Application of a new pelletised organic bed for tex biodegradation in conventional biofilters

La problemática de las emisiones de gases contaminados generadas por las actividades humanas ha obligado al desarrollo de distintas tecnologías de tratamiento cuyo objetivo es minimizar el efecto de las mismas sobre el medio ambiente.La biofiltración es una de estas tecnologías de bajo coste que además es respetuosa con el entorno. Básicamente consiste en hacer pasar un gas contaminado a través de un medio poroso donde anida la biomasa que lleva a cabo la degradación de los contaminantes, generando productos no nocivos. El presente estudio se ha centrado en aportar soluciones a una de las principales limitaciones que presentan estos sistemas biológicos: el excesivo tiempo empleado por la biomasa para adaptarse a los contaminantes y degradarlos eficazmente.Se ha desarrollado una sistemática de aclimatación que ha permitido acortar el tiempo de adaptación de la biomasa específica para la eliminación de compuestos orgánicos volátiles (COVs). Estos compuestos, más específicamente los TEX (tolueno, p-xileno y etilbenceno), son uno de los grupos de contaminantes más habituales a nivel industrial, e incluso en ambientes interiores. La optimización de los parámetros de operación que afectan a esta tecnología (el nivel de humedad del soporte, temperatura, la interacción de varios contaminantes presentes en la misma corriente gaseosa, entre otros), ha llevado a la consecución de eficacias de depuración muy elevadas en el biotratamiento en continuo de corrientes gaseosas contaminadas.

Environmental Concerns in Water Pricing Policy: an Application of Data Envelopment Analysis (DEA)

25 p.

Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture

The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni's FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry.

Evaluation of marine phytoplankton toxicity by application of marine invertebrate bioassays

The dinoflagellate Alexandrium minutum and the haptophyte Prymnesium parvum are well known for their toxin production and negative effects in marine coastal environments. A. minutum produces toxins which cause paralytic shellfish poisoning in humans and can affect copepods, shellfish and other marine organisms. Toxins of P. parvum are associated with massive fish mortalities resulting in negative impacts on the marine ecosystem and large economic losses in commercial aquaculture. The aim of this work is to improve our knowledge about the reliability of the use of marine invertebrate bioassays to detect microalgae toxicity, by performing: (i) a 24- to 48-h test with the brine shrimp Artemia franciscana; (ii) a 48-hour embryo-larval toxicity test with the sea urchin Paracentrotus lividus; and (iii) a 72-h test with the amphipod Corophium multisetosum. The results indicate that A. franciscana and P. lividus larvae are sensitive to the toxicity of A. minutum and P. parvum. LC50 comparison analysis between the tested organisms reveals that A. franciscana is the most sensitive organism for A. minutum. These findings suggest that the use of different organizational biological level bioassays appears to be a suitable tool for A. minutum and P. parvum toxicity assessment.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques-Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description-using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.