Fluoranthene biotreatment using prominent freshwater microalgae: physiological responses of microalgae and artificial neural network modeling of the bioremoval process

Abstract

Due to the intensified industrial activities and other anthropogenic actions, contamination of polycyclic aromatic hydrocarbons (PAHs) has been growing at an alarming rate, turning in to a serious environmental concern. Bioremediation, as an eco-friendly and sustainable removal technology, can be used by organisms to reduce the resulting contaminations. In the present study, the ability of Tetradesmus obliquus to remove of fluoranthene (FLA) was evaluated. It was confirmed that FLA removal efficiency was managed by various environmental parameters and pH was found to be one of the most important influencial factors. The reusability of the algae in long-term repetitive operations confirmed the occurrence of biodegradation along with other natural attenuation and 10 intermediate compounds were identified in the FLA biodegradation pathway by GC-MS. As a result of physiological assays, induced antioxidant enzymes activities and augmentation of phenol and flavonoids contents, after the treatment of the microalgae by a high concentration of FLA, confirmed the ability of the microalgae to upregulate its antioxidant defense system in response to the toxic effects of FLA. An artificial neural network (ANN) model was then developed to predict FLA biodegradation efficiency and the appropriate predictive performance of ANN was confirmed by comparing the experimental FLA removal efficiency with its predicted amounts (R² = 0.99).

GRAPHICAL ABSTRACT

Highlights

• Estimation of the effects of operational parameters on FLA bioremoval by Tetradesmous obliquus.

• Physiological responses of the microalgae to FLA toxicity.

• Identification of the intermediate metabolites of FLA biodegradation process.

• Artificial neural network modeling of FLA biotreatment by the microalgae.

Novelty statement

The present study, for the first time, focused on the feasibility and assessment of the bioremediation potential of Tetradesmus obliquus in the treatment of fluoranthene (FLA). Moreover, FLA removal efficiency was managed by various environmental parameters and their impact was assessed by applying artificial neural network (ANN) modeling. 10 intermediate compounds were identified by GC-MS in the biodegradation pathway of FLA.

Keywords:

• Artificial neural network (ANN) modeling
• bioremediation
• obliquus
• PAHs
• tetradesmus

Acknowledgement

The authors thank Urmia University for the support of present study.