A Preliminary Assessment of Soil Samples Around a Filling Station in Diobu , Port Harcourt , Rivers State , Nigeria

This research study is carried out to evaluate the effects of fuel station on surrounding soil. Twelve (12) soil samples were collected at intervals of four years (2000 and 2004) and were analyses. Generally, in both years, strong correlation coefficients suggest anthropogenic inputs while weak to moderate correlations implies natural sources. The 2000 and 2004 data also revealed higher concentrations at 2000 than 2004. This observation could be due to dilution, diffusion and dispersion over the period. The data acquired were subjected to multivariate statistical analysis and Contamination Factor (CF) index calculation. Both factor and cluster analyses show that natural and anthropogenic influences contributed to the variable sources. Directly linked to the fuel station were TOC, N, OG, Pb, Ni and Mn while PO4 and NO3 were related to chemical fertilizer application on farmlands. Other variables such as Cu, Ca, Mg, Zn and Fe may be due to natural processes. Contamination Factor (CF) for Zn, Fe and Cu in both years suggests some degree of enrichment. While the rest heavy metals recorded low contamination, Fe revealed moderate contamination.


INTRODUCTION
Soils constitute part of the vital environmental, ecological and agricultural resource that has/must be protected for sustainable development and for generations yet unborn.The determination of elemental and organic status of cultivated lands is necessary to identify yielding limiting deficiencies/enrichment of essential micronutrients, polluted soils and groundwater resources.This is important in Diobu because the inhabitants are mostly farmers and fishermen.
This study focuses on the organic constituents of oil and gas related activities and the heavy metals associated with it.The potential for these to constitute pollution nuisance in the area is high.The availability of these heavy metals and the presence of factors capable of mobilizing, distributing and storing them in pedologic system are critical.The heavy metals and other organic constituents of oil and gas have been implicated in various disease conditions in many other areas (Adaikpoh et al., 2005).There are concerns about the potential harmful effects of these heavy metals in soils in the area (Adaikpoh et al., 2005).So far, studies on heavy metals and organic constituents of oil and gas has been scanty, local and therefore, there is the need for regional update and integration to determine the geological and geochemical affinities of these oil and gas constituents and their consequences on soil at higher/ critical concentrations.
The objective of study is to determine the various constituents of oil and gas due to the fuel station, distribution and concentrations by comparing acquired values with standards.
Location of the study area and geology: Diobu is a district in Port Harcourt, Southern Nigeria and located within the Niger Delta Basin, delimited by Latitude 4° 40'N and 5° 00'N and Longitude 6° 45'E and 7° 10'E (Fig. 1).The area lies within the subequatorial wetland climate that spreads across a number of ecological zones.
Niger Delta consists of three dichronous units, namely from bottom, the Akata, Agbada and Benin Formations (Olobaniyi et al., 2007).The study area is underlain by the Miocene-Recent Benin Formation.The formation is aquiferous and is probably the most prolific groundwater producer in Southern Nigeria (Oteze, 1981;Ofodile, 1992;Ofoma et al., 2005).The formation which is about 2100m thick at the basin centre generally consists of unconsolidated and friable sandy beds with intercalation of gravely units and clay lenses (Olobaniyi et al., 2007).The upper section of the formation is the quaternary deposits which is about 40-150 m thick and comprises rapidly alternating sequences of sand and silt/clay with the later becoming increasingly more prominent seawards (Etu-Efeotor and Akpokodje, 1990;Ofoma et al., 2005).

MATERIALS AND METHODS
To evaluate the level of effects of the filling station on soils, a total of 12 soil samples were collected radially over intervals of few meters from the filling station between the period of 2000 and 2004.
Soil sample collection: Sample points were located and recorded using GPS (Fig. 2).The samples were collected radially from the filling station and evenly distributed around the filling station.The soil samples were sun-dried, disaggregated (not crushed) using a pestle and mortar and sieved to min 80 meshes (0.177 mm) with cellulose nitrate filter.1.0 g of each sample was digested with 3 mL of 1: 2 mixtures of perchloric acid and hydrofluoric acid.The concentrations of seven heavy metals were determined by AAS.Analytical procedures, operational parameters, calibration and standardization used in this study are according to APHA (2002).All analyses were performed in Fugro Nig. Ltd.Laboratory, Port Harcourt, Nigeria.
Analytical methods: Insitu measurements of pH and Ec were determined intrusively with appropriate probes.Spectrophotometer (Model Genesys 20) was used to determine the concentrations of Ca.Atomic absorption spectrophotometer (Model 210 VGP) was used to determine the concentrations of Ni, Cr, Pb, Zn, Mg, Mn and Fe.PO 4 2-and NO 3 -were determined by titration while TOC and N were analysed using CHN/S analyzer and OG was determined using standard methods (APHA, 2002).
SPSS 15.0 was used to perform all data analysis after auto-scaling for all parameters.Mathematically, PCA and PFA involve the following five major steps: • Code variables to have zero means and unit variance.• Calculate covariance matrix • find eigenvalues and corresponding eigenvectors • Discard any component that account for small proportion of variation in data set • Develop the factor loading matrix and perform varimax rotation on the factor loading matrix to infer the principal parameters (Afshin and Moore, 2007;Ata et al., 2009).
In this study, only components or factors exhibiting an eigenvalue greater than one were retained.

Hierarchical cluster analysis:
Cluster analysis comprises of a series of multivariate methods used to find the true groups of data (Praveena et al., 2007).Objects are grouped such that similar objects fall into the same class.Hierarchical clustering which joins the most similar observations and successively the next most similar observations was employed.The levels of similarity at which observations are merged were used to construct dendrogram.The squared Euclidean distance method is used to construct dendrogram.A low distance shows that the two objects are similar or close together whereas a large distance indicates dissimilarity (Praveena et al., 2007;Reghunath et al., 2002).

Factor analysis:
The raw data were treated first to Zscale transformation for standardization.Multivariate data analysis was utilized to identify the correlations among the measured parameters.Principal component analysis was used to reduce the number of input variables.Spearman's correlation matrix was performed to illustrate the correlation coefficients among the variables (Aprile and Bouvy, 2008).

Determination of contamination factor:
To evaluate the degree of contaminants in the soils, CF were computed for each location relative to the abundances of species in source materials to the control/background value and the following equation as proposed by Hakanson (1980) and used by Mohiuddin et al. (2010) and Sekabira et al. (2010) was employ to assess degree of contamination, understand the distribution of elements of anthropogenic origin.The CF = C metal concentration /C background value .
In 2004, mean concentration trend of the heavy metals were Fe (816.33 mg/L) >Zn (32.70 mg/L) >Cu (29.55 mg/L) >Mn (13.95 mg/L) >Ni (0.04 mg/L) > Cr/Pb (0.02 mg/L).The cations and anions varied thus: Ca (492.0 mg/L) >Mg (73.0 mg/L) and anions NO 3 (40.98mg/L) >PO 4 (0.38 mg/L).The organic components recorded thus: OG (33.08) >TOC (0.86) >N (0.04).The soil percentages recorded this order:  From all variables analyses, mean concentrations were higher in 2000 than in 2004.This trend may not be unconnected with dissolution, diffusion and dispersion over time (Navarro et al., 2008).Apart from this, sand constitutes over 80% of soil body and as such very low retentive capacity.This means significant vertical infiltration and dispersion processes are the dominant factors (Navarro et al., 2008).Expectedly, the pH values increases during this period due to increased oxidation from surface environment (Chakravarty and Patgiri).From the filling station outwards, variables did not show a distinctive order of concentrations.The significantly higher Fe concentrations could be due to tropical climatic conditions, pyrites and marcasites associated with local sale found in the study area (Nganje et al., 2010;Adaikpoh et al., 2005).
In 2004, the varimax rotated factor analysis generated four factors.Factor 1 has eigenvalue of 5.310 and variance of 27.950%.Factor 1 consists of high factor loadings of Pb, pH, Ni, Ec and moderate loadings of PO 4 , Zn, Mn, Ca and Cr.Ni has inverse relation with other variables.Factor 2 is an association of high factor loadings of Mg, silt, Cr, Fe, Ca and sand.Factor 2 has eigenvalue of 4.795 and variance of 25.238%.Factor 3 recorded high factor loadings of NO 3 , TOC, N, Mn and Cu; moderate loading of Zn and weak loading of Fe.It recorded eigenvalue 000000of 4.671 and variance of 24.582 %.Factor 4 yielded high loadings of clay, OG; moderate loading of PO 4 and weak loadings of Cu and sand.Factor 4 has eigenvalue of 3.236 and total variance of 17.031% (Table 6).R-mode cluster analysis of 2000 soils yielded four clusters.Cluster 1 is an association of TOC, N, Ec, clay, OG, Cu and silt.Maximum similarities were observed between TOC, N and Ec in cluster 1. Cluster 2 consists of PO 4 , Zn, Pb, sand, Mn and Cr.Attached to cluster 2 at a farther distance is cluster 3, which consists uniquely of NO 3 .This uniqueness could imply different factor from factors affecting other variables.Cluster 4 In 2004, three clusters were obtained from R-mode analysis.Cluster 1 consists of TOC, N, NO 3 , Cu, sand, Mg, Cr, Fe and Ca with maximum similarities observed between TOC and N followed by Cr and Fe.Cluster 2 revealed maximum similarities between OG and clay; Ec and Pb; pH and Mn and PO 4 and Zn.This cluster consists of OG, clay, Ec, Pb, pH, Mn, PO 4 and Zn.Cluster 3 consists of Ni and silt at the farthest Euclidean distance.Though Ni and silt in the same cluster, greatest dissimilarity was observed between them (Fig. 3).
In 2004, Ni, Cr, Pb, Zn, Mn and Cu revealed low contamination in all locations (CF<1) while only Fe recorded moderate contamination (1≥CF≥3).When compared, 2000 CF values were higher in all sampled points than 2004 and particularly with respect to Fe, Zn, Cu and Mn (Fig. 5 and Table 8).

DISCUSSION
Between the period under study, mean concentrations of the variables were higher in year 2000 than 2004 (Table 1 and 2; Fig. 4 and 5).This may be due to dissolution, diffusion and dispersion from the source over time (Navarro et al., 2008).The composition of the soil is significantly made up of sand.This means low retentive capacity of soil and more of infiltration and lateral dispersion (Chakravarty and Patgiri, 2009).
In 2000, R-mode factor analysis yielded four factors.Factor 1 which consists of high factor loadings of TOC, N, Ec, Cu, clay, OG and Pb are directly related to activities arising from the fuel station and hence anthropogenic (Yang et al., 2009).The OG, TOC and N are plants matter related to fuel station.Ca, Zn have weak loadings and are not directly linked to the fuel station.The Ec high loading could be attributed to other variables which were retained by the presence of clay.Factor 2 is also anthropogenic.While Ni, Pb, Mn and Cr may be due to fuel station, NO 3 and PO4 may have arisen from chemical fertilizer/or phosphate mineral dissolution.The pH was influenced by the presence of PO 4 and NO 3 (Tauhid et al., 2008;Armah et al., 2010).Factor 3 on the other hand was naturally influenced given the high prevalence/association of Fe, Mg, Ca and clay in the area.Factor 4 was also anthropogenic in nature.While NO 3 could be due to fertilizer/manure applications, which is associated with pH, OG is linked directly to the fuel station.The Cu and Ca may or may not be associated with the fuel station, the presence of silt helps to absorbed/and retain these ions (Yang et al., 2009).In 2004, factor 1 may be due to anthropogenic activities within the study area while PO 4 could be agricultural/domestic effects (Afshin and Moore, 2007).Factor 2 was due significantly to natural processes.Factor 3 and 4 in 2004 were related directly to "petrochemical source" station (Yang et al., 2009).Fe is naturally high in the tropics and given its negative loading, natural influences may be more significant (Nganje et al., 2010;Adaikpoh et al., 2005).The clay in factor 4 is significant in its retentive capacity over sand hence the very high loading.Factor 4 is anthropogenic in origin and results from oil, gas, domestic and agricultural influences.
Cluster 4 in 2000 suggests influences arisen from natural processes operating in the soil.Cluster 3 consists of only NO 3 attached at a farther distance to cluster 1 and 2. This cluster suggests unique factor such as fertilizer application on farmland where NH 4 + , the main components of chemical fertilizer is easily oxidised to NO 3 due to nitrification process (Afshin and Moore, 2007).Clusters 1 and 2 were due to anthropogenic factors.While cluster 2 implies a mixture of agrochemical products, cluster 1 is related wholly to anthropogenic input from the oil and gas activities in the area (Ji-Hoon et al., 2003;Afshin and Moore, 2007).In 2004, clusters 1 and 2 were both anthropogenic and natural activities.This is because in cluster 1, Cu, Mg, Fe and Ca and in cluster 2, Zn cannot be easily be said to be associated with the human activity in the area.The rest variables in clusters 1 and 2 were easily related to anthropogenic influences (Armah et al., 2010).
Of the 7 heavy metals, Zn, Mn, Fe and Cu showed some level of contamination.During this period of study also, the CF were higher in 2000 than in 2004.While factors earlier suggested maybe responsible for these variations, low activity within the station around 2004 could have also contributed to this observation.Also note worthy is the fact that these heavy metals which shows some level of contamination cannot be directly linked to oil and gas related activities.

CONCLUSION
The activities around the fuel station were responsible for the presence of TOC, N, OG, Ni and Pb.The presence of NO 3 and PO 4 may have been due to agricultural/domestic wastes on the soil.The clay and silt were also responsible for the high retention of the relatively enhanced variables.

Table 1 :
Results of 2000 soil samples * CSA: Cylinder Storage Area; NBH: Near Borehole; OUF: Outside the Fence; OG = Oil and Grease

Table 2 :
Results of 2004 soil samples

Table 5 :
Varimax rotated factor analysis of 2000 soil Factor

Table 7 :
CF of heavy metals in 2000 soilsVariables -

Table 8 :
CF of heavy metals in 2004 soils Variables -