Impairment of the Bacterial Biofilm Stability by Triclosan

The accumulation of the widely-used antibacterial and antifungal compound triclosan (TCS) in freshwaters raises concerns about the impact of this harmful chemical on the biofilms that are the dominant life style of microorganisms in aquatic systems. However, investigations to-date rarely go beyond effects at the cellular, physiological or morphological level. The present paper focuses on bacterial biofilms addressing the possible chemical impairment of their functionality, while also examining their substratum stabilization potential as one example of an important ecosystem service. The development of a bacterial assemblage of natural composition – isolated from sediments of the Eden Estuary (Scotland, UK) – on non-cohesive glass beads (,63 mm) and exposed to a range of triclosan concentrations (control, 2 – 100 mg L21) was monitored over time by Magnetic Particle Induction (MagPI). In parallel, bacterial cell numbers, division rate, community composition (DGGE) and EPS (extracellular polymeric substances: carbohydrates and proteins) secretion were determined. While the triclosan exposure did not prevent bacterial settlement, biofilm development was increasingly inhibited by increasing TCS levels. The surface binding capacity (MagPI) of the assemblages was positively correlated to the microbial secreted EPS matrix. The EPS concentrations and composition (quantity and quality) were closely linked to bacterial growth, which was affected by enhanced TCS exposure. Furthermore, TCS induced significant changes in bacterial community composition as well as a significant decrease in bacterial diversity. The impairment of the stabilization potential of bacterial biofilm under even low, environmentally relevant TCS levels is of concern since the resistance of sediments to erosive forces has large implications for the dynamics of sediments and associated pollutant dispersal. In addition, the surface adhesive capacity of the biofilm acts as a sensitive measure of ecosystem effects

Fate of the herbicide glyphosate and its metabolite AMPA in soils and their transfer to surface waters: A multi-scale approach in the Lavaux vineyards, western Switzerland

The use of herbicides in agriculture may lead to environmental problems, such as surface water pollution, with a potential risk for aquatic organisms. The herbicide glyphosate is the most used active ingredient in the world and in Switzerland. In the Lavaux vineyards it is nearly the only molecule applied. This work aimed at studying its fate in soils and its transfer to surface waters, using a multi-scale approach: from molecular (10-9 m) and microscopic scales (10-6 m), to macroscopic (m) and landscape ones (103 m). First of all, an analytical method was developed for the trace level quantification of this widely used herbicide and its main by-product, aminomethylphosphonic acid (AMPA). Due to their polar nature, their derivatization with 9-fluorenylmethyl chloroformate (FMOC-Cl) was done prior to their concentration and purification by solid phase extraction. They were then analyzed by ultra performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). The method was tested in different aqueous matrices with spiking tests and validated for the matrix effect correction in relevant environmental samples. Calibration curves established between 10 and 1000ng/l showed r2 values above 0.989, mean recoveries varied between 86 and 133% and limits of detection and quantification of the method were as low as 5 and 10ng/l respectively. At the parcel scale, two parcels of the Lavaux vineyard area, located near the Lutrive River at 6km to the east of Lausanne, were monitored to assess to which extent glyphosate and AMPA were retained in the soil or exported to surface waters. They were equipped at their bottom with porous ceramic cups and runoff collectors, which allowed retrieving water samples for the growing seasons 2010 and 2011. Results revealed that the mobility of glyphosate and AMPA in the unsaturated zone was likely driven by the precipitation regime and the soil characteristics, such as slope, porosity structure and layer permeability discrepancy. Elevated glyphosate and AMPA concentrations were measured at 60 and 80 cm depth at parcel bottoms, suggesting their infiltration in the upper parts of the parcels and the presence of preferential flow in the studied parcels. Indeed, the succession of rainy days induced the gradual saturation of the soil porosity, leading to rapid infiltration through macropores, as well as surface runoff formation. Furthermore, the presence of more impervious weathered marls at 100 cm depth induced throughflows, the importance of which for the lateral transport of the herbicide molecules was determined by the slope steepness. Important rainfall events (>10 mm/day) were clearly exporting molecules from the soil top layer, as indicated by important concentrations in runoff samples. A mass balance showed that total loss (10-20%) mainly occurred through surface runoff (96%) and, to a minor extent, by throughflows in soils (4%), with subsequent exfiltration to surface waters. Observations made in the Lutrive River revealed interesting details of glyphosate and AMPA dynamics in urbanized landscapes, such as the Lavaux vineyards. Indeed, besides their physical and chemical properties, herbicide dynamics at the catchment level strongly depend on application rates, precipitation regime, land use and also on the presence of drains or constructed channels. Elevated concentrations, up to 4970 ng/l, observed just after the application, confirmed the diffuse export of these compounds from the vineyard area by surface runoff during main rain events. From April to September 2011, a total load of 7.1 kg was calculated, with 85% coming from vineyards and minor urban sources and 15% from arable crops. Small vineyard surfaces could generate high concentrations of herbicides and contribute considerably to the total load calculated at the outlet, due to their steep slopes (~10%). The extrapolated total amount transferred yearly from the Lavaux vineyards to the Lake of Geneva was of 190kg. At the molecular scale, the possible involvement of dissolved organic matter (DOM) in glyphosate and copper transport was studied using UV/Vis fluorescence spectroscopy. Combined with parallel factor (PARAFAC) analysis, this technique allowed characterizing DOM of soil and surface water samples from the studied vineyard area. Glyphosate concentrations were linked to the fulvic-like spectroscopic signature of DOM in soil water samples, as well as to copper, suggesting the formation of ternary complexes. In surface water samples, its concentrations were also correlated to copper ones, but not in a significant way to the fulvic-like signature. Quenching experiments with standards confirmed field tendencies in the laboratory, with a stronger decrease in fluorescence intensity for fulvic-like fluorophore than for more aromatic ones. Lastly, based on maximum concentrations measured in the river, an environmental risk for these compounds was assessed, using laboratory tests and ecotoxicity data from the literature. In our case and with the methodology applied, the risk towards aquatic species was found negligible (RF<1).

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.

Improved detection of Rhodococcus coprophilus with a new quantitative PCR assay.

Agricultural practices, such as spreading liquid manure or the utilisation of land as animal pastures, can result in faecal contamination of water resources. Rhodococcus coprophilus is used in microbial source tracking to indicate animal faecal contamination in water. Methods previously described for detecting of R. coprophilus in water were neither sensitive nor specific. Therefore, the aim of this study was to design and validate a new quantitative polymerase chain reaction (qPCR) to improve the detection of R. coprophilus in water. The new PCR assay was based on the R. coprophilus 16S rRNA gene. The validation showed that the new approach was specific and sensitive for deoxyribunucleic acid from target host species. Compared with other PCR assays tested in this study, the detection limit of the new qPCR was between 1 and 3 log lower. The method, including a filtration step, was further validated and successfully used in a field investigation in Switzerland. Our work demonstrated that the new detection method is sensitive and robust to detect R. coprophilus in surface and spring water. Compared with PCR assays that are available in the literature or to the culture-dependent method, the new molecular approach improves the detection of R. coprophilus.

Wet Oxidation of Concentrated Wastewaters: Process Combination and Reaction Kinetic Modeling

The accumulation of aqueous pollutants is becoming a global problem. The search for suitable methods and/or combinations of water treatment processes is a task that can slow down and stop the process of water pollution. In this work, the method of wet oxidation was considered as an appropriate technique for the elimination of the impurities present in paper mill process waters. It has been shown that, when combined with traditional wastewater treatment processes, wet oxidation offers many advantages. The combination of coagulation and wet oxidation offers a new opportunity for the improvement of the quality of wastewater designated for discharge or recycling. First of all, the utilization of coagulated sludge via wet oxidation provides a conditioning process for the sludge, i.e. dewatering, which is rather difficult to carry out with untreated waste. Secondly, Fe2(SO4)3, which is employed earlier as a coagulant, transforms the conventional wet oxidation process into a catalytic one. The use of coagulation as the post-treatment for wet oxidation can offer the possibility of the brown hue that usually accompanies the partial oxidation to be reduced. As a result, the supernatant is less colored and also contains a rather low amount of Fe ions to beconsidered for recycling inside mills. The thickened part that consists of metal ions is then recycled back to the wet oxidation system. It was also observed that wet oxidation is favorable for the degradation of pitch substances (LWEs) and lignin that are present in the process waters of paper mills. Rather low operating temperatures are needed for wet oxidation in order to destruct LWEs. The oxidation in the alkaline media provides not only the faster elimination of pitch and lignin but also significantly improves the biodegradable characteristics of wastewater that contains lignin and pitch substances. During the course of the kinetic studies, a model, which can predict the enhancements of the biodegradability of wastewater, was elaborated. The model includes lumped concentrations suchas the chemical oxygen demand and biochemical oxygen demand and reflects a generalized reaction network of oxidative transformations. Later developments incorporated a new lump, the immediately available biochemical oxygen demand, which increased the fidelity of the predictions made by the model. Since changes in biodegradability occur simultaneously with the destruction of LWEs, an attempt was made to combine these two facts for modeling purposes.

Enrichment cultures should be performed in the detection of bacterial oral human pathogens in DUWLs

Water delivered by dental units during routine dental practice is densely contaminated by bacteria. The aim of this study was to determine actual isolation of the microorganisms sprayed from Dental Unit Water Lines (DUWLs) when enrichment cultures are performed and to compare frequencies with those obtained without enrichment cultures. Moreover, the antimicrobial susceptibilities of the microorganisms isolated were also studied. Water samples were collected from one hundred dental equipments in use at Dental Hospital of our University in order to evaluate the presence/absence of microorganisms and to perform their presumptive identification. Aliquots from all of the samples were inoculated in eight different media including both enrichment and selective media. Minimal inhibitory concentrations (MIC) were determined by the broth dilution method. The results herein reported demonstrate that most of the DUWLs were colonized by bacteria from human oral cavity; when enrichment procedures were applied the percentage of DUWLs with detectable human bacteria was one hundred percent. The results showed that in order to evaluate the actual risk of infections spread by DUWLs the inclusion of a step of pre-enrichment should be performed. The need for devices preventing bacterial contamination of DUWLs is a goal to be achieved in the near future that would contribute to maintain safety in dental medical assistance

Oral pathogens in duwl's: risk of infection and strategies for their control

Water lines are an important source of potentíal contamination. Every dental unit is equipped with small-bore flexible plastic tubing to bring water to different hand pieces, such as the air/water syringe, the ultrasonic scaler or the high-speed hand piece. Most dental units are connected directly to municipal distribution systems for potable water and chlorinated or not, this water contains diverse...

Diel pattern of Chironomids drift in the Llobregat river

Drift has recived considerable attention in recent times as a method to collect chironomid pupal exuviae (COFFMAN, 1973; LAVILLE, 1979, 1981) for taxonomie as well as water pollution studies (WILSON, 1977).

25-years of biomonitoring in two mediterranean streams (Llobregat and Besos basins, NE Spain)

Biological water quality changes in two Mediterranean river basins from a network of 42 sampling sites assessed since 1979 are presented. In order to characterize the biological quality, the index FBILL, designed to characterize these rivers" quality using aquatic macroinvertebrates, is used. When comparing the data from recent years to older ones, only two headwater sites from the 42 had improved their water quality to good or very good conditions. In the middle or low river basin sites or even in headwater localities were river flow is reduced, the important investment to build up sewage water treatment systems and plants (more than 70 in 15 years) allowed for a small recovery from poor or very poor conditions to moderate water quality. Nevertheless still a significant number (25 %) of the localities remain in poor conditions. The evolution of the quality in several points of both basins shows how the main problems for the recovery of the biological quality is due to the water diverted for small hydraulic plants, the presence of saline pollution in the Llobregat River, and the insufficient water depuration. In the smaller rivers, and specially the Besòs the lack of dilution flows from the treatment plants is the main problem for water quality recovery.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.

Study of seawater biofiltration by measuring adenosine triphosphate (ATP) and turbidity

In the present study, we examined seawater biofiltration in terms of adenosine triphosphate (ATP) and turbidity. A pilot biofilter continuously fed with fresh seawater reduced both turbidity and biological activity measured by ATP. Experiments operated with an empty bed contact time (EBCT) of between 2 and 14 min resulted in cellular ATP removals of 32% to 60% and turbidity removals of 38% to 75%. Analysis of the water from backwashing the biofilter revealed that the first half of the biofilter concentrated around 80% of the active biomass and colloidal material that produces turbidity. By reducing the EBCT, the biological activity moved from the first part of the biofilter to the end. Balances of cellular ATP and turbidity between consecutive backwashings indicated that the biological activity generated in the biofilter represented more than 90% of the detached cellular ATP. In contrast, the trapped ATP was less than 10% of the overall cellular ATP detached during the backwashing process. Furthermore, the biological activity generated in the biofilter seemed to be more dependent on the elapsed time than the volume filtered. In contrast, the turbidity trapped in the biofilter was proportional to the volume filtered, although a slightly higher amount of turbidity was found in the backwashing water; this was probably due to attrition of the bed medium. Finally, no correlations were found between turbidity and ATP, indicating that the two parameters focus on different matter. This suggests that turbidity should not be used as an alternative to cellular concentration.

Study on the removal of biodegradable NOM from seawater using biofiltration

Despite the low biodegradability of seawater NOM, problems associated with biofouling are common in facilities that handle seawater. In this work, a fixed-film aerobic biofilter is proposed as an effective unit for preventing biofouling in such facilities. A packed-bed biofilter with an EBCT = 6 - 11 min was employed. The results demonstrated that the DOC is reduced by 6% and the BOD7 is reduced up to 15%. The LC-OCD analysis revealed that biofiltration abates the LMW neutrals and biopolymer fractions by 33 and 17%, respectively. However, the fractionation with UF membrane showed that the biofiltration process is able to degrade the more biodegradable compounds that have molecular weights that are greater than 1 kDa and compounds with molecular weights of less than 1 kDa. After biofiltration, the biological activity measured in terms of ATP removal was reduced by 60%. Finally, a test to evaluate the biofilm formation capacity of a water sample revealed reductions of ~94% when comparing biofiltered and non-biofiltered seawater. Therefore, a fixed-film aerobic biofiltration process could be a useful treatment for the removal of biodegradable organic matter from seawater and for improving the water quality in terms of less biofilm formation capacity.

Photocatalytic Activity of Nanostructured Anatase Coatings Obtained by Cold Gas Spray

This article describes a photocatalytic nanostructured anatase coating deposited by cold gas spray (CGS)supported on titanium sub-oxide (TiO22x) coatings obtained by atmospheric plasma spray (APS) onto stainless steel cylinders. The photocatalytic coating was homogeneous and preserved the composition and nanostructure of the starting powder. The inner titanium sub-oxide coating favored the deposition of anatase particles in the solid state. Agglomerated nano-TiO2 particles fragmented when impacting onto the hard surface of the APS TiO22x bond coat. The rough surface provided by APS provided an ideal scenario for entrapping the nanostructured particles, which may be adhered onto the bond coat due to chemical bonding; a possible bonding mechanism is described. Photocatalytic experiments showed that CGS nano-TiO2 coating was active for photodegrading phenol and formic acid under aqueous conditions. The results were similar to the performance obtained by competitor technologies and materials such as dip-coating P25 photocatalysts. Disparity in the final performance of the photoactive materials may have been caused by differences in grain size and the crystalline composition of titanium dioxide.

New methods for the concentration of viruses from urban sewage using quantitative PCR.

Viruses are among the most important pathogens present in water contaminated with feces or urine and represent a serious risk to human health. Four procedures for concentrating viruses from sewage have been compared in this work, three of which were developed in the present study. Viruses were quantified using PCR techniques. According to statistical analysis and the sensitivity to detect human adenoviruses (HAdV), JC polyomaviruses (JCPyV) and noroviruses genogroup II (NoV GGII): (i) a new procedure (elution and skimmed-milk flocculation procedure (ESMP)) based on the elution of the viruses with glycine-alkaline buffer followed by organic flocculation with skimmed-milk was found to be the most efficient method when compared to (ii) ultrafiltration and glycine-alkaline elution, (iii) a lyophilization-based method and (iv) ultracentrifugation and glycine-alkaline elution. Through the analysis of replicate sewage samples, ESMP showed reproducible results with a coefficient of variation (CV) of 16% for HAdV, 12% for JCPyV and 17% for NoV GGII. Using spiked samples, the viral recoveries were estimated at 30-95% for HAdV, 55-90% for JCPyV and 45-50% for NoV GGII. ESMP was validated in a field study using twelve 24-h composite sewage samples collected in an urban sewage treatment plant in the North of Spain that reported 100% positive samples with mean values of HAdV, JCPyV and NoV GGII similar to those observed in other studies. Although all of the methods compared in this work yield consistently high values of virus detection and recovery in urban sewage, some require expensive laboratory equipment. ESMP is an effective low-cost procedure which allows a large number of samples to be processed simultaneously and is easily standardizable for its performance in a routine laboratory working in water monitoring. Moreover, in the present study, a CV was applied and proposed as a parameter to evaluate and compare the methods for detecting viruses in sewage samples.