The use of antioxidant enzymes in freshwater biofilms: temporal variability vs. toxicological responses.

This study aims to investigate the potential of antioxidant enzyme activities (AEA) as biomarkers of oxidative stress in freshwater biofilms. Therefore, biofilms were grown in channels for 38 days and then exposed to different concentrations (0-150 µg L(-1)) of the herbicide oxyfluorfen for 5 more weeks. Under control conditions, the AEA of biofilms were found to change throughout time with a significant increase in ascorbate peroxidase (APX) activity during the exponential growth and a more important role of catalase (CAT) and glutathione reductase (GR) activities during the slow growth phase. Chronic exposure to oxyfluorfen led to slight variations in AEA, however, the ranges of variability of AEA in controls and exposed communities were similar, highlighting the difficulty of a direct interpretation of AEA values. After 5 weeks of exposure to oxyfluorfen, no clear effects were observed on chl-a concentration or on the composition of other pigments suggesting that algal group composition was not affected. Eukaryotic communities were structured clearly by toxicant concentration and both eukaryotic and bacterial richness were reduced in communities exposed to the highest concentration. In addition, during acute exposure tests performed at the end of the chronic exposure, biofilms chronically exposed to 75 and 150 µg L(-1) oxyfluorfen showed a higher CAT activity than controls. Chronic exposure to oxyfluorfen provoked then structural changes but also functional changes in the capacity of biofilm CAT activity to respond to a sudden increase in concentration, suggesting a selection of species with higher antioxidant capacity. This study highlighted the difficulty of interpretation of AEA values due to their temporal variation and to the absence of absolute threshold value indicative of oxidative stress induced by contaminants. Nevertheless, the determination of AEA pattern throughout acute exposure test is of high interest to compare oxidative stress levels undergone by different biofilm communities and thus determine their antioxidant capacity.

Water toxicity assessment and spatial pollution patterns identification in a Mediterranean River Basin District. Tools for water management and risk analysis.

In compliance with the requirements of the EU Water Framework Directive, monitoring of the ecological and chemical status of Catalan river basins (NE Spain) is carried out by the Catalan Water Agency. The large amount of data collected and the complex relationships among the environmental variables monitored often mislead data interpretation in terms of toxic impact, especially considering that even pollutants at very low concentrations might contribute to the total toxicity. The total dataset of chemical monitoring carried out between 2007 and 2008 (232 sampling stations and 60 pollutants) has been analyzed using sequential advanced modeling techniques. Data on concentrations of contaminants in water were pre-treated in order to calculate the bioavailable fraction, depending on substance properties and local environmental conditions. The resulting values were used to predict the potential impact of toxic substances in complex mixtures on aquatic biota and to identify hot spots. Exposure assessment with Species Sensitivity Distribution (SSD) and mixture toxicity rules were used to compute the multi-substances Potentially Affected Fraction (msPAF). The combined toxicity of the pollutants analyzed in the Catalan surface waters might potentially impact more than 50% of the species in 10% of the sites. In order to understand and visualize the spatial distribution of the toxic risk, Self Organising Map (SOM), based on the Kohonen's Artificial Neural Network (ANN) algorithm, was applied on the output data of these models. Principal Component Analysis (PCA) was performed on top of Neural Network results in order to identify main influential variables which account for the pollution trends. Finally, predicted toxic impacts on biota have been linked and correlated to field data on biological quality indexes using macroinvertebrate and diatom communities (IBMWP and IPS). The methodology presented could represent a suitable tool for water managers in environmental risk assessment and management.

Patterning ecological risk of pesticide contamination at the river basin scale.

Ecological risk assessment was conducted to determine the risk posed by pesticide mixtures to the Adour-Garonne river basin (south-western France). The objectives of this study were to assess the general state of this basin with regard to pesticide contamination using a risk assessment procedure and to detect patterns in toxic mixture assemblages through a self-organizing map (SOM) methodology in order to identify the locations at risk. Exposure assessment, risk assessment with species sensitivity distribution, and mixture toxicity rules were used to compute six relative risk predictors for different toxic modes of action: the multi-substance potentially affected fraction of species depending on the toxic mode of action of compounds found in the mixture (msPAF CA(TMoA) values). Those predictors computed for the 131 sampling sites assessed in this study were then patterned through the SOM learning process. Four clusters of sampling sites exhibiting similar toxic assemblages were identified. In the first cluster, which comprised 83% of the sampling sites, the risk caused by pesticide mixture toward aquatic species was weak (mean msPAF value for those sites<0.0036%), while in another cluster the risk was significant (mean msPAF<1.09%). GIS mapping allowed an interesting spatial pattern of the distribution of sampling sites for each cluster to be highlighted with a significant and highly localized risk in the French department called "Lot et Garonne". The combined use of the SOM methodology, mixture toxicity modelling and a clear geo-referenced representation of results not only revealed the general state of the Adour-Garonne basin with regard to contamination by pesticides but also enabled to analyze the spatial pattern of toxic mixture assemblage in order to prioritize the locations at risk and to detect the group of compounds causing the greatest risk at the basin scale.