INQUISAL   20936
INSTITUTO DE QUIMICA DE SAN LUIS "DR. ROBERTO ANTONIO OLSINA"
Unidad Ejecutora - UE
artículos
Título:
Actinobacteria consortium as an efficient biotechnological tool for mixed polluted soil reclamation: Experimental factorial design for bioremediation process optimization
Autor/es:
RAIMONDO, ENZO EMANUEL; POLTI, MARTA ALEJANDRA; GIL, RAÚL ANDRÉS; APARICIO, JUAN DANIEL; BENIMELI, CLAUDIA SUSANA
Revista:
JOURNAL OF HAZARDOUS MATERIALS.
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Año: 2019 vol. 342 p. 408 - 417
ISSN:
0304-3894
Resumen:
The objective of the present work was to establish optimal biological and physicochemical parameters in order to remove simultaneously lindane and Cr(VI) at high and/or low pollutants concentrations from the soil by an actinobacteria consortium formed by Streptomyces sp. M7, MC1, A5, and Amycolatopsis tucumanensis AB0. Also, the final aim was to treat real soils contaminated with Cr(VI) and/or lindane from the Northwest of Argentina employing the optimal biological and physicochemical conditions. In this sense, after determining the optimal inoculum concentration (2 g kg−1), an experimental design model with four factors (temperature, moisture, initial concentration of Cr(VI) and lindane) was employed for predicting the system behavior during bioremediation process. According to response optimizer, the optimal moisture level was 30% for all bioremediation processes. However, the optimal temperature was different for each situation: for low initial concentrations of both pollutants, the optimal temperature was 25 °C; for low initial concentrations of Cr(VI) and high initial concentrations of lindane, the optimal temperature was 30 °C; and for high initial concentrations of Cr(VI), the optimal temperature was 35 °C. In order to confirm the model adequacy and the validity of the optimization procedure, experiments were performed in six real contaminated soils samples. The defined actinobacteria consortium reduced the contaminants concentrations in five of the six samples, by working at laboratory scale and employing the optimal conditions obtained through the factorial design.