Monitoring Noise Pollution with Smartphone Application and Sound Level Meter at UTS Campus

This study evaluates the impact of noise pollution in UTS campus and compares data produced by a smartphone app and sound level meter. It discusses noise pollution, its impacts, sound level meter, and the results and discussion of the experiment. The aim is to analyze the key factor responsible for noise pollution and to compare data produced by the noise producing factor of a Smartphone app and the sound level meter. The study concludes that a smartphone application could not replace the standard sound level meter used in industries, but it can be used for an accurate estimation.

MONITORING NOISE POLLUTION WITH THE SMARTPHONE
APPLICATION AND SOUND LEVEL METER AT UTS CAMPUS

SNO TABLE OF CONTENTS PAGENO
1. AIM AND OBJECTIVES 3
2. NOISE POLLUTION 3
3. IMPACTS OF NOISE POLLUTION 4
4. SOUND LEVEL METER 5
5. RESULT AND DISCUSSION 7
6. CONCLUSION 10
REFERENCES 10

AIM AND OBJECTIVES:
The aim of the study is to evaluate the impact on environment due to the noise produced in the
UTS campus and the objectives of our experiment are as follows.
 To analyze the key factor that is responsible for the cause of noise pollution in the UTS
laboratory.
 To compare the data produced by the noise producing factor of a Smartphone app and the
sound level meter.
NOISE POLLUTION:
Noise means that the raise in the level of sound energy which could be beyond an
acceptable level and produce exasperation. The Latin work for “noise” is “nausea” which we
have heard in the medical term meaning sickness. The noise can produce physical and
psychological effect for any type of individual especially for children and old ones. These can
also affect the students and children at schools and colleges. This could affect the student
accomplishment which is being reported by many studies at the present scenario. The social
atmosphere of the educational institution plays a major role in the development of the student’s
individual capacity and capability. As a result, educational zone needs a poised atmosphere
without any disturbances and the disturbance causing factor. At university, there are several
sources of noise making factor. A university, college, school, library, laboratory and hospital
come under the category of silent Zone. These places require the maximum silent atmosphere
and any sound occurs in this silent nature is considered to be a noise making factor. For instance,
consider the place of library which needs a pin drop silence. In this situation when a student
cough or sneeze could produce a sound.
The main aim of the World Health Organization (WHO) is to promote the highest
attainable quantity of health to all the people. The definition of the health according to the
World health organization is, “the state of complete physical, mental and social well-being and
not merely the absence of disease or infirmity”. The extensive source of noise occurs due to the

industry, vehicles and the community. The two-third of the pollution caused mainly(Santini,
Ostermaier, and Vitaletti, 2008) due to the traffic making vehicle noise.
At recent days, Smartphone plays a major role in the field of communication and have
become more pervasive. The development in the technology (Maisonneuve, Stevens, Ochab,
2010) and their wide spread popular application make the Smartphone to be the most popularly
used device. Research says that with the help of the smart phone application (Zichermann,
Cunningham, 2011), we can able to measure the level of noise produced with the greater
accuracy level due to the microphone fixed in them which make them to be more accurate when
compared to the professional device, i.e., about 35–95 dB. In this experiment we have estimated
the maximum and minimum sound level (db) with the sound level meter and also with the
Smartphone application.
IMPACTS OF NOISE POLLUTION:
The noise producing factors at the university is mainly due to the vehicle, students and
some generator. The factor which produces noise in and around the area is the foremost factor to
be considered. When the place is located near any port or station then the noise could be
produced at a most considerable rate. The noise produced by the vehicle is due to the following
reasons: 1) horns 2) engines 3) exhaust system 4) interaction of the tire with the road 5) due to
the gearboxes, brakes etc. The major causes of the noise pollution are as follows:
 Due to the increase in the population and increase in the number of vehicle
 The vehicle manufacturing type and their functionality mode
 Road position and dimension where the interaction with them could produce noise
 Signal system positioned at the city center cross road
 Industrial activity near the urbanized area
 Flight landing and take over noise
 Disobedience maintained in the traffic rule system.
Noise pollution couldn’t be noticed which is not like a chemical reaction (Maisonneuve,
Stevens, Niessen, Hanappe, Steels, 2009). These are the waves which could traverse in the air

and could cause significant effect. This could greatly affect the social welfare of the people. The
noise which are raised by various factors and their decibel range are
 Noise level produced by the aircraft: 90-100 dB (A)
 Noise produced at the railway junction, busy store: 70-90 dB (A).
 Noise produced at the residential areas close to traffic: 60-80 dB (A).
 Other noise sources that are away from the traffic: 40-60 dB (A).
Figure 1: various source of noise with their decibel range
SOUND LEVEL METER:
Many types of the sound measuring devices can be used to measure (Huang, Kanhere, &
Hu, 2010) the noise. Many electronic element composed together to form an equipment. The
system which measures the noise should have the following elements: 1) The microphone which
captures the sound is the transducer 2) The amplifier and attenuator 3) Frequency weighing
networks and filters 4) Data storage capacity 5) The display. Not all elements can be used for the
measurement. Some elements could be neglected depending upon the usage and accuracy rate.
The block diagram of the sound level meter is represented in the below figure

Figure 2: Sound level meter block diagram
The two main characteristic of a sound level meter are as follows
Frequency response: This is said to be the deviation value that occurs between the frequency
response of the measured value and the true value. Since the human ear is capable of hearing
sound of about 20-20,000 Hz, the sound level meter that we use should be capable of hearing
sound of about 1dB range.
The dynamic range: This is said to be the range at which the measured value is proportional to
the true value, at a given frequency (typically 1000 Hz). The range is usually denoted by decibel.
There are certain limitations in this range in which the low level signals can be restricted by
electrical background noise of the instrument and at high level signal could be restricted by the
signal distortion elicited by overloading amplifiers.
The sound level meter could be connected with the pc with the help of the cable supplied.
The sound level meter software will analyze all the measurements that are stored in the meter’s
memory. We can able to download them all and store the information for reference. We can also
display the information in the form of tabular as well as graphical manner. The Start time,
Sampling rate, Maximum and Minimum values are noted in this software.
Equivalent Continuous Sound Pressure Level (Leq/LAeq) is defined as the constant noise level that
would result in the same total sound energy being produced over a given period. This equivalent
continuous sound pressure level could convert the varying sound level into single level at a given
period. The mathematic equation of Leq is,
Leq=10 log10 ( 1
Tm ∫
Q
T m
( Pt
P0 )
2
dt )

Where,
Leq is the equivalent continuous linear weighted sound pressure level that is obtained from a
required time interval
P(t) is the instantaneous sound pressure of the given signal
P0 is the sound pressure taken as a reference (20 μPa).
At least we require about ten Leq values to get a proper estimated value. After that we have to
take the antilog of the estimated value which is given by the formula,
Total Leq= 10 log10 (10
Leq 1
10 +10
Leq 2
10 +10
Leq3
10 + …+10
Leqn
10 )
n
RESULT AND DISCUSSION:
We had measured the noise level both in the sound level meter as well as in the smart
phone application. The data was taken on the month of April around the University of
Technology, Sydney. The graph drawn with the help of the data obtained from the sound level
meter is shown below.
Start Time:4/10/2018 11:50:38
AM
Sampling Rate:1
DataNo:1530
Avg.:68.1
Maximum:67.9@4/10/2018 11:51:05 AM
Minimum:63.0@4/10/2018 11:50:41 AM
Cursor A:80.9@4/10/2018 11:59:08 AM
Cursor B:74.1@4/10/2018 12:07:38 PM
Max.Between A and B:87.9@4/10/2018 12:00:34
PM
Min.Between A and B:59.0@4/10/2018 12:06:07
PM
Avg. Between A and B:73.1

50:39.052:15.053:51.055:27.057:03.058:39.000:15.001:51.003:27.005:03.006:39.008:15.009:51.011:27.013:03.014:39.0
0
10
20
30
40
50
60
70
80
90
100
Chart Title
Figure 3: Graph obtained with the help of the sound level meter.
The noise level was recorded at each and every second between 11:50:38 AM till 12:16:01 PM.
The highest level of the noise was recorded at the time of about 12:11:58 PM. Similarly the
graph was estimated with the smart phone application. This was carried out on 16th of April at the
time of 2:43:12 PM. The estimated graph is shown below.
Start Time:4/16/2018 2:43:12 PM
Sampling Rate:1
DataNo:980
Avg.:64.4
Maximum:89.3@4/16/2018 2:50:27 PM
Minimum:44.8@4/16/2018 2:46:02 PM
Cursor A:63.4@4/16/2018 2:48:38 PM
Cursor B:73.3@4/16/2018 2:54:05 PM
Max.Between A and B:89.3@4/16/2018
2:50:27 PM
Min.Between A and B:63.4@4/16/2018
2:48:38 PM
Avg. Between A and B:72.1

41:16.8 44:09.6 47:02.4 49:55.2 52:48.0 55:40.8 58:33.6 01:26.4
0
10
20
30
40
50
60
70
80
90
100
Figure 4: Graph estimated with the help of smart phone application
The maximum and the minimum value were estimated with the help of the sound level meter on
that particular date. NIOSH noise researchers had done an experiment to capture the noise data
using various smart phones (Kardous, & Shaw, 2014) as requested by the stake holders, public
and various safety professionals. This particular experiment was done in an ambient temperature.
Finally, they made a study regarding various smart phone applications and the data they could
capture from the environment. The estimation was carried out at the NIOSH acoustics testing
laboratory at a resonating noise chamber. They also found that android based application was
inadequate of certain features when compared to an iOS application. This was due to the iOS
application advancement features in the audio capacity. There were many challenges faced due
to the usage of the smart phone in collecting and storing of data such as collection of personal
data (Drosatos, Efraimidis, Athanasiadis, D’Hondt, & Stevens, 2012), chronic motivation, data
corruption and process of storing and gaining access to the data. At last, it was concluded by
them that the smart phone application serves as an empowerment of several workers while
working in an industry. They can use their mobile phones for the noise level capture and
therefore they can avoid any harmful effects (Foerster, Jirka, 2010) in their workplace. This is
very useful for the for industrial hygienists and OS&H managers (Williams and Sukara, 2013).
Moreover sound level meter can be used for an accurate estimation.

CONCLUSION:
The effects of the noise pollution and their estimation are made in this experiment. The
study also shows us regarding the smart phone application and a sound level meter in
determining the data. The functionality of the smart phone could be increase with the use of the
external microphone of amplifier in estimating the data. Due to this, the sound level could be
determined at a minute level and also we could able to calibrate the changes in the data. Various
studies also says that a smart phone application could not replace the standard sound level meter
used in the industries which was send into the process of testing and calibration that could
determine more accurate data. The result also says that a smart phone (Nielsen, 2013) application
will not adhere to ANSI or IEC standard instrument in the near future. But with the help of this
experiment we can take some precaution with the approximated data so that the educational
institutes could be aware of various noise producing factors.
REFERENCES:
Drosatos, G., Efraimidis, P. S., Athanasiadis, I. N., D’Hondt, E., & Stevens, M. (2012). A
privacy-preserving cloud computing system for creating participatory noise maps. Computer
Software and Applications Conference (COMPSAC), 2012 IEEE 36th Annual (pp. 581-586).
IEEE.
Santini, S. Ostermaier, B. and Vitaletti, A. (2008) First Experiences Using Wireless Sensor
Networks for Noise Pollution Monitoring. In Proceedings of the 3rd ACM Workshop on Real-
World Wireless Sensor Networks (REALWSN’08) ACM. 1st April.
Maisonneuve, N., Stevens, M., Ochab, B.(2010): Participatory noise pollution monitoring using
mobile phones. Information Polity 2010 15, 51–71.

Maisonneuve, N., Stevens, M., Niessen, M., Hanappe, P., Steels, L.(2009). Citizen Noise
Pollution Monitoring. In Proceedings of the 10th International Digital Government
Research Conference.
Zichermann, G., Cunningham, C. (2011). Gamification by Design. Implementing Game
Mechanics in Web and Mobile Apps. O’Reilly Media, Inc.
Huang, K. L., Kanhere, S. S., & Hu, W. (2010). Are you contributing trustworthy data? the case
for a reputation system in participatory sensing. In Proceedings of the 13th ACM international
conference on Modeling, analysis, and simulation of wireless and mobile systems (pp. 14-22).
ACM.
Kardous, C. A., & Shaw, P. B. (2014). Evaluation of smartphone sound measurement
applications. The Journal of the Acoustical Society of America, 135(4), EL186-
EL192; http://dx.doi.org/10.1121/1.4865269
Bugs, G., Granell, C., Fonts, O., Huerta, J., Painho, M. (2010): An assessment of Public
Participation GIS and Web 2.0 technologies Canela, Brazil: urban planning practice.pp.172–181
Mobile Application for Noise Pollution... (PDF Download Available). Available from:
https://www.researchgate.net/publication/259052847_Mobile_Application_for_Noise_Pollution_
Monitoring_through_Gamification_Techniques [accessed Apr 28 2018].
Kardous, C. A., & Shaw, P. B. (2016). Evaluation of smartphone sound measurement
applications using external microphones – A follow-up study. J. Acoust. Soc. Am. 140, EL327
(2016); http://dx.doi.org/10.1121/1.4964639
Nielsen (2013). Mobile Majority: U.S. Smartphone ownership tops 60%. Retrieved June 23,
2013, from http://www.nielsen.com/us/en/newswire/2013/mobile-majority–u-s–smartphone-
ownership-tops-60-.html

Williams W. and Sukara Z. (2013). Simplified noise labelling for plant or equipment used in
workplaces. Journal of Health and Safety, Research and Practice, Vol. 5 (2), 18-22
Foerster, T., Jirka, S., et al. (2010): Integrating Human Observations and Sensor Observations –
the Example of a Noise Mapping Community. Berlin, Germany: Proceedings of Towards Digital
Earth Workshop at Future Internet Symposium. vol. 640