4. DISCUSSION
4.2. REGULATION OF BCL6 BY EBV-ENCODED microRNAS
area recorded the highest and lowest average emission levels of carbon dioxide from its road transportation. An Arc GIS presentation of the emissions variations and analysis will also be made for presentations at a glance.
11.5 RESULTS AND DISCUSSION
11.5.1 Results
Appendices A, B and C contains the raw data of the field work conducted in Minna, Bida and Suleja respectively to determine the emission levels of CO2. In each of the towns, a total of 96 data counts were recorded, from which analysis of the emission level of CO2 was based upon. The 96 data counts range from peak to off-peak period, which corresponds to the rate of flow of transportation vehicles. The variation in the rate of traffic flow at peak and off-peak periods subsequently resulted in variations in the value of the readings obtained, with peak periods having higher emission levels than off-peak periods. The analysis was individually carried out in each town with reference to the international safe limit of CO2 emission in an environment (350 PPM)
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and comparatively to determine the level of significant difference existing between the wide ranges of data obtained in the study areas. The latter, entails the application of suitable statistical examinations at an appropriate level of significance.
11.5.1.1 Descriptive Statistics of CO2 Emission Data Generated in Minna, Bida and Suleja
Tables 4.1, 4.2 and 4.3 represent a summarised description of the CO2 emission data generated in Minna, Bida and Suleja respectively. More details about the data generated are also shown in Appendices A, B and C for Minna, Bida and Suleja respectively.
Table 4.1 Descriptive Statistics of CO2 Emissions Data Generated in Minna
Parameter Value
Mean 2688.542
Standard Error 42.5116
Median 2810
Mode 3050
Standard Deviation 416.5269 Sample Variance 173494.7
Kurtosis 6.338264
Skewness -2.37921
Range 2000
Minimum 1200
Maximum 3200
Sum 258100
Count 96
Confidence Level (95.0%) 84.3962
Table 4.2 Descriptive Statistics of CO2 Emissions Data Generated in Bida
Parameter Value
Mean 2518.125
Standard Error 23.30307209
Median 2500
Mode 2450
Standard Deviation 228.3225442
Sample Variance 52131.18421
Kurtosis 0.144140262
Skewness 0.414743198
Range 950
Minimum 2110
Maximum 3060
Sum 241740
Count 96
Confidence Level (95.0%) 46.26244614
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Table 4.3 Descriptive Statistics of CO2 Emissions Data Generated in Suleja
11.5.2 Summary of the CO2 Emission Data Generated in Minna, Suleja and Bida
From table 4.4, it can be observed that the highest average emission level of CO2 is obtained in Suleja, followed by Minna, with Bida having the least average emission level.
Table 4.4 Summary of CO2 Emission levels in the Study Areas
11.5.3 Data Analysis
11.5.3.1 Graphical Analysis of CO2 Emissions in Study Areas
Figure 4.1 shows the variations in the average emission levels of CO2 between the three study areas. Suleja is having the highest average emission level of 2856.458PPM while Bida is having the least emission level of 2518.125PPM compared to the other two towns.
Parameter Value
Mean 2856.458333
Standard Error 28.94716117
Median 2880
Mode 2880
Standard Deviation 283.6230974
Sample Variance 80442.0614
Kurtosis 0.710814391
Skewness -0.87947656
Range 1140
Minimum 2110
Maximum 3250
Sum 274220
Count 96
Confidence Level (95.0%) 57.46737937
Groups Count Average Variance
Minna 96 2731.146 78892.36
Suleja 96 2856.458 80442.06
Bida 96 2518.125 52131.18
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Figure 4.1: Average Emission Levels of CO2 in the Study Areas
Figure 4.2: Deviation of Average Emission Levels of CO2 in Study Area from the Internationally acceptable Safe Limit.
2731.146 2856.458
2518.125
0 500 1000 1500 2000 2500 3000
CO2 Emissions in ppm
Minna Suleja Bida
350
2731.146 2856.458
2518.125
0 500 1000 1500 2000 2500 3000
CO2 Emissions in ppm
Safe Limit Minna Suleja Bida
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It can be observed from figure 4.2 that the average emission levels of CO2 in the study areas deviate by more than five times from the internationally acceptable safe limit of 350PPM. This entails that the study areas are also contibuting to the global phenomenon of climate change through CO2 emission.
11.5.4 Statistical Analysis of the CO2 Emission Data
11.5.4.1 Analysis of Variance (ANOVA) of the three CO2 Emission Samples ANOVA entails the testing of hypothesis on the means of three or more population sample, making use of the degree of variability (measure of variance), within each sample as well as between the samples, taken independently from these populations.
The primary aim of ANOVA between population samples is to hypothetically test for significant difference between the population samples. Since the emission samples in Minna, Suleja and Bida comprises of just a single factor (CO2), a Single Factor ANOVA (ANOVA One Way) was used to analyse the three population samples of CO2 emission. The analysis provides a test of the hypothesis that each sample is drawn from the same underlying probability distribution against the alternative hypothesis that underlying probability distributions are not the same for all samples.
Table 4.5: Single Factor ANOVA between the CO2 Emission Sample Data
Source of Variation
SS Df MS F
P-value
F crit
Between Groups 5617617 2 2808809 39.84774 5.5E-16
3.027443265
Within Groups 20089232 285 70488.53
Total 25706850 287
From the foregoing analysis, table 4.5, shows the summary of a single factor ANOVA on the three samples of emission data at 0.05 significant levels. The analysis was carried out based on a null hypothesis that there are no significant differences between the means of the three CO2 emission samples under the assumption that the variances of the three samples are all equal. The alternative hypothesis is that there is a significant difference between the means of the emission samples. The purpose of the analysis is to statistically accept the null hypothesis while the alternative hypothesis is rejected or to reject the null hypothesis while the alternative hypothesis is accepted.
From the summary table of the ANOVA analysis in table 4.5, it can be observed that the computed value of F (39.84774) is greater than the critical value of F (3.027443265); this sets up a critical region for the analysis i.e. a region for rejecting the null hypothesis. In other words, since the computed value of F is greater than the
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critical value of F (F crit), we reject the null hypothesis and accept the alternative hypothesis. Thus, there is a significant difference between the means of the three emission samples at 0.05 significant levels.
This difference is justifiable qualitatively due to the variation in the traffic conditions in Minna, Suleja and Bida. The variation in traffic condition is due to the differences in the commercial and industrial activities of the towns. In ascending order, commercial and industrial activities in the three towns vary as follows, Bida, Minna and Suleja.
11.5.5 Correlation between the Three CO2 Emission Samples
Correlation analysis measures the relationship existing between two or more data samples. This relation is determined by the establishment of a scaled factor called correlation coefficient from which the relationship is measured.
The correlation coefficient is a measure of the extent to which two measurement variables "vary together". The correlation coefficient is scaled so that its value is independent of the units in which the two measurement variables are expressed. The value of any correlation coefficient must be between -1 and +1 inclusive. Correlation coefficient of +1 represents a condition of perfect correlation (perfect relationship) while correlation coefficient of 0 represents a condition of no correlation (no relationship). The result of the correlation analysis carried out between the three data samples is presented in the table 4.6.
Table 4.6: Correlations between the three samples of data
Location Parameter Minna Suleja Bida Minna Pearson Correlation 1 .348** .390**
Sig. (2-tailed) .001 .000
N 96 96 96
Suleja Pearson Correlation .348** 1 .057
Sig. (2-tailed) .001 .579
N 96 96 96
Bida Pearson Correlation .390** .057 1 Sig. (2-tailed) .000 .579
N 96 96 96
From the results above it can be seen that the relationship between the CO2 emission in Minna is somehow related to the emission level in Bida since the correlation coefficient between them is 0.39; but the relationship between the emission levels in Suleja and Bida is very wide since the correlation coefficient between them is 0.057.
Also, there is a relationship between the emission level in Suleja and Minna, but the relationship is less than that existing between Minna and Bida. This is seen with the correlation coefficient between Suleja and Bida less than that between Minna and Bida.
i.e 0.348 is less than 0.39.
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11.6 CONCLUSION AND RECOMMENDATION
This research has established the presence of CO2 from vehicular emissions in the study area. The research clearly indicates the high level of the presence of this greenhouse gas in the atmosphere of the research area at an average level exceeding the internationally acceptable safe limit of 350PPM in the atmosphere. The study also shows the variations in the average emission levels of CO2 between the three study areas. Suleja is having the highest average emission level of 2856.458PPM followed by Minna with 2731.146PPM while Bida is having the least 2518.125PPM.
Furthermore, these differences in emission level is due to the nature of economic activities in individual towns. Suleja having highest emission level is characterised with the highest commercial activities and this is prominent with the high level of vehicle traffic usually experienced. Suleja is a neighbouring town to Abuja the Federal Capital Territory and most of the workers in Abuja live in the town. Thus, there is always a scenerio of high traffic conditions at the morning and evening peak period and consequently accounts for the high level of vehicular carbon dioxide emissions. Bida having the least CO2 emission is due to a lower level of vehicular traffic condition resulting from lower economic activities compared to Minna and Suleja. From a study carried out earlier by Ndoke et al (2006), the average emission levels recorded at locations in Kaduna and Abuja shows higher values of carbon dioxide concentration for heavily congested areas: 1840 PPM for Sabo, Kaduna, 1780 PPM for Stadium round-about, Kaduna, and 1530 PPM for A.Y.A. Junction, Abuja – and lower values of carbon dioxide concentration for areas with minimal traffic – 1170 PPM for Jabi road, Kaduna, and 1160 PPM for Asokoro (behind ECOWAS), Abuja.
Although the study areas are not highly industrialised towns, the research has depicted the hazardous condition expected in highly industrialised towns since the level of CO2 in such areas would definitely exceed the average emission recorded in the study area.
It is also obviously evident in this study that the level of vehicular emission of CO2 increases with the rate of the traffic volume. This is evident in Suleja which has a greater traffic volume compared to Minna and Bida having a corresponding greater average emission level of CO2. With the internationally acceptable safe limit of CO2 in the atmosphere to be 350PPM, the average emission levels of CO2 in the entire study areas is of great concern on the global warming implications. However, aaccording to Greiner (1995), this emission quantities are not high enough to cause health hazards but as vehicular traffic grows in number and age, the quantity of carbon dioxide that will be released in the near future in these study areas will be enough to make the government of the day worry.
The research has clearly shown that Minna, Bida and Suleja are largely contributing to the high level emission of CO2 globally via road transportation; thus, contributing to the global phenomenon of climate change and the recommendations from this study are as follows:
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Introduction of the urban mass transit would definitely reduce the number of traffic on the roads, hence the emissions from several vehicles that would have been on the road. This would also be a cheaper means of transportation for the masses.
Encouraging the use of vehicles using alternative sources of energy like solar, electric and biofuel will also result in less emission levels as the these alternative energy sources other than fossil fuel originated diesel and petrol vehicles do not emit especially carbon dioxide.
Research programs should be initiated to come out result providing alternative sources of fuels whose final products would not emit greenhouse gases.
Afforestation programs should be put in place by the government to help salvage the environment by absorbing CO2 being emitted.
REFERENCES