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268 results for “mercury”
Experimental evidence for the recovery of mercury-contaminated fish populations
<p>Anthropogenic releases of mercury (Hg) are a human health issue because the potent toxicant methylmercury (MeHg), formed primarily by microbial methylation of inorganic Hg in aquatic ecosystems, bioaccumulates to high concentrations in fish consumed by humans. Predicting the efficacy of Hg pollution controls on fish MeHg concentrations is complex because many factors influence the production and bioaccumulation of MeHg. Here we conducted a 15-year whole-ecosystem, single-factor experiment to determine the magnitude and timing of reductions in fish MeHg concentrations following reductions in Hg additions to a boreal lake and its watershed. During the seven-year addition phase, we applied enriched Hg isotopes to increase local Hg wet deposition rates fivefold. The Hg isotopes became increasingly incorporated into the food web as MeHg, predominantly from additions to the lake because most of those added to the watershed remained there. Thereafter, isotopic additions were stopped, resulting in an approximately 100% reduction in Hg loading to the lake. The concentration of labelled MeHg quickly decreased by more than 85% in lower trophic level organisms, initiating rapid decreases of 38–76% of MeHg concentration in large-bodied fish populations in eight years. Although Hg loading from watersheds may not decline in step with lowering deposition rates, this experiment clearly demonstrates that any reduction in Hg loadings to lakes, whether from direct deposition or runoff, will have immediate benefits to fish consumers.</p>
Mercury isotope trace magma mixing and crust-mantle interactions in the Yidun arc, eastern Tibetan Plateau
<p>Magma mixing between mafic and felsic melts is widespread in open magmatic process. However, tracing the magma sources of different endmembers is challenging, because elemental and isotopic information of different endmembers commonly achieved equilibrium during magma interactions. Mantle and crustal reservoirs show distinct signatures of mercury (Hg) isotope mass-independent fractionation, making Hg isotope an emerging tool to trace mantle- and crustal-derived magmas. Here we report the Hg isotope data of two types (Type-I and Type-II) of mafic microgranular enclaves (MMEs) and their host granitoids, which have similar whole-rock Sr-Nd and zircon Hf isotope composition, from the Daocheng-Cuojiaoma batholith, Eastern Tibetan Plateau, SW China. Zircon U-Pb dating indicates both the host granitoids and two types of MMEs formed coevally at ca. 216 – 217 Ma, coherent to the subduction of Garzê–Litang ocean (a branch of Paleo-Tethys ocean). The host granitoids are metaluminous to weakly peraluminous characteristics (A/CNK = 0.98 – 1.05) and exhibit negative to slightly positive ∆<sup>199</sup>Hg values (-0.2 to 0.02 ‰), indicating their source magma was a mixture of terrestrial sediments- and mantle-derived melts. Type-I MMEs display arc-like trace element patterns, low SiO<sub>2</sub> (53.8 to 55.0 wt%) and positive ∆<sup>199</sup>Hg values (0.00 to 0.10 ‰), indicating their derivation from a subduction-related fluid/melt metasomatized mantle source. Type-II MMEs show intervening concentrations of major/trace elements, and intermediate ∆<sup>199</sup>Hg values (-0.18 to 0.02), suggesting they were generated via mixing between the temporally and spatially coexisting first two magmas (i.e., type-I MMEs and granitoid). This study demonstrates the powerful use of Hg isotope for understanding magma sources and crustal-mantle interactions.</p>
Rivers as the largest source of mercury to coastal oceans worldwide
<p>Mercury is a potent neurotoxic substance and accounts for 250,000 intellectual disabilities annually. Worldwide, coastal fisheries contribute the majority of human exposure to mercury through fish consumption. Recent global mercury cycling and risk models attribute all the mercury loading to the ocean to atmospheric deposition. Nevertheless, new regional research has noted that the riverine mercury export to coastal oceans may also be significant to the oceanic burden of mercury. Here we construct an unprecedented high-spatial-resolution dataset estimating global river mercury and methylmercury exports. We find that rivers annually deliver 1,000 (minimum–maximum: 893–1,224) Mg mercury to coastal oceans, threefold greater than atmospheric deposition. Furthermore, high flow events, which are becoming more common with climate change, are responsible for a disproportionately large percentage of the export. Coastal oceans constitute 0.2% of the entire ocean volume but receive 27% of the external mercury input to the ocean. We estimate that the river mercury export could be responsible for a net annual export of 350 (interquartile range: 52–640) Mg mercury across the coastal–open-ocean boundary, although there is still high uncertainty around this estimate. Our results show that river export is the largest source of mercury to coastal oceans worldwide, and continued mercury risk modelling should incorporate the impact of rivers.</p>
Gaseous elementary mercury and other air pollutants data during COVID-19
<p>This dataset contains gaseous elementary mercury, particulate ions, organics, and trace metals, and meteorological parameters measured at the Dianshan Lake site in Shanghai during the 2020 COVID-19 period.</p>
Non-detected changes of dark spots on Mercury in 30 Earth months
<p>Central coordinates for each of the cataloged dark spots on Mercury.</p>
A high spatial resolution dataset for anthropogenic atmospheric mercury emissions in China during 1998-2014
<p>This database contains gridded atmospheric mercury emissions in 30 provinces of China by sectors from 1998 to 2014 at the resolution of 1 km×1 km. We distribute atmospheric mercury emissions in four sectors, i.e., agriculture, industry, service industry, and residences, based on China's land use data, enterprise data, road data, and population data. Gridded estimates of the total Hg (THg) and the three species, i.e., gaseous elemental Hg (Hg<sub>0</sub>), gaseous oxidized mercury (Hg<sub>II</sub>), and particulate-bound mercury (Hg<sub>p</sub>), are given separately.</p>
Transport and distribution of sodium ions in Mercury's magnetosphere: results from multi-fluid MHD simulations
<p>This dataset contains VTK files of the four simulations that are presented in the paper<em> </em><a href="https://essopenarchive.org/doi/full/10.22541/essoar.171629607.76912814/v1">Transport and distribution of sodium ions in Mercury’s magnetosphere: results from multi-fluid MHD simulations</a> .</p> <p>The physical parameters for each of the simulations are summarized below:</p> <div> <div>Sim-1: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-2: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-3: IMF = [0,0,-8.5] nT, V_sw = [-400,0,0] km/s</div> <div>Sim-4: IMF = [-15.2,8.4,-8.5] nT, V_sw = [-400,50,0] km/s</div> <div> <div> </div> <div>The Hall term is switched off in sim-2, but switched on in the rest of the simulations. The sodium source is used in all the simulations except for sim-3. In all the simulations, the solar wind density is 40 amu/cc with a temperature of 7.5 eV. We refer the readers to the aforementioned paper for more details.</div> </div> </div> <p>These VTK files can be opened with Paraview.</p>
Experimental Data for understanding melting and phase relations at the Mercury silicate mantle compositions as a function of temperature at 7 GPa
<p><span>High pressure-temperature experiments were performed at 7 GPa and 1700-2100 C using a cubic press to understand melting relations at the core-mantle boundary on Mercury. Mer8 was in the stability field of orthopyroxene whereas Mer15 first crystallized olivine. With cooling, both compositions reached a cotectic surface with olivine + orthopyroxene, followed by garnet and clinopyroxene. A sulfide phase (FeS + MgCaFeS) was present in all experiments.</span></p>
Postdepositional fractionation of mercury isotope between sedimentary organics and pyrite
<p>Raw data for postdepositional fractionation of mercury isotope between sedimentary organics and pyrite</p>
Mercury's Impact structure inventory
<h4>This data set display an inventory of impact structures larger than 100 km on Mercury. The gravity measurements were determined using a amore precise approach of calculating the avearge of multiple profiles along the impact structure.</h4> <h4>Variables:</h4> <ol> <li><strong>ID</strong>: Identification number or the name of the impact structure.</li> <li><strong>Diam (km)</strong>: The diameter of the impact structure in kilometers.</li> <li><strong>Lon</strong>: Central longitude.</li> <li><strong>Lat</strong>: Central latitude.</li> <li><strong>Id_source: </strong>Primarly identification source ( Gravity data / DTM/ Mosaic model).</li> <li><strong>Certainity</strong>: Grade of certainity ( Certain, probable or tentative).</li> <li><strong>Class</strong> : Geomorphological classification (complex, central peak basins, peak ring basin, multi ring basins, modified basin).</li> <li><strong>Terraces</strong>: Presence of terraces. </li> <li><strong>Rim</strong>: Rim preservation state.</li> <li><strong>Depth [km]</strong>: Depth measurements in km.</li> <li><strong>D/d</strong>: Depth to diameter ratio.</li> <li><strong>Filling</strong>: Presence of volcanis infill.</li> <li><strong>Smooth plains</strong>: Filling classified as part of the smooth plains.</li> <li><strong>Bouguer_center</strong>: Measured Bouguer anomaly (mGal) of LOS gravity model (Goossens et al., 2022) associated with the center of the impact structure.</li> <li><strong>Bouguer_outer</strong>: Measured Bouguer anomaly (mGal) of LOS gravity model (Goossens et al., 2022) associated with the outer area of the impact structure.</li> <li><strong>Bouguer_rimcrest</strong>: Measured Bouguer anomaly (mGal) of LOS gravity model (Goossens et al., 2022) associated with the rim area of the impact structure. </li> <li><strong>Bouguer_contrast: </strong>Bouguer anomaly contrast (mGal).</li> <li><strong>Stdev_center</strong>: Standard deviation of the Bouguer anomaly in the center.</li> <li><strong>Stdev_outer</strong>: Standard deviation of the Bouguer anomaly in the outer area.</li> <li><strong>Stdev_rimcrest</strong>: Standard deviation of the Bouguer anomaly at the rimcrest.</li> <li><strong>CC</strong>: Crater counts of N(20).</li> <li><strong>Area</strong>: The area of the basin in km².</li> <li>N(20)/Area: Value that refelcts the relative age of impact structures.</li> <li><strong>CT_center</strong>: Measured crustal thickness (km) associated with the center of the impact structure.</li> <li><strong>CT_outer</strong>: Measured crustal thickness (km) associated with the outer area of the impact structure.</li> <li><strong>CT_rim: </strong>Measured crustal thickness (km) associated with the rim area of the impact structure.</li> <li><strong>Temperature [K]: </strong>Crustal temperature of impact structures at 39.4km depth at 3.0 Ga.</li> </ol>
Supplementary Information on Origin and partitioning of mercury in the polluted Scheldt Estuary and adjacent coastal zone. STOTEN Volume 878, 20 June 2023, 163019
<p>Dataset on Hg speciation in surface water in the Scheldt estuary and Belgian coastal zone in 2020 and 2021</p>
BepiColombo/Mio MSA, MIA and MEA 2 Data during the third Mercury flyby
Open the record for dataset details and reuse information.
Raw diffraction images of mercury-bound human muscarinic acetylcholine receptor
<p>Raw data for <a href="https://www.rcsb.org/structure/5YC8">5YC8</a> (S110R-BRIL–NMS:Hg).</p>
Data for Mercury's crustal porosity as constrained by the planet's bombardment history
<p>This archive contains grids and spherical harmonic files for <em>Mercury's crustal porosity as constrained by the planet's bombardment history Broquet A. & Rolser F. et al, submitted to Geophysical Research Letters (2024). </em></p> <p>The different grid files are provided in netcdf format and are expanded up to spherical harmonic degree 300 for crustal thickness and 80 for porosity. The spherical harmonic model uses the <a href="https://shtools.github.io/SHTOOLS/index.html">SHTOOLs</a> convention. Higher resolution models are available on demand. </p> <p>Porosity_M1.netcdf: Nominal porosity model using the parameters that best-fit gravity-derived porosity estimates</p> <p>Porosity_M2.netcdf: Porosity model using the lunar parameters</p> <p>CrustalThickness_M1.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M1 and assuming an average grain density of 2950 kg m-3.</p> <p>CrustalThickness_M2.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M2 and assuming an average grain density of 2950 kg m-3.</p> <p>CrustalThickness_M1_Beu20.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M1 and lateral grain density variations from Beuthe et al. (2020).</p> <p>CrustalThickness_M2_Beu20.netcdf: Crustal thickness (km) considering the effect of crustal porosity from model M2 and lateral grain density variations from Beuthe et al. (2020).</p> <p>CrustalThickness_2800.netcdf: Crustal thickness (km) considering the an average crustal bulk density of 2800 kg m-3.</p> <p>N20.netcdf: N20 count using a 1000-km moving window.</p> <p>Topo_degstr.sh: Spherical harmonic coefficients for the spectrally truncated Mercury Laser Altimeter model. The truncation uses the degree-strength map of Konopliv et al. (2020). </p>
Datasets concerning "Variations of Heat Flux and Elastic Thickness of Mercury from Thermal Evolution Modeling"
<p><strong>Datasets concerning timeseries from average temperature profiles:</strong></p> <p>Average_profiles.rar</p> <p>Tables containing the time evolution of the elastic lithospheric thickness calculated with the average mantle temperature profile.<br>Calculations have been done with dry and wet rheologies (crust and mantle), using the conversion package from :<br>"Adrien Broquet. AB-Ares/Te_HF_Conversion: 0.2.3 (Version 0.2.3). Zenodo. <a href="http://doi.org/10.5281/zenodo.4973893" rel="nofollow">http://doi.org/10.5281/zenodo.4973893</a>"<br>In total 32 tables, 16 for each rheology.</p> <p> </p> <p><strong>Datasets concerning timeseries from localized temperature profiles:</strong></p> <div> <div>Localized_profiles.rar</div> </div> <p>Tables containing the time evolution of the elastic lithospheric thickness calculated with the respective localized mantle temperature profile of each investigated point of interest (Caloris Basin, Discovery Rupes, Goossens et al., 2022 points 1-4).<br>Calculations have been done with dry and wet rheologies (crust and mantle), using the conversion package from :<br>"Adrien Broquet. AB-Ares/Te_HF_Conversion: 0.2.3 (Version 0.2.3). Zenodo. <a href="http://doi.org/10.5281/zenodo.4973893" rel="nofollow">http://doi.org/10.5281/zenodo.4973893</a>"<br>In total 32 tables, 16 for each rheology.</p> <p> </p> <p><strong>Datasets concerning maps of CMB heat flux at present day:</strong></p> <p>LatLon_Maps.rar</p> <p>Tables containing present day output of the CMB heat flux for each case investigated<br>Format in each file is : <br>Longitude | Latitude | CMB heat flux <br>1 degree of resolution<br>A python code is provided to visualize easily the data (Map_visualization.py)</p> <div> </div> <div> </div> <div>Sh_Maps.rar</div> <div> </div> <div>Tables containing present day output of the CMB heat flux for each case investigated under the form of spherical harmonics coeffcients, up to the spherical harmonic degree 59.</div> <div>A python code is provided in order to plot easily the spherical harmonics data (PlottingSH_maps.py).</div> <div> </div> <div> </div> <p> </p>
Hollows on Mercury: A Comprehensive Analysis of Spatial Patterns and Their Relationship to Craters and Structures
<p><strong><span>Supporting Material Content </span></strong></p> <p><span> </span></p> <p><span>The raw data collected and produced in this paper are shown in the tables provided as supplementary information to the main text of the article.</span><span> </span><span>Specifically, the contents of each table are as follows:</span></p> <p><span> </span></p> <p><strong><span><span>1-<span> </span></span></span></strong><strong><span>Matrix 1</span></strong></p> <p><span>This table shows the Boolean matrix in which all the data collected for each distinctive trait (header descriptions are reported in Table 1 in the main text) for each hollow location are collected. In Matrix 1 and 2, the ID progressive numbering used in Thomas et al., (2014a) have been maintained. When a new location was added to the list we used the same Id number of the closest identified location by Thomas et al., (2014a). For further clarity an univocal new progressive numbering has been assigned to each location. In addition, (i) the coordinates of the centroid of the mapped polygon for each location (latitude and longitude are provided in decimal degrees) and (ii) the automatically extracted minimum, maximum and mean elevations are given for each polygon.</span></p> <p><strong><span><span>2-<span> </span></span></span></strong><strong><span>Matrix 2</span></strong></p> <p><span>This table shows the Boolean matrix in which the occurrences of degradation classes and geologic units are collected for all those hollows contained within craters. These data are reported both as single column cumulative data (e.g., for each location, when available, the degradation class code is reported) and as Boolean matrix. When data are not available for the given location the cells have been left empty.</span></p> <p><span>Crater diameters are also reported along with elevations related to crater morphologies.</span></p> <p><strong><span><span>3-<span> </span></span></span></strong><strong><span>Matrix 3</span></strong></p> <p><span>This table shows the matrix that collects the results of equations 1, 2 (tab P) and 3 (tab I), described in the methods section, for the entire population of hollows. The data herein reported are the machine-readable version of the data reported in Table 2 in the main text.</span></p> <p><strong><span><span>4-<span> </span></span></span></strong><strong><span>Matrix 4</span></strong></p> <p><span>This table shows the matrix that collects the results of equations 1, 2 (tab P) and 3 (tab I), described in the methods section, for the population of hollows contained within craters. This dataset also includes the results of the above equations by taking into account parameters such as degradation classes and geological units (names reported in the headers correspond to the ones used in Matrix 2 which are taken from geological mapping literature. The full literature list can be found in the main text in the methods section).</span></p> <p><span> </span></p> <p><span>In addition to these tables, we also provided the GIS-ready shapefile containing all the polygons showing the areas where the hollows were observed, the attributes are the same as those included in Matrix 1.</span></p>
Derived data for: "Variability of the interplanetary magnetic field as a driver of electromagnetic induction in Mercury's interior"
<p>Derived data for "Variability of the interplanetary magnetic field as a driver of electromagnetic induction in Mercury’s interior", accepted for publication in the Journal of Geophysical Research: Space Physics.</p>
2016 Mercury Transit Observations at BBSO / FISS
<p>Data on the May 9 2016 solar transit of Mercury from the FISS instrument at Big Bear Solar Observatory. Covers both the sodium D lines at R~1.4e5</p>
Derived data for: 'Modelling the Time-Dependent Magnetic Fields that BepiColombo will use to Probe Down into Mercury's Mantle'
<p>Derived data for the manuscript entitled 'Modelling the Time-Dependent Magnetic Fields that BepiColombo will use to Probe Down into Mercury’s Mantle'.</p>
Data for Field evidence for Asian outflow and fast depletion of total gaseous mercury in the polluted coastal atmosphere
<p>Hourly data of TGM at Tai Mo Shan in Hong Kong</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.