1. Introduction

ACS

Atmospheric and Climate Sciences

2160-0414

Scientific Research Publishing

10.4236/acs.2022.122025

ACS-116483

Articles

Earth&Environmental Sciences

Impact of Concentration Levels of Atmospheric Pollutants on Local Climate of Delta State, Nigeria

Ifeanyi

Innocent Onwosi

¹Emmanuel

Fartiyahcha Nymphas

Department of Physics, University of Ibadan, Ibadan, Nigeria

Department of Physics and Engineering, Delaware State University, Dover, DE, USA

10022022

120242144018, February 20229, April 2022 12, April 2022

2014

This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/

Studies in various regions of the world have revealed that air pollution can have a significant influence on local climate. This study therefore considers the impact of concentration levels of atmospheric pollutants on local climate of Delta state, Nigeria. Monthly and annual averaging of the daily pollutant concentrations and meteorological parameters within the period of investigation was carried out. Descriptive Statistics, correlation analysis, coefficient of determination (R²) analysis and least squares regression analysis of the selected meteorological parameters with CH₄ and O₃ concentrations for the period of 2003 to 2012 and NO₂ and CO₂ concentrations for the period of 2011 to 2014 were carried out. The regression relationship was then used to obtain predicted values for the meteorological parameters within the period of investigation. The results of the descriptive statistics of annual averages of CH₄, O₃, NO₂ and CO₂ concentrations within the period of investigation revealed that the emission levels breached FEPA and EGASPIN limits . The results of the correlation analysis indicated that CO₂ had a strong significant positive correlation with temperature with a correlation coefficient of 0.962, while a moderate negative correlation coefficient of 0.549 was obtained for CH₄, and very weak correlation coefficients of - 0.167 and 0.077 were obtained for O₃ and NO₂ respectively. CH₄, O₃ and CO₂ had a moderately significant positive correlation with solar radiation with correlation coefficients of 0.661, 0.571 and 0.656 respectively, while a weak negative correlation coefficient of 0.106 was obtained for NO₂. CH₄ had a strong significant positive correlation with relative humidity with a correlation coefficient of 0.859, while moderate correlation coefficients of - 0.516 and 0.646 were obtained for NO₂ and CO₂ respectively, and a weak correlation coefficient of 0.345 was obtained for O₃. CO₂ and CH₄ had a strong significant correlation with wind speed with correlation coefficients of 0.951 and -0.906 respectively, while a moderate negative correlation coefficient of 0.518 was obtained for O₃, and a weak negative correlation coefficient of 0.317 was obtained for NO₂. The predicted values of the meteorological parameters showed a significant level of agreement with their measured values. Therefore, among the atmospheric pollutants postulated as influencing meteorological parameters, CO₂ appears to be the most strongly significant in explaining temperature variations in this region of Niger Delta, with correlation coefficient of 96.2% and coefficient of determination (R²) of 0.926, implying that CO₂ influenced 92.6% variation in temperature in this part of Niger Delta within the period of investigation.

Air Pollution Atmospheric Pollutants Local Climate Meteorological Parameters

1. Introduction

Air pollution is a major environmental problem facing the Niger Delta region [1]. Air pollution is the contamination of the atmosphere by noxious gases and particulates.

Studies have revealed that air pollution can have a major effect on local climate [1]. Due to the spatial distributions of atmospheric pollutants with higher concentration levels mostly found near emission sources, variations in emission and concentration levels of air pollutants can cause a significant influence on local climate [2] [3] and in some situations regional climate by means of teleconnections in the atmosphere [4].

About 8 billion cubic meters of gas is flared yearly at various oil production sites in Nigeria [1]. It has been reported that the Niger Delta region of Nigeria has more than 123 gas flaring sites making Nigeria one of the major emitters of greenhouse gases in Africa [5]. Nigeria is accountable for almost one-sixth of the gas flared worldwide [6]. Nearly 75% of Nigeria’s natural gas is being flared and all occur in the Niger Delta region. The flares have contributed more greenhouse gases thereby causing climate change which could possibly lead to increased occurrence of flooding in the region [7].

There have been occurrences of acidified rain in the Niger Delta region due to the introduction of a high concentration of sulphur and oxides of nitrogen into the atmosphere [8] [9]. Combustion processes in gas flaring sites give rise to the emission of lower fractions of hydrocarbons and oxides of nitrogen and the presence of ultraviolet radiation produces smog which could cause a reduction in visibility [10].

About 45.8 billion kilowatts of heat are released into the atmosphere of the Niger-Delta from 1.8 billion ft³ of gas daily [11]. Heat production destroys vegetation in the vicinity of the heat source [12]. [13] undertook a study on the analysis of carbon monoxide concentration levels with some selected meteorological parameters such as wind speed, relative humidity and temperature in ten major cities in the south eastern part of Nigeria. The result of the correlation analysis showed that out of all the meteorological parameters studied, only wind speed showed a strong correlation with carbon monoxide.

[14] undertook a study on the use of greenhouse gases as climate proxy data in explaining variability in climate. The standard deviation of CH₄ and CO₂ concentrations showed good correlations with the years associated with warming and can be used as good climate proxies. Furthermore, [15] carried out a study on the effect of meteorological parameters on distribution of atmospheric pollutants in Bayelsa State, Nigeria. The results revealed that wind speed showed a strong correlation with O₃ and CH₄ concentration levels.

Even though some amount of work has been done on the sources and distribution of air pollutants, so far, no major study has been undertaken on the effect of atmospheric pollutants on local climate in the Niger Delta. This work is aimed at increasing research efforts on understanding the association of atmospheric pollutants and related climate and environmental impacts in the Niger Delta Area.

2. Study Station, Materials and Method2.1. Study Station

Figure 1 is the map showing gas flaring sites and highlighting study station (Warri). The city of Warri (5.52˚N, 5.75˚E) is a major center of petroleum activities in

southern Nigeria. It has a population of over 311,970 (2006 census) [16]. The climate is marked by two different seasons: the rainy season (May to October) and the dry season (November to April). Over the course of the year, temperature typically varies from 20.56˚C to 31.11˚C and is rarely below 16.11˚C or above 33.33˚C. Rainfall periods vary from January to December with annual rainfall amount of about 2768.8 mm.

2.2. Materials

Data description

The data on daily methane (CH₄) with tropospheric ozone (O₃) concentration levels (for the period of 2003 to 2012) and daily nitrogen dioxide (NO₂) with carbon dioxide (CO₂) concentration levels (for the period of 2011 to 2014) used in this study were obtained from the National Aeronautics and Space Administration (NASA). The data on meteorological parameters (wind speed, solar radiation, temperature and relative humidity) for the period of 2003 to 2014 were acquired from the Nigerian Meteorological Agency (NIMET), Lagos.

2.3. Method

Monthly and annual averaging of the daily pollutant concentrations (NASA data) and meteorological parameters (NIMET data) within the period of investigation was carried out. The statistical analysis of weather parameters in this region of the Niger Delta with CH₄ and O₃ concentrations for the period of 2003 to 2012 and with NO₂ and CO₂ concentrations for the period of 2011 to 2014 were carried out. The regression relationship:

Y = a + b X (1)

was used to obtain predicted values for the meteorological parameters within the period of investigation, so that by comparing the level of agreement between the predicted and measured values, we could ascertain the reliability of the model in this part of Niger Delta. Where:

Y = meteorological parameter (predicted);

X = atmospheric pollutant concentration;

b and a are the slope and intercept respectively and are given as:

b = n Σ x y − Σ x Σ y n Σ x 2 − ( Σ x ) 2 (2)

a = Σ y n − b Σ x n (3)

3. Results and Discussion3.1. Average Annual Concentration Levels of the Atmospheric Pollutants and Meteorological Parameters

Table 1 shows the values of average annual concentration levels of CH₄ with O₃ for the period of 2003 to 2012 and selected meteorological parameters for the period of 2003 to 2014, while Table 2 shows the values of average annual concentration

Table 1 Values of average annual concentration levels of CH4 and O3 for the period of 2003 to 2012 and selected meteorological parameters for the period of 2003 to 2014

Year	Mean CH₄ (ppmv)	Mean O₃ (ppmv)	Solar radiation (MJ/m²)	Relative humidity (%)	Temperature (˚C)	Wind speed (m/s)
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014	1740.994 1738.120 1737.331 1730.918 1740.740 1743.628 1750.178 1746.312 1759.923 1773.787	56.262 57.253 54.501 57.139 55.272 58.556 57.259 58.033 58.319 56.800	22.973 22.723 21.970 23.072 22.791 23.062 22.251 23.548 23.921 24.012 23.582 24.963	81.567 81.207 79.705 80.560 79.938 78.871 84.289 84.700 85.781 87.538 87.920 87.388	23.361 23.215 23.381 23.229 23.037 22.977 23.536 23.693 22.669 22.724 22.898 23.315	2.327 2.329 2.415 2.346 2.341 2.324 2.235 2.242 2.068 2.093 2.092 2.122

Table 2 Values of average annual concentration levels of NO2 and CO2 for the period of 2011 to 2014

Year	Mean NO₂ (ppmv)	Mean CO₂ (ppmv)
2011 2012 2013 2014	135.695 127.516 133.934 132.747	382.370 385.108 388.111 392.186

levels of NO₂ and CO₂ with selected meteorological parameters for the period of 2011 to 2014.

3.2. Descriptive Statistics of the Atmospheric Pollutants and Selected Meteorological Parameters

Table 3 shows the descriptive statistics of annual averages of CH₄, O₃, NO₂ and CO₂ concentrations, while Table 4 shows the descriptive statistics of annual averages of selected meteorological parameters.

3.3. Impact of the Atmospheric Pollutants Concentration on Meteorological Parameters

The impact of the concentration of atmospheric pollutants on selected meteorological parameters was determined using correlation analysis, coefficient of determination (R²) analysis and least squares regression analysis.

3.3.1. Correlation Analysis

Table 5 shows the correlation coefficients between selected meteorological parameters and CH₄, O₃, NO₂ and CO₂ concentrations.

Figures 2(a)-(d) show temperature correlation with CH₄, O₃, NO₂ and CO₂ concentrations respectively, Figures 3(a)-(d) show solar radiation correlation

Table 3 Descriptive statistics of annual averages of CH4, O3, NO2 and CO2 concentrations within the period of investigation

	Mean	Standard Deviation	Minimum	Maximum
CH₄ (ppmv) O₃ (ppmv) NO₂ (ppmv) CO₂ (ppmv)	1746.193 56.939 132.473 386.944	12.500 1.300 3.519 4.208	1730.918 54.501 127.516 380.139	1773.787 58.556 135.695 392.186

Table 4 Descriptive statistics of annual averages of selected meteorological parameters within the period of 2003 to 2014

	Mean	Standard Deviation	Minimum	Maximum
Solar radiation (MJ/m²) Relative humidity (%) Temperature (˚C) Wind speed (m/s)	23.239 83.289 23.170 2.245	0.822 3.355 0.315 0.121	21.970 78.871 22.669 2.068	24.963 87.920 23.693 2.415

Table 5 Correlation coefficients between selected meteorological parameters (dependent variables) and CH4, O3, NO2 and CO2 concentrations (independent variables) within the period of investigation

	CH₄ (ppmv)	O₃ (ppmv)	NO₂ (ppmv)	CO₂ (ppmv)
Solar radiation (MJ/m²) Relative humidity (%) Temperature (˚C) Wind speed (m/s)	0.661* 0.859* −0.549 −0.906	0.571 0.345 −0.167 −0.518	−0.106 −0.516 0.077 −0.317	0.656 0.646 0.962* 0.951*

with CH₄, O₃, NO₂ and CO₂ concentrations respectively, Figures 4(a)-(d) show relative humidity correlation with CH₄, O₃, NO₂ and CO₂ concentrations respectively, while Figures 5(a)-(d) show wind speed correlation with CH₄, O₃, NO₂ and CO₂ concentrations respectively.

3.3.2. Coefficient of Determination (R2) and Least Squares Regression Analysis

Figures 6(a)-(d) to Figures 9(a)-(d) show the coefficient of determination (R²) of the selected meteorological parameters with the concentration of atmospheric pollutants.

1) Coefficient of determination (R²) analysis

The coefficient of determination analysis gives us the measure of the variation in the meteorological parameters (dependent variable) that is predictable from the atmospheric pollutants (independent variable).

Methane (CH₄) had coefficients of determination (R²) of 0.820, 0.738, 0.437 and 0.302 with wind speed, relative humidity, solar radiation and temperature respectively. Tropospheric ozone (O₃) had coefficients of determination (R²) of 0.326, 0.268, 0.119 and 0.028 with solar radiation, wind speed, relative humidity and temperature respectively. Nitrogen dioxide (NO₂) had coefficients of determination (R²) of 0.266, 0.101, 0.011 and 0.006 with relative humidity, wind speed, solar radiation and temperature respectively. Carbon dioxide (CO₂) had coefficients of determination (R²) of 0.926, 0.904, 0.430 and 0.417 with temperature, wind speed, solar radiation and relative humidity respectively.

2) Least squares regression analysis

The Least squares regression analysis gives us the line of best fit enabling us to predict the behavior of the meteorological parameters.

CO₂ had the highest R² of 0.926 with temperature, as shown in Figure 6(d). To obtain predicted values for temperature, we substitute the values of a (intercept) and b (slope) from Figure 6(d) into the regression relationship Y = a + b X , where Y is temperature (predicted) and X is CO₂ concentration to obtain Equation (4) as:

Temperature = − 2.972 + 0.067 ( CO 2 ) (4)

Therefore by substituting the values of CO₂ concentration into Equation (4), we obtain predicted values for temperature within the period of investigation. Table 6 shows predicted and measured values of temperature, while Figure 10 shows the graph of predicted and measured values of temperature.

CH₄ had the highest R² of 0.437 with solar radiation, as shown in Figure 7(a). To obtain predicted values for solar radiation, we substitute the values of a (intercept) and b (slope) from Figure 7(a) into the regression relationship Y = a + b X , where Y is solar radiation (predicted) and X is CH₄ concentration to obtain Equation (5) as:

Solar radiation = − 37.859 + 0.035 ( CH 4 ) (5)

Therefore by substituting the values of CH₄ concentration into Equation (5),

Table 6 Predicted and measured values of temperature

Year	Mean CO₂ (ppmv)	Temperature ˚C (measured)	Temperature ˚C (predicted)
2011 2012 2013 2014	382.370 385.108 388.111 392.186	22.669 22.724 22.898 23.315	22.647 22.830 23.031 23.304

we obtain predicted values for solar radiation within the period of investigation. Table 7 shows predicted and measured values of solar radiation, while Figure 11 shows the graph of predicted and measured values of solar radiation.

Table 7 Predicted and measured values of solar radiation

Year	Mean CH₄ (ppmv)	Solar radiation MJ/m² (measured)	Solar radiation MJ/m² (predicted)
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012	1740.994 1738.120 1737.331 1730.918 1740.740 1743.628 1750.178 1746.312 1759.923 1773.787	22.973 22.723 21.970 23.072 22.791 23.062 22.251 23.548 23.921 24.012	23.076 22.975 22.948 22.723 23.069 23.168 23.397 23.262 23.738 24.224

CH₄ had the highest R² of 0.738 with relative humidity, as shown in Figure 8(a). To obtain predicted values for relative humidity, we substitute the values of a (intercept) and b (slope) from Figure 8(a) into the regression relationship Y = a + b X , where Y is relative humidity (predicted) and X is CH₄ concentration to obtain Equation (6) as:

Relative humidity = − 270.779 + 0.202 ( CH 4 ) (6)

Therefore by substituting the values of CH₄ concentration into Equation (6), we obtain predicted values for relative humidity within the period of investigation. Table 8 shows predicted and measured values of relative humidity, while Figure 12 shows the graph of predicted and measured values of relative humidity.

CO₂ had the highest R² of 0.904 with wind speed, as shown in Figure 9(d). To obtain predicted values for wind speed, we substitute the values of a (intercept) and b (slope) from Figure 9(d) into the regression relationship Y = a + b X , where Y is wind speed (predicted) and X is CO₂ concentration to obtain equation (7) as:

Table 8 Predicted and measured values of relative humidity

Year	Mean CH₄ (ppmv)	Relative humidity % (measured)	Relative humidity % (predicted)
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012	1740.994 1738.120 1737.331 1730.918 1740.740 1743.628 1750.178 1746.312 1759.923 1773.787	81.567 81.207 79.705 80.560 79.938 78.871 84.289 84.700 85.781 87.538	80.902 80.321 80.162 78.866 80.850 81.434 82.757 81.976 84.725 87.526

Wind speed = 0.162 + 0.005 ( CO 2 ) (7)

Therefore by substituting the values of CO₂ concentration into Equation (7), we obtain predicted values for wind speed within the period of investigation. Table 9 shows predicted and measured values of wind speed, while Figure 13 shows the graph of predicted and measured values of wind speed.

3.4. Discussion

The results of the descriptive statistics of annual averages of selected meteorological parameters within the period of investigation showed that relative humidity had the highest standard deviation value of 3.355%, while wind speed had the lowest standard deviation value of 0.121 m/s. Solar radiation and temperature had standard deviation values of 0.822 MJ/m² and 0.315˚C respectively.

Table 9 Predicted and measured values of wind speed

Year	Mean CO₂ (ppmv)	Wind speed m/s (measured)	Wind speed m/s (predicted)
2011 2012 2013 2014	382.370 385.108 388.111 392.186	2.068 2.093 2.092 2.122	2.074 2.088 2.103 2.123

Therefore relative humidity values were the most dispersed or spread out around the mean of 83.289%, while wind speed values were the least dispersed around the mean of 2.245 m/s.

The results of the descriptive statistics of annual averages of CH₄, O₃, NO₂ and CO₂ concentrations within the period of investigation revealed that the mean values were higher than the acceptable ambient values [17]. The continuous increase in concentration level of these pollutants is due to the activities of the artisanal petroleum refineries in the Niger Delta region. The emission levels of these pollutants breached FEPA and EGASPIN limits [18] [19] [20]. The results also showed that CH₄ had the highest standard deviation value of 12.500 ppmv while O₃ had the lowest standard deviation value of 1.300 ppmv. NO₂ and CO₂ had standard deviation values of 3.519 ppmv and 4.208 ppmv respectively. Therefore CH₄ concentration values were the most dispersed or spread out around the mean of 1746.193 ppmv, while O₃ concentration values were the least dispersed around the mean of 56.939 ppmv. Methane (CH₄) had higher standard deviation (S.D) values than carbon dioxide (CO₂), showing that on a per molecule basis, proportional rise in CH₄ concentration is much more efficient as a greenhouse gas than a comparable rise in CO₂ concentration [14]. However, CO₂ has a greater influence than CH₄ on climate change due to its higher atmospheric concentration.

The results of the correlation analysis between the selected meteorological parameters (dependent variables) and CH₄, O₃, NO₂ and CO₂ concentrations (independent variables) within the period of investigation as shown in Table 5, indicated that CO₂ had a strong significant positive correlation with temperature with a correlation coefficient of 0.962, while a moderate negative correlation coefficient of 0.549 was obtained for CH₄, and very weak correlation coefficients of −0.167 and 0.077 were obtained for O₃ and NO₂ respectively. Therefore, among the atmospheric pollutants postulated as influencing temperature, CO₂ appears to be the most strongly significant (P < 0.05) in explaining temperature variations in this region of Niger Delta, with a correlation coefficient of 96.2%. CH₄, O₃ and CO₂ had a moderately significant positive correlation with solar radiation with correlation coefficients of 0.661, 0.571 and 0.656 respectively, while a weak negative correlation coefficient of 0.106 was obtained for NO_2. Therefore, among the atmospheric pollutants postulated as influencing solar radiation, CH₄ appears to be the most strongly significant (P < 0.05) in explaining variations in solar radiation in this region of Niger Delta, with a correlation coefficient of 66.1%. CH₄ had a strong significant positive correlation with relative humidity with a correlation coefficient of 0.859, while moderate correlation coefficients of −0.516 and 0.646 were obtained for NO₂ and CO₂ respectively, and a weak correlation coefficient of 0.345 was obtained for O₃. Therefore, among the atmospheric pollutants postulated as influencing relative humidity, CH₄ appears to be the most strongly significant (P < 0.01) in explaining variations in relative humidity in this region of Niger Delta, with a correlation coefficient of 85.9%. CO₂ and CH₄ had a strong significant correlation with wind speed with correlation coefficients of 0.951 and −0.906 respectively, while a moderate negative correlation coefficient of 0.518 was obtained for O₃, and a weak negative correlation coefficient of 0.317 was obtained for NO₂. Therefore, among the atmospheric pollutants postulated as influencing wind speed, CO₂ appears to be the most strongly significant (P < 0.05) in explaining variations in wind speed in this region of Niger Delta, with a correlation coefficient of 95.1%.

The results of the coefficient of determination (R²) analysis revealed that Methane (CH₄) had coefficients of determination (R²) of 0.820, 0.738, 0.437 and 0.302 with wind speed, relative humidity, solar radiation and temperature respectively. This implies that CH₄ influenced 82.0% variation in wind speed, 73.8% variation in relative humidity, 43.7% variation in solar radiation and 30.2% variation in temperature in this region of Niger Delta within the period of investigation. Tropospheric ozone (O₃) had coefficients of determination (R²) of 0.326, 0.268, 0.119 and 0.028 with solar radiation, wind speed, relative humidity and temperature respectively. This implies that O₃ influenced 32.6% variation in solar radiation, 26.8% variation in wind speed, 11.9% variation in relative humidity and 2.8% variation in temperature in this part of Niger Delta within the period of investigation. Nitrogen dioxide (NO₂) had coefficients of determination (R²) of 0.266, 0.101, 0.011 and 0.006 with relative humidity, wind speed, solar radiation and temperature respectively. This implies that NO₂ influenced 26.6% variation in relative humidity, 10.1% variation in wind speed, 1.1% variation in solar radiation and 0.6% variation in temperature in this region of Niger Delta within the period of investigation. Carbon dioxide (CO₂) had coefficients of determination (R²) of 0.926, 0.904, 0.430 and 0.417 with temperature, wind speed, solar radiation and relative humidity respectively. This implies that CO₂ influenced 92.6% variation in temperature, 90.4% variation in wind speed, 43.0% variation in solar radiation and 41.7% variation in relative humidity in this part of Niger Delta within the period of investigation.

The predicted values of the meteorological parameters showed a significant level of agreement with their measured values as shown in Figures 10-13.

4. Conclusion

Changes in emission and concentration levels of atmospheric pollutants can significantly affect local climate and in some situations regional climate by means of teleconnections in the atmosphere. Among the atmospheric pollutants postulated as influencing meteorological parameters, CO₂ appears to be the most strongly significant in explaining temperature variations in this region of Niger Delta, with a correlation coefficient of 96.2% and a coefficient of determination (R²) of 0.926, implying that CO₂ influenced 92.6% variation in temperature in this part of Niger Delta within the period of investigation. The emission levels of the atmospheric pollutants breached FEPA and EGASPIN limits.

Acknowledgements

I. I. Onwosi gratefully thanks Dr. Chinonyelum Vivian Onwosi for being his source of inspiration. The authors gratefully acknowledge the valuable discussions with Emmanuel Iruka Njoku.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

Cite this paper

Onwosi, I.I. and Nymphas, E.F. (2022) Impact of Concentration Levels of Atmospheric Pollutants on Local Climate of Delta State, Nigeria. Atmospheric and Climate Sciences, 12, 421-440. https://doi.org/10.4236/acs.2022.122025

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