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.