Dual augmentation for aerobic bioremediation of MTBE and TCE pollution in heavy metal-contaminated soil

In this work we isolated from soil and characterized several bacterial strains capable of either resisting high concentrations of heavy metals (Cd2+ or Hg2+ or Pb2+) or degrading the common soil and groundwater pollutants MTBE (methyl-tertbutyl ether) or TCE (trichloroethylene). We then used soil microcosms exposed to MTBE (50 mg/l) or TCE (50 mg/l) in the presence of one heavy metal (Cd 10 ppm or Hg 5 ppm or Pb 50 or 100 ppm) and two bacterial isolates at a time, a degrader plus a metalresistant strain. Some of these two-membered consortia showed degradation efficiencies well higher (49–182% higher) than those expected under the conditions employed, demonstrating the occurrence of a synergetic relationship between the strains used. Our results show the efficacy of the dual augmentation strategy for MTBE and TCE bioremediation in the presence of heavy metals.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.