4. Results

4.1. Frequency Distribution of Oil Spills

The results of factors responsible for the oil spills are presented in the 1985-2008 graphs. Figure 4(a) reports the oil spills caused by interdiction where the peaks were observed in 1991, 1993, 2001 and 2007, while the lowest numbers were observed in 1987, 1988, 1989, 1996 and 2004. The oil spills by corrosion has seen a somewhat different trend than the interdiction. The highest peak for the corrosion factor was observed in year 1994 (Figure 4(b)). Whereas, the highest peak for production error was observed in the year 1995 (Figure 4(c)) and unknown factors peaked in 2007 (Figure 4(d)). The spills due to unknown factors showed fluctuations that is sometime no incident of oil spills. The frequency and quantity (severity) of spills showed that interdiction factor discharged the largest quantity of oil spills among all factors, even when it is responsible for less than 32% of oil spill incidents in the period under examination.

Oil spills due to production error occurred 154 times, which is the highest number, while the lowest corresponding to unknown factors, total 36 (see Table 1). Though, production factors correspond to the highest frequency of oil spills, it discharged almost 3.5 times less than interdiction. This is due to spills from production

(a) Interdiction (b) Corrosion(c) Production Error (d) Unknown

Figure 4. Flow chart for the methodology adopted for the study showing four spatial techniques resulting in diferent analysis.

Table 1. Frequency and statistics of oil spills during time interval.

YTD: are yet to be determined cases.

error being under-reported or controlled, while the opposite may be the case with interdiction, where spills must first be discovered and reported before contingency plan is activated [2] [62] . For this reason, it may take longer for operators to dictate on-going spills from interdiction due to ineffective leak detection system and poor spill contingency protocol [2] . The distribution of oil spills caused by all factors did not reveal any definite patterns.

4.2. Spatial Analysis of Oil Spills

To establish pattern and evaluate relationships between frequency, quantity and locations of oil spills, the spill incidents are divided into six categories of four years interval (see Table 2). The present study investigates the

Table 2. List of top 20 communities with the highest total spill incidents in the period.

pattern and cluster of oil spills distribution using four different spatial analysis techniques to determine the susceptibility of the communities around oil facilities. The outcomes of all four spatial techniques are discussed below:

i) The Average Nearest Neighbour analysis of the spill distribution indicates that there is less than 1% likelihood that the distribution is random, with an index ratio of 0.19, and the distribution tends toward cluster with an index ratio of less than 1 at 0.01 significant levels. The observed and expected mean distances are 196.14 m and 1042.44 m respectively with a Z-score of −32.69; p-value = 0.000 also at 0.01 significant level.

ii) Getis-Ord General G-test analysis was performed to locate the hot spots (clusters of high values) as well as cold spots (cluster of low values). The calculation is based on a neighbourhood distance within which cluster is expected to occur [58] , hence the Distance Band Neighbour (DBN) count was calculated to determine average equidistance for six neighbouring spill sites at 923.96 m (Min = 0 m, Max. = 14142.6 m). The DBN of 923.96 was rounded to 1000 m for calculating the General G test and to determine the extent of Z-scores. The determination of Z-scores and estimation of high and low cluster zones was analysed for 1 km at 200 m intervals as shown in Figure 5, which showed that at a distance of 400 m, the Z-score is highest i.e. 0.47 (p-value of 0.63 at 0.10 significant level). This means that there is significant cluster of high quantity spills at every 400 m within the study sites.

iii) Spatial autocorrelation (global Moran’s I), was used to assess the oil spills cluster by location and values. This method was applied for a distance of 1 km at 100 m intervals (see Figure 6). Since, the oil spills are in points but the method is suitable for polygons only, we created grid cells of 100 × 100 m to count spills in each polygon using spatial join command in ArcGIS. The neighbouring oil spill values were compared, and difference between each pair of neighbouring oil spill values was obtained and cluster determined by locating the highest Z-Score shown in Figure 6. The analysis revealed a general decrease in cluster with increase in distance, so oil spills are inversely proportional to the distance from oil facilities/oil sources. Therefore, it is inferred that

Figure 5. High and low clustered oil spill sites with similar quantities within 200 m interval.

Figure 6. Spatial autocorrelation showing cluster of oil spills by location and quantity at 100 m intervals.

large quantities of oil spills are found within 100 m and low quantities oil spills are seen at farthest distance (see Figure 6).

The Moran’s index for a 1 km distance is equal to 1.75 which is greater than 0. Thus, signifying cluster pattern i.e. “similar values are found together”. The highest critical value for 100 m is 3.83 with p-value = 0.000130 at 0.01 significant level. Given the Z-score of 1.75 at the 1000 m interval, there is less than 1% likelihood that the cluster is the result of a random chance and there is high probability of finding larger quantities of oil spills within 100 m. Based on the result, it is safe to assume that there is a very high certainty that the source of spill responsible for discharging such large quantities of crude oil may be located in close proximity e.g. to storage tanks, oil gathering terminals or points of massive interdiction.

iv) Cluster and Outlier Analysis, of the spill pattern and results, revealed the locations with high cluster pattern as “hot spot” and low cluster as “cold spot” as shown in Figure 7(a). The technique identifies clusters with large quantities or high values (denoted by “HH”), small quantities or low value clusters (denoted by “LL”). “HL” denotes large quantities or high value outliers surrounded by low values while “LH” represents small quantities or low value outliers surrounded by large quantities or high values as shown in Figure 7(b).

The cluster and outlier analysis is shown in Figure 7(b), revealing oil spill clusters in terms of quantity according to “HH” and “HL” represents clusters of large quantity of oil spills and outliers surrounded by small

(a)

Figure 7. Showing cluster and Outlier Analysis for (a) hot and coil spots assessment of repeated oil spill incidents and (b) quantity discharged in each spill.

quantity spills respectively. The LMi Z-score (Local Moran I) represents the significant level showing that most spills (quantity) are between −1.0 - 1.0 and >2.0 at 0.05 significant levels. In this result, it is clear with the aid of Figure 7(b) that the large quantity spills are closer to communities compared to the smaller ones. Figure 7(a) on the other hand, shows sites with high and low incidents for the oil spills in the studied communities. The “HH” locations are places where there has been consecutive spill occurance in the 24 years under examination, likewise the “LL” locations are places with less repeated spill incidents. The “HL” and “LH” are outliers describing locations with either few repeated spills or more, but not clustered.

4.3. Exposure Susceptibility

In deciding exposure scenarios, the concept of contaminated land is adopted to provide basis for establishing source-pathways-receptor relationship. Here, the historic oil spill sites conform to the UK’s definition of contaminated land as described in Part IIA of the Environmental Protection Act, 1990. Figure 7 includes exposure analysis in terms of its impact over several factors such as human dwellings and Land use land cover (LULC) of

Figure 8. Oil Spill Impacted areas (communities) indicating period of spill incident and location of possible susceptible communities (human receptors).

the study site including freshwater swamp, plantations, mangrove forests, urban settlements (major and minor), and rivers (major and minor). Even human dwelling and resources are exposed to the oil spill incidents located near or far from oil installation and pipelines as shown in Figure 7. The exposure is related to the distance of the communities from the oil spill incident sites that happened during different time-periods from 1985 to 2008, demonstrated in 4 years slots. Human symbol illustrates the settlement communities in the figure and red line symbolises the pipelines network while oil installation is represented with red box.

5. Discussion

A total of 59 communities were found to have had one or more spill incidents at one time or another in the period under study. The communities listed in Table 2 are arranged in order of severity (frequency) and shown in Figure 8 to indicate susceptibility, which can be influenced by: a) repeated occurrence of oil spill incidents, b) proximity and presence of source of hydrocarbon discharge and c) land use exposure opportunities. These communities represent about 15.8% of communities in the study area with a population of 168,747, being 13.5% of entire population in the study area. Collectively they had 443 spill incidents and discharged a total of 129,578 bbl of crude oil from 1985-2008 (Table 1).

There is an indication of severe oil pollution in communities with multiple spill incidents. This is supported by the ability of some hydrocarbon components to persist longer in the environment thereby increasing the concentration of none degradable and trapped hydrocarbons in the environment [40] [51] . Although the affected sites may seem like fixed-point hazards, oil plume can migrate to adjourning land areas through surface and subsurface pathways. The United Nations report on the assessment of polluted sites in Ogoni-land revealed high concentrations of Total Petroleum Hydrocarbon (TPH) in samples collected several meters away from discharge points [51] . This information is important because the people depend on subsistence lifestyles of farming, gathering, fishing, hunting etc. and may be in constant contact with hydrocarbon contaminants in soils, water and air in their daily land use activities, which can make them vulnerable to unnecessary exposure opportunities and increase their susceptibility to health risks associated with hydrocarbon contaminants over time. The exposure duration relative to age, sex and type of land use has been estimated in Table A1 while Table A2 indicates target and intervention values legally allowed for benzene, toluene, ethylbenzene and xylene in ground water and soil sediments by the regulatory agency (DPR).

Although it is difficult to separate acts of political sabotage from theft, agitation for resource control and environmental movement in the Niger Delta struggle, has become intertwined and can only be resolved if the political, social, environmental and economic issues generated by uneven distribution of the cost and benefit of oil production are treated holistically. Perhaps this is why communities located in and around oil installation tolerate or participate in pipeline interdiction for either monetary gain or in protest. In addition, the nature of cluster of large quantity oil spills within 100 m revealed by the spatial analysis methods, and the theissen polygon allocation of spill sites supports community involvement. Also the presence of waterways and river channels in the creeks can provide easy access, which may encourage theft and evacuation of stolen crude oil for sale to artisanal (locally made) refineries or international black market.

Thus, results presented analysis of four techniques in Niger Delta region. Average nearest neighbourhood showed that the probability of oil spill distribution is a cluster distribution for the oil spills in the region (ratio < 1 with index value 0.19). The Getis-Ord General G tests indicated significant cluster of high quantity spills within every 400 m of a spill sites. The Moran’s I index indicated the cluster pattern of the oil spills value ranges from 0.396 - 3.83 at interval of 200 m. Outlier method analysed the quantity of oil spills incident in the region and frequency of the oil spill incidents. The cause of oil spills notwithstanding, it is obvious that the pipeline monitoring system of oil companies has not been effective. Otherwise the cluster pattern of large quantity of oil spill sites would be minimal, except if the remote nature of the area hinders rapid response time, and/or presents difficulties for regular pipeline integrity management.

6. Conclusions

The aim of the study was to use geo-spatial analytical techniques to determine and assess the distribution pattern and susceptibility of communities living near oil facilities. Four techniques were used in the analysis to identify communities that are susceptible to oil spills in the study area.

・ The average neighbourhood index indicated oil spill distributions to be significantly clustered while the Getis-Ord General G-test technique showed significant cluster of high quantity spills at every 400 m within the study sites. Spatial autocorrelation (global Moran’s I) on the other hand inferred that large quantities of oil spills are found within 100 m and low quantities of oil spills at farthest distance.

・ The potential effect of oil pollution on human health is of great concern due to impact of oil spills in social, physical and economic terms. The health condition of people living in the area is at risk because of their daily interactions with contaminated environmental media while conducting their socio-economic land use activities. Although some of the people may have the choice of relocation, others may have no option other than to continue using the same contaminated space because of land scarcity and poverty.

・ According to the pattern of spill distribution, pipeline monitoring and response plans can be centralised along the hotspots or cluster points where settlements exist. As a result, geospatial technology can be used to map pipeline distribution, pattern and proximity to sensitive environmental receptors in the region, and identify the type of land use practice of communities located along pipelines routes.

・ Thus, results from this study can be useful to oil regulators and operators in addressing future occurrences. It may also be useful to consider alternative location by relocating oil facilities to minimize impact on nearby communities.

・ A framework for integrating oil producing communities in oil production decision-making process would give the people a sense of belonging and motivate them to fight against interdiction of oil facilities, and by extension reduce the rate of oil spill incidents and oil pollution.

・ The oil company’s environmental standards must be regulated by government, which must enforce “best available technology” and “good oilfield practice” as well as educate oil producing communities on the environmental impact of oil spill and risks of prolonged exposure to petroleum hydrocarbons.

Acknowledgements

Some procedures presented in this paper were developed as part of a PhD research programme by the main author Shittu Whanda Ja’afaru while at the School of Geography, University of Nottingham. The authors would like to acknowledge the Departments of Petroleum Resources in Nigeria for their intermediary role in data gathering, the Nigerian National Petroleum Corporation, the National Petroleum Investment Management Services, staff of Shell Petroleum Development Company for their assistance and the Petroleum Technology Development Fund (PTDF) Abuja for funding the PhD.

Appendix

Table A1. Land-use potential receptors, estimated exposure duration and averaging time (Shittu, 2014).

Table A2. EGASPIN target and intervention values for soil and groundwater (EGASPIN, 2002) [63] .

NOTES

*Corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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