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516 results for “Chloride”

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edi56/100

Chloride and sulfate concentrations in 1918 Marsh, Madison, WI, 2012-2024

Beginning in 2012, chloride and sulfate concentrations were collected at a suite of sampling locations in and around 1918 Marsh, a small wetland on the University of Wisconsin–Madison campus from 2012-2024. Water temperature, water depth, and ice thickness are provided for each sampling event where applicable. Since fall 2014 water was collected via syringes, and filtered through a 25 mm 0.45µm GMF filter into plastic scintillation vials in the field. All samples were analyzed on an ion chromatograph (Dionex ICS 2100) using an electro-chemical suppressor. Prior to September 2014 samples were taken by dipping the opening of a plastic sample bottle below the surface and the sample was filtered in the Laboratory prior to chemical measurements. Samples were largely from the fall/winter season and taken at ca. 3-week intervals. In these early samples dissolved oxygen was also measured with a DO meter in the field. The University of Wisconsin–Madison deposits plowed snow each winter onto a gravel lot located adjacent to the snowpile. Photos of the snowpile from 2013 to 2024 are also provided, with a vertical pole for scale.

openCC (other)Jun 2025View details →
edi56/100

Chloride Concentrations, Conductivity, and Water Temperature Data from Lake Mendota and Lake Monona Madison, WI: December 2019 – April 2021

Conductivity and chloride were measured for 2 years in Lake Mendota and Lake Monona in Madison, WI. Conductivity was continuously measured (every 30 minutes) on under-ice buoys in the eplimnia (1-2m below the surface) and hypolimnia (1m off the bottom of the lake) of the lakes. Depth-discrete chloride grab samples were collected from the lakes quarterly. Profile sampling in Mendota, which is approximately 25 m deep, occurred every 5m from 0-20m and at 23.5m. Profile sampling in Monona, which is approximately 21m deep, occurred every 4m from 0-20m. This data was needed for a master’s research thesis with the goal of identifying the lakes' mixing dynamics and how salinization may impact them.

openCC (other)Dec 2022View details →
zenodo52/100

Non-refractory particulate sulfate and chloride data from a time of flight aerosol chemical speciation monitor around the Southern Ocean in the austral summer of 2016/17, during the Antarctic Circumnavigation Expedition (ACE).

<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE) campaign was conducted between 20th December 2016 and 19th March 2017. The time of flight aerosol chemical speciation monitor (ToF-ACSM, Aerodyne Research Inc.) was deployed. It is capable of providing 10-minute resolution chemical compositions of NR-PM1 (non-refractory particulate matter with aerodynamic diameter smaller than 1 &micro;m), including sulphate, nitrate, ammonium and organics. Chloride is refractory and can only be measured qualitatively, that is relative changes in intensity are trustworthy while absolute concentrations are a clear underestimation, because most of the chloride is in refractory form as part of sea salt in the marine environment. Since this ACSM dataset was collected on the ship, the ship exhaust will occasionally interfere with the natural signal. Therefore data gaps exist. The overall concentrations of particulate organics, nitrate and ammonium remained low, mostly below detection limit, except during the polluted periods. Thus, we do not report these three components. Only sulphate can be retrieved as a quantitative variable from this dataset.</p> <p>This dataset provides limited information on the chemical composition of sub-micron non-refractory aerosol in the Southern Ocean and gives hints on potential sources. Chloride clearly reflects the contribution of sea salt to the aerosol population. This can be checked by relating the particulate chloride to wind speed (Landwehr et al., 2019; 10.5281/zenodo.3379590) and particles with large diameters (Schmale et al., 2019; 10.5281/zenodo.2636709). Particulate sulphate may originate from a variety of sources: sea salt (minor contribution), anthropogenic emissions and natural marine emissions of dimethylsulfide, which is converted to SO2 and sulphuric acid in the atmosphere and can subsequently partition into the particle phase via gas-phase or aqueous phase reactions (Schmale et al., 2019).</p> <p><strong>Dataset contents</strong></p> <ul> <li>raw_chl_SO4_mz_55_57_manual_with_flags.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>calibration_info.csv, metadata, comma-separated values</li> </ul>

opencc-by-4.0Dec 2019View details →
edi52/100

Summary statistics and annual trends in chloride concentration in urban Minnesota lakes and streams

These data tables describe statistical summaries of chloride concentration, temporal trends in annual chloride, and projected risk of future chloride pollution in lakes and streams of the 17 most urban counties in Minnesota. Data were summarized separately for lakes and for streams, and include statistics (mean, median, standard deviation, upper and lower confidence intervals, and maximum) over the entire data record, over the warm season (May - October), and over the most recent 5 years (i.e., since 2018). Trends were computed on annual means and medians. For lakes, data were aggregated by lake basin (MN DNR Lake ID, or DOW) as well as by depth of sample (surface and deep). For streams, data were aggregated by individual site level as well as by stream reach (per MN Pollution Control Agency assessment units). Risk of chloride pollution was also determined for sites with longer records (10+ years) based on current concentration, number of exceedances of chronic standards, and projected chloride concentration based on current trends. Raw data were extracted from two sources: (1) the National Water Quality Portal (USGS & EPA) and (2) the Metropolitan Council Environmental Information Management System. The data were originally collected by a large number of entities, including watershed management authorities in the state of Minnesota, tribal groups, the Minnesota Pollution Control Agency, municipalities, university researchers, private consultants, and the Metropolitan Council. Some data records begin as early as the 1950's or 1960's, with many sites still including active data collection. A total of approximately 45,000 observations of chloride were included for lakes and wetlands, and approximately 70,000 observations for streams. Nearly 1600 stream sites and 700 lake/wetland sites were represented in the raw data, with 356 stream sites and 600 lakes represented in the summaries (after filtering out sites with less than 1 year of data collection). Primary data retr

openCC (other)Jan 2026View details →
edi52/100

Modeling the effects of lake morphology on chloride retention and salt-driven stratification in two urban lakes in St. Paul, MN

Road salt inputs have caused widespread salinization of urban lakes in northern temperate regions. Watershed characteristics are known to be important drivers of lake chloride concentrations, but there has been less focus on how lake morphometry influences seasonal and interannual dynamics in lake chloride, and how these chloride levels may alter mixing in the water column. We analyzed chloride retention for two urban lakes (Como Lake and Lake McCarrons) in Saint Paul, Minnesota, that are in adjacent watersheds and have similar surface areas, but differ in depth and water residence time. Summer chloride concentrations were negatively related to total summer precipitation for Como Lake (maximum depth 2.2 m), but the relationship was less strong for Lake McCarrons (maximum depth 7.6 m). We used a zero-dimensional model to simulate chloride dynamics in both lakes and tracked the fate of chloride over time. In Como Lake, the mass of chloride in the lake turns over within three years, whereas chloride inputs are retained for >10 years in Lake McCarrons. We then used a one-dimensional hydrodynamic lake model (GLM-AED) to examine how lake depth affects how current chloride loading rates alter lake mixing. Salt inputs significantly extended the duration of summer stratification for simulated lakes with depths of 8 m or more, and salt inputs increased the number of days of hypoxia and anoxia across all depths. These results underscore the importance of considering lake morphometry in understanding the effects of salt inputs on lake ecosystems.

openCC (other)Aug 2025View details →
edi52/100

Chloride Concentrations, Conductivity, and Water Temperature Data from Upper Yahara River Watershed Tributaries in Dane County, WI: December 2019 – April 2021

Conductivity and chloride were measured for 2 years in nine tributaries of Lake Mendota and Lake Monona in Dane County, WI. HOBO Conductivity loggers continuously measured absolute conductivity and water temperature every 30 minutes. Breaks in data collection were due to a calibration period or if the loggers were out of the water. Grab samples for chloride concentration occurred weekly or biweekly. Conductivity and water temperature were measured with a field meter at each sampling excursion. This data was needed for a master’s research thesis with the goal of characterizing the spatial distribution and loading of chloride in the Upper Yahara River Watershed.

openCC (other)Dec 2022View details →
zenodo48/100

Dataset for Reaction-Induced Formation of Stable Mononuclear Cu(I)Cl Species on Carbon for Low-Footprint Vinyl Chloride Production

<p>This dataset complements the publication entitled &quot;Reaction-Induced Formation of Stable Mononuclear Cu(I)Cl Species on Carbon for Low-Footprint Vinyl Chloride Production&quot;&nbsp;by Dario Faust Akl, Georgios Giannakakis, Andrea Ruiz-Ferrando, Mikhail Agrachev, Juan D. Medrano-Garc&iacute;a, Gonzalo Guill&eacute;n-Gos&aacute;lbez, Gunnar Jeschke, Adam H. Clark, Olga V. Safonova, Sharon Mitchell, N&uacute;ria L&oacute;pez, Javier P&eacute;rez-Ram&iacute;rez.</p>

opencc-by-4.0Nov 2022View details →
edi48/100

Lake chloride concentrations and model predictions for 49,432 lakes in the Midwest and Northeast United States.

Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but we have little knowledge of the prevalence and spatial distribution of the problem. The majority of salt pollution in north temperate regions stems from road salt application but other chloride sources include water softeners, synthetic fertilizers, and livestock excretion. Although chloride contamination of lakes is well documented, it is unknown how many lakes are at risk of long-term salinization. We used a quantile regression forest to leverage information from 2,773 lakes to predict the chloride concentration of all 49,432 lakes greater than 4 ha in a 17-state area. The QRF used 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest interstate and road. Model predictions had an r2 of 0.94 for all chloride observations, and 0.87 for predictions of the mean chloride concentration observed at each lake.

openCC (other)Apr 2020View details →
zenodo44/100

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

<p>Methodology: &nbsp;</p> <p><span>The </span><strong><span>TOMCAT simulation</span></strong><span> was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized </span><strong><span>ERA-5 reanalysis data</span></strong><span>.</span></p> <h3><span>HCl Profile Processing and Bias Correction</span></h3> <p><strong><span>Collocated HCl profiles</span></strong><span> are organized into five distinct latitude bins:</span></p> <ul> <li> <p><strong><span>NH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>NH mid-lat</span></strong><span>: </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>Tropics</span></strong><span>: </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>SH mid-lat</span></strong><span>: </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> <li> <p><strong><span>SH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> </ul> <p><span>Initially, </span><strong><span>differences between TOMCAT and satellite measurements</span></strong><span> (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from </span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> <p><strong><span>Separate XGBoost regression models</span></strong><span> are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate </span><strong><span>HCl bias corrections</span></strong><span> for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles.</span></p> <p><strong><span>Height-resolved HCl profile data</span></strong><span> are then interpolated onto 28 standard pressure levels (from </span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions.</span></p> <h3><span>Data Files</span></h3> <p><span>The dataset includes two files containing daily mean zonal mean HCl profiles:</span></p> <ul> <li> <p><code><span>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>height level data</span></strong><span> (</span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> </li> <li> <p><code><span>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>pressure level data</span></strong><span> (</span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>).</span></p> </li> </ul> <h3><span>Reference Publication</span></h3> <p><span>This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:</span></p> <p><span>Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105&ndash;5120, </span><a title="null" href="https://doi.org/10.5194/essd-15-5105-2023"><span>https://doi.org/10.5194/essd-15-5105-2023</span></a><span>, 2023</span></p>

opencc-by-4.0Feb 2023View details →
zenodo44/100

Dataset for publication "Scaling of planar sodium-metal chloride battery cells to 90 cm2 active area"

<p><span lang="EN-US">Sodium-metal chloride batteries; high-temperature ZEBRA batteries; molten-salt batteries; scalable stationary energy storage; alkali-metal anode</span></p>

opencc-by-4.0May 2024View details →
zenodo44/100

Dataset for publication "Cell design strategies for sodium-zinc chloride (Na-ZnCl2) batteries, and first demonstration of tubular cells with 38 Ah capacity"

<p><span lang="EN-US">stationary energy storage; ZEBRA battery; high-temperature metal chloride battery; molten-salt battery; molten sodium anode.</span></p> <p>Measured data to recreate Figures 1-8 in the above manuscript.</p>

opencc-by-4.0Nov 2022View details →
zenodo44/100

Dataset for publication "Influence of precursor morphology and cathode processing on performance and cycle life of sodium-zinc chloride (Na-ZnCl2) battery cells"

<p>High-temperature sodium-metal battery; sodium-metal halide battery (ZEBRA); molten-salt battery; zinc battery for stationary energy storage; alkali metal anode.</p> <p>Datasets used in the above manuscript.&nbsp;</p>

opencc-by-4.0Aug 2023View details →
edi44/100

Sodium, chloride and specific conductance in stream water and groundwater in New Hampshire 1991, 2000 – 2021

Stream water was collected at weekly to monthly intervals at 29 stream sites in New Hampshire (USA). Ten of the stream sites were instrumented with high‐frequency sensors. Twenty-one of the stream sites (including 5 sensor sites) are in the Lamprey River Hydrologic Observatory (LRHO; Wymore et al 2021) and two stream sites were nearby the LRHO. Groundwater was collected from two riparian well fields (JF, 14 wells and WHB, 13 wells). Wells were installed in 2004 and sampled monthly through May 2007, then quarterly until December 2009, after which a subset (JF, 6 and WHB, 5) was generally sampled quarterly. Stream and groundwater samples span a 17-year collection period and were analyzed for sodium, chloride and specific conductance. Methods and findings are described in the associated Limnology and Oceanography Letters manuscript.

openCC (other)Feb 2022View details →
edi44/100

Global Lake Ecological Observatory Network: Long term chloride concentration from 529 lakes and reservoirs around North America and Europe: 1940-2016

This dataset compiles long term chloride concentration data from 529 freshwater lakes and reservoirs in Europe and North America. All lakes in the dataset had greater than or equal to ten years of data. For each lake the following landscape and climate metrics were calculated: mean annual precipitation, mean monthly air temperatures, road density and impervious surface in 100 to 1500 m buffer zones, sea salt deposition. The dataset includes three files: 1) Descriptive data of lake sites (physical lake metrics, climate, land-cover characteristics), 2) Chloride time-series, and 3) GIS shapefiles.

openCC0May 2017View details →
zenodo40/100

Conversion of fluoride and chloride catalized by SAM-dependent fluorinase in Nocardia brasiliensis

<p>Data sets show the following reactions:</p> <p>- Fluorinase catalized conversion of fluoride and SAM to 5&#39;-FDA and L-methionine (Explanation file: Figure 2).</p> <p>- Fluorinase catalyzed conversion of chloride and SAM to 5&rsquo;-ClDA and L-methionine in the presence of L-amino acid oxidase (Explanation file: Figure 3).</p>

opencc-zeroFeb 2014View details →
zenodo40/100

Supplementary data for the paper: "Enhancing concrete durability in chloride-rich environments through manual application of healing agents"

<p>Supplementary data for the paper: &nbsp;&ldquo;Enhancing concrete durability in chloride-rich environments through manual application of healing agents&rdquo;.<br><br>Open data concerning experimental work. The paperinvestigates the use of three potential healing agents: water-repellent agent (WRA), sodium silicate (SS), and polyurethane (PU). The agents are tested separately under two conditions: exposure to a 3.3% NaCl concentration at 20&deg;C and exposure to cyclic freeze-thaw conditions. The agents are manually injected into the cracks and evaluated for their healing efficiency using the capillary absorption test and microscopy analysis. Additionally, their resistance to freeze-thaw cycles by monitoring mass loss and chloride bulk diffusion is measured using sprayed silver nitrate, titration, and EDX mapping.&nbsp;</p>

opencc-by-4.0Mar 2024View details →
zenodo40/100

Fig. 1 in Use of sodium chloride and zeolite during shipment of Ancistrus triradiatus under high temperature

Fig. 1. Linear regression between the number of Ancistrus triradiatus that maintained the swimming axis and time of exposure to hyperosmotic saline solution for control and zeolite groups.

opencc-by-4.0Oct 2011View details →
zenodo40/100

DATASET: Evaluation of sorbents for high temperature removal of tars, hydrogen sulphide, hydrogen chloride and ammonia from biomass-derived syngas by using Aspen Plus

<p>Effect of S/B ratio on syngas; Effect of temperature on syngas; sensitivity analysis of H2S concentration respect to steam content in syngas; sensitivity analysis of H2S concentration respect to temperature.</p>

opencc-by-4.0Dec 2021View details →
zenodo40/100

Particulate methylsulfonic acid (MSA), sodium and chloride concentrations from high-volume air filter samples over the Southern Ocean during austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).

<p><strong>Dataset abstract</strong></p> <p>Aerosol particles come from a variety of sources: a look at the chemical composition gives insights on the particle origin. Ion chromatography was performed for aerosol particles smaller than 10 micrometers (PM10 inlet), giving concentrations of sodium and chloride, as well as particulate methylsulfonic acid (MSA). For this, aerosol particles where sampled on quartz fibre filters for 24 hours each. The sampled filters were stored at -20 degrees C on the research vessel, transported frozen back to the chemistry lab of TROPOS and analysed for main ions. Temporal coverage is from December 20, 2016 to March 20, 2017. We give 24-hour quality controlled particulate MSA, sodium and chloride concentrations in microgram per cubic meter for the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ particulate_MSA_Sodium_Chloride_PM10, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul>

openDec 2021View details →
zenodo40/100

Fig 1 in Sub-lethal toxic effect of cadmium chloride (CdCl ) on freshwater murrel Channa punctata (BLOCH)

Fig 1: Protein Content of the tissues (gill and liver) of C. punctata at 5 ppm and 10 ppm of CdCl2 after 7 days of exposure

opencc-by-4.0Dec 2023View details →

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