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  • PANGAEA  (60)
  • Wiley  (4)
  • Aschendorff Verlag  (2)
  • Oxford University Press  (2)
  • American Physical Society
  • 2020-2024  (68)
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Keywords
Language
Year
  • 1
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    Aschendorff Verlag
    Publication Date: 2024-04-05
    Description: Dieser Beitrag bietet eine Deutung dessen, worum es sich beim Anthropozän handelt: nämlich um eine geistige Umweltkrise. Zu diesem Zweck wird das Anthropozän zunächst in gebotener Kürze als Umweltkrise dargestellt. Darauf folgt die Skizze einer Krise, die als eine Krise der Geistlosigkeit erscheinen könnte, da sie sich mit „Anti-Universalismus“ und „logischer Narzissmus“ charakterisieren lässt. Das führt zu der Frage, was unter dem Geistigen zu verstehen ist, und diese Frage wird aus Gründen, die dabei plausibel werden sollen, im Rahmen eines undogmatischen Panpsychismus beantwortet. Diese Antwort hilft wiederum, geistige Aspekte des Anthropozäns zu würdigen, und mit dem so ermöglichten Verständnis dieser Zeit als geistiger Umweltkrise ist das angestrebte Ziel erreicht.
    Keywords: Umweltkrise ; Anthropozän ; thema EDItEUR::Q Philosophy and Religion::QD Philosophy
    Language: German
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  • 2
    Publication Date: 2024-04-05
    Description: Der in diesem Band vorausgehende Beitrag des Verf. legt es nahe, das Anthropozän als eine geistige Umweltkrise zu verstehen. Demnach fühlt es sich auf eine bestimmte Weise an, in einer technisch überformten Umwelt zu leben, und dies führt wiederum zu einer anti-universalistischen narzisstischen Verstimmung. Der davon begünstigte logische Narzissmus erschwert es, auf die Herausforderungen dieser im Wandel begriffenen Umwelt sinnvoll zu reagieren.
    Keywords: Umweltkrise ; Anthropozän ; thema EDItEUR::Q Philosophy and Religion::QD Philosophy
    Language: German
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  • 3
    Publication Date: 2024-02-07
    Description: Probing seismic anisotropy of the lithosphere provides valuable clues on the fabric of rocks. We present a 3-D probabilistic model of shear wave velocity and radial anisotropy of the crust and uppermost mantle of Europe, focusing on the mountain belts of the Alps and Apennines. The model is built from Love and Rayleigh dispersion curves in the period range 5–149 s. Data are extracted from seismic ambient noise recorded at 1521 broad-band stations, including the AlpArray network. The dispersion curves are first combined in a linearized least squares inversion to obtain 2-D maps of group velocity at each period. Love and Rayleigh maps are then jointly inverted at depth for shear wave velocity and radial anisotropy using a Bayesian Monte Carlo scheme that accounts for the trade-off between radial anisotropy and horizontal layering. The isotropic part of our model is consistent with previous studies. However, our anisotropy maps differ from previous large scale studies that suggested the presence of significant radial anisotropy everywhere in the European crust and shallow upper mantle. We observe instead that radial anisotropy is mostly localized beneath the Apennines while most of the remaining European crust and shallow upper mantle is isotropic. We attribute this difference to trade-offs between radial anisotropy and thin (hectometric) layering in previous studies based on least-squares inversions and long period data (〉30 s). In contrast, our approach involves a massive data set of short period measurements and a Bayesian inversion that accounts for thin layering. The positive radial anisotropy (VSH 〉 VSV) observed in the lower crust of the Apennines cannot result from thin layering. We rather attribute it to ductile horizontal flow in response to the recent and present-day extension in the region.
    Type: Article , PeerReviewed
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  • 4
    Publication Date: 2024-02-07
    Description: To constrain seismic anisotropy under and around the Alps in Europe, we study SKS shear wave splitting from the region densely covered by the AlpArray seismic network. We apply a technique based on measuring the splitting intensity, constraining well both the fast orientation and the splitting delay. Four years of teleseismic earthquake data were processed, from 723 temporary and permanent broad-band stations of the AlpArray deployment including ocean-bottom seismometers, providing a spatial coverage that is unprecedented. The technique is applied automatically (without human intervention), and it thus provides a reproducible image of anisotropic structure in and around the Alpine region. As in earlier studies, we observe a coherent rotation of fast axes in the western part of the Alpine chain, and a region of homogeneous fast orientation in the Central Alps. The spatial variation of splitting delay times is particularly interesting though. On one hand, there is a clear positive correlation with Alpine topography, suggesting that part of the seismic anisotropy (deformation) is caused by the Alpine orogeny. On the other hand, anisotropic strength around the mountain chain shows a distinct contrast between the Western and Eastern Alps. This difference is best explained by the more active mantle flow around the Western Alps. The new observational constraints, especially the splitting delay, provide new information on Alpine geodynamics.
    Type: Article , PeerReviewed
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  • 5
    Publication Date: 2024-02-07
    Description: We present the results of P-to-S receiver function analysis to improve the 3D image of the sedimentary layer, the upper crust, and lower crust in the Pannonian Basin area. The Pannonian Basin hosts deep sedimentary depocentres superimposed on a complex basement structure and it is surrounded by mountain belts. We processed waveforms from 221 three-component broadband seismological stations. As a result of the dense station coverage, we were able to achieve so far unprecedented spatial resolution in determining the velocity structure of the crust. We applied a three-fold quality control process; the first two being applied to the observed waveforms and the third to the calculated radial receiver functions. This work is the first comprehensive receiver function study of the entire region. To prepare the inversions, we performed station-wise H-Vp/Vs grid search, as well as Common Conversion Point migration. Our main focus was then the S-wave velocity structure of the area, which we determined by the Neighborhood Algorithm inversion method at each station, where data were sub-divided into back-azimuthal bundles based on similar Ps delay times. The 1D, nonlinear inversions provided the depth of the discontinuities, shear-wave velocities and Vp/Vs ratios of each layer per bundle, and we calculated uncertainty values for each of these parameters. We then developed a 3D interpolation method based on natural neighbor interpolation to obtain the 3D crustal structure from the local inversion results. We present the sedimentary thickness map, the first Conrad depth map and an improved, detailed Moho map, as well as the first upper and lower crustal thickness maps obtained from receiver function analysis. The velocity jump across the Conrad discontinuity is estimated at less than 0.2 km/s over most of the investigated area. We also compare the new Moho map from our approach to simple grid search results and prior knowledge from other techniques. Our Moho depth map presents local variations in the investigated area: the crust-mantle boundary is at 20–26 km beneath the sedimentary basins, while it is situated deeper below the Apuseni Mountains, Transdanubian and North Hungarian Ranges (28–33 km), and it is the deepest beneath the Eastern Alps and the Southern Carpathians (40–45 km). These values reflect well the Neogene evolution of the region, such as crustal thinning of the Pannonian Basin and orogenic thickening in the neighboring mountain belts.
    Type: Article , PeerReviewed
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  • 6
    Publication Date: 2024-02-23
    Description: The understanding of silicate weathering and its role as a sink for atmospheric CO 2 is important to get a better insight into how the Earth shifts from warm to cool climates. The lithium isotope composition (δ 7 Li) of marine carbonates can be used as a proxy to track the past chemical weathering of silicates. A high‐resolution δ 7 Li record would be helpful to evaluate the role of silicate weathering during the late Cretaceous climate cooling. Here, we assess chalk as a potential archive for reconstructing Late Cretaceous seawater Li isotope composition by comparing Maastrichtian chalk from Northern Germany (Hemmoor, Kronsmoor) to a Quaternary coccolith ooze from the Manihiki Plateau (Pacific Ocean) as a lithological analog to modern conditions. We observe a negative offset of 3.9 ± 0.6‰ for the coccolith ooze relative to the modern seawater Li isotope composition (+31.1 ± 0.3‰; 2SE; n = 54), a value that falls in the range of published offsets for modern core‐top samples and for brachiopod calcite. Further, the negative offset between the Li isotope compositions of Manihiki coccolith ooze and modern planktonic foraminifera is 2.3 ± 0.6‰. Although chalk represents a diagenetically altered modification of pelagic nannofossil ooze, manifested by changes in the composition of trace elements, we observe a consistent offset of Li isotope data between Maastrichtian chalk and Maastrichtian planktonic foraminiferal data (−1.4 ± 0. 5‰) that lies within the uncertainty of modern values. We therefore suggest that chalk can be used as a reliable archive for δ 7 Li reconstructions. Key Points Chalk is a reliable archive for the Li isotope composition of seawater Coccolith ooze has a negative offset of 3.9 ± 0.6‰ from modern seawater for Li isotope ratios The estimated mean value for the late Maastrichtian seawater Li isotope composition is +27.5 ± 1.0‰
    Type: Article , PeerReviewed
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  • 7
    Publication Date: 2024-03-05
    Description: Offshore wind energy is a steadily growing sector contributing to the worldwide energy production. The impact of these offshore constructions on the marine environment, however, remains unclear in many aspects. In fact, little is known about potential emissions from corrosion protection systems such as organic coatings or galvanic anodes composed of Al and Zn alloys, used to protect offshore structures. In order to assess potential chemical emissions from offshore wind farms and their impact on the marine environment water and sediment samples were taken in and around offshore wind farms of the German Bight between 06.03.2019 and 24.03.2019.
    Keywords: Helmholtz-Zentrum Hereon; Hereon
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 8
    Publication Date: 2024-02-24
    Description: This dataset is a synthesis of published nitrous oxide (N2O) fluxes from permafrost-affected soils in Arctic, Antarctic, and Alpine permafrost regions. The data includes mean N2O flux rates measured under field (in situ) conditions and in intact plant-soil systems (mesocosms) under near-field conditions. The dataset further includes explanatory environmental parameters such as meteorological data, soil physical-chemical properties, as well as site and experimental information. Data has been synthesized from published studies (see 'Further details'), and in some cases the authors of published studies have been contacted for additional site-level information. The dataset includes studies published until 2019. We encourage linking additional N2O flux data from unpublished and future studies with similar metadata structure to this dataset, to produce a comprehensive, findable database for N2O fluxes from permafrost regions.
    Keywords: Abisko_N2O; Alexandra_Fjord_N2O; Ammonium; Analytical method; Antarctica; Ardley_Island_N2O; Area/locality; Boniface_River_N2O; Canada; Cape_Bounty_N2O; Carbon/Nitrogen ratio; China; Churchill_N2O; Country; Daring_Lake_N2O; Daxing-an_Mountains_N2O; Day; Denmark; Density, active layer, bulk; Disturbance Type; Dome_Desert_N2O; Eagle_Plains_N2O; Eboling_Mountains_N2O; Ecosystem; Event label; Expedition_Fjord_N2O; Experimental treatment; Fenghuo_Mountains_N2O; Fildes_Peninsula_N2O; Finland; Garwood_Valley_N2O; Geermu_N2O; Great_Hing-an_Mountains_N2O; Haibei_N2O; Hemeroby/disturbance; Inner_Mongolia_N2O; Kilpisjaervi_N2O; LATITUDE; Location; LONGITUDE; Luanhaizi_N2O; Month; Nagqu_N2O; Nitrate; Nitrogen, soil; Nitrous oxide, flux, in mass nitrous oxide; Niwot_Ridge_N2O; Norway; Number of measurements; Number of measurement seasons; Number of points; Ny-Alesund_N2O; Okse_Bay_N2O; Organic carbon, soil; Original unit; Original value; Patterson_River_N2O; Permafrost extent; pH, soil; Precipitation, annual mean; Presence/absence; Publication of data; Reference of data; Replicates; Russia; Sample code/label; Seida_I_N2O; Seida_II_N2O; Site; Sodankylae_N2O; Soil moisture; Soil organic matter; Soil water content, gravimetric; Soil water content, volumetric; Sweden; Temperature, air; Temperature, air, annual mean; Temperature, soil; Thaw depth of active layer, maximum; Thaw depth of active layer, mean; Time in minutes; Truelove_Lowland_N2O; Tura_N2O; Type of chamber; Type of study; United States of America; Utsjoki_N2O; Vegetation type; Water filled pore space; Water filled pore space, calculated; Water holding capacity; Wudaoliang_N2O; Yakutsk_N2O; Year of observation; Yukon_Delta_N2O; Zackenberg_N2O; Zone
    Type: Dataset
    Format: text/tab-separated-values, 10302 data points
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  • 9
    Publication Date: 2024-05-17
    Description: During MOSAiC-ACA field campaign in late summer 2020 the Basler BT-67 research aircraft Polar 5 based in Spitzbergen (78.24 N, 15.49 E) was equiped with an advanced in-situ cloud payload by the DLR including a combination of the Cloud Droplet Probe, Cloud Imaging Probe and Precipitation Imaging Probe. The data sets provides data from all DLR particle measurement instruments including micropysical cloud properties like particle size distribution, total particle number concentration, effective diameter, median volume diameter and an estimated cloud/liquid/ice water content. In combination the dataset includes all particle sizes from 2.8 - 6400.0µm in diameter. In addition to the particle measurement systems the Nevzorov probe provides bulk measurements of the liquid and total water content. These cloud measurements were mainly conducted in low and midlevel clouds in the Fram Strait over sea ice and the open ocean. This measurement campaign is embedded in the Transregional Collaborative Research Centre TR 172 (ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3.
    Keywords: AC; AC3; Aircraft; Arctic; Arctic Amplification; Binary Object; Binary Object (File Size); CDP; CIP; Cloud droplet probe; Cloud imaging probe; Cloud Microphysics; clouds; Date/Time of event; Date/Time of event 2; Event label; Fram Strait; In-situ; In-Situ Measurements; Latitude of event; Longitude of event; mixed-phase clouds; MOSAiC; MOSAiC20192020; MOSAiC-ACA; Multidisciplinary drifting Observatory for the Study of Arctic Climate; NEVZ; Nevzorov probe; P5_223_MOSAiC_ACA_2020_2008310301; P5_223_MOSAiC_ACA_2020_2009020501; P5_223_MOSAiC_ACA_2020_2009040601; P5_223_MOSAiC_ACA_2020_2009070701; P5_223_MOSAiC_ACA_2020_2009080801; P5_223_MOSAiC_ACA_2020_2009100901; P5_223_MOSAiC_ACA_2020_2009111001; P5_223_MOSAiC_ACA_2020_2009131101; P5-223_MOSAiC_ACA_2020; Particle measurement system; PIP; PMS; POLAR 5; Precipitation imaging probe; Svalbard
    Type: Dataset
    Format: text/tab-separated-values, 40 data points
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  • 10
    Publication Date: 2024-06-25
    Description: Offshore wind energy is a steadily growing sector contributing to the worldwide energy production. The impact of these offshore constructions on the marine environment, however, remains unclear in many aspects. In fact, little is known about potential emissions from corrosion protection systems such as organic coatings or galvanic anodes composed of Al and Zn alloys, used to protect offshore structures. In order to assess potential chemical emissions from offshore wind farms and their impact on the marine environment water and sediment samples were taken in and around offshore wind farms of the German Bight between 06.03.2019 and 24.03.2019 within the context of the Hereon-BSH project OffChEm. The surface sediment samples were taken by a box grab, homogenized, freeze-dried and wet-sieved to gain the 〈20 µm grain size fraction. The 〈20 µm grain size fraction was acid digested and measured by ICP-MS/MS for their (trace) metal mass fractions. The Sr and Pb isotope ratios were measured by MC ICP-MS after an automated matrix separation with a prepFAST MCTM system.
    Keywords: Aluminium; Aluminium, limit of detection; Aluminium, limit of quantification; Aluminium, uncertainty; Arsenic; Arsenic, limit of detection; Arsenic, limit of quantification; Arsenic, uncertainty; AT275; AT275_Stat_S_097_HELW5; Atair; Atair275; Atair275_11; Atair275_12; Atair275_13; Atair275_14; Atair275_17; Atair275_18; Atair275_19; Atair275_2; Atair275_20; Atair275_21; Atair275_22; Atair275_23; Atair275_24; Atair275_25; Atair275_26; Atair275_27; Atair275_28; Atair275_29; Atair275_30; Atair275_31; Atair275_32; Atair275_33; Atair275_34; Atair275_35; Atair275_36; Atair275_39; Atair275_4; Atair275_40; Atair275_41; Atair275_42; Atair275_43; Atair275_44; Atair275_45; Atair275_46; Atair275_47; Atair275_48; Atair275_49; Atair275_5; Atair275_52; Atair275_53; Atair275_54; Atair275_55; Atair275_56; Atair275_57; Atair275_58; Atair275_60; Atair275_61; Atair275_62; Atair275_64; Atair275_65; Atair275_67; Atair275_68; Atair275_69; Atair275_7; Atair275_70; Atair275_71; Atair275_72; Atair275_73; Atair275_75; Atair275_78; Atair275_79; Atair275_8; Atair275_80; Atair275_81; Atair275_82; Atair275_83; Atair275_84; Atair275_85; Atair275_86; Atair275_87; Atair275_88; Atair275_89; Atair275_9; Atair275_91; Atair275_92; Atair275_93; Atair275_94; Atair275_95; Atair275_96; Atair275_97; Barium; Barium, limit of detection; Barium, limit of quantification; Barium, uncertainty; Beryllium; Beryllium, limit of detection; Beryllium, limit of quantification; Beryllium, uncertainty; Bismuth; Bismuth, limit of detection; Bismuth, limit of quantification; Bismuth, uncertainty; Cadmium; Cadmium, limit of detection; Cadmium, limit of quantification; Cadmium, uncertainty; Caesium; Caesium, limit of detection; Caesium, limit of quantification; Caesium, uncertainty; Calcium; Calcium, limit of detection; Calcium, limit of quantification; Calcium, uncertainty; Cerium; Cerium, limit of detection; Cerium, limit of quantification; Cerium, uncertainty; Chromium; Chromium, limit of detection; Chromium, limit of quantification; Chromium, uncertainty; Cobalt; Cobalt, limit of detection; Cobalt, limit of quantification; Cobalt, uncertainty; DEPTH, sediment/rock; Dysprosium; Dysprosium, limit of detection; Dysprosium, limit of quantification; Dysprosium, uncertainty; Element analysis grain size fraction 〈 20 microns via ICP-MS (total digest); Erbium; Erbium, limit of detection; Erbium, limit of quantification; Erbium, uncertainty; Europium; Europium, limit of detection; Europium, limit of quantification; Europium, uncertainty; Event label; Gadolinium; Gadolinium, limit of detection; Gadolinium, limit of quantification; Gadolinium, uncertainty; Gallium; Gallium, limit of detection; Gallium, limit of quantification; Gallium, uncertainty; Germanium; Germanium, limit of detection; Germanium, limit of quantification; Germanium, uncertainty; Helmholtz-Zentrum Hereon; Hereon; Holmium; Holmium, limit of detection; Holmium, limit of quantification; Holmium, uncertainty; Indium; Indium, limit of detection; Indium, limit of quantification; Indium, uncertainty; International Generic Sample Number; Iron; Iron, limit of detection; Iron, limit of quantification; Iron, uncertainty; Lanthanum; Lanthanum, limit of detection; Lanthanum, limit of quantification; Lanthanum, uncertainty; Lead; Lead, limit of detection; Lead, limit of quantification; Lead, uncertainty; Lead-206/Lead-204 ratio; Lead-206/Lead-204 ratio, uncertainty; Lead-207/Lead-204 ratio; Lead-207/Lead-204 ratio, uncertainty; Lead-207/Lead-206 ratio; Lead-207/Lead-206 ratio, uncertainty; Lead-208/Lead-204 ratio; Lead-208/Lead-204 ratio, uncertainty; Lead-208/Lead-206 ratio; Lead-208/Lead-206 ratio, uncertainty; Lead-208/Lead-207 ratio; Lead-208/Lead-207 ratio, uncertainty; Lithium; Lithium, limit of detection; Lithium, limit of quantification; Lithium, uncertainty; Lutetium; Lutetium, limit of detection; Lutetium, limit of quantification; Lutetium, uncertainty; Magnesium, limit of detection; Magnesium, limit of quantification; Magnesium, uncertainty; Manganese; Manganese, limit of detection; Manganese, limit of quantification; Manganese, uncertainty; Mercury; Mercury, limit of detection; Mercury, limit of quantification; Mercury, uncertainty; Molybdenum; Molybdenum, limit of detection; Molybdenum, limit of quantification; Molybdenum, uncertainty; MULT; Multi-collector ICP-MS (MC-ICP-MS), Nu Plasma II, Wrexham, UK; External intra-elemental calibration using NIST SRM 981; Multi-collector ICP-MS (MC-ICP-MS), Nu Plasma II, Wrexham, UK; External intra-elemental calibration using NIST SRM 987; Multiple investigations; Neodymium; Neodymium, limit of detection; Neodymium, limit of quantification; Neodymium, uncertainty; Nickel; Nickel, limit of detection; Nickel, limit of quantification; Nickel, uncertainty; Niobium; Niobium, limit of detection; Niobium, limit of quantification; Niobium, uncertainty; North Sea; Phosphorus; Phosphorus, limit of detection; Phosphorus, limit of quantification; Phosphorus, uncertainty; Potassium; Potassium, limit of detection; Potassium, limit of quantification; Potassium, uncertainty; Praseodymium; Praseodymium, limit of detection; Praseodymium, limit of quantification; Praseodymium, uncertainty; Rubidium; Rubidium, limit of detection; Rubidium, limit of quantification; Rubidium, uncertainty; S_002_AMWE4; S_004_AMWE3; S_005_AMWE7; S_007_AMWE5; S_008_AMWE6; S_009_AMWE15; S_011_AMWE19; S_012_AMWE20; S_013_AMWE21; S_014_AMWE22; S_017_NOST4; S_018_NOST1; S_019_NOST5; S_020_NOST6; S_021_NOST7; S_022_NOST3; S_023_NOST42; S_024_NOST43; S_025_NOST35; S_026_TI7; S_027_MEWI1; S_028_MEWI3; S_029_MEWI6; S_030_TI13; S_031_MEWI7; S_032_MEWI36; S_033_MEWI37; S_034_MEWI38; S_035_MEWI40; S_036_MEWI41; S_039_DOLW1; S_040_ALVE5; S_041_ALVE4; S_042_ALVE2; S_043_ALVE3; S_044_ALVE1; S_045_BKRI5; S_046_BKRI4; S_047_BKRI3; S_048_BKRI2; S_049_BKRI1; S_052_GOWI10; S_053_GOWI6; S_054_GOWI7; S_055_GOWI9; S_056_GOWI11; S_057_GOWI4; S_058_GOWI3; S_060_GOWI2; S_061_GOWI1; S_062_GOWI8; S_064_GOWI54; S_065_GOWI59; S_067_GOWI26; S_068_GOWI24; S_069_GOWI21; S_070_GOWI25; S_071_GOWI20; S_072_GOWI22; S_073_GOWI23; S_075_GOWI29; S_078_GOWI55; S_079_GOWI57; S_080_DOLW7; S_081_VEJA02; S_082_VEJA03; S_083_VEJA04; S_084_VEJA05; S_085_VEJA06; S_086_VEJA08; S_087_VEJA09; S_088_VEJA10; S_089_VEJA11; S_091_DOLW8; S_092_DOLW10; S_093_DOLW9; S_094_VEJA16; S_095_HELW1; S_096_HELW4; Samarium; Samarium, limit of detection; Samarium, limit of quantification; Samarium, uncertainty; Sample code/label; Sample method; Scandium; Scandium, limit of detection; Scandium, limit of quantification; Scandium, uncertainty; Selenium; Selenium, limit of detection; Selenium, limit of quantification; Selenium, uncertainty; Silver; Silver, limit of detection; Silver, limit of quantification; Silver, uncertainty; Sodium; Sodium, limit of detection; Sodium, limit of quantification; Sodium, uncertainty; Station label; Strontium; Strontium, limit of detection; Strontium, limit of quantification; Strontium, uncertainty; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, uncertainty; Tantalum; Tantalum, limit of detection; Tantalum, limit of quantification; Tantalum, uncertainty; Tellurium; Tellurium, limit of detection; Tellurium, limit of quantification; Tellurium, uncertainty; Terbium; Terbium, limit of detection; Terbium, limit of quantification; Terbium, uncertainty; Thallium; Thallium, limit of detection; Thallium, limit of quantification; Thallium, uncertainty; Thorium; Thorium, limit of detection; Thorium, limit of quantification; Thorium, uncertainty; Thulium; Thulium, limit of detection; Thulium, limit of quantification; Thulium, uncertainty; Titanium; Titanium, limit of detection; Titanium, limit of quantification; Titanium, uncertainty; Tungsten; Tungsten, limit of detection; Tungsten, limit of quantification; Tungsten, uncertainty; Uranium; Uranium, limit of detection; Uranium, limit of quantification; Uranium, uncertainty; Vanadium; Vanadium, limit of detection; Vanadium, limit
    Type: Dataset
    Format: text/tab-separated-values, 17568 data points
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