and characterizetraffic noise pollution problems (Sommerhoff et al., 2004; Piccolo et al., 2004;Georgiadou, 2004; Theebe, 2004; Abo-Qudais and Alhiary, 2004; Pandya, 2003;Ramis et al., 2003; Bhadram, 2003; Zannin et al., 2001, 2002; Abdel-Raziq et al.,2000; Zeid et al., 2000; Suksaard et al., 1999; Funk and Rabl, 1999; Stoilova andStoilov, 1998; Mateu and Vicente, 1997; Zheng, 1996).

The noise pollution situation in the Greater Municipality of Amman, Jordan,is similar to that in many urban areas. Actual traffic noise data and informationin Amman are very scarce. Data collection and agglomeration is one of the im-portant elements in the assessment and management of urban environmental noise(Sommerhoff et al., 2004). Analysis of traffic noise generally constitutes an im-portant component of any environmental impact assessment which is needed forhighway development and improvement (Bhattacharya et al., 2002). Additionally,measurement and estimation of traffic noise are significant tasks that can lead tothe development of efficient methods of control (Geordiadou, 2004).

The city of Amman is a relatively large city with a population of about 2 mil-lion. The city has been expanding continuously in all directions in the past twodecades. Many significant changes have been experienced in terms of urbanization,industrialization, expansion of road network and infrastructure. The city has beensubjected to persistent increase in road traffic due to overall increase in prosperity,fast development and expansion of economy, and travel and tourism.

Very few studies have been carried out to investigate and assess noise pollutionin the City of Amman. Abdelazeez and Hammad (1987) studied the traffic noise andrelated annoyance in the City of Amman. Many recent changes in the demography

EVALUATION OF TRAFFIC NOISE POLLUTION IN AMMAN, JORDAN 501

and urban boundaries of the city have taken place; and consequently, further in-vestigation of this phenomenon is needed. Abo-Qudais and Alhiary (2004) studiedthe effect of distance from road intersection on developed traffic noise levels. Pre-diction of noise pollution along with community response to such noise need to beemphasized to render such investigation significant for city planning.

Data on the growth in the national vehicle registration between the years 1970and 1997 shows that the number of vehicles in Jordan has increased from about20,000 to approximately 310,000 in that 27 year-period (JAPS, 1997). This indicatesthat the number of vehicles nationwide has increased by more than fifteen folds,which constitutes an average annual increase of about 53%. Between the years1997 and 2005, the national vehicle registration jumped from 310,000 to 575,000(JEWS, 2005) which represents an average annual increase of about 11%. Theseexperienced percentages of annual increase are much higher than the reportedpercentages of annual increase of vehicle ownership in most of the European andAsian countries for the same period (Dargay and Gately, 1999; Pandya, 2003).Abdelazeez and Hammad (1987) reported that the vehicles operating in the City ofAmman constitute about 82.7% of the total number of vehicles operating in Jordan.This indicates that problems associated with traffic noise are expected to be moresignificant.

Figures 1 and 2 show; respectively, the distribution of vehicle registration inJordan and in the city of Amman for the year 1997. The figures show that thepercentage of registered vehicles; excluding passenger cars, in both Jordan andAmman is 32.5% and 27.5%; respectively. This is somewhat comparable to thepercentages reported for many of the European and Asian countries (Dargay andGately, 1999; Pandya, 2003; Piccolo et al., 2004). This high percentage of heavyvehicles along with the continuously increasing rate of growth in vehicle ownership

Figure 1. Distribution of vehicle registration in Jordan for the year 1997 (JAPS, 1997).


Figure 2. Distribution of vehicle registration in the City of Amman for the year 1997 (JAPS, 1997).

Figure 3. Car ownership per 1000 inhabitants in various countries; including Jordan and the City of

Amman (Dargay and Gately, 1999; JAPS, 1997; JEWS, 2005).

in Amman indicates that the noise pollution problem is expected to exacerbate.Accordingly, data collection and investigation of noise pollution in the City ofAmman and on the national level is justified and should receive significant attention.

Figure 3 summarizes the car ownership levels (defined as the number of cars per1000 inhabitants) in various countries; including Jordan and the city of Amman. It


should be noted that historical data regarding car ownership between the years 1970and 1992 in various countries around the world revealed that the average annualgrowth rate in car ownership is almost constant and less than 2.0% for the majorityof developed countries such as The United States, Great Britain, Austria, France,Germany, Finland, Spain, Italy, Norway, Netherlands, Sweden, Japan and Australia(Dargay and Gately, 1999). Less developed countries showed a much higher averageannual growth rate in car ownership. This trend in average growth rate was alsoobserved for heavy vehicles in both developed and developing countries. Thisindicates that problems associated with traffic noise pollution are expected to worsenin developing countries such as Jordan.

Objectives and Methodology

The importance of road traffic noise (RTN) stems from the fact that it constitutesthe major source of urban noise pollution. Aside from environmental and healthimpacts, predictions and measurements of road traffic noise levels are essential forroadway planning, residential entitlement for sound insulation, and for the control ofnoise. Additionally, traffic noise monitoring and prediction can be used to estimatethe traffic intensity and the queue lengths of vehicles (Stoilova and Stoilov, 1998).

Authorities in Jordan are increasingly aware of the effects of road traffic noise(RTN) in urban areas. However, it is not a common practice for planners to considerroad traffic noise in their studies in Jordan. Moreover, roads are being built throughresidential areas to relieve congestion without paying attention to the main effectsof road traffic noise on residents. These facts justify this investigation. In addition,the response of the community to this growing problem needs to be addressed.

The primary objectives of this investigation are (1) to evaluate the environmentalnoise pollution in the Greater Municipality of Amman due to traffic noise, (2)to assess and rate noise exposure in the different urban zones of the city, (3) topredict the traffic noise levels in the city by the use of Calculation of Road TrafficNoise (CRTN) prediction model, starting from the knowledge of traffic flow andcomposition, and (4) to compare measured and predicted noise levels in the city inorder to examine the applicability of the mathematical model.

Noise is measured by a sound level meter; which is an instrument which re-sponds to sound in approximately the same way as the human ear and which givesreproducible measurements of sound level (Mato and Mufuruki, 1999). The equiv-alent continuous equal energy level (Leq) is applied to fluctuating noise level. TheLeq is defined as the constant noise level that expends the same amount of energy asthe fluctuating level over the same time period (Davis and Masten, 2004). The timeperiod over which Leq is defined has to be relatively long (1, 8, 12 or 24 h), and theLeq is measured for traffic noise along with the statistical levels L10, L50, and L90

which are the noise levels exceeded 10%, 50%, and 90% of the time; respectively(Georgiadou et al., 2004). O’Cinneide (1997) stated that L10 provides an indication


of the upper end of the level range; while L90 constitutes the background level inthe absence of nearby noise sources.

The general practice in Amman is to use the same L10(18 h) descriptor as usedin the United Kingdom, or the Leq used in United States. This is due to the factthat there is not any international agreement on the index which should be used todetermine road traffic noise. The assigned noise index can result in a reasonableoutcome if it correlates well with dissatisfaction, and if it contains accurate set ofdesign rules for predicting the index (Salter and Hothersal, 1977).

A Reten Electronic RS103 sound level meter with an A-filter and a sound levelcalibrator was used to measure noise level throughout this investigation, and thenoise was given as dB(A). Road traffic noise was measured at 28 locations in theCity of Amman. These locations were selected because they represent residentialareas that cover most of the City of Amman, in addition to the fact that theselocations suffer from a persisting traffic noise problem. The locations chosen forthe study are listed in Table I and shown on the map of the City of Amman illustratedin Figure 4.

The noise meter was held in the arms about 1.5 m above the ground. The soundindex that was measured is the L10(1 h). Noise level readings were taken twice a day;from 07:00 to 08:00 during the early morning rush hour and from 07:00 to 08:00during the early evening rush hour. It should be noted that noise measurementstook place during a week day in the summer where streets were dry, and that allmeasurements took place at the same time. Due to the residential nature of the

TABLE I

Locations chosen for the noise level measurements in the City of Amman (See Figure 4)

Designated Designated

number Location number Location

1. Interior Roundabout 15. Sweileh Roundabout

2. First Circle 16. University Street

3. Second Circle 17. Sports City Roundabout

4. Third Circle 18. Safeway-Gardens Junction

5. Fourth Circle 19. Gardens Street

6. Fifth Circle 20. Al-Abdali

7. Sixth Circle 21. Jabal Al-Hussein

8. Seventh Circle 22. Jordan (Al-Urdon) Street

9. Eighth Circle 23. Al-Mahata Street

10. Airport Highway 24. South Buses Terminal

11. Abdoun 25. Ras El-Ein

12. Abdoun Roundabout 26. King Hussein Street

13. Sweifeyah 27. Raghdan Buses Terminal

14. Al-Sina’a Street 28. Al-Istiqlal Street


Figure 4. Overview of the City of Amman showing the locations of noise measurements throughout

this study (Courtesy; Web Site of His Majesty King Hussein, www.kinghussein.gov.jo).

locations chosen for this investigation; noise level measurements were carried outtwice at each location; once in the presence of an existing sound barrier and oncein the absence of a sound barrier.

The Calculation of Road Traffic Noise (CRTN) prediction model (Kuglar et al.,1976) was employed to predict noise levels at the 28 locations chosen for the study.This method of road traffic noise prediction allows the calculation of L10(1 h), whichis based on observation of the noise of freely flowing traffic along with data col-lection. The input data for the prediction method were collected from the differentlocations of the study. These data include traffic volume and vehicle registration,speed, road surface, road gradient, road obstructions, distance, noise path, inter-vening ground, effects of shielding whether man made or natural, and the angleof view. The vehicle registrations include cars, jeeps, and 4 wheel-drives, taxis,service vehicles, pick-ups and vans, mini-buses, buses, light good vehicles (LGV),medium good vehicles (MGV) and heavy good vehicles (HGV).

The CRTN prediction method was first applied to estimate the basic noise levelL10 (1 h) from the traffic flow assuming no heavy vehicles, zero gradient, measuredmean speed, conventional road surface, and 7-m distance from center of the road.This was followed by application of correction factors through a series of manual


steps and calculations using simple nomograms. Corrections were applied for speedand percentages of heavy vehicles. Road gradient was taken at 3% throughoutAmman, except for Omar Matar Street and King Hussein Street where the gradi-ents were taken at 7% and 5%, respectively. Road surface throughout Amman wasassumed to be impervious, and hard ground was assumed between the source and re-ceiver to account for ground absorption. No correction was applied for obstruction ofsound propagation path, and a view angle of 10◦ was assumed throughout Amman,except for Omar Matar Street where 18◦ was assumed. Finally, to account for reflec-tions, it was assumed that the sound level was measured at a distance of 1 m from afacade and that no substantial reflection surface exists on the far side of the trafficflow. Traffic noise prediction was carried at the 28 locations of the study using theCTRN method and the measured as well as assumed conditions. Predicted noise lev-els were then compared to measured noise levels during the day-time and night-time.

Results and Discussion

Results of background noise level (L90), the statistical noise level L10(1 h), andtraffic noise index (TNI) in the presence and absence of barriers at the 28 locationsselected for the study in Amman are givin in Tables II and III for day and night,respectively. It should be noted that the background noise level corresponds to noiselevel in the absence of nearby noise sources, while the statistical noise level L10

corresponds to the upper end of the noise level range (Georgiadou et al., 2004).The traffic noise index (TNI) is a method used to estimate annoyance responsesdue to traffic noise, which is computed using the following formula (Langdon andScholes, 1968):

TNI = 4 × (L10 − L90) + (L90 − 30)

Investigation of Table II shows that the day-time statistical noise level L10 through-out Amman has an average of 69 dB(A) and ranges between 46 and 81 dB(A) in thestreets. These levels are somehow less in the presence of a barrier, and have an aver-age of 58 dB(A) and range between 45 and 76 dB(A). These noise levels are similarto those reported for other cities around the world in Italy, Brazil, Greece and India(Piccolo et al., 2004; Zannin et al., 2002; Georgiadou et al., 2004; Pandya, 2003).Additionally, these noise levels are higher than those reported by Abdelazeez andHammad (1987) for many of the locations chosen for the study in the city of Am-man. It should be noted that these noise levels are mostly considered unacceptable,resulting in the fact that voice must be raised to be understood, and are intolerablefor phone use even behind a barrier similar to office setting. The traffic noise index(TNI) shown in Tables II and III indicates that the locations of Al-Mahata Streetand Abdoun Mall have the highest and lowest annoyance responses due to trafficnoise, respectively. These locations experience TNI of 114 and 19 in the absence


TABLE II

Measured day-time background noise level (L90), statistical noise level L10(l hr), and traffic noise index

(TNI) with and without barrier at 28 locations in the City of Amman

W/O Barrier W/Barrier

No. Location Time L90 L10 TNI L10 TNI

1 Ministry of Interior Roundabout 7:30–8:30 55 67 73 56 29

2 1st Circle 7:30–8:30 56 66 66 57 30

3 2nd Circle 7:30–8:30 56 66 66 60 42

4 3rd Circle 7:30–8:30 56 68 74 56 26

5 4th Circle 7:30–8:30 50 65 80 55 40

6 5th Circle 7:30–8:30 53 66 75 55 31

7 6th Circle 7:30–8:30 52 62 62 53 26

8 7th Circle 7:30–8:30 54 65 68 55 28

9 8th Circle 7:30–8:30 55 64 61 56 29

10 Airport Street 7:30–8:30 55 69 81 45 –

11 Abdoun Mall 7:30–8:30 45 46 19 45 15

12 Abdoun Roundabout 7:30–8:30 45 53 47 51 39

13 Sweifeyah 10:00–11:00 50 61 64 48 12

14 Al-Sina’a Street 7:30–8:30 55 68 77 59 41

15 Sweileh Roundabout 7:30–8:30 60 78 102 69 66

16 Al-Jam’a Street 7:30–8:30 60 76 94 64 46

17 Sports City Roundabout 7:30–8:30 60 70 70 66 54

18 Safew Ay-Gardens Inter- Junction 7:30–8:30 56 73 94 67 70

19 Gardens Street 7:30–8:30 57 73 91 58 31

20 Al-Abdali 7:30–8:30 57 70 79 60 39

21 Jabalal-Hussein 10:00–11:00 56 72 90 56 26

22 Al-Urdon Street 7:30–8:30 54 73 100 64 64

23 Al-Mahata Street 7:30–8:30 60 81 114 76 94

24 South Buses Terminal 7:30–8:30 54 70 88 – –

25 Gam (Omarmatar ST.) 7:30–8:30 55 67 73 60 45

26 Al-Malek Hussein ST. 7:30–8:30 62 77 92 65 44

27 Raghdan Buses Terminal 7:30–8:30 60 80 110 – –

28 Al-Istiklal Street 7:30–8:30 58 78 108 61 40

Minimum 45 46 19 45 12

Maximum 62 81 114 76 94

Average 55 69 79 58 40

Standard deviation 4.1 7.7 20.3 7.2 18.2

of a barrier, and 94 and 15 in the presence of a barrier. The table also shows thatSweifeyah enjoys a very low TNI in the presence of a noise barrier. It should benoted that a TNI of 74 dB(A) has been reported to be associated with less than 3%annoyance in social surveys.


TABLE III

Measured night-time background noise level (L90), statistical noise level L10(l hr), and traffic noise

index (TNI) with and without barrier at 28 locations in the City of Amman

w/o Barrier w/Barrier

No. Location Time L90 L10 TNI L10 TNI

1 Ministry of Interior Roundabout 7:00–8:00 53 65 71 55 31

2 1st Circle 7:00–8:00 53 61 55 56 35

3 2nd Circle 7:00–8:00 52 60 54 51 18

4 3rd Circle 7:00–8:00 54 65 68 52 16

5 4th Circle 7:00–8:00 49 65 83 55 43

6 5th Circle 7:00–8:00 56 61 46 52 10

7 6th Circle 7:00–8:00 52 59 50 55 34

8 7th Circle 7:00–8:00 53 58 43 56 35

9 8th Circle 7:00–8:00 58 69 72 53 8

10 Airport Street 7:00–8:00 52 65 74 43 –

11 Abdoun Mall 7:00–8:00 50 58 52 53 32

12 Abdoun Roundabout 7:00–8:00 45 60 75 56 59

13 Sweifeyah 7:00–8:00 54 67 76 48 0

14 Al-Sinaa Street 7:00–8:00 50 63 72 57 48

15 Sweileh Roundabout 7:00–8:00 56 66 66 57 30

16 Al-Jam’a Street 7:00–8:00 57 62 47 57 27

17 Sports City Roundabout 7:00–8:00 57 67 67 59 35

18 Safew Ay-gardens Inter- junction 7:00–8:00 55 70 85 65 65

19 Gardens Street 7:00–8:00 55 69 81 57 33

20 Al-Abdali 7:00–8:00 50 62 68 56 44

21 Jabalal-Hussein 7:00–8:00 54 66 72 56 32

22 Al-Urdon Street 7:00–8:00 53 68 83 57 39

23 Al-Mahata Street 7:00–8:00 56 71 86 66 66

24 South Buses Terminal 7:00–8:00 50 68 92 60 60

25 Gam (Omarmatar ST.) 7:00–8:00 54 63 60 58 40

26 Al-Malek Hussein ST. 7:00-8:00 52 64 70 55 34

27 Raghdan Buses Terminal 7:00–8:00 55 69 81 – –

28 Al-Istiklal Street 7:00–8:00 57 70 79 57 27

Minimum 45 58 43 43 0

Maximum 58 71 92 66 66

Average 53 65 69 56 35

Standard deviation 2.9 3.9 13.4 4.5 16.5

Noise levels reported in Table III show that the night-time statistical noise levelL10 throughout Amman has an average of 65 dB(A) and ranges between 58 and71 dB(A) in the streets. These levels are somehow less in the presence of a barrier,and have an average of 56 dB(A) and range between 43 and 66 dB(A). These


night-time noise levels are very comparable to those reported for the day-timenoise. Additionally, these noise levels are very much higher than the levels reportedfor living rooms. The bedroom noise level of 25–30 dB(A) reported by Davis andMasten (2004) has been exceeded in all locations of Amman even during the night-time and behind the barrier, resulting in more possible sleep disturbance due to trafficnoise. It should be noted that the World Health Organization recommends a noiselevel of less than 35 dB(A) based on the continuous equal energy concept for therestorative process of sleep (Mufuruki, 1997). The traffic noise index (TNI) shownin the table indicates that the locations of South Buses Terminal, Al-Mahata Streethave the highest annoyance due to traffic noise, while 7th Circle and Sweifeyahhave the lowest annoyance responses due to traffic noise.

The data presented in Tables II and III are further investigated in order to betterunderstand the relative importance of the day-time versus the night-time L90 andL10 noise levels, and the effect of barriers on the day-time and the night-time L90

and L10 noise levels. Figures 5 and 6, respectively, show the measured day-timeand night-time statistical noise levels, L10(1 h), with and without barrier at the28 locations in the City of Amman. The figures also reflect the fact that barriersresult in a consistent, but variable noise reduction. This noise reduction is generallyassociated with the type and height of barrier present, as well as the distance fromthe noise sources as will be investigated later. Figures 7, 8 and 9, respectively, showcomparisons of measured day-time and night-time statistical background noiselevels L90, day-time and night-time statistical noise levels L10(1 h) without barrier,and day-time and night-time statistical noise levels L10(1 hr) with barrier at the 28locations in the City of Amman. With very few exceptions, the figures show that thenight-time noise level is less than the day-time noise level. Figure 7 shows that somelocations in Amman such as the 5th Circle, 8th Circle, Abdoun Mall, and Suweifahexperience higher background noise level during the night than during the day.

Figure 5. Measured day-time statistical noise levels, L10 (1 hr), with and without barrier at 28 locations

in the City of Amman.


Figure 6. Measured night-time statistical noise levels, L10 (1 hr), with and without barrier at 28

locations in the City of Amman.

Figure 7. Comparison of measured day-time and night-time statistical background noise levels, L90,

at 28 locations in the City of Amman.

Since background noise level corresponds to noise level in the absence of nearbynoise sources, it can then be argued that the background noise at these locations ismainly due to the prevailing life-style in these areas of Amman where night-timeactivities are more than those during day-time activities. Figure 8 shows that themeasured statistical noise level L10 without a barrier during the night-time is morethan that during the day-time at locations such as 8th Circle, Abdoun Mall, AbdounRoundabout, and Sweifeyah. Figure 8 shows that the measured statistical noise levelL10 without a barrier during the night-time is more than that during the day-time atlocations such as 8th Circle, Abdoun Mall, Abdoun Roundabout, and Sweifeyah.Similar results are indicated by Figure 9 for the measured statistical noise levelL10 with a barrier during the night-time is more than that during the day-time atlocations such as 6th Circle, 7th Circle, Abdoun Mall, and Abdoun Roundabout.


Figure 8. Comparison of measured day-time and night-time statistical noise levels, L10 (1 hr), without

barrier at 28 locations in the City of Amman.

Figure 9. Comparison of measured day-time and night-time statistical noise levels, L10 (1 hr), with

barrier at 28 locations in the City of Amman.

Further investigation in order to understand and compare the noise measurementdata presented in Tables II and III and Figures 5–8 and 9 is shown in Table IV. Thetable shows a summary of statistical inference on the noise data collected throughoutthe study showing the 90% confidence interval on the difference in means. The tableshows that the average day-time noise level in the absence of barrier is greater thanthe average day-time noise level in the presence of barrier by 7.6–14.4 dB(A). Thetable also shows that the average night-time noise level in the absence of barrier isgreater than the average night-time noise level in the presence of barrier by 7.1–10.9 dB(A). Table IV also shows that in the absence of noise barriers, the averageday-time noise level is greater than the average night-time noise level by 1.3–6.8


TABLE IV

Summary of statistical inference on the noise data collected throughout the study showing the 90%

confidence interval on the difference in means

Degree of Lower Upper

Sample type Variable Size Mean Variance freedom t-value limit limit

Day-Time Noise w/Barrier 26 58 7.2 54 1.675 7.60 14.40

w/o Barrier 28 69 7.7

B.G. Level 28 55 4.1 40 1.684 0.29 5.71

w/Barrier 26 58 7.2

B.G. Level 28 55 4.1 42 1.683 11.23 16.77


Night-Time Noise w/Barrier 27 56 4.5 53 1.686 7.08 10.92


B.G. Level 28 53 2.9 46 1.688 1.27 4.73

w/Barrier 27 56 4.5

B.G. Level 28 53 2.9 52 1.686 10.45 13.55


Day-Night Noise B.G.Level Day 28 55 4.1 50 1.678 0.41 3.59

B.G. Level Night 28 53 2.9

w/Barrier Day 26 58 7.2 43 1.682 −0.79 4.79

w/Barrier Night 27 56 4.5

w/o Barrier Day 28 69 7.7 41 1.683 1.25 6.75

w/o Barrier Night 28 65 3.9

w/Barrier: with barrier.

w/o Barrier: without barrier.

B.G. Level: background level.

dB(A). Additionally, day-time and night-time noise levels are statistically similarin the presence of noise barriers. These findings indicate that the employed noisebarriers throughout the locations of the study are effective in reducing noise, as willbe investigated further.

As stated earlier, the statistical noise level L10 (1 h) represents the upper end ofthe sound level range. Accordingly, measurement and reporting of L10 to representtraffic noise pollution would become more reasonable when the background soundlevel is dominated by the sound of road traffic. For non-constant noise source, theequivalent continuous sound level Leq is generally reported to represent the constantnoise level containing the same quantity of sound energy over a time period as theactual varying noise level (Georgiadou et al., 2003). The equivalent continuousnoise level Leq is related to the statistical noise level L10 by the following empiricalrelationship (Nelson, 1987; Tang and Chu, 2001; Piccolo et al., 2004):

L10 = Leq + 3.0 dB(A)


Figure 10. Relationship between measured day-time and night-time statistical background noise level

(L90) and the calculated equivalent continuous noise level Leq without barrier in 28 locations in the

City of Amman.

Background noise level data L90 reported in Tables II and III and equivalent continu-ous noise level Leq calculated from the above equation were correlated to investigatethe presence of a relationship between the two types of noise data. Figure 10 presentsthe relationship between measured day-time and night-time statistical backgroundnoise level (L90) and the calculated equivalent continuous noise level Leq withoutbarrier in 28 locations in the City of Amman. The figure indicates that the two noiselevels are linearly related according to the following relationship:

L90 = 0.467 × Leq + 24.60

The prevailing coefficient of determination R2 is 0.644. This coefficient indicatesthat a high percentage of the equivalent continuous noise level Leqcan be explainedby the variation in the background noise level data L90 (Montgomery and Runger,1999), indicating that the background noise level is dominated by the noise of roadtraffic. Tang and Chu (2001) pointed out that the scattering of noise data reflectthe randomness of noise level fluctuations in the outdoor environment. In addition,high scatter of noise data can be attributed to the sensitivity of Leq noise levels toother sources of noise and short-duration noisy events.

Barriers can result in significant reduction in noise levels as can be seen from thedata presented in Tables II and III and Figures 5 and 6. This noise reduction due tobarriers is investigated further in Tables V and VI; which present the reduction in thestatistical noise level L10(1 hr) due to presence of barrier at the 28 locations in theCity of Amman during day-time and night-time, respectively. The noise reductionpresented in Tables V and VI was calculated using the following relationship (Garg,1994):

Noise Reduction = 10 × log

(20H 2

λR

)This relationship was reported to be valid for any distance (D) farther from thesource than the barrier. In a mathematical form, D ≥R and R � H , where H refers


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54


to the height of the barrier, D refers to the distance between barrier and receiver, Rrefers to the distance between source and barrier, and λ refers to the wave length ofsound. Garg (1994) reported that when noise reduction due to barriers is assessed,substantial noise reductions are defined as those ranging from 5–10 dB(A). Noisereductions calculated and presented in Tables V and VI for the day-time and night-time indicate that significant reductions have been achieved due to the presenceof barriers, with a minimum noise reduction of 9 dB(A) at the 8th Circle, and amaximum noise reduction of 17 dB(A) at University Street. The tables also presentvalues of the theoretical noise level, which are calculated as the difference betweenthe prevailing noise levels without barrier and the noise reduction due to the barrier.This theoretical noise level was compared to the background noise level L90 toillustrate the effectiveness of barriers in reducing noise. Statistical inference on thedifference between means of background noise and theoretical noise levels resultedin a 90% confidence interval of (−1.54, 5.54) dB(A) for day-time noise levels, and(1.79, 6.21) dB(A) for night-time noise levels. This indicates that during day-time,the barriers successfully counterbalanced the noise originating from sources otherthan the background. On the other hand, the average night-time noise level in thepresence of barrier was greater than the average background noise by 1.79–6.21dB(A), indicating that traffic noise constitutes a more noticeable problem duringnight-time than during day-time.

Results of relevant traffic data are collected in order to assist in traffic noiseprediction using the CRTN method. Collected data is shown in Table VII, whichpresents the average speed and distribution of vehicle registration at the 28 loca-tions chosen for the study in the City of Amman. The data presented include trafficvolume, and the vehicle registrations include cars, jeeps, and 4 wheel-drives, taxis,service vehicles, pick-ups and vans, mini-buses, buses, light good vehicles (LGV),medium good vehicles (MGV) and heavy good vehicles (HGV). The vehicle reg-istration data presented in Table VII are summarized in Figure 11. Investigation ofTable VII and Figure 11 shows that passenger vehicles (car, jeep, and 4 WD) con-stitute the majority of vehicles at the 28 locations chosen for the study in Amman.This follows a similar trend to that presented in Figures 1 and 2 which show thedistribution of vehicle registration for Amman and Jordan in the year 1997.

The CRTN prediction method along with the data presented in Table VII wereused to estimate the statistical noise level L10 (1 h) for the 28 study locations inAmman. Table VIII and Figure 12 both present a comparison of day-time andnight-time measured statistical noise levels with the predicted L10(1 hr) obtainedusing the CRTN method at the 28 locations in the City of Amman. The resultspresented indicate that the predicted noise levels for both day-time and night-timeare not significantly distant from the measured levels. This indicates that the CTRNmethod can be applied to predict road traffic noise for the conditions of road andtraffic flow in Amman. The results presented in Figure 12 indicate that the CTRNmethod was more reasonable in predicting night-time noise level than day-timenoise level. This can be attributed to the relatively higher day-time background


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TABLE VIII

Comparison of day-time and night-time measured noise levels with the predicted statistical noise

level L10 (1 hr) obtained using the CRTN method at 28 locations in the City of Amman

Noise Level, dB(A)

No. Location Day-Time Night-Time Predected

1 Ministry of Interior Roundabout 67 65 66

2 1st Circle 66 61 62

3 2nd Circle 66 60 63

4 3rd Circle 68 65 64

5 4th Circle 65 65 66

6 5th Circle 66 61 65

7 6th Circle 62 59 65

8 7th Circle 65 58 67

9 8th Circle 64 69 66

10 Airport Street 69 65 70

11 Abdoun Mall 46 58 59

12 Abdoun Roundabout 53 60 63

13 Sweifeyah 61 67 62

14 Al-Sina’a Street 68 63 63

15 Sweileh Roundabout 78 66 67

16 Al-Jam’a Street 76 62 70

17 Sports City Roundabout 70 67 68

18 Safeway-Gardens Inter-Junction 73 70 66

19 Gardens Street 73 69 66

20 Al-Abdali 70 62 62

21 Jabal Al-hussein 72 66 65

22 Al-Urdon Street 73 68 64

23 Al-Mahata Street 81 71 63

24 South Buses Terminal 70 68 70

25 Gam (Omar Matar ST.) 67 63 62

26 Al-Malek Hussein ST. 77 64 63

27 Raghdan Buses Terminal 80 69 70

28 Al-Istiklal Street 78 70 68

Minimum 46 58 59

Maximum 81 71 70

Average 69 65 65

Standard deviation 7.7 3.9 2.94

noise level which indicates higher noise day-time level in the absence of trafficnoise.

Statistical inference on the data presented in Table VIII to test the differencebetween means of predicted noise level and measured day-time noise level resulted


Figure 11. Distribution of vehicle registration at the 28 locations chosen for the study in the City of

Amman.

Figure 12. Comparison of day-time and night-time measured statistical noise levels with the predicted

L10 (1 hr) obtained using the CRTN method at 28 locations in the City of Amman.

in a 90% confidence interval of (1.37, 6.63) dB(A). Similarly, a 90% confidenceinterval of (−1.56, 1.56) dB(A) was obtained for the difference between meansof predicted noise level and measured night-time noise level. This indicates thatthe predicted and measured night-time noise levels are statistically similar, with apossible mean error of up to 1.56 dB(A). This is consistent with the findings of Lang-don and Griffiths (1981) who reported a prediction accuracy of the CTRN methodranging from +1.4 dB(A) to −1.2 dB(A) for actual noise range of 50–85 dB(A).


Conclusions

This study was carried out to evaluate the environmental noise pollution in the cityof Amman due to traffic noise, to investigate the diurnal variations of traffic noiselevels in the city, to assess and rate noise exposure in the different urban zones ofthe city, to predict traffic noise levels in the city using the CTRN method startingfrom the knowledge of traffic flow and composition.

The study concluded that the day-time statistical noise level L10 throughoutAmman has an average of 69 dB(A) and ranges between 46 and 81 dB(A) in thestreets. These levels are somehow less in the presence of a barrier, and have anaverage of 58 dB(A) and range between 45 and 76 dB(A). These noise levels aresimilar to those reported for other cities around the world.

The night-time statistical noise level L10 throughout Amman has an averageof 65 dB(A) and ranges between 58 and 71 dB(A) in the streets. These levels aresomehow less in the presence of a barrier, and have an average of 56 dB(A) andrange between 43 and 66 dB(A). These night-time noise levels are very comparableto those reported for the day-time noise. Additionally, these noise levels are verymuch higher than the levels reported for living rooms and bedrooms even duringthe night-time and behind the barrier, resulting in more possible sleep disturbancedue to traffic noise.

Noise reductions for the day-time and night-time indicate that significant reduc-tions have been achieved due to the presence of barriers, with a minimum noisereduction of 9 dB(A) at the 8th Circle, and a maximum noise reduction of 17 dB(A)at University Street. During day-time, the barriers successfully counterbalancedthe noise originating from sources other than the background. The average night-time noise level in the presence of barrier was greater than the average backgroundnoise by 1.79–6.21 dB(A), indicating that traffic noise constitutes a more noticeableproblem during night-time than during day-time.

The study concluded that the CTRN method can be applied to predict road trafficnoise for the conditions of road and traffic flow in Amman. The CTRN method wasmore reasonable in predicting night-time noise level than day-time noise level. Thiscan be attributed to the relatively higher day-time background noise level whichindicates higher noise day-time level in the absence of traffic noise.

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evaluation of traffic noise pollution in amman, jordan

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