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205 results for “Wastewater”
Electrochemical ammonia recovery and co-production of chemicals from manure wastewater
<p class="MsoNormal"><span>Livestock manure wastewater, containing high level of ammonia, is a major source of water contamination, posing serious threats to aquatic ecosystems. Because ammonia is an important nitrogen fertilizer, efficiently recovering ammonia from manure wastewater would have multiple sustainability gains from both the pollution control and the resource recovery perspectives. Here, we develop an electrochemical strategy to achieve this goal by using an ion-selective potassium nickel hexacyanoferrate (KNiHCF) electrode as a mediator. The KNiHCF electrode spontaneously oxidizes organic matter and uptakes ammonium ions (NH₄⁺) and potassium ions (K⁺) in manure wastewater with a nutrient selectivity of ~100%. Subsequently, nitrogen- and potassium-rich fertilizers are produced alongside the electrosynthesis of H₂ (green fuel) or H₂O₂ (disinfectant) while regenerating the KNiHCF electrode. The preliminary techno-economic analysis indicates that the proposed strategy has notable economic potential and environmental benefits. This work provides a powerful strategy for efficient nutrient (NH₄⁺ and K⁺) recovery and decentralized fertilizer and chemical production from manure wastewater, paving the way to sustainable agriculture.</span></p>
Harnessing waterfleas for water reclamation: A nature-based tertiary wastewater treatment technology
<p>Urbanisation, population growth, and climate change have put unprecedented pressure on water resources, leading to a global water crisis and the need for water reuse. However, water reuse is unsafe unless persistent chemical pollutants are removed from reclaimed water.. State-of-the-art technologies for the reduction of persistent chemical pollutants in wastewater typically involves high operational and energy costs and potentially generates toxic by-products (e.g., bromate from ozonation). Nature-base solutions are preferred to these technologies for their lower environmental impact. However, so far, bio-based tertiary wastewater treatments have been inefficient for industrial-scale applications. Moreover, they often demand significant financial investment and large infrastructure, undermining sustainability objectives. Here, we present a scalable, low-cost, low-carbon, and retrofittable nature-inspired solution that could be retrofitted into current wastewater treatment systems to remove persistent chemical pollutants. The technology uses the water flea <em>Daphnia </em>to non-selectively uptake and retain persistent chemical pollutants (pharmaceutical, pesticides and industrial chemicals). We showed <em>Daphnia's </em>removal efficiency at laboratory scale ranging between 50% for PFOS and 90% for diclofenac. We validate the removal efficiency of diclofenac at prototype scale showing sustained performance over four weeks in outdoor seminatural conditions. A techno-commercial analysis on the <em>Daphnia</em>-based technology suggests several technical, commercial and sustainability advantages over established and emerging treatments at comparable removal efficiency, benchmarked on available data on individual chemicals. Further testing of the technology is underway in open flow environments holding real wastewater. The technology has the potential to improve the quality of wastewater effluent meeting requirements to produce water appropriate for reuse in irrigation, industrial application, and household use. By preventing persistent chemicals from entering waterways, this technology has the potential to maximise the shift to clean growth, enabling water reuse, reducing resource depletion and preventing environmental pollution.</p>
Dataset on drug use in 2020 (COVID-19 lockdown) in Spain and Portugal by wastewater-based epidemiology
<div><strong>This datase contains the metadata associated with this publication:</strong></div> <div> </div> <div><em>A. Estévez-Danta, L. Bijlsma, R. Capela, R. Cela, A. Celma, F. Hernández, U. Lertxundi, J. Matias, R. Montes, G. Orive, A. Prieto, M.M. Santos, R. Rodil, J.B. Quintana</em></div> <div><em>Use of illicit drugs, alcohol and tobacco in Spain and Portugal during the COVID-19 crisis in 2020 as measured by wastewater-based epidemiology</em></div> <div><em>Science of the Total Environment, 2022, 836, 155697</em></div> <div><a href="https://doi.org/10.1016/j.scitotenv.2022.155697" target="_blank" rel="noopener">https://doi.org/10.1016/j.scitotenv.2022.155697</a></div> <div> </div> <div>The data is deposited in ZENODO:</div> <div><a href="../doi/10.5281/zenodo.10829752">https://zenodo.org/doi/10.5281/zenodo.10829752</a></div> <div> </div> <div><strong>If you reuse the data, please cite the publication and ZENODO deposit mentioned above</strong></div> <div> </div> <div><strong>Explanation of the different sheets of the Excel file (All_Data_STOTEN_2022_155697) or different individual CSV files (named as below):</strong></div> <ul> <li><em>WWTP_details</em>: explanation of wastewater treatment plats (WWTPs) sampled, flow rates, etc.</li> <li><em>Concentrations</em>: concentrations measured in the samples</li> <li><em>PNDL</em>: population normalized daily loads calculated per each sample</li> <li><em>Consumption</em>: estimated drug use (see the publication for correction factors)</li> <li><em>EF</em>: enantiomeric fraction, expressed as fraction of the R-enantiomer for the samples analyzed</li> </ul> <div> </div> <div><strong>Abreviations</strong></div> <ul> <li>AMP Amphetamine</li> <li>MAMP Methamphetamine</li> <li>MDMA 3,4-Methylenedioxymethamphetamine</li> <li>BE Benzoylecgonine</li> <li>COC Cocaine</li> <li>THC-COOH 11-Nor-9-carboxy-Δ9-tetrahydrocannabinol</li> <li>THC Δ9-Tetrahydrocannabinol</li> <li>COT Cotinine</li> <li>OH-COT Trans-3'-Hydroxycotinine</li> <li>NIC Nicotine</li> <li>EtS Ethyl sulfate</li> </ul>
Soil microbiome dataset from the University of Wisconsin Arlington and Lancaster agricultural research stations and cheese maker and vegetable processor wastewater land application sites
<p>Cheese making and vegetable processing are trillion-dollar industries globally. However, they generate immense volumes of high nitrogen wastewater that must be processed safely and cost effectively. Land application systems are frequently used by rural medium and smaller processing facilities that lack ready access to wastewater resource recovery facilities. This study utilized soil microbial data to determine system differences leading to high denitrification rates observed in incubation studies in agricultural soil collected from University of Wisconsin Agricultural Research Stations (ARS), Arlington and Lancaster stations, compared to industry cheese making and vegetable processing land application water treatment facilities. It was hypothesized that decade long frequent treatment with facility wastewater would alter the microbial communities in the system soils, but this is not the case. No clear correlations were found between soil denitrification rates and biotic or abiotic system factors and the microbial communities observed in the industry systems are similar to the ARS soils under agricultural production and to literature reported denitrifying systems such as wetlands and wastewater resource recovery facilities. Knowing that land application system management does not alter the microbial biome will allow any management advances that increase denitrification efficiency in other denitrifying systems to be readily applied to industry wastewater land application facilities. </p>
Prediction of COVID-19 case numbers using state-space modeling and wastewater virus datasets
Open the record for dataset details and reuse information.
Antibiotic Resistance in Hospital Wastewater in West Africa: A Systematic Review and Meta-Analysis
Open the record for dataset details and reuse information.
Wastewater alkalinity addition enhancement for carbon emission reduction and marine CO2 removal
<p>The ROMS_RCA model settings of 2010 runs. The reference date of the 'TIME' variable is 1983-01-01.</p> <p>The files with 'Y2010_' in their names contain the boundary data and initial fileds for the model run. The file "ROMSeutro_1strun.inp" lists all the model parameters, while the files with 'CPB_WWTP_ps' in the names are the settings of the discharges from each WWTP outlet. </p> <p>The data used to generate the figures are provided in the MAT file.</p>
Tracking SARS-CoV-2 variants in wastewater in San Pedro de la Paz, Chile
<p>Various studies have shown the presence of SARS-CoV-2 RNA in the feces of patients<br>with COVID-19, both symptomatic and asymptomatic. This allowed determining the<br>viral load in wastewater samples from Wastewater Treatment Plants (WWTPs),<br>carrying out wastewater-based surveillance (WBS) of the virus in the community, as a<br>complement to person-to-person testing. The appearance of SARS-CoV-2 variants,<br>which can increase transmissibility and/or immune evasion, creates an imperative<br>need to implement specific and permanent surveillance methods to control the COVID19 pandemic. For variant detection, we performed a real-time RT-qPCR assay with a<br>commercial kit to detect five virus variants (Alpha, Beta, Gamma, Lambda, and Delta)<br>in the municipality of San Pedro de la Paz, Chile, from January to November 2021.<br>Detection of variants in wastewater was consistent with available clinical data and<br>provided additional information for community surveillance, identifying lambda and<br>delta variants as the most frequently detected during the second and third wave of<br>infections in the population of this area. Furthermore, in some cases we detected<br>specific variants in wastewater before local authorities confirmed the first clinical cases.<br>The study demonstrates that WBS is a tool that allows a rapid and cost-effective<br>detection of specific mutations associated with SARS-CoV-2 variants using RT-qPCR.<br>However, Illumina amplicon sequencing confirms that there are more optimal methods<br>to sequence this type of matrices. This method can be used to complement clinical<br>data during outbreaks and is especially useful when clinical care is insufficient or<br>collapsed and/or cost is very high, as is the case in many countries.</p>
Imputed Multiple Sequence Alignment used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'
<p>Multiple Sequence Alignment of imputed SARS-CoV-2 sequences used in 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'</p>
LC-HRMS files from wastewater samples collected in Portugal
<p>LC-HRMS files corresponding to wastewater samples associated to the paper:</p> <p><em><strong><a href="https://doi.org/10.1016/j.scitotenv.2021.152518">M.F.T. Sá et al. Tracking pollutants in a municipal sewage network impairing the operation of a wastewater treatment plant. Science of the Total Environment, 2022, 817, 152518. DOI: 10.1016/j.scitotenv.2021.152518</a></strong></em></p> <p>These files contain the LC-QTOF-HRMS data associated with the manuscript:</p> <p>Please note:</p> <p>Data was acquired with an Agilent 6550 QTOF in both:<br> - AutoMSMS (data-dependent acquisition, DDA) <br> - All Ions (data-independent acquisition, DIA)</p> <p>Data is provided in Agilent MassHunter B.10.00 format.</p> <p>In the case of AutoMSMS the iterative mode with two injections per sample was employed.<br> Further details on how samples were processed is presented in the manuscript.</p> <p>Data is therefore structure in two folders, one for DDA and one for DIA.<br> Each folder is then further divided between positive and negative mode.</p> <p>Files are named accordingly to the sample, using the same codes than in the manuscript.<br> The number after the code refers to the sample campaign.<br> Besides, there are "A" and "B" files in the DDA files, corresponding to the first and second iterative injection, respectively.</p> <p>As an example, the file:<br> "PVZ-2 2_B.d" in the AutoMSMS_DDA (either pos or neg) folder:<br> corresponds to the sample PVZ-2 from the second sampling campaign and second injection</p> <p><strong>If you reuse this data, please cite this dataset (<a href="https://doi.org/10.5281/zenodo.5830725">DOI: 10.5281/zenodo.5830725</a>) and the orginal article cited above (<a href="https://doi.org/10.1016/j.scitotenv.2021.152518">DOI: 10.1016/j.scitotenv.2021.152518</a>)</strong></p>
CAD file and chromatographic data for determination of diclofenac in wastewater using a 3D printed immunosorbent device
<p>CAD file of the 3D-printed device (in FreeCAD) and chromatographic data for determination of diclofenac in wastewater associated to Fig. S5 (in CSV) of the paper "A 3D printed spinning cup-shaped device for immunoaffinity solid-phase<br> extraction of diclofenac in wastewaters" published in Microchimica Acta 2022 (DOI:10.1007/s00604-022-05267-9.)</p>
Dataset of Paper "Microplastics in fresh- and wastewater are potential contributors to antibiotic resistance - A minireview"
<p>Dataset of Paper "Microplastics in fresh- and wastewater are potential contributors to antibiotic resistance - A minireview"</p> <ul> <li>Table 2. Reported abundance and characteristics of MPs in freshwater and wastewater in literature.</li> <li>Table 3. Abundance and characteristics of antibiotic resistant elements in freshwater and wastewater.</li> </ul>
Quantification of SARS-CoV-2 RNA in Wastewater Treatment Plants Mirrors the Pandemic Trend in Hong Kong
<p>The dataset included the SARS-CoV-2 and PMMoV virus concentration of WWTPs from December 24, 2020 to June 30, 2021 in Hong Kong, China.</p>
Long-term observation of the end-of- treatment sludge quality from a Parisian WWTP treating wastewater from 6.5 M inhabitants
<p>Wastewater contains a wide range of biological and chemical markers related to human activity. The collection and analysis of these markers in the effluents (wastewater and sludge) allow to qualify the public health of the population and to evaluate the impact of the chemicals used in daily life on the environment.</p> <p> </p> <p>In line with these studies, the Innovation Department of SIAAP and its scientific partners (ISA and LEESU) has created an Observatory of the city with the objectives of:</p> <p>• Monitoring the long-term dynamics of known and/or potential pollutants according to French regulations.</p> <p>• Contributing to studies aimed at a better understanding of anthropogenic activities.</p> <p>• Structuring and centralizing data (wastewater and end-of-treatment sludge), ensuring their storage for 10 years.</p> <p>• Providing technical and scientific knowledge by publishing every two years the data generated by the Observatory of the city via the open platform Zenodo.</p> <p>Regarding the end-of-treatment sludge, the Seine Aval (SAV) plant has been chosen as a strategic location, since the catchment area corresponds to the Parisian west catchment (6.5 million inhabitants). Data on the sludge micropollution (in metals, polycyclic aromatic hydrocarbons – PAHs and polychlorinated biphenyls – PCBs) are available since 1980 and are provided in the present datasets.</p> <p> </p> <p> </p> <p> </p>
Wastewater Treatment data simulated by WEST software
<p>This data was simulated by "WEST" software running for 30 days. This process is an oxidation wastewater treatment process. This data set includes all the necessary quality related variables, particularly, effluent qualities.</p>
Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: a valuable addition to COVID-19 surveillance
<p>Datasets used for the manuscript: <em>Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: a valuable addition to COVID-19 surveillance</em></p> <p><em>pandemic_daily_passenger_counts.tsv</em>: An overview of daily passenger arrival counts at Amsterdam Schiphol Airport per continent of origin during the study period 16-02-2020 - 04-09-2022</p> <p><em>pre-pandemic_daily_passenger_averages.tsv: </em>An overview of mean daily passenger arrival counts at Amsterdam Schiphol Airport in the pre-pandemic period 2017-2019.</p> <p><em>viral_load_data.tsv: </em>Sample metadata (sample identifier, sampling date, flow, average # particles per ml, and flow-corrected viral-load) for samples taken at the wastewater treatment plant of Amsterdam Schiphol Airport.</p> <p><em>wastewater_variant_frequencies.tsv: </em>SARS-CoV-2 lineage estimates in samples taken at the wastewater treatment plant of Amsterdam Schiphol Airport, analyzed using whole-genome tiled amplicon sequencing.</p> <p> </p> <p> </p> <p> </p>
Investigation of Oil Well Blowouts Triggered by Wastewater Injection in the Permian Basin, USA.
<p>This dataset is a part of research work titled: "Investigation of Oil Well Blowouts Triggered by Wastewater Injection in the Permian Basin, USA."</p> <p>Authors: <br>Vamshi Karanam, Zhong Lu, Jin-Woo Kim, Roger P Denlinger</p> <p><br>The folder contains six datasets. They are explained in detail below.</p> <p>1. Deformation rate map<br>filename: deformation_rate.geojson<br>The data can be accessed using QGIS, ArcGIS or other GIS software<br>The attribute table contains:<br>field_0: point number<br>field_1:Code<br>field_2:height<br>field_3: height standard deviation<br>field_4:deformation rate (mm/yr)<br>field_5:standard deviation of deformation rate<br>field_6: Coherence<br>field_7: effective area</p> <p><br>2. Monthly njection volumes<br>filename: injection_volumes.geojson<br>The data can be accessed using QGIS, ArcGIS or other GIS software<br>The attributes table contains:<br>field_0: API Number<br>field_1 to field_150: Monthly injection volumes from 20100101 to 20220601 (m^3)<br>field_151: Longitude<br>field_152 : Latitude</p> <p><br>3. Depth to the top of formations<br>filename: main_formations_new.geojson<br>The data can be accessed using QGIS, ArcGIS or other GIS software<br>The attribute table contains:<br>field_0: API Number<br>field_1 to field_12: Depth to top of different formations (m)<br>field_13: True depth of the well (m)<br>field_14: Latitude<br>field_15: Longitude</p> <p><br>4. Blowout Modeling results<br>This dataset contains six files as follows:<br>i. blowout_bestfit_alpha.mat: best fit parameters for the Modeling of sill alpha. <br>ii. blowout_bestfit_alpha_summary.mat: summary of the best fit parameters for the Modeling of sill alpha<br>iii. blowout_bestfit_beta.mat: best fit parameters for the Modeling of sill beta<br>iv. blowout_bestfit_beta_summary.mat: summary of the best fit parameters for the Modeling of sill beta<br>v. blowout_bestfit_results.csv: Best fit results of the penny crack modeling. The csv file contains five columns: Longitude, Latitude, Observed deformation, Modeled deformation and Residual<br>vi. blowout_input_data.mat: Input data for the modeling of blowout. The mat file contains five files. InSAR Phase (Phase), Incidence angle (Inc), Heading angle (Heading), Longitude (Lon), Latitude (Lat)</p> <p><br>5. Cumulative uplift Modeling results<br>This dataset contains six files as follows:<br>i. uplift_bestfit_alpha.mat: best fit parameters for the Modeling of sill alpha. <br>ii. uplift_bestfit_alpha_summary.mat: summary of the best fit parameters for the Modeling of sill alpha<br>iii. uplift_bestfit_beta.mat: best fit parameters for the Modeling of sill beta<br>iv. uplift_bestfit_beta_summary.mat: summary of the best fit parameters for the Modeling of sill beta<br>v. uplift_bestfit_results.csv: Best fit results of the penny crack modeling. The csv file contains five columns: Longitude, Latitude, Observed deformation, Modeled deformation and Residual<br>vi. uplift_input_data.mat: Input data for the modeling of cumulative uplift. The mat file contains five files. InSAR Phase (Phase), Incidence angle (Inc), Heading angle (Heading), Longitude (Lon), Latitude (Lat)</p> <p>6. Metadata for the Sentinel-1 datasets used in this study</p> <p>i. sentinel_1_datasets_descending.csv: CSV file with filenames, date of acquisition and other metadata along with URL to access the datasets in descending geometry</p> <p>ii. sentinel_1_datasets_ascending.csv: CSV file with filenames, date of acquisition and other metadata along with URL to access the datasets in ascending geometry</p>
Dataset of concentrations of bisphenol A, F and S sulfates in wastewater from Spain and Portugal and back-calculation of human exposure by wastewater-based epidemiology
<p>Dataset of concentrations of bisphenol A, F and S sulfates in wastewater from Spain and Portugal and back-calculation of human exposure by wastewater-based epidemiology.</p> <p>Data is provided in MS Excel (xlsx) and CSV formats and contains details on WWTP characteristics, concentrations of bisphenols sulfates and extrapolated population-normalizad daily loads and different extrapolations of human exposure to bisphenols.</p> <p><strong>Further details are provided in the associated publication:</strong></p> <p><em>A. Estévez-Danta, R. Montes, A. Prieto, M.M. Santos, G. Orive, U. Lertxundi, J.B. Quintana, R. Rodil. </em><em>Wastewater-Based Epidemiology Methodology To Investigate Human Exposure To Bisphenol A, Bisphenol F and Bisphenol S.<br>Water Research 2024, 122016. DOI: 10.1016/j.watres.2024.122016.<br><a href="https://doi.org/10.1016/j.watres.2024.122016" target="_blank" rel="noopener">https://doi.org/10.1016/j.watres.2024.122016 </a><br></em></p> <div>The data is deposited in ZENODO:</div> <div><a href="../doi/10.5281/zenodo.10459047">https://zenodo.org/doi/10.5281/zenodo.10459047</a></div> <div> </div> <div><strong>If you reuse the data, please cite the publication and ZENODO deposit mentioned above</strong></div>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset3
<p>This dataset contains the data underlying the following publication: Li Qimeng, Wu Ji, Hua Ming, Zhang Guang, Li Wentao, Shuang Chendong, Li Aimin. (2017). Preparation of Permanent Magnetic Resin Crosslinking by Diallyl Itaconate and Its Adsorptive and Anti-fouling Behaviors for Humic Acid Removal. <em>Scientific Report. </em> <a href="https://doi.org/10.1038/s41598-017-17360-8">https://doi.org/10.1038/s41598-017-17360-8</a></p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset2
<p>This dataset contains the data underlying the following publication: Hassen W, Neifar M, Cherif H, Najjari A, Chouchane H, Driouich RC, Salah A, Naili F, Mosbah A, Souissi Y, Raddadi N, Ouzari HI, Fava F and Cherif A (2018) Pseudomonas rhizophila S211, a New Plant Growth-Promoting Rhizobacterium with Potential in Pesticide-Bioremediation. Front. Microbiol. 9:34. doi: 10.3389/fmicb.2018.00034</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.