27 research outputs found

    A web-based support system for biometeorological research

    Full text link
    [EN] Data are the fundamental building blocks to conduct scientific studies that seek to understand natural phenomena in space and time. The notion of data processing is ubiquitous and nearly operates in any project that requires gaining insight from the data. The increasing availability of information sources, data formats and download services offered to the users, makes it difficult to reuse or exploit the potential of those new resources in multiple scientific fields. In this paper, we present a spatial extract-transform-load (spatial-ETL) approach for downloading atmospheric datasets in order to produce new biometeorological indices and expose them publicly for reuse in research studies. The technologies and processes involved in our work are clearly defined in a context where the GDAL library and the Python programming language are key elements for the development and implementation of the geoprocessing tools. Since the National Oceanic and Atmospheric Administration (NOAA) is the source of information, the ETL process is executed each time this service publishes an updated atmospheric prediction model, thus obtaining different forecasts for spatial and temporal analyses. As a result, we present a web application intended for downloading these newly created datasets after processing, and visualising interactive web maps with the outcomes resulting from a number of geoprocessing tasks. We also elaborate on all functions and technologies used for the design of those processes, with emphasis on the optimisation of the resources as implemented in cloud servicesArroquia-Cuadros, B.; Marqués-Mateu, Á.; Sebastiá Tarín, L.; Fdez-Arroyabe, P. (2021). A web-based support system for biometeorological research. International Journal of Biometeorology. 65(8):1313-1323. https://doi.org/10.1007/s00484-020-01985-yS13131323658Aime MD, Lioy A, Pomi PC, Vallini M (2011) Security plans for SaaS. In: Agrawal D et al (eds) New frontiers in information and software as services. Service and application design challenges in the cloud. LNBIP 74. Springer, Berlin, pp 81–111Bermudez L (2017) New frontiers on open standards for geo-spatial science. Geo Spatial Inform Sci 20:126–133. https://doi.org/10.1080/10095020.2017.1325613Bhat S (2018) Practical Docker with Python. Apress, BangaloreBorkar VR, Deshmukh K, Sarawagi S (2000) Automatically extracting structure from free text addresses. IEEE Data Eng Bull 23:27–32Canfield DE, Ngombi-Pemba L, Hammarlund EU, Bengtson S, Chaussidon M, Gauthier-Lafaye F, Meunier A, Riboulleau A, Rollion-Bard C, Rouxel O, Asael D, Pierson-Wickmann AC, El Albani A (2013) Oxygen dynamics in the aftermath of the great oxidation of Earth’s atmosphere. Proc Natl Acad Sci U S A 110:16736–16741. https://doi.org/10.1073/pnas.1315570110Chubukov LA (1956) Climate fundaments of climatotherapy [in Russian]. In: Basis of Climatotherapy, Vol. 1. Medical Ed., MoscowCook J (2017) Docker for data science. Apress, Santa MonicaCrikard P III (2014) Leaflet.js essentials. Packt Publishing, BirminghamCrowe SA, Døssing LN, Beukes NJ, Beukes NJ, Bau M, Kruger SJ, Frei R, Canfield DE (2013) Atmospheric oxygenation three billion years ago. Nature 501:535–538. https://doi.org/10.1038/nature12426Dai J, Fdez-arroyabe P, Sheridan SC (2019) Foreword for IJB Special Issue on Asian Biometeorology Spring news from the eastern hemisphere: recent advances of biometeorology in Asia. Int J Biometeorol 63:563–568. https://doi.org/10.1007/s00484-019-01725-xDar U, Krosing H, Mlodgenski J, Roybal K (2015) PostgreSQL server programming, second edn. Packt Publishing, BirminghamDas H, Barik RK, Dubey H, Roy DS (eds) (2019) Cloud computing for geospatial big data analytics. Springer, Chamde Freitas CR, Grigorieva EA (2015) A comprehensive catalogue and classification of human thermal climate indices. Int J Biometeorol 59:109–120. https://doi.org/10.1007/s00484-014-0819-3De Smith MJ, Goodchild MF, Longley P (2007) Geospatial analysis: a comprehensive guide to principles, techniques and software tools. Troubador Publishing Ltd, LeicesterESA (2019) Copernicus. Europe’s eyes on Earth. https://www.esa.int/Applications/Observing_the_Earth/Copernicus. Accessed 25 October 2019Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17:37–37. https://doi.org/10.1609/aimag.v17i3.1230Fdez-Arroyabe P (2015) Climate change, local weather and customized early warning systems based on biometeorological indexes. IJEE 5(3). https://doi.org/10.17265/2159-581X/2015.03.002Fdez-Arroyabe P, Roye D (2017) Co-creation and participatory design of big data infrastructures on the field of human health related climate services. In: Bhatt C, Dey N, Ashour A (eds) Internet of things and big data technologies for next generation healthcare. Studies in Big Data, vol 23. Springer, Cham, pp 199–226Fdez-Arroyabe P, Lecha Estela L, Schimt F (2018) Digital divide, biometeorological data infrastructures and human vulnerability definition. Int J Biometeorol 62:733–740. https://doi.org/10.1007/s00484-017-1398-xFdez-Arroyabe P, Soliño Fernández D, Bilbatua Andrés J (2019) Work environment and healthcare: a biometeorological approach based on wearables. In: Dey N, Ashour A, Fong S, Bhatt CM (eds) Wearable and implantable medical devices applications and challenges. Vol. 7 in Advances in ubiquitous sensing applications for healthcare. Elsevier, London, pp 141–161Fernández de Arroyabe P, Lecha Estela L (2008). Validación en el norte de España de dos sistemas de alerta sanitarios basados en la idea del contraste meteorológico extremo. In: Publicaciones de la Asoc. Española Climatología: El cambio climático regional y sus impactos, Serie A (6) Ponencia V. Tarragona. ISBN: 978–84–612-6051-5GDAL/OGR contributors (2019) GDAL/OGR geospatial data abstraction software library. Open Source Geospatial Foundation. https://gdal.org. Accessed 20 October 2019Hempelmann N, Ehbrecht C, Alvarez-Castro C, Brockmann P, Falk W, Hoffmann J, Kindermann S, Koziol B, Nangini C, Radanovics S, Vautard R, Yiou P (2018) Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0). Comput Geosci 110:65–72. https://doi.org/10.1016/j.cageo.2017.10.004Hillar G (2018) Django RESTful web services. Packt Publishing, BirminghamKitchin R (2014) The data revolution: big data, open data, data infrastructures and their consequences. SAGE, Los AngelesKlein T, Samourkasidis A, Athanasiadis IN, Bellocchi G, Calanca P (2017) webXTREME: R-based web tool for calculating agroclimatic indices of extreme events. Comput Electron Agric 136:111–116. https://doi.org/10.1016/j.compag.2017.03.002Lecha Estela LB (2018) Biometeorological forecasts for health surveillance and prevention of meteor-tropic effects. Int J Biometeorol 62:741–771. https://doi.org/10.1007/s00484-017-1405-2Lecha Estela LB (2019) Pronósticos biometeorológicos. Citmatel, La Habana, p 2019Lodovici M, Bigagli E (2011) Oxidative stress and air pollution exposure. J Toxicol 2011:487074–487079. https://doi.org/10.1155/2011/487074McInerney D, Kempeneers P (2014) Open source geospatial tools. Springer, New YorkMehdipoor H, Vanos JK, Zurita-Milla R, Cao G (2017) Emerging technologies for biometeorology. Int J Biometeorol 61(1):81–88. https://doi.org/10.1007/s00484-017-1399-9Miell I, Hobson A (2019) Docker in practice, Second edn. Manning Publications Co, Shelter IslandMitchell T, GDAL contributors (2014) Geospatial power tools. Open source GDAL/OGR command line utilities. Locate Press, ChugiakMwange C, Mulaku GC, Siriba DN (2016) Technology trends for spatial data infrastructure in Africa. In Proceedings of the GSDI 15 World Conference, Taipei, TaiwanNOAA (2019) NCEP Data Products GFS and GDAS. https://www.nco.ncep.noaa.gov/pmb/products/gfs/. Accessed 20 October 2019Ovcharova VF (1963) Changes in the superior nervous activity and the gas exchange during the adaptation process of laboratory animals exposed to seasonal climate variations [in Russian]. In: Problems of Complex Climatology, USSR Academy of Sciences Ed., Moscow, pp 141-149Qin CZ, Zhan LJ, Zhu AX (2014) How to apply the geospatial data abstraction library (GDAL) properly to parallel geospatial raster I/O? Trans GIS 18:950–957. https://doi.org/10.1111/tgis.12068Reitz K, Schlusser T (2016) The Hitchhiker's guide to Python: best practices for development. O'Reilly Media, Inc., SebastopolRichards M (2015) Software architecture patterns. O'Reilly Media, Inc., SebastopolRisal A, Lima KJ, Bhattarai R, Yang JE, Noh H, Pathak R, Kim J (2018) Development of web-based WERM-S module for estimating spatially distributed rainfall erosivity index (EI30) using RADAR rainfall data. Catena 161:37–49. https://doi.org/10.1016/j.catena.2017.10.015Robichaud PR, Elliot WJ, Pierson FB, Hall DE, Moffet CA (2007) Predicting postfire erosion and mitigation effectiveness with a web-based probabilistic erosion model. Catena 71:229–241. https://doi.org/10.1016/j.catena.2007.03.003Rountree D, Castrillo I (2013) The basics of cloud computing: understanding the fundamentals of cloud computing in theory and practice. Elsevier, WalthamRutledge GK, Alpert J, Ebisuzaki W (2006) NOMADS: a climate and weather model archive at the National Oceanic and Atmospheric Administration. Bull Am Meteorol Soc 87:327–342. https://doi.org/10.1175/BAMS-87-3-327Salazar Loor J, Fdez-Arroyabe P (2019) Aerial and satellite imagery and big data: blending old technologies with new trends. In: Dey N, Bhatt C, Ashour A (eds) Big data for remote sensing: visualization, Analysis and Interpretation. Springer, Cham, pp 39–59Sample JT, Ioup E (2010) Tile-based geospatial information systems: principles and practices. Springer Science & Business Media, New YorkServon LJ (2002) Bridging the digital divide, technology, community and public policy. Wiley-Blackwell Publishing, OxfordSoulignac V, Pinet F, Lambert E, Guichard L, Trouche L, Aubin S (2019) GECO, the French web-based application for knowledge management in agroecology. Comput Electron Agric 162:1050–1056. https://doi.org/10.1016/j.compag.2017.10.028Stasch C, Foerster T, Autermann C, Pebesma E (2012) Spatio-temporal aggregation of European air quality observations in the sensor web. Comput Geosci 47:111–118. https://doi.org/10.1016/j.cageo.2011.11.008Suryanto W, Irnaka TM (2016) Web-based application for inverting one-dimensional magnetotelluric data using Python. Comput Geosci 96:77–86. https://doi.org/10.1016/j.cageo.2016.08.006Vance TC, Merati N, Yang C, Yuan M (eds) (2016) Cloud computing in ocean and atmospheric sciences. Elsevier, LondonVillar A, Zarrabeitia MT, Fdez-Arroyabe P, Santurtún A (2018) Integrating and analyzing medical and environmental data using ETL and business intelligence tools. Int J Biometeorol 62:1085–1095. https://doi.org/10.1007/s00484-018-1511-9Voronin IM (1954) Experimental study of the effects of climatotherapy in human organism [in Russian]. In: Proceedings of the 2nd Interdisciplinary Conference on Applications of Climatotherapy. Moscow, November; 25:27Wang XZ, Zhang HM, Zhao JH, Lin QH, Zhou YC, Li JH (2015) An interactive web-based service analysis framework for remote sensing cloud computing. ISPRS Annals II-4(W2):43–50. https://doi.org/10.5194/isprsannals-II-4-W2-43-2015Wang W, Cui Y, Luo Y, Li Z, Tan J (2019) Web-based decision support system for canal irrigation management. Comput Electron Agric 161:312–321. https://doi.org/10.1016/j.compag.2017.11.018Warschauer M (2004) Technology and social inclusion. Rethinking the digital divide. The MIT Press, MassachusettsYang C, Goodchild M, Huang Q, Nebert D, Raskin R, Xu Y, Bambacus M, Fay D (2011) Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? Int J Digit Earth 4:305–329. https://doi.org/10.1080/17538947.2011.58754

    Electrografting of diazonium salt for SPR application

    No full text
    International audienceA general method to develop diazonium-based biochip for SPRi analysis is presented. The electrografting of carboxybenzene diazonium salt is optimized in order to fit with the requirement of the SPR technology. The influence of the surface preparation on plasmon quality is discussed and the performance of this original modified biochip is investigated for determining the binding constants between circulating ovalbumin and spotted anti-ovalbumin antibody. The kinetic constants are calculated and compared with literature and similar performances are reached confirming that diazonium based biochip is a good complement to commercial SPRi sensor

    Utilisation de la modélisation 3D et de la télédétection pour une meilleure compréhension des hétérogénéités spatio-temporelles de l'abondance du phytoplancton dans les grands lacs

    No full text
    International audienceLake biological parameters show important spatio-temporal heterogeneities. This is why explaining the spatial patchiness of phytoplankton abundance has been a recurrent ecological issue and is an essential prerequisite for objectively assessing, protecting and restoring freshwater ecosystems. The drivers of these heterogeneities can be identified by modelling their dynamics. This approach is useful for theoretical and applied limnology. In this study, a 3D hydrodynamic model of LakeGeneva (France/Switzerland) was created. It is based on the Delft3D suite software and includes the main tributary (Rhône River) and two-dimensional high-resolution meteorological forcing. It provides 3D maps of water temperature and current velocities with a 1 h time step on a 1 km horizontal grid size and with a vertical resolution of 1 m near the surface to 7 m at the bottom of the lake. The dynamics and the drivers of phytoplankton heterogeneities were assessed by combining the outputs of the model and chlorophyll-a concentration (Chl-a) data from MERIS satellite images between 2008 and 2012. Results highlight physical mechanisms responsible for the occurrence of seasonal hot-spots in phytoplankton abundance in the lake. At the beginning of spring, Chl-a heterogeneities are usually caused by an earlier onset of phytoplankton growth in the shallowest and more sheltered areas; spatial differences in the timing of phytoplankton growth can be explained by spatial variability in thermal stratification dynamics. In summer, transient and locally higher phytoplankton abundances are observed in relation to the impact of basin-scale upwelling

    Utilisation de la modélisation 3D et de la télédétection pour une meilleure compréhension des hétérogénéités spatio-temporelles de l'abondance du phytoplancton dans les grands lacs

    No full text
    International audienceLake biological parameters show important spatio-temporal heterogeneities. This is why explaining the spatial patchiness of phytoplankton abundance has been a recurrent ecological issue and is an essential prerequisite for objectively assessing, protecting and restoring freshwater ecosystems. The drivers of these heterogeneities can be identified by modelling their dynamics. This approach is useful for theoretical and applied limnology. In this study, a 3D hydrodynamic model of LakeGeneva (France/Switzerland) was created. It is based on the Delft3D suite software and includes the main tributary (Rhône River) and two-dimensional high-resolution meteorological forcing. It provides 3D maps of water temperature and current velocities with a 1 h time step on a 1 km horizontal grid size and with a vertical resolution of 1 m near the surface to 7 m at the bottom of the lake. The dynamics and the drivers of phytoplankton heterogeneities were assessed by combining the outputs of the model and chlorophyll-a concentration (Chl-a) data from MERIS satellite images between 2008 and 2012. Results highlight physical mechanisms responsible for the occurrence of seasonal hot-spots in phytoplankton abundance in the lake. At the beginning of spring, Chl-a heterogeneities are usually caused by an earlier onset of phytoplankton growth in the shallowest and more sheltered areas; spatial differences in the timing of phytoplankton growth can be explained by spatial variability in thermal stratification dynamics. In summer, transient and locally higher phytoplankton abundances are observed in relation to the impact of basin-scale upwelling

    An integrated approach for assessing the impact of urban stormwater discharge on the fecal contamination in a recreational lake near Paris

    No full text
    International audienceUrban stormwater discharges contribute to the fecal contamination of recreational lakes. It is essential to assess the spatial and temporal distribution of fecal bacterial indicators in the receiving waterbodies, to prevent public health risks. This study develops for the first time in continental waters an integrated monitoring and modelling approach, linking the SWMM and Delft-3D models and including a detailed monitoring of the stormwater discharge and at different points of the lake, for assessing Escherichia coli (E.coli) dynamics in stormwater discharges and the receiving urban lake. This integrated approach is applied to a recreational shallow lake and its adjacent urban catchment, with a single stormwater outlet which discharges into the lake. The SWMM model parameters are calibrated and validated with continuous measurements of the flow rate and mean concentration of E. coli in the stormwater discharge measured during each rainfall event. Using the simulated flow rate and E. coli concentration at the sewer outlet, the Delft3D-FLOW-WAQ model simulates E. coli transport in the urban lake with previously calibrated hydrodynamic parameters and default values of E. coli parameters. Comparing simulations with E. coli concentrations measured at different points in the lake, this integrated modelling approach yields promising results. Further studies will focus on the development of automatic model coupling and parameter optimisation, as well as on the evaluation of long-term impacts and management scenarios

    A multi-lake comparative analysis of the General Lake Model (GLM): stress-testing across a global observatory network

    Get PDF
    The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required

    Impacts de l'hétérogénéité spatiale dans l'évaluation de la qualité de l'eau des grands lacs : évaluation par la télédétection et la modélisation tri-dimensionnelle

    No full text
    International audienceContext : In 2000, the European Parliament set out a framework (European Water Framework Directive) for managing and protecting water bodies in Europe. Classification of water bodies into «ecological status» is a key issue for the implementation of that framework. For lakes, the assessment of this status is based on biological (e.g . fish, phytoplankton and macrophyte), physical-chemical and hydro-morphological indicators. The physical-chemical and phytoplankton indicators are based on 4 observations given by integrated samples over the growing season during one year for a 6 years management plan. The sampling occur in the euphotic zone over the deepest point of the lake. However, in large lakes, ecological parameters, used for water quality assessment (e.g . chlorophyll-a concentration and Secchi depth), exhibit strong spatial-temporal heterogeneities. Consequently, the representativeness of those data versus the whole lake might be questionable and needs to be verified (Kiefer et al., 2015). The objective is to evaluate how spatial and temporal heterogeneities in chlorophyll-a may impact the assessment of Lake Geneva ecological status; we propose to combine remote sensing data and 3D modellin

    AgroPEPS, un outil web collaboratif de gestion des connaissances pour Produire, Echanger, Pratiquer, S'informer sur les systèmes de culture durables

    No full text
    International audienceAgroPEPS is a collaborative web tool of knowledge management focused on agro-ecology, designed for and with future and potential users of the Joint technology network for ‘innovative cropping system'.Its aims are to capitalize knowledge and experiences in order to allow several actors in agriculture to design, pilot, manage and train innovative cropping systems, which are performant economically, environmentally and socially. Nowadays, developed as a prototype, Agro-PEPS makes available 150 techniques, described and structured according to 5 issues of sustainable development (water, air, soil, fossil resources, biodiversity). To reach them, Agro-PEPS has functionalities of syntactic research by keywords and of semantic research allowing a better targeting. This tool concept finds a favorable echo in the French R&D and training system: we recorded in January 2015 about 240 000 connections on the web site since its start which was confidential due to its prototype status. However its evolution in an operational web tool remains to be formalized.AgroPEPS est un outil web collaboratif de gestion des connaissances dédié à l'agroécologie, conçu pour et avec les futurs utilisateurs dans le cadre du RMT Systèmes de culture innovants. Il a pour objectifs de capitaliser les connaissances et expériences disponibles afin de permettre à différents acteurs du monde agricole de concevoir, piloter, gérer et faire l'apprentissage de systèmes de culture innovants et performants d'un point de vue économique, environnemental et social. Aujourd'hui développé sous forme de prototype il met a disposition des utilisateurs 150 techniques, décrites et structurées en fonction de cinq enjeux de développement durable (l'eau, l'air, le sol, les ressources fossiles et la biodiversité). Pour y accéder, AgroPEPS dispose de fonctionnalités de recherche syntaxique classique par moteur de recherche et de fonctionnalités de recherche sémantique permettant un meilleur ciblage. Ce concept d'outil trouve un écho très favorable dans le monde du développement, de la recherche et de la formation : on dénombre fin janvier 2015 près de 240.000 connexions au site depuis sa mise en ligne pourtant restée "confidentielle" compte-tenu du caractère de prototype de l'outil. Mais son évolution sous la forme d'un outil abouti reste encore à formalise

    Isomorphism of graph classes related to the circular-ones property

    Get PDF
    We give a linear-time algorithm that checks for isomorphism between two 0-1 matrices that obey the circular-ones property. This algorithm leads to linear-time isomorphism algorithms for related graph classes, including Helly circular-arc graphs, \Gamma-circular-arc graphs, proper circular-arc graphs and convex-round graphs.Comment: 25 pages, 9 figure

    Fully dynamic recognition of proper circular-arc graphs

    Get PDF
    We present a fully dynamic algorithm for the recognition of proper circular-arc (PCA) graphs. The allowed operations on the graph involve the insertion and removal of vertices (together with its incident edges) or edges. Edge operations cost O(log n) time, where n is the number of vertices of the graph, while vertex operations cost O(log n + d) time, where d is the degree of the modified vertex. We also show incremental and decremental algorithms that work in O(1) time per inserted or removed edge. As part of our algorithm, fully dynamic connectivity and co-connectivity algorithms that work in O(log n) time per operation are obtained. Also, an O(\Delta) time algorithm for determining if a PCA representation corresponds to a co-bipartite graph is provided, where \Delta\ is the maximum among the degrees of the vertices. When the graph is co-bipartite, a co-bipartition of each of its co-components is obtained within the same amount of time.Comment: 60 pages, 15 figure
    corecore