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apps Other research productkeyboard_double_arrow_right Research Object 2024Publisher:CNR-ISMAR Authors: Susanna Mesghez; ANTONIO PETRIZZO; Fantina Madricardo; Taha Lahami;Susanna Mesghez; ANTONIO PETRIZZO; Fantina Madricardo; Taha Lahami;Bathymetric data collected in three diffrent surveys (October 2021, May 2022 and November 2022) in Sacca Fisola, Venice, Italy, by CNR- ISMAR VE within the MAELSTROM project.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=w3id________::411b6273b980d62b6472db910d6dfb52&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Model 2024 EnglishPublisher:Zenodo Authors: Noack, Vera; Scheck-Wenderoth, Magdalena; Cacace, Mauro; Schneider, Michael;Noack, Vera; Scheck-Wenderoth, Magdalena; Cacace, Mauro; Schneider, Michael;Abstract We provide grid files that collectively allow recreating 3D structural subsurface model of Brandenburg discussed in the papers (Noack, et al., 2010), (Noack, et al., 2012) and (Noack, 2013). The crustal-scale model covers an area of 250 km in E–W direction and 210 km in N–S direction and is located in the Northeast German Basin (NEGB). We provide ASCII files with top surface elevation and thickness of individual stratigraphic layers. There are two versions of the stratigraphic layering: one with the Rupelian clay layer and one without it. The coordinate system used is Gauss Krueger zone 4. The model without the Rupelian complements the following published papers: 1. Sensitivity of 3D thermal models to the choice of boundary conditions and thermal properties: a case study for the area of Brandenburg (NE German Basin).(Noack, et al., 2012). 2. Assessment of the present-day thermal field (NE German Basin)—Inferences from 3D modelling (Noack, et al., 2010) The model with the Rupelian is a complement to the publication of the paper: 1. Influence of fluid flow on the regional thermal field: Results from 3D numerical modelling for the area of Brandenburg (North German Basin) (Noack, et al., 2013). The provided structural model without the Rupelian consists of 15 layers starting from quaternary to the Lithosphere-Asthenosphere Boundary(LAB) and the one with the Rupelian consists of 17 layers starting from quaternary to the base (Moho). The files provided here include information on the regional variation of these geological units in terms of their depth and thickness, both attributes being allocated to regularly spaced grid nodes with horizontal spacing of 1 km. The model has originally been developed to obtain a basis for numerical simulations of heat transport, to calculate the lithospheric-scale conductive thermal field and assess the related geothermal potentials. The contents and structure of the grid files provided here with are described in the Technical Information section. We provide two archived compressed folders corresponding to the models with and without the Rupelian layer. The model grids, the top surface elevations and thicknesses of corresponding layers of the 3-D model are provided as space-separated ASCII files, one for each individual layer of the model. The head of the file contains License, citation and column names. The columns in each file are identical: As indicated by the headers of these files, column #1 contains the easting (X coordinate), column #2 contains the northing (Y coordinate), column #3 contains TOP (m.a.s.l.) and column #4 contains THICKNESS (m) in Gauss Krueger zone 4 coordinate system. The models have a grid resolution of 1 km and 250 x 210 grid points in horizontal direction for all model units. The vertical resolution of the final 3D model is heterogeneous since it corresponds to the variable thickness of its units. The model without Rupelian comprises of the following layers: 1. Quaternary 2. Tertiary 3. Upper Cretaceous 4. Lower Cretaceous 5. Jurassic 6. Keuper 7. Muschelkalk 8. Buntsandstein 9. Zechstein 10. Sedimentary Rotliegend 11. Permo-Carboniferous Volcanics 12. Pre-permian 13. Upper crust 14. Lower crust 15. Mantle The model with Rupelian layers comprises of the following layers: 1. Quaternary 2. Tertiary-post Rupelian 3. Tertiary Rupelian 4. Tertiary-pre-Rupelian 5. Upper Cretaceous 6. Lower Cretaceous 7. Jurassic 8. Keuper 9. Muschelkalk 10. Buntsandstein 11. Zechstein 12. Sedimentary Rotliegend 13. Permo-Carboniferous Volcanics 14. Pre-permian 15. Upper crust 16. Lower crust 17. Mantle Model area In model CRS(Gauss Krueger zone 4): X: 4450000 to 4700000 Y: 5720000 to 5930000 Longitude: from 11.2766811 to 15.01020321 Latitude: from 51.61219482 to 53.4636479 The presented model is the result of integration of several datasets such as depth and thickness maps, data from former regional models and deep well data. The first step in modelling is the interpolation of the compiled datasets into 2D grids using a minimum tension gridding technique and considering the trace of faults as interpolation barriers (Earth Vision, Dynamic Graphics Ltd., Version 8.0). Both depth and thickness data are interpolated to obtain the following stratigraphic units composing the 3D model from top to bottom: Quaternary, Tertiary, Upper Cretaceous, Lower Cretaceous, Jurassic, Triassic Keuper, Triassic Muschelkalk, Triassic Buntsand-stein, Permian Zechstein, Permian Rotliegend and Permo-Carboniferous Volcanics. Subsequently, the 2D grids are integrated in a 3D structural model with the software Geological Modelling System (GMS developed at GFZ Potsdam). Finally, we complete the model downward adding a layer of pre-Permian crust obtained as the difference between the base of Permo-Carboniferous Volcanics and the depth of the crust-mantle boundary as compiled by (Scheck-Wenderoth, 2005). More details about the original datasets can be found in Table 1 (Noack, et al., 2010) and references herein.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Event 2024 EnglishPublisher:Zenodo Authors: Centre for Research on Ecology and Forestry Applications;Centre for Research on Ecology and Forestry Applications;Air quality in urban environments is a matter of public health, environmental sustainability, and fortifying the resilence of our cities against the challenges of climate change. Nowadays it is commonly shared that decision-making in this regard needs to be based on reliable data that comes from scientific sources. For this reason, having access to scientific data in a simple, integrated and interoperable way can help both policymakers and citizens as a whole to understand the existing pollution levels and implement targeted strategies. We invite you to participate in this open day if you are a person in charge of managing or designing air quality measures in cities, if you are a provider of air quality data or if you are a citizen or group committed to this problem and want to support the availability of scientific data for desicion making.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:Zenodo Authors: Sakhaee, Ali;Sakhaee, Ali;The Support Vector Regression (SVR) model was trained using a combined dataset comprising the German Agricultural Soil Inventory and LUCAS data to generate spatial prediction (100m) for organic carbon content (gkg-1) in German mineral agricultural topsoil (0-30cm). Details regarding how the map was created can be found in the associated journal article (https://doi.org/10.5194/soil-8-587-2022).
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:ICOS ERIC - Carbon Portal ICOS RI; Bergamaschi, Peter; Colomb, Aurélie; De Mazière, Martine; Emmenegger, Lukas; Kubistin, Dagmar; Lehner, Irene; Lehtinen, Kari; Leuenberger, Markus; Lund Myhre, Cathrine; Marek, Michal; Platt, Stephen Matthew; Plaß-Dülmer, Christian; Ramonet, Michel; Schmidt, Martina; Apadula, Francesco; Arnold, Sabrina; Blanc, Pierre-Eric; Brunner, Dominik; Chen, Huilin; Chmura, Lukasz; Conil, Sébastien (ORCID: 0000-0001-6767-821X); Couret, Cédric; Cristofanelli, Paolo; Delmotte, Marc (ORCID: 0000-0003-3805-818X); Forster, Grant; Frumau, Arnoud; Gerbig, Christoph; Gheusi, François; Hammer, Samuel; Haszpra, Laszlo; Hatakka, Juha; Heliasz, Michal; Henne, Stephan; Hensen, Arjan; Hoheisel, Antje; Kneuer, Tobias; Laurila, Tuomas; Leskinen, Ari; Levin, Ingeborg; Lindauer, Matthias; Lunder, Chris; Mammarella, Ivan; Manca, Giovanni; Manning, Andrew; Martin, Damien; Meinhardt, Frank; Mölder, Meelis; Müller-Williams, Jennifer; Necki, Jaroslaw; Noe, Steffen Manfred; O'Doherty, Simon; Ottosson-Löfvenius, Mikaell; Philippon, Carole; Piacentino, Salvatore; Pitt, Joseph; Rivas-Soriano, Pedro; Scheeren, Bert; Schumacher, Marcus; Sha, Mahesh Kumar; Spain, Gerard; Steinbacher, Martin; Sørensen, Lise Lotte; Vermeulen, Alex; Vítková, Gabriela; Xueref-Remy, Irène; di Sarra, Alcide; Conen, Franz; Kazan, Victor; Roulet, Yves-Alain; Biermann, Tobias; Heltai, Daniela; Hermansen, Ove (ORCID: 0000-0001-7353-057X); Komínková, Kateřina; Laurent, Olivier; Levula, Janne; Marklund, Per; Morguí, Josep-Anton; Pichon, Jean-Marc; Smith, Paul; Stanley, Kieran; Trisolino, Pamela; ICOS Carbon Portal; ICOS Atmosphere Thematic Centre; ICOS Flask And Calibration Laboratory; ICOS Central Radiocarbon Laboratory;This data package contains high accuracy CO2 dry air mole fractions from xx ICOS and non-ICOS European observatories at in total xxx observation levels, collected by the ICOS Atmosphere Thematic Centre (ATC) and provided by the station contributors. The package is the first 2024 update of the Globalview EU data product (version 9.1), released in 2023 and is intended for use in carbon cycle inverse modeling, model evaluation, and satellite validation studies. Please report errors and send comments regarding this product to the ObsPack originators. Please read carefully the ObsPack Fair Use statement and cite appropriately. Please review the release notes for the associated Obspack product at www.esrl.noaa.gov/gmd/ccgg/obspack/release_notes.html. Metadata for this product are available at https://commons.datacite.org/doi.org/10.18160/47KN-KV28. Please visit http://www.gml.noaa.gov/ccgg/obspack/ for more information on Obspack.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024 Spain EnglishPublisher:World Health Organization. Regional Office for Europe Authors: World Health Organization. Regional Office for Europe; Centre for Environment & Health (BON); Living & Working Environments (LWE); Cañas Portilla, Ana Isabel; +1 AuthorsWorld Health Organization. Regional Office for Europe; Centre for Environment & Health (BON); Living & Working Environments (LWE); Cañas Portilla, Ana Isabel; Castaño, Argelia;handle: 20.500.12105/17224
Ana Isabel Cañas Portilla y Argelia Castaño Calvo del Centro Nacional de Sanidad Ambiental (ISCIII) han participado en este informe como expertos proporcionando comentarios técnicos. Benzene, arsenic, cadmium, lead, mercury and nickel are ubiquitous pollutants in ambient air. The main sources are industrial processes, electricity generation and fuel combustion. The main routes of exposure are inhalation for benzene, and diet for arsenic, cadmium, lead, nickel and mercury. Inhalation of benzene, arsenic and cadmium is relevant for exposure in active tobacco smokers and people exposed to second-hand tobacco smoke. Epidemiological studies show that exposure to these pollutants is associated with adverse effects on the cardiovascular (cadmium, lead, mercury); haematological (benzene, lead); immunological, neurological and reproductive (benzene, lead, mercury); respiratory (cadmium, nickel); renal (cadmium, lead); and skeletal (cadmium) systems. Limited epidemiological evidence on ambient air pollution suggests adverse effects on the cardiovascular system (arsenic and nickel). Since benzene, arsenic, cadmium and nickel are classified as carcinogenic, the lowest possible exposure level is suggested to minimize the risk for cancer development in view of the no-effect threshold paradigm. Lead and methylmercury compounds are classified as possibly carcinogenic to humans. However, the available evidence is insufficient to warrant updating the air quality guidelines for these air pollutants. Evidence gaps are identified and these should guide future research efforts. Sí
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024 EnglishPublisher:World Data Center for Climate (WDCC) Authors: Finn Burgemeister; Dr. Marco Clemens; Prof. Dr. Felix Ament;Finn Burgemeister; Dr. Marco Clemens; Prof. Dr. Felix Ament;An operational, single-polarized X-band weather radar (WRX) provides measurements in Hamburg’s city center since 2013. This local area weather radar (LAWR) is located on the rooftop of the high-rise building "Geomatikum" in Hamburg (HHG), which is the location of the Meteorological Institute of the Universität Hamburg. The radar operates at one beam elevation angle with a high temporal 30 s, range 60 m, and sampling 1° resolution refining observations of the German nationwide C-band radars within a 20 km scan radius. Several sources of radar-based errors were adjusted gradually improving the measurement variables, e.g. the radar calibration, alignment, attenuation, noise, non-meteorologial echoes. This experiment includes data sets of the equivalent radar reflectivity factor (dbz) in level 1 (without attenuation correction) and the rainfall rate (rr) in level 2 (applied attenuation correction). The WRX/LAWR HHG measurements were calibrated and evaluated with measurements of micro rain radars (MRR). With this high-quality and -resolution weather radar product, refined studies on the spatial and temporal scale of urban precipitation will be possible. For example, the data sets will be used for further hydrological research in an urban area within the project Sustainable Adaption Scenarios for Urban Areas – Water from Four Sides of the Cluster of Excellence Climate Climatic Change, and Society (CliCCS). This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany‘s Excellence Strategy – EXC 2037 'CLICCS - Climate, Climatic Change, and Society' – Project Number: 390683824, contribution to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg. Changes in Version 2: - We provide daily instead of hourly files to reduce the number of files for better data handling. For the days 23.09.2014, 12.03.2015, 09.06.2015, 05.07.2017, and 01.02.2018 there are two files to avoid additional time dependencies of variables because of changes in calibration or alignment parameters. - We changed the data type (double to int64) and the unit days since 1970-01-01 to seconds since 1970-01-01 of the time coordinate. - We changed the standard names / long names of the variables azimuth, range and ele. - We added the integer variable grid_mapping with the attributes grid_mapping_name ("radar_lidar_radial_scan"), latitude_of_projection_origin, longitude_of_projection_origin and height_of_projection_origin, as suggested by the CfRadial conventions. Since the grid_mapping variable provides the same information as the variables lat_center, lon_center and zsl_center, we removed them. We added the attribute grid_mapping to the variable rr and dbz.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=r3730f562f9e::f7e49fcd926be3874756ddf121f3be5b&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:Taylor & Francis Guo, Tonglei; BM, Sifan Tian; Xin, Henan; Du, Jiang; Cao, Xuefang; Feng, Boxuan; BS, Yijun He; He, Yongpeng; BS, Dakuan Wang; BS, Bin Zhang; BS, Zisen Liu; BS, Jiaoxia Yan; Shen, Lingyu; Di BS, Yuanzhi; BS, Yanxiao Chen; Jin, Qi; BS, Shouguo Pan; Kioumourtzoglou, Marianthi-Anna; Gao, Lei; Gao, Xu;Evidence showed that air pollution was associated with an increased risk of tuberculosis (TB). This study aimed to study the impact of long-term exposure to ambient particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) on the acquisition of LTBI and on the risk of subsequent active disease development among rural older adults from a multicentre cohort, which have not yet been investigated to date. A total of 4790 older adults were included in a population-based, multicenter, prospective cohort study (LATENTTB-NSTM) from 2013 to 2018. The level of long-term exposure to PM2.5 for each participant was assessed by aggregating satellite-based estimates. Logistic regression and time-varying Cox proportional hazards models with province-level random intercepts were employed to assess associations of long-term exposures to PM2.5 with the risk of LTBI and subsequent development of active TB, respectively. Out of 4790 participants, 3284 were LTBI-free at baseline, among whom 2806 completed the one-year follow-up and 127 developed newly identified LTBI. No significant associations were identified between PM2.5 and the risk of LTBI. And among 1506 participants with LTBI at baseline, 30 active TB cases were recorded during the 5-year follow-up. Particularly, an increment of 5 μg/m3 in 2-year moving averaged PM2.5 was associated with a 50.6% increased risk of active TB (HR = 1.506, 95% CI: 1.161-1.955). Long-term air pollution might be a neglected risk factor for active TB development from LTBI, especially for those living in developing or less-developed areas where the air quality is poor.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research product2024 SpainValdés S; Doulatram-Gamgaram V; Maldonado-Araque C; García-Escobar E; García-Serrano S; Oualla-Bachiri W; García-Vivanco M; Garrido JL; Gil V; Martín-Llorente F; Calle-Pascual A; Castaño L; Delgado E; Menéndez E; Franch-Nadal J; Gaztambide S; Girbés J; Chaves FJ; Galán-García JL; Aguilera-Venegas G; Vallvé JC; Amigó N; Guardiola M; Ribalta J; Rojo-Martínez G;We aimed to assess the associations of exposure to air pollutants and standard and advanced lipoprotein measures, in a nationwide sample representative of the adult population of Spain.We included 4647 adults (>18 years), participants in the national, cross-sectional, population-based di@bet.es study, conducted in 2008-2010. Standard lipid measurements were analysed on an Architect C8000 Analyzer (Abbott Laboratories SA). Lipoprotein analysis was made by an advanced 1 H-NMR lipoprotein test (Liposcale®). Participants were assigned air pollution concentrations for particulate matter <10 μm (PM10 ), <2.5 μm (PM2.5 ) and nitrogen dioxide (NO2 ), corresponding to the health examination year, obtained by modelling combined with measurements taken at air quality stations (CHIMERE chemistry-transport model).In multivariate linear regression models, each IQR increase in PM10 , PM2.5 and NO2 was associated with 3.3%, 3.3% and 3% lower levels of HDL-c and 1.3%, 1.4% and 1.1% lower HDL particle (HDL-p) concentrations (p < .001 for all associations). In multivariate logistic regression, there was a significant association between PM10 , PM2.5 and NO2 concentrations and the odds of presenting low HDL-c (<40 mg/dL), low HDL-p (
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:ICOS ERIC -- Carbon Portal Authors: Becker, Meike; Olsen, Are;Becker, Meike; Olsen, Are;Surface ocean and atmospheric fCO2 observations taken by a General Oceanics system on Ship of Opportunity Tukuma Arctica during 2023. Also includes previous data not previously submitted.
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apps Other research productkeyboard_double_arrow_right Research Object 2024Publisher:CNR-ISMAR Authors: Susanna Mesghez; ANTONIO PETRIZZO; Fantina Madricardo; Taha Lahami;Susanna Mesghez; ANTONIO PETRIZZO; Fantina Madricardo; Taha Lahami;Bathymetric data collected in three diffrent surveys (October 2021, May 2022 and November 2022) in Sacca Fisola, Venice, Italy, by CNR- ISMAR VE within the MAELSTROM project.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Model 2024 EnglishPublisher:Zenodo Authors: Noack, Vera; Scheck-Wenderoth, Magdalena; Cacace, Mauro; Schneider, Michael;Noack, Vera; Scheck-Wenderoth, Magdalena; Cacace, Mauro; Schneider, Michael;Abstract We provide grid files that collectively allow recreating 3D structural subsurface model of Brandenburg discussed in the papers (Noack, et al., 2010), (Noack, et al., 2012) and (Noack, 2013). The crustal-scale model covers an area of 250 km in E–W direction and 210 km in N–S direction and is located in the Northeast German Basin (NEGB). We provide ASCII files with top surface elevation and thickness of individual stratigraphic layers. There are two versions of the stratigraphic layering: one with the Rupelian clay layer and one without it. The coordinate system used is Gauss Krueger zone 4. The model without the Rupelian complements the following published papers: 1. Sensitivity of 3D thermal models to the choice of boundary conditions and thermal properties: a case study for the area of Brandenburg (NE German Basin).(Noack, et al., 2012). 2. Assessment of the present-day thermal field (NE German Basin)—Inferences from 3D modelling (Noack, et al., 2010) The model with the Rupelian is a complement to the publication of the paper: 1. Influence of fluid flow on the regional thermal field: Results from 3D numerical modelling for the area of Brandenburg (North German Basin) (Noack, et al., 2013). The provided structural model without the Rupelian consists of 15 layers starting from quaternary to the Lithosphere-Asthenosphere Boundary(LAB) and the one with the Rupelian consists of 17 layers starting from quaternary to the base (Moho). The files provided here include information on the regional variation of these geological units in terms of their depth and thickness, both attributes being allocated to regularly spaced grid nodes with horizontal spacing of 1 km. The model has originally been developed to obtain a basis for numerical simulations of heat transport, to calculate the lithospheric-scale conductive thermal field and assess the related geothermal potentials. The contents and structure of the grid files provided here with are described in the Technical Information section. We provide two archived compressed folders corresponding to the models with and without the Rupelian layer. The model grids, the top surface elevations and thicknesses of corresponding layers of the 3-D model are provided as space-separated ASCII files, one for each individual layer of the model. The head of the file contains License, citation and column names. The columns in each file are identical: As indicated by the headers of these files, column #1 contains the easting (X coordinate), column #2 contains the northing (Y coordinate), column #3 contains TOP (m.a.s.l.) and column #4 contains THICKNESS (m) in Gauss Krueger zone 4 coordinate system. The models have a grid resolution of 1 km and 250 x 210 grid points in horizontal direction for all model units. The vertical resolution of the final 3D model is heterogeneous since it corresponds to the variable thickness of its units. The model without Rupelian comprises of the following layers: 1. Quaternary 2. Tertiary 3. Upper Cretaceous 4. Lower Cretaceous 5. Jurassic 6. Keuper 7. Muschelkalk 8. Buntsandstein 9. Zechstein 10. Sedimentary Rotliegend 11. Permo-Carboniferous Volcanics 12. Pre-permian 13. Upper crust 14. Lower crust 15. Mantle The model with Rupelian layers comprises of the following layers: 1. Quaternary 2. Tertiary-post Rupelian 3. Tertiary Rupelian 4. Tertiary-pre-Rupelian 5. Upper Cretaceous 6. Lower Cretaceous 7. Jurassic 8. Keuper 9. Muschelkalk 10. Buntsandstein 11. Zechstein 12. Sedimentary Rotliegend 13. Permo-Carboniferous Volcanics 14. Pre-permian 15. Upper crust 16. Lower crust 17. Mantle Model area In model CRS(Gauss Krueger zone 4): X: 4450000 to 4700000 Y: 5720000 to 5930000 Longitude: from 11.2766811 to 15.01020321 Latitude: from 51.61219482 to 53.4636479 The presented model is the result of integration of several datasets such as depth and thickness maps, data from former regional models and deep well data. The first step in modelling is the interpolation of the compiled datasets into 2D grids using a minimum tension gridding technique and considering the trace of faults as interpolation barriers (Earth Vision, Dynamic Graphics Ltd., Version 8.0). Both depth and thickness data are interpolated to obtain the following stratigraphic units composing the 3D model from top to bottom: Quaternary, Tertiary, Upper Cretaceous, Lower Cretaceous, Jurassic, Triassic Keuper, Triassic Muschelkalk, Triassic Buntsand-stein, Permian Zechstein, Permian Rotliegend and Permo-Carboniferous Volcanics. Subsequently, the 2D grids are integrated in a 3D structural model with the software Geological Modelling System (GMS developed at GFZ Potsdam). Finally, we complete the model downward adding a layer of pre-Permian crust obtained as the difference between the base of Permo-Carboniferous Volcanics and the depth of the crust-mantle boundary as compiled by (Scheck-Wenderoth, 2005). More details about the original datasets can be found in Table 1 (Noack, et al., 2010) and references herein.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Event 2024 EnglishPublisher:Zenodo Authors: Centre for Research on Ecology and Forestry Applications;Centre for Research on Ecology and Forestry Applications;Air quality in urban environments is a matter of public health, environmental sustainability, and fortifying the resilence of our cities against the challenges of climate change. Nowadays it is commonly shared that decision-making in this regard needs to be based on reliable data that comes from scientific sources. For this reason, having access to scientific data in a simple, integrated and interoperable way can help both policymakers and citizens as a whole to understand the existing pollution levels and implement targeted strategies. We invite you to participate in this open day if you are a person in charge of managing or designing air quality measures in cities, if you are a provider of air quality data or if you are a citizen or group committed to this problem and want to support the availability of scientific data for desicion making.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:Zenodo Authors: Sakhaee, Ali;Sakhaee, Ali;The Support Vector Regression (SVR) model was trained using a combined dataset comprising the German Agricultural Soil Inventory and LUCAS data to generate spatial prediction (100m) for organic carbon content (gkg-1) in German mineral agricultural topsoil (0-30cm). Details regarding how the map was created can be found in the associated journal article (https://doi.org/10.5194/soil-8-587-2022).
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:ICOS ERIC - Carbon Portal ICOS RI; Bergamaschi, Peter; Colomb, Aurélie; De Mazière, Martine; Emmenegger, Lukas; Kubistin, Dagmar; Lehner, Irene; Lehtinen, Kari; Leuenberger, Markus; Lund Myhre, Cathrine; Marek, Michal; Platt, Stephen Matthew; Plaß-Dülmer, Christian; Ramonet, Michel; Schmidt, Martina; Apadula, Francesco; Arnold, Sabrina; Blanc, Pierre-Eric; Brunner, Dominik; Chen, Huilin; Chmura, Lukasz; Conil, Sébastien (ORCID: 0000-0001-6767-821X); Couret, Cédric; Cristofanelli, Paolo; Delmotte, Marc (ORCID: 0000-0003-3805-818X); Forster, Grant; Frumau, Arnoud; Gerbig, Christoph; Gheusi, François; Hammer, Samuel; Haszpra, Laszlo; Hatakka, Juha; Heliasz, Michal; Henne, Stephan; Hensen, Arjan; Hoheisel, Antje; Kneuer, Tobias; Laurila, Tuomas; Leskinen, Ari; Levin, Ingeborg; Lindauer, Matthias; Lunder, Chris; Mammarella, Ivan; Manca, Giovanni; Manning, Andrew; Martin, Damien; Meinhardt, Frank; Mölder, Meelis; Müller-Williams, Jennifer; Necki, Jaroslaw; Noe, Steffen Manfred; O'Doherty, Simon; Ottosson-Löfvenius, Mikaell; Philippon, Carole; Piacentino, Salvatore; Pitt, Joseph; Rivas-Soriano, Pedro; Scheeren, Bert; Schumacher, Marcus; Sha, Mahesh Kumar; Spain, Gerard; Steinbacher, Martin; Sørensen, Lise Lotte; Vermeulen, Alex; Vítková, Gabriela; Xueref-Remy, Irène; di Sarra, Alcide; Conen, Franz; Kazan, Victor; Roulet, Yves-Alain; Biermann, Tobias; Heltai, Daniela; Hermansen, Ove (ORCID: 0000-0001-7353-057X); Komínková, Kateřina; Laurent, Olivier; Levula, Janne; Marklund, Per; Morguí, Josep-Anton; Pichon, Jean-Marc; Smith, Paul; Stanley, Kieran; Trisolino, Pamela; ICOS Carbon Portal; ICOS Atmosphere Thematic Centre; ICOS Flask And Calibration Laboratory; ICOS Central Radiocarbon Laboratory;This data package contains high accuracy CO2 dry air mole fractions from xx ICOS and non-ICOS European observatories at in total xxx observation levels, collected by the ICOS Atmosphere Thematic Centre (ATC) and provided by the station contributors. The package is the first 2024 update of the Globalview EU data product (version 9.1), released in 2023 and is intended for use in carbon cycle inverse modeling, model evaluation, and satellite validation studies. Please report errors and send comments regarding this product to the ObsPack originators. Please read carefully the ObsPack Fair Use statement and cite appropriately. Please review the release notes for the associated Obspack product at www.esrl.noaa.gov/gmd/ccgg/obspack/release_notes.html. Metadata for this product are available at https://commons.datacite.org/doi.org/10.18160/47KN-KV28. Please visit http://www.gml.noaa.gov/ccgg/obspack/ for more information on Obspack.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024 Spain EnglishPublisher:World Health Organization. Regional Office for Europe Authors: World Health Organization. Regional Office for Europe; Centre for Environment & Health (BON); Living & Working Environments (LWE); Cañas Portilla, Ana Isabel; +1 AuthorsWorld Health Organization. Regional Office for Europe; Centre for Environment & Health (BON); Living & Working Environments (LWE); Cañas Portilla, Ana Isabel; Castaño, Argelia;handle: 20.500.12105/17224
Ana Isabel Cañas Portilla y Argelia Castaño Calvo del Centro Nacional de Sanidad Ambiental (ISCIII) han participado en este informe como expertos proporcionando comentarios técnicos. Benzene, arsenic, cadmium, lead, mercury and nickel are ubiquitous pollutants in ambient air. The main sources are industrial processes, electricity generation and fuel combustion. The main routes of exposure are inhalation for benzene, and diet for arsenic, cadmium, lead, nickel and mercury. Inhalation of benzene, arsenic and cadmium is relevant for exposure in active tobacco smokers and people exposed to second-hand tobacco smoke. Epidemiological studies show that exposure to these pollutants is associated with adverse effects on the cardiovascular (cadmium, lead, mercury); haematological (benzene, lead); immunological, neurological and reproductive (benzene, lead, mercury); respiratory (cadmium, nickel); renal (cadmium, lead); and skeletal (cadmium) systems. Limited epidemiological evidence on ambient air pollution suggests adverse effects on the cardiovascular system (arsenic and nickel). Since benzene, arsenic, cadmium and nickel are classified as carcinogenic, the lowest possible exposure level is suggested to minimize the risk for cancer development in view of the no-effect threshold paradigm. Lead and methylmercury compounds are classified as possibly carcinogenic to humans. However, the available evidence is insufficient to warrant updating the air quality guidelines for these air pollutants. Evidence gaps are identified and these should guide future research efforts. Sí
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024 EnglishPublisher:World Data Center for Climate (WDCC) Authors: Finn Burgemeister; Dr. Marco Clemens; Prof. Dr. Felix Ament;Finn Burgemeister; Dr. Marco Clemens; Prof. Dr. Felix Ament;An operational, single-polarized X-band weather radar (WRX) provides measurements in Hamburg’s city center since 2013. This local area weather radar (LAWR) is located on the rooftop of the high-rise building "Geomatikum" in Hamburg (HHG), which is the location of the Meteorological Institute of the Universität Hamburg. The radar operates at one beam elevation angle with a high temporal 30 s, range 60 m, and sampling 1° resolution refining observations of the German nationwide C-band radars within a 20 km scan radius. Several sources of radar-based errors were adjusted gradually improving the measurement variables, e.g. the radar calibration, alignment, attenuation, noise, non-meteorologial echoes. This experiment includes data sets of the equivalent radar reflectivity factor (dbz) in level 1 (without attenuation correction) and the rainfall rate (rr) in level 2 (applied attenuation correction). The WRX/LAWR HHG measurements were calibrated and evaluated with measurements of micro rain radars (MRR). With this high-quality and -resolution weather radar product, refined studies on the spatial and temporal scale of urban precipitation will be possible. For example, the data sets will be used for further hydrological research in an urban area within the project Sustainable Adaption Scenarios for Urban Areas – Water from Four Sides of the Cluster of Excellence Climate Climatic Change, and Society (CliCCS). This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany‘s Excellence Strategy – EXC 2037 'CLICCS - Climate, Climatic Change, and Society' – Project Number: 390683824, contribution to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg. Changes in Version 2: - We provide daily instead of hourly files to reduce the number of files for better data handling. For the days 23.09.2014, 12.03.2015, 09.06.2015, 05.07.2017, and 01.02.2018 there are two files to avoid additional time dependencies of variables because of changes in calibration or alignment parameters. - We changed the data type (double to int64) and the unit days since 1970-01-01 to seconds since 1970-01-01 of the time coordinate. - We changed the standard names / long names of the variables azimuth, range and ele. - We added the integer variable grid_mapping with the attributes grid_mapping_name ("radar_lidar_radial_scan"), latitude_of_projection_origin, longitude_of_projection_origin and height_of_projection_origin, as suggested by the CfRadial conventions. Since the grid_mapping variable provides the same information as the variables lat_center, lon_center and zsl_center, we removed them. We added the attribute grid_mapping to the variable rr and dbz.
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:Taylor & Francis Guo, Tonglei; BM, Sifan Tian; Xin, Henan; Du, Jiang; Cao, Xuefang; Feng, Boxuan; BS, Yijun He; He, Yongpeng; BS, Dakuan Wang; BS, Bin Zhang; BS, Zisen Liu; BS, Jiaoxia Yan; Shen, Lingyu; Di BS, Yuanzhi; BS, Yanxiao Chen; Jin, Qi; BS, Shouguo Pan; Kioumourtzoglou, Marianthi-Anna; Gao, Lei; Gao, Xu;Evidence showed that air pollution was associated with an increased risk of tuberculosis (TB). This study aimed to study the impact of long-term exposure to ambient particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) on the acquisition of LTBI and on the risk of subsequent active disease development among rural older adults from a multicentre cohort, which have not yet been investigated to date. A total of 4790 older adults were included in a population-based, multicenter, prospective cohort study (LATENTTB-NSTM) from 2013 to 2018. The level of long-term exposure to PM2.5 for each participant was assessed by aggregating satellite-based estimates. Logistic regression and time-varying Cox proportional hazards models with province-level random intercepts were employed to assess associations of long-term exposures to PM2.5 with the risk of LTBI and subsequent development of active TB, respectively. Out of 4790 participants, 3284 were LTBI-free at baseline, among whom 2806 completed the one-year follow-up and 127 developed newly identified LTBI. No significant associations were identified between PM2.5 and the risk of LTBI. And among 1506 participants with LTBI at baseline, 30 active TB cases were recorded during the 5-year follow-up. Particularly, an increment of 5 μg/m3 in 2-year moving averaged PM2.5 was associated with a 50.6% increased risk of active TB (HR = 1.506, 95% CI: 1.161-1.955). Long-term air pollution might be a neglected risk factor for active TB development from LTBI, especially for those living in developing or less-developed areas where the air quality is poor.
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For further information contact us at helpdesk@openaire.euapps Other research product2024 SpainValdés S; Doulatram-Gamgaram V; Maldonado-Araque C; García-Escobar E; García-Serrano S; Oualla-Bachiri W; García-Vivanco M; Garrido JL; Gil V; Martín-Llorente F; Calle-Pascual A; Castaño L; Delgado E; Menéndez E; Franch-Nadal J; Gaztambide S; Girbés J; Chaves FJ; Galán-García JL; Aguilera-Venegas G; Vallvé JC; Amigó N; Guardiola M; Ribalta J; Rojo-Martínez G;We aimed to assess the associations of exposure to air pollutants and standard and advanced lipoprotein measures, in a nationwide sample representative of the adult population of Spain.We included 4647 adults (>18 years), participants in the national, cross-sectional, population-based di@bet.es study, conducted in 2008-2010. Standard lipid measurements were analysed on an Architect C8000 Analyzer (Abbott Laboratories SA). Lipoprotein analysis was made by an advanced 1 H-NMR lipoprotein test (Liposcale®). Participants were assigned air pollution concentrations for particulate matter <10 μm (PM10 ), <2.5 μm (PM2.5 ) and nitrogen dioxide (NO2 ), corresponding to the health examination year, obtained by modelling combined with measurements taken at air quality stations (CHIMERE chemistry-transport model).In multivariate linear regression models, each IQR increase in PM10 , PM2.5 and NO2 was associated with 3.3%, 3.3% and 3% lower levels of HDL-c and 1.3%, 1.4% and 1.1% lower HDL particle (HDL-p) concentrations (p < .001 for all associations). In multivariate logistic regression, there was a significant association between PM10 , PM2.5 and NO2 concentrations and the odds of presenting low HDL-c (<40 mg/dL), low HDL-p (
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For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2024Publisher:ICOS ERIC -- Carbon Portal Authors: Becker, Meike; Olsen, Are;Becker, Meike; Olsen, Are;Surface ocean and atmospheric fCO2 observations taken by a General Oceanics system on Ship of Opportunity Tukuma Arctica during 2023. Also includes previous data not previously submitted.
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