Response Surface Methodology for Optimization of Operational Parameters To Remove Ciprofloxacin from Contaminated...

Ciprofloxacin (CFX) is a broad-spectrum fluoroquinolone antibiotic that is widely used to treat bacterial infections in humans and other animals. However, its unwanted occurrence in any (eco)system can affect nontarget bacterial communities, which may also impair the performance of the natural or artificially established bioremediation system. The problem could be minimized by optimization of operational parameters via modeling of multifactorial tests. To this end, we used a Box-Behnken design in response surface methodology (RSM) to generate the experimental layout for testing the effect of the CFX biodegradation for four important parameters, that is, temperature (°C), pH, inoculum size (v/v %), and CFX concentration (mg L^-1). For inoculation, a consortium of three bacterial strains, namely, Acenitobacter lwofii ACRH76, Bacillus pumilus C2A1, and Mesorihizobium sp. HN3 was used to degrade 26 mg L^-1 of CFX. We found maximum degradation of CFX (98.97%; initial concentration of 25 mg L^-1) at 2% inoculum size, 7 pH, and 35 °C of temperature in 16 days. However, minimum degradation of CFX (48%; initial concentration of 50 mg L^-1) was found at pH 6, temperature 30 °C, and inoculum size 1%. Among different tested parameters, pH appears to be the main limiting factor for CFX degradation. Independent factors attributed 89.37% of variation toward CFX degradation as revealed by the value of the determination coefficient, that is, R ² = 0.8937. These results were used to formulate a mathematical model in which the computational data strongly correlated with the experimental results. This study showcases the importance of parameter optimization via RSM for any bioremediation studies particularly for antibiotics in an economical, harmless, and eco-friendly manner.