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Factorial Designs Application to Study Enhanced Bioremediation of Soil Artificially Contaminated with Weathered Bonny Light Crude Oil through Biostimulation and Bioaugmentation Strategy 被引量:2

Factorial Designs Application to Study Enhanced Bioremediation of Soil Artificially Contaminated with Weathered Bonny Light Crude Oil through Biostimulation and Bioaugmentation Strategy
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摘要 The objective of this study was designed to evaluate the effects of biostimulation and bioaugmentation amendment agents (NPK fertilizer, Tween 80 and mixed culture) on the bioremediation of tropical soil samples artificially contaminated with Weathered Bonny Light Crude Oil (WBLCO). Response Surface Methodology (RSM) with Box Behnken Design (BBD) was used with three levels and three factors of NPK fertilizer (2 - 6 g), Tween 80 (5 - 15 mg/l) and mixed culture (0.5 - 1.5 g/l) as independent variables and WBLCO removal as dependent variable (response) in a six weeks remediation period. The results showed that the rate of WBLCO removal generally increased with increase in the amount of NPK fertilizer, Tween 80 and mixed culture (biomass), respectively. A statistically significant (P < 0.0001) second-order quadratic regression model for WBLCO removal (using design-expert statistical program (v. 6.0.8)) with a coefficient of determination, R (=0.9996) was obtained. Numerical optimization technique based on desirability function was carried out to optimize the bioremediation process. The optimum values for biostimulation and bioaugmentation amendment agents to achieve a predicted maximum WBLCO removal of 84.88 percent were found to be: NPK fertilizer, 4.25 g;Tween 80, 10.22 mg/l and mixed culture, 0.46 g/l. At this optimum point, the observed WBLCO removal was found to be 83.79 percent. The statistical analyses and the closeness of the experimental results and model predictions show the reliability of the regression model and thus, biostimulation and bioaugmentation of indigenous microbial density and activity can reduce remediation period of petroleum hydrocarbon contaminated environment and subsequently the cost of remediation. The objective of this study was designed to evaluate the effects of biostimulation and bioaugmentation amendment agents (NPK fertilizer, Tween 80 and mixed culture) on the bioremediation of tropical soil samples artificially contaminated with Weathered Bonny Light Crude Oil (WBLCO). Response Surface Methodology (RSM) with Box Behnken Design (BBD) was used with three levels and three factors of NPK fertilizer (2 - 6 g), Tween 80 (5 - 15 mg/l) and mixed culture (0.5 - 1.5 g/l) as independent variables and WBLCO removal as dependent variable (response) in a six weeks remediation period. The results showed that the rate of WBLCO removal generally increased with increase in the amount of NPK fertilizer, Tween 80 and mixed culture (biomass), respectively. A statistically significant (P < 0.0001) second-order quadratic regression model for WBLCO removal (using design-expert statistical program (v. 6.0.8)) with a coefficient of determination, R (=0.9996) was obtained. Numerical optimization technique based on desirability function was carried out to optimize the bioremediation process. The optimum values for biostimulation and bioaugmentation amendment agents to achieve a predicted maximum WBLCO removal of 84.88 percent were found to be: NPK fertilizer, 4.25 g;Tween 80, 10.22 mg/l and mixed culture, 0.46 g/l. At this optimum point, the observed WBLCO removal was found to be 83.79 percent. The statistical analyses and the closeness of the experimental results and model predictions show the reliability of the regression model and thus, biostimulation and bioaugmentation of indigenous microbial density and activity can reduce remediation period of petroleum hydrocarbon contaminated environment and subsequently the cost of remediation.
出处 《Journal of Environmental Protection》 2012年第8期748-759,共12页 环境保护(英文)
关键词 BIOREMEDIATION Box-Behnken DESIGNS CRUDE Oil SECOND-ORDER Quadratic Regression Model Bioremediation Box-Behnken Designs Crude Oil Second-Order Quadratic Regression Model
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