Ziel des Vorhabens ist es, die in GerES VI gewonnenen Morgenurinproben der Erwachsenen bzw. die vorhandenen Rückstellproben der Kinder und Jugendlichen aus GerES V auf Schadstoffe zu analysieren, die eine besondere Gesundheitsrelevanz aufweisen, wie bspw. die Gruppe der Biozide/Pestizide. In GerES IV (ehemals Kinder-Umwelt-Survey, KUS) wurden letztmalig Morgenurine von 3- bis 14-Jährigen auf Organophosphate und Pyrethroide untersucht. Jedoch gibt es für zahlreiche Pestizide keine aktuellen, repräsentativen Daten zur korporalen Belastung der Bevölkerung in Deutschland. Daher wird momentan die mögliche Belastung über Berechnungen basierend auf dem Lebensmittelverzehr und Belastungsdaten der verzehrten Lebensmittel geschätzt. Um diese Datenlücke zu schließen, wurden Biozide/Pestizide als priorisierte Substanzgruppe im Rahmen des Projekts HBM4EU erkannt. Zur Festlegung des Analytspektrums wurde im Rahmen von HBM4EU eine Auswertung zu deutschen und europäischen Anwendungsdaten der Pestizide/Biozide und eine Auswertung der in Europa vorhandenen Expositionsdaten durchgeführt. Zusammen mit den Ergebnissen der Befragung der teilnehmenden Personen liefern die Analysen der Morgenurine repräsentative Informationen zur Belastung der in Deutschland lebenden Bevölkerung.
This dataset comprises new chemical, isotopic and geochronological analyses for 3 samples from the Cenomanian Serra do Cuó olivine basalts from northeast Brazil. Whole rock major, trace element and Sr-Nd-Pb isotope compositions as well as mineral oxide compositions for pyroxenes, plagioclase, olivine, and Fe-Ti oxides. New analyses on 3 samples are presented in the bulk and in-situ data templates developed by EarthChem. A compilation of all new analyses and previous whole-rock data from Sial (1978) are also provided. Analyses were carried out at the Geoanalítica Core Facility, Isotope Geology Research Center and Geochronological Research Center (CPGeo) at the Instituto de Geociências, University of São Paulo, Brazil. This dataset is supplementary to: Macêdo Filho, A. A., Oliveira, A. L., Klöcking, M., Janasi, V. A., Archanjo, C. J., & Lino, L. M. (2025). Petrology of Cenomanian basalts on the Brazilian equatorial margin: Implications for the tectonomagmatic evolution of the drift phase. Geochemistry, 126248. https://doi.org/10.1016/j.chemer.2025.126248. The data publication includes the following Excel Tables: (1) 2025-002_MacedoFilho_BulkSample_Analyses (DIGIS/EarthChem Template, EarthChem Team, 2022a): Whole rock major, trace element and Sr-Nd-Pb isotope compositions and 40Ar/39Ar age; with additional information on sample collection and analytical methods. (2) 2025-002_MacedoFilho_InSitu_Analyses (DIGIS /EarthChem Template, EarthChem Team, 2022b): Mineral oxide compositions for pyroxene, plagioclase, olivine, and Fe-Ti oxides; with additional information on sample collection and analytical methods. (3) 2025-002_MacedoFilho_suppl-compiled: supplementary data tables from Macêdo Filho et al. (2025). Excel file with the six spreadsheets: Table A1. whole-rock chemistry; Table A2. Feldspar chemistry; Table A3. Pyroxene chemistry; Table A4. Olivine chemistry; Table A5. Titanomagnetite chemistry; Table A6. Ar-Ar Geochronology. Table A1 compiles analyses from Sial (1978) as well as new data. Reference: Sial, A. N. (1978). Major and trace chemistry of the Tertiary basaltic suite of Rio Grande do Norte and Paraíba, northeast Brazil. Jornal de Mineralogia, 7, 119-128.
This dataset comprises new chemical, isotopic and geochronological analyses for 14 samples from the Angicos Plutonism (Angicos Batholith and Poço da Oiticica Stock) from northern Borborema Province, NE Brazil. Whole rock major and trace element compositions as well as mineral oxide compositions for feldspars, biotite, and Fe-oxides. New analyses on 14 samples are presented in the bulk and in-situ data templates developed by EarthChem. A compilation of all new analyses and previous whole-rock data from Jardim de Sá (1994) are also provided. Analyses were carried out at the Geoanalítica Core Facility at the Instituto de Geociências, University of São Paulo, Brazil. The data are reported with the EarthChem/ DIGIS data templates (IEDA, 2022).
The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). The following attributes are defined (not all are observable in the RRVS survey): code description lon longitude in fraction of degrees lat latitude in fraction of degrees object_id unique id of the building surveyed MAT_TYPE Material Type MAT_TECH Material Technology MAT_PROP Material Property LLRS Type of Lateral Load-Resisting System LLRS_DUCT System Ductility HEIGHT Height YR_BUILT Date of Construction or Retrofit OCCUPY Building Occupancy Class - General OCCUPY_DT Building Occupancy Class - Detail POSITION Building Position within a Block PLAN_SHAPE Shape of the Building Plan STR_IRREG Regular or Irregular STR_IRREG_DT Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE Type of Irregularity NONSTRCEXW Exterior walls ROOF_SHAPE Roof Shape ROOFCOVMAT Roof Covering ROOFSYSMAT Roof System Material ROOFSYSTYP Roof System Type ROOF_CONN Roof Connections FLOOR_MAT Floor Material FLOOR_TYPE Floor System Type FLOOR_CONN Floor Connections. For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.
Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
Multi-resolution exposure model for seismic risk assessment in Turkmenistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Turkmenistan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process) For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
Multi-resolution exposure model for seismic risk assessment in Kazakhstan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Kazakhstan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
Multi-resolution exposure model for seismic risk assessment in Tajikistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra (submitted). The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Tajikistan (provided as a separate file). The model integrates around 1'000 building observations (see related dataset Pittore et al. 2019a). The following specific modelling parameters have been employed: Prior strength=10, 100 Epsilon=0.001 For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
EMMA – End Member Modelling Analysis of grain-size data is a technique to unmix multimodal grain-size data sets, i.e., to decompose the data into the underlying grain-size distributions (loadings) and their contributions to each sample (scores). The R package EMMAgeo contains a series of functions to perform EMMA based on eigenspace decomposition. The data are rescaled and transformed to receive results in meaningful units, i.e., volume percentage. EMMA can be performed in a deterministic and two robust ways, the latter taking into account incomplete knowledge about model parameters. The model outputs can be interpreted in terms of sediment sources, transport pathways and transport regimes (loadings) as well as their relative importance throughout the sample space (scores).
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