Optimization of the Biodegradation of Aliphatic, Aromatic, and UCM Hydrocarbons from Light Crude Oil in Marine Sediment Using Response Surface Methodology (RSM).

This study describes the optimization of the biodegradation of total aliphatic (tAHCs), total aromatic (tPAHs), and unresolved complex mixture (UCM) hydrocarbons from light crude oil in marine sediment. The response surface methodology (RSM), with a Box-Behnken design, was used to optimize the hydrocarbon fraction degradation, reported as degradation efficiency (E (%)), using four independent variables (inoculum, dispersant, light oil concentration, and carbon/nitrogen ratio), all at three levels. Analysis of variance (ANOVA) showed R2 values of 0.976, 0.974, and 0.975 for tAHCs, tPAHs, and UCM, respectively. All fractions exhibited a statistically significant effect (P < 0.05) in the second-order quadratic regression model for degradation. According to the models, the optimal degradation prediction was: 81.03% for tAHCs, 85.96% for tPAHs, and 92.86% for UCM. This work highlights the possibility of carrying out efficient biodegradation, of more than 80%, through an optimization process using different light oil concentrations, opening up possibilities of multiple response optimization.

Characterization of sediment microbial communities at two sites with low hydrocarbon pollution in the southeast Gulf of Mexico.

BACKGROUND: Coastal ecosystems are prone to hydrocarbon pollution due to human activities, and this issue has a tremendous impact on the environment, socioeconomic consequences, and represents a hazard to humans. Bioremediation relies on the ability of bacteria to metabolize hydrocarbons with the aim of cleaning up polluted sites. METHODS: The potential of naturally occurring microbial communities as oil degraders was investigated in Sisal and Progreso, two port locations in the southeast Gulf of Mexico, both with a low level of hydrocarbon pollution. To do so, we determined the diversity and composition of bacterial communities in the marine sediment during the dry and rainy seasons using 16S rRNA sequencing. Functional profile analysis (PICRUTSt2) was used to predict metabolic functions associated with hydrocarbon degradation. RESULTS: We found a large bacterial taxonomic diversity, including some genera reported as hydrocarbon-degraders. Analyses of the alpha and beta diversity did not detect significant differences between sites or seasons, suggesting that location, season, and the contamination level detected here do not represent determining factors in the structure of the microbial communities. PICRUTSt2 predicted 10 metabolic functions associated with hydrocarbon degradation. Most bacterial genera with potential hydrocarbon bioremediation activity were generalists likely capable of degrading different hydrocarbon compounds. The bacterial composition and diversity reported here represent an initial attempt to characterize sites with low levels of contamination. This information is crucial for understanding the impact of eventual rises in hydrocarbon pollution.