Survey Data on Noise Pollution Levels and Influential Factors

Published: 4 May 2022| Version 1 | DOI: 10.17632/67yzy5x3fb.1


Over the last decade, the amount and type of activity has skyrocketed mainly as a result of global population rise. Human activities which constitute noise e.g., traffic noise, commercial noise, residential noise, and religious noise had all presented the world noisier than before. Noise is the unpleasant pressure variation in sound level that human ear can sense over a period of time. Noise survey of most city in Nigeria has been documented, whereby the selected location in this research work has not being documented. This research work gave a noise data survey which were collected for three weeks across 21 selected locations in Omu-Aran township , Irepodun-Ifelodun local government of Kwara State, Nigeria. Also the survey includes data of some influential factors that determine the noise pollution e.g., population, noise source,temperature, humidity, wind direction etc...


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Material and method The sound level of each location was measured with a sound level meter of model SL4010. The sound meter has the following features such as a measurement quality microphone, a mic preamp, frequency weighting networks, an RMS detector circuit, averaging circuits, meter display, AC and DC outputs used to feed other measurement devices or for recording, its calibrated with a low sound level between 30-100dB and a high noise level has 60-130dB. The Sound level meter was comfortably held at hand with the microphone pointed towards the noise source at a distance of approximately 2m above ground level. The average distance between each of the selected locations within 0mu-Aran township is 200± 50m and an average distance of 1km between each zone. 21 locations were selected and data were collected between the hours of 7 am-9 am, 12 pm-2 pm, and 4 pm-6 pm daily in the morning, afternoon, and evening respectively for six working days within the duration of 3-weeks. The survey data for the factors that influence noise pollution was also collected, some by calculations while others by inquiry (visiting the Independent National Electoral Commission, Local Government). The collected data are categorized into two types of data as uploaded in the Excel file presented. The first type of data was the results of noise levels for 21 different locations in Omu-Aran in Irepodun Ifelodun local government, Kwara State Nigeria while the other set of data were readings of both natural and artificial factors that influenced the level of noise pollution at each of the selected locations. The 21 selected locations in Omu-Aran were studied for 3-weeks, and the data was collected twice a day (morning, afternoon, and evening). A total of 1134 data on noise levels were collected within the 8-working hours of the day, aside from the input variable data used in building the ANN model. All data were analyzed using Statistical Package for Social Science (SPSS), and one-way ANOVA at a P-value ≤0.05 level of significance was considered. The SPSS was used to calculate the mean total and standard deviation for all the locations, the mean total and standard deviation calculated were later used to determine the equivalent average noise level (Leq.) using the logarithm total formula as given in equation (3.3), all this was done in agreement to OSHA, EPA, and NIOSH (Fink, 2017); Munzel et al. (2017, 2020) prescription. The data revealed some natural and artificial factors that influence noise pollution, it brings us to the awareness of the harmful effect of noise pollution on public health and the data set is valuable in mitigating the factors that influence noise pollution in any location. Moreso, the dataset reveals that the local authority needs to take urgent actions against noise pollution as it's more dangerous yet less attention is paid to it. The research instrument that was used (noise level meter) can reproduce the noise levels of any selected location..


Landmark University College of Science and Engineering


Environmental Health of the Built Environment