Noise pollution - PHYPHOX APP PDF

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Experimental Investigation of Noise Pollution by Examining the Characteristics of Noise Source By Md Sarwer Hossain Master of Professional Engineering – Mechanical Engineering Student ID # 13661842

Abstract To determine noise pollution from various sound source two different methods are applied in this experiment. Three different sets of data are collected with the help of a smartphone application and then calibrated and then compared with the noise mapping (sound level modelling). To analyse and compare the data different matrices like mean, median, maximum, and cumulative sum are calculated. To achieve the best possible result from the data and the sound level modelling different factors are considered. Temperature, relative humidity, wall factor, ground factor, and height of source and receiver are the main factors that influence the whole experiment. The deviation and discrepancy of the data from smartphone application with sound level modelling are examined in the discussion part. The location of the experiment is observed and inspected for the sake of noise mapping. The effect of noise pollution at the locality of Wiley Park, NSW also measured and discussed in conclusion.

Introduction Noise pollution is considered as unfavourable effects on peoples’ health, living creatures or organisms, and environment which is caused by unwanted excessive sound.(Ozkurt et al., 2014)

defined as the contamination of the atmosphere.(Murphy & King, 2016) Noise pollution is a growing problem that has both economic and well-being losses. Mental stress and annoyance are the primary health issues of noise pollution which consequently lead to blood pressure, heart rate, and cardiovascular disease. (Berglund et al., 1999) Like other pollution, it is important to study the topic for the betterment of this environmental problem.

With the global industrialization and advancement of technology, our life gets a lot of comfort and pace with exposure to health degradation due to pollution. Among the different types of pollution, noise causes hearing impairment (Ibrahim, 2012) and serious other health hazards. (Arafa et al., 2007) Unwanted or excessive sound that can adversely affect human health (Moudon, 2009) and quality of the environment is considered as noise pollution. Noise pollution can be also

In this investigation, noise pressure level is examined at Wiley Park, a suburb at southwestern Sydney, New South Wales, Australia. Different noise sources are being 1

generator and the Phyphox application, calibration is done by taking 10 seconds of data twice with the difference of before calibrated mean and after calibrated mean which is added to the field value.

considered around the area and characterized for mapping purposes. The purpose of this experiment is to assess noise pollution obtained from a smartphone app and compare the data with the sound pressure modelling estimates in a residential area.

We use Microsoft excel to determine the mean, median and maximum value for this investigation.

Methodology

The primary sound sources of a residential area comprise construction work, transportation, train station, bus stops, schools, temple and vehicle.

The location of interest, Wiley Park near the Wiley Park Public School is situated 17 kilometres south-west of the Sydney central business district. The suburb is in the Canterbury-Bankstown Council and on the opposite side of King Georges Road. It is a residential area having a population of only around ten thousand where most of the buildings are a maximum of three stories. The main public transport is train from the Wiley Park station which is 1 kilometre far from the studied location. There is a temple, three schools, King Georges highway, a construction site, and an apartment complex along with the train station and several bus stops.

Sound pressure level are selected in air at standard atmospheric pressure as per the appendix A The weather condition is windy at the time of sample collection. The temperature is around 13.5 degrees Celsius and the relative humidity is about 76% on 7 May 2020. The weather data is obtained from the Bureau of Meteorology from Canterbury Racecourse AWS (station 066194). (The Bureau of Meteorology, 2020)

The experimental noise sample is collected by Phyphox app with an iOS operated smartphone at three different times on 7 May 2020. Phyphox app uses the sensors of smartphones to collect samples. In this experiment, the built-in microphone of the Apple iPhone XR is used to measure the sound pressure level in dB from surrounding sound sources. The exported data in the spreadsheet is calibrated with the given sound pressure level data of Samsung Galaxy 8 smartphone and the testo 816-1 sound level meter. It is done by the linear calibration curve method using Microsoft Excel.

To create the model in dbmap, first of all, the area map is dragged into the grid, and the size is adjusted using corner nodes. Firstly, the receiver is added just outside the building at 1 meter receiving height. Then point sources are added as objects. Height, frequency, and sound power levels are edited as per table 1 for different point sources. The building is added as per the visual inspection and sourcing from google map. Building heights are edited and wall characteristics for the building are defined. Lastly from the global setting option, ground factor, temperature, and humidity are set as per the condition of experimental location of interest.

The revised calibration method is applied to get the final sound pressure level value by using an online tone generator from the website szynalski.com. From the tone

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noises that consider as impulse noise. (Wong et al., 2012)

Results The three different experimental data were collected at 10 a.m., 3 p.m. and 8 p.m. by the phyphox smartphone application for 15 minutes (900 seconds) enabling timed run. The time vs. sound pressure level data are plotted in scatter graph. Figure 1, figure 2 and figure 3 show the data for 10 a.m., 3 p.m. and 8 p.m. respectively.

It is observed that sound pressure level in the morning is comparatively higher that the other two sample. In support, it is also obtained that mean value for the sound pressure level at 10 a.m. is 75.91 dB, whereas 65.8 dB and 65.6 at 3 p.m. and 8 p.m. On the other hand, median values are 72.05 dB, 64.24 dB and 65.2 dB for the three set of data taken in three different time of the day. Another matrix is the maximum value at three different time’s experimental data. Maximum sound pressure level is 117.4 dB, 102.3 dB and 92.5 dB for the following samples.

In the first two set of data the type of noise is continuous and intermittent. But during the night there are a few sudden sharp

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plotting all the point source and enabling, sound pressure level is obtained 63 dB at the receiving point.

Figure 4 shows the google map of the local area surrounding the residence where the point of interest is located. This is used as model input. In figure 5, all the point source is showed along with the building and receiver. In figure 6 The final sound pressure model map of local residence is showed. In the dbmap modelling, after

In the figure 7, the cumulative sound pressure level is depicted for fifteen minutes time period at different time of the day.

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0.0 27.3 54.7 82.0 109.3 136.5 163.9 191.3 218.5 245.8 273.1 300.4 327.7 355.0 382.3 409.6 437.0 464.2 491.4 518.7 546.0 573.3 600.6 627.9 655.2 682.6 709.9 737.2 764.5 791.8 819.2 846.5 873.9

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Discussion We observed the sound pressure model gives us data which is less from the experimental collected data from smartphone application. It is due to the lack of perfection of sound source point. There might be some other sound source like conversation between people, birds chattering around the receiver point that was omitted at dbmap model. Again, the model shows under value of sound pressure level which can be caused by the unreliable data calibration as well as overvalued of collected sample.

From the dbmap model, it is observed that the receiver gets a huge sound pressure level from the construction site source in the morning which closes in the afternoon and results less sound pressure level. This evidenced the reason of low mean sound pressure level at 3 p.m. and 8 p.m. Another reason of this phenomenon of less mean sound pressure level is the vehicle movement at the nearest busy King Georges road. Normally the movement of vehicle is less at afternoon and in the night, whereas vehicle movement is higher in the morning which result higher noise pollution.

The mean sound pressure level at various time of day was different as obtained from the retrieved data at 10 a.m., 3 p.m. and 8 p.m. In the morning, 10 a.m. the mean noise level was 75.91 dB which was higher compared to mean noise level 65.8 dB at 3 p.m. and 65.6. at 8 p.m. It is inevitable because busyness of the area clearly influences the mean sound pressure level as well as noise pollution.

The quality of collected data depends on the microphone of the smartphone device. We cannot claim the data is accurate as the performance of the smartphone’s microphone is differed with the brand and model although it was calibrated. To get more accurate data, a professional sound level meter like testo 816-1 can be used.

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The location of interest, Wiley Park is a suburb and the site are considered as residential area. As per the Noise Guide for Local Government, the sound pressure level should be 40-50 dB in a suburban area, (Noise Guide for Local Government) but in this case the noise level is higher which has an impact over health of the habitant of the area. (Suter, 2014). Continuous exposures to the higher sound pressure level causes the loss of hearing over the time. (Bronzaft, 2004)

From the cumulative sound pressure level graph, it is estimated that in the cumulative sound pressure increases sharply at 10 a.m. At 3 p.m. it is less soared and at night 8 p.m. the inclination is very less. From this we understand, in the taken fifteen minutes data at morning the sound pressure level is reasonably constant at high pressure. In the afternoon sound pressure level gets high at irregular intervals. And lastly at night there are a few impulse noises that causes less cumulative sound pressure level.

The noise pollution is increasing day by day in this area as the vehicle movement increases on the road. The experiment was performed in the COVID-19 situation when people restricted themselves inside the house and hardly uses their personal motor vehicle. Thus, the sound pressure level is much lower than the normal situation. From a survey among the old habitants around the area it is learned that during the last couple of years sound pressure level increased dramatically with the increase of population, motor vehicle, school etc.

Conclusion The experimental investigation of noise pollution of Wiley Park area by two methods helps to understand the surrounding sound pressure level from different noise source at different time of a day. The investigation also gives a clear concept of retrieve data using the smartphone and calibration process. In spite of some discrepancy we can use smartphone devices to measure noise pollution around us to avoid noise hazard.

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Appendix A Potential Pollutant Source

Estimated sound pressure level (dB)

Train station

85

Temple

60

School

50

Convenient Store

60

Apartment Complex

50

Bus stop

65

Passenger car

65

Construction site

90

Reference

IAC Accoustics. (2020). Comparative Examples of Noise Levels. Retrieved from https://www.iacacoustics.com/blogfull/comparative-examples-of-noise-levels.html Chepesiuk, R. (2005). Decibel Hell: The Effects of Living in a Noisy World. Environmental Health Perspectives, 113(1), A34-A41. doi:10.1289/ehp.113-a34 Chepesiuk, R. (2005). Decibel Hell: The Effects of Living in a Noisy World. Environmental Health Perspectives, 113(1), A34-A41. doi:10.1289/ehp.113-a34 Chepesiuk, R. (2005). Decibel Hell: The Effects of Living in a Noisy World. Environmental Health Perspectives, 113(1), A34-A41. doi:10.1289/ehp.113-a34 Healthlinkbc. (2018, October 11). Harmful noise levels. Retrieved from https://www.healthlinkbc.ca/healthtopics/tf4173 Chepesiuk, R. (2005). Decibel Hell: The Effects of Living in a Noisy World. Environmental Health Perspectives, 113(1), A34-A41. doi:10.1289/ehp.113-a34 IAC Accoustics. (2020). Comparative Examples of Noise Levels. Retrieved from https://www.iacacoustics.com/blogfull/comparative-examples-of-noise-levels.html Chepesiuk, R. (2005). Decibel Hell: The Effects of Living in a Noisy World. Environmental Health Perspectives, 113(1), A34-A41. doi:10.1289/ehp.113-a34

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