Industrial Air Pollutants Investigation in the Niger Delta Region of Nigeria

Oluseyi Enitan Ogunsola¹, Emmanuel Iruka Njoku^1*, Olalekan David Ayokunnu²

¹Department of Physics, University of Ibadan, Ibadan, Nigeria.
²Department of Physics, The Polytechnic, Ibadan, Nigeria.

DOI: 10.4236/oalib.1110319                

Abstract

The discovery and exploitation of crude oil in the Niger Delta Region of Nigeria by the various Petroleum Companies have greatly enhanced the nation’s economy. However, practices linked to their discovery, growth and production operations have important and detrimental local effects on the ambient atmosphere. A good understanding and quantification of the concentrations of the greenhouse gases in this region including those of CO₂, CH₄, O₃ and NO₂ as a by-product of crude oil and pollutants could assist in their mitigation. Thus, this work investigates the anomalous variation of these pollutants and their trends in the Niger Delta region to develop control strategies that will enhance the mitigations leading to air quality improvement in this region of Nigeria. The CH₄ and NO₂ data utilized in this work were sourced from the European Space Agency (ESA) for 10 years from January 2003 to December 2012. The same data period of 10 years was obtained for the tropospheric ozone (O₃) concentrations from the National Aeronautics and Space Administration (NASA), while a data period of six (6) years was obtained for CO₂ concentrations from six (6) experimental sites around the gas-flaring stations in the Niger Delta region from January 2005 to December 2010. However, 17 other sites with no gas-flaring records were selected as the control in both the Northern and Western regions of Nigeria. The analyses of the concentrations of these Pollutants were carried out using a descriptive statistical approach including regression and correlation analysis. The One-Way ANOVA was also utilized in comparing the concentrations of these pollutants in the flare region of the Niger Delta region to those of the non-flare region of Nigeria to be able to determine their statistical significance. The results of analyses showed that CH₄ concentrations were the main contributor to the air pollution problem in the Niger Delta region of Nigeria followed by CO₂. While for the non-flare stations considered, NO₂ has the highest concentration index aside from CO₂.

Keywords

Industrial Air Pollutants, Crude Oil Pollutants, Anomalous Variation, Air Quality Improvement and Air Pollution Problem

1. Introduction

The Niger Delta region of Nigeria has been identified as one of the most polluted areas on the planet earth with air pollution as one of the most serious environmental issues ravaging the region [1] . The disposal of crude oil-related gases through flaring has been a crucial issue for the Nigerian oil and gas industries due to their non-economic viability. The subsequent repercussions of flaring these gases include the harm done to the environment as a result of the production of acid rain, the greenhouse effect, global warming, and ozone depletion. Although it was anticipated that the exploitation of natural resources such as crude oil and natural gas would accelerate and maintain the local development of the economy. Moreover, fossil fuel combustion had been producing greenhouse gases with additional pollutants enhancing climate change [2] [3] . However, in the Niger Delta, gas-flaring is a substantial cause of pollution. Also, the rapid oxidation or burning of natural gas and crude oil had been releasing heat matter and gaseous particulate into the atmosphere, which is harmful to the health of ecosystems [4] [5] . Greenhouse gases, volatile organic compounds, precursor gases, toxins (including benzene, hydrogen sulfide and toluene), and black carbon are all key components of flared gases damaging and destroying the environment [5] [6] [7] [8] [9] . Nigeria was ranked the fifth internationally among gas-flaring nations in 2014 as an improvement after consistently ranking second for three decades [5] . In 1970, 99% of the gases produced in Nigeria were flared. This fell to 51% in 2001 before rebounding to 53% in 2002 [10] . Although, in the year 2004/2005 the amount of gas flared accounted for about 39% of the total gas generated with only 10% of this flared in 2018. Thus, confirming a steady drop that began in 2002 [11] . However, the Nigerian government attempted to capitalize on the use of the associated gas by constructing a Liquefied Natural Gas (LNG) facility at Bonny. Nevertheless, there are still conflicting claims about the flaring of gases, in which the year 2018 was indicated as the year with the highest volume of gas emissions since the year 2012 [12] . In essence, there are indications that the practice of gas-flaring in Nigeria is worsening rather than improving despite that gas-flaring was originally outlawed in 1984, especially with another end-to-flaring date fixed for the year 2030 [13] [14] . Thus, this work is aimed at investigating the anomalous variation of greenhouse gases and their trends in the Niger Delta region with a view to developing control strategies that will enhance the mitigations leading to air quality improvement in this region of Nigeria.

2. Materials and Methods

The data for CH₄ and NO₂ concentrations 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), for the period of ten (10) years from January 2003 to December 2012 and that of Carbon dioxide (CO₂) for the period of six (6) years from January 2005 to December 2010. These data were collected from six (6) experimental sites around the gas-flaring station in the Niger Delta with additional seventeen (17) other sites with no gas-flaring records selected as the control in the Northern and the Western region of Nigeria.

The analysis of the concentrations of these pollutants (CH₄, NO₂, CO₂ and O₃) in this region of the Niger Delta was carried out using a descriptive statistical approach including regression and correlation analysis. The One-Way ANOVA was also utilized in comparing their concentrations in the flare region (Figure 1) with that of the non-flare region used as control (Table 1 and Figure 2) in order to determine their statistical significance.

Figure 1. Map showing states in the Niger delta area with oilfields and wells concentration. Courtesy: UNDP, Niger Delta Human Development Report, 2006 [19] .

Table 1. List of control stations used in this study.

Figure 2. Map showing control stations and their pollutions emission points. (Source: NASRDA).

2.1. Regression Analysis

The linear regression involves examining the relationship between one independent variable (x) and another dependent variable (y). It is usually expressed as:

$y=a+bx$ (1)

where,

a is the intercept;

b is the regression coefficient.

The intercept could also be expressed as:

$a=\frac{{\displaystyle \sum y}-b{\displaystyle \sum x}}{n}$ (2)

While, the regression coefficient (i.e. slope or gradient) could be written as:

$b=\frac{n{\displaystyle \sum xy}-\left({\displaystyle \sum x}\right)\left({\displaystyle \sum y}\right)}{n\left({\displaystyle \sum x^2}\right)-{\left({\displaystyle \sum x}\right)}^2}$ (3)

Also, linear regression could be expressed as:

$y=a+bx+\epsilon$ (4)

where,

a is the intercept;

b is the regression coefficient;

ε is a random error component.

2.2. Trend Detection Using the ANOVA Test

The ANOVA test is a null hypothesis test used in analyzing the differences among the means of various groups. The observations in each group are independent of each other and obtained by a random sampling. The equations are generally written as:

$R^2=\frac{{\text{SS}}_{\text{R}}}{{\text{SS}}_{\text{T}}}=1-\frac{{\text{SS}}_{\text{E}}}{{\text{SS}}_{\text{T}}}$ (5)

${\text{SS}}_{\text{T}}={\displaystyle {\sum }_i{\left(y_i-\bar{y}\right)}^2}$ (6)

${\text{SS}}_{\text{R}}={\displaystyle {\sum }_i{\left({\hat{y}}_i-\bar{y}\right)}^2}$ (7)

${\text{SS}}_{\text{E}}={\displaystyle {\sum }_i{\left(y_i-{\hat{y}}_i\right)}^2}$ (8)

where,

SS_T is the total sum of squares;

SS_R is the sum of squares due to treatment;

SS_E is the sum of squares due to error.

A one-way ANOVA uses the following null and alternative hypotheses:

H₀ (null hypothesis): ${\mu }_1={\mu }_2={\mu }_3=\cdots ={\mu }_k$ (The data are normally distributed).

H₁ (The data are not normally distributed): at least one data mean is different from the rest.

3. Result and Discussion

The average mean concentration of CH₄ in Niger Delta stations is 1740.77 ppm, while that of the non-flare stations is 3.55 ppm. Meanwhile, two (2) stations (Bayelsa and Portharcourt) in the Niger Delta stations have their mean CH₄ concentrations lower than that of the mean concentration of the entire Niger Delta stations considered (Tables 2-4). Also, five (5) stations (Abuja, Kano, Nasarawa, Taraba and Jigawa) in the non-flare stations have their mean methane concentrations lower than that of the mean concentration of the entire non-flare stations considered. However, it was observed that the closer the stations are to the source point (flare site), the higher the concentrations index except in Cross River station in 2008 (1597.793 ppm) which may be attributed to instrumentation breakdown or strong meteorological conditions within the station at that year. While in the non-flare station, the concentration did not follow the same trend as was observed in the Niger Delta stations.

Tables 2-4, further show that the standard deviation (SD) for methane in Bayelsa, Rivers and Delta states are 4.96, 4.98 and 4.80 respectively, while in Kano, Nasarawa and Jigawa (northern region) with less flares activities has SD of 0.02, 0.11 and 0.02 respectively, which are lower than unity (1). Thus, SD values in the northern region are lower than those of the Niger Delta region for the

Table 2. Comparison between mean concentrations of all the pollutants in Bayelsa station and other non-flare stations.

Table 3. Comparison between mean concentrations of all the pollutants in Rivers station and other non-flare stations.

Table 4. Comparison between mean concentrations of all the pollutants in Delta station and other non-flare stations.

same period of study. In essence, gas-flaring has more impact on the production and distribution of atmospheric pollution especially methane than any other sources, as a result of uncontrolled burning of natural gas and subsequent release to the atmosphere. Similarly, the SD values of CO₂ concentrations in Bayelsa, Rivers and Delta stations are 2.10, 2.10 and 2.12 respectively, while those of Kano, Nasarawa, Taraba and Jigawa (i.e. non-flares northern region) are 1.51, 1.34, 1.42 and 1.61, respectively, showing clear evidence that the CO₂ effluence is higher in the industrialized locations. This is due to the fact that Carbon dioxide is the most abundant pollutant gas in flare sites apart from methane due to its resident time in the atmosphere [15] .

The result of ANOVA shows that CH₄ has the highest concentration index in the Niger Delta region where there was gas-flaring as compared to the stations without gas-flaring in the Western (Ogun and Lagos) and Northern (Abuja, Kano, Nasarawa, Taraba and Jigawa) locations. Thus, methane concentration is higher in the Niger Delta stations than those in the non-flare stations showing that methane is one of the major pollutants abundant in the gas flare stations (Figures 3(a)-(c)). This could be attributed to uncontrolled gas flare and some meteorological factors such as prevalent rainfall activities in the region that enhance its accumulation [16] .

Figures 4(a)-(c) show the comparison between atmospheric Ozone (O₃) in Niger Delta (Bayelsa, Rivers and Delta) stations with non-flare stations. The results of the linear trend showed that the mean concentration plots for O₃ in the non-flare stations are increasing than those in Niger Delta stations. This indicates that tropospheric ozone is more abundant in other anthropogenic sources such as fossil fuel combustion, power plant and vehicular emission than in the gas-flaring sources.

Figures 5(a)-(c) show a non-linear trend in the mean annual concentration index of NO₂. Three (3) stations in the non-flare stations (Ogun, Lagos and

Figure 3. Comparison of methane between the flare region and non flare region.

Figure 4. Comparison of ozone between the flare region and non flare region.

Figure 5. Comparison of NO₂ between the flare region and non flare region.

Taraba) showed high concentration index than those in the Niger Delta stations. The concentration of NO₂ in the Jigawa station showed low statistical significance when compared with the Rivers (Port Harcourt) station. Also, it was observed that NO₂ concentrations in Kano and Nasarawa stations are not statistically significant when compared with Niger Delta (Delta) stations.

Figures 6(a)-(c) show the comparative CO₂ concentration index between Niger Delta stations and non-flare stations. The result showed a strong statistical relationship in CO₂ concentration in both flare and non-flare stations as there is a homogeneous monotonic increase in the concentration index of CO_2. In all the stations considered. This result shows that CO₂ is one of the most abundant pollutants in the flare stations aside from CH₄ while for non-flare stations, CO₂ has the highest index followed by NO₂.

The result of the regression statistics (Tables 2-4) showed that gas-flaring has a greater influence on the concentration of CH₄ and CO₂ respectively. The increase in the gas-flaring activities seems to increase the concentration of the pollutants for they tend to accumulate near the source point but decrease in the non-flaring locations. However, the decreases in the concentrations of these pollutants are caused by wind, dry depositions and other mitigation processes.

The results of the statistical averages (Tables 5-7) of all the pollutants in Bayelsa state with their standard deviation and standard error showed that year

Figure 6. Comparison of CO₂ between the flare region and non flare region.

Table 5. Monthly averages of air pollutant concentrations in Bayelsa State with their standard deviation and standard error for the period 2003-2012.

Table 6. Monthly averages of air pollutant concentrations in Rivers State with their standard deviation and standard error for the period 2003-2012.

Table 7. Monthly averages of air pollutant concentrations in Delta State with their standard deviation and standard error for the period 2003-2012.

2012 has the highest methane effluence (1767.42 ppm) in all the stations within the years considered, while low value of the concentration was recorded in year 2006 (1722.22 ppm).

Figures 7(a)-(c) show the non-linear trends of the pollutants in Niger Delta stations and revealed that methane has the highest uniform variations among all the pollutants considered in the region because of its abundance in the flare source point [17] .

Figures 8(a)-(c) show that the concentration of methane pollutant follows a

Figure 7. Average concentrations of the pollutants in Niger Delta. (a) Bayelsa State; (b) Rivers State; (c) Delta State.

Figure 8. Trend pattern of CH₄, NO₂ and O₃ in the Niger Delta region. (a) CH₄ (ppm); (b) NO₂ (ppm); (c) O₃ (ppm).

regular increasing pattern throughout the years considered except in Cross-River state in 2008 where there is a drastic drop in the concentration, while NO₂ and O₃ followed a non-linear trend in all the stations considered.

4. Conclusion

This work utilized satellite pollution data to quantify air emissions in the Niger Delta region (flare zone) in comparison with that of the non-flared zone. The results obtained showed that gas-flaring tends to contribute significantly to air emissions in the Niger Delta region. However, it was observed that both CH₄ and CO₂ gases were the most abundant pollutants in this region due to gas-flaring. Moreover, the pollution decreases as the distance increases from the flare site. Also, O₃ increases mostly in locations with little or no gas-flaring rates than in locations that are deeply involved in the flaring cycle. It was further observed that due to vehicular emissions ozone precursors such as NO₂, carbon monoxide and volatile organic compounds are also prevalent in this region of Nigeria [18] . Likewise, NO₂ though more prevalent and dominant in the states with less flare capacity also experiences irregular patterns in concentrations. Moreover, it was observed that major cities and towns situated far away from flaring sites such as Lagos, Abuja, Kano and Ogun states are also polluted beyond the recommended limits due to pollution from diverse sources. Hence, the results of this study will further assist in the management and regulation of air pollution in Nigeria, especially in the Niger Delta region.

Conflicts of Interest

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

References

[1]	Mbachu, D. (2020) The Toxic Legacy of 60 Years of Abundant Oil. https://www.bloomberg.com/features/2020-niger-delta-oil-pollution/
[2]	Oni, S.I. and Oyewo, M.A. (2011) Gas Flaring, Transportation and Sustainable Energy Development in the Niger-Delta, Nigeria. Journal of Human Ecology, 33, 21-28. https://doi.org/10.1080/09709274.2011.11906345
[3]	Flannigan, M.D., Stocks, B.J. and Wotton, B.M. (2000) Climate Change and Forest Fires. Science of the Total Environment, 262, 221-229. https://doi.org/10.1016/S0048-9697(00)00524-6
[4]	Edino, M.O., Nsofor, G.N. and Bombom, L.S. (2010) Perceptions and Attitudes towards Gas Flaring in the Niger Delta, Nigeria. The Environmentalist, 30, 67-75. https://doi.org/10.1007/s10669-009-9244-2
[5]	Giwa, S.O, Adama, O.O. and Akinyemi, O.O. (2014) Baseline Black Carbon Emissions for Gas Flaring in the Niger Delta Region of Nigeria. Journal of Natural Gas Science and Engineering, 20, 373-379. https://doi.org/10.1016/j.jngse.2014.07.026
[6]	Giwa, S.O., Nwaokocha, C.N., Kuye, S.I. and Kayode, O.A. (2019) Gas Flaring Attendant Impacts of Criteria and Particulate Pollutants: A Case of Niger Delta Region of Nigeria. Journal of King Saud University—Engineering Sciences, 31, 209-217. https://doi.org/10.1016/j.jksues.2017.04.003
[7]	Ubani, E.C. and Onyejekwe, I.M. (2013) Environmental Impact Analyses of Gas in the Niger Delta Region of Nigeria. American Journal of Scientific and Industrial Research, 4, 246-252. https://doi.org/10.5251/ajsir.2013.4.2.246.252
[8]	Yaduma, N., Kortelainen, M. and Wossink, A. (2013) Estimating Mortality and Economic Costs of Particulate Air Pollution in Developing Countries: The Case of Nigeria. Environmental and Resource Economics, 54, 361-387. https://doi.org/10.1007/s10640-012-9598-7
[9]	Fawole, O.G., Cai, X.M. and MacKenzie, A.R. (2016) Gas Flaring and Resultant Air Pollution: A Review Focusing on Black Carbon. Environmental Pollution, 216, 182-197. https://doi.org/10.1016/j.envpol.2016.05.075
[10]	Fagbeja, M.A., Chatterton, T.J., Longhurst, J.W., Akinyede, J.O., and Adegoke, J.O. (2008) Air Pollution and Management in the Niger Delta—Emerging Issues. WIT Transactions on Ecology and the Environment, 116, 207-216. https://doi.org/10.2495/AIR080221
[11]	PWC (2019) Assessing the Impact of Gas Flaring on the Nigerian Economy. https://www.pwc.com/ng/en/assets/pdf/gas-flaring-impact1.pdf
[12]	Chimezie, I.C. (2020) Gas Flaring and Climate Change: Impact on Niger Delta Communities. Tansian University Journal of Arts, Management and Social Sciences, 6, 106-123.
[13]	Okoro, E.E., Adeleye, B.N, Okoye, L.U, and Maxwell, O. (2021) Gas Flaring, Ineffective Utilization of Energy Resource and Associated Economic Impact in Nigeria: Evidence from ARDL and Bayer-Hanck Cointegration Techniques. Energy Policy, 153, Article ID: 112260. https://doi.org/10.1016/j.enpol.2021.112260
[14]	Zabbey, N., Sam, K., Newsom, C.A., and Nyiaghan, P.B. (2021) The COVID-19 Lockdown: An Opportunity for Conducting an Air Quality Baseline in Port Harcourt, Nigeria. The Extractive Industries and Society, 8, 244-256. https://doi.org/10.1016/j.exis.2020.12.011
[15]	Obiekezie, T.N. and Agbo, G.A. (2008) Day to Day Variability of Sq (H) Variation in the Indian Sector. JANS, 3, 81-85.
[16]	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
[17]	Ogunsola, O.E. (2012) Effects of CO2 and CH4 Emissions on Climate Variability in the Tropics. University of Ibadan, Ibadan.
[18]	Abam, F.I. and Unachukwu, G.O. (2009) Vehicular Emissions and Air Quality Standards in Nigeria. European Journal of Scientific Research, 34, 550-560.
[19]	United Nations Development Programme UNDP (2006) Human Development Report: Niger Delta Human Development Report. New York. http://www.ng.undp.org/