Biocomplementation of SVE to achieve clean-up goals in soils contaminated with toluene and xylene

Soil vapor extraction (SVE) and bioremediation (BR) are two of the most common soil remediation technologies. Their application is widespread; however, both present limitations, namely related to the efficiencies of SVE on organic soils and to the remediation times of some BR processes. This work aimed to study the combination of these two technologies in order to verify the achievement of the legal clean-up goals in soil remediation projects involving seven different simulated soils separately contaminated with toluene and xylene. The remediations consisted of the application of SVE followed by biostimulation. The results show that the combination of these two technologies is effective and manages to achieve the clean-up goals imposed by the Spanish Legislation. Under the experimental conditions used in this work, SVE is sufficient for the remediation of soils, contaminated separately with toluene and xylene, with organic matter contents (OMC) below 4 %. In soils with higher OMC, the use of BR, as a complementary technology, and when the concentration of contaminant in the gas phase of the soil reaches values near 1 mg/L, allows the achievement of the clean-up goals. The OMC was a key parameter because it hindered SVE due to adsorption phenomena but enhanced the BR process because it acted as a microorganism and nutrient source.

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.