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484 results for “water quality”
Water quality data for Narragansett Bay, RI (USA) from 2014 to 2018
<p>This dataset includes results of a monthly monitoring program conducted in Narragansett Bay, RI (USA) between 2014 and 2018. Eight stations were sampled at three depths (surface, middle, and bottom); at each station, water clarity was noted (Secchi depth). and dissolved oxygen, salinity, and pH were measured. Discrete water samples were analyzed in the laboratory for chlorophyll-a, dissolved inorganic nutrients (nitrate, nitrite, ammonium, and orthophosphate), dissolved organic carbon, stable isotopes of particulate carbon and nitrogen, and total suspended solids. These water quality data are being made available to use, in conjunction with pre-existing datasets collected by others, to better understand the ecological dynamics of the Narragansett Bay ecosystem.</p> <p>A description of the sample collection methods and data analyses are included as a companion file to the dataset.</p> <p> </p>
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018. Paper ". Impacts of climate change on water quality, benthic mussels and suspended mussel culture in a shallow, eutrophic estuary by Maar et al. Heliyon,
<p>SWAT river water, TN & TP loads to Limfjorden under climate change scenarios (Delta change) + baseline SWAT loads 2009-2018 </p>
Water Quality Index from Tiete River
<p>Data from WQI (response variable) and 9 physical, chemical, and biological parameters (independent variables), namely: hydrogen potential (pH), dissolved oxygen (do), biochemical oxygen demand (bod), thermotolerant coliforms (tc), total nitrogen (tn), total phosphorus (tp), total solids (ts), turbidity (turb), temperature (temp) were obtained from reports issued by the Environmental Company of the State of São Paulo (CETESB). Annual report available at the link (<a href="https://cetesb.sp.gov.br/aguas-interiores/publicacoes-e-relatorios/">https://cetesb.sp.gov.br/aguas-interiores/publicacoes-e-relatorios/</a>). On this website the documents are available only in Portuguese and from 1978. The WQI used presents a range between 0 and 100, divided into ranges of values and by color: very bad (0 and ≤ 19, purple); bad (> 19 and ≤ 36, red); medum (> 36 and ≤ 51, yellow); good (> 51 and ≤ 79, green) and excellent (> 79, blue). Its object of study data obtained from 78 measurement points located on the Tiete River and its tributaries, from 1994 to 2019. </p><p><strong>The database is part of the doctoral thesis, which addresses the use of machine learning techniques in classifying and predicting water quality indicators.</strong></p><p><strong>CRediT authorship contribution statement: Mario Elias Carvalho do Nascimento: </strong>Conceptualization, Software, Formal analysis, Investigation, Data Curation. <strong>Ralpho Rinaldo dos Reis</strong>: Supervision, Project administration. </p><p><strong>For any inquiries, please contact </strong>marioelias_carvalho@yahoo.com. The .csv will be updated as required to correct issues or to add data from additional surveys. Please check for updated versions periodically.</p><p>Data description:</p><ul><li><strong>ugrhi:</strong> Water Resources Management Units</li><li><strong>macro_land_use:</strong> Classified according to macro land use agriculture, conservation, industrialization, and industrial</li><li><strong>measurement_points: </strong>Measurement points located on the Tiete River and its tributaries</li><li><strong>pH</strong>: hydrogen potential [ ]</li><li><strong>do</strong>: dissolved oxygen [mg/L]</li><li><strong>bod</strong>: biochemical oxygen demand [mg/L]</li><li><strong>tc</strong>: thermotolerant coliforms [NMP/100ml]</li><li><strong>tn</strong>: total nitrogen [mg/L]</li><li><strong>tp</strong>: total phosphorus [mg/L]</li><li><strong>ts</strong>: total solids [mg/L]</li><li><strong>turb</strong>: turbidity [UNT]</li><li><strong>temp</strong>: temperature [ºC]</li><li><strong>wqi</strong>: Range of water quality index [ ]</li><li><strong>classification</strong>: Water quality classes [very_bad, bad, medium, good, excelent]</li></ul><p> </p>
ROSSyndicate Cameron Peak Fire (CPF) reservoir water quality data: Latest Release: 2021- 11/2023 Dataset
<p><strong>Data Description:</strong> The majority of this dataset is water chemistry grab sample data collected post-Cameron Peak Fire in the Cache la Poudre Watershed between the years of 2021 and 2023. This dataset also includes historical data collected pre Cameron Peak Fire by the Rhoades lab at the US Forest Service Rocky Mountain Research Station. These data are focused on basic water quality parameters, as well as cations and anions. Data were collected at various reservoirs in the Cache la Poudre watershed as well as the mainstem of the Cache la Poudre River. This project is ongoing and additional data will be released as it is analyzed.</p> <p><strong>Background Information:</strong> The 2020 Cameron Peak wildfire (CPF) was the largest wildfire in Colorado history at over 200,000 acres. The CPF burned a large proportion of the Cache la Poudre watershed, in particular areas surrounding high elevation reservoirs. This work is funded to support ongoing source water protection programs by the City of Fort Collins, Greeley, Thornton and Northern Water. In collaboration with the Rocky Mountain Research Station (USFS, RMRS), we are sampling various reservoir, tributary, and mainstem sites of the Cache la Poudre watershed. This field campaign allows us to analyze trends in water quality focusing on nutrients and other key constituents mobilized post-fire. The goal of this project is to understand how these nutrients affect algal growth in reservoirs and how those changes are propagated downstream. The reservoirs studied are the following: Barnes Meadow Reservoir, Chambers Lake, Comanche Reservoir, Hourglass Reservoir, Joe Wright Reservoir, Long Draw Reservoir, and Peterson Lake. Historical data (prior to 2021) was collected by the Rhoades Lab at the USFS' Rocky Mountain Research Station.</p> <p><strong>The primary data file is data/cleaned/CPF_reservoir_chemistry_up_to_202301027.csv.</strong> Column definitions and units are defined in the file <em>metadata/Units_Cam_Peak.xlsx</em>. Methods used to collect these data are outline below or in <em>metadata/rmrs_procedures.png</em></p> <p>Location metadata file is <em>data/metadata/cpf_sites.csv</em>. A basic map showing all sampling locations is available at cpf_sites_map.html.</p> <p>Code is housed in the <em>scripts</em> folder and contains the following files:</p> <p>- <em>00_analysis_setup.R</em> provides loads packages and metadata files to be collated in <em>01_chem_prep.qmd</em>.</p> <p>- <em>01_chem_prep.qmd</em> adds metadata to most recent .csv of water chemistry data supplied by RMRS lab.</p> <p>- <em> distance_finder.R</em> uses NHDflowlines to calculate distances from furthest downstream site, PBD.</p> <p>- <em>cpf_sites_map.R</em> uses location metadata to create <em>cpf_sites_map.html</em></p> <p>- <em>demo.R</em> provides an example of how to download data from Zenodo directly in RStudio</p> <p><strong>Data are housed in the data folder and it contains the following:</strong></p> <p>- cleaned: This folder contains the most recently available dataset and has associated burn severity and location data added to the chemistry data. The addition of the metadata was accomplished using the `01_chem_prep.qmd` R script.</p> <p>- cleaned_archive: This folder contains an archive of previously cleaned data. <strong>Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv`</strong> in the `cleaned` directory.</p> <p>- raw: These data were directly received by the ROSSyndicate from RMRS lab managers. Downstream users are encouraged to use the collated data file `CPF_reservoir_chemistry_up_to_20231027.csv` in the `cleaned` directory.</p> <p>- metadata: this contains location data, parameter/column name definitions, units, and methods used at the RMRS Lab. The `README` file in this folder explains burn severity classifications used in the files `sbs_watershed.csv`,`sbs_watershed.csv` and `cpf_sites.csv`</p> <p><strong>Sample Collection</strong></p> <p>Field measurements were taken using a Thermo Orion Star with RDO Optical and Conductivity probes. Time data, when present, are listed in MST. Samples were collected and processed using the Rocky Mountain Research Station's Biogeochemistry Lab, overseen by Timothy Fegel and Charles Rhoades, according to the methods described in rmrs_procedures.png</p> <p><strong>Version: v2023.12.13</strong></p>
Urban Riparian Wetland Water Quality Dataset_Stormwater Capture in Beaver-mediated Wetlands along Walnut Creek, Raleigh, North Carolina, USA
<p><span>This is the initial release of a </span><strong><span>water quality</span></strong><span> dataset pertaining to the <strong>riparian floodplain wetlands</strong> alongside Walnut Creek in Raleigh, North Carolina USA. Walnut Creek is the main drainage channel in an <strong>urbanized watershed</strong> (HUC-12: 030202011101) in central North Carolina. There are several riparian floodplain wetlands along the creek which are largely supplied by <strong>urban stormwater</strong> runoff including directed <strong>storm sewer flows</strong> and regular <strong>overbank flooding</strong> events. In many of these wetlands local water retention and residence time in the surface ponds is mediated by the damming activity of <strong>North American beavers (</strong><em><strong>Castor canadensis</strong></em><strong>)</strong>. This dataset contains data specific to the water quality values of <strong>Walnut Creek</strong>, its tributary <strong>Little Rock Creek</strong>, and the surface ponds and groundwater at the <strong>Walnut Creek Wetland Park</strong> which is actively influenced by resident beavers. The period of this dataset is from <strong>January </strong></span><strong><span>5</span><span>, 2023 through </span></strong><strong><span>October 28</span><span>, 2023</span></strong><span>. </span></p> <p><span>This dataset includes a variety of common <strong>water quality parameters</strong> measured in situ by use of a <strong>YSI Pro water quality meter</strong>, as well as <strong>dissolved nutrient values</strong> determined by <strong>laboratory analysis</strong> of collected water samples.<span> </span>YSI data was collected on a <strong>weekly</strong> basis and water samples were collected for laboratory analysis on a <strong>monthly</strong> basis. Additional measurements and collection took place during <strong>six large rainfall events</strong> to allow comparison between baseflow and stormflow conditions across the site.<span> </span>This dataset aims to provide a comprehensive look at the water quality of Walnut Creek in comparison with the surface ponds and groundwater in the Walnut Creek Wetland Park, which are all ultimately sourced from <strong>urban stormwater runoff</strong>. </span></p> <p><span>This water quality dataset is intended to accompany the <u>separate</u> <strong>hydrology dataset</strong> published on Zenodo at URL: <a href="https://doi.org/10.5281/zenodo.10709630">https://doi.org/10.5281/zenodo.10709630</a>. Together, these datasets are meant to support an improved understanding of the water availability and water quality found in connection with beaver-mediated stormwater capture in an urbanized watershed in the North Carolina Piedmont.</span></p> <p><span> </span><span>This dataset resulted from research supported with a Graduate Student Research Grant awarded by the <strong>North Carolina Water Resources Research Institute (WRRI)</strong>, under Project Number 23-10-W: "Stormwater Diversion, Storage, and Treatment by Beaver-enhanced Floodplain Wetlands in Piedmont Urban Watersheds". </span></p> <p><span>This material is based upon work supported by the <strong>National Science Foundation (NSF)</strong> Graduate Research Fellowship Program (GRFP) under Grant No. (DGE 2137100). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.</span></p> <p><span>Special thanks to <strong>Raleigh Parks</strong> and <strong>Walnut Creek Wetland Park</strong> for making this work possible.</span></p> <p><span>Laboratory analysis support for evaluation of dissolved nutrients (nitrate+nitrite, TKN, total phosphorus, and total organic carbon) was provided by the <strong>NC State Environmental and Agricultural Testing Services (EATS)</strong> laboratory, Department of Crop and Soil Sciences.</span></p> <p><span> </span><span>Additional laboratory analysis support for evaluation of dissolved nutrients (TKN and total phosphorus) was provided by the <strong>NC State Environmental Analysis Laboratory (EAL)</strong>, Department of Biological and Agricultural Engineering (BAE).</span></p> <p><span> </span><span>Usage of and technical support for the YSI Pro water quality meter used in this study was made possible by the <strong>Osburn Lab</strong>, Department of Marine, Earth and Atmospheric Sciences (MEAS), NC State University.</span></p>
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA).</p> <p>In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</a>. </p>
Global surface water quality data from 1980 - 2019, derived from the dynamical surface water quality model (DynQual) at 30 arcmin spatial resolution
<p>Global ~50km (30 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual, monthly and daily temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Simulations were originally made at 5-arcmin resolution and aggregated to 30 arcmin 0.5 degree by summing the in-stream (routed) loadings and channel storage over the aggregated area (at daily, monthly and annual timesteps), and subsequently calculating in-stream concentrations. Please note the aggregation technique is provisional and thus the data is subject to change.</p> <p>Note. A minimum discharge threshold of 0.1 m3 s-1 was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.</p> <p>Hydrology and water quality simulations made at DynQuals native spatial resolution (5 arcmin) can be found at: <a href="https://zenodo.org/records/14673871">https://zenodo.org/records/14673871</a>.</p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
Water quality dataset from stream water in the Khibiny massif, Kola Peninsula (Russia) in August 2017
<p>The dataset includes base chemistry, elemental concentrations, and sulfur isotope (δ34S_SO4) measurements from 11 stream water sampling locations.</p> <p>The measurements were taken in the Belaya and Vuonnemiok stream systems within the Khibiny massif (Kola Peninsula, Russia) during 25-30th of August 2017 and were analyzed at laboratories at Stockholm University, Sweden.</p> <p>The data was gathered to investigate potential pollution spreading in hydrological pathways from active apatite mining within the catchments, and it can be used for comparison with other mining-impacted sites in the Arctic.</p>
Data and R code for long-term study of fire and climate effects on water quality in Clear Lake, California
<p>Long-term relationships between water quality, fire and climate for Clear Lake, California. Although the watershed has historically experienced frequent fire, the 2018 Mendocino Complex, which was the largest wildfire complex in state history, burned approximately 40% of the watershed, sparking concerns about drinking water quality and lake ecosystem health. This analysis spans approximately 1968-2021.</p>
Lake Cadagno 2017 CTD and water quality monitoring
<p>This repository contains an almost bi-daily CTD (conductivity, temperature and depth) and water quality profile dataset conducted during the Summer of 2017 in Lake Cadagno, Ticino (TI), Switzerland.</p> <p>Associated publications:</p> <ul> <li> <p>Sepúlveda Steiner, O., Bouffard, D. and Wüest, A. (2021). Persistence of bioconvection-induced mixed layers in a stratified lake. Limnol Oceanogr, 66: 1531-1547. <a href="https://doi.org/10.1002/lno.11702">https://doi.org/10.1002/lno.11702</a> [Check this publication for temperature-corrected conductivity (e.g., C_20) and water density calculations specific to Lake Cadagno]</p> </li> <li> <p>Janssen, D. J., Rickli, J., Wille, M., Sepúlveda Steiner, O., Vogel, H., Dellwig, O., et al. (2022). Chromium cycling in redox-stratified basins challenges δ<sup>53</sup>Cr paleoredox proxy applications. Geophys. Res. Lett., 49, e2022GL099154. <a href="https://doi.org/10.1029/2022GL099154">https://doi.org/10.1029/2022GL099154</a></p> </li> </ul> <p> </p>
Rinker's Treatment Wetland Water Quality monitoring data (2017 - 2019), South Daytona, Florida
<p><span>Water quality was monitored at a constructed treatment wetland in South Daytona, Florida through </span>a<span> </span><span>two-year</span><span> </span><span>project</span><span> </span><span>(Oct</span><span> </span><span>2017-</span><span> </span>Sept<span> </span><span>2019). </span></p> <p><strong><span>The 2017-2018 INDIAN<span> </span><span>RIVER</span><span> </span><span>LAGOON</span><span> </span><span>NATIONAL</span><span> </span><span>ESTUARY</span><span> </span><span>PROGRAM funded Project</span></span></strong></p> <p><strong><span>Submitted to the IRL Council</span></strong></p> <p><strong><span>Oct 8<sup>th</sup>, 2018</span></strong></p> <p><span> </span></p> <p><strong><span>Project<span> </span>Title</span></strong><span>: <span><span> </span></span>Reed<span> </span><span>Canal</span> <span><span> </span></span>Basin <span><span> </span></span>Stormwater <span><span> </span></span>Improvement <span><span> </span></span>through <span><span> </span></span>Treatment <span><span> </span></span>Wetland</span><span> </span><span>Construction<span> </span>in<span> </span>South<span> </span>Daytona,<span> </span>FL</span></p> <p><strong><span>Project<span> </span>Applicant:<span> </span></span></strong><span>Bethune-Cookman</span><span> </span><span>University</span><span> </span><span>(B-CU)</span></p> <p> </p> <p><strong><span>Amount</span></strong><strong><span> </span></strong><strong><span>of</span></strong><strong><span> <span>Request</span></span></strong><span>:</span><span> </span><span>$181,148</span></p> <h3><span>Other</span><span> </span><span>Funding</span> <span>Sources</span><span> </span><span>and</span> <span>Amount</span><span> </span><span>of</span> <span>Total</span><span> </span><span>Match</span><span>:</span><span> </span><span>$183,095</span></h3> <p><span><span>B-C</span></span>U <span><span> </span></span><span>($74,631);</span> <span><span> </span></span><span>EPA</span> <span><span> </span></span><span>319</span> <span><span> </span></span><span>($11,250);</span> <span><span> </span></span><span>Local</span> <span><span> </span></span><span>wetland/tree/shoreline</span> <span><span> </span></span><span>restoration</span> <span><span> </span></span><span>funds</span> <span><span> </span></span><span>(pending</span></p> <p><span>$60,000); AE-Group ($5,000); </span><span>Volunteers</span><span> </span><span>($2,214);</span><span> </span><span>Project </span>H2O<span> </span><span>Academy</span><span> </span><span>and</span><span> </span><span>partners</span><span> </span><span>($30,000)</span></p> <p><span>The project goals were </span><span>to</span><span> </span><span>design,</span><span> </span><span>construct,</span><span> </span><span>and</span><span> </span><span>assess</span><span> </span>a<span> </span><span>treatment</span><span> </span><span>wetland,</span><span> </span><span>retrofit</span><span> </span><span>Rinker’s</span><span> </span><span>pond</span><span> </span><span>within<span> </span><span>the</span><span> </span><span>city</span></span><span>-owned</span> <span>plat, monitor water quality,</span><span> and</span><span> </span><span>conduct plant</span><span> </span><span>surveys</span><span> </span><span>to</span> test<span> the</span> <span>effectiveness</span><span> </span><span>of </span><span>treatment wetlands</span><span> </span>in<span> stormwater</span> <span>management </span><span>and</span><span> habMayitat quality improvement.</span><span> </span><span>This</span><span> </span><span>project</span><span> </span><span>will</span><span> </span><span>engage</span><span> </span><span>the</span><span> </span><span>residents</span><span> </span><span>into</span><span> </span><span>public</span><span> </span><span>education</span><span> </span><span>programs</span><span> </span>in<span> </span><span>order</span><span> </span><span>to</span><span> </span><span>help</span><span> </span><span>them</span><span> </span><span>enhance</span><span> </span><span>their</span><span> </span><span>awareness</span><span> </span><span>of</span> <span>issues</span><span> </span>of <span>and</span><span> solutions</span><span> </span><span>to</span> <span>stormwater</span><span> </span><span>associated</span> <span>problems.</span></p>
The compiled 8-year dataset (2012-2019) consisting of weekly river water quality indicators (CODMn, DO, NH3-N and PH ) in majors 10 sub-basin of Yangtze river based on imputation of machine learning
<p>Water quality is significantly affected by global climate change and human activities, with diverse critical factors shaping its state in rivers and lakes. In the study, we utilized four indicators to characterize water quality: the physical water quality parameters included dissolved oxygen (DO, mg/L) and PH, while the chemical water quality parameters encompassed chemical oxygen demand (CODMn, mg/L) and ammonia nitrogen (NH3-N, mg/L). This study establishes weekly water quality models for typical 10 sub-basins along the Yangtze River using machine learning methods, which incorporate the impacts of hydro-meteorological and anthropogenic factors.These 10 sub-basins represent the principal tributaries of the Yangtze River basin and include Dongting Lake, the upper Han River, the lower Han River, the Jialing River, the Jinsha River, the Li River, the Min River, Poyang Lake, the Xiang River, and the Yuan River. This data collection was performed by National Environmental Monitoring Centre (http://www.cnemc.cn/sssj/szzdjczb/index_1.shtml). The water quality indicators discussed in this study are assessed in accordance with the national standard GB 3838-2002. Please refer to the paper for details.</p>
Fig 2 in Water quality, yield and cost-benefit analysis of rain water ponds of Cuttack district: A comparison between Indian major carp and GIFT Tilapia
Fig 2: Average share of various cost in IMC poly-culture and GIFT mono-sex culture in T1 & T2 (2018-19)
Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4 in Investigation Of Common Reed Regrowth On The Shores Of Recreational Lakes
Fig. 3. Correlation coefficients estimated between water quality indicators and morphological Fig. 4. The number of holidaymakers near parameters of common reeds on the shores of Bridvaisis, Gaustvinis and Gilius lakes (in the Bridvaisis, Gaustvinis and Gilius Lakes. The order from the bottom to the top) and differences boundary of the continuous line side indicates in morphological parameters of plants after the cases where p <0.01; dashed lines, where p <0.05. holidaymakers' visits.
Fig. 6 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 6. DNA damage scores (mean ± SEM, n = 8) in erythrocytes of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).
Fig. 2 in Integrated biomarker response index using a Neotropical fish to assess the water quality in agricultural areas
Fig. 2. Activity (mean ± SEM, n = 8) of glutathione S-transferase in liver (A) and gills (B) of A. altiparanae exposed in situ for seven days in five sites along Água das Araras stream (S1, S2, S3, S4, and S5) and in a reference site (Ref). Different letters indicate significant differences between sites (P <0.05).
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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