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Relationship between PM2.5 and Climate Variability in Niger Delta, Nigeria

V.O. Shaibu*, V.E. Weli

Department of Geography and Environmental Management, University of Port Harcourt, Port Harcourt, Nigeria

Abstract: Fine particulate matter PM2.5 has attracted much attention both scientific and public, due to its effects on human health as meteorological conditions serves as one of the factors that have important effect on PM2.5 mass concentration. The study examined the relationship between certain meteorological elements and PM2.5 concentration in selected cities of the Niger delta region of Nigeria. Meteorological data were obtained from the Nigerian Meteorological Agency. PM2.5 data that was used for this study was Aerosol Optical Depth (AOD), it was acquired from remotely sensed satellite data from National Aeronautics and Space Administration (NASA’s) earth observing system data and information system, PM2.5 concentration data and meteorological monthly data were obtained from 2001 to 2015 and multiple regression analysis was employed to test the relationship between PM2.5 concentration and the meteorological elements (Temperature, Rainfall, Relative Humidity and Wind Speed). The correlation analysis result showed that temperature had a positive correlation, rainfall had a negative correlation and wind speed also had a positive but low correlation. This has an effect on PM2.5 concentration because as temperature increases and rainfall decreases with low wind speed, PM2.5 concentration increases and this can lead to adverse health effects on human beings.

Keywords: Niger delta, climate variability, PM2.5, multiple regression

Citation: V.O. Shaibu, V.E. Weli, Relationship between PM2.5 and Climate Variability in Niger Delta, Nigeria. American Journal of Environmental Protection, 5 (1). 20-24. January 2017. doi:10.12691/env-5-1-4

1. Introduction

Weather and climate elements are steadily changing. Variations in these elements can be day to day for weather, and over seasons and years for climate; this can serve as the main drivers of climate-induced changes in particulate matter (PM). Climate Variability refers to variations in the mean state of the climate on all temporal and spatial scales beyond that of individual events. The time scale could be in months to years [1]. Particulate matter is a complex mixture of anthropogenic, biogenic, and natural materials, suspended as aerosol particles in the atmosphere with major components as sulphate, nitrate, ammonium, organic carbon, elemental carbon, sea salt, and dust [2]. PM is a primary air pollutant and includes all solids and/or liquids suspended in the atmosphere and may or may not be visible as soil particles, soot and lead [3]. PM2.5 or respirable particles because they are small enough to be inhaled and have the potential to cause health effect; they penetrates the respiratory system further than larger particles, it is made up of sulphate and nitrate particles, elemental and organic carbon and soil. PM2.5 material is primarily formed from chemical reactions in the atmosphere and through fuel combustion (motor vehicles, power generation, industrial facilities, residential fire places, wood stoves and agricultural burning). Exposure to fine PM has been associated with hospital admissions, asthma, cardiovascular or lung disease including premature death.

Many aspects of climate variability have the potentials of influencing PM, air pollution and its implications for environmental management in Niger delta Nigeria over the coming decades. The varying climate may affect PM directly through changing meteorology and circulation patterns, and indirectly by affecting wildfires, dust suspension, biogenic emissions [4]. Changes in human activities and the energy generation mix will also affect PM concentrations, through emissions of PM and its precursors, and also through changes to the natural systems that will alter emissions [4]. Climate variability could affect local to regional air quality directly through changes in chemical reaction rates, boundary layer heights that affect vertical mixing of pollutants, and changes in synoptic airflow pattern that govern pollutant transport [5]. Indirect effects could result from increasing or decreasing anthropogenic emissions via changes in human behavior or from altering the level of biogenic emissions because of higher temperatures and land cover change; higher temperatures can increase emissions of isoprene (a volatile hydrocarbon emitted by many woody plant species).

Air pollution has intensified strongly since the industrial revolution, that is, during the epoch known as the Anthropocene [6]. Ground-level fine PM with a diameter of 2.5 micron has increased substantially, not only in most urbanized and industrialized areas but also in rural and even remote regions [7,8,9]. PM2.5 can have serious health impacts by causing cardiovascular and respiratory disease and lung cancer, and especially chronic exposure is associated with morbidity and premature mortality [10,11]. Urban PM2.5 exposure is responsible for approximately 712,000 cardiopulmonary disease (CPD) and 62,000 lung cancer deaths in 2000 [12], while anthropogenic PM2.5 is associated with 3.5 million CPD and 220,000 lung cancer mortalities annually [9].

In Nigeria, almost the entire country has PM2.5 concentration above the WHO guideline of 25µg/m³ (24 hour mean) and 10µg/m³ (annual mean) [13] this presents an environmental health burden in relation to potential risk of continuous exposure to dangerous level of PM2.5 [3].

[14] applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables, They found strong positive correlations of PM2.5 components with temperature in most of the US.

[15,16,17] investigated the relationship between PM2.5 and climate variables and reported that temperature, rainfall, wind speed and relative humidity have a clear influence on PM2.5 concentration and distribution; and the effects of increased temperature were an increase in PM2.5 concentrations.

The relationship between PM2.5 and climate variables leads to the prevailing increase in temperature over the other climate variables confirmed that PM2.5 was rather uncomfortable at certain times of the year (dry season), and that temperature mostly influenced the formation of PM2.5 while wind speed also influenced the transportation of winds. People with prolonged exposure or activities under this condition are in danger of health hazards. [18] used Statistical Regressions to identify factors influencing PM₂.₅ concentrations in the Pittsburgh supersite as a Case they found that temperature, relative humidity, their squared terms, and their interactions explain much of the variation in airborne concentrations of PM₂.₅ in the city. [19] investigated the influence of present climate on PM2.5 concentrations over Europe by representing it using a weather regimes/types approach. They started by exploring the relationships between classical weather regimes, meteorological variables and PM2.5 concentrations over five stations in Europe, using the EMEP air quality database. The pressure at sea level was used in the classification as it effectively describes the atmospheric circulation. The result showed that rain rate is the variable that impacts PM2.5 concentrations the most.

2. Study Area

The study area is the Niger Delta region of Nigeria with latitude 4.05°N to 7.55°N and longitude 4.20°E to 9.30°E (Figure 1). The area around this coastline is interrupted by series of distributaries that form the Niger Delta swamp at the middle where the lower Niger River system drains the waters of Rivers Niger and Benue into the Atlantic Ocean. This delicate mangrove swamp of the Niger Delta covers a coastline of over 450km, about two-thirds of the entire coastline of Nigeria and the wetland in this region is traversed and criss-crossed by a large number of rivers, rivulets, streams, canals and creeks. The Niger Delta is a rich mangrove swamp in the southernmost part of Nigeria within the wetlands of 70,000km² formed primarily by sediment deposition. It is the largest mangrove swamp and wetland in Africa, maintaining the third largest drainage basin in the continent, and is also the third largest wetland in the world after Holland and Mississippi [20].

Figure 1. Niger Delta Region showing States and their Capital Cities

2.1. Climate

The Niger delta lies mainly in the wet equatorial climate region (Koppen’s Af climate) but in the northern extremities, the climate is tropical wet-and-dry climate (Koppen’s Aw climate). As a result of the nearness of this region to the equator, cloud cover is very high, sunshine hours are low and the air is damp for most of the year due to the very high relative humidity of the air. The climate of the Niger delta is characterized by a long rainy season from March-April through October. Precipitation increases from the north of the delta (with an average of 2,500 millimeters) to the coastal area where mean annual rainfall averages around 4,000 millimeters (mm), making it one of the wettest areas in Africa. The wet season peaks in July, and the only dry months are January and February. However, even during this dry period an average monthly mean of 150 mm rainfall is recorded. Relative humidity rarely dips below 60% and fluctuates between 90% and 100% for most of the year. During most of the rainy season cloud cover is nearly continuous resulting in 1,500 mean annual sunshine hours and an average annual temperature of approximately 28°C. The most important determinant of biological variation in the delta is its hydrology. In addition to precipitation, the major variation in the hydrological regime comes from the Atlantic Ocean's tidal movements and the Niger River flood. This flood begins toward the end of the rainy season in August, peaks in October, and tapers off in December. Some fluctuation in flow is determined by the yearly variation in rainfall, but after the completion of the Kainji dam on the Niger at Bussa in 1968 the timing and level of flooding is also determined by the opening and closing of the dam's sluices [21].

2.2. Land Use

The Niger delta is resource-rich and abundantly blessed with expanse of agricultural/aquatic resources and vast reserves of petroleum hydrocarbon [22]. Most of Nigeria’s more than 600 oil fields are in the Niger delta (60% onshore), with a proven oil reserve of over 35 billion barrels and production rate of 2.5 million barrels a day [23]. Over time, this region has played important roles in the global economy (through palm oil trade and now fossil fuels export) and documented human economic activities in the Niger delta dates back to more than a century [24]. At the lowest levels of society, inhabitants of this region eke out their living by subsistent harvesting of natural resources (fishes, forest products, backyard farms). At higher levels, resource-exploitation takes the form of profiteering and range from profitable plantation farming to petroleum hydrocarbon exploitation.

3. Materials and Method

3.1. Climate Data

The data for climatic variables was obtained from the Nigerian Meteorological Agency (NIMET), Oshodi, Lagos which is the sole custodian of climate data of Nigeria. The climate data that was used for this study are Air Temperature, Rainfall, Relative Humidity and Wind Speed from 2001 to 2015. The choice of these climatic variables that were used as drivers was based on their importance and effectiveness in the formation, concentration, transportation and variability of particulate matters.

3.2. PM2.5 Data

The type of PM2.5 data that was used for this study was Aerosol Optical Depth (AOD), it is raster in nature, this dataset was acquired from remotely sensed satellite data from National Aeronautics and Space Administration (NASA’s) earth observing system data and information system, this dataset was used because it is readily available and has global coverage. This dataset represents latest advances in the field of remote sensing and environmental modeling. Data for the study area was derived from this dataset and MODIS sensor, located on the Terra and Aqua satellite platforms, which has 36 spectral channels. This MODIS instrument onboard the NASA’s Terra satellite global observations of AOD, is a measure of light extinction by aerosol in the atmospheric column above the earth’s surface. AOD reflects aerosol optical extinction of the total column; High AOD values imply very high levels of air pollution and associated negative impact on human health, while low AOD values represent good air quality [25]. MODIS is a multi-spectral radiometer, designed for the retrieval of aerosol microphysical and optical properties over land and ocean, it was also designed to provide a wide variety of information about land, ocean and atmospheric conditions, MODIS has good spatial and temporal resolutions; it has a large number of spectral bands and has the ability to use this spectral information to derive biogeophysical results. Other data required were obtained from journals, textbooks and other relevant literatures.

The Satellite PM2.5 data that was used for this study is a new technology; this dataset provides higher accuracy, longer temporal range and higher resolution. The data was from collection 6 and it is level 2 Aqua Terra MODIS collected at 5 minutes interval daily, this was averaged to obtain monthly averages for each year. The global annual average PM2.5 gridded datasets from MODIS from 2001-2015 was downloaded from SEDAC, the PM2.5 that was used for the study was a finer resolution of 3km because the study is on a regional scale not on a global scale but both algorithms are similar. A Python script was written to extract the monthly averages across the study area over the years after downloading the data.

The [26] formular (PM2.5 = n * AOD) was used to derive the PM2.5, so we divided the satellite derived AOD by the SEDAC PM2.5 values which gave us n (conversion factor). To get a long term average we computed 10 year mean and used the value to compute the PM2.5 for the study area. The Niger Delta annual average PM2.5 gridded datasets for the period 2001-2015 were extracted by ArcGIS software and were transformed to the same coordinate system as the meteorological datasets. ArcGIS 10.2 software was preferred for this analysis because of its versatility, functionality in the sense that it has some extensions that are operational which are not found in ArcGIS 9 versions, its inter operationality which means it can accept data from other softwares like Ms Access and Ms Excel (Data Integration) and its robustness.

Multiple regression analysis was used to model the relationship between PM2.5 and the four climatic variables. This was used to quantify the relationship between climatic variables and PM2.5 across the selected cities of the Niger Delta which was concerned with measuring the effect of numerous independent variables on a single dependent variable. Multiple Regression analysis was used for this study to know the influence of temperature, wind speed, rainfall and relative humidity on PM2.5 to see which of these parameters relate more with or have more influence on PM2.5 concentration and then possible autocorrelation.

[picture] | (1)

a = Intercept on Y- axis

b₁, b₂, b₃…...bn = the regression coefficients of independent variables.

X₁ X₂ X₃…...Xn = the independent variables

Where: X₁ = Temperature, X2 = Precipitation, X₃ = Relative Humidity, X4 = Wind Speed.

e = the residual or random error term.

4. Results and Discussion

It has been shown in Table 1 that across the cities of the Niger Delta from 2001-2015, the mean Temperature is 27.5°C, mean Rainfall is 123.5mm, mean Relative Humidity is 78.6% and Wind Speed is 3.5m/s, also it was identified that rainfall has the highest mean (123.526mm)/standard error (4.198), standard deviation (119.780) and variance (14347.228).

The coefficients were used to write regression equations for each models as follows:

Model 1: Y = a + bT; where T= Temperature

When the expected Temperature value is 27.461, then the expected PM2.5 is

Also, if the expected Temperature value is 30, then the expected PM2.5 is

Model 1 showed that as temperature increases, PM2.5 concentration increases across the cities of the Niger delta. Temperature has a high influence on the concentration of PM2.5 across the cities Niger delta.

Model 2: Y = a + bT + cR; where T=Temperature, R= Rainfall. When the expected Temperature and Rainfall values are 27.461 and 123.526 respectively. Then the expected PM2.5 is:

Model 2 showed that rainfall had a minimal influence on PM2.5 concentration; therefore, an increase in temperature with a decrease in rainfall increases PM2.5 concentration.

Model 3: Y = a + bT + cR + dW.; where T= Temperature, R= Rainfall, and W= Wind speed. When the expected Temperature, Rainfall and Wind Speed values are 27.46, 123.526 and 3.518 respectively. Then the expected PM2.5 is

Model 3 showed that an increase in temperature increases PM2.5 concentration, a decrease in rainfall increases PM2.5 concentration and an increase in wind speed increases PM2.5 concentration. This confirmed that temperature is most significant in PM2.5 concentration.

Table 1. Summary Statistics of the Parameters used for the Analysis

Table 2. R Square Value for the Three Models Identified by Stepwise Techniques

5. Conclusion

The increase in rate of PM2.5 concentration and high temperature had its attending effect on both the environment and the health of residents thereby causing hospital admissions, asthma, cardiovascular or lung disease including premature death. People with asthma, cardiovascular or lung disease, as well as children and elderly people, are considered to be the most sensitive to the effects of fine (PM).

The general air temperature and PM2.5 concentration confirmed that Niger delta region is rather uncomfortable at certain times of the year, and people in this region will be in danger of hospital admissions, asthma, cardiovascular or lung disease including premature death with prolonged exposure or activity under this condition. Adequate space management and planning is therefore sure for averting PM2.5 related disasters in the cities. This is particularly important as the issues of global warming are at the front burner of environmental sustainability.

Acknowledgements

This study was supported by the Nigerian Meteorological Ageny Oshodi, Nigeria.

References

[1]   | IPCC, Climate Change: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Third Assessment Report. J J McCarthy, O Canziania, N A Leary, D J Dokken and K S White. Cambridge, Cambridge University Press for Intergovernmental Panel on Climate Change. 2001b.
[2]   | John, P.D., Bryan, J.B., Darrel, A.W., and Christopher, P.W., “Understanding the Meteorological Drivers of U.S. Particulate Matter Concentrations in a Changing Climate,” Journal of American Meteorological Society, 521-532. 2014.
[3]   | Lawal, O. and Asimiea, A.O., “Spatial Modelling of Population at Risk and PM2.5 Exposure index”: A Case Study of Nigeria. Ethiopian Journal of Environmental Studies & Management, 8 (1): 69-80. 2015.
[4]   | U.S EPA: Assessment of the impacts of global change on regional U.S. air quality: A synthesis of climate change impacts on ground-level ozone (an interim report of the U.S. EPA Global Change Research Program). U.S. Environmental Protection Agency Rep. EPA/600/R-07/094F, 131. 2009a
[5]   | Kristie, L. E. and Glenn, M., “Climate Change, Tropospheric Ozone and Particulate Matter, and Health Impacts”: Environmental Health Perspectives. 116 (11). 2008.
[6]   | Crutzen, P.J., “Geology of mankind” Nature, 415, 23. 2002.
[7]   | Akimoto, H., “Global air quality and pollution”, Science, 302, 1716-1719. 2003.

[8]  
Schulz, M., Textor, C., Kinne, S., Isaksen, I.S.A., Iversen, T., Koch, D., Kirkevag,A., Liu, X., Montanaro, V., Myhre, G., Penner, J.E., Pitari, G., Reddy, S., Seland, O., Stier, P., and Takemuna, T., “Radiative forcing by aerosols as derived from the AeroCom present day and pre-industrial simulations” Atmos. Chem. Phys. 6, 5225-5246. 2006.

[9]   | Anenberg, S.C., Horowitz, L.W., Tong, D.O., and West, J.J. “An estimate of the global burden of anthropogenic ozone and fine particulate matter on premature human mortality using atmospheric modeling”. Environ. Health Persp, 118, 1189-1195. 2010.
[10]   | Dockery, D.W., Pope, C.A., Xu, X., Spengler, J.D., Ware, J.H., Fay, M.E., Ferris Jr, B.G., and Speizer, F.E., “An association between air pollution and mortality in six U.S. cities”. N. Engl. J. Med, 329, 1753-1759. 1993.
[11]   | McDonnell, W.F., Nishino-Ishikawa, N., Petersen, F.F., Chan, L.H., and Abbey, D.E., “Relationships of mortality with the fine and coarse fractions of long-term ambient PM₁₀ concentrations in nonsmokers”, J. Expo. Anal. Env. Epid, 10, 427-436. 2000.
[12]   | Cohen, A.J., Anderson, H.R.,Ostro, B., Pandey, K.D., Krzyzanowski, M., Kunzli, N., Gutschmidt, K., Pope, C.A., Romieu, I., and Samet, J.M., “Urban air pollution”, Comparative Quantification of Health Risks. 2, 1353-1433. 2004.
[13]   | WHO, “WHO Air quality guidelines for PM, Ozone, Nitrogen dioxide and Sulphur dioxide”: Global Update, summary of risk assessment. 2005.

[14]  
Tai, A.P.K., Mickley, L.J., Jacob, D.J., Leibensperger, E.M., Zhang, L., Fisher, J.A., and Pye, H.O.T., “Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change”. Atmos. Chem. Phys. 12, 3131-3145. 2012.

[15]   | Pohjola, M., Rantamaki, M., Kukkonen, J., Karppinen, A., and Berge, E., “Meteorological evaluation of a severe air pollution episode in Helsinki on 27-29 December 1995”, Boreal Environ. Res., 9. 75-87. 2004.
[16]   | Dawson, J.P., Adams, P.J., and Pandis, S.N., “Sensitivity of ozone to summertime climate in the eastern USA”: A modeling case study. Atmos. Environ, 41. 1494-1511. 2007.
[17]   | Chen, L., Watson J., Chow, J., Green, M., Inouye, D., and Dick, K., “Wintertime particulate pollution episodes in an urban valley of the Western US”: a case study. Atmos. Chem. Phys., 12 (21): 10051-10064. 2012.
[18]   | Nanjun, C., Joseph B.K., and Cliff I.D., “Using Statistical Regressions to Identify Factors Influencing PM2.5 Concentrations”: The Pittsburgh Supersite as a Case Study. Aerosol Science and Technology, 44(9). 766-774, 2010.
[19]   | Lecoeur, E., Seigneur, C., Terray, L., and Pagé, C., “Influence of Climate on PM2.5 Concentrations over Europe”: a Meteorological Analysis using a 9-year Model Simulation. Geophysical Research Abstracts. 14. EGU2012-1420. 2012.
[20]   | Ekubo, A., and Abowei, J., “Aspects of Aquatic Pollution in Nigeria”, Research Journal of Environmental and Earth Sciences, 3(6). 673-693. 2011.
[21]   | McGinley, M., “Conservation Biology Biodiversity and Ecology in The Encyclopaedia of Earth”. World Wildlife Fund. 2014.
[22]   | Irikana, G.J., “Mangrove resources utilization in Nigeria: An analysis of the Andoni mangrove resources crisis”. Sacha, J. Env. Stud, 1(1). 49- 63. 2011.
[23]   | Egberongbe, F.O.A., Nwilo, P.C., and Badejo, O.T., “Oil Spill Disaster Monitoring Along Nigerian Coastline”. Promoting Land Administration and Good Governance 5th FIG Regional Conference Accra, Ghana, March 8-11. 2006.
[24]   | Enemugwem, J.H., “The Development of the Niger Delta of Nigeria 1900-1966”. J. Sust. Dev. Afr, 10(4). 162-178. 2009.
[25]   | Cao, C., Zheng, S., and Singh, R. P., “Dust Event of April 23-24, 2014 in the Northwest China and Changes in Air Quality, Atmospheric and Meteorological Parameters”. American Geophysical Union, Fall Meeting, 2014.
[26]   | Van Donkelaar, A., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., and Paul J. Villeneuve, P.J., “Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth”: Development and Application, Environ Health Perspect. 118(6). 847-855. 2010.