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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 (CH
_{4}, NO
_{2} and O
_{3}) 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 CH
_{4} and NO
_{2} data utilized in this study were sourced from the European Space Agency (ESA), while that of tropospheric ozone (O
_{3}) 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 (CH
_{4}, NO
_{2} and O
_{3}) 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 CH
_{4}, NO
_{2} and O
_{3} concentrations with their previous day’s concentrations showed a strong significance in regression analysis for both CH
_{4} and O
_{3}. The coefficient of determination of CH
_{4} and O
_{3 }was obtained as 0.654 and 0.810 respectively, while a very weak correlation was obtained for NO
_{2}. However, despite that a very strong negative correlation of -0.809 and -0.900 was obtained between wind speed and both the CH
_{4} and O
_{3} pollutants respectively, a moderate correlation was obtained between the wind speed and NO
_{2}. 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 CH
_{4} and O
_{3} concentrations, but with a little influence on the NO
_{2} concentrations.

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) [

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 [

This work is focused on the Bayelsa state of Nigeria (

The CH_{4} and NO_{2} data utilized in this study were sourced from the European Space Agency (ESA), while that of tropospheric ozone (O_{3}) 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 CH_{4} and NO_{2} data utilized in this study were sourced from the European Space Agency (ESA), while that of tropospheric ozone (O_{3}) 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 (CH_{4}, NO_{2} and O_{3}) 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.

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

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 (

Years | CH_{4} (measured) | Wind Speed | CH_{4} (predicted) | O_{3} (measured) | Wind Speed | O_{3} (predicted) |
---|---|---|---|---|---|---|

2003 | 1733.685 | 4.828965 | 1735.945 | 0.005655 | 4.828965 | 0.005657 |

2004 | 1729.42 | 6.687811 | 1728.642 | 0.005602 | 6.687811 | 0.005525 |

2005 | 1729.149 | 6.676661 | 1728.685 | 0.005479 | 6.676661 | 0.005526 |

2006 | 1722.8 | 6.471819 | 1729.49 | 0.005629 | 6.471819 | 0.00554 |

2007 | 1734.205 | 6.729039 | 1728.48 | 0.005566 | 6.729039 | 0.005522 |

2008 | 1736.168 | 4.23183 | 1738.291 | 0.005741 | 4.23183 | 0.005699 |

2009 | 1741.963 | 4.02259 | 1739.113 | 0.005784 | 4.02259 | 0.005714 |

2010 | 1740.801 | 3.91419 | 1739.539 | 0.005746 | 3.91419 | 0.005722 |

SR | RH | WNDSPD | WNDDR | CC | TMIN | TMAX | |
---|---|---|---|---|---|---|---|

Methane Pearson Correlation | −0.357 | 0.236 | −0.809* | -0.332 | 0.118 | −0.219 | −0.329 |

NO_{2} Pearson Correlation | −0.412 | 0.396 | 0.213 | 0.568 | 0.375 | 0.337 | 0.181 |

OZONE Pearson Correlation | −0.310 | 0.364 | −0.900** | −0.445 | 0.018 | −0.288 | 0.059 |

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 _{4} (

Year | Mean | Standard deviation | Variance | Minimum | Maximum |
---|---|---|---|---|---|

2003 | 1733.686 | 21.807 | 475.546 | 1701.84 | 1775.74 |

2004 | 1729.420 | 13.769 | 189.583 | 1704.10 | 1747.80 |

2005 | 1729.149 | 8.200 | 67.243 | 1718.19 | 1741.13 |

2006 | 1722.800 | 14.219 | 202.175 | 1705.71 | 1754.33 |

2007 | 1732.205 | 15.786 | 249.204 | 1709.77 | 1758.43 |

2008 | 1736.168 | 14.241 | 202.814 | 1703.48 | 1754.65 |

2009 | 1741.963 | 11.775 | 138.641 | 1723.35 | 1764.90 |

2010 | 1740.801 | 7.691 | 59.146 | 1728.65 | 1756.84 |

2011 | 1755.575 | 22.918 | 525.236 | 1722.90 | 1793.17 |

2012 | 1763.733 | 14.424 | 202.045 | 1740.48 | 1785.71 |

Year | Mean (×10^{−5 }) | Standard deviation (×10^{−6}) | Minimum (×10^{−5}) | Maximum (×10^{−5}) |
---|---|---|---|---|

2003 | 5.246 | 3.141 | 4.9 | 6.0 |

2004 | 4.637 | 4.542 | 4.0 | 5.3 |

2005 | 4.307 | 4.860 | 3.9 | 5.7 |

2006 | 4.092 | 3.555 | 3.6 | 4.6 |

2007 | 3.771 | 2.708 | 3.5 | 4.4 |

2008 | 3.834 | 2.110 | 3.5 | 4.1 |

2009 | 3.752 | 1.554 | 3.6 | 4.0 |

2010 | 3.849 | 1.969 | 3.4 | 4.2 |

2011 | 3.943 | 1.611 | 3.7 | 4.2 |

2012 | 3.982 | 2.056 | 3.7 | 4.3 |

Year | Mean (×10^{−3}) | Standard deviation (×10^{−4}) | Minimum (×10^{−3}) | Maximum (×10^{−3}) |
---|---|---|---|---|

2003 | 5.655 | 3.006 | 5.243 | 6.109 |

2004 | 5.602 | 2.367 | 5.289 | 5.944 |

2005 | 5.479 | 1.948 | 5.091 | 5.732 |

2006 | 5.630 | 3.389 | 5.098 | 6.012 |

2007 | 5.566 | 2.004 | 5.251 | 5.792 |

2008 | 5.741 | 3.580 | 5.228 | 6.174 |

2009 | 5.784 | 2.742 | 5.360 | 6.132 |

2010 | 5.746 | 2.934 | 5.316 | 6.138 |

2011 | 5.789 | 2.224 | 5.434 | 6.078 |

2012 | 5.689 | 3.037 | 5.238 | 6.073 |

Year | Mean | Standard deviation | Variance | Minimum | Maximum |
---|---|---|---|---|---|

2009 | 387.149 | 28.710 | 824.269 | 372.948 | 477.398 |

2010 | 381.162 | 4.386 | 19.239 | 372.887 | 387.056 |

2011 | 382.833 | 4.778 | 22.826 | 372.579 | 388.765 |

2012 | 386.839 | 4.639 | 21.524 | 380.198 | 398.513 |

2013 | 388.241 | 4.004 | 16.035 | 382.363 | 393.243 |

Pollutants | Mean | Standard deviation | Variance | Minimum | Maximum |
---|---|---|---|---|---|

Methane | 1738.7500 | 12.50933 | 156.483 | 1722.80 | 1763.73 |

NO_{2} | 4.139 × 10^{−5} | 4.771 × 10^{−6} | - | 3.8 × 10^{−5} | 5.2 × 10^{−5} |

Ozone | 5.668 × 10^{−2} | 1.01 × 10^{−4} | - | 5.4790 × 10^{−2} | 5.7887 × 10^{−2} |

CO_{2 } | 385.245 | 3.0675 | 9.409 | 381.1618 | 388.2413 |

YEARS | CH_{4} | U(t_{i}) | U'(t_{i}) | NO_{2} | U(t_{i}) | U'(t_{i}) | OZONE | U(t_{i}) | U'(t_{i}) |
---|---|---|---|---|---|---|---|---|---|

2003 | 1733.685 | 0.154 | −0.77 | 5.25E−06 | 3.696 | −1.848 | 0.005655 | 4.466 | −2.618 |

2004 | 1729.42 | 1.848 | −0.62 | 4.64E−06 | 2.464 | −0.195 | 0.005602 | 2.926 | −2.464 |

2005 | 1729.149 | 0.154 | −0.62 | 4.31E−06 | 1.386 | −1.694 | 0.005479 | 5.389 | −3.542 |

2006 | 1722.8 | 2.002 | −0.154 | 4.09E−06 | 3.079 | −1.232 | 0.005629 | 2.772 | −2.772 |

2007 | 1734.205 | 1.539 | 0.308 | 3.77E−06 | 0.769 | −0.769 | 0.005566 | 4.004 | −2.156 |

2008 | 1736.168 | 0 | −1.694 | 3.83E−06 | 2.926 | −1.078 | 0.005741 | 4.158 | −2.772 |

2009 | 1741.963 | 3.079 | −1.232 | 3.75E−06 | 2.156 | −0.616 | 0.005784 | 4.928 | −3.388 |

2010 | 1740.801 | −0.154 | −2.309 | 3.82E−06 | 1.848 | −1.848 | 0.005746 | 3.388 | −2.002 |

2011 | 1755.575 | 0.769 | 1.078 | 3.94E−06 | 0.154 | −0.616 | 0.005789 | 3.542 | −3.079 |

2012 | 1763.733 | 0.616 | −0.616 | 3.98E−06 | 0.769 | −1.848 | 0.00569 | 3.849 | −3.388 |

YEARS | CO_{2} | U(t_{i}) | U'(t) |
---|---|---|---|

2009 | 387.1489 | 1.694 | −1.078 |

2010 | 381.1618 | 1.539 | −1.232 |

2011 | 382.8326 | 0.769 | 1.078 |

2012 | 386.8387 | 2.309 | 0.924 |

2013 | 388.2413 | 1.386 | −1.078 |

Y = a + b X (1)

where,

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} (

Y = a + b X (2)

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).

_{4}, NO_{2}, O_{3} and CO_{2}. While _{2} and average temperature.

The Mann-Kendal rank statistics showed that the standardization variables U'(t_{i}) 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 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 (

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 (

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.

_{4} and NO_{2} concentration decreases with increasing temperature, while Ozone_{ }concentration is the opposite in which it increases as temperature also increases (_{3 }and CH_{4}) concentrations (P < 0.01 for O_{3} and CH_{4}) (_{3}, NO_{2} and CH_{4} concentrations_{ }in the region of the Niger Delta, giving as high as 81% and 64% for O_{3} and CH_{4} respectively (_{2} (

Wind speed, temperature, and solar radiation are effective meteorological variables in decreasing CH_{4 }concentration. Solar radiation is also effective meteorological variable in decreasing NO_{2} concentration. Wind speed, solar radiation, wind direction, and minimum temperature are effective meteorological variable in decreasing O_{3,} concentration. Whereas, maximum temperature, relative humidity and cloud cover promotes O_{3 }concentration although it is only the effect of wind speed that is strongly significant (P < 0.01) [

The spatial and temporal distribution of daily CH_{4} (Methane), NO_{2} (nitrogen dioxides) and O_{3} (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 CH_{4} and O_{3} pollutants respectively, and a moderate correlation was obtained between the wind speed and NO_{2}. 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 CH_{4} and O_{3} concentrations, but with a little influence on the NO_{2} concentrations.

The authors declare no conflicts of interest regarding the publication of 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