Home Email this page Print this page Bookmark this page Decrease font size Default font size Increase font size
Noise & Health  
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Email Alert *
Add to My List *
* Registration required (free)  

   Sampling and Mea...
   Results and Disc...
   Article Figures
   Article Tables

 Article Access Statistics
    PDF Downloaded210    
    Comments [Add]    
    Cited by others 5    

Recommend this journal


ARTICLES Table of Contents   
Year : 2006  |  Volume : 8  |  Issue : 32  |  Page : 101-107
An estimation of annoyance due to various public modes of transport in Delhi

School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India

Click here for correspondence address and email

Measurements of noise levels associated with different types of vehicles plying the roads in Delhi were made. From the data, noise level indices L 10 , L 90 and Leq were determined. In addition, spectra of noise for different vehicles at 1- octave band frequencies were also obtained. The time-averaged noise spectra reveal that the noise intensities are significantly higher in the frequency range of 0.5 kHz to 2 kHz for all types of vehicles. Perceived noise levels (PNdB) and the total noisiness measured on NOY scale indicate that rural transport vehicles (RTVs) are most annoying, followed by buses, auto-rickshaws and taxis.

Keywords: Annoyance, loudness index, PNdB, traffic noise

How to cite this article:
Prakash A, Joute K, Jain V K. An estimation of annoyance due to various public modes of transport in Delhi. Noise Health 2006;8:101-7

How to cite this URL:
Prakash A, Joute K, Jain V K. An estimation of annoyance due to various public modes of transport in Delhi. Noise Health [serial online] 2006 [cited 2023 Dec 9];8:101-7. Available from: https://www.noiseandhealth.org/text.asp?2006/8/32/101/33950
Noise pollution in urban areas is now being recognized as a major environmental issue around the world. With increasing awareness of the adverse impacts of noise on human health, more and more people are becoming less tolerant to environmental noise. Annoyance is the most commonly encountered human response to traffic noise in major cities. Griffiths et al. [1] studied the subjective effects of traffic noise exposure on human beings in the London area. A similar study that focused on the behavior of human beings exposed to traffic noise carried out in two French cities by Lambert et al. [2] and Bluhn et al. [3] concluded that even at low levels, traffic noise exposure was associated with annoyance and sleep disturbance. It is also found to be associated with loss of memory in different age groups. [4]

Further, Miedema and Oudshoorn [5] established a relationship between traffic noise annoyance and day night level (DNL) index. Other studies [6],[7],[8],[9],[10],[11] have also established correlations between traffic noise pollution and annoyance. In some cases, a positive correlation between traffic noise and heart diseases was also found. [12],[13]

The criteria for assessing annoyance caused by traffic noise have also been developed. Scholes [14] found that the A-weighted equivalent sound pressure level LAeq and a Traffic Noise Index (TNI) based on the A-weighted 10 and 90 percentile levels, LA10 and LA90 , correlate satisfactorily with human annoyance. The use of percentile levels as the noise criteria was further investigated by Langdon and Griffiths. [15] LA10 is now generally adopted in 'traffic noise control' practices. PNdB and NOY were the two indices put forward by Kryter [16] in an attempt to measure the perceived noisiness of individual noise events such as a jet aircraft flying overhead by observers on the ground. Kryter developed equal noisiness contours for converting 1- octave or 1/3 octave bands of noise into NOYs (units of noisiness).

A great deal of effort has been devoted to extensive noise surveys and modeling studies in various cities of the world, namely. [17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27],[28],[29],[30] Although a number of surveys have been carried out in Delhi, which is considered as one of the noisiest cities in the world, no attempt has been made to assess the annoyance caused by various modes of transport in Delhi. This prompted us to undertake the present study with a view to estimating the annoyance from different types of vehicles plying on the roads of Delhi. First, noise levels emitted from individual types of vehicles were monitored at a location where the traffic flow could be considered as free flow. The estimates of annoyance on PNdB and NOY scales were obtained for different types of vehicles by using the methodology of Kryter.

  Sampling and Measurement Procedure Top

A magnetic tape recorder (Sony TCM-400DV cassette-corder) was used for recording the noise of the various CNG-driven modes of public transport plying in the city of Delhi, viz., Delhi Transport Corporation (DTC) buses; rural transport vehicles (RTVs), which were recently introduced as small passenger vehicles having a capacity of 10-15 seats; auto-rickshaws and taxis. The tape recorder has a frequency response of 2 dB from 50 Hz to 12 kHz and signal-to-noise ratio of 50 dB. The recorder is calibrated by using an octave analyzer (CRL, 2.37A). The analyzer is a precision type I sound level meter with a facility to measure noise in linear mode at 1-octave band of central frequencies in the range of 31.5 Hz to 16 kHz. The signal from the recorder is then stored into a computer using Gold Wave software [31] at a sampling rate of 16 kHz in stereo channel.

To examine the noise levels associated with different vehicles, lonely stretches of road were chosen as the sites for recording so that one can record the sound of a single vehicle as it passes by. Recordings of sound emissions were made at a distance of 3 m from the line of motion of the vehicles for a total of 30 vehicles for each mode of transport, i.e., buses (30), RTVs (30), auto-rickshaws (30) and taxis (30). Recording was done at a height of 1 m above ground level, and the average speed of the vehicles at the time of recording was ≥30 km/h, i.e., under free-flow condition. The measurements for each vehicle were made for a total duration of 10s (5s before it approaches the line of the site of the instrument and 5s as it recedes from it).

The recorded signals were analyzed using PRAAT software. [32] With the help of this software, the spectro-temporal diagram, i.e., spectrogram of the signals, was obtained. The horizontal direction of the spectrogram represents time, the vertical direction represents frequency. Grayness of a part of the spectrogram represents the energy density of sound. The typical spectrogram of a three wheeler is given in [Figure - 1]. The window length of the spectrogram was kept at 5 ms. The spectral slice of the spectrogram gives the frequency content of the signal. The sound pressure levels at the octave band frequencies were noted at an interval of 1s for the duration of measurement, i.e., 10 s.

  Results and Discussion Top

Determination of peak (L10), background (L90) and equivalent sound pressure levels (L eq)

The data of noise levels associated with each vehicle were arranged in ascending order of magnitude through a simple computer program. Then, various percentile indices, i.e., L 10 , L 50 and L 90 , were obtained. L eq levels were then estimated using the expression

[Table - 1] shows the values of various indices for different types of vehicles. Under free-flow conditions, the factors contributing to noise level are the engine noise, aerodynamic turbulence, rattling of window panes and body and the road surface-tyre contact. It is found that the L 10 indices (which represent peak noise) are the highest for auto-rickshaws, followed by RTVs, buses and taxis. But there is no significant difference observed in the peak noise level for RTVs and buses.

For L 90 levels (which represent background noise), RTVs have maximum value, followed by auto-rickshaws, buses and taxis. [Table - 1] also shows the values of indices for noise environment inside the vehicles, obtained in an earlier study. [30] It may be noted that L 90 values in this study are much lower - by as much as ~20 dB. The reason for this may be the fact that this study was conducted outside the vehicles and measurements were taken along a lonely stretch of road where only a single vehicle passes by at a time, whereas the earlier study was conducted for the noise inside the vehicles where the background noise due to people sitting inside the vehicles, as well as due to other vehicles on the road, may have had an influence on the overall background noise level. For the L eq values buses have the highest value, followed by RTVs, auto-rickshaws and taxis respectively. The reason for auto-rickshaws and buses having higher L eq indices is their lower L 90 levels compared with that of RTVs. An examination of L eq values reveals that the values inside the vehicles are significantly lower than those outside. This is due to the fact that the range of noise fluctuations represented by (L 10 - L 90 ) is much higher outside than inside the vehicles. As a consequence, from equation (i), the Leq values would be significantly higher outside for all types of vehicles.

Spectral analysis

The noise data of each type of vehicle was subjected to spectral analysis using PRAAT software. [32] The spectral distribution of sound pressure level (SPL) at 1-octave band center frequencies is shown in [Figure - 2]. The perusal of these graphs reveals that the spectral nature is rather similar for all types of vehicles. The maximum noise levels were observed at 1 kHz center frequency for all four types of vehicles, followed by 500 Hz, 4 kHz and 2 kHz respectively. The SPL values at 2 kHz and 4 kHz are comparable for all types of vehicles except for taxis, where the difference in SPL between the two frequencies is apparent. The lowest SPL value is observed at 8 kHz in all types of vehicles except for RTVs, where it is at 250 Hz. With the exception of RTV, the noise levels at 125 Hz and 250 Hz are almost similar over the recorded time span. At the lower end of the spectra, the highest SPL value is at 63 Hz, followed by 125 Hz and then 250 Hz. Among all the center frequencies, the SPL at 8 kHz fluctuated the most with time for all vehicle types, as indicated by the values of standard deviation in [Table - 2].

The time-averaged noise levels (mean of SPL values over the duration of measurement for a given frequency) as a function of frequency in all types of vehicles are shown in [Figure - 3]. Though the general trend of noise levels as a function of frequency in all types of vehicles is similar, there are certain notable features. It is clear from [Figure - 3] that noise levels in RTVs are higher at higher frequency range (1-8 kHz); whereas at lower frequency range (63-250 Hz), buses have higher noise levels than those of other vehicles. The noise levels are lowest in taxis at all center frequencies except at 8 kHz, where auto-rickshaws have the lowest value. In general, noise levels fall with increase in frequency from 63 Hz to 125 Hz and then increase in the interval 125 Hz to 1 kHz and fall thereafter. The decline in the range 63-125 Hz is more pronounced in case of RTVs as compared to other vehicles.

It is worth noting from [Figure - 3] that the SPL levels are significantly higher between 500 Hz and 4000 Hz for all types of vehicles. Given the fact that the human ear is most sensitive around 1,000-4,000 Hz, these vehicles are likely to induce adverse impacts on human health.

Loudness index, annoyance and perceived noise levels

The loudness level on Phon and Sone scales and annoyance in terms of perceived noisiness on NOY and PNdB scales were also evaluated. The total loudness S t in sones was obtained from the spectral distribution of the noise associated with various types of vehicles using Steven's method. The methods involve calculating St from the following expression:

St = Sm + F (ΣS - Sm)


Sm = maximum contribution in sone in a given band

ΣS = sum of all contributions in sone of a given band of frequencies

F = 0.15 for 1/3- octave band

= 0.30 for 1- octave band

Then, the total loudness P in phons is obtained using the relation

St = 2(P - 40) /10

The noisiness of the vehicles is computed from their spectral distribution using Kryter's method. First, the total noisiness N t , on NOY scale, is calculated from the expression

Nt = Nm + F (Σ N - Nm)


Nm = maximum contribution in phon in a given band

ΣN = sum of all contributions in NOY of a given band of frequencies

F = 0.15 for 1/3- octave band

= 0.30 for 1- octave band

From Nt , the perceived noise level (PNdB), x, is calculated using the relation:

Nt = 2 (x - 40) /10

The computed values of loudness and noisiness levels are shown in [Table - 3], where it can be seen that auto-rickshaws have the highest loudness level on sone and phon scales, followed by RTVs, buses and taxis. However, the loudness levels are not the best indicators of annoyance caused by the vehicular noise. The perceived noise level (PNdB) has been devised as an estimate of annoyance due to a discrete noise event such as jet aircraft noise.

It is unlikely that such a concept could be extended in general to road traffic noise, which is a continuous noise in contrast to discrete number of noises, which enable each aircraft to be given annoyance value. Since in the present study, the measured noise levels were due to individual vehicles of various types, this concept of PNdB has been extended here to evaluate the extent of annoyance caused by them. An examination of PNdB values in [Table - 3] reveals that RTVs have the highest annoyance value, followed by buses, auto-rickshaws and then taxis.

  Conclusions Top

The background and peak levels of noise represented by L 90 and L 10 indices respectively are found to be the highest for auto-rickshaws, followed by RTVs, buses and taxis. The time-averaged noise spectra consist of significantly higher noise levels in the frequency range of 0.5-2 kHz for all types of vehicles. Although maximum loudness indices (on Phon and Sone scales) are found to be associated with auto-rickshaws, the maximum annoyance (represented by PNdB) is caused by RTVs, followed by buses, auto-rickshaws and taxis in decreasing order.

  Acknowledgement Top

The authors would like to thank the Council for Scientific and Industrial Research (CSIR), New Delhi, India, for providing necessary financial support to carry out this study.

  References Top

1.Griffiths ID, Langdon FJ, Swan MA. Subjective effects of traffic noise exposure: Reliability and seasonal effects. J Sound Vib 1980;71:227-40.  Back to cited text no. 1    
2.Lambert J, Simonnet F, Vallet M. Patterns of behaviour in dwellings exposed to road traffic noise. J Sound Vib 1984;92:159-72.  Back to cited text no. 2    
3.Bluhm G, Nordling E, Berglind N. Road traffic noise and annoyance: An increasing environmental health problem. Noise Health 2004;6:43-9.  Back to cited text no. 3    
4.Boman E, Enmarker I, Hygge S. Strength of noise effects on memory as a function of noise source and age. Noise Health 2005;7:11-26.  Back to cited text no. 4  [PUBMED]  [FULLTEXT]
5.Miedema HM, Oudshoorn CG. Annoyance from transportation noise: Relationships with exposure metrics: DNL and DENL and their confidence intervals. Environ Health Perspec 2001;109:409-16.  Back to cited text no. 5    
6.Sato T, Yano T, Bjorkman M, Rylander R. Road traffic noise annoyance in relation to average noise level, number of events and maximum noise level. J Sound Vib 1999;223:775-84.  Back to cited text no. 6    
7.Mortensen FR, Poulsen T. Annoyance of low frequency noise and traffic noise. J Low Freq Noise Vib Active Control 2001;20:193-6.  Back to cited text no. 7    
8.Ouis D. Annoyance caused by exposure to road traffic noise: An update. Noise Health 2002;4:69-79.  Back to cited text no. 8  [PUBMED]  [FULLTEXT]
9.Rylander R, Bjorkman M. Road traffic noise annoyance and window orientation in dwellings. J Sound Vib 2002;249:828-31.  Back to cited text no. 9    
10.Ali SA, Tamura A. Road traffic noise levels, restrictions and annoyance in Greater Cairo, Egypt. App Acoust 2003;64:815-23.  Back to cited text no. 10    
11.Klaeboe R, Amundsen AH, Fyhri A, Solberg S. Road traffic noise - the relationship between noise exposure and noise annoyance in Norway. App Acoust 2004;65:893-912.  Back to cited text no. 11    
12.Kempen EE, Kruize H, Boshuizen HC, Ameling CB, Staatsen BA, Hollander AE. The association between noise exposure and blood pressure and ischemic heart disease: A meta-analysis. Environ Health Perspect 2002;110:307-17.  Back to cited text no. 12  [PUBMED]  [FULLTEXT]
13.Jarup L, Dudley ML, Babisch W, Houthuijs D, Swart W, Pershagen G, et al. Hypertension and exposure to noise near airports (hyena): Study design and noise exposure assessment. Environ Health Perspect 2005;113:1473-8.  Back to cited text no. 13  [PUBMED]  [FULLTEXT]
14.Scholes WE. Traffic noise criteria. Appl Acoust 1970;3:1-21.  Back to cited text no. 14    
15.Langdon FJ, Griffiths ID. Subjective effects of noise exposure II: Comparisons of noise indices, response scales and the effect in changes in noise levels. J Sound Vib 1982;83:171-80.  Back to cited text no. 15    
16.Kryter KD. The meaning and measurement of perceived noise level. Noise Control 1960;6:12-7.  Back to cited text no. 16    
17.Tempest W, Bryan ME. Low frequency sound measurement in vehicles. Appl Acoust 1972;5:133-9.  Back to cited text no. 17    
18.Attenborough K, Clark S, Utley WA. Background noise levels in the United Kingdom. J Sound Vib 1974;48:359-75.  Back to cited text no. 18    
19.Cannelli GB. Traffic noise pollution in Rome. Appl Acoust 1976;7:103-15.  Back to cited text no. 19    
20.Ko NW. Traffic noise in a high-rise city. Appl Acoust 1978a;11:225-39.  Back to cited text no. 20    
21.Ko NW. Indoor traffic noise in a high - rise city. J Sound Vib 1976b;47:599-601.  Back to cited text no. 21    
22.Don CG, Rees IG. Road traffic sound level distribution. J Sound Vib 1985;100:41-53.   Back to cited text no. 22    
23.Ishiyama T, Tateishi K, Arai T. An analysis of traffic noise propagation around main roads in Tokyo. Noise Control Eng J 1991;36:65-72.  Back to cited text no. 23    
24.Baverstock SJ, Pocock RL, Attenborough K. Development of area - based methods for predicting ambient noise. Appl Acoust 1991;33:303-12.  Back to cited text no. 24    
25.Rao PR, Rao MG. Prediction of LA10T traffic noise levels in the city of Visakhapatnam India. Appl Acoust 1991;34:101-10.  Back to cited text no. 25    
26.Kumar K, Jain VK. A study of noise in various modes of transport in Delhi. App Acoust 1994;43:57-65.  Back to cited text no. 26    
27.Singh BB, Jain VK. A comparative study of noise levels in some residential, Industrial and commercial areas of Delhi. Env Monitr Assess 1995;35:1-11.  Back to cited text no. 27    
28.Chakrabarty D, Santra SC, Mukherjee A, Roy B, Das P. Status of road traffic noise in Calcutta metropolis, India. J Acoust Soc Am 1997;101:943-9.  Back to cited text no. 28    
29.Kumar K, Jain VK, Rao DN. A predictive model of noise for Delhi. J Acoust Soc Am 1998;103:1677-9.  Back to cited text no. 29    
30.Sanyogita, Prakash A, Jain VK. A study of noise in CNG driven modes of transport in Delhi. App Acoust 2004;65:195-201.  Back to cited text no. 30    
31.Available from: http://www.goldwave.com/.  Back to cited text no. 31    
32.Available from: http://www.fon.hum.uva.nl/praat/download_win.html.  Back to cited text no. 32    

Correspondence Address:
V K Jain
School of Environmental Sciences, Jawaharlal Nehru University, New Delhi
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/1463-1741.33950

Rights and Permissions


  [Figure - 1], [Figure - 2], [Figure - 3]

  [Table - 1], [Table - 2], [Table - 3]

This article has been cited by
1 Road traffic noise: A risk factor for myocardial infarction?
Kankaria, A. and Sanjeev, K.G.
National Medical Journal of India. 2013; 26(1): 40-41
2 Road traffic noise assessment and modeling in Bhubaneswar, India: A comparative and comprehensive monitoring study
Swain, B.K. and Goswami, S. and Panda, S.K.
International Journal of Earth Sciences and Engineering. 2012; 5(5): 1358-1370
3 Road traffic noise assessment and modeling of Sambalpur city, India: A comprehensive, comparative and complete study
Pradhan, A.C. and Swain, B.K. and Goswami, S.
Journal of Ecophysiology and Occupational Health. 2012; 12(3-4): 51-63
4 Dynamics of road traffic noise in Bhadrak city, India
Swain, B.K. and Panda, S.K. and Goswami, S.
Journal of Environmental Biology. 2012; 33(6): 1087-1092
5 Road traffic noise, annoyance and community health survey - A case study for an Indian city
Agarwal, S. and Swami, B.L.
Noise and Health. 2011; 13(53): 272-276