In a biopurification system such as a biobed, the rhizosphere of the grass layer may be a significant factor for promoting pesticide dissipation in the biomixture. The rhizosphere effect of a Lolium perenne, Festuca arundinacea and Trifolium repens mixture on the dissipation of a pesticide combination that was composed of atrazine, chlorpyrifos and isoproturon was studied. The assay was performed using glass pots divided into two separate compartments (root surface and root-free), each filled with an organic biomixture (oat husk, top soil and peat) and contaminated with the pesticide mixture at 5 mg kg-1.Non-planted and non-contaminated pots were also used as controls. The results indicated that there were high atrazine, chlorpyrifos and isoproturon dissipation in the planted pots compared with the unplanted pots. An inverse correlation was found throughout the assay between phenoloxidase activity and residual pesticide (0.684 to 0.952). Indeed, fungal biomass was positively correlated with phenoloxidase activity on day 1 (r =0.825) and day 30 (r =0.855). Besides, exudation of oxalic and malic acid in contaminated pots was higher than in the control without pesticides, associated with oxidation of the pesticide mixture in the biomixture of a bioded system. Therefore, the grass layer enhances pesticide removal in biobeds.

Degradation of pentachlorophenol (PCP) in the rhizosphere of Lolium multiflorum (ryegrass) was studied in an Andisol of southern Chile. A rhizotron system was designed to evaluate the effect of different PCP concentrations (50, 100 and 250 mg kg-1) on plant biomass, organic acid exudation, dehydrogenase and the ß-glucosidase activity of L. multiflorum. Moreover, the microbial biomass was estimated using a molecular technique in different sections of rhizotrons. More than 96% of PCP was removed in the rhizotron system by adsorption in the soil and degradation by the rhizosphere of L. multiflorum. The plant biomass decreased when the PCP concentration was increased in the soil, the biomass for the treatment with 250 mg kg-1 being significantly lower. The estimation of the microbial biomass and ß-glucosidase activity in the soil was not affected when the PCP concentration was increased, indicating a low toxicity of the contaminant due to its lesser availability. However, the dehydrogenase activity decreased when increasing the PCP concentration, showing negative effects on microorganisms. The organic acid exudates varied during the incubation time, influenced by the stress of the different treatments and the distribution of roots. Therefore, Lolium multiflorum is a promising candidate for designing field scale phytoremediation processes.

Bioremediation processes are negatively affected by the low aqueous solubility of some contaminants; therefore their bioavailability may be enhanced by the addition of surfactants. These compounds are organic molecules that can be chemically and biologically produced. Surfactants contain both hydrophilic and hydrophobic groups, therefore reducing surface and interfacial tensions of immiscible fluids and increasing the solubility and sorption of hydrophobic organic and inorganic compounds. This article provides an overview of characteristics of natural and synthetic surfactants and the effects of biosurfactants on solubility, sorption and biodegradation of hydrophobic organic contaminants; as well as the effects of biosurfactants on degrader microorganisms as white-rot fungi. Finally, some examples of application of natural surfactants for bioremediation of contaminated soils are shown. In general, this overview indicates the great potential of biosurfactants on the remediation of contaminated sites.

A hybrid neural network model for simulating the process of enzymatic reduction of fructose to sorbitol process catalyzed by glucose-fructose oxidoreductase in Zymomonas mobilis CP4 is presented. Data used to derive and validate the model was obtained from experiments carried out under different conditions of pH, temperature and concentrations of both substrates (glucose and fructose) involved in the reaction. Sonicated and lyophilized cells were used as source of the enzyme. The optimal pH for sorbitol synthesis at 30º C is 6.5. For a value of pH of 6, the optimal temperature is 35º C. The neural network in the model computes the value of the kinetic relationship. The hybrid neural network model is able to simulate changes in the substrates and product concentrations during sorbitol synthesis under pH and temperature conditions ranging between 5 and 7.5 and 25 and 40º C, respectively. Under these conditions the rate of sorbitol synthesis shows important differences. Values computed using the hybrid neural network model have an average error of 1.7·10-3 mole.