Triennial project approved by an international expert panel selected by F.C.T with a classification of Very Good; Participating Research Centres: ICETA Instituto de Ciências e Tecnologias Agrárias e Agro-Alimentares e CIGAR. Financing 52 555 €.

The incorrect deposition of some wastes, inadequate practices in some industrial activities and environmental accidents haves can cause environmental problems related with soil contamination. A typical Portuguese contamination case is the leaking of gasoline  products from underground storage tanks located in gas stations. This problem becomes more serious due the number of existing gas stations and the fact that a small amount of an organic compound is enough to contaminate big volumes of soil and groundwater becoming a serious risk to public health.

For this kind of contamination, there are several in-situ technologies, namely soil vapour extraction (SVE) and bioremediation (BR). SVE is a simple, efficient and relatively economical; it uses the application of vacuum to soil contaminated with volatile organic compounds. The vacuum produces the movement of the contaminant towards extraction wells that conduces it to adequate treatment units. BR  uses the degradative power of special micro-organisms to destruct organic contaminants present in soil. This technology has low costs, high efficiencies however is relatively slow. The combination of these two technologies have as objective, in the first stage, using the initial high efficiency of SVE decreasing the possible inhibition of the high contaminant load on the micro-organisms, and in a second stage, using BR that allow better efficiencies with no significant costs.

One of the great difficulties in choosing the most appropriate remediation technology is the lack of models that can predict the remediation time accurately, originating economical errors in projects.

Aiming at the creation of an accurate prediction tool, this project has as objectives: i) identify the most appropriate micro-organisms to degrade the proposed COVs; ii) simulate contaminated soils previously remediated by SVE; iii)  bioremediate the prepared soils analyzing the remediation time and process efficiency; iv) using artificial neural networks (ANN), obtain a model that can predict bioremediation time and efficiency of contaminated soils pre-treated with SVE, based on the soil´s physical and chemical properties; v) integrate simple mechanistic models to predict bioremediation time; vi) bioremediate real soils equally treated with SVE in order to validate the developed model; vii) study the best optimum of both  techniques to obtain the most effective and economical remediation.

The project considers soils constituted by sand, clay and organic matter contaminated with petroleum compounds (Benzene, Toluene, Ethylbenzene and Xylene). Appropriate chromatographic methodologies to monitor the process and quantify pollutants will be developed.