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205 results for “Wastewater”
Integrated Treatment of Per- and Polyfluoroalkyl Substances in Existing Wastewater Treatment Plants─Scoping the Potential of Foam Partitioning
<p><span>This</span><span> file corresponds to the publication </span><span>Integrated Treatment of Per- and Polyfluoroalkyl Substances in Existing Wastewater Treatment Plants─Scoping the Potential of Foam Partitioning</span><span>: https://doi.org/10.1021/acsestengg.3c00091 </span></p> <p> </p> <p><span>All concentrations are in ng/L and were rounded to two significant digits.</span></p> <p><span>When all</span><span> triplicates had concentrations below the LOQ, the concentration is reported as < LOQ. </span></p> <p><span>When at least one triplicate had a concentration above the LOQ, the other triplicates' concentrations were set to half the LOQ, and the average of the three is reported.</span></p> <p> </p> <p><span>Sheet names correspond to the site identifiers (Table 1 of main text).</span></p>
Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)
<h4><strong>Publication</strong></h4> <p>Please cite this publication if you use the dataset:</p> <p>Sarrazin, F. J., Attinger, A., Kumar, R., Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019), submitted to Earth System Science Data.</p> <p>Please also refer to the above publication for methodological details.</p> <h4><strong>License</strong></h4> <p>The "Gridded dataset of nitrogen and phosphorus point sources from wastewater in Germany (1950-2019)" is freely available under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0), in compliance with the terms of use of the Food and Agriculture Organization of the United Nations (FAO) data and German Cosmetic, Toiletry, Perfumery and Detergent Association (IKW) data that underlie the dataset.</p> <h4><strong>Data description</strong></h4> <p>The dataset includes estimates of nitrogen (N) and phosphorus (P) emissions from wastewater in Germany (1950-2019) at (1) grid level, and at different levels of aggregation, namely (2) at Nomenclature of Territorial units for statistics level 1 (NUTS-1), that correspond to the 16 German federal states (for this, we used the 2020 NUTS classification; BKG, 2020) and (3) at river basin level for 3778 river basins of the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013). It also includes the input and calibration data (at NUTS-1 and grid level) that were used to estimate the N and P emissions.</p> <ol> <li> <p>Input and calibration data at NUTS-1 level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019 (input data), 1987-2019 (calibration data)<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: input data, calibration data, parameter sample<br><strong><em>file format</em></strong>: CSV<br><em><strong>number of files</strong></em>: 3<br><br></p> </li> <li> <p>Input data at grid level:<br><strong><em>spatial extent</em></strong>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><strong><em>time period</em></strong>: 1950-2019 (population data), 2020 (NUTS-1 map)<br><strong><em>frequency</em></strong>: annual<br><strong><em>variables</em></strong>: urban and rural population counts, NUTS-1 map<br><em><strong>file format</strong></em>: netCDF<br><strong><em>number of files</em></strong>: 2<br><br></p> </li> <li>Emission data at grid level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: 0.015625°<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P wastewater treatment plants (WWTPs) outgoing emissions (treated point sources)<br>- N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: netCDF<br><em><strong>number of files</strong></em>: 100<br> </li> <li>Emission data aggregated at NUTS-1 (federal state) level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: NUTS-1<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: <br>- N and P gross emissions<br>- N and P total point sources (sum of treated and untreated components)<br>- N and P treated point sources (WWTPs outgoing load)<br>- N and P untreated point sources (emissions collected in the public sewer system that are not treated in WWTPs)<br>- N and P emissions that are removed during treatment in WWTPs<br>- N and P emissions lost during wastewater collection and transport<br>- N and P emissions applied to agricultural soils in sewage farms<br>- N and P emissions that are not collected in the sewer system nor treated in WWTPs<br>- N and P incoming WWTPs load<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 100 realisations corresponding to 100 different parameter sets<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 18<br> </li> <li>Emission data aggregated at river basin level:<br><em><strong>spatial extent</strong></em>: Germany<br><em><strong>spatial resolution</strong></em>: river basins from the HydroBASINS v1.c of the HydroSHEDS database (HydroSHEDS, 2014; Lehner and Grill, 2013).<br><em><strong>time period</strong></em>: 1950-2019<br><em><strong>frequency</strong></em>: annual<br><em><strong>variables</strong></em>: N and P WWTP outgoing emissions (treated point sources), N and P emissions collected in the public sewer system that are not treated in WWTPs (untreated point sources)<br><em><strong>unit</strong></em>: kg yr-1<br><em><strong>realisations</strong></em>: 200 realisations corresponding to 100 different parameter sets and 2 spatial disaggregation methods for the treated point sources<br><em><strong>file format</strong></em>: CSV<br><em><strong>number of files</strong></em>: 600</li> </ol> <h4><strong>Acknowledgements and underlying datasets</strong></h4> <p>Partial support for this work was provided by the Global Water Quality Analysis and Service Platform (GlobeWQ) project financed by the German Ministry for Education and Research (grant number 02WGR1527A). We thank Olaf Büttner for providing the WWTPs data that were collected from the authorities of the German federal states (<a href="https://www.hydroshare.org/resource/2f2c2fa04e6e417ba0eb7b0fb14b1090/">Büttner et al., 2020</a>). The dataset produced in this work builds on the NUTS map of the German Federal Agency for Cartography and Geodesy © GeoBasis-DE/BKG that is under a <a href="https://www.govdata.de/dl-de/by-2-0">dl-de/by-2-0 license</a>; the History Database of the Global Environment (HYDE) dataset available under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>; protein data provided by the Food and Agriculture Organization of the United Nations © FAO provided under a <a href="https://creativecommons.org/licenses/by-nc-sa/3.0/igo/">CC BY-NC-SA 3.0 IGO license</a>; detergent data from the German Cosmetic, Toiletry, Perfumery and Detergent Association © IKW (<a href="https://www.ikw.org/impressum">license here</a>); data from the statistical offices of Germany and the federal states and the German and federal state authorities (details on data sources in the publication reported above: Sarrazin et al., submitted to Earth System Science Data); WWTP data available in the Waterbase dataset from the European Environment Agency © EEA under a <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0 license</a>. The river basins come from © HydroSHEDS (<a href="https://www.hydrosheds.org/terms-of-use">license here</a>).</p> <h4><strong>Contact</strong></h4> <p>Fanny Sarrazin (<a href="mailto:fanny.sarrazin@ufz.de">fanny.sarrazin@inrae.fr</a>)<br>Rohini Kumar (<a href="mailto:rohini.kumar@ufz.de">rohini.kumar@ufz.de</a>)</p> <h4><strong>References</strong></h4> <p>BKG (Bundesamt für Kartographie und Geodäsie) (2020), NUTS regions 1 : 250 000, 31.12.2020, GeoBasis-DE [data set], Leipzig, Germany, https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html (last access: 1 November 2022).</p> <p>Büttner, O. (2020), DE-WWTP - data collection of wastewater treatment plants of Germany (status 2015, metadata), HydroShare [data set],<br>https://doi.org/10.4211/hs.712c1df62aca4ef29688242eeab7940c.</p> <p>HydroSHEDS (2014), HydroBASINS v1.c, https://www.hydrosheds.org/products/hydrobasins (last access: 23 October 2023).<br><br>Lehner, B. and Grill, G. (2013), Global river hydrography and network routing: baseline data and new approaches to study the world's large river<br>systems, Hydrological Processes, 27, 2171–2186, https://doi.org/10.1002/hyp.9740.</p> <p><strong>Changes compared to v1.0 dataset version</strong></p> <p>Compared to the version 1.0 of the dataset, this version v1.1 contains the input and calibration data at NUTS-1 level and the input data at grid level used to calculate the emissions.</p>
Data from: Priority setting for global WASH challenges in the age of wastewater-based epidemiological surveillance
<p>Background: It is not known how the number of deaths due to COVID-19 compare to the number of deaths due to "unsafe water, sanitation, and handwashing" during the COVID-19 global health emergency.</p> <p>Methods: A dataset of deaths due to COVID-19 was downloaded from the World Health Organization. A dataset summarizing deaths due to unsafe water, sanitation, and handwashing was obtained from the Institute for Health Metrics and Evaluation (IHME). </p> <p>Results indicate that COVID-19 deaths in Africa and South East Asia regions exceeded those due to unsafe water, sanitation, and hygiene. </p>
Raw data for "Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition"
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SI-B for "Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition"
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Agronomic and Environmental Performance of Lemnaminor Cultivated on Agricultural Wastewater Streams—A Practical Approach
<p>This study investigated the potential of Lemna minor to valorise agricultural wastewater in protein-rich feed material in order to meet the growing demand for animal feed protein and reduce the excess of nutrients in certain European regions. For this purpose, three pilot-scale systems were monitored for 175 days under outdoor conditions in Flanders. The systems were fed with the effluent of aquaculture (pikeperch production—PP), a mixture of diluted pig manure wastewater (PM), and a synthetic medium (SM). PM showed the highest productivity (6.1 ± 2.5 g DW m<sup>−2</sup> d<sup>−1</sup>) and N uptake (327 ± 107 mg N m<sup>−2</sup> d<sup>−1</sup>). PP yielded a similar productivity and both wastewaters resulted in higher productivities than SM. Furthermore, all media showed similar P uptake rates (65–70 P m<sup>−2</sup> d<sup>−1</sup>). Finally, duckweed had a beneficial amino acid composition for humans (essential amino acid index = 1.1), broilers and pigs. This study also showed that the growing medium had more influence on the productivity of duckweed than on its amino acid composition or protein content, with the latter being only slightly affected by the different media studied. Overall, these results demonstrate that duckweed can effectively remove nutrients from agriculture wastewaters while producing quality protein.</p>
Simulations from 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'
<p>Simulations used in "Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples"</p>
Data for the original research article 'Enhanced anaerobic digestion of dairy wastewater in a granular activated carbon amended sequential batch reactor'
<p>All data that support the findings of the study 'Enhanced anaerobic digestion of dairy wastewater in a granular activated carbon amended sequential batch reactor' published in GCB Bioenergy Journal are openly available in the file.</p>
Long-term observation of the wastewater from the Parisian conurbation
<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 make it possible to qualify the public health of the population and to evaluate the impact of the chemicals used in daily life on the environment.<br> In line with these studies, the Innovation Department of SIAAP and its scientific partners (ISA and LEESU) have created an Observatory of the city with the objectives of:<br> • Monitoring the long-term dynamics of known and/or potential pollutants according to French regulations.<br> • Contributing to studies aimed at a better understanding of anthropogenic activities.<br> • Structuring and centralizing data (wastewater and end-of-treatment sludge), ensuring their storage for 10 years.<br> • Providing technical and scientific knowledge by publishing every two years the data generated by the Observatory of the city via the open platform Zenodo.<br> Regarding the pre-treated wastewater, the Clichy pretreatment plant has been chosen as a strategic location because it collects wastewater from the Paris catchment area (2.7 million inhabitants). Data on the wastewater quality during dry weather and on rain events (biological and physicochemical global quality parameters, metals, polycyclic aromatic hydrocarbons – PAHs and polychlorinated biphenyls – PCBs) is available since 2000 (for dry weather) and from 2005 (for rain events).</p> <p> </p> <p><strong>Dataset description</strong></p> <p>The dataset “Dataset _Clichy-SEC-Inlet.xlsx” includes the monthly analysis of various micro pollutants (trace metals, PAHs and PCBs) and usual physico-chemical parameters. The oldest analyses date back to 2000.</p> <p>The dataset “Dataset _Clichy-CSO.xlsx” includes the monthly quantified analysis of various micro pollutants (trace metals, PAHs and PCBs) and usual physico-chemical parameters for samples collected during 384 rain events. The oldest analyses date back to 2005.</p> <p>Details on each analysis are provided in the “Dataset_analyticalmethods_Clichy.xlsx”</p> <p> </p> <p>The following files are available:</p> <ul> <li>Dataset _Clichy-SEC-Inlet.xlsx</li> <li>Dataset _Clichy-CSO.xlsx</li> <li>Dataset_analyticalmethods_Clichy.xlsx</li> </ul>
Alan Turing Data Stories - Covid19 Wastewater
<p>Data set used in the Turing Data Stories article on COVID19 monitoring through wastewater data collection</p>
Included samples for wastewater
<p>List of the subset of wastwater samples included in the analysis, with ENA sample codes, unique ID, and reads after quality control.</p>
Testing sunfish as zooplankton control agents for wastewater-algal cultivation: Nutrient, invertebrate, and algae data
<p>Wastewater-algal cultivation is a promising means of recovering polluting nutrients and converting them into useful algal biomass. However, open raceway ponds used for wastewater-algal cultivation are prone to contamination by zooplankton, which often severely reduces algal yields. We conducted an experiment to test the potential for bluegill sunfish to improve yields by suppressing zooplankton. We used cages to protect the fish from the paddlewheels in 230-L raceway ponds. In nine raceways, the cage surrounded the paddlewheel and fish, if present, had access to the remaining tank area. In another nine raceways, the cage was on the opposite side from the paddlewheel, and contained the fish when present. Six fishless controls were implemented across both cage placements. We filled the raceways with synthetic wastewater and local plankton, and one juvenile bluefill sunfish (aside from controls). After 19 days we sampled dissolved nutrients, algae, and invertebrates in each raceway. This dataset consists of four csv files containing data from laboratory analyses of these samples. The data include dissolved nutrients, algal dry weights, zooplankton and benthic invertebrate counts, algal biovolumes, and algal nutrient content for each of the 18 raceway ponds.</p>
Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste [Data]
<p>Data of article <em>Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste</em></p>
Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste
<p>Data of article <em>Data-driven approaches to bestow environmental management through linking wastewater data to source estimation of hazardous waste</em></p>
Use of Fly Ash for Heavy Metals Removal from Wastewater A Ftir Spectroscopy Study
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
WasteWater Quality Monitoring
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SARS-CoV-2 in ferry wastewater
<p>Data for the journal paper "Use of wastewater from passenger ships to assess the movement of COVID-19 and other pathogenic viruses across maritime international boundaries" published in Frontiers in Public Health. doi: 10.3389/fpubh.2024.1377996</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
<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>
Dataset - Selection of cyanobacteria over green algae in a photo-sequencing batch bioreactor fed with wastewater
<p>The data set attached consists of an excel file where the data from the article “<strong>Selection of cyanobacteria over green algae in a photo-sequencing batch bioreactor fed with wastewater”</strong>, published in Science of the Total Environment (vol. 653, 25<sup>th</sup> February 2019, 485-495.) can be found, as well as the different equations and formulae that were used to obtain the published results.</p>
Data (electrical conductivity, rainfall depth, water level and turbidity) registered at Bologna wastewater treatment plant from mid-May to December 2024
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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.