NOISE IMPACT

ASSESSMENT AND
CONTROL

ASSIGNMENT 02

SUBMITTED BY
SUBHAMOY GHOSH
219MN1410
DEPT. OF MINING ENGG.
NIT, ROURKELA
 CONTENTS
SUBJECT PAGE No.

 Sound, Noise and their characteristics......................................1

 Case Study 1:
Modelling of traffic noise pollution...........................................2

 Case Study 2:
Noise level and annoyance of industrial factories: A study
of Duhok City...........................................................................13

 Case Study 3:
Studies on noise pollution model for Chennai..........................24

 Case Study 4:
Modelling of noise pollution due to heterogeneous
highway traffic in India..............................................................31

 Case Study 5:
A study of transport related noise pollution
in Asansol town, West Bengal using modelling
techniques...................................................................................49

 Case Study 6:
Noise dispersion modelling in small urban areas
with Custic 3.2 Software............................................................56

 Case Study 7:
Far from the noisy world? Modelling the relationships
between park size, tree cover and noise levels in urban
green spaces of the city of Puebla, Mexico.............................64
Sound, Noise and Their Characteristics:

Sound is a form of energy giving auditory sensation, of hearing and is

produced by longitudinal and mechanical waves in matter including solid, liquid
and gases and transmitted by oscillation of molecules of the matter. It is
produced when an object vibrates alternately compressing and expanding the
air. Usually, it is characterised by its pitch, loudness and tone quality. In
physical terms however, sound is defined as a fluctuation in the pressure in an
elastic medium and is characterised by its properties such as frequency,
intensity and wave form. Sound travels with different speeds in different media
depending on its density and elasticity. The denser and less elastic the substance
is, the slower the speed of sound wave is. Sound travels in air with a velocity of
330 m/s at normal atmospheric pressure and at a temperature of 20ºC. Decibel
is the basic unit of sound. The most important characteristics of a sound wave
are its frequency and amplitude.

Noise is a sound that is unwanted and it is usually associated with

annoyance. Even sounds that are pleasant at one volume can become noise to us
as they get louder. Noise, then, has both an objective, physical component; as
well as a subjective component that takes account of a person's individual
perception, or reaction, to a sound.
As a result of increasing mechanisation, the use of increasingly
voluminous and complicated machineries, noise is becoming an increasingly
widespread and serious source of discomfort and an unjustifiable interference
and imposition upon human comfort, health and the quality of modern life.
Before initiating any administrative, engineering and medical measures
against the noise hazards, noise surveys are essential. They help in identifying
the noise sources and quantifying the exposure risks of the people exposed to it.
Effective anti-noise measures can accordingly be formulated and implemented
to bring the menace within the permissible limits.
Therefore, a number of case studies have been presented below which
uses a mathematical theory to understand or predict the way noise in the
environment behave and affects the people living in it. This type of modelling is
also used to run scenarios and test the various theories available to understand
the impact on the environment and humans that are exposed to unnatural levels
of noise for prolonged duration.

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CASE STUDY 1:

MODELLING OF TRAFFIC NOISE POLLUTION

Harmful Effects Of Noise On Human Beings:

Noise is considered a serious threat to the environmental health. Some of

the adverse effects of noise pollution are given below:

 It interferes with speech. In the presence of noise we may not able to

follow, what the other person is saying.
 Noise leads to emotional and behavioral stress. A person may feel
disturbed in the presence of loud noise such as produced by heating of
drums.
 Noise may permanently damage hearing. A sudden loud noise can cause
severe damage to the eardrum.
 Noise increases the chances of occurrence of diseases such as headache,
blood pressure, heart failure, etc.
 Noise leads to increased heart beat, constriction of blood vessels and
dilation of pupil.
 Noise is a problem especially for patients who need rest.
 Noise may cause damage to liver, brain and heart.

Noise Measurement Techniques & Instruments:

Noise measuring devices typically use a sensor to receive the noise

signals emanating from a source. The sensor, however, not only detects the
noise from the source, but also any ambient background noise. Thus, measuring
the value of the detected noise is inaccurate, as it includes the ambient
background noise. Many different type of instruments are available to measure
sound levels and the most widely used are sound level meters. Most
environmental noise investigations begin with measurements using a Sound
Level Meter. The scope of the measurements will usually decide what type of
instrument should be used and with which standards it must comply. A wide
variety of Sound Level Meters is available covering requirements for simple
noise surveys, where only dB(A) levels need to be measured, to elaborate
measurements where Equivalent Continuous Levels (LAeq, r ), Sound Exposure
Levels (SEL), maximum, minimum, impulse and peak levels are required.
Furthermore, there are Sound Level Meters available for national standards
which differ from international standards.

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 Table: Different Standards Of Noise Level For Various Areas Of A
Community

Typical Traffic Noise Levels:

 Areas with heavy traffic or close to blaring loud speakers: 80 –105 dB

(A).
 Areas with over flying aircrafts: 90-100dBA.
 At Railway Stations, Traffic Junctions, Busy markets; 70 –90 dB (A).
 Residential Areas close to traffic, industries and markets: 60 –80 dB (A).
 Residential areas away from heavy traffic roads or other noisy Sources:
40 –60 dB (A).

Introduction To Vehicular Traffic Noise:

Traffic noise will continue to increase in magnitude and severity because

of population growth, urbanization, and the associated growth in the use of
automobiles. It will also continue to grow because of sustained growth in of
vehicles. Highway noise is the sum of the total noise produced at the observer
point by all the moving vehicles on the highway. Thus the fundamental
component is the noise produced by the individual vehicles, which depend on
the vehicle type and its mode of operation. The overall noise is also dependent
on the characteristics of the vehicle flow and the relative proportions of the
vehicle types included in the flow. Knowledge of these factors is thus necessary
to define the characteristics of highway noise and to subsequently predict the
associated noise level in the surrounding area. The amount of information

3
required depends on the degree of accuracy desired in the predictions, which in
turn is a function of the method selected to characterize the temporal variation
of the noise. Thus the complexity of highway noise model will depend on the
noise descriptor selected.

Vehicle Noise Sources:

It is well established fact that vehicular traffic noise is a major source of

community annoyance especially near highway carrying fast traffic. Many
people consider the truck noise to be the principal offender. Numerous
components of noise sources contribute to the overall truck noise. These
sources, however, can logically be grouped into the major categories as under:

 Power Plant and Transmission Noise Sources- engine, exhaust, intake,

cooling system, drive train and so on,

 Running gear Noise Sources – tyre road interaction, differential, propeller

shaft. Noise from the power-plant increases as engine speed increases.
While noise from tyre increases as vehicle speed increases. Trucks tend to
operate at a nominally constant engine speed, so that engine and exhaust
noise do not vary appreciably with vehicle speed. Therefore, at lower
highway speeds the engine-exhaust noise is dominant, while at higher
vehicle speeds tyre-pavement interaction becomes the dominant source of
noise. The exact speed at which the tyre-roadway noise starts to dominate
over the power-plant-associated noise is a highly complicated function of
such variables as tyre characteristics, engine-exhaust characteristics, road
surface, and vehicle design and condition. As a tyre rolls over a road
surface, it displaces macroscopic and microscopic volumes of air. The
‘macroscopic’ applies to volume displacements of the same order as the
volume of the tyre itself, and ‘microscopic’ applies to much smaller
volumes. These air displacements generated pressure disturbances in the
surrounding air. Pressure disturbances in the audio frequency range and
of sufficient amplitude are responsible for the production of noise along
the roadway.

4
 Table: Factors Affecting Traffic Noise

Noise Prediction Models:

 Traffic noise prediction models are required as aids in the design of

highways and other roads and sometimes in the assessment of existing or
envisaged changes in traffic noise conditions. They are commonly needed
to predict sound pressure levels, specified in terms of (Leq)L10,etc., set
by government authorities.
 Environmental laws require the Environmental Impact Statement(EIS) to
take into account the effect of the proposed noise on all existing and
potential elements of the environment, not only statutory criteria. This
calls for a variety of descriptors and criteria. Special descriptors are
sometimes required for the assessment of complaints about road traffic
noise.

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Experimental Investigation - Nature Of Noise Problem:

Traffic noise prediction models are required as aids in the design of roads
and sometimes in the assessment of existing or envisaged changes in traffic
noise conditions. They are commonly needed to predict sound pressure levels,
specified in terms of Leq, L10 etc. Several models have been developed by
regression analysis of experimental data from fundamental variables such as
traffic flow, vehicle speed, etc. A survey of the area revealed that the major
contribution to the noise is from traffic with substantially high percentage of
heavy vehicles. The average speed of the vehicles was found within range of
35-60 kmph. The noise nuisance was aggravated by the indiscriminate horn
blowing, rapid accelerations and overtaking.

Site Selection:

To develop mathematical model for predicting the traffic noise, the first
task was site selection. So, according to surveys of different areas and nature of
noise problem, a two lane straight stretch where continuous uninterrupted flow
of vehicles occurs, without any obstructions like speed breaker, junctions,
traffic signals etc. was selected on Dindigul – Bangalore road (NH-209) at
Ganesapuram, between Coimbatore and Annur . Also it is better to take noise
measurement only in hot sunny days and it is better to avoid the rainy season.
During rainy days, the level of noise due to tyre - road interaction will be more
than that on normal days.

Measurement Procedure:

For traffic noise problems it is useful to know the equivalent continuous

sound level Leq and the 10 percentile exceeded sound level L10. Such
information is obtained using a sound level meter (SL 4010, Lutron make). The
sound level meter was suitably calibrated before taking the measurements. The
sound level meter was placed on a tripod at a height of about 1.20 m above the
existing road level at distance of 1.50m from the BT edge as shown in Fig. 4.
The sound level meter was provided with a windscreen for minimizing the
influence of wind during the measurement. The following data were recorded at
the site:
 Location plan
 Atmospheric characteristics such as air temperature, humidity and surface
temperature
 Vehicle count based on various classification of vehicles
 Speed of vehicles
 Noise level for different type of vehicles

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 Fig. Location Plan Of The Noise Measurement Site
Location plan:

The location plan was recorded in a separate sheet for showing the
following details regarding the existing road:
 Name of the road.
 Category of the road.
 Location of measurement.
 Type of pavement.
 Road width.
 No. of lanes.
 Type of carriageway (Divided / Undivided).
 Width of median.
 Height of median.

In addition to the above details, a line sketch showing the location of Km

or Hm stones, their distances, distance of nearby junctions, distance of nearby
culverts / bridges, north point, etc. and measured distances to the closest
residences are marked in the location plan.

Atmospheric Data:

Atmospheric data such as air temperature, humidity and surface

temperature were measured at the time of measurement. The air temperature in
°C and humidity in % were recorded using a hand held digital thermo-
7
hygrometer and the surface temperature in °C was measured using a hand held
laser thermometer.

Vehicle Count:

The vehicle count was done for various classification of vehicles based
on the Indian Roads Congress guidelines given in IRC:9-1972 for the full time
period of 12 hours at each measurement time on both sides of the road.

Speed Of Vehicles:

The uninterrupted free flowing speed of each category of vehicles was

recorded in a separate form by using a hand held speed radar gun (Bushnell
make) in kmph. The speed radar gun was also placed on the berm portion of the
road.

Fig. Speed Measurement of Vehicles

Noise Measurements:

The Sound Level Meter (SLM) with a wind screen was placed on a tripod
at a distance of 1.50m from the existing road edge at a height of 1.20m from the
existing road level. The noise levels created by each category of vehicles were
recorded manually in a separate form using the sound level meter (SL 4010,
Lutron make) in decibels. The speed of wind seriously affects the accuracy of a
measurement. This wind noise can be reduced significantly by the use of wind
screen. These screens are commonly spherical balls or porous foamed plastic
that fit over the microphone, and have negligible effect on the frequency
response of the microphone. The noise measurements were taken on both sides
of the road. In every hour, the measurements were taken on the left side of road
between 0 to 20 minutes (20 minutes). After that, the instrument is shifted to the
opposite side (right side) and the noise measurements were taken between 30 to

8
50 minutes (20 minutes) of every hour. The 10 minutes time duration is
considered for shifting the instrument from the left side to the right side.

Fig. Measurement of Noise

Irregular Noise Events:

During the measurement period, the irregular noise events such as low-
flying planes, dogs barking, passing of ambulances, fire service and VIP
vehicles, etc. were measured and marked in red colour for easy identification of
them.

Results And Discussions:

Modeling Of Traffic Noise:

Modeling of traffic noise has several uses, including estimating current

noise exposure along roadways, assessing the effect of roadway changes, and
predicting the performance of noise abatement options. The basic elements of
traffic noise modelling are the traffic source levels and the propagation or
attenuation of sound between traffic and receiver. Typical source related inputs
to traffic noise models are the speed and volume of vehicle types, operating
mode of the vehicles and the length of roadway with line of sight to the receiver
location. Propagation related inputs include the acoustic characteristics of the
ground, the number of lanes of travel, site geometry and topography and the
type of geometry of any barriers or buildings present. Most models also
consider the type of pavement at the site in regard to tyre-pavement noise
generation, the prevailing wind and temperature conditions and interrupted
traffic flows. There are a number of resources that can be consulted for
additional, detailed information about traffic noise prediction methods. To
develop road traffic noise a relationship is found between two or more variables
and these relationships are expressed in mathematical form. Followings are
steps followed:

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Step 1:

Collection of various parameters for this study such as Date & Time,
Total vehicle count, Q, Average Speed, V (kmph), Atm. Temp., Ta (°C),
Surface Temp., Ts (°C), Equivalent noise level, Leq (dB)Relative Humidity, H
(%)

Step 2:

Analysis of the collected data using DATAFIT (Version 8.10) to find out
the correlation between the various parameters and the noise level. A nominal
distribution test is also applied to test the model for its goodness of fit.

Step 3:

Plotting of graphs for Leq Vs Q, Leq Vs V, Leq Vs Ta, Leq Vs TsQ, and
Leq Vs H.
From the scatter diagram it is possible to visualize a nature of relationship
between variables.
Based on the data taken in different days between 6.00A.M. to 6.00 P.M.,
the data analysis was done using DATAFIT (Version 8.10). The best form of
regression equation obtained is given below:

Leq = 75.58 + 0.0024Q – 0.0064V + 0.0469Ta – 0.00451Ts + 0.0306H (R2 = 0.523)

Where, Q = Total vehicle count in both directions, V = Average speed of

vehicles in kmph, Ta = Average atmospheric temperature in °C, Ts = Average
Surface temperature in °C, H = Relative humidity in %, and R2 = Coefficient of
correlation. This equation can be used for predicting traffic noise in a two lane
road.

Testing The Goodness of Fit of The Model:

As a part of this study, the statistical goodness-of-fit test is provided to

test the prediction values against the field observed data. Since the total number
of samples is 168, the normal distribution test was carried out to find out the
goodness of fit, which shows that this model can be effectively used in analysis
and prediction of highway traffic noise conditions in India.

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 Total Vehicle Count Vs Leq

Average Vehicle Speed Vs Leq

Atmospheric Temperature Vs Leq

11
 Surface Temperature Vs Leq

Relative Humidity Vs Leq

Conclusion:

Collected data on noise generating parameters was applied to calculate

the predicted noise level with the help of regression analysis. The comparison
tests were made in order to examine the goodness of fit, between the predicted
and measured noise level from the collected field data and to suggest a suitable
model for Indian conditions.

From the present study following conclusions are drawn:

 Correlation equations have been obtained between Leq with total number
of vehicles (Q), Vehicle speed (V), Atmospheric temperature (Ta),
Surface temperature (Ts), and Relative humidity (H). Percentage errors
between the measured and predicted volumes are of negligible amount.
 R2 value for the equation was found to be 0.523. The value of R 2 can be
improved by incorporating variations by taking number of different
locations and taking more data sets.

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  Since the number of data are 168, the goodness of fit for large samples
was applied to study about the fitness of the equation. The null
hypothesis, μ=0, that is the mean value of the differences between pairs
of measured noise and predicted noise is equal to zero. The results of
nominal distribution at 5% level of significance shows that the null
hypothesis is accepted, that is the mean value of difference between
measured and predicted noise level is zero.
 The scatter plots of Leq Vs Q, Leq Vs V, Leq Vs Ta, Leq Vs Ts, and Leq
Vs H were plotted to determine the influence of the parameter on the
level of noise. In the above plots, if there are more data sets of different
locations and timings are adopted then it may have better correlation.

The following results conclusions were arrived from that plots:

a) The noise level increases with increased total number of
vehicles.
b) The noise level increases with increase in speed of vehicles.
c) The noise level decreases with increased atmospheric
temperature.
d) The noise level decreases with increased surface temperature.
e) The noise level increases with increased humidity.

CASE STUDY 2:

NOISE LEVEL AND ANNOYANCE OF INDUSTRIAL FACTORIES: A

STUDY OF DUHOK CITY

Introduction:

The sound is a mechanical wave that results from variations, or

oscillations, in elastic medium like water, air and solids and “Noise” generally
can be defined as unpleasant and disturbing sounds which disturb the human
being physically and physiologically and cause environmental pollution by
destroying environmental properties and it is one of the physical environmental
factors affecting our health in today's world and harmful to people. Noise can be
measured by determining the pressure created by the sound waves in the human
ear and is measured in (dB), which is used as an international measure of the
intensity of sound.
Due to the rapid growth and development in technology which is
occurred in the developing countries, noise pollution has become one of the

13
major threats that face the environment and the cost of reducing it in future
years is expected to be insurmountable and it is considered to be the
commonest reason of annoyance and permanent hearing loss.
One of the important problems of noise sources is industrial noise. The
general effect of industrial noise on the health of workers has been a topic of
debate among scientists for a number of years. The large, medium and small
scale hand tool manufacturers of developing countries are lagging far behind in
implementing hearing conservation, noise control programs, occupational health
and safety programs. These industries have a plenty of devices and machines
that considered as a source of noise such as: rotors, cutting machines, motors,
compressors, electrical machines, internal combustion engines, drilling,
crushing, fans and transportation resources. The noise level generated depends
mainly on the type of the noise source such as the kinds of machines, distance
from the source to the employee or receiver and the nature of the working
environment. Consequently the workers of hand tool industrial are exposed to
the noise levels overstep the permissible limits. High noise exposure in
industries not only affects the communication among the workers, but also leads
to the other psychological and physiological effects on the workers.

Materials and Methods:

This study includes the measurement of noise and a questionnaire based

survey.

Industrial samples:

This study has been carried out in thirteen different size industries spread
through Duhok city. The industries have been classified into seven groups based
on products such as: metal or mourning, joiner or carpenter, aluminum, auto
mechanic, washing carpet, printing house and tailor. This study was conducted
between September and November 2013.

Workers samples:

The current study was conducted on a group of workers who worked in

thirteen various types of factories distributed in several locations in Duhok city.
The samples of this study were ninety workers, distributed in these different
factories or industries (Table 1). Daily working hours are between (4-12h/d),
workers are given (1h) lunch break excluding the work time of (8-12h/d). The
sample's ages were between (14-55) years.

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Questionnaire or social survey:

Employee surveys were distributed to thirteen industrial sites. The

questionnaire for the survey comprised of (38) question contained questions
about gender, age, education level, knowledge of noise exposure, knowledge of
hearing protection, knowledge of protection them self, physiological and
psychological effects, maximum daily and year exposure duration.
Physiological and psychological equations are about annoyance, headache,
dizziness, disturbs their peace of mind, nervousness, stressful, speech
interference, insomnia, ringing in ears and pain or harm in the ears. Each
question was provided with five choices of responses, i.e. always, often,
sometime, seldom and never. The other questions were having yes or no
response.

Measurement of sound pressure levels in industries:

The measurement of industrial noise was carried out by author at

investigation areas. The chosen locations represented all types of industrial
work place. The measurement time interval was adjusted for (15min) each time
on each work section. A sound pressure level meter measuring instrument
Model SL-200 (VOLTCRAFT) was used in these measurements, it is designed
to approximate the loudness level sensitivity of the human ear. Measurements
results have been recorded by holding the instrument at a height equal to the ear
level of the workers and sound pressure level was recorded at least five times in
different locations where the workers actually work or movement of the
workers was most frequent in order to determine the noise levels to which the
workers are exposed. Measurements were carried out for every kind of
industries for all hours of working.

Statistical analysis

All questionnaires and collected data were recorded as Microsoft Excel

2010. Statistical Package for Social Scientists (SPSS) and Chi-square was used
for analysis to determine whether the effects of noise on workers are significant
or not.

Results and Discussion:

Noise Level Measurements:

The number of industries, the results of the noise level ranges

measurements during all working hours in all kinds of industries and the
number of workers who responded to the survey from each industry are shown

15
in the following tabular column. This table also shows the percentage of
respondent from each factory and mean value of noise level.

Results shows that majority of industries have high noise level. In larger,
medium and small size industries, the major source of noise is from industrial
machines. The workers who operate these machines are significantly exposed to
high noise levels compared to other workers that they not operate it, the
majority of workers are machine operators and because the size of many
factories are not large noise exposure was not limited to machine operators,
sound pressure levels are approximately equal in all locations except in the
joiner (J3) and printing house factories sound pressure levels are not equal in all
locations because the size of both industries are larger than other factories, old
machines are responsible for most of the noises. Our survey included questions
on daily working hours and total machine operation time or daily exposure
hours to high noise levels in order to accurately assess duration of employee
noise exposure. 93.34% of workers operated 8h/d and more than 8h/d in six
d/wk i.e. operating time is 48h/wk as shown in (Table 2). The workers daily
exposure hours were between 1-12h/d as shown in (Table 2).
The Occupational Safety and Health Administration (OSHA) specify a
legal limit on noise exposure in the workplace. It put the occupational noise
standards to protect humans health [28,29]. By comparing (OSHA) law with
actual maximum daily exposure duration to industrial noise, it can be observed
from (Table1 and 2) that the majority of the study has level values that don't
correspond with working hours per day as the (OSHA) illustrated. It was
observed in case of noise level 115 dB (A), the maximum daily exposure
duration set by (OSHA) is 0.25h/d or less but workers perform 2h/d in auto
mechanic and exposure to 123.6 dB(A) noise. In many factories, the workers

16
perform 8h/d in noise level 100 dB(A), but maximum daily exposure duration to
100 dB(A) noise set by (OSHA) is 2h/d. In case of noise level 95 dB(A), the
maximum daily exposure duration sets by (OSHA) is 4h/d but workers perform
8h/d in noise level 95 dB(A) and greater than 95 dB(A) as shown in (Table 1).
This means that maximum daily exposure duration which set by (OSHA) to
protect health of workers was not taken in consideration for any level of
industrial noise in Duhok city factories. For long duration 10-15 years, exposure
to high intensity noise in 6-8 h/d can cause biochemical changes which make
the workers prone to cardiovascular pathology, depression and other
psychological conditions.

Questionnaire or Social Survey Results and Evaluation:

The questionnaire has been applied on (90) workers selected from all
different industries. All the respondents or workers are males. Distribution of
the workers has been examined according to their factories, ages, educational
situation and Duration of employment. Distribution of the workers according to
their industries is: 12.22% metal or mourning, 32.22% joiner, 13.33%
aluminum, 5.56% auto mechanic, 6.67% washing carpet, 22.22% printing house
and 7.78% tailor (Table 3). The workers ages were between 14 to 55 years (with
mean value of 29.12 year). Ages of interviewed workers exhibit a wide range:
13.33% were < 20 years, 21.11% were 21-25 years, 27.78% were 26-30 years,
17.78% were 31-35 years, 11.11% were 36-40 years, 7.78% were 41-50 years
and 1.11% was > 50 (Table 3). Distribution of the workers according to their
educational level is: 5.56% no educational, 27.78% primary school, 31.11%
intermediate, 27.78% high school, 5.56% technical and 2.22% university
education (Fig. 1). It has been determined that their distribution with respect to
their working periods is: 36.66% were 1-5years, 26.67% were 6-10 years,
15.56% were 11-15 years, 11.11% were 16-20 years, 5.56% were 21-25 years,
3.33% were 26-30 years and 1.11% more than 30 years (Table 3). When the
industries, ages, education levels and working periods were compared, it has
been specified that the majority of the workers were from joiner industry, age
range of 26-30 years, service period range of 1-5 years and generally
intermediate graduates.

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Noise annoyance:

81.11 % of workers were annoyed by industrial noise and 18.89% said

that they are not annoyed because:
(1) respondents consider that noise is result of their job and they must be patient
related that matter,
(2) they have accepted noise as a part of their works, and they get adapted to
noisy job conditions with increasing work exposure to high noise levels. Five
step semantic scale were used to respond about worker's attitudes from
industrial noise. 23.33% of the workers reported 'always' annoyed, 31.11 %
'often', 21.11 % 'sometimes', 5.56 % of workers 'seldom annoyed' and 18.89 %
'never' felt annoyed by high noise levels (Fig. 2).

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 By examination of the rates of disturbance in the workers depending on
their duration of employment years, it has been observed that majority of the
workers who reported noise annoyance were with less than and equal to five
years of work exposure, as long as exposure increases workers get adapted to
noise and noise annoyance is reduced as shown in (Table 4). From Chi-square
distribution , P≤0.05 and degree of freedom (D.F)=5, the critical value of Chi-
square obtain is 11.070. The Chi-square for (Table 4) is 17.407. This result
shows that there is significant relationship between duration of employment
years and feeling annoyance.

The results of the questionnaire interviews about psychological and

physiological effects of industrial noise in seven factors, were analyzed.
Analytical survey from these seven factors which are threatened the worker's
health has been done. The number and percentage of these seven factors are
represent clearly in (Table 5) and (Fig. 3). (Table 5 and Fig.3) display that most

19
of responds to these seven variables was always and sometimes. 41.11% of
respondents reported sometimes headache from high noise levels, 44.44%
reported sometimes having dizziness during or after work due to high noise
levels, 41.11% reported had disturbs their peace of mind, 47.78% reported
always nervousness, 48.89 reported always feeling stressful during work in a
noisy area, 35.56% reported sometime had speech interference and 43.33%
reported always had insomnia.

(Fig. 4) show the relationship between effects of noise on workers in

seven factors for three ranges of noise level and the percentage of the workers in
five responds. This figure display that in noise level ranges 90-100 dB(A) and

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>100 dB(A) the respond never is approximately disappear in all factors that
means when noise level increase the effects on the health of workers increase.
(Table 6) shows the total Chi-square value obtained from different noise level
ranges and different responds. From Chi-square distribution , P≤0.05 and degree
of freedom (D.F)=8, the critical value of Chi-square obtained is 15.507. (Table
6) shows that the computerized test statistics for the effect of noise on headache,
disturbs their peace of mind, nervousness, stressful, speech interference or
problem in speech communication and insomnia exceeds the critical value,
hence the null hypothesis is rejected. This means that there is significant
relationship between this type of effects of noise on workers and the types of
noise level ranges. In case of dizziness factor the null hypothesis is accepted,
because the computerized test statistics for it is less than critical value so that
there is no significant relationship between dizziness effect on workers and the
types of noise level ranges.

21
 Also respondents were asked about reaction against industrial noise, they
asked “Do you want to move to a quieter work” and asked “Do you consider
that the noise in your factory is dangerous”. It is shown in (Fig. 5) that 97.78%
of respondents said that they like to move to a quieter work and 2.22% don‟t
like to move to a quieter work. 83.33% of respondents said that they consider
that the noise in their industrial is dangerous, 12.22% said that is not dangerous
and 4.44% of workers said that they do not know if the noise in their industries
is dangerous or not.

Effect Of Noise in Ear and Ear Protection Equipment (EPE) :

Effects on ear were considered significant. When the workers asked about
effect of noise on their ears, 65.56% of workers stated that noise causes
“hearing impairment and harms their hearing or pain in their ears” and 34.44%
responded that it “gives them ringing in ears drum”. When the workers asked
how to protect themselves from hearing loss, in all industries the majority of
them know how to protect their ears, but only 10% of them were used EPE and
the others said they are not used EPE, the reasons for not using EPE are:
17.78% feel uncomfortable, 21.11% due to negligence, 8.89% do not know why
they are not used and 42.22% said management did not provide EPE at work
place (Fig. 6).

22
Conclusion:

Industrial noise exposure has been identified as a very obvious danger

especially in small and hand tool industries which are still not mechanized.
During our measurements it has been determined that the noise levels in the
majority industries are exceeds the maximum (OSHA) exposure limits.

According to results of the questionnaire applied to the all workers in all

industries:
 Majority of workers in the industries are annoyance from the noise in
their workplaces.
 Mostly workers are of little education even under primary, primary and
intermediate therefore they are not fully aware of the hazardous effects of
noise.
 There is strong relation between duration of employment years and
feeling annoyed, and this relation is statistically important with given
Chi-square=17.407.
 Psychological and physiological effects of industrial noise in seven
factors (headache, dizziness, disturbs their peace of mind, nervousness,
stressful, speech interference and insomnia) was analytic and the answer
of these seven variable was in sometimes and always choice options. The
Chi-square calculated for each these factors are: 18.965, 13.942, 20.716,
31.701, 34.246, 26.654 and 42.708 respectively.
 97.78% of workers like to move to a quieter work, 2.22% of them like
their factory sound insulated and 83.33% said that the noise in their
industries is dangerous.
 Almost all the workers are highly exposed to high noise levels compared
to (OSHA) limits without proper ear protection, and the majority of them
are suffering hearing problems.

23
CASE STUDY 3:

STUDIES ON NOISE POLLUTION MODEL FOR CHENNAI

Introduction:

Noise pollution refers to sounds in the environment that are caused by

humans and threaten the health and welfare of human or animal inhabitants.
Environmental noise consists of all the unwanted sounds in our communities
except that which originates in the workplace. Environmental noise pollution is
a threat to health and well-being. It is more severe and widespread than ever
before, and it will continue to increase in magnitude and severity because of
population growth, urbanization and the associated growth in the use of
increasingly powerful, varied, and highly mobile source of noise. Vehicular
noise problem is contributed by various kinds of vehicles like heavy, medium
trucks/buses, automobiles and two wheelers.
The traffic noise problem is not properly recognized despite the fact that
it is steadily growing in developing countries. The permissible noise levels
specified by Tamil Nadu Pollution Control Board (TNPCB) are given in Table
1:

Table 1: Permissible Noise Levels

Objectives and Study Area:

The objective of this work is to develop a road traffic noise prediction

model for the busy corridors of Chennai based on the primary and secondary
data. The developed model is capable of predicting the combined traffic noise
generated from vehicles in highways. Traffic flow data will be used for
constructing this model consisting of vehicle noise, vehicle volume, vehicle
speed and the distance from the traffic flow line.
Chennai, the capital of Tamil Nadu forms one of the developed urban
centres of India with a population of 4.68 million as per 2011 census. It is

24
located towards South East along the coastal Plains of India. It is one amongst
the four metropolitan cities of India. The vehicle population in Chennai as of
2012 is 3,760,000 vehicles. The total length of road network in Chennai is 2780
Km. The city is continuously growing in terms of population in geometric
progression. The city being a hub of commercial and business activities is
facing ever-increasing vehicular traffic. This has resulted in multifaceted traffic
problems such as accidents, peak hour congestion etc. The road network of
Chennai city is of Radial Pattern having major highways to the North, West and
Southwest. The other major roads are the arterial road along the coast and the
road parallel to NH4. However, the existing road network in the city is unable to
accommodate the present day vehicular traffic.

Data Collection And Analysis:

Fifteen busy commercial corridors of Chennai city were selected for the
present study. Busy corridor is one which is having traffic volume higher at all
times of the day. In this corridor the traffic volume on either direction is almost
same and the non-peak hour traffic is more than 50 % of peak hour traffic. The
study road extends from Tambaram intersection to Koyambedu and
Thiruvanmiyur intersection. This corridor is busy corridor in Chennai as the
moffusil bus stand is located near to this corridor and traffic volume is more
throughout the day. Each site has its unique characteristics, i.e., having typical
road width, roadside housing pattern, traffic flow pattern. At each of these
locations, measurements were made when there was a reasonable traffic activity
(in general from 8 a.m. to 8 p.m.). “Sound Level Meter having digital display
was used to record the equivalent noise level at different selected locations. The
noise level locations are depicted in fig.1.
The following locations are selected for this study:
 Little Mount
 Anna University
 Madhya Kailash
 Adyar
 Kottivakkam
 Palavakkam
 Thiruvanmiyur
 Neelankarai
 Vettuvankani
 Echampakkam
 Pallavaram
25
  Chrompet
 Tambaram
 Perungalathur
 Vandalur

Fig. 1 Observed Noise Level Locations In The Study Area

Noise levels were recorded at these locations and simultaneously the

volume and speed of traffic streams were also monitored. The data pertaining to
Thiruvanmiyur Junction for ground floor is presented in Fig. 2. Noise level is
gradually increasing from morning 6:00am to 10:00am and then reaching
morning peak from 10:00 am to 11:00 am. It is observed that the noise level is
maximum during evening peak hours between 4:00pm and 6:00pm. Further the
noise level is decreasing from 11:00 am onwards till 2:00 pm and 2:00 pm to
4:00pm the noise level increases gradually. The noise is decreasing from
6:00pm onwards. It is also observed that the noise level is decreasing towards
buildings from the source. The noise level is higher on far side than that of near
side and the reason for this is the return trip to home is more. Similarly for all
the 15 locations, noise, traffic volume and speed data were collected and
analysed.

26
 Fig. 2 Observed Noise Level near Thiruvanmiyur in ground floor

Development Of Regression Model:

Prediction is a very important part of noise impact assessment. The basic

prediction procedure involves consideration of the nature and noise level of the
sources, propagation along the paths between sources and receivers and the
location of the receivers. Each vehicle in the stream having mixed traffic system
has different noise generation characteristics and this fact makes the road traffic
noise problem little complex. In order to understand the behaviour of road
traffic noise or to understand the various relationships, it is necessary to convert
all vehicles into some equivalence based on their noise generation
characteristics. The Passenger Car Noise Equivalence (PCNE) of particular
vehicles represents that, how many times the vehicle is noisier than car. PCNE
values can be used to quantify noise produced by different types of vehicles into
a common unit and is used in modelling process.
Mathematical models for prediction of traffic noise usually extract the
functional relationship between the parameter of noise emission, Leq, and
measurable parameters of traffic and roads. The classical functional
relationships available in literature have been stated based on data measured
through semi-empirical models, typically regression analysis. In practice the
relationship is found to exist between two or more variables. In the present
study, the important parameters that affect traffic noise like hourly traffic
volume, speed (kmph), distance of observer from edge of pavement (meters) in
both directions are taken as independent variables.
A Multiple Linear Regression Model is developed. Though there are a
number of independent variables which may affect traffic noise, in this study,
the important parameters like traffic volume, spot speed and distance of

27
observer from edge of pavement in both directions were taken as independent
variables.
A common form of Mathematical model is
Leq = a0 + a1 V + a2 S + a3 Dg +…………..…….(1)

Where,
Leq = Equivalent Traffic Noise Level in dB (A)
a0 = Constant
a1, a2, a3 = Coefficients to be determined adopting Multiple Linear Regression
S = Mean speed of traffic (kmph)
V = Volume of traffic (veh/hr)
Dg = Geometric mean of roadside section
Dg = (Df . Dn)(1/2)
Df = Distance from observer to centre line of far side (m)
Dn = Distance from observer to centre line of near side (m)
Dg0 = Geometric mean of roadside section for ground floor

 Average traffic flow speed is always greater than 0

 The highway stretch is straight and level
 The model is applied under normal weather conditions

The regression model is built separately for different heights and different
distances from the source of noise emission for each direction of traffic. The
traffic volume is directly used in this study. In this study a different approach is
used to find out the relationship between distance and height of the recipient
from the source of noise. The factors such as traffic volume speed, distance and
observed noise were kept constant but the height was changed.

In this study the model developed for the noise, speed and distance from
source of noise. The model is described in the following form:

Leq = a0 + a1 S + a2 Dg +…………..…….(2)

Where,
Leq = Equivalent Traffic Noise Level in dB (A)
a0 = constant
a1, a2, a3 = Coefficients to be determined adopting Multiple Linear Regression
S =Mean speed of traffic of observer (kmph)
Dg =Geometric mean of roadside section
Dg = (Df . Dn)(1/2)
Df =Distance from observer to centre line of far side (m)

28
Dn =Distance from observer to centre line of near side (m)
Dg0 = Geometric mean of roadside section for ground floor

Calculation of Dg0, arrived as per the following procedure for padi.

Edge of the road on near side is taken as half of the road width and far side is
taken as width of the road

Road width = 30m

Floor height = 0
Df = 30m
Dn =15m
Dg0 = (Df . Dn)(1/2)
Dg0 = ( 15 * 30)(1/2) = 21.21 m

The geometric mean distances arrived for each road is given in Table 2.
The model is developed separately for ground floor, for each road. The noise
level and speed vary at different times of the day. Hence the model is developed
separately for each road. Table3 shows the model that is developed for all study
areas respectively.

Table 2 Geometric Mean Distance for Ground Floor

Speed varies from location to location due to volume of traffic and road
width. Noise levels are taken near the junction where the speeds change
drastically depending upon the signal phases. The noise level will be higher at

29
lower stream speeds and higher stream speeds but at medium stream speed the
noise level will be lower. Lower the speed, higher is the noise level since the
driver decelerates the vehicles during red signal and accelerates during starting
of green signal timings. The noise level is also higher during higher speed after
green signal timings. Hence this speed on the near side and for side has positive
impact on the noise. From Table 4, it is observed that the value of R2 in most
cases lies between 0.85 and 0.96. This fact characterizes a good correlation.
However, at certain locations, namely Little Mount, Anna University, Madhya
Kailash and Adyar, the values of R2 are below 0.85. The reduction in the R2
values, and hence in the noise levels, can be attributed to an increase in vehicle
volume coupled with a decrease in vehicle speed.

Model Validation:

To validate the model two equations are taken one at the 15m for ground
floor equation. Corresponding values of speed, volume of traffic and geometric
mean of roadside were substituted and the results are presented in the Table 4. It
is seen that the differences of observed and calculated noise level is between 0.1
dB to 2.45 dB. The difference between observed and estimated noise level for
Delhi lies in the range of 1dB - 11 dB. The error various from -9.88% to 0.73%.
Hence the model developed for busy corridor in Chennai can be applied for any
stretch of the road in Chennai and also used for other similar cities in India.
Hence the model developed for noise for busy corridor for Chennai is in
acceptable limits.

Table 3 Comparisons of Observed and Calculated Noise Levels

30
Conclusion:

The noise levels are observed in the range of 65-86 dB (A) during peak
and non-peak hours. Further the noise levels decreased with increase in distance
and height that is noise levels are lower at higher level floors compared to the
ground floor. The noise level also decreases when the distance from the carriage
way increases. These noise levels are in excess of the prescribed limits given in
Table 1.

The following are the future scope of the study:

 The effects of a range of bituminous road surfaces on road traffic noise
needs to be expanded.

These surfaces include:

 dense grade asphaltic concrete for a range of formulation types,
 Open graded porous asphalts especially speciality ‘low noise
‘formulations.
 Cement concrete roads.

CASE STUDY 4:

MODELLING OF NOISE POLLUTION DUE TO HETEROGENEOUS

HIGHWAY TRAFFIC IN INDIA

Introduction:

A recent study conducted by World Health Organisation (2011) reported

that "one million people are losing their lives every year due to traffic noise in
western Europe alone." This proves the severity and necessity of mitigating the
traffic noise from every delicate corner possible. This inevitable requirement led
to the need for taking up traffic noise studies by researchers globally including
India, to study the core factors leading to road noise levels. Compared to most
of the countries across the world, the unique phenomena that worries the road
planners and traffic engineers in India is the heterogeneity in traffic flow on
most of the roads. Accordingly, one of the significant agonizing factors
affecting the road noise levels in India being the vehicle itself. Moreover, a
drastic increase in different vehicle classes hitting the Indian roads are dreadful,
as they grew at the Compound Annual Growth Rate (CAGR) of 10.5% between

31
2002 and 2012 (Guite, 2017; Hindu business line, 2009). This rise in different
classes of vehicles in the traffic stream has made the nature of heterogeneity of
Indian traffic into more complex phenomena. Thus, there is a need for better
traffic noise prediction models especially for the mixed traffic conditions. This
is because, with the presence of different vehicle sizes, different engine
characteristics and manoeuvring abilities, the road traffic movements results in
the spectrum of noise levels. This is because, vehicle speeds holds a direct
logarithmic relation with the tyre/road noise, and is dominant at speeds
exceeding 50 kmph. On the contrary, propulsion noise is the dominant noise
source at lower vehicle speeds. Thus, the road traffic noise from the vehicle
fleet is defined as the combination of aerodynamic noise, propulsion noise, and
tyre/road noise levels. As aerodynamic noise effect is very less on overall noise
emission, and is experienced only by the person sitting in the vehicle, the
concentration of the noise levels due to tyre/road interaction and propulsion
noise sources were majorly considered in traffic noise quantification. As both of
these sources are highly dependent on the vehicular speeds, it can be said that
tyre/road interaction and engine propulsion are the most contributing noise
sources on the Indian roads. Thus, it is inevitable to consider the speed spectrum
along with the possible vehicle classes for developing the noise prediction
model for Indian conditions to use as a design aid for future.

Study Area and Data Collection:

The study area selected for the current study covers the important
highways in the states of Andhra Pradesh and Telangana in India, that are
grouped in Table 1.

Table 1. List Of Survey Locations

Usually, noise levels were measured through near field and far field
measurements. Placing the microphones on the roadside and capturing the noise
levels from the moving traffic is classified under far-field methodology and is
adopted in the current study. A class 1 sound level meter was placed at a

32
predefined distance of 1.5 meters from the adjacent traffic lane, at the height of
1.5 meters above the ground, and the continuous noise levels were measured
with a data logging of 1-second interval using the time averaging method.
Accordingly, SVAN 945A pocket sound level meter (SLM) was used to
measure the noise levels and are analysed using SVAN PC suite by transferring
the data to the computer. The measured noise indices in the current study are
equivalent A-Weighed continuous sound level [Leq or Leq (dBA)], Sound
Pressure Level [SPL], Sound Exposure Level [SEL] and the noise level
exceeded for 10% of the measurement time [L10]. Along with these noise level
measurements, traffic volume and spot speed studies were carried out
simultaneously. Classified traffic volume on both the directions of the selected
road was collected. In order to achieve this task, four trained enumerators were
employed in each direction of the vehicle movement. Accordingly, vehicles are
classified as Bus (B), Mini Bus (MB), Motor Cycle (MC), Scooter (SC),
Bicycle (CY), Cycle Rickshaw (OT), Auto Rickshaw (A), Small Car (CS), Big
Car (CB), Tractor-Trailer (TT), Light Commercial Vehicle (LT), Two-Axle
Truck (HT) and Multi-axle Truck (MT). As consideration of these classes on
the same roadway will lead to heterogenic traffic volume, classes of all vehicles
were converted into Passenger Car Units (PCU’s). Spot speeds of the vehicles
were recorded using the RADAR speedgun. Along with these traffic
parameters, geometric factors such as the width of the carriageway, the number
of lanes and the shoulder width were recorded for each highway location. All
the measurements were carried out from 10 am to 5 pm continuously.

Results and Discussion:

Various vehicle types travelling on the highways would generate different

noise levels with respect to the vehicle and roadway characteristics. To account
for these variations over a continuous noise exposure level on the commuters,
the captured noise levels were averaged for a data logging of 15 minutes and
one-hour time intervals, and the governing Leq (dBA) and L10 (dBA) were
analysed with respect to the vehicle volumes and speeds as shown in Figures
1(a) to 8 (c).

33
 Figure 1(a). Traffic volume v/s Noise levels on Vijayawada-Kolkata highway

Figure 1(b). Average traffic speed v/s Noise levels on Vijayawada-Kolkata highway

Figure 1(c). Mode share on Vijayawada-Kolkata highway

34
 Figure 2(a). Traffic volume v/s Noise levels on Vijayawada-Chennai highway

Figure 2(b). Average traffic speed v/s Noise levels on Vijayawada-Chennai highway

Figure 2(c). Mode share on Vijayawada-Chennai highway

35
 Figure 3(a). Traffic volume v/s Noise levels on Warangal-Khammam highway

Figure 3(b). Average traffic speed v/s Noise levels on Warangal-Khammam highway

Figure 3(c). Mode share on Warangal-Khammam highway

36
 Figure 4(a). Traffic volume v/s Noise levels on Hyderabad-Nagpur highway

Figure 4(b). Average traffic speed v/s Noise levels on Hyderabad-Nagpur highway

Figure 4(c). Mode share on Hyderabad-Nagpur highway

37
 Figure 5(a). Traffic volume v/s Noise levels on Hyderabad-Vijayawada highway

Figure 5(b). Average traffic speed v/s Noise levels on Hyderabad-Vijayawada highway

Figure 5(c). Mode share on Hyderabad-Vijayawada highway

38
 Figure 6(a). Traffic volume v/s Noise levels on Hyderabad-Bangalore highway

Figure 6(b). Average traffic speed v/s Noise levels on Hyderabad-Bangalore highway

Figure 6(c). Mode share on Hyderabad-Bangalore highway

39
 Figure 7(a). Traffic volume v/s Noise levels on Hyderabad-Pune highway

Figure 7(b). Average traffic speed v/s Noise levels on Hyderabad-Pune highway

Figure 7(c). Mode share on Hyderabad-Pune highway

40
 Figure 8(a). Traffic volume v/s Noise levels on Hyderabad-Warangal highway

Figure 8(b). Average traffic speed v/s Noise levels on Hyderabad-Warangal highway

Figure 8(c). Mode share on Hyderabad-Warangal highway

41
 It was observed that motorcycles have a dominant share in traffic flow
among most of the selected sections. Figure 1(a) shows that maximum Leq (15
minutes) of 107.1 dB (A) was observed for the vehicle volume of 176 (pcu's).
On the same section, for the highest volume (pcu’s) of 238.5, the Leq (15
minutes) was observed to be 103.5 dB (A) between 11:00 to 11:15 am. This
shows that maximum Leq (15 minutes) need not necessarily corresponds to the
maximum traffic volume, and vice-versa. Whereas, on Warangal-Khammam
highway, maximum Leq (15 minutes) of 99.5 dB (A) was observed for the
highest 15-minute volume of 136 (pcu’s) shown in Figure 3(a). Thus, the
variation of the proportion of the vehicle type can play a significant role in the
generation of traffic noise levels, irrespective of the traffic volumes. Similar
results were observed on other highways, as shown in Figures 2(a), 4(a), 5(a),
6(a), 7(a) and 8(a).
As the continuous noise exposure over time is more fatal than the
instantaneous noise source for commuter’s health, along with traffic volumes,
average speeds were taken for each 15-minute time interval. It was observed
that individual speeds of vehicles on all the highways were ranging between 10
to 95 kmph, with an average 15-minute speed of 30- 65 kmph. With the
variation being drastic, the effect of speed on the noise level will be significant
too. This is because, crossover speed between the engine propulsion and
tyre/road interaction for highway traffic vary between 30-50 kmph. Moreover,
literature concluded that noise levels from the vehicles will vary linearly with
speed. On a contradicting tone, for a highest 15-minute average traffic speed of
60.06 kmph in Figure 1(b), Leq (15 minutes) and L10 (15 minutes) appeared as
105.8 dB (A) and 107.8 dB (A). On the same section, for an average speed of
55.44 kmph during 10:45 am to 11:00 am, highest Leq (dB) of 107.1 dB (a) was
observed. Similar results were observed in Figure 2(b), 3(b), 4(b), 5(b), 6(b),
and 7(b). On another side, for a maximum 15 minutes, average traffic speed of
58.46 kmph, highest Leq (15 minutes) and L10 (15 minutes) indices of 102.2
dB (a) and 106.1 dB (A) was observed as shown in Figure 8(b). This clearly
shows the fact that, unlike the individual traffic speeds and noise levels, average
noise levels over the time frame will strongly depend upon the combination of
vehicle proportion, size, and speeds, that are shown in Figures 1(c), 2(c), 3(c),
4(c), 5(c), 6(c), 7(c) and 8(c). This is because, a weight of the vehicle can be a
judgemental factor in the noise generation. This concludes that the proportion of
vehicle volumes and road speed combination will play a major role in
generating the continuous highway noise levels. Moreover, the carriageway
width of the selected highways was different, which may affect the driving
pattern apart from the volume and speeds. Accordingly, the consideration of all
these independent variables can be vital for analysing the noise levels for the
development of the prediction model for the highway. In order to confirm it,
scatter plots are developed between the captured noise levels and these
independent parameters, to observe the relationship between them. The

42
developed scatter diagrams for both Leq (dBA) and L10 (dBA) are shown in
Figures 9 to 10.

From Figures 9 and 10, it can be observed that the noise levels have
shown a significant relation with speed and volumes whereas the relationship
with carriageway width is questionable. Accordingly, data collected at all the
study locations were taken and averaged for 15 minutes and 1-hour intervals,
and datasets were prepared for both Leq and L10, that were processed using
SPSS package to develop the linear noise models for all the highways selected
in this study. For obtaining hourly Leq from the 15-minutes Leq data, the
following equations are employed:
Leq(hr) = 10*log [10(L1/10)*t1+ 10(L2/10)*t2+10(L3/10)*t3+10(L4/10)*t4],................(1)
L10(hr) = 10*log [10(L1/10)*t1+ 10(L2/10)*t2+10(L3/10)*t3+10(L4/10)*t4],................(2)

where,
Leq(hr) = A - Weighed equivalent sound pressure level for one hour,
L10(hr) = A - Weighed noise level exceeded for the 10% of the total
observations for one hour, and

L1,L2,L3 and L4 are fluctuating noise levels for an interval of t1, t2,t3 and
t4.
Models are developed for the prediction of the noise levels, and these models
are tested for the logical sign for every coefficient. Student t-test values are
compared with the table values to know their significance of contribution to
43
explain the variation in noise levels. Table 2 presents the best form of regression
equations obtained for each highway location for both Leq (dBA) and L10
(dBA) noise descriptors, with the highest R2 value. Data corresponding to each
highway passing through a particular city was combined and the models are
developed accordingly.

44
 With the data pertaining to the two major highway locations near
Vijayawada city, the models have been proposed as in Table 2. On a similar
note, data collected at five important national highways covering the
Hyderabad-Nagpur, Hyderabad-Vijayawada, Hyderabad-Pune, Hyderabad-
Bengaluru, and Hyderabad-Warangal Highways was averaged for 15 minutes
and one hour, and the respective calibrated models are shown in Table 3.

Finally, field data collected at all the study locations were taken and
averaged for 15 minutes and 1-hour time intervals, and datasets were prepared
for both Leq (dBA) and L10 (dBA), and the comprehensive noise prediction
models are developed, that are shown in Tables 4 to 8.

45
46
 To check the validity of the comprehensive model developed in this
study, 180 sample observations within the collected noise data from all the
highway sections is utilized. Non-parametric testing (chi-square test) for all
models were conducted to know the difference between observed and predicted
values. Accordingly, Chi-square test (χ2) was performed between the observed
and predicted values of Leq (dB), where χ2 (calculated) is appeared to be 22.825
and χ2 (Critical) at the 5% level of significance is 69.90. Since the χ2
(calculated) is less than χ2 (critical), it can be concluded that difference between
observed and predicted values are insignificant, that are shown in Table 9.

47
Conclusions:

Measured noise levels for all the selected highways revealed that both
Leq (dBA) and L10 (dBA) are exceeding the noise limits, which can annoy the
road users in a menacing way. Moreover, the measured noise levels in the time
frame of 15 minutes and one-hour time intervals have shown a clear correlation
with both the traffic variables, including volume and speed. At the same time,
the results revealed that the combination of volume proportion and road speeds
would play a significant role in highway noise level generation. Along with that,
observed R2 values are higher in the models developed with an hourly data [Leq

48
[hr] and L10 [hr]], compared to Leq [15 min] and L10 [15 min]. It indicates that
the noise and traffic data averaged over a one-hour interval is close to reality
than 15-minutes. Accordingly, models for 15-min time frame resulted in a poor
fit compared to the Leq [hr] and L10 [hr]. The comprehensive models
developed in this study were validated resulting in a predicted difference of 1 to
10 dB (A) with the observed values. Henceforth, the developed comprehensive
model can be effectively used for the noise prediction for the highways with the
similar traffic and geometric conditions. Moreover, the proposed model can be
effectively used in noise assessment for heterogenic traffic conditions, as the
considered vehicle classes for the study covers the most possible modes on
Indian highways. Moreover, the study shows that, percentage of two and three
wheelers have been dominating the volume proportion in most of the highways
selected, showing the need for improved public transportation facilities to keep
the average noise levels within the limits prescribed by Central Pollution
Control Board (2000) of India. The study can be further extended by assessing
the noise levels inside the vehicle on the same highways, in order to compare
the noise levels affected by both the commuters (occupants of the vehicle) and
the road users including the pedestrians, which is more beneficial in order to
formulate the traffic noise regulations.

CASE STUDY 5:

A STUDY OF TRANSPORT RELATED NOISE POLLUTION IN

ASANSOL TOWN, WEST BENGAL USING MODELLING
TECHNIQUES

Introduction:

Noise is unwanted sound and a cause of concern for all the urban areas
around the world. Motor vehicles, which are significant part of the urban
environment, are the main source of noise emission, contributing about 55%to
the total noise pollution. The growing vehicle population gives rise to
unrestrained noise pollution associated with significant health effects. Noise can
cause both short term as well as long term psychological and physiological
disorders, particularly among those living in close proximity to busy roads,
streets and highways. However, because of complexity, variability and the
interaction of noise with other environmental factors, the adverse health effects
of noise do not lend themselves to a straight forward analysis. Noise is a very
complex phenomenon in its physical dimension, as well as its manifestations in
psychological and medical dimensions. In consequence, it is practically
indispensable to measure, predict or describe noise in a simplified way. The
Central Pollution Control Board of India (CPCB), in its notification on Ambient

49
Air Quality (AAQ) standards has included noise as an air pollutant under
Section 20 of the Amended Air Act of 1987 and has laid down the ambient
noise standards (CPCB 2000, 2001).
The prediction and assessment of noise levels from transport activities
can be achieved either by measurement, by computation or by hybrid methods
using measurement results in computations. For reasons of reproducibility, the
methods used for measuring or calculating noise levels within the framework of
legal regulations have been standardized on an international scale.
Research works conducted based on traffic noise modelling and
prediction include studies carried out by Bhattacharya et al. (2003) relating to
development of highway noise prediction model under Indian condition.
Banerjee & Chakraborty (2005) attempted to compare CORTN, FHWA & Lyon
noise models under Indian road conditions and developed a regression model
for road noise prediction. Work done by Nirjar et al. (2003) relating to
prediction of road traffic noise in New Delhi using FHWA, CORTN and STOP
& GO Models can be cited as significant contribution to this field of research.
The objective of the present work was to record noise level and traffic
flow parameters along with site geometry at representative locations in an urban
environment and fit the measured data into two noise prediction models, namely
Federal Highway Administration (FHWA) and Calculation of Road Traffic
Noise (CORTN) to compare the predicted noise levels with actual monitored
values and evaluate the suitability of the models under Indian road conditions
using suitable statistical tests.

Materials and Methods:

Sampling:
For the present study data were collected at ten locations in the Asansol city, an
urban-industrial area situated in the eastern part of India. The town is a fast
growing and rapidly urbanizing industrial zone of this subcontinent. The area
has 100% urban population consisting of 50 wards with 4,75,439 inhabitants.
The town has grown in all sectors, and in recent times has become an important
hub for business and commercial activities. The increase in new settlement
areas, business and commercial units have increased the personal vehicle
population of the town. Although there is good public transport system, but
personal vehicles are preferred. Along with the infrastructure growth there
have been no expansion of the road network or its improvement, resulting in
overflowing traffic conditions chocking the major roads and giving rise to
severe noise pollution all around the town. The Delhi-Kolkata National
Highway (NH-2) passes through this town which is well connected by rail and
bus routes with all major locations of the country. The city has diverse types of
roads i.e., arterial roads, secondary roads and also dense network of lanes and
50
by lanes, all of which are used for the movement of vehicles. The data
collection sites were chosen based on presence of vulnerable receptors, land use
pattern and distance of road from housing quarters. The sampling locations
represented different zones like commercial, residential and sensitive areas. The
locations used for field data collection are shown in Fig. 1.
A digital sound level meter, Type 2 with frequency weighting network as
per IEC651 specifications, frequency range of 31.5Hz to 8,000Hz and
measuring range between 0-150 dB was used for the study. A ‘B& K’ (Bruel &
Kjaer) multi-function acoustic calibrator (Model: 4226) was used for calibration
at 94.0 dB(A) before and after sampling. All readings were taken on the ‘A-
weighting’ frequency network, at a height of 1.5 meters from ground level and
on the ‘Fast’ range Time Weighting. The A-weighting characteristic and ‘Fast’
range is simulated as ‘Human Ear Listening’ response. This is typically required
if making the environmental sound level measurement. All measurements were
carried out during working days and under suitable climatic condition setting.
Concurrent recording of on site traffic flow parameters like classified vehicle
volume and average speed etc. were also done along with collection of
geometric parameters of location, including road type, width, ground cover
reflective facade and angle of view, etc. for each site.

Modelling and Prediction:

Fig 1: Map Showing Study Area And Field Data Collection Locations.

51
 For the present investigation two different empirical noise prediction
models were used, namely FHWA and CORTN. The comparative features of
the two models are given in Table 1. The noise levels predicted by the models
were compared with data collected from field using regression analysis and
statistical tests. The paired t-test technique was applied to compare the output of
the models with the actual road noise level to examine how well the models
work under Indian conditions (Nirjar et al. 2003). The null hypothesis taken into
consideration was that, the ‘mean value of difference between pair of measured
and predicted traffic noise is equal to zero’. The null hypothesis was tested
using the paired t-test.

FHWA Model:

The Federal Highway Authority in USA, developed this very useful and
important model for road noise prediction. In this model traffic noise levels in
and around roadways can be predicted on the basis of individual noise levels,
vehicle volume and observer distance, etc. The FHWA Model can be written as:

Leq = L0 + Avs + Ad + Ab + Af + Ag + As

In this model vehicles are classified into categories like car/jeep/van,

scooters/motorcycles, light commercial vehicles, bus/truck, auto rickshaw and
tractor trailers. Here, the reference energy level for each category of vehicles is
separate and obtained in the form Y =A + B log S, where S is speed of vehicles
in kmph. The hourly Leq value for each category is calculated using the
following equation:

Leq = Lo + Avs + Ad + As

The equation for volume and speed correction is given as:

Avs = 10 log10 d0 v/s – 25

The distance corrections at the receiver point are given by:

Ad = log10 (d0/d)1-

Noise level for each lane is evaluated and then combined logarithmically
to get the total Leq value, and the combined hourly Leq value is calculated by
logarithmic summation of hourly Leq values for each category.

52
CORTN Model:

Developed by UK Department of Transport in 1988, this model can

predict noise at an observer location at a certain distance from the road. The
parameters on which this model works are vehicle composition, road surface,
type, ground absorptive factors, site geometry and reflections, etc. This model
does not take into account background noise such as trains, aeroplanes and
industry, etc. Here vehicles are not classified into categories and it combines the
flow of all vehicles in vehicles/hour and takes into account hourly flow of all
vehicles and heavy vehicles in particular. The standard equation for L10 (hourly)
is used for the calculations. The basic model for this calculation is given by:

L10 = 42.2 + Log10 q

The hourly L10 value for each category (light and heavy vehicles) is calculated
using the following equation:

L10 = L0 + Abv + Ad

Results and Discussion:

The data collected from field were analysed and have been presented
here. The Noise standards as prescribed by the Central Pollution Control Board
of India are given in Table 2. The results of the noise prediction using the two
models along with their summarized based criteria results of testing are given in
Table 3. The scatter plot for FHWA model, showing relation between observed
and calculated Leq values is given in Fig. 2, and for CORTN model showing
relation between observed and calculated L10 values in Fig 3.
For proper analysis of data, the traffic counts have been summarized in
the form of total traffic volume for both directions for every hour, whereas the
traffic speeds have been summarized in the form of average traffic speed values,
calculated for every hour, for both the directions . In order to correctly compare
the noise level predicted by the models in relation to that observed in the field,
three criteria were used for deciding the suitability of the models, i.e., difference
between observed and estimated noise level, the r value and paired t-test
estimated at 5 % level of significance.

53
FHWA Model Analysis:

Applying the three criteria system to validate the model, it is observed

that the difference between observed and estimated noise level varies between 1
to 6 dB(A); the best fit regression plot between observed and estimated Leq
produced an r value of 0.8031 (Fig 2); whereas the paired t-test between
observed and estimated Leq gives a ‘t’ value of 4.58 at 5% level of significance
and 88 degrees of freedom. The noise variation range is within the acceptable
criteria and also the r value, which is very much in the acceptable range, since
any r value above 0.7 up to 1.0 is a good correlation. It is observed that the
54
predicted values tend to differ much, when the actual levels are on the higher
side. The t-test value of 4.58, does not lie within the acceptable region and
hence the null hypothesis can be rejected. Since majority of the acceptable
criteria among the three are satisfied by the model, it can be said that the FHWA
model can predict noise level satisfactorily under urban Indian conditions.

CORTN Model Analysis:

Applying the same three criteria system to validate the model, it is

observed that the difference between observed and estimated noise level varies
between 1 and 4 dB(A). The best fit regression plot between observed and
estimated Leq produced an r value of 0.8313; whereas the paired t-test between
observed and estimated Leq gives a ‘t’ value of 5.22 at 5% level of significance
and 88 degrees of freedom. The noise variation range is within the acceptable
range and also the r value is in the satisfactory region. The t-test value of 5.22 is
outside the acceptable limit, hence the null hypothesis can be rejected. Since
majority of the acceptable criteria among the three are satisfied by the model, it
can be said that the CORTN model can predict noise level satisfactorily under
urban Indian conditions.

55
Conclusion:

The comparison of the two models based on statistical analysis is given in

Table 3. The statistical data analysis between predicted and observed noise
levels collected from field investigation was carried out to determine the
suitability of the FHWA and CORTN model for use under the Indian road
conditions. It can be concluded that the both FHWA and CORTN models give
desirable results. Among the two, the correlation coefficient estimated value for
CORTN model is marginally better than FHWA model. The assessment test
shows that both models can predict the noise level within acceptable limits in
comparison to observed noise values with certain degree of error. Overall
CORTN gives better prediction based on the present study conditions and
limitations as evident from the three tests conducted to evaluate their suitability
under Indian road conditions.

CASE STUDY 6:

NOISE DISPERSION MODELLING IN SMALL URBAN AREAS WITH

CUSTIC 3.2 SOFTWARE

Introduction:

Noise, as a major risk factor affecting the environment, has been an area
of concern and research subject for considerable length of time, especially in
major urban commercial and residential areas where the levels of noise
exposure are high and the problem is already visible. The effects of rapid
urbanization and rising standard of living, especially in smaller communities
have created a major problem affecting their central residential, public and
commercial areas.
Noise pollution, both in large and small urban areas is regarded as a
growing problem of communities. There are various factors that contribute to
increase of noise levels. One of main factors is increasing urban population,
which contributes to higher traffic volume and intensity. In most urban areas,
the corridors are developed in a close proximity to residential areas, due to
limited space thus increase the number of high rise buildings. Numerous
countries have implemented new technologies to control noise pollution in
urban areas.

56
 The Green Paper estimates that, in terms of the number of people affected
by noise, 20% of the population (i.e., 120 million people) suffers from
unacceptable noise levels that cause sleep disturbance, annoyance and adverse
health effects [3]. An additional 170 million citizens in Europe live in areas
where noise levels cause serious annoyance during the daytime. In financial
terms, environmental noise costs society an estimated 0.2% to 2% of the Gross
Domestic Product. Even the lower of these figures represents an immense cost.
Noise Dispersion Models (NDM) simulates outdoor sound propagation
and predicts noise levels from known noise sources for close and distant
locations.
Commercially available NDM software’s are usually cumbersome, data
dependent (a lot of input data not readily available), expensive and only
extensively trained users can utilize them.
Completely opposite, CUSTIC 3.2 is simple to use, low cost software
solution which certainly does not have all possibilities and precision of standard
NDM solutions, but can be used as indicative toll for basic modelling purposes.
Example of CUSTIC 3.2 application in noise dispersion modelling in small
urban areas is discussed below.

Custic 3.2 Noise Pollution Modelling Software:

The Custic 3.2 use numerical algorithms for noise dispersion modelling
that give us the possibility to study the noise pollution that we find in our
environment. Mathematical model that the software uses, provide options to
model noise emissions from a wide range of sources that might be present at
industrial areas and urban areas. The modelling is based on estimates for
dispersion of noise in free field by mean of numerical simulations which give as
results approximate values for the noise levels, regardless of source type (point,
line or area).
It must be noted that only approximate values are obtained, since the
noise dispersion is a complex physical phenomenon (that involves turbulences,
non-linear dynamics and thermodynamics of irreversible processes) that the
simulations software must represent with simple equation.
The basis of this mathematical model is linear sound propagation
equation, which is used to model simple point source emissions from vehicles,
industries, aircrafts…
CUSTIC model calculates attenuation due to noise source enclosures and
other noise control measures, the distance from the source to the receiver, the
noise source size, type and directivity, barriers and natural topographical
features and sound absorption in the air.

57
Excess attenuation from ground absorption effects such as those due to
vegetation, bare ground or hard surfaces are derived using the most recently
available scientific theories. Weather conditions such as wind speed and
direction, relative humidity and the vertical temperature gradient of the
atmosphere are also accounted for.
The CUSTIC 3.2 Software accepts meteorological data records to define
the conditions for sound propagation. The model estimates the noise level for
each source/receptor combination and calculates user-selected averages.
This software allows for creation of robust and useful numerical simulations
that fully make use of the graphical user interface.

Fig. 1 presents the input and output data of the CUSTIC 3.2 software.

As shown on the figure above input data include: type of source (point,
line ore area), ambient (climate) data, grid size and scale. Based on data entered
the software calculates noise levels and presents those levels in form of iso-
lines, numerical grid or colour gradient.
Ambient data used in sample calculation (actually measured during noise
level assessment in central part of city of Stip) are given on Fig. 2 (temperature
15ºC, relative humidity 50%). Those data are used to describe the change of
sound levels as one move toward or away from a sound source. Attenuation
coefficient related with temperature and relative humidity, describes the
reduction of sound per unit distance.

58
 If the source size is small compared with the size of the area included in
simulation, that source presents point source. Point source data entry window is
shown on figure below (Fig. 3).

59
 Traffic roads presents line noise source. If road traffic noise level data are
available, then we can easily enter them through the window shown below
(Fig.4).

Also with very simple and convenient tool, road traffic noise levels can
be estimated through average vehicle velocity and vehicles number data (Fig.5).
In our specific case, road traffic noise levels are estimated with following
assumptions; maximum permissible speed of vehicles approximately equal to
40 km/h and average number of vehicles per hour approximately equal to1000.

60
 In order to increase model precision, spot noise level measurements were
performed in the vicinity of the buildings at the most exposed facade. All data
from the measurements were entered in the model as s point sources.
Calculations of noise dispersion was performed at 4 meters height from
the surface (Fig.6).

As two different models (Classical CUSTIC model and ISO- 9613) are
included in the software, through the option Calculation Models (Fig.7) user can
select which calculation method will be used. The ISO calculation is mostly
used for point sources considering humidity, temperature and the solid angle for
the source. In the case of roads, only angle solid effects will be considered when
the ISO option is activated.

61
Role Of GIS for Noise Dispersion Model:

Through a simple GIS set of tools CUTIC 3.2 allows for storing and
retrieving, transforming and displaying spatial data from the real world for
particular set purposes. A cataloguing and metadata management system of GIS
could be used to track data manipulation at each stage of process. These
include: changes in input data, data simplification, interpolation methods,
calculation methods, calculation settings, other factors, which could influence
the accuracy of results. Also GIS facilitates the visual presentation of the noise
effects and an additional tool for analyzing the results. Proper integration of GIS
with noise prediction models provides fast and accurate assessment of the
environmental impact of noise.
Noise contours generated with interpolation techniques available are
presented through included GIS tools, thus making possible to generate a
continuous spatial model of noise levels within software windows.

Noise Dispersion Model Of Stip:

Fig.8 presents central part of Stip for which we have developed noise
level dispersion modelling, using CUSTIC 3.2.
Based on estimated and measured data for different point and line sources
within the Stip City Center, this software generated noise levels model which
can be presented in form of iso-lines, numerical grid or colour gradient.
Noise dispersion models of Stip central part in forms of iso-lines and
colour gradient are shown on Fig. 9 and Fig. 10.

Figure 8. Satellite Image On The Central Part Of Stip

62
 Figure 9. Noise Dispersion Model In Central Part Of Stip Represented By Iso-Lines

Figure 10. Noise Dispersion Model In Central Part Of Stip Represented By

Colour Gradient

Conclusion :
CUSTIC 3.2 Noise Pollution Modelling Software can be used to model
different types of noise, such as industrial noise, traffic noise and aircraft noise.
It provides a range of prediction algorithms easily selected and applied by the
user.

63
 In the same time CUSTIC 3.2 is low cost and simple tool that allows fast
and easy simulation of the process of noise pollution, this making him ideal for
day to day usage.
This modelling toll can be with easy used for indications of areas affected
by noise, determining the number of sensitive buildings, new project
development decisions and so on. Although not so sophisticated this indicative
toll can point to the problems in early stages and determine need of further
analyses and protection measures, thus saving time and labour.

CASE STUDY 7:

FAR FROM THE NOISY WORLD? MODELLING

THE RELATIONSHIPS BETWEEN PARK SIZE,
TREE COVER AND NOISE LEVELS IN URBAN GREEN SPACES OF
THE CITY OF PUEBLA, MEXICO

Introduction:

A city is a patchy ecosystem that appears as a highly developed matrix

in which remnants of vegetation can be scattered. Notwithstanding that less than
20% can remain as vegetated areas in urban locations, city parks and other open
green spaces are important leisure resources in these environments, where they
can contribute to a cleaner air or to reduce noise pollution depending on their
size and location. Urban vegetation may act as an acoustic screen between noise
sources and receivers. When large areas are provided in urban ecosystems, the
tree and other plant cover may play an important role in the reduction of noise
level; however, given the often small size of urban green spaces and the
expected large scale of urban pollution, some authors are sceptical about the
usefulness of green spaces in improving environmental quality in cities.
In this paper noise pollution is evaluated in green spaces with variable
amounts of greenery in the city of Puebla, Mexico, a developing country in the
middle of urban transition. Probably more than in other countries, a large
number of problematic situations regarding noise have been reported in Mexico,
where urban settlements are growing in a chaotic and irresponsible way. Human
population in Mexico is greater than 105,000,000 inhabitants, with a high 70%
concentrated in urban areas, which is aggravating water, soil and air pollution in
urban environments. Currently, the city of Puebla and its metropolitan area
(henceforth, AMPC) is absorbing part of the human growth of Mexico City; the
human population in the AMPC has multiplied 3.2 times between 1970 and
2000, and by 2005 it was ~1 700 000 inhabitants. A number of factors reduce
environmental quality in the AMPC, among which the increase in noise
pollution has been properly remarked by local authorities.

64
 The primary goal of the study was to evaluate noise pollution in the urban
green spaces of the city of Puebla and its metropolitan area. Comparisons
between measured noise levels and permissible limits set by both regional and
international criteria were performed, and it was also assessed whether urban
green spaces can reduce environmental noise pollution as a function of
park size, tree cover, or a combination of both.
Materials and Methods:

Study area

The city of Puebla, the capital of the State of Puebla, Mexico, is located
in the Angelopolis region of the State; ~2 200 000 inhabitants live in this region,
where the near San Andrés Cholula and San Pedro Cholula also form part of the
conurbation. During the last 30 years, the population has grown at a more
accelerated pace in the AMPC than in any other region of the State.
A total of 21 urban green spaces were considered in this study, located in
the following three sectors of the AMPC (Table I; Figure 1): a) the downtown
area (sector 1), the main square of the city of Puebla, characterized by a square-
meshed road network with low buildings and narrow roads; b) the inner city
(sector 2); and c) the outer city (sector 3). The 21 sampling sites included: 1
abandoned, brown field; 2 cemeteries; 3 main squares; the campuses of 4 local
schools or universities; 8 urban parks, and 2 other semi natural, green spaces;
besides, a large parking lot was included. Urban park size ranged from the very
small site #5 (0.28ha, an urban park in downtown Puebla) to the largest one, site
#1 (140.9ha, a semi natural area in the outer city); however, differences in park
sizes between urban sectors were not statistically significant (one-way analysis-
of-variance: F2,18= 0.98; P= 0.394). A very large (~702ha) protected natural
area (Reserva Ecológica Flor del Bosque) was also sampled as a positive
reference site (site #22; Table I; Figure 1).

Tree Vegetation:

Following methods in Brower et al. (1997) from 3 to 7 sampling quadrats

(20×20m) were located at each of the 21 urban green spaces, and all the trees
with a diameter at breast height (DBH) ≥ 5cm inside each quadrat were
counted, identified accelerand their DBH-measured. The tree vegetation
structure at each sampling site was described in terms of density (trees/ha) and
canopy cover (m2 canopy/ha; see Barillas Gómez, 2004; Bonache Regidor,
2005). Canopy cover (CC) was calculated for each individual tree by measuring
its DBH and applying a species- specific linear model describing the true
relationship between CC and DBH. Models were empirically obtained from an
average number of 20 trees of 13 species present in the same study area (9

65
angiosperm, 1 palm and 3 gymnosperm trees). Canopy cover of some taxa
(Acacia sp., Phoenix canariensis or Salix sp.) was estimated by applying models
of closely related species (Acacia retinodes, Washingtonia robusta and Salix
humboldtiana, respectively). For more details and models linking DBH to tree
canopy cover.

Noise Levels:

All noise sampling was done on working days, from 7 to 11am, under
ideal meteorological conditions (no wind and no rain). Noise levels, expressed
in A weighted decibels (dBA) were measured by using an Extech 407735 digital
sound level meter. At each sampling site, between 8 and 27 randomly selected
sampling points were considered and, at each sampling point, instantaneous
noise levels were registered every 10sec during 1min; then a cumulative noise
metric was calculated, the equivalent sound level (Leq) for 1min. In theoretical
terms, Leq may be thought of as the constant sound level over the period of
interest that contains as much sound energy as the actual time varying
sound level; in practical terms, Leq is the usual variable employed in acoustical
studies (Wilson, 1989; Canter, 1996; Berglund et al., 1999). After pooling all
the sampling points at each sampling site, the corresponding equivalent sound
levels (LeqN) were obtained. For example, Leq23 is the equivalent sound level
registered at the 23 sampling points located in a site; in this paper, LeqN values
were considered as measurements of “time average noise levels”.
No legislation has been approved in Mexico (country) or Puebla (state)
for the regulation of ambient noise levels. So, in order to put noise pollution
within a broader context, equivalent noise levels in the green spaces of the city
of Puebla and its conurbation have been compared with 1) the limits set for
community noise in outdoor living areas by the World Health Organization
(WHO maximum 1, for moderate annoyance, 50dBA; and WHO maximum 2,
for serious annoyance, 55dBA; Berglund et al., 1999), and 2) the recently
passed NADF-005- AMBT-2006 environmental standard for noise emissions in
Mexico’s Distrito Federal, of 65dBA between 6am and 10pm.

Statistical Analyses:

Both nonparametric and parametric statistical tests were used. Since

equivalent noise level is not a linearly additive variable (i.e., for different
background levels, a given noise source does not produce the same increase in
the variable; the contrasting of arithmetic means by standard parametric tests is
not applicable. Instead, the possible existence and statistical significance of
spatial differences in equivalent sound levels for 1 minute (Leq1) were explored
by means of the nonparametric Kruskal-Wallis 1-way ANOVA by ranks (1 way

66
K-W ANOVA), with Leq1 as dependent variable and park (21 levels) or sector
(3 levels: downtown, inner city and outer city) as categorical predictors. In case
of statistical significance (a= 0.05), the 1-way K-W ANOVA was followed by
nonparametric multiple comparisons tests. Given the unbalanced nature of the
design (groups of sites had different numbers of data), the Dunn test was
employed for this purpose, being a more powerful test than others like the
Tukey-type Nemenyi test. Nonparametric analyses were performed by running
the ‘ANOVA on ranks’ module as implemented in the SigmaStat (1997)
software.
In addition, by means of simple and multiple linear regressions (a robust,
parametric statistical technique with respect to possible violations of underlying
assumptions; Zar, 1996), relationships were evaluated between average noise
levels in urban green spaces (LeqN, measured in dBA) and the following
quantitative predictors: park size (area, in ha), total density of trees (TDENS, in
trees/ha) and total canopy of trees (TCOV, in m2 canopy/ ha). As possible
predictors of LeqN were also considered the density and cover of both the
gymnosperm species Casuarina equisetifolia, Cupressus sp.pl. and Pinus sp.pl.,
and the non-gymnosperm ones (a total of 30 species more; see Barillas Gómez,
2004; Bonache Regidor, 2005; González- Oreja et al., in press). The existence
of a linear relationship between the response variable (y) and the possible
predictors (x)was previously explored by means of xy biplots; cases with
standardized residuals >2.1 were considered as outliers, and removed from
further analyses. Parametric analyses were done by running the ‘multiple
regression’ module of the Statistica vers. 6.0 (StatSoft, 2001) software.

67
Results:

Tree vegetation:

A total of 33 tree species were registered in the 21 urban green spaces,

although richness per site ranged from only 2 species (site #2, a brown field) to
10 (site #4, a university campus). Total density (Figure 2) varied between urban
green spaces from a minimum of 55 trees/ha (site #14, a semi natural area) to a
maximum of 405 (site #8, an urban park), with mean= 232 trees/ha and
coefficient of variation= 40%. According to their density, the dominant trees
were the natives Fraxinus udhei (16.6% of total density) and Cupressus lindleyi
(15.0%), and the exotics Eucalyptus camaldulensis (16.7%) and Ligustrum
japonicum (10.7%). Total canopy cover (Figure 2) ranged from 1465 (site #15,
a parking lot) to 14065 (site #21, a main square) m2 canopy/ha, with mean=
7342 m2 canopy/ha and coefficient of variation= 47%. According to their
canopy cover, the main tree species were the native F. udhei (27.3% of total
canopy cover) and the exotic E. camaldulensis (25.4%).

Noise levels:

A total of 2230 instantaneous noise measurements were registered,

corresponding to a total of 373min (8-27min per site). For urban sites, a)
instantaneous noise levels ranged from a low 40.2dBA (site #12, in fact, a
cemetery) to a very high 91.9 (site #17, an urban park in downtown Puebla); b)
minimum equivalent sound levels for 1 minute (min Leq1; Figure 3) ranged
from 41.1dBA (site #12) to 65.2 (site #3, a different downtown urban park),
whereas maximum ones (max Leq1; Figure 3) varied between 52.3 dBA (site
#12) and 88.6 (site #17); and c) average noise levels (LeqN; Figure 3) varied
from a minimum Leq23= 47.3 (site #12), or the close Leq18= 49.6 (site #13, a
different cemetery), to a maximum Leq14= 78.4dBA (site #17). At the positive
reference, all noise measurements were lower than those registered in urban
green spaces; instantaneous noise varied from 35.2 to 43.9dBA; Leq1 ranged
36.8-43.2dBA, and average level was Leq15= 38.9dBA.
Average noise levels (LeqN) were ≥65dBA in 9 urban sites (~43% of the
total, 21 urban green spaces), although max equivalent noise levels in 1min
were above that figure in 3 of the remaining 12 sites (#7, 15 and 20; Figure 3);
max Leq1 was <65dBA in 9 urban green spaces (continually quiet sites; open
triangles in Figure 3), whereas min Leq1 was >65dBA in 2 sites (permanently
noisy sites: #3 and 16; black squares in Figure 3). LeqN was above WHO
maximum 2 (55 dBA) in 16 sites (~76% of the total) and, finally, 1 (50dBA) in
19 urban green spaces (a large ~90.5%). Only the positive reference (site #22;
Figure 3) conformed all the standards considered.

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 The differences in Leq1 amongst urban green spaces were statistically
significant (1-way K-W ANOVA H20= 272.97, P<0.001). Also, differences in
Leq1 between regions were statistically significant (H3= 50.77, P<0.001);
multiple comparisons test vs a control group (sector 4, the positive reference,
where median Leq1= 39.12) showed that Leq1 values registered at all the urban
regions were statistically above those measured at the positive reference site
(downtown, sector 1, with median Leq1= 61.12, Dunn’s test Q= 7.06; inner
city, sector 2 median= 59.30, Dunn’s test Q= 6.21; outer city, sector 3 median
Leq1= 68.35, Dunn’s test Q= 5.50; in all three cases, P<0.001); the remaining
differences between urban sectors were not statistically significant
(0.84<Dunn’s test Q<2.62, P>0.05).

Effect Of Park Size And Tree Vegetation On Noise Levels:

For the 21 urban green spaces, both park size (area) and total canopy
cover (tcov) were statistically and negatively correlated to average noise levels
(LeqN): for area, r= -0.47, P= 0.032 (Figure 4a); for tcov, r= -0.44, P= 0.048
(Figure 4b); total tree density (tdens) was not linked to LeqN (r= -0.01, P=
0.950). Tree density for both gymnosperm and non-gymnosperm species was
not linearly correlated to average noise levels (in both cases, P>0.1), and the
same was obtained for canopy cover by groups of species (P>0.1). Since area
was not statistically correlated with TCOV (r= 0.27, n= 21, P= 0.220), a
multiple linear regression of LeqN was performed on these two variables as
quantitative predictors. Although the model as a whole was statistically
significant (P= 0.031), simple regression coefficients were not (b1 [area], P=
0.079; b2 [TCOV], P= 0.120). In fact, site #11 (a small urban park in the inner
city) was identified as an outlier (observed LeqN= 51.24dBA; predicted LeqN=
67.33dBA). After excluding this case from the multiple linear regression
analysis, the new model was statistically significant, both globally (F2, 17=
7.49; P= 0.005) and individually (b1 [area], P= 0.029; b2 [TCOV], P = 0.030)
and expressed as:

LeqN (dBA)= 73.46 - 0.0998×area (ha) - 0.0011×tcov (m2 canopy/ha)

where, n= 20, R= 0.68 and standard error of the estimate= 6.66dBA. The
goodness-of-fit of the whole model in Eq. 1 (R2= 46.84%) was clearly higher
than that previously obtained with only one parameter (area, R2 = 21.96%; tcov,
R2= 18.91%).

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Discussion:

There are no universally accepted noise criterion for parkland and

conservation areas, or for urban parks and other green spaces. For example,
whereas WHO recommends an equivalent sound level of 55dBA as threshold
for outdoor living areas the local authority of Mexico’s Distrito Federal has
passed a higher standard of 65dBA for noise emissions. The present results
show that 9 out of the 21 urban green spaces studied in the city of Puebla and its
metropolitan area exceeded even the more relaxed level set by the cited local
authority. The problem is aggravated if the more stringent limits set by WHO
are taken into account, since equivalent noise levels in some 3/4 of urban green
spaces were above the intermediate 55dBA limit, and a large 9/10 exhibited
levels above the low 50dBA criterion. Only at the positive reference site, a
distant and large protected natural area, were noise equivalent levels below all
these standards. Daytime guidance values for noise annoyance have been set by
WHO at 50-55dBA, below which the majority of the adult population will be
protected from becoming moderately to seriously annoyed. It has been
concluded that, among other elements, noise levels contributeto the subjective
experience of quietness that, in turn, improves the quality of urban life. In fact,
there is a large body of evidence that access to quiet green spaces close to
residences may provide an important way to escape from tense and challenging
everyday situations, like those derived from chronic noise pollution in urban
settlements. Nonetheless, taking into account the abovementioned standards and
results, there is no evidence to suggest that people living in the Puebla-Cholula
conurbation can expect to find a calm environment even in the hypothetically
quiet urban green spaces of the city. In the case study, it is probably naïve to
think that, by visiting urban green spaces, people can escape from
environmental stressors or to rest and relax from the stressful rhythm of the city.

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 Noise pollution is not exclusive of large cities in rich, developed
countries, since it has been documented in medium and small-size localities all
over the world, including developing countries. For instance, noise levels in
about 97% of sites in Kahramanmaraş (Turkey) were ≥55dBA, and 63% were
≥65dBA . In Latin America, the equivalent sound level in a large 96.3% of sites
in Curitiba, Brazil, was >55dBA, whilst ~60% of points in Valdivia, Chile,
exceeded 65dBA. In Mexico, noise pollution in the city of Guadalajara has been
considered a severe environmental problem, since levels were <70dBA only in
3.9% of the cases (Orozco Medina, 2001; Orozco Medina et al., 2007). Noise
pollution in green spaces has been studied by for urban parks in Curitiba, the
‘ecological capital’ of Brazil; the average sound levels measured in some of
them were well above the limit established for parks by the local law of the city,
of 55dBA.
Noise pollution in urban environments is characterized by a large
diversity of sources although traffic flow has been recognized as a main source
in both developed and developing countries. Additional factors should be taken
into account to better understand the role of traffic noise in developing
countries, such as the poor maintenance of circulating vehicles or the lack of
modern traffic control equipments and planning . Special attention should be
paid to heavy vehicles (with weights >3500kg, such as buses, trucks or vans;
since they can be considered as strong point sources like the privately owned
outdated and/ or poorly maintained mass transportation vehicles in Turkey. In
developing Mexico, the problem of noise has been tracked, among others, to the
following main causes: large vehicle fleet, poor urban transport, shortage of
open green spaces and insufficient regulation, inspection and legal frame. This
also applies to the AMPC, so measures to control noise pollution should be
directed toward improving traffic problems. But, can park size and tree canopy
cover help to reduce noise pollution in our urban green spaces, is the question.
Changes in noise levels have been documented as a consequence of park
size. Other factors being equal, noise pollution levels in larger parks should be
lower because of noise reduction from the source with distance, which can
follow different attenuation models. In the AMPC we have documented a
statistically significant reduction in noise levels due to increasing park size
(Eq.1); thus, noise pollution in urban green spaces is expected to decrease if
large green, urban spaces are designed and created from the beginning.
However, park size, shape or location in the AMPC have generally been
determined by forces out of intelligent urban design; because of this, the
capacity of urban parks and open green spaces to improve local acoustic
environment could be limited. In this context, can urban trees ameliorate the
situation? Changing the path of noise from the source to the receiver, and
attenuating noise by absorption, have been included among the steps that can be
taken to minimize the magnitude of noise pollution . Mitigation measures for
traffic noise include barriers to obstruct or dissipate sound emissions, the

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absorption effects of landscaping by means of trees, bushes and shrubs (Canter,
1996), or the incorporation of porous noise absorbing surfaces into the urban
fabric. In fact, it has been reported that if large vegetated areas remain around
noisy streets, with the suitable species composition, in the right densities and
with the right shapes, it is possible to provide a considerable amount of noise
reduction (Fang and Ling, 2003, 2005; De Ridder, 2004; De Ridder et al., 2004;
Ozer et al., 2007). Mansouri et al. (2006) documented a drop in noise levels of
2.5dBA between sites due to the damping effect of green trees, and
recommended the development of dense barriers of trees at both sides of streets.
In the city of Erzurum, Turkey, it has been documented that coniferous pine
trees (Pinus sylvestris) resulted more effective in noise reduction along roads
than deciduous poplar trees (Populus nigra); a significant difference of 6.3dBA
was found between the two species at a distance of 25m. We also found a
significant, reducing effect of tree canopy cover on noise levels in urban green
spaces (Eq.1); however, no difference was found for gymnosperm or
nongymnosperm tree species. Thus, noise pollution in the green spaces of the
AMPC could be significantly abated if tree canopy cover is enhanced,
irrespectively of the park location inside the city or the tree species employed.
What is more, the relationships between park size, tree canopy cover and
average noise levels documented throughout this paper (Eq. 1) could be used to
guide the solution to the following questions: in the city of Puebla and the
AMPC, which should be the minimum park size for LeqN values to be
<65dBA, the environmental standard for noise emissions (Gaceta, 2006) in
Mexico’s DF? Or, which should be the minimum tree canopy cover for LeqN
values to be <65 dBA? In the AMPC, if area= 20ha, then it is enough with
TCOV= 0.6ha canopy/ ha to get an expected value of average noise level LeqN=
65dBA (see Figure 4a; 67.46 to 68.41 being the corresponding 95% confidence
interval or CI). Also, if TCOV= 0.75ha canopy/ha, then it suffices with area=
3ha to expect LeqN= 65dBA ( Figure 4b; 61.13 to 68.85 being the corresponding
95% CI). Thus, if these minimum values of park size and tree canopy cover are
observed in the design and management of the urban green areas in the study
area, average noise levels are expected to be <65dBA. Similar procedures could
be applied to search for minimum values of park size and tree canopy cover in
order to expect different noise levels, such as those established by WHO. This
conclusion reinforces the important environmental role of urban green spaces,
as reported by De Ridder (2004) and clearly contradicts Lam et al. (2005), who
suggested that urban parks and other green areas should be designed to
emphasize their social rather than environmental functions, at least in dense
cities.

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Noise Management Recommendations:
The increasing prominence of urban areas worldwide is reason enough
to study them, but ecologists also should inform decision makers in order to
manage cities in a way that ensures that they are reasonable places to live in the
near future. Urban green spaces are one of the typical subjects of open space
design, and play an important role in the daily life of the citizens. To strive for
lower sound levels, to assure access to “noise-free” places, and to protect,
preserve and even increase the supply of urban green spaces, have been deemed
critical in order to attain a more sustainable and health-promoting urban
residential environment.
However, it has been also detected that even those leisure places can be a
potential source of health problems, not fulfilling its intended role. Several
authors have pointed out that noise pollution abatement is less of a scientific
problem than a policy problem, and this is not yet understood in cities located in
developing countries. Inaction would mean that noise problems could further
increase, and, likely, future mitigation measures will become more expensive to
implement.
In the city of Puebla and its metropolitan area, it is necessary to think
of a noise pollution abatement plan, as has been recently suggested by the local
authority for the noise produced by heavy vehicles (buses and trucks; Puebla,
2007). This task should come only after a more comprehensive study of noise
problems in the AMPC is done. Up to now, the results of the present study
suggest that noise pollution could be improved (reduced) if tree canopy cover in
urban green spaces is enhanced.

73
References:

 Tripathy, D.P, (1998), Noise Pollution, A.P.H Publishing Corporation,

New Delhi, pp. 1-2.
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Venkaiah Chowdary, C.S.R.K Prasad; Sciencedo - Transport and
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 Case study 7 - Far from the noisy world? Modelling the relationship
between park size, tree cover and noise levels in urban green spaces of
the city of Puebla, Mexico by Jose Antonio Gonzalez-Oreja, Carolina
Bonache-Regidor and Arturo Andres De La Fuente-Diaz-Ordaz;
Interciencia, Vol 35, no.7, July 2010, pp. 486-492

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