A Predictive Model of Crude Oil Dispersion in Water: A Case Study of the Niger-Delta Area of Nigeria

Abdulkareem, A. S.; Odigure, J. O.; Otaru, M. D. O.; Kuranga, M. B.; Afolabi, Ayo Samuel

doi:10.1080/15567030903468528

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…Some of the identified sources of noise pollution are loud music from concerts, religious buildings like churches and mosques, noise emitting generators [9], political rallies, road advertisement, traffic [10] and air transportation [11], sporting events, construction and industrial activities. In all the mentioned sources, areas that have high risk of noise pollution are residential places near to major roads [12] and airports and manufacturing industries [13]; for example, small scale industries [14], [15], steel rolling industries [16], oil and gas industry [17], [18] and so on.…”

Section: Introductionmentioning

confidence: 99%

A Study of Noise Pollution Measurements and Possible Effects on Public Health in Ota Metropolis, Nigeria

Oguntunde¹,

Okagbue²,

Oguntunde³

et al. 2019

Open Access Maced J Med Sci

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BACKGROUND: Noise pollution has become a major environmental problem leading to nuisances and health issues. AIM: This paper aims to study and analyse the noise pollution levels in major areas in Ota metropolis. A probability model which is capable of predicting the noise pollution level is also determined. METHODS: Datasets on the noise pollution level in 41 locations across Ota metropolis were used in this research. The datasets were collected thrice per day; morning, afternoon and evening. Descriptive statistics were performed, and analysis of variance was also conducted using Minitab version 17.0 software. Easy fit software was however used to select the appropriate probability model that would best describe the dataset. RESULTS: The noise levels are way far from the WHO recommendations. Also, there is no significant difference in the effects of the noise pollution level for all the times of the day considered. The log-logistic distribution provides the best fit to the dataset based on the Kolmogorov Smirnov goodness of fit test. CONCLUSION: The fitted probability model can help in the prediction of noise pollution and act as a yardstick in the reduction of noise pollution, thereby improving the public health of the populace.

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