Elsevier

Bioresource Technology

Volume 101, Issue 24, December 2010, Pages 9455-9460
Bioresource Technology

Optimal conditions for bioremediation of oily seawater

https://doi.org/10.1016/j.biortech.2010.07.077Get rights and content

Abstract

To determine the influence of nutrients on the rate of biodegradation, a five-level, three-factor central composite design (CCD) was employed for bioremediation of seawater artificially contaminated with crude oil. Removal of total petroleum hydrocarbons (TPH) was the dependent variable. Samples were extracted and analyzed according to US-EPA protocols. A significant (R2 = 0.9645, P < 0.0001) quadratic polynomial mathematical model was generated. Removal from samples not subjected to optimization and removal by natural attenuation were 53.3% and 22.6%, respectively. Numerical optimization was carried out based on desirability functions for maximum TPH removal. For an initial crude oil concentration of 1 g/L supplemented with 190.21 mg/L nitrogen and 12.71 mg/L phosphorus, the Design-Expert® software predicted 60.9% hydrocarbon removal; 58.6% removal was observed in a 28-day experiment.

Introduction

With advances in biotechnology, bioremediation has become one of the most rapidly developing fields of environmental restoration. Bioremediation methods use microorganisms to reduce the concentration and toxicity of various chemical pollutants, such as petroleum hydrocarbons, polycyclic aromatic hydrocarbons, pesticides, industrial solvents, and metals (Dua et al., 2002).

Hydrocarbon pollution in marine ecosystems has potentially dangerous effects on organism and human health through bioaccumulation and biomagnification via food chains. The majority of petroleum hydrocarbons compounds (i.e., saturates, aromatics) is degradable at different rates, and some components that are recalcitrant to biodegradation may be metabolized over long periods of time. Bioremediation strategies attempt to enhance and accelerate this process. The use of bioremediation for large-scale field application gained significant attention in 1989 when beaches contaminated with crude oil from the Exxon Valdez spill were seeded with fertilizer to promote the growth of hydrocarbon-degrading bacteria (Atlas, 1995).

The addition of dispersant chemicals can affect the environmental cycling processes of petroleum and has been used to treat oil spills in temperate marine environments for many years (Kirby and Law, 2008). Dispersants are a group of chemicals which disseminate organics in aqueous systems and are extensively employed in marine pollution applications (Lee et al., 2008, Hua, 2006). A major reason for using these chemicals is to prevent spilled oil from reaching the shore. Bioremediation, like other biotechnologies has limitations. Bioremediation processes are highly heterogeneous and complex and are correspondingly difficult to characterize. The success of oil spill bioremediation depends on the availability of the appropriate microorganisms, multiple environmental factors and the composition of the oil spilled (Zahed et al., 2010, Gandolfi et al., 2010, Saeki et al., 2009, Zhu et al., 2001).

Response surface methodology (RSM) is a useful mathematical and statistical method for analyzing the effects of several independent variables on process outcomes (response) (Myers and Montgomery, 2002, Draper and John, 1988). In many processes, the relationship between the response and the independent variables is usually unknown; therefore, the first step in RSM is to approximate the function (response) in terms of independent variables. Usually, this process employs a low-order polynomial equation in a pre-determined region of the independent variables, which is subsequently analyzed to identify the optimum values of the independent variables for the best response.

Several studies have been performed on the impact of nitrogen and phosphorus supplementation on bioremediation of crude oil in marine environments (Nikolopoulou and Kalogerakis, 2008, Adesodun and Mbagwu, 2008, Kirkpatrick et al., 2006, Ruiz et al., 2006, Knezevich et al., 2006). However, the optimal duration of treatment and nutrient concentration is still not known for bioremediation of pure and mixed crude oils in seawater. To address these unknowns, the objective of this research was to study the effect of parameters describing the bioremediation of petroleum hydrocarbons in seawater contaminated with a high concentration of crude oil. Classical optimization involves changing one variable at a time while fixing all other variables and studying the effect on the response. This is a time-consuming, expensive, and complicated process for a multi-variable system. Therefore, RSM was employed for design, modeling and optimization of crude oil bioremediation.

In recent years, RSM has been successfully applied to biodegradation optimization (e.g., polycyclic aromatic hydrocarbons (PAH), n-alkanes, diesel fuel) in different matrixes (Mohajeri et al., 2010a, Huang et al., 2008, Vieira et al., 2007; Nasrollahzadeh et al., 2007).

Section snippets

Sampling

Seawater and coastal sediments were collected from the Butterworth Channel, Penang, Malaysia, for both acclimatization and experiments.

Bacterical concertum were isolated from seawater, as follows: 1 L seawater, 10 g soil sample and 1 mL crude oil were transferred to a 2-L conical flask containing growth medium. Bacteria were cultured in a media containing 1 g/L NH4NO3, 1 g/L KH2PO4, 1 g/L K2HPO4, 0.2 g/L MgSO4·7H2O, 0.05 g/L FeCl3, and 0.02 g/L CaCl2 as described by Mohajeri et al., 2010a, Mohajeri et

Statistical testing and mathematical modeling

The experimental data obtained from the design were fitted and analyzed by the response surface regression procedure via Eq. (3), a second-order polynomial equation:Y=β0+i=1kβixi+i=1kβiixi2+i=1ij=1βijxixj+εwhere Y denotes the response (TPH removal); β0 is the value of the fixed response at the center point of the design; βi, βii, and βij are the linear, quadratic and interaction-effect (cross-product coefficients) regression terms, respectively; xi and xj are the coded values of

Conclusions

RSM and CCD were employed to optimize biodegradation of dispersed crude oil in seawater samples. Statistical and diagnostics analyses indicated that RSM is a reliable tool to optimize crude oil bioremediation. A significant (R2 = 0.9645, P < 0.0001) quadratic polynomial mathematical model was generated.

Under optimum conditions, 58.6% DCO removal was observed in a 28-day experiment, compared to 53.3% removal in experiments without optimization and 22.6% removal in experiments in which natural

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