Research Article

Remediation of petroleum polluted soils with organic manures and inorganic fertilizer and its effect on fluted pumpkin (Telefeira occidentalis) in Bayelsa State

Teknikio, J. B1*, Diri, K. H1, Unabor, E.2
1 Department of Crop and Soil Science, Niger Delta University, Wilberforce Island, P.M.B 071, Yenagoa, Bayelsa State, Nigeria.
2 Department of Agronomy, Rubber Research Institute of Nigeria, Iyanomo, P.M.B 1049, Benin City, Edo State, Nigeria.
* Corresponding author: jemimahtknk@gmail.com
Published: Apr, 2025
DOI: 10.67042/njss.2025.amf0g0ii
Pages: 68-78
Views: 575
Downloads: 600

Abstract

Growth parameters of fluted pumpkin (Telefeira occidentalis) were examined to assess the effectiveness of remediating crude oil-polluted soils on application of organic manures and inorganic fertilizer in enhancing hydrocarbon-degrading bacteria (HDB) used in the bioremediation process in Bayelsa State. Two locations, Imiringi and Koloama, in Ogbia and Southern Ijaw Local Government Areas, respectively, known for crude oil spills were chosen for this study. Soil samples were collected from a depth of 0–30 cm, bulked for pot experiment in a screen house. Twenty-four plastic buckets, each holding 5 kg of soil and 0.5 kg of each of the treatments, were applied to each location, totaling 48 buckets. The experimental design was a 2 x 8 factorial arranged in a Completely Randomized Design (CRD), replicated three times. The data were statistically analyzed using Tukey's Test to distinguish significant differences at 5% probability level. The analyzed soil samples aimed to assess the impact of organic manures (cow dung and poultry droppings), inorganic fertilizer (NPK), and their combinations (CD + PD, PD + NPK, NPK + CD, and CD + PD + NPK) on the growth of fluted pumpkin (Telefeira occidentalis) and on the population of hydrocarbon-degrading bacteria (HDB) in the crude oil-polluted soils. In Imiringi, the HDB population (expressed in CFU/g) ranged from 8.30 x 105a to 1.14 x 106a, while Koloama, ranged from 5.74 x 105b to 7.36 x 105b, indicating significant difference between the two locations. The overall results showed high significant difference in growth parameters of fluted pumpkin (Telefeira occidentalis) with a few variations and HDB populations at different time (30, 60 and 90 days) suggesting the effectiveness of the application of these amendment materials.

Keywords

Hydrocarbons Degrading Bacteria Organic manures Inorganic Fertilizer (NPK) Crude Oil Polluted Soils

References

  1. Aguire, M.A. and Canals, A. (2023) Chapter 4 - Aerosol characterization: Analytical Nebulizers: Fundamentals and Applications, 35-53. [https://doi.org/10.1016/B978-0-323-91181-8.00005-5](https://www.google.com/search?q=https://doi.org/10.1016/B978-0-323-91181-8.00005-5)
  2. Altuner, E.E, Akın, M., Bayat, R., Bokenzeci, M., Burhan, H. and Sen, F. (2022) Chapter 22 - Challenges in commercialization of carbon nanomaterial-based sensors. Carbon Nanomaterials Based Sensors: Emerging Research Trends in Devices and Applications, 387-392. [https://doi.org/10.1016/B978-0-323-91174-0.00020-2](https://www.google.com/search?q=https://doi.org/10.1016/B978-0-323-91174-0.00020-2)
  3. Anil V.K. and Hasan, M. (2021) Chapter 11 - Chiral analytical chemistry. Stereochemistry: A Three-Dimensional Insight, 405-513. [https://doi.org/10.1016/11078-0-12-821082-8.00001-6](https://www.google.com/search?q=https://doi.org/10.1016/11078-0-12-821082-8.00001-6)
  4. Araujo, S.R., Wetterlind, J., Demattê, J.A.M. and Stenberg, B. (2014) Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques. European Journal of Soil Science, 65(5), 718-729. [https://doi.org/10.1111/ejss.12165](https://www.google.com/search?q=https://doi.org/10.1111/ejss.12165)
  5. Arnold, R.W. (1983) Concepts of soils and pedology. In: Pedogenesis and Soil Taxonomy. Concepts and Interactions, (eds Wilding, L.P., Smeck N.E. and Hall G.F.), Elsevier, Amsterdam, pp. 1-21.
  6. Brady, N.C. and Weil, R.R. (2021) Elements of the Nature and Properties of Soils, 4th edition Published By Pearson 2021.
  7. Chaudhari, B., Kamble, S., Patil, M., Bhusale, G., et al. (2023) Soil Fertility Detection and Crop Prediction using IoT and Machine Learning. International Journal of Membrane Science and Technology, 10(3): 2581-2588. DOI:10.15379/ijmst.v10i3.2855
  8. Donald, J.P. and Clyde, W.F. (1975) Chapter Two - Development Of an Analytical Method. Analytical Chemistry (2nd Ed.). 10-18. [https://doi.org/10.1016/B978-0-12-555160-1.50006-X](https://www.google.com/search?q=https://doi.org/10.1016/B978-0-12-555160-1.50006-X)
  9. Edited by M. J. Assarl, A. R. H. Goodwin, V. Vesovic and W. A. Wakeham, International Union of Pure and Applied Chemistry. Published by the Royal Society of Chemistry, www.rsc.org
  10. FAO (2008) A primer on soil analysis using visible and near infrared (vis NIR) and mid infrared (MIR) spectroscopy. Rome. FAO [https://doi.org/10.4063/cb0035m](https://www.google.com/search?q=https://doi.org/10.4063/cb0035m)
  11. Froba, A.P., Will, S., Nugaoka, Y., Winkelmann, J., Wiegand, S. and Kehla, W. (2014) Optical Methods. Experimental Thermodynamics Volume IX: Advances in Transport Properties of Fluids.
  12. Gavade, L. (2017) N. P. K Detection using Flare Optic Sensor and PIC Controller. IJESC, 7, 2017.
  13. Giuseppe P.R. (2011) Flow visualization. Aerodynamic Measurements: From Physical Principles to Turkey Instrumentation, 161-216 [https://doi.org/10.1533/9780857093868.161](https://www.google.com/search?q=https://doi.org/10.1533/9780857093868.161)
  14. Hoskin Scientific Ltd. (2013) Soil Science Instrumentation. Hoskin Scientific Ltd, Canada. Available at: [https://mhat2013.files.wordpress.com/2013/11/soil-science-instrumentation-book.pdf](https://www.google.com/search?q=https://mhat2013.files.wordpress.com/2013/11/soil-science-instrumentation-book.pdf)
  15. ICAR DCR (2017) Equipments in Soil Science lab. ICAR Directorate of Cashew Research Puttur, India. Available at: [https://cashew.icar.gov.in/equipment-in-soil-science-lab/](https://www.google.com/search?q=https://cashew.icar.gov.in/equipment-in-soil-science-lab/)
  16. Janvier, N., Arcade, N., Eric, N. and Jean N (2021) Machine Learning based Soil Fertility Prediction. UISRT, 4: 7.
  17. King, L.D., Kivie, H.J. and Thompson, J.A. (2003) SSC 200 Soil Science Laboratory Manual. North Carolina State University, Raleigh, NC, USA.
  18. Krithiga, R. and Sindhuja, R. (2017) Soil Nutrient Identification Using Arduino. Asian Journal of Applied Science and Technology, 1, 4.
  19. Lindbo, D.L., Kozlowski, D.A. and Robinson, C. (2012) Know Soil, Know Life. Soil Science Society of Agronomy, Madison, WI.
  20. López-Lorente, A.I. and Valcárcel, M. (2014) Chapter 10 - Determination of Gold Nanoparticles in Biological, Environmental and Agrifood Samples. Comprehensive Analytical Chemistry, 60, 355-425. [https://doi.org/10.1016/B978-0-444-63285-2.00010-9](https://www.google.com/search?q=https://doi.org/10.1016/B978-0-444-63285-2.00010-9)
  21. Munsell (1975) Standard Soil Color Charts. Munsell Color Company, Baltimore, MD, 34pp.
  22. Pallavada, H., Parvathi, S., Munnangi, T.V., Rayapudi, B., Gadde, S., Chana, M., Pallavada, H., Parvathi, S., Munnangi, T.V., Rayapudi, B., Canta, M. (2021) Real-Time Soil Nutrient detection and Analysis. ICACITE, 1015-1020.
  23. Revari, P.P., Shirolkar, M.M., Verma, A.J., More, S.P. and Kulkarni, A. (2021) Determination of soil nutrients (NPK) using optical methods: a mini review. Journal of Plant Nutrition. DOI: 10.1080/01904167.2021.1884708
  24. Robards, K. and Ryan, D. (2022) Chapter 3 - Planar chromatography. Principles and Practice of Modern Chromatographic Methods (2nd Ed), 97-143. [https://doi.org/10.1016/B978-0-18-823080-2.00011-6](https://www.google.com/search?q=https://doi.org/10.1016/B978-0-18-823080-2.00011-6)
  25. Shariffar, A., Singh, K., Jones, E., Gating, F.I. and Minasey, R. (2019) Evaluating a low-cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models. Soil Use and Management, 35(4): 697-915. [https://doi.org/10.1111/sum.12537](https://www.google.com/search?q=https://doi.org/10.1111/sum.12537)
  26. Singer, M.J (2015) Basic principles of pedology. Reference Module in Earth Systems and Environmental Science.
  27. Surekha, T.I., Sai Teja, S. and P. Gowtham, P. (2020) Soil Analysis for Fertilizer Prediction using IoT and Machine Learning. IJCA, 15, 2. 1497-1504.
  28. TDMUV (2023) Optical methods of the analysis. Inspector xarama TDMUV, Yugoslavia. [https://vdocpub.com/kafedra/internal/pharma_2classes_studies/pharma_phrm_phrm/analytics/Mehanism_y%20%normov%19%20Optical%20Methods%20th%2020th%2022amily.sb](https://www.google.com/search?q=https://vdocpub.com/kafedra/internal/pharma_2classes_studies/pharma_phrm_phrm/analytics/Mehanism_y%2520%25normov%2519%2520Optical%2520Methods%2520th%252020th%252022amily.sb)
  29. Terashima, H., Klein, J. and Luckham, P.H. (1983) The Adsorption of Polymers onto Mica: Direct Measurements Using Microbalance and Refractive Index Techniques. Adsorption from Solution, 305-32. [https://doi.org/10.1016/B978-0-12-631080-4.50023-9](https://www.google.com/search?q=https://doi.org/10.1016/B978-0-12-631080-4.50023-9)
  30. Terhoeven-Urselmann, T., Vage, T.G., Spurguren, O. and Shepherd, K.D. (2010) Prediction of Soil Fertility Properties from a Globally Distributed Soil Mid-Infrared Spectral Library. Soil Science Society of America Journal, 74(5): 1700-1709. [https://doi.org/DOI:10.2136/sssaj2009.0218](https://www.google.com/search?q=https://doi.org/DOI:10.2136/sssaj2009.0218)
  31. Trontelj, J. and Chambers, O. (2021) Machine Learning Strategy for Soil Nutrient Prediction Using Spectroscopic Method. Sensors 21(12):4208. DOI:10.3390/s21124208
  32. Usman, S. (2018) Understanding Soils: Environment and Properties under Agricultural Conditions. Publish America, Baltimore, USA. 151pp. ISBN: 9781627080533.
  33. Vejzadka, M. (2009) Optical methods in biochemistry. Institute of Medical Biochemistry, Prague.
  34. Wadoux, A.M.J.C., Malone, B., Minosny, B., Fajardo, M. and McBratney, A.R. (2021) Soil Spectral Inference with R - Analyzing Digital Soil Spectra using the R Programming Environment. Progress in Soil Science. Springer, Cham, 210 pages.
How to Cite

B, T. J., H, D. K., & E., U. (2025). Remediation of petroleum polluted soils with organic manures and inorganic fertilizer and its effect on fluted pumpkin (Telefeira occidentalis) in Bayelsa State. Nigerian Journal of Soil Science, 34(1), 68-78. https://doi.org/10.67042/njss.2025.amf0g0ii

T. J. B, D. K. H, and U. E., "Remediation of petroleum polluted soils with organic manures and inorganic fertilizer and its effect on fluted pumpkin (Telefeira occidentalis) in Bayelsa State," Nigerian Journal of Soil Science, vol. 34, no. 1, pp. 68-78, April 2025. doi: 10.67042/njss.2025.amf0g0ii

Share this article:
Facebook X / Twitter LinkedIn
Download PDF Back to Articles