HydroQual: Visual analysis of river water quality

International audienceEconomic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40]. River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56]. Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotem-poral data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts

Un système décisionnel pour l’analyse de la qualité des eaux de rivières

National audienceThis article describes a decisional system developed to allow the analysis of data about hydro-ecosystem functioning; there are numerous and various data, from several sources. The implemented system includes an integrated database, a datawarehouse for exploring data dimensions, and data mining tools for answering hydroecologists’ questions.Cet article décrit un système décisionnel développé pour permettre l’analyse des données concernant le fonctionnement des hydro-écosystèmes ; ces données sont nombreuses, diverses et issues de sources variées. Le système mis en place comporte une base de données intégrée, un entrepôt permettant l’exploration des dimensions associées aux données, et des outils de fouille permettant de répondre aux questions des hydro-écologues

Retour d'Expérience sur la constitution d'un système d'information multi-source pour l'étude de la qualité de l'eau dans le cadre du projet ANR FRESQUEAU

International audienc

Impact de la source énergétique alimentaire sur les métabolites digestifs microbiens chez le poulet

The increasing use of unconventional feedstuffs in broi ler's diets, resulting in the substitution of starch by lipidsas the main energy source, could influence the functionality oftheir digestive microbiota. To evaluate this effect,chicken divergently selected on their fatness (Fat or Lean) were fed isoenergetic, isoproteic diets with high (8 %)or low (2 %) lipid content from 22 to 63 days of age. At 63 days, birds were slaughtered and digestivemetabolites in the small intestine (jejunum) and the caeca were studied by proton NMR ('H). The number ofmetabolites detected and identified in the two digestive segments was higher in the caeca compared to the smallintestine ( 41 instead of 29). Among metabolites th at might originate from microbiota, sorne compounds werefound in the two segments such as short chain fatty acids (SCFA), two organic acids, a secondary amine and anami no ac id derivative. Severa) metabolites were found only in the caeca, as different SCF A, a derivative ofSCFA, a dicarboxylic acid, ketoacids, a tertiary amine, the pyrimidine derivative, monosacharides and an aminoacid. The difference is likely due to the higher microbial diversity in the caeca compared to jejunum.Multivariate statistical analysis of the data highlighted a high inter-individual variability, and a difference in thej ejunum between dietary treatments due to severa) metabolites, whose one may come from bacteria.L'utilisation croissante de matières premières non conventionnelles dans les aliments destinés aux volailles,entraînant la substitution de l'amidon par des lipides comme source énergétique, pourrait influencer lafonctionnalité du microbiote digestif des poulets de chair. Pour évaluer cet effet, des poulets sélectionnés demanière divergente sur leur engraissement (maigres ou gras) ont été alimentés de 22 à 63 jours avec des régimesisoénergétiques et isoprotéiques présentant des teneurs en lipides élevées (8%, HL) ou faibles (2%, BL) mais desrapports identiques en type d'acides gras. A l'abattage à 63 jours, les métabolites digestifs de l' intestin grêle etdes caeca ont été étudiés par RMN du proton ('H). Les métabolites détectés et identifiés sont présents en plusgrand nombre dans les caeca (41) par rapport à l' intestin grêle (29). Parmi les métabolites pouvant provenir dumicrobiote, certains sont présents dans les 2 segments comme certains acides gras à chaine courte (AGCC), deuxacides organiques, une amine secondaire et un dérivé d'acide aminé. Plusieurs métabolites ne sont détectés quedans les caeca, comme certains AGCC, un dérivé d'AGCC, un acide dicarboxylique, des a-cétoacides, uneamine tertiaire, une base pyrimidique, des monosacharides et un acide aminé. Cette différence est probablementliée à la diversité plus importante du microbiote dans les caeca par rapport au jéjunum. L' analyse statistiquemultivariée des données a révélé une forte variabilité inter-individuelle et a mis en évidence une différence dansle jéjunum entre les deux régimes due à plusieurs métabolites, dont l'un deux pourrait être d'originemicrobienne

Une expérience de constitution d’un système d’information multi-sources pour l’étude de la qualité de l’eau

Atelier SI et environnement - Inforsid 2014 - LyonTo better understand hydrosystem functioning, several and various data can be used: data on water quality, data characterizing sampling reaches, data describing the hydrographic network, etc. All these data are spatial and complex to structure and to interconnect because of their volume and their nature. They are characterized by a high heterogeneity due to their origins, their values, their spatial structures and their temporal variability. This article reports problems encountered for data gathering, modeling and integration. The inventory is carried out on two french districts: Rhine-Meuse and Rhône-Mediterranean and Corsica.Pour mieux appréhender le fonctionnement des hydro-écosystèmes, sont disponibles des données nombreuses et diverses : données relatives à la qualité de l’eau ou aux stations de mesures, données décrivant le réseau hydrographique, etc. Ces données spatialement définies sont complexes à structurer et à relier de par leur volume et leur nature variables. Elles ont des origines, des valeurs, des structures spatiales et des répartitions temporelles diverses. Cet article fait l’état des problématiques rencontrées dans la collecte et la structuration de ces don- nées, pour les deux zones étudiées, les districts Rhin-Meuse et Rhône-Méditerranée et Corse

Feedbacks on data collection, data modeling and data integration of large datasets: application to Rhin-Meuse and Rhone-Mediterranean districts (France)

To better understand hydrosystem functioning, we need to improve our knowledge of hydrobiological processes as well to identify and to quantify the associated pressures. In this context, the ANR11-MONU14 Fresqueau project associates data miners and hydrobiologists to define a new knowledge discovery process from datasets provided by public databases to fully meet the expert requirements. The required data are grouped into five major categories: (i) data on water quality, (ii) data characterizing sampling reaches, (iii) data describing the hydrographic network (iv) data estimating human activities (land use and waste water treatment plant) and (v) climate and environmental forcing variables. All these data are spatial and complex to structure and to inter-connect because of their volume and their nature. The studied data are characterized by a high heterogeneity due to their origin (values from measurements or expertise), their value that can be quantitative, semi-quantitative or qualitative, and their structure (point, line, surface polygon) as well as because of their temporal variability (sampling duration and frequency). The objective of this presentation is to introduce the first phase of our work for data gathering, data modeling and data integration. The inventory is carried out on two french districts: Rhine Meuse (33 000 km2) and Rhône Mediterranean and Corsica (130 000 km2). We present the main operational lessons of the work performed on the 16 concerned public databases (access, rights of use, data format, etc.). As a result we present the conceptual data model (data standardization and positioning linking)

Feedbacks on data collection, data modeling and data integration of large datasets: application to Rhin-Meuse and Rhone-Mediterranean districts (France)

To better understand hydrosystem functioning, we need to improve our knowledge of hydrobiological processes as well to identify and to quantify the associated pressures. In this context, the ANR11-MONU14 Fresqueau project associates data miners and hydrobiologists to define a new knowledge discovery process from datasets provided by public databases to fully meet the expert requirements. The required data are grouped into five major categories: (i) data on water quality, (ii) data characterizing sampling reaches, (iii) data describing the hydrographic network (iv) data estimating human activities (land use and waste water treatment plant) and (v) climate and environmental forcing variables. All these data are spatial and complex to structure and to inter-connect because of their volume and their nature. The studied data are characterized by a high heterogeneity due to their origin (values from measurements or expertise), their value that can be quantitative, semi-quantitative or qualitative, and their structure (point, line, surface polygon) as well as because of their temporal variability (sampling duration and frequency). The objective of this presentation is to introduce the first phase of our work for data gathering, data modeling and data integration. The inventory is carried out on two french districts: Rhine Meuse (33 000 km2) and Rhône Mediterranean and Corsica (130 000 km2). We present the main operational lessons of the work performed on the 16 concerned public databases (access, rights of use, data format, etc.). As a result we present the conceptual data model (data standardization and positioning linking)