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.
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.
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:
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
Figure 1: various source of noise with their decibel range
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 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,
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.
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Start Time:4/10/2018 11:50:38 AM |
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Sampling Rate:1 |
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DataNo:1530 |
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Avg.:68.1 |
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Maximum:[email protected]/10/2018 11:51:05 AM |
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Minimum:[email protected]/10/2018 11:50:41 AM |
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Cursor A:[email protected]/10/2018 11:59:08 AM |
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Cursor B:[email protected]/10/2018 12:07:38 PM |
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Max.Between A and B:[email protected]/10/2018 12:00:34 PM |
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Min.Between A and B:[email protected]/10/2018 12:06:07 PM |
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Avg. Between A and B:73.1 |
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.
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Start Time:4/16/2018 2:43:12 PM |
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Sampling Rate:1 |
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DataNo:980 |
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Avg.:64.4 |
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Maximum:[email protected]/16/2018 2:50:27 PM |
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Minimum:[email protected]/16/2018 2:46:02 PM |
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Cursor A:[email protected]/16/2018 2:48:38 PM |
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Cursor B:[email protected]/16/2018 2:54:05 PM |
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Max.Between A and B:[email protected]/16/2018 2:50:27 PM |
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Min.Between A and B:[email protected]/16/2018 2:48:38 PM |
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Avg. Between A and B:72.1 |
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
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; https://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.172–18
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); https://dx.doi.org/10.1121/1.4964639
Nielsen (2013). Mobile Majority: U.S. Smartphone ownership tops 60%. Retrieved June 23, 2013, from https://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
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