Contaminación de las aguas por nitratos y efectos sobre la salud

Publicado en la página web de la Consejería de Salud: www.juntadeandalucia.es/salud (Consejería de Salud / Ciudadanía / Nuestra Salud / Medio ambiente y Salud / Aguas de Consumo Público)

Calidad microbiológica de las aguas de baño del embalse Conde del Guadalhorce, de Málaga (España), 2000-2005.

Background. Determine the presence and evolution of indicators microorganisms of water pollution in “Conde del Guadalhorce” reservoir, Málaga city, Spain. A second objective was to analyze pollution degree and evaluate the sanitary quality of bathing water and compliance with European Directive 76/160/CE. Method. A total of 120 water samples were collected in two bathing freshwater sites during May to September sampling period between 2000 to 2005, and the numbers of total coliforms (CT), faecal coliforms (CF) and faecal streptococci (EF) were enumerated using the membrane filtration method. We used the log-normal distribution method and calculate the logarithmic means, percentile points, ratios CF:EF, ANOVA and Pearson correlations. Results. Only two samples overcome CF limit values at Camping sampling station during 2000 year. Ratios CF:EF values were higher (> 4) during 2000 to 2002, and lower (< 0,7) during 2003 to 2005. Significant differences (ANOVA F = 3,41, ∝ < 0,01) was only observed with EF during evaluated period. There was no significant difference between concentration means at bathing water sites (ANOVA, F = 3,395, ∝ < 0,01). The counts of CT and CF were significantly correlated in Kiosko water samples, while in Camping water, significant correlation (t = 0,632, p < 0,05) was only observed with EF at the Camping station during 2000, 2003 and 2005 years. Conclusions. “Conde del Guadalhorce” reservoir showed hygienic conditions for safety bathing. Globally, water bathing quality is good. CT, CF y EF indicators were agreed with UE Directive during 2000- 2005, with exception CF at Camping station in 2000 year. CT y CF concentrations at Camping were frecuently higher than Kiosko, it could be caused to swimmers abundance and recreational activities. There was a trend towards rising EF, it could be caused to faecal pollution source of animal origin, needed to research it.

Perspectivas de salud ambiental en la investigación de brotes epidémicos asociados con una zona de abastecimiento de aguas de consumo humano.

Introduction. The purpose of this work was studied the magnitude, possible causes and contributing environmental factors in the waterbourne outbreaks appearance, in the performance area of the locality of Benaoján (Town of Málaga. Spain). Material/Methods. Analysis of the potability of the water and disinfection controls. Evaluation of the fulfillment of the quality of the drinking water and sanitary technical requirements of water supplies, pursuant to the Spanish regulation on public consumption waters. Results. We have been accomplished 110 potability analysis, proving that 13,4% of the samples do not comply with the potability criteria of the water. It were practiced 647 controls of disinfection, of those which 53% resulted not accordant. The design of the supply net is of the branching type and at least presents 30 blind branches, points where the water remains stagnant. The municipal waters service doesn´t make analytics controls of the quality of the water neither has implanted Standard Operating Procedures (SOPs) of the facilities of the supply. Discussion. Environmental research suggests that the public water supply net is a source of infection, problem related to the epidemic outbreaks appearance. Because of this the water consumption not treaty must be avoided.

Antibiotic resistant bacteria/genes dissemination in lacustrine sediments highly increased following cultural eutrophication of Lake Geneva (Switzerland).

This study investigates faecal indicator bacteria (FIB), multiple antibiotic resistant (MAR), and antibiotic resistance genes (ARGs), of sediment profiles from different parts of Lake Geneva (Switzerland) over the last decades. MARs consist to expose culturable Escherichia coli (EC) and Enterococcus (ENT) to mixed five antibiotics including Ampicillin, Tetracycline, Amoxicillin, Chloramphenicol and Erythromycin. Culture-independent is performed to assess the distribution of ARGs responsible for, β-lactams (blaTEM; Amoxicillin/Ampicillin), Streptomycin/Spectinomycin (aadA), Tetracycline (tet) Chloramphenicol (cmlA) and Vancomycin (van). Bacterial cultures reveal that in the sediments deposited following eutrophication of Lake Geneva in the 1970s, the percentage of MARs to five antibiotics varied from 0.12% to 4.6% and 0.016% to 11.6% of total culturable EC and ENT, respectively. In these organic-rich bacteria-contaminated sediments, the blaTEM resistant of FIB varied from 22% to 48% and 16% to 37% for EC and ENT respectively, whereas the positive PCR assays responsible for tested ARGs were observed for EC, ENT, and total DNA from all samples. The aadA resistance gene was amplified for all the sediment samples, including those not influenced by WWTP effluent water. Our results demonstrate that bacteria MARs and ARGs highly increased in the sediments contaminated with WWTP effluent following the cultural eutrophication of Lake Geneva. Hence, the human-induced changing limnological conditions highly enhanced the sediment microbial activity, and therein the spreading of antibiotic resistant bacteria and genes in this aquatic environment used to supply drinking water in a highly populated area. Furthermore, the presence of the antibiotic resistance gene aadA in all the studied samples points out a regional dissemination of this emerging contaminant in freshwater sediments since at least the late nineteenth century.

Biodièsel: impacte en origen del biodièsel consumit a Catalunya i a Espanya

Actualment la situació del mercat espanyol i català del biodièsel es caracteritza per les grans importacions d’oli de palma africana. Per a produir aquesta matèria primera s’estan establint plantacions a gran escala d’Elaeis guineensis (palma africana) a Indonèsia. El monocultiu d’Elaeis guineensis i la producció de l’oli tenen associats grans impactes ambientals i socials. Per una banda, els impactes ambientals són principalment la desforestació, el canvi d’ús del sòl, la pèrdua de biodiversitat, l’erosió del sòl i la contaminació de l’aire, del sòl de l’aigua. Per altra banda, els impactes socials més destacats són la violació dels drets humans dels pobles indígenes, els conflictes d’adquisició de terres i que es compromet la seguretat alimentària del país. Per tant, l’ús del biodièsel produït amb oli de palma africana redueix les emissions de GEH a Espanya i a Catalunya provocant un gran impacte ambiental i social a Indonèsia.

Sulfur and strontium isotope composition of the Llobregat River (NE Spain): Tracers of natural and anthropogenic chemicals in stream waters

The use of sulfur and strontium isotopes as tracers for the source/s of water contaminants have been applied to the water of the Llobregat River system (NE Spain). Surface water samples from June 1997 were collected from the Llobregat River and its main tributaries and creeks. The chemistry of most stream waters are controlled mainly by the weathering of Tertiary chemical sediments within the drainage basin. The largest variation in delta(34)S values were found in the small creeks with values ranging from -9.9 to 15parts per thousand, whilst in the main river channels values ranged from 6.3 to 12.4parts per thousand. The Sr-87/Sr-86 ratio for dissolved strontium ranged from 0.70795 for a non-polluted site to 0.70882 for a polluted one. Most of the waters with high NO3 and low Ca/Na ratio converge to the same Sr-87/Sr-86 value, pointing to dominant pollutant end member contribution or a mixing of pollutants with an isotopic composition around 0.7083-0.7085. Although the concentration of the natural inputs in the river for sulfate and strontium are high, as a result of the sulfate outcrops within the geology of the basin, their isotopic characteristics suggest that they can be used as a discriminating device in water pollution problems. However to establish the detailed characteristics of the isotopes as geochemical tools, specific high-resolution case studies are necessary in small areas, where the inputs are well known.

Simple and fast methods based on solid-phase extraction coupled to liquid chromatography with UV detection for the monitoring of caffeine in natural and waste waters as marker of anthropogenic impact

Two concentration methods for fast and routine determination of caffeine (using HPLC-UV detection) in surface, and wastewater are evaluated. Both methods are based on solid-phase extraction (SPE) concentration with octadecyl silica sorbents. A common “offline” SPE procedure shows that quantitative recovery of caffeine is obtained with 2 mL of an elution mixture solvent methanol-water containing at least 60% methanol. The method detection limit is 0.1 μg L−1 when percolating 1 L samples through the cartridge. The development of an “online” SPE method based on a mini-SPE column, containing 100 mg of the same sorbent, directly connected to the HPLC system allows the method detection limit to be decreased to 10 ng L−1 with a sample volume of 100 mL. The “offline” SPE method is applied to the analysis of caffeine in wastewater samples, whereas the “on-line” method is used for analysis in natural waters from streams receiving significant water intakes from local wastewater treatment plants

Surface and subsurface decomposition of a desiccated grass pasture biomass related to erosion and its prediction with RUSLE

Erosion is deleterious because it reduces the soil's productivity capacity for growing crops and causes sedimentation and water pollution problems. Surface and buried crop residue, as well as live and dead plant roots, play an important role in erosion control. An efficient way to assess the effectiveness of such materials in erosion reduction is by means of decomposition constants as used within the Revised Universal Soil Loss Equation - RUSLE's prior-land-use subfactor - PLU. This was investigated using simulated rainfall on a 0.12 m m-1 slope, sandy loam Paleudult soil, at the Agriculture Experimental Station of the Federal University of Rio Grande do Sul, in Eldorado do Sul, State of Rio Grande do Sul, Brazil. The study area had been covered by native grass pasture for about fifteen years. By the middle of March 1996, the sod was mechanically mowed and the crop residue removed from the field. Late in April 1996, the sod was chemically desiccated with herbicide and, about one month later, the following treatments were established and evaluated for sod biomass decomposition and soil erosion, from June 1996 to May 1998, on duplicated 3.5 x 11.0 m erosion plots: (a) and (b) soil without tillage, with surface residue and dead roots; (c) soil without tillage, with dead roots only; (d) soil tilled conventionally every two-and-half months, with dead roots plus incorporated residue; and (e) soil tilled conventionally every six months, with dead roots plus incorporated residue. Simulated rainfall was applied with a rotating-boom rainfall simulator, at an intensity of 63.5 mm h-1 for 90 min, eight to nine times during the experimental period (about every two-and-half months). Surface and subsurface sod biomass amounts were measured before each rainfall test along with the erosion measurements of runoff rate, sediment concentration in runoff, soil loss rate, and total soil loss. Non-linear regression analysis was performed using an exponential and a power model. Surface sod biomass decomposition was better depicted by the exponential model, while subsurface sod biomass was by the power model. Subsurface sod biomass decomposed faster and more than surface sod biomass, with dead roots in untilled soil without residue on the surface decomposing more than dead roots in untilled soil with surface residue. Tillage type and frequency did not appreciably influence subsurface sod biomass decomposition. Soil loss rates increased greatly with both surface sod biomass decomposition and decomposition of subsurface sod biomass in the conventionally tilled soil, but they were minimally affected by subsurface sod biomass decomposition in the untilled soil. Runoff rates were little affected by the studied treatments. Dead roots plus incorporated residues were effective in reducing erosion in the conventionally tilled soil, while consolidation of the soil surface was important in no-till. The residual effect of the turned soil on erosion diminished gradually with time and ceased after two years.

Cambios en el estado ecologico de tres rios producidos por el vertido de depuradoras

En el marco del proyecto Sostaqua se ha llevado a cabo la evaluación del efecto de las aguas residuales tratadas en tres depuradoras (dos en la provincia de Barcelona y una en Andorra), sobre la comunidad de macroinvertebrados bentónicos. En cada una de ellas se evaluó mensualmente el estado ecológico aguas arriba y aguas abajo del punto de vertido, utilizando varios índices biológicos. Mientras que en el río de andorra no se observó un efecto sobre los índices biológicos, en los ríos de la provincia de Barcelona (ríos ya perturbados y con una menor disolución del vertido) el efecto fue importante.

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.