The Influence of Atmospheric Parameters on Production and Distribution of Air Pollutants in Bayelsa : A State in the Niger Delta Region of Nigeria

Air pollution is a primary environmental problem in the Niger Delta region of Nigeria due to oil spills including the gas emissions associated with industrial effluents. However, a good understanding and quantification of atmospheric parameters (wind speed, wind direction, temperature, relative humidity, solar radiation and cloud cover) that influence air pollution (CH4, NO2 and O3) concentrations in this region could assist in the mitigation and distribution of these pollutants. This work examines the influence of atmospheric parameters on the production and distribution of air pollutants in the Niger Delta region of Nigeria for the development of control strategies that will enhance the mitigation and amelioration of the significant impacts that these atmospheric pollutants could have on the populace in this part of the country. 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 was 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. The results of the correlations of CH4, NO2 and O3 concentrations with their previous day’s concentrations showed a strong significance in regression analysis for both CH4 and O3. The coefficient of determination of CH4 and O3 was obtained as How to cite this paper: Njoku, E.I., Ogunsola, O.E. and Oladiran, E.O. (2019) The Influence of Atmospheric Parameters on Production and Distribution of Air Pollutants in Bayelsa: A State in the Niger Delta Region of Nigeria. Atmospheric and Climate Sciences, 9, 159-171. https://doi.org/10.4236/acs.2019.91011 Received: December 1, 2018 Accepted: January 14, 2019 Published: January 17, 2019 Copyright © 2019 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access


Introduction
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 (O 3 ), carbon monoxide (CO), sulphur dioxide (SO 2 ), nitrogen oxides (NO x ), hydrogen sulphide (H 2 S), hydrogen fluoride (HF) and volatile organic compounds (VOC) [1].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 [2].
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 [3] [4].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 [5].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 [6].Furthermore, [4] 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, [7] 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 [8] [9] [10] [11].
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.

Materials and Methods
The CH

Results and Discussion
The result of the regression statistics showed that wind speed has a greater negative influence on the concentration of CH 4 and Ozone (O 3 ) 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 (CH 4 and O 3 ) 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 O 3 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 Y is the CH 4 concentration, X is the wind speed, a = 174.918 is the intercept, b = 3.929, and the slope, R 2 = 0.654 (significant at 1 percentile).
Also, the concentration of the variables were input into the regression equation and Methane (CH 4 ) with all the weather parameters show a weak significance in the statistical analysis with none of the parameters meeting the entry requirement for NO 2 when analysed in the regression equation.
The regression analysis between the measured and predicted O 3 (Figure 3) a relationship expressed as: where, Y is the O 3 concentration, X is the wind speed, a = 0.006 is the intercept, b = 7.101E−005, and the slope, R 2 = 0.810 (significant at 1 percentile).The CH 4 , NO 2 and O 3 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 CH 4 .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 CH 4 because of their very weak significant values in the statistical analysis.For NO 2 , none of the parameters meet up with the entry requirement in the equation because all the other parameters showed a weak correlation with it (NO 2 ), 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.There was a very strong correlation and a good coefficient of determination of about (81%) between O 3 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 CH 4 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 CH 4 has a very good dependence on these factors; where as 35% is undetermined.However, because of the weak dependence of NO 2 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.CH 4 , O 3 and NO 2 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 be-

Figure 1 .
Figure 1.Map of Bayelsa State showing the study area (Apoi Creek) Southern Ijaw, sourced from NARSDA.

Figure 4 (
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 CH 4 , NO 2 , O 3 and CO 2 .While Figure 5(C) shows the mean annual concentrations of NO 2 and average temperature.

Figure 4 (Figure 3 .Figure 4 .
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 CH 4 , NO 2 , O 3 and CO 2 .While Figure 5(C) shows the mean annual concentrations of NO 2 and average temperature.

Figure 5 .
Figure 5. (A) NO 2 concentration correlation with (a) wind speed; (b) relative humidity; (c) cloud cover; (B) Ozone concentration correlation with all the parameters; (C) NO 2 concentration correlation with wind direction and temperature.

Figure 6 .
Figure 6.(A) Graph of Man-Kendall trend validation statistics for Methane; (B) Graph of Man-Kendall trend validation statistics for NO 2 ; (C) Graph of Man-Kendall trend validation statistics for ozone; (D) Graph of Man-Kendal trend validation statistics for CO 2 .
4and NO 2 data utilized in this study were sourced from the European

Table 1 .
The measured and the predicted values of CH 4 and O 3 .

Table 2 .
Pearson correlation of some meteorological parameters against the pollutant concentrations.
deducing the spatial interpretations of the pollutants' concentrations in Niger Delta while Table8and Table9shows the Man-Kendal rank statistical table within the period of studies.The regression analysis between the measured and predicted CH 4 (Figure2) has a relationship expressed as:E.I.Njoku et al.DOI: 10.4236/acs.2019.91011163 Atmospheric and Climate Sciences

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 NO 2 .

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 CO 2 .

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 CO 2 .