Nnaemeka Okorondu, Justin and Izunobi, Lucy and Ifunanya Okorondu, Sylvester and Ikechukwu Nwachukwu, Joseph and Abrakasa, Selegha (2023) Forecasting the Rate of Biostimulated Bioremediation Using Biodegradation Models. International Research Journal of Pure and Applied Chemistry, 24 (1). pp. 27-35. ISSN 2231-3443
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Abstract
There have been several remediation techniques for oil spill-impacted soil in the Nigerian Niger Delta which has not given the much-desired results as the methods used were either inappropriate for the environment or ineffective for the different soil types in the Niger Delta. Bioremediation is a cost-effective and environmentally friendly technology that exploits the capabilities of microorganisms to degrade organic pollutants leading to complete mineralization. It has become the most preferred technique for oil spill remediation on soil in Nigeria. This study is aimed at developing a biodegradation model using biodegradation ratios of a biostimulated biodegradation experiment on crude oil polluted/spiked soil. The model design criteria involve inoculating varying amounts of nutrients (N.P.K fertilizer) into a soil media impacted with crude oil at a ratio of 10kg/kg (10% w/w). The medium for the presentation of the nutrient was water and the volume of water used varied from 30% to 80% saturation. Samples were taken at an interval of about three months to monitor the changes in diagnostic ratios (nC17/Pr, nC18/Ph, (nC17+nC18)/(Pr+Ph) using gas chromatography (GC-FID). Results obtained were used to develop a biostimulated biodegradation model to forecast/predict the rate of bioremediation assuming the design considerations are consistent. The model adopted was constrained to the diagnostic ratio (nC17+nC18)/(Pr+Ph) which describes the biostimulated biodegradation for all the sample sets. A linear regression model equation, y=c+bx was employed in the model.
Item Type: | Article |
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Subjects: | Academic Digital Library > Chemical Science |
Depositing User: | Unnamed user with email info@academicdigitallibrary.org |
Date Deposited: | 03 Feb 2023 04:21 |
Last Modified: | 11 May 2024 04:36 |
URI: | http://publications.article4sub.com/id/eprint/574 |