Abstract
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.
Keywords: Environmental toxicity, Distribution, Nigeria, Noise Pollution, Public health
Introduction
Noise pollution is one of several environmental pollutions across the world. It can be described as the propagation of noise with a harmful impact on the physiological and psychological lives of humans or animals [1]. Noise or sound pollution is usually not studied compared with other forms of pollution such as air [2], [3], [4], water [5], soil [6], light and radioactive. The reason is that the adverse effects of other forms of pollution on humans are more pronounced. Notwithstanding, noise pollution remains a serious health concern in the study area (Ota, Nigeria) in particular and the entire planet [7], [8]. 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.
The health effects of noise pollution cannot be over-emphasised. This has prompted the World Health Organization (WHO) and the Federal Environment Protection Agency (FEPA) (Nigeria) to set standards and limits of allowable noise levels. Noise pollution occurs when it is observed that those standards are exceeded as seen in [19], [20].
The most common manifestation of noise pollution is hearing loss or impairment [21]. Hearing impairment is mostly classified as occupational hazards especially when the individual is affiliated with industry that propagates loud sound or noise. Moreover, several physiological and psychological effects of noise pollution exist. The combination of noise and air pollution is associated with respiratory ailments, dizziness and tiredness in school children [22], [23]. In adults, noise pollution has been found to be associated with high blood pressure [24] and cognitive difficulties [25].
A look at the literature showed the abundance of evidence of the adverse effects of noise pollution on the general public health. The worsening situation of noise pollution is that it has not been upgraded to the level of the other forms of pollution. Also, recommendations suggested by several authors on the different strategies on tackling noise pollution has not been considered and implemented. However, noise pollution continues to impact negatively on fetal development [26], annoyance and anxiety [27], mental health crisis [28], sleep disturbance and insomnia [29], [30], cardiovascular disorders in pregnant women [31], cardiocerebrovascular diseases [32], type 2 diabetes incidence [33] and medically unexplained physical symptoms [34]. Other auditory and non-auditory effects of noise on health are myocardial infarction incidence [35], peptic ulcers [36] and disruption of communication and retentive capabilities in children [37].
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.
Material and Methods
The dataset used in this research was gotten from [38]. It represents the noise level in 41 major locations in Ota metropolis, Nigeria. These major areas include industrial areas, commercial areas, passenger loading parks, busy roads and junctions. The readings were taken using the SLM (Sound Level Meter). Measurements were taken three different times of the day; morning (7 am to 9 am), afternoon (1 pm to 3 pm) and evening (6 pm to 8 pm). Particularly, the noise pollution level (NLP) was considered and analysed in this present research.
Analysis of Variance
Analysis of variance is conducted in this research to know if there is a significant difference between the effect of noise pollution level in the morning, afternoon and evening in Ota metropolis. The hypothesis tested is:
H0: The effects of the noise pollution level are the same for morning, afternoon and evening
Versus
H1: The effects of the noise pollution level are not the same for at least one of either morning, afternoon or evening.
The level of significance used is 0.05, and the null hypothesis is considered rejected if the p-value is less or equal to the level of significance. The structure of the ANOVA table is such as presented in Table 1.
Table 1.
A typical example of a one-way ANOVA Table
| Source of Variation | Degree of Freedom | Sum of Square | Mean Square | F-value |
|---|---|---|---|---|
| Factor | f-1 | SSF | MSF = SSF/f-1 | MSF/MSE |
| Error | n-f | SSE | MSE = SSE/n-f | |
| Total | n-1 | SST |
where, ‘f’ is the number of factors which is 3 according to this research; morning, afternoon and evening. ‘n’ is the overall sample size.
The goodness of Fit Test
The goodness of fit test is performed in this research to select the probability model that best fits the dataset. The Kolmogorov Smirnov (KS) test, the Anderson Darling (AD) test and Chi-square test are examples of the goodness of fit tests.
The KS test was adopted in this research because it is the most popular and others might give similar results. The null hypothesis tests whether the data follow a specified distribution. If represent ordered data points, the KS statistic is:
where are the ordered data and is the cumulative distribution function (cdf) of the continuous distribution tested.
Results
Descriptive Analysis of the Dataset
The summary for the LNP measurements is provided in Figures 1 to 3 while the summary for the mean measurement across the 41 locations is provided in Figure 4.
Figure 1.
Summary report for morning measurements on LNP
Figure 3.
Summary report for evening measurements on LNP
Figure 4.
Summary report for the mean measurements of LNP across all locations in Ota
Figure 2.
Summary report for afternoon measurements on LNP
Result for the Analysis of Variance
The analyses of the means of the various measurements are presented in Table 2.
Table 2.
Analysis of the Means
| Factor | N | Mean | Standard Deviation | 95% Confidence Interval |
|---|---|---|---|---|
| LNP_Morning | 41 | 90.78 | 7.89 | (88.16, 93.39) |
| LNP_Afternoon | 41 | 90.64 | 9.31 | (88.03, 93.26) |
| LNP_Evening | 41 | 90.72 | 8.11 | (88.10, 93.34) |
The 95% confidence interval (CI) plot for the means is displayed in Figure 5.
Figure 5.
The 95% confidence interval (C.I) plot for the means
The result of the analysis of variance is presented in Table 3.
Table 3.
Analysis of Variance (ANOVA) Table
| Source | Degree of Freedom | Sum of Square | Mean Square | F-value | p-value |
|---|---|---|---|---|---|
| Factor | 2 | 0.36 | 0.1805 | 0.00 | 0.997 |
| Error | 120 | 8585.85 | 71.5487 | ||
| Total | 122 | 8586.21 |
The result in Table 3 shows that the generated p-value is 0.997 which is far greater than the level of significance (0.05). Hence, there is no enough evidence to reject the null hypothesis, and it can, therefore, be concluded that there is no significant difference in the means of the noise level measurements taken in the morning, afternoon and evening. This result is further confirmed by Turkey’s post-hoc test which is summarized in Figure 6.
Figure 6.
Summary of Turkey’s post-hoc analysis
It can be observed in Figure 6 that all the intervals contained zero; this is an indication that there is no significant difference in the pair of each of the measurements considered.
Fitting of Probability Models
To determine the appropriate probability model that describes the mean noise pollution level in Ota metropolis, Easyfit (trial version) software was used to select distribution with the best fit. The Kolmogorov-Smirnov (KS) test of goodness of fit was used to select the best model. The software fitted sixty distributions to the dataset, but the best five was reported in this research. The result is presented in Table 4.
Table 4.
Fitted Distributions
| Distributions | KS Statistic | Rank |
|---|---|---|
| Log-Logistic (3P) | 0.06236 | 1 |
| Burr | 0.06846 | 2 |
| Hypersecant | 0.07131 | 3 |
| Logistic | 0.08415 | 4 |
| Johnson SU | 0.08629 | 5 |
From Table 4, the best-fitted model is the three-parameter Log-logistic distribution; this selection/decision is based on the Kolmogorov Smirnov statistic. A graph showing the best distribution fitted to the dataset on mean noise pollution level is presented in Figure 7.
Figure 7.
Graph of log-logistic distribution on the histogram of the dataset
In conclusion, further analyses of the noise pollution level in Ota metropolis has been provided in this research. The mean noise level in the morning was 90.78 which is higher than (though very close to) that of afternoon and evening with means 90.6 and 90.72 respectively. This is reasonable as more activities are expected during this time; pupils are going to school, workers going to the office, traffic at some junction and major bus stops. However, the analysis of variance result indicated that the time of the day (morning, afternoon and evening) have the same effect on the environment and populace. Also, the noise pollution level in Ota metropolis can be modelled using the log-logistic distribution as evident from the goodness of fit test. The model can now be used in predicting and managing noise pollution in that area. Furthermore, the model can be used in different geographical settings where noise pollution poses a perceived threat to the public health of the populace.
Footnotes
Funding: This research received financial support from the Covenant University
Competing Interests: The authors have declared that no competing interests exist
References
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