Phthalates, organotin compounds and per-polyfluoroalkyl substances in semiconfined areas of the Spanish coast: occurrence, sources and risk assessment

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Environmentally friendly analysis of emerging contaminants by pressurized hot water extraction-stir bar sorptive extraction-derivatization and gas chromatography-mass spectrometry

This work describes the development, optimiza- tion, and validation of a new method for the simultaneous determination of a wide range of pharmaceuticals (beta- blockers, lipid regulators ... ) and personal care products (fragrances, UV filters, phthalates ... ) in both aqueous and solid environmental matrices. Target compounds were extracted from sediments using pressurized hot water ex- traction followed by stir bar sorptive extraction. The first stage was performed at 1,500 psi during three static extrac- tion cycles of 5 min each after optimizing the extraction temperature (50 – 150 °C) and addition of organic modifiers (% methanol) to water, the extraction solvent. Next, aqueous extracts and water samples were processed using polydime- thylsiloxane bars. Several parameters were optimized for this technique, including extraction and desorption time, ionic strength, presence of organic modifiers, and pH. Fi- nally, analytes were extracted from the bars by ultrasonic irradiation using a reduced amount of solvent (0.2 mL) prior to derivatization and gas chromatography – mass spectrome- try analysis. The optimized protocol uses minimal amounts of organic solvents (<10 mL/sample) and time ( ≈ 8 h/sam- ple) compared to previous ex isting methodologies. Low standard deviation (usually below 10 %) and limits of de- tection (sub-ppb) vouch for the applicability of the method- ology for the analysis of target compounds at trace levels. Once developed, the method was applied to determin

Compuestos orgánicos en aguas del estuario del Río Miño. Proyecto "TEAM-Miño"

Comunicación presentada al II Congreso Internacional de Ingeniería Civil y Territorio. Agua, Cultura y Sociedad. Vigo, 20 y 21 de Mayo de 2013.Los resultados de este trabajo se engloban en el Marco del proyecto TEAM-Miño “Transferencia de herramientas para la Evaluación, Ordenación, Gestión y Educación Ambiental en Estuarios” financiado por la Unión Europea (POCTEP 2007-2013), que pretende desarrollar herramientas comunes para la tipificación y clasificación del estado ecológico de las masas de agua de transición del sur de Galicia y norte de Portugal, con la finalidad de colaborar en la implantación de la Directiva Marco del Agua. Se han analizado 19 muestras de agua recogidas a lo largo del río Miño y sus afluentes. En ellas se han determinado diferentes contaminantes de amplio interés industrial y ambiental, como hidrocarburos aromáticos policíclicos (HAP), compuestos organoestánnicos, alquilfenoles y bisfenol A. Salvo excepciones, los niveles encontrados en las muestras no son elevados (<0,1 μg/L). La mayoría de los HAPs se encuentran por debajo de los límites de cuantificación del método analítico (MQL), siendo el compuesto mayoritario el naftaleno. Los compuestos organoestánnicos tampoco han sido detectados. Los alquilfenoles están presentes en todos los puntos muestreados (<MQL-1 μg/L), mientras que el bisfenol A se ha encontrado únicamente en dos de ellos, pero a altas concentraciones (>3 μg/L). En ningún caso las concentraciones medidas superan los límites establecidos en la Directiva 2008/105/CE

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Bioaccumulation of PCB-153 and effects on molecular biomarkers acetylcholinesterase, glutathione-S-transferase and glutathione peroxidase in Mytilus galloprovincialis mussels

In this study, PCB-153 bioaccumulation kinetics and concentration-response experiments were performed employing wild Mytilus galloprovincialis mussels. In addition, the activity of three enzymatic biomarkers: glutathione S-transferase (GST), glutathione peroxidase (GPx) and acetylcholinesterase (AChE), were measured in the mussel gills. The experimental data fitted well to an asymptotic accumulation model with a high bioconcentration factor (BCF) of 9324 L Kg-1 and a very limited depuration capacity, described by a low excretion rate coefficient (Kd = 0.083 d-1). This study reports by first time in mussels significant inhibition of GST activity and significant induction of GPx activity as a result of exposure to dissolved PCB-153. In contrast, AChE activity was unaffected at all concentrations and exposure times tested. The effects on both enzymes are time-dependent, which stresses the difficulties inherent to the use of these biomarkers in chemical pollution monitoring programs.Versión del editor3,746