Air pollution is the emission of chemical effluents from numerous sources into the atmosphere which could cause harm to both man and plants including damage to life and property. These pollutants are of many forms including ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2), nitrogen oxides (NOx), hydrogen sulphide (H2S), hydrogen fluoride (HF) and volatile organic compounds (VOC) . Also, the chemical effluents being referred to as pollutants are been influenced by so many factors including wind speed, temperature and humidity. The wind speed influences the quantity of the pollutants to be dispersed, while temperature assists in transforming the pollutants to other forms .
The discovery of oil has been causing series of negative environmental effects in the Niger Delta region, where all the petroleum exploration and production has been taking place in Nigeria  . In this region, gas flaring which is thought to be very important in the elimination of gas, especially when the volume is thought to be economically insufficient to warrant recovery or collection is on the increase in recent years, thereby causing many health hazards both to people and to animals . The increasing effect of the rapid population growth in the Niger Delta region, including the industrialization, and increased use of vehicles has also made the situation in this region to become worse. Moreover, the Niger Delta has been witnessing water and land contamination with consequent degradation of the agricultural land with the effective enforcement of regulatory measures yielding no measurable results. Activities related to petroleum exploration, development and production operations have local disadvantages and effects on the atmosphere, soils and sediments, surfaces and groundwater, marine environment, biologically diversity and sustainability of terrestrial ecosystems in the Niger Delta . Furthermore,  carried out systematic studies of the air quality of the Niger Delta region and found out that carbon monoxide, nitrogen dioxide, sulphur dioxide and carbon dioxide effluents vary in the Niger Delta. Also,  carried out the analysis of carbon monoxide concentrations with some selected meteorological variables such as temperature, relative humidity and wind speed in ten major urban centres in the south eastern part of Nigeria. The correlation analysis reveals that among the meteorological parameters studied; only wind speed is strongly correlated with carbon monoxide in the south eastern Nigeria. However, there are other sources of pollution in Nigeria which include those from vehicular sources    .
This work is focused on the Bayelsa state of Nigeria (Figure 1) which is one of the nine states in the Niger Delta region, due to its been exposed to much environmental degradation and health hazards as a result of oil spills and gas emissions associated with the industrial effluents in this area.
2. Materials and Methods
The CH4 and NO2 data utilized in this study were sourced from the European Space Agency (ESA), while that of tropospheric ozone (O3) was obtained from the National Aeronautics and Space Administration (NASA), and the atmospheric parameters data were provided by the Nigeria Meteorological Agencies (NIMET), Lagos.
The CH4 and NO2 data utilized in this study were sourced from the European Space Agency (ESA), while that of tropospheric ozone (O3) was obtained from the National Aeronautics and Space Administration (NASA), and the atmospheric parameters data were provided by the Nigeria Meteorological Agencies (NIMET), Lagos. The analysis of the daily pollutants (CH4, NO2 and O3) including the atmospheric parameters in this region of the Niger Delta for the period 2003 to 2010 were carried out using standard statistical approach including the graphical method, stepwise regression model, least-square method, and correlation analysis. The Mann-Kendal rank statistics was also utilized in identifying the meaningful long-term trends, validation and testing of the homogeneity of the concentrations of the pollutants.
3. Results and Discussion
The result of the regression statistics showed that wind speed has a greater negative influence on the concentration of CH4 and Ozone (O3) respectively. The decrease in the wind speed increases the concentration of the pollutants for they tends to accumulate near the source point but the decrease in the wind speed decreases the concentrations of these pollutants as much pollutants will be dispersed by wind. The measured and the predicted values of these pollutants (CH4 and O3) as observed from the regression equation were presented in Table 1.
The result of the correlation analysis showed that only wind speed among all the meteorological parameters considered has the strongest negative influence on these pollutants with the value 81% and 91% for CH4 and O3 respectively (Table 2). Tables 3-7 which showed the minimum and the maximum annual trends values of the pollutants within the period considered was utilized in
Figure 1. Map of Bayelsa State showing the study area (Apoi Creek) Southern Ijaw, sourced from NARSDA.
Table 1. The measured and the predicted values of CH4 and O3.
Table 2. Pearson correlation of some meteorological parameters against the pollutant concentrations.
SR = Solar Radiation, RH = Relative Humidity, WNDSPD = Wind Speed, WNDDR = Wind direction, CC = Cloud Cover, TMIN = Minimum Temperature, TMAX = Maximum Temperature.
deducing the spatial interpretations of the pollutants’ concentrations in Niger Delta while Table 8 and Table 9 shows the Man-Kendal rank statistical table within the period of studies. The regression analysis between the measured and predicted CH4 (Figure 2) has a relationship expressed as:
Table 3. Basic statistics of monthly averages of air pollutant concentrations and their maximum and minimum values within the period of investigation Methane.
Table 4. Basic statistics of monthly averages of air pollutant concentrations and their maximum and minimum values within the period of investigation NO2.
Table 5. Basic statistics of monthly averages of air pollutant concentrations and their maximum and minimum values within the period of investigation ozone.
Table 6. Basic statistics of monthly averages of air pollutant concentrations and their maximum and minimum values within the period of investigation CO2.
Table 7. Basic statistics of annual averages of air pollutant concentrations and their maximum and minimum values within the period of investigation.
Table 8. Man-Kendall rank statistical table for various pollutants.
Table 9. Man-Kendall rank statistical table for CO2.
Figure 2. Graph of agreement between measured and predicted value of methane.
Y is the CH4 concentration,
X is the wind speed,
a = 174.918 is the intercept,
b = 3.929, and the slope,
R2 = 0.654 (significant at 1 percentile).
Also, the concentration of the variables were input into the regression equation and Methane (CH4) with all the weather parameters show a weak significance in the statistical analysis with none of the parameters meeting the entry requirement for NO2 when analysed in the regression equation.
The regression analysis between the measured and predicted O3 (Figure 3) a relationship expressed as:
Y is the O3 concentration,
X is the wind speed,
a = 0.006 is the intercept,
b = 7.101E−005, and the slope,
R2 = 0.810 (significant at 1 percentile).
Figure 4(A)-(D) showed that the pollutants’ trends in the Niger Delta are temporal but with high concentration during the dry season.
Figure 4(A)-(C), Figure 5(A) and Figure 5(B) respectively showed a non-linear trend in the mean annual concentration plots for CH4, NO2, O3 and CO2. While Figure 5(C) shows the mean annual concentrations of NO2 and average temperature.
Figure 3. Graph of agreement between measured and predicted value of ozone.
(A) (B) (b) (c) (d)(C)
Figure 4. (A) Methane Correlation with Solar radiation; (B) Methane correlation with wind speed; (C) Methane concentration correlation with (a) Relative humidity, (b) cloud cover, (c) wind direction and (d) temperature.
(a) (b) (c) (A) (a) (b) (c) (d) (e) (f) (g)(B) (C)
Figure 5. (A) NO2 concentration correlation with (a) wind speed; (b) relative humidity; (c) cloud cover; (B) Ozone concentration correlation with all the parameters; (C) NO2 concentration correlation with wind direction and temperature.
The Mann-Kendal rank statistics showed that the standardization variables U'(ti) for all the pollutants between the period of studies (2003-2012) has a sequential fluctuating behaviour around a zero level and which confirms validity of the trends used and the homogeneity of the pollutants considered in the region Figures 6(A)-(D).
The CH4, NO2 and O3 concentrations are the dependent variables, while meteorological factors are the independent variables. In this study, because the statistical analysis of the relative humidity showed an insignificant value, it was therefore not imputed into the equation for CH4. It was only the wind speed that survived among the parameter utilized in this work because of its very high significance value in the statistical analysis. Also, the other parameters such as temperature, cloud cover and solar radiations were eliminated from the regression equation for CH4 because of their very weak significant values in the statistical analysis. For NO2, none of the parameters meet up with the entry requirement in the equation because all the other parameters showed a weak correlation with it (NO2), hence the equation terminated when the regression analysis was carried out.
The remaining parameters considered in this work also showed weak relationship with tropospheric ozone except the wind speed which showed a very strong relationship with ozone. Hence, it was the only surviving parameter in the regression equation analysis.
(A) (B) (C) (D)
Figure 6. (A) Graph of Man-Kendall trend validation statistics for Methane; (B) Graph of Man-Kendall trend validation statistics for NO2; (C) Graph of Man-Kendall trend validation statistics for ozone; (D) Graph of Man-Kendal trend validation statistics for CO2.
There was a very strong correlation and a good coefficient of determination of about (81%) between O3 concentration and the previous year’s ozone concentration with the following parameters: wind speed, temperature, relative humidity, cloud cover, in which 19% is undetermined. In a similar way, there was also a strong dependence of CH4 concentration and the previous year’s concentration on the following parameters: wind speed, temperature, relative humidity. The regression model in Equation (1) showed that 65% of CH4 has a very good dependence on these factors; where as 35% is undetermined. However, because of the weak dependence of NO2 concentration on these factors, it was not possible for it to be modelled. The value from the correlation table is very low (0.213) for wind speed. This value cannot be modelled as the model will not survive the values below 0.5. The coefficient of determination was not obtained and so the rate of dependence is generally indeterminate.
A Pearson correlation was carried out on the 10 years data set. CH4, O3 and NO2 monthly concentrations were correlated against monthly meteorological parameters (Table 2). This correlation was carried out to ascertain which of the atmospheric parameters were important in describing the behaviour of pollutants.
The Pearson correlation coefficient shows that solar radiation has a negative correlation with methane indicating that the increase in solar radiation causes a decrease in methane’s concentration. This behaviour may be attributed to increase in heat flux which causes dry deposition and pollutant fall out.
Wind speed has a very strong negative correlation with methane concentration in Niger Delta (Figure 2 and Figure 3). This implies that the high decrease in speed of wind causes much increase in the production of methane. This is because at low wind speed, the emitted pollutant (methane) tends to accumulate near the source area and disperses with an increasing wind speed due to higher ventilation.
Close observations reveals that solar radiation lowers the concentration of ozone in Niger Delta region as it shows negative correlation with ozone concentration. The concentration wind of ozone with speed shows a strong negative value. This implies that increase in wind speed decreases the accumulation of ozone as much speed of the wind tends to disperse the pollutants and decreases the concentrations due to higher ventilation. Relative humidity shows a moderate positive correlation with ozone concentration while cloud cover and temperature shows a very weak positive correlation with ozone. This implies that the increase in these parameters causes a slight increase in ozone concentration.
Figure 4(C) and Figure 5(C) showed that CH4 and NO2 concentration decreases with increasing temperature, while Ozone concentration is the opposite in which it increases as temperature also increases (Figure 5(B)). There is a very strong negative correlation between wind speed and the pollutant (O3 and CH4) concentrations (P < 0.01 for O3 and CH4) (Figure 4(B) and Figure 5(B)). This implies that wind speed, among all the meteorological parameters studied, has more influence on the variation of O3, NO2 and CH4 concentrations in the region of the Niger Delta, giving as high as 81% and 64% for O3 and CH4 respectively (Figure 2 and Figure 3), while all the parameters are of less significance with NO2 (Figure 5(B) and Figure 5(C)).
Wind speed, temperature, and solar radiation are effective meteorological variables in decreasing CH4 concentration. Solar radiation is also effective meteorological variable in decreasing NO2 concentration. Wind speed, solar radiation, wind direction, and minimum temperature are effective meteorological variable in decreasing O3, concentration. Whereas, maximum temperature, relative humidity and cloud cover promotes O3 concentration although it is only the effect of wind speed that is strongly significant (P < 0.01) .
The spatial and temporal distribution of daily CH4 (Methane), NO2 (nitrogen dioxides) and O3 (ozone) concentration in the Niger Delta region was observed depend on the variations in atmospheric parameters. A very strong negative correlation was obtained between wind speed and both the CH4 and O3 pollutants respectively, and a moderate correlation was obtained between the wind speed and NO2. This implies that amongst the atmospheric parameters considered in this study for the region of the Niger Delta in Nigeria, wind speed has much influence on the variation of both CH4 and O3 concentrations, but with a little influence on the NO2 concentrations.
 Anochie, U.C. and Onyinye, O.M. (2015) Evaluation of Some Oil Companies in the Niger Delta Region of Nigeria: An Environmental Impact Approach. International Journal of Environmental and Pollution Research, 3, 13-31.
 Ngele, S.O., Eboatu, A.N. and Onwu, F.K. (2012) Preliminary Study of the Influence of Some Meteorological Parameters on the Concentration of CO in South Eastern Part of Nigeria. Chemical Science Transactions, 1, 702-708.
 Iyoha, M.A. (2009) The Environmental Effects of Oil Industry Activities on the Nigerian Economy: A Theoretical Analysis. A Paper Presented at National Conference in the Management of Nigeria’s Petroleum Resources, Organised by the Department of Economics, Delta State University.
 Ojo, O.O.S. and Awokola, O.S. (2012) Investigation of Air Pollution from Automobiles at Intersections on Some Selected Major Roads in Ogbomoso, South Western, Nigeria. IOSR Journal of Mechanical and Civil Engineering, 1, 31-35.
 Weli, V.E. (2014) Spatial and Seasonal Influence on Meteorological Parameters on the concentration of Suspended Particulate Matter in an industrial city of Port Harcourt, Nigeria. Developing Country Studies, 4, 112-121.