Enhancing geostatistical prediction of total hydrocarbon content (THC) using Bayesian maximum entropy (BME) in a region of Niger Delta, Nigeria
Keywords:
Niger Delta Region, hydrocarbon spillage, total hydrocarbon content (THC), electrical conductivity (EC), bayesian maximum entropy (BME)Abstract
In this study, Bayesian maximum entropy (BME) was implemented to take advantage of the limited data available for reliable prediction of total hydrocarbon content (THC) in the soil because it allows assimilate of secondary and its uncertainty data. Thirteen (13) physico-chemical soil variables were obtained. Upon the application of multiple regression analysis to the soil variables, electrical conductivity, EC was found as a covariate to the eleven (11) data. Twenty-eight (28) data were then generated by regression model between and EC and their 28 uncertainty data were also derived using probability density function. Therefore, 11 , 28 and 28 error data in the computation of THC soft were integrated at BME technique to produce BME prediction and BME standard deviation maps of THC. The BME predicted THC value in the area ranges from 0 – 16 . The BME prediction map provides the mean variable of the estimation posterior pdf of THC. It shows the spatial variability of THC as the concentration decreases from the highest concentrated zone of THC.
Downloads
References
Akinlo, A.E. (2012). How important is oil in Nigeria’s economic growth? J Sustain Dev 5:165–179. doi:10.5539/jsd.v5n4p165
Awobajo, S. A. (1981). An analysis of oil spill incidents in Nigeria, 1976-1980. In Proc. Seminar on Petroleum Industry and the Nigeria Environment, NNPC/FMOW and Housing, P.T.I., Ward, Nigeria.
BMElab Department of Environmental Sciences and Engineering School of Public Health, University of North Carolina (2014). BMEGUI3.0.1 User Manual. Retrieved from https://mserre.sph.unc.edu/BMEGUI_web/BMEGUI3.0.1_web/BMEGUI3.0.1_DOCs/PDF/BMEGUI3.0.1_UserManual_v01.pdf
Christakos, G. (1990). A Bayesian/maximum-entropy view to the spatial estimation problem. Math Geol 22(7):763–777
Christakos, G. (1992). Random field models in earth sciences: Academic Press. San Diego, CA, 474 p
Christakos, G. (2000), Modern spatiotemporal geostatistics. Oxford University Press, New York.
Christakos, G., & Li, X.Y. (1998) Bayesian maximum entropy analysis and mapping: a farewell to kriging estimators? Math Geol., 30(4):435–462
Christakos, G., & Serre, M.L. (2000). BME analysis of spatiotemporal particulate matter distributions in North Carolina. Atmos Environ 34(20):3393–3406
Christakos, G., Kolovos, A., Serre, M.L., & Vukovich, F. (2004). Total ozone mapping by integrating databases from remote sensing instruments and empirical models. IEEE Trans Geosci Remote Sens., 42(5):991–1008.
Cressie, N., & Hawkins, D.M. (1980). Robust estimation of the variogram: I. J Int Assoc Math Geol., 12(2):115–125.
Douaik, A., Van Meirvenne, M., & Toth, T. (2005). Soil salinity mapping using spatio-temporal kriging and Bayesian maximum entropy with interval soft data. Geoderma 128(3–4):234–248.
Dumitru, M., & Vladimirescu, A. (2017). Loads Limits Values of Soils with Petroleum Hydrocarbons. Geophysical Research Abstracts, 19.
Echefu, N., & Akpofure, E. (2002). Environmental impact assessment in Nigeria: regulatory background and procedural framework. EIATrain Resour Man, 63–74.
Elum, Z.A., Mopipi, K., & Henri-Ukoha, A. (2016). Oil exploitation and its socioeconomic effects on the Niger delta region of Nigeria. Environ. Sci. Pollut. Res. Int. 23, 12880-12889. https://doi.org/10.1007/s11356-016-6864-1
Kadafa, A.A. (2012). Oil exploration and spillage in the Niger Delta of Nigeria. Civ Environ Res 2(3):31–51
Kellett, J. (1990). The environmental impact of wind energy developments. Town Plan Rev 61:139–155
Lark, R.M. (2000). A comparison of some robust estimators of the variogram for use in soil survey. Eur J Soil Sci 51(1):137–157
Matheron, G. (1963). Principles of geostatistics. Econ Geol 58(8):1246–1266
Modis K, Vatalis K, Papantonopoulos G, Sachanidis C (2010) Uncertainty management of a hydrogeological data set in a greek lignite basin, using BME. Stoch Environ Res Risk Assess., 24(1):47–56
Olaseni, M., & Alade, W. (2012). Vision 20:2020 and the challenges of infrastructural development in Nigeria. J Sustain Dev 5:63–76. doi:10.5539/jsd.v5n2p63
Okpokwasili, G.C. & Amanchukwua, S.C. (1988). Petroleum hydrocarbon degradation by Candida species. Environment International, 14(3):243-247. https://doi.org/10.1016/0160-4120(88)90145-6
Oguine, I. (1999). Nigeria’s oil revenues and the oil producing areas. J Energy Nat Res Law 2:111–120
Rezaei, S., Khojasteh, E.R., & Faridazad, M. (2020). Improving geostatistical predictions of two environmental variables using Bayesian maximum entropy in the Sungun mining site. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-020-01863-4
Shi, T.T., Yang, X.M., Christakos, G., Wang, J.F., & Liu, L. (2015). Spatiotemporal
interpolation of rainfall by combining BME theory and satellite rainfall estimates. Atmosphere 6(9):1307–1326
Ukhurebor, K.E., Athar, H., Adetunji, C.O., Aigbe, U.O., Onyancha, R.B., & Abifarin, O. (2021). Environmental implications of petroleum spillages in the Niger Delta region of Nigeria: A review. Journal of Environmental Management, Volume 293. https://doi.org/10.1016/j.jenvman.2021.112872
Zhang, C.S., Tang, Y., Luo, L., & Xu, W.L. (2009) Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land. Environ Pollut 157(11):3083–3090.
Zhang, C., & Yang, Y. (2019). Improving the spatial prediction of soil Zn by converting outliers into soft data for BME method. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-018-1641-y
Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2022). Post-pandemic health and its sustainability: Educational situation. International Journal of Health Sciences, 6(1), i-v. https://doi.org/10.53730/ijhs.v6n1.5949
Suryasa, I.W., Sudipa, I.N., Puspani, I.A.M., Netra, I.M. (2019). Translation procedure of happy emotion of english into indonesian in kṛṣṇa text. Journal of Language Teaching and Research, 10(4), 738–746
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
