Remediation of sandy soils contaminated with hydrocarbons and halogenated hydrocarbons by soil vapour extraction

This paper presents the study of the remediation of sandy soils containing six of the most common contaminants (benzene, toluene, ethylbenzene, xylene, trichloroethylene and perchloroethylene) using soil vapour extraction (SVE). The influence of soil water content on the process efficiency was evaluated considering the soil type and the contaminant. For artificially contaminated soils with negligible clay contents and natural organic matter it was concluded that: (i) all the remediation processes presented efficiencies above 92%; (ii) an increase of the soil water content led to a more time-consuming remediation; (iii) longer remediation periods were observed for contaminants with lower vapour pressures and lower water solubilities due to mass transfer limitations. Based on these results an easy and relatively fast procedure was developed for the prediction of the remediation times of real soils; 83% of the remediation times were predicted with relative deviations below 14%.

Soil vapor extraction in sandy soils: influence of airflow rate

Airflow rate is one of the most important parameters for the soil vapor extraction of contaminated sites, due to its direct influence on the mass transfer occurring during the remediation process. This work reports the study of airflow rate influence on soil vapor extractions, performed in sandy soils contaminated with benzene, toluene, ethylbenzene, xylene, trichloroethylene and perchloroethylene. The objectives were: (i) to analyze the influence of airflow rate on the process; (ii) to develop a methodology to predict the remediation time and the remediation efficiency; and (iii) to select the most efficient airflow rate. For dry sandy soils with negligible contents of clay and natural organic matter, containing the contaminants previously cited, it was concluded that: (i) if equilibrium between the pollutants and the different phases present in the soil matrix was reached and if slow diffusion effects did not occur, higher airflow rates exhibited the fastest remediations, (ii) it was possible to predict the remediation time and the efficiency of remediation with errors below 14%; and (iii) the most efficient remediation were reached with airflow rates below 1.2 cm3 s 1 standard temperature and pressure conditions.

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.

Estimation of pollutant partition in sandy soils with different water contents

The objectives of this work were: (1) to identify an isotherm model to relate the contaminant contents in the gas phase with those in the solid and non-aqueous liquid phases; (2) to develop a methodology for the estimation of the contaminant distribution in the different phases of the soil; and (3) to evaluate the influence of soil water content on the contaminant distribution in soil. For sandy soils with negligible contents of clay and natural organic matter, contaminated with benzene, toluene, ethylbenzene, xylene, trichloroethylene (TCE), and perchloroethylene (PCE), it was concluded that: (1) Freundlich’s model showed to be adequate to relate the contaminant contents in the gas phase with those in the solid and non-aqueous liquid phases; (2) the distribution of the contaminants in the different phases present in the soil could be estimated with differences lower than 10% for 83% of the cases; and (3) an increase of the soil water content led to a decrease of the amount of contaminant in the solid and non-aqueous liquid phases, increasing the amount in the other phases.

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.