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268 results for “Mercury”
Mercury in soil, vegetation, and organisms across Niwot Ridge, Saddle Catchment, and Green Lakes Valley, 2020 - 2023.
This dataset includes soil, vegetation, water, atmospheric deposition, litterfall, incubation, and organism data from the Niwot Ridge, Saddle Catchment, and Green Lakes Valley collected during 2020 and 2021 to investigate the storage, transformation, and mobilization of mercury in the Colorado Rocky Mountains. During Summer 2020, we collected soil cores (10cm x 3cm) across vegetation plant functional groups in wet meadows, moist meadows, dry meadows, krummholz, subalpine forest, shrub areas, as well as at the inlet and outlet of the Green Lakes in Green Lakes Valley. At each of these sites, we collected leaves from forbs, graminoids, and shrubs, as well as litter (and moss if present). For organisms, we sampled pika hairs from nine different pika trapped on the West Knoll, in addition to caddisfly pupae found in wet meadows in the Saddle Catchment. We analyzed hairs from weasel specimens at the CU Boulder Natural History Museum that were trapped either on, or near, Niwot Ridge. Finally, we analyzed dust samples collected by Dr. Ruth Heindel in 2018 and 2019 on Niwot Ridge. We analyzed soil samples for organic matter; pH; water content; percent carbon, nitrogen, and sulfur; stable carbon, nitrogen, and sulfur isotopes; total mercury; and methylmercury. We analyzed vegetation samples for percent carbon, nitrogen, and sulfur; stable carbon, nitrogen, and sulfur isotopes; total mercury; and methylmercury. We analyzed organism and dust samples for total mercury and methylmercury. During Spring 2021, we collected composite snow cores from 4 sites in the Saddle region and 3 sites in the subalpine forest. We measured snow depth and density to calculate snow water equivalent and then analyzed these samples for sulfate, nitrate, chloride, dissolved organic carbon, dissolved organic nitrogen, total mercury, and methylmercury concentrations. During Summer 2021, we collected soil cores (10cm x 3cm) every other week from June through September from a solifluction lobe, alpine wet
Data associated with the FLooded Upland Dynamics EXperiment (FLUDEX), conducted at the IISD Experimental Lakes Area 1997 to 2003, investigating reservoir flooding impacts on ecosystems, particularly the release of mercury and greenhouse gases.
The data included in this repository were collected over the course of the FLooded Upland Dynamics Experiment (FLUDEX) conducted at the IISD Experimental Lakes Area (IISD-ELA) from 1997 to 2003. A plethora of data was collected over five years of flooding three upland reservoir sites, in order to examine the relationship between the amount of flooded, and thus decomposed, terrestrial organic matter and the production of methylmercury (MeHg), total mercury (THg), and greenhouse gases (GHGs) in the reservoirs. Findings from this experiment suggest that the amount of organic carbon stored in a flooded site does not directly influence the amount of THg, MeHg, and GHGs produced, but it does affect the persistence of mercury in the reservoir and food web. This version of the repository contains data collected on water chemistry, benthic invertebrate (chironomid) emergence, mercury and methylmercury concentrations in the water and food web, stable isotopes of carbon and nitrogen in emerging insects and zooplankton, and abundance and biomass of zooplankton, phytoplankton, and bacteria. This data package contains only some of the data from the FLUDEX project. IISD-ELA hopes to add more data in subsequent versions.
Atmospheric Gaseous Elemental Mercury Fluxes at Harvard Forest EMS Tower 2019-2020
In terrestrial ecosystems, dry deposition of atmospheric gaseous elemental mercury (GEM) is considered the dominant source of mercury accounting for 54% to 94% of mercury loads observed in soils, yet direct quantification of GEM deposition across forests is largely missing. The goal of this project is to quantify atmosphere-surface exchange of GEM at Harvard Forest for one full year, providing the first such record in a non-polluted forest. GEM exchange is measured using micrometeorological techniques using a large measurement tower, the only available method for direct, non-intrusive and time-extended measurements of net GEM exchange at the ecosystem level encompassing all underlying sinks and sources. A second objective was to partition GEM fluxes into canopy and soil contributions via deployment of two corresponding flux systems: one system was deployed above the forest canopy to measure ecosystem-level GEM exchange; a second system was deployed below the canopy to quantify soil contributions. This dataset contains an 18-month record of gaseous elemental mercury concentrations and fluxes measured at the EMS tower at Harvard Forest from May 2019 to August 2020.
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Landscape Position Project at North Temperate Lakes LTER: Fish Growth and Mercury Contaminant Data 1998 - 1999
As part of the Landscape Position Project, yellow perch were collected for mercury and isotope analysis by a combination of angling, beach seining, vertical gill net, fyke net and electrofishing in the summers of 1998 and 1999. A total of 86 yellow perch from 25 lakes were analyzed. Scales were used to determine age and length at ages 1 to 3 years. The nitrogen stable isotope signature indicates the relative food-web position of the fish relative to cladocerans collected from the same lake. The N_SIGNATURE value divided by 3.2 gives trophic position relative to cladoceran Sampling Frequency: one survey on each lake in late June through late July of 1998 or 1999 Number of sites: 25
Landscape Position Project at North Temperate Lakes LTER: Fish Mercury Level 1998 - 1999
As part of the Landscape Position Project, yellow perch were collected for mercury and isotope analysis by a combination of angling, beach seining, vertical gill net, fyke net and electrofishing in the summers of 1998 and 1999. A total of 183 yellow perch from 43 study lakes with approximate length of 150 mm were analyzed. Sampling Frequency: one survey on each lake in late June through August of 1998 or 1999 Number of sites: 43
Thermal infrared emissivity spectral library of silicates measured under the Mercury simulated environment
<p>This is the thermal emissivity spectral library of silicates measured as a function of temperature under Mercury simulated environment. Data is measured at the Planetary Spectroscopy Laboratory (PSL), Institute of Planetary Research, German Aerospace Center (DLR), Berlin. The spectral library will be used for mineral identification of Mercury surface using MERTIS datasets. The manuscript related to this work is submitted to Icarus on the title "<strong>Thermal Infrared Spectroscopy (7-14 µm) of Silicates under Simulated Mercury Daytime Surface Conditions and their Detection: Supporting MERTIS onboard the BepiColombo Mission".</strong></p>
Measurements and model simulations of iodine monoxide (IO) radical, water vapor (H2O), nitrogen dioxide (NO2) radical, formaldehyde (HCHO), gaseous elemental mercury (Hg0), and oxidized mercury (HgII) at Storm Peak Laboratory, Colorado, during April 2022
<p>This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to <em>AGU Geophysical Research Letters</em>.</p> <p> </p> <p><strong>file01</strong> contains two example spectral proofs for iodine monoxide (IO) radical measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Storm Peak Laboratory, CO (SPL; 3220 meters above sea level; 40.455 degrees North; 106.745 degrees West) during April 2022.</p> <p><strong>file02</strong> contains oxygen collision-induced absorption (O2-O2) slant column densities (SCDs) measured in a spectral fit window from 350 to 388 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file03</strong> contains O2-O2 SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file04</strong> contains IO SCDs measured in a spectral fit window from 417.5 to 438 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file05</strong> contains water vapor (H2O) SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file06</strong> contains nitrogen dioxide (NO2) radical SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file07</strong> contains formaldehyde (HCHO) SCDs measured in a spectral fit window from 328,5 to 359 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file08</strong> contains the profiles of pressure, temperature, O2-O2, ozone (O3), NO2, and H2O derived from ECMWF CAMS reanalysis (April 2022 at SPL) and used in the radiative transfer model McArtim3 to calculate weighting functions for the trace gas profile inversions of IO, H2O, NO2, and HCHO.</p> <p><strong>file09</strong> contains the a priori profiles used for the IO profile inversions during April 2022 at SPL. One profile assumes a "flat" profile shape with a constant volume mixing ratio of 0.10 pptv throughout the atmosphere. The other profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file10</strong> contains the a priori profile used for the H2O profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file11</strong> contains the a priori profile used for the NO2 profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file12</strong> contains the a priori profile used for the HCHO profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file13</strong> contains the IO tropospheric vertical column densities (VCDtrop; surface to 12 km), volume mixing ratios near instrument altitude (VMRinstr), and degrees of freedom (DoF) measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file14</strong> contains the H2O VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file15</strong> contains the NO2 VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file16</strong> contains the HCHO VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file17</strong> contains GEOS-Chem simulated temperature, relative humidity, IO VCDtrop & VMRinstr, H2O VCDtrop & VMRinstr, NO2 VCDtrop & VMRinstr, HCHO VCDtrop & VMRinstr, and bromine monoxide (BrO) radical VCDtrop & VMRinstr at SPL from April 1 to April 30, 2022.</p> <p><strong>file18</strong> contains the gaseous elemental mercury (Hg0) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file19</strong> contains the oxidized mercury (HgII) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file20</strong> contains the GEOS-Chem simulated Hg0 and HgII at SPL from April 1 to April 30, 2022.</p> <p><strong>file21</strong> contains the profiles of pressure, temperature, relative humidity, BrO, bromine atom (Br), methane (CH4), chlorine monoxide (ClO) radical, chlorine atom (Cl), carbon monoxide (CO), Hg0, peroxy radical (HO2), IO, iodine atom (I), NO2, hydroxyl radical (OH), and O3 used as constraints for the gas-phase mercury box model. All profiles except IO and I are adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. The IO profile was calculated by scaling the GEOS-Chem April 2022 daytime (SZA < 85) average below 12 km by the average observed IO VCDtrop during April 2022. The I atom profile was calculated by multiplying the scaled IO profile by the ratio of unscaled I / unscaled IO profiles from GEOS-Chem.</p> <p> </p> <p><strong>file22</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file23</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file24</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file25</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file26</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file27</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file28</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file29</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file30</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file31</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file32</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file33</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file34</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file35</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file36</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file37</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file38</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file39</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p> </p> <p><strong>file40</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file41</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file42</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file43</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file44</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file45</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file46</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file47</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file48</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file49</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file50</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file51</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file52</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file53</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file54</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file55</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file56</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file57</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p>
Northeastern TIME Lakes Dragonfly Mercury and supporting lake geochemistry
We sampled lake water and dragonfly larvae in 74 northeastern US lakes (TIME, or Temporally Integrated Monitoring of Ecosystems, lakes) that are part of the US EPA Long-Term Monitoring Network. The lakes are a statistical population of acid-sensitive lakes, a subset of US EPA EMAP lakes originally sampled in the early 1990s (Stoddard et al. 1996). The TIME lakes are 45 lakes in New York, 43 of which are in the Adirondacks, plus 29 lakes in New England. All lakes were sampled in a late-summer index period during 2012; lake water samples were collected manually from the epilimnion via boat, and dragonfly larvae were collected near shore using dip nets. Major ions, acid-base chemistry, total mercury and methylmercury in lake water, and total mercury and methylmercury in dragonfly larvae were analyzed. GIS analysis of lake watersheds and integration of selected EMAP-derived characteristics provides landscape and some morphometry variables for each lake. Additional annual geochemistry data for the lakes beginning in 1992 (with EMAP sampling) and ending in 2016 are available through US EPA.
Data and results of the example used in the SI-Hg D1 protocol for the SI-traceable calibration of elemental mercury (Hg0) gas generators used in the field
<p>During the SI-Hg project a metrological traceable protocol for the calibration of mercury gas generators used in the field was developed and validated. The SI-Hg calibration protocol specifies the procedures for establishing traceability to the SI units for the quantitative output of elemental mercury generators that are employed in regulatory applications for emission monitoring or testing. This protocol provides methods for</p><ul><li>the experimental procedures to compare the output of elemental mercury gas generators</li><li>the data processing for determination of mercury concentration and the expanded uncertainty of the mercury concentration obtained from the elemental mercury gas generator.</li></ul><p>In the protocol examples are given to explain the data processing, determining the mercury concentration and corresponding uncertainty. In this repository the raw data and results used for the example calculated with the data processing script can be found.</p>
Spherical harmonic models of the shape of Mercury
<p>The data used to generate these spherical harmonic models is the global digital elevation model (DEM) of Mercury, produced by the U.S. Geological Survey (USGS). The DEM was derived from from stereo image pairs (stereo photogrammetry) captured by the Mercury Dual Imaging System (MDIS) narrow-angle camera (NAC) and multispectral wide-angle camera (WAC) on board the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft. </p> <p>The global DEM was downloaded throught the <a href="https://astrogeology.usgs.gov/search/map/Mercury/Topography/MESSENGER/Mercury_Messenger_USGS_DEM_Global_665m_v2">Astropedia catalog</a> in geoTIFF format and equirectangular projection. Using the <a href="https://rasterio.readthedocs.io/en/stable/">rasterio</a> package, the dataset was loaded in python, scaled to the local height and radius (described in the Astropedia documentation), and exported into .dat format. Then, the file was converted into a netcdf format and resampled into a gridline registration using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> as follows:<br><code>gmt xyz2grd filename.dat -Gfilename.grd -R0/360/-90/90 -I0.015625/0.015625 -ZTLd -fg -rp</code><br><code>gmt grdsample filename.grd -Gfilename_gridline.grd -T</code></p> <p>The resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software with the <code>SHGrid.from_netcdf()</code> and expanded into spherical harmonics using the function <code>SHGrid.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)m. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <p>- Mercury_shape_5759.sh.gz<br>- Mercury_shape_2879.sh.gz<br>- Mercury_shape_1439.sh.gz<br>- Mercury_shape_719.sh.gz</p> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>The spherical harmonic coefficients can be loaded with pyshtools as follows:<br><code>SHCoeffs.from_file("filename.sh.gz", format='bshc')</code></p>
Datasets for "Large subglacial source of mercury from the southwestern margin of the Greenland Ice Sheet"
<p>Geochemical measurements and hydrochemical datasets linked to the publication "Large subglacial source of mercury from the southwestern margin of the Greenland Ice Sheet" in Nature Geoscience. Presented are (1) data for mercury concentrations in glacial meltwater outflows from the Greenland Ice Sheet taken in 2012, 2015 and 2018, (2) data for mercury concentrations in fjord waters from Nuup Kangerlua, Ameralik Fjord and Søndre Strømfjord, and (3) all associated hydrochemical data presented in the manuscript. For additional details (analytical techniques, precision, accuracy and limits of detection) please refer to the methodology in the publication.</p> <p>This third version has additional riverine data added the the 2012 dataset. </p>
MacFarlane Australian Anthropogenic Mercury Emissions
<p><strong>Australian anthropogenic mercury emissions inventory.</strong></p> <p>A detailed description of the emissions is provided in MacFarlane et al., currently (as of March 2022) in review for <em>Environmental Science: Processes and Impacts</em> and available as a pre-print on EarthArXiv (<a href="https://doi.org/10.31223/X5RK84">https://doi.org/10.31223/X5RK84</a>).</p> <p>The dataset posted here includes:</p> <ul> <li>Total annual emissions for each sector (kg), summed over Australia as a whole, as a .csv file</li> <li>Gridded emissions for each sector (kg/m<sup>2</sup>/s), as netcdf (.nc) files</li> </ul> <p>The netcdf files are provided in GEOS-Chem compliant format, with metadata included within the files. Note that the gridded files do not all have the same horizontal resolution, with distributed emissions at 0.25° resolution and point-source emissions at 0.1° resolution.</p>
Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change
<p>Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Artic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (<em>Alle alle</em>), a keystone Artic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. These datasets contain the behavioral, blood Hg and environmental data (SST, SIC) used in our analyses. Details about the datasets are found in the accompanying word document.</p>
Supplementary material to: Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC
<p>We produced a new higher-resolution topographic map of Mercury’s south polar region (75°-90° South, covering ~1.3 million km<sup>2</sup>) by using data collected by the NASA MESSENGER spacecraft’s Mercury Dual Imaging System (MDIS; Hawkins et al, 2007) over the years 2011-2015. This new map enables, <em>e.g.</em>, the first detailed modeling of illumination and thermal conditions in these southern radar-bright locations and the first constraints on the nature and history of volatiles residing there, but it is also intended as a resource for other geophysical analyses and for the preparation of the BepiColombo mission, currently en-route to the planet.</p> <p>For more details, please visit <a href="https://pgda.gsfc.nasa.gov/products/88">https://pgda.gsfc.nasa.gov/products/88</a>.</p> <p><strong>Products:</strong></p> <p>DEM (interpolated), DEM (filled), Slopes, PSR masks</p> <p>All these files (except the PSR masks shapefile) are 250 m/pix GeoTiffs with south polar stereographic X/Y coords in meters.</p> <p><br> <em>If using these products, please cite:</em><br> Bertone, S., E. Mazarico, M.K. Barker, M. Siegler, J. M. Martinez Camacho, C. Hamill, A. Glatzenberg, N. L. Chabot, 2022: <em>Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC</em>. The Planetary Science Journal, 02/2023, <a href="http://dx.doi.org/10.3847/PSJ/acaddb">doi:10.3847/PSJ/acaddb</a></p>
Datasets for Wohlfarth et al. (2023) An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer
<p>This document describes the datasets and modeling results presented and discussed in our full research article.<br> <br> Wohlfarth, K., Wöhler, C., Hiesinger, H., Helbert, J. 2023, An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer, Astronomy and Astrophysics, 672<br> <br> <a href="https://doi.org/10.1051/0004-6361/202245343">https://doi.org/10.1051/0004-6361/202245343</a><br> <br> We provide several visualization scripts that read and display the results for convenience. Access to the original MATLAB® code for the thermal model implementation is available upon request (<a href="mailto:kay.wohlfarth@tu-dortmund.de">kay.wohlfarth@tu-dortmund.de</a>).<br> <br> More info in Dataproducts.pdf</p>
Lists of Magnetopause and Bow Shock Crossings of Mercury by MESSENGER Spacecraft
<p>The dataset titled “Lists of Magnetopause and Bow Shock Crossings of Mercury by MESSENGER Spacecraft” employs the measurements from the MESSENGER spacecraft’s Magnetometer (MAG) and Fast Imaging Plasma Spectrometer (FPIS) instruments to identify magnetopause and bow shock crossings during MESSENGER's orbit of Mercury. MESSENGER's data orbiting Mercury were collected between 23-03-2011 and 30-04-2015 and are available from the Planetary Data System’s Planetary Plasma Interactions (PDS/PPI) Node at https://pds-ppi.igpp.ucla.edu.</p> <p> </p> <p>The dataset includes four lists:</p> <p>a, Bow_Shock_Out_Time_Duration_public_version_WeijieSun_20230829.txt</p> <p>b, Bow_Shock_In_Time_Duration__public_version_WeijieSun_20230829.txt</p> <p>c, MagPause_In_Time_Duration__public_version_WeijieSun_20230829.txt</p> <p>d, MagPause_Out_Time_Duration_public_version_WeijieSun_20230829.txt</p> <p> </p> <p>Here are examples for the time in the list:</p> <p>Example A</p> <p>2011 03 23 15 39 10.5 2011 03 23 16 24 02.4 BSO m </p> <p>This entry represents multiple bow shock crossings. The first six columns indicate the time of the first boundary crossing, while the next six columns indicate the time of the last boundary crossing. “BSO” stands for outbound crossing of the bow shock, and “m” indicates that this is a multiple bow shock crossing made by MESSENGER. Only the first and last boundaries were selected out, we did not identify the boundary crossings in between.</p> <p> </p> <p> </p> <p>Example B</p> <p>2011 03 25 13 04 24.2 2011 03 25 13 04 24.0 BSI s </p> <p>This entry represents a single bow shock crossing. The first six columns and the next six columns are identical, indicating that this is a single event. “BSI” stands for inbound crossing of the bow shock, and “s” indicates that this is a single bow shock crossing made by MESSENGER.</p> <p><br> </p> <p>The dataset does not include magnetopause and bow shock crossings during the following time intervals:</p> <p>a. From 03:02 to 20:00 on 05-04-2011</p> <p>b. From 24-05-2011 to 03-06-2011</p> <p>c. From 17:50 to 22:53 on 16-04-2012</p> <p>d. From 09-06-2012 to 13-06-2012</p> <p>e. From 07:30 on 08-01-2013 to 16:00 on 09-01-2013</p> <p>f. From 07:55 to 18:33 on 28-02-2013</p> <p>g. From 14:22 to 17:38 on 26-12-2014</p> <p> </p> <p>The current version is updated on 29 August 2023.</p> <p> </p> <p>This work was supported by NASA Discovery Data Analysis Program (DDAP) Grant #80NSSC22K1061 (PI Weijie Sun).</p>
Data and code for the publication: "Deforestation as an anthropogenic driver of mercury pollution"
<p>A. Feinberg, Sep 2023<br> arifeinberg@gmail.com</p> <p>Essential data and code for the publication: Feinberg et al. : Deforestation as an anthropogenic driver of mercury pollution</p> <p>The directories include:<br> 1) analysis_scripts/ - all analysis scripts used to produce input data and figures for paper<br> 2) Erosion_data/ - Erosion model (GloSEM) output<br> 3) GC_code/ - Archived GEOS-Chem code used to simulate the runs in this paper<br> 4) GC_data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:<br> HIST - run0311<br> BAU - run0312<br> GOV- run0313<br> SAV - run0315<br> RFR - run0314<br> Deforesting different regions for EF calculations:<br> DFR_Afrotropic - run0321<br> DFR_Indomalayan - run0322<br> DFR_China - run0323<br> DFR_Neotropic - run0324<br> DFR_Palearctic - run0325<br> DFR_Australasia - run0326<br> DFR_Nearctic - run0327<br> DFR_Amazon = SAV - run0315</p> <p>5) input_data/ - input data used to run GEOS-Chem</p> <p>Please refer to other README.md files within sub-directories and contact me for any questions</p>
South Bay Salt Pond Restoration Project – Phase-1 (2014-2017) Fish Sampling for Mercury Studies.
The South Bay Salt Pond Restoration Project, the largest wetland restoration project in the western United States, is conducting an adaptive management experiment to restore tidal flows to the tidally muted pond A8 complex. The A8 pond complex is a series of interconnected ponds (A8, A7, and A5) located at the interface of the Guadalupe River with Alviso Marsh in Lower South San Francisco Bay that has had a legacy of mercury contamination from cinnabar open-pit mining in the Guadalupe River watershed. As a result, restoration of salt pond habitats in the Alviso Marsh has taken an adaptive management approach whereby fish populations, water and sediment were monitored for mercury contamination before and after restoration actions and data was used to inform further restoration actions in the A8 complex. In this study, UC Davis conducted fish sampling to collect 2-sentinel species, the Three-spine Stickleback and Mississippi Silverside to assess whole-body mercury concentrations. Fish were collected seasonally (4-5 surveys-year) from spring 2014 through winter 2017 at two slough locations exchanging water with the A8 complex, one in upper Alviso Slough (ALSL-2) at the Alviso Municipal Marina boat launch and on in lower Alviso Slough (ALSL-3) near the pond A7 water control structure, and in two reference sloughs, Artesian/Mallard Slough just downstream from the San Jose-Santa Clara Regional Wastewater Facility outfall and Guadalupe Slough at the confluence with the Sunnyvale Wastewater Treatment Plant. Fish sampling consisted of beach seine, minnow trap and fyke netting with various levels of effort to capture the requisite number and size of sentinel fishes. Therefore, much of the sampling efforts were not conducted in a manor conducive of making fish abundance or species assemblage comparisons amongst the sampling locations. In addition to the slough sampling, we sampled for fish in tidal muted ponds A8, A7, A5, A3N, A3W and A16 from spring 2014 through winter of 201
Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) breast feather total mercury concentrations from the Florida Everglades, Florida, USA: breeding seasons 2016 - 2018
This dataset was used to determine hydrologic parameters influencing Cape Sable Seaside Sparrow (CSSS) mercury exposure and potential mercury effects on their reproductive success in the Florida Everglades. We collected breast feathers for total mercury determination from juvenile and adult CSSS during (or shortly after) three breeding seasons (March 1 to July 31) and monitored the same individuals' breeding performance (mate status, number of nest attempts, number of successful nest attempts, total productivity of nests, clutch size, total count of eggs, and hatch success). Hydrologic parameters (average water depths, drought length, water recession rate, and hydroperiod) were estimated using the Everglades Depth Estimation Network and in situ depth measurements. Data collection is complete.
ScienceDex guides
Understand access before you commit
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.