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SCHEER (Scientific Committee on Health, Environmental and Emerging Risks), Final Opinion on Draft Environmental Quality Standards for Priority Substances under the Water Framework Directive - fluoranthene, 22 November 2022
No full textSCHEER (Scientific Committee on Health, Environmental and Emerging Risks), Preliminary Opinion on Draft Environmental Quality Standards for Priority Substances under the Water Framework Directive - Erythromycin, 28 February 2022.
No full textSCHEER (Scientific Committee on Health, Environmental and Emerging Risks), Final Opinion on Draft Environmental Quality Standards for Priority Substances under the Water Framework Directive - bisphenol-A, 12 October 2022
No full textEffects of aquaculture waste feeds and antibiotics on marine benthic ecosystems in the Mediterranean Sea
No full textRedes de sensores de medición continua de parámetros de calidaddel agua relacionados con la potencial presencia de cianobacterias. Elsistema de monitorización del proyecto CianoMOD
No full text
Nada de fangos, biomasa bacteriana bien caracterizada
No full textEn el sector del agua contamos con una microbiología no patogénica que se encarga de eliminar de los ecosistemas acuáticos todos los residuos generados por el resto de seres vivos.
Explotamos este tipo de microbiología para depurar nuestras aguas y garantizar una calidad de vertido al medio natural a través de la eliminación de contaminantes. En ocasiones, estos mal llamados contaminantes son en realidad compuestos que contienen nitrógeno o fósforo, con capacidad nutricional y que deberían intentar recuperarse en vez de destruirse
Comparison of soil quality indexes calculated by network and principal component analysis for carbonated soils under different uses
No full textThere is an urgent need to conserve and improve the quality of agricultural soils in the coming decades. Decision tools capable of providing reliable information about soil quality are needed, and soil quality index (SQI) is one of the most used. Principal component analysis (PCA) is the common methodology to calculate it, however in some cases fails to differentiate soil quality properly. Therefore, the aim of this work is to assess a SQI through a different methodology as network analysis (NTA) and compare it with PCA, assuming that soil uses affect soil qualities differently. From soils with different uses (rainfed, olive grove and forest) network analysis and principal component analysis have been used to select a minimum dataset (MDS) to generate SQI from 36 physical, chemical and biological soil variables. Using NTA, geometric mean of the enzyme activities (GMEAN), bulk density (BD) and phosphatase activity (phos) where selected as indicators, while PCA selected total organic carbon (TOC), free Fe oxides (FeF), crystalline Mn oxides (MnX), pH, electrical conductivity (EC) and percentage of coarse sand (CS). Four SQI were calculated from each MDS through linear and non-linear scoring equations and by additive integration and weights. The SQI generated by NTA were more useful than those generated by PCA, as in addition to having fewer indicators they were able to better differentiate the uses in the study. This greater resolution capacity of the NTA would be the consequence of a better selection of indicators using this method than using PCA
