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63 results for “water detection”
Table S27: Target and identified unknown organic micropollutants detected in surface water samples taken during heavy rain events
<p>In the following table, peak intensities of detected organic micropollutants in water samples are displayed.</p> <p>This data table is part of the appendix of Chapter 4 of the PhD thesis “Novel approaches to identify drivers of chemical stress in small rivers” by Liza-Marie Beckers prepared at RWTH Aachen University and at the Helmholtz Centre for Environmental Research-UFZ. In Chapter 4, precipitation-related pollutant patterns and indicator compounds during heavy rain events were identified in the Holtemme River by nontarget screening and cluster analysis. The table contains peak heights of organic micropollutants detected in water samples taken during heavy rain events in the Holtemme River (Saxony – Anhalt, Germany). The table is structured into the following columns: Compound name, use class of compound (e.g., pharmaceutical or pesticide), distinction between target or identified unknown compounds, mass-to-charge ratio (m/z), retention time (RT), assignment to a pattern identified by cluster analysis (i.e., “Base” or “Quick”), the probability of belonging to the assigned pattern as number between 0 and 1 as well as the peak height of the compound in each sample. The samples are indicated by "B" for "bottle" and a number from 1-16. The use class “NA” indicates that now major use class for this compound could be identified.</p> <p>The sampling was triggered by combined sewer overflow at a wastewater treatment plant upstream of the sampling point. Samples were taken by an automated sampler in 30-min composite samples for 8 hours resulting in 16 samples per rain event. In total, 6 heavy rain events from May to September 2016 were sampled during this study. The table is divided into 6 subtables (i.e., Table S27 A-F). Each subtable displays compounds and their peak heights detected in samples from one heavy rain event. The different rain events are abbreviated by the sampling date:</p> <p>Table S27A displays results from the rain event samples May 29<sup>th</sup> 2016 : E2905</p> <p>Table S27B displays results from the rain event samples June 01<sup>st</sup> 2016 : E0106</p> <p>Table S27C displays results from the rain event samples June 24<sup>th</sup> 2016 : E1306</p> <p>Table S27D displays results from the rain event samples June 13<sup>th</sup> 2016 : E2406</p> <p>Table S27E displays results from the rain event samples July 13<sup>th</sup> 2016 : E1307</p> <p>Table S27F displays results from the rain event samples September 17<sup>th</sup> 2016 : E1709</p> <p>Chemical analysis of the water samples was performed by liquid chromatography (UltiMate 3000 LC system (Thermo Scientific)) coupled to high resolution mass spectrometry (Q Exactive Plus, Thermo Scientific) with a heated electrospray ionization (HESI) source. Nontarget screening was performed as it allows for a comprehensive characterization of the chemical exposure during heavy rain events. However, only annotated target compounds and unknown compounds identified by structure elucidation are presented in the table. Details on data evaluation methods are described in Chapter 4 of the PhD thesis.</p> <p>Beckers, L.M. (2019): Novel approaches to identify drivers of chemical stress in small rivers. RWTH Aachen University, Aachen.</p>
Site environmental, climate, water levels and temperatures, vegetation cover, and GIS change detection for assessing permafrost change in fens on the Tanana Flats, central Alaska
This data package provides data used to assess the roles of climate extremes, ecological succession, and hydrology in repeated permafrost aggradation and degradation in fens on the Tanana Flats, central Alaska. The package provides data on site environmental information, Fairbanks climate, vegetation cover, water levels and temperatures, as well as GIS files for fen change detection. The Site data include information on observers, locations, geomorphology, hydrology, soils, vegetation, and disturbance. The table has numerous fields that uses coding for class characteristics and these codes are described in the metadata as well as compiled in the ELS_Arctic_Boreal_Site_Soil_Veg_Code_Sheet_2020.docx. Alaska Climate records for Fairbanks (UAF Experiment Station) from 1904 to 2019 were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/). Additional data were obtained for the Nenana station (about 70 km southwest of Fairbanks), to fill in small data gaps (particularly precipitation/snow depth ruler measurements) in the Fairbanks record. We attributed the data with fields for summer (May-September) and winter periods (November-March) and hydrologic year (October-September) and calculated mean air temperature, precipitation, and snow depth by seasonal period (average of daily values) and year. The broad summer and winter periods were of interest because warmer and wetter summers increase soil heat input and warmer and snowier winters reduce soil heat loss. Fen hydrology data include information on fen water level/pressure and temperatures collected every two hours at seven sites within fens from 2011 to 2014. Vegetation composition and cover of fens, scrub, and forests was sampled to assess effects of thermokarst on vegetation change. Plant cover was determined by point-sampling at 100 points (including repetitive “hits” for all layers) distributed along 5 equally spaced rows (4-m long, 20 points per row) across the 10-m l
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>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
Land Water Transition Zone Change Detection - Hydroperiod Maps - WQeMS raster products
<p>Within this dataset, Hydroperiod maps of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy are available in GeoTIFF raster format. The maps provide information about the total number of days each pixel is inundated within a specific time period.They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and period during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps which were provided as input for the production of the hydroperiod maps.</p>
Land Water Transition Zone Change Detection - Transition Maps between two dates - WQeMS raster products
<p>Within this dataset, land water transition zone maps, which indicate the land-water transition between two instances in time, of the Polyphytos open surface water reservoir in Greece and Giaretta reservoir in Italy, are available in GeoTIFF raster format. The maps depict the transition zones by indicating the change of the pixel status from non-inundated to inundated and vice versa between two provided dates. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project. Each raster file in the dataset is named according to the water body and the two dates during which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps on the two dates.</p>
Detecting Lunar and Martian Water via Backscattered Cosmic Particles using Muon Tomography
<p><strong>Introduction</strong></p> <p>The search for water on the Lunar and Martian surfaces is a cornerstone of space exploration, playing a key role in expanding our understanding of the history and evolution of these celestial bodies. Despite its importance, current knowledge about the distribution, concentration, origin, and migration of water on the Moon and Mars is still limited. This study aims to address these gaps by employing a novel approach that leverages cosmic-ray muon detectors and backscattered radiation. Through the use of advanced muon tracking systems and preliminary simulations conducted with GEANT4, the research suggests that muon tomography holds significant promise for improving our understanding of water-ice content on the Lunar and Martian surfaces.</p> <p><strong>Data Description</strong></p> <p>Data and detector models were generated using GEANT4. The simulations include:</p> <ul> <li>Lunar and Martian dry regolith</li> <li>Lunar and Martian regolith with water-ice beneath the surface</li> </ul> <p><strong>Contents</strong></p> <p>This record includes:</p> <ul> <li><code>*.csv</code>: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type.</li> <li><code>backscatter_eventselection.py</code>: Python code to filter events and generate a CSV file of selected backscattered events.</li> <li><code>*.tiff</code>: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events.</li> <li><code>ml_classifier.py</code>: Python code for machine learning tasks to classify backscattered events.</li> <li><code>OP_Muographers_2023.pdf</code>: Detailed description of chemical composition and simulated scenarios.</li> <li>Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The provided datasets are simulated samples suitable for conceptual R&D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.</p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The supplementary figures contain 2380 precipitation-type flood events recorded by the DFO compared with results derived based on GRACE and MODIS. "DFO-GRACE-MODIS" folder means comparison among flood derived from DFO, GRACE and MODIS while "DFO-GRACE" folder means comparison between DFO and GRACE due to lack of MODIS-derived flood inundation. As for detailed information, please refer to the corresponding paper. </p>
Data release for "Measurements of muon-antineutrino and muon-neutrino+muon-antineutrino charged-current cross-sections without detected pions nor protons on water and hydrocarbon at mean antineutrino energy of 0.86 GeV"
<p>This data release is associated with the publication "Measurement of charged-current cross-sections on water and hydrocarbon without detected pions nor protons using the T2K anti-neutrino beam at an off-axis angle 1.5 degrees". It is available in <a href="https://doi.org/10.1093/ptep/ptab014">Progress of Theoretical and Experimental Physics</a> and <a href="https://arxiv.org/abs/2004.13989">arXiv:2004.13989 [hep-ex]</a>.<br><br>The data release contains:</p> <ul> <li>The "histograms.root" file contains several histograms related to the cross-sections. <ul> <li>flux_numubar_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>flux_numu_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>Err_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numubar cross-sections and their uncertainties.</li> <li>Err_numu_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numu+numubar cross-sections and their uncertainties.</li> <li>xsec_numubar_* -> 1D histogram with the predicted flux-integrated numubar cross-sections by NEUT (5.3.3).</li> <li>xsec_numu_numubar_* -> 1D histogram with the predicted flux-integrated numu+numubar cross-sections by NEUT (5.3.3).</li> </ul> </li> <li>The "Covariance_Matrix_Numubar.root" file contains the covariance matrix for the flux-integrated numubar cross-sections, considering all the uncertainties.</li> <li>The "Covariance_Matrix_Numu+Numubar.root" file contains the covariance matrix for the flux-integrated numu+numubar cross-sections, considering all the uncertainties.</li> <li>The "flux" file contains the (anti-)muon neutrino flux prediction at the WAGASCI module or the Proton Module of the T2K experiment</li> </ul>
Data from: Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: testing multiple eDNA workflows.
<p>The European weather loach (<i>Misgurnus fossilis</i>) is classified as highly endangered in several countries of Central Europe. Populations of <i>M. fossilis</i> are predominantly found in ditches with low water levels and thick sludge layers and are thus hard to detect using conventional fishing methods. Therefore, environmental DNA (eDNA) monitoring appears particularly relevant for this species. In previous studies, <i>M. fossilis</i> was surveyed following eDNA water sampling protocols, which were not optimized for this species. Therefore, we created two full factorial study designs to test six different eDNA workflows for sediment samples and twelve different workflows for water samples. We used qPCR to compare the Threshold cycle (Ct) values of the different workflows, which indicate the target DNA amount in the sample, and spectrophotometry to quantify and compare the total DNA amount inside the samples. We analyzed 96 water samples and 48 sediment samples from a pond with a known population of <i>M. fossilis</i>. We tested several method combinations for long-term sample preservation, DNA capture and DNA extraction. Additionally, we analyzed the DNA yield of samples from a ditch with a natural <i>M. fossilis</i> population monthly over one year to determine the optimal sampling period. Our results showed that the long-term water preservation method commonly used for eDNA surveys of <i>M. fossilis </i>did not lead to optimal DNA yields, and we present a valid long-term sample preservation alternative. A cost-efficient high salt DNA extraction led to the highest target DNA yields and can be used for sediment and water samples. Furthermore, we were able to show that in a natural habitat of <i>M. fossilis</i>, total and target eDNA were higher between June and September, which implies that this period is favorable for eDNA sampling. Our results will help to improve the reliability of future eDNA surveys of <i>M. fossilis</i>.</p>
Data from: Surface Acoustic Wave-based Lab-On-a-Chip for the fast detection of Legionella pneumophila in water
<p><span>Surface acoustic wave (SAW) -based immuno-biosensors are used for several applications, thanks to their versatility and faster response than conventional analytical methods. SAW immuno-biosensors can be usefully applied to promptly detect bacteria and prevent bacterial infections that can lead to severe diseases. Here, we present a SAW immuno-biosensor to detect <em>Legionella</em> <em>pneumophila</em> in water. Our device, working at ultra-high frequency (740 MHz), is functionalized with an anti-<em>L</em>. <em>pneumophila</em> antibody to maximize the specificity. We report the characteristic curve of the sensor, calculated measuring bacterial samples at known densities, and its related parameters. We also measure <em>L</em>. <em>pneumophila</em> samples contaminated with different Gram-positive and Gram-negative bacterial species (<em>Escherichia</em> <em>coli</em> and <em>Enterococcus</em> <em>faecium</em>) and samples diluted in mains waters. The proposed device is able to detect <em>L</em>. <em>pneumophila</em> in the range from 1</span><span>×</span><span>10<sup>6</sup> to 1</span><span>×</span><span>10<sup>8</sup> CFU/mL, with a limit of blank of 1.22</span><span>×</span><span>10<sup>6</sup> CFU/mL and a limit of detection of 2.01</span><span>×</span><span>10<sup>6</sup> CFU/mL. The nonspecific signal due to contaminant bacteria is very limited and measurements of <em>L</em>. <em>pneumophila</em> are not affected by contamination. We obtain a good detection also in mains water, representing a realistic matrix for <em>L</em>. <em>pneumophila</em>. Our results are encouraging and pave the way to the use of fast, easy-to-use, reliable and precise sensors to prevent bacterial infections in human activities.</span></p>
Data from: Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept and sediment samples
<p>Climate warming will impact the sustainability of arid and semi-arid zone environments so we need to understand the influence of changes in arid lands on vertebrate populations. However, biomonitoring and biodiversity assessment in arid environments can be prohibitively time-consuming, expensive, and logistically challenging due to their often remote and inhospitable nature. Sampling of environmental DNA (eDNA) coupled with high-throughput sequencing is an emerging biodiversity assessment method. Here we explore the application of eDNA metabarcoding and various sampling approaches to estimate vertebrate richness and assemblage at human-constructed and natural water sources in a semi-arid region of Western Australia. Three sampling methods: sediment samples, filtering through a membrane with a pump, and membrane sweeping in the water body, were compared using two eDNA metabarcoding assays, 12S-V5 and 16smam, for 120 eDNA samples collected from four gnammas (gnamma: Australian Indigenous Noongar language term – granite rock pools) and four cattle troughs in the Great Western Woodlands, Western Australia. We detected higher vertebrate richness in samples from cattle troughs and found differences between assemblages detected in gnammas (more birds and amphibians) and cattle troughs (more mammals, including feral taxa). Total vertebrate richness was not different between swept and filtered samples, but all sampling methods yielded different assemblages. Our findings indicate that eDNA surveys in arid lands will benefit from collecting multiple samples at multiple water sources to avoid underestimating vertebrate richness. The high concentration of eDNA in small, isolated water bodies permits the use of sweep sampling which simplifies sample collection, processing, and storage, particularly when assessing vertebrate biodiversity across large spatial scales.</p>
Machine Learning Integrated High Quantum Yield Blue Light Carbon Dots for Real-time and On-site Detection of Cr(VI) in Groundwater and Drinking Water
<p>RGB和Kmeans提取后含有Cr(VI)水样的图像数据</p>
Comparative Efficacy of Water & Indigo Carmine vs. Water or Air Method on Adenoma Detection Rate (ADR) - a Randomized Controlled Trial (RCT)
ClinicalTrials.gov study NCT01607255. IPD Sharing: NO. Countries: 1. Publications: 13.
Water Immersion and Polyp Detection: A Randomized Controlled Trial
ClinicalTrials.gov study NCT03231917. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Surface Acoustic Wave-based Lab-On-a-Chip for the fast detection of Legionella pneumophila in water
Open the record for dataset details and reuse information.
Data from: Detection of the endangered European weather loach (Misgurnus fossilis) via water and sediment samples: testing multiple eDNA workflows.
Open the record for dataset details and reuse information.
Data from: Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept and sediment samples
Open the record for dataset details and reuse information.
Data from: Detection of invasive mosquito vectors using environmental DNA (eDNA) from water samples
Repeated introductions and spread of invasive mosquito species (IMS) have been recorded on a large scale these last decades worldwide. In this context, members of the mosquito genus Aedes can present serious risks to public health as they have or may develop vector competence for various viral diseases. While the Tiger mosquito (Aedes albopictus) is a well-known vector for e.g. dengue and chikungunya viruses, the Asian bush mosquito (Ae. j. japonicus) and Ae. koreicus have shown vector competence in the field and the laboratory for a number of viruses including dengue, West Nile fever and Japanese encephalitis. Early detection and identification is therefore crucial for successful eradication or control strategies. Traditional specific identification and monitoring of different and/or cryptic life stages of the invasive Aedes species based on morphological grounds may lead to misidentifications, and are problematic when extensive surveillance is needed. In this study, we developed, tested and applied an environmental DNA (eDNA) approach for the detection of three IMS, based on water samples collected in the field in several European countries. We compared real-time quantitative PCR (qPCR) assays specific for these three species and an eDNA metabarcoding approach with traditional sampling, and discussed the advantages and limitations of these methods. Detection probabilities for eDNA-based approaches were in most of the specific comparisons higher than for traditional survey and the results were congruent between both molecular methods, confirming the reliability and efficiency of alternative eDNA-based techniques for the early and unambiguous detection and surveillance of invasive mosquito vectors. The ease of water sampling procedures in the eDNA approach tested here allows the development of large-scale monitoring and surveillance programs of IMS, especially using citizen science projects.
Detection of allophane by Zhurong rover indicative of limited water-alteration at Utopia Planitia of Mars
<p><span>Tianwen-1 mission is China’s first Martian exploration mission, and the Zhurong rover has successfully landed in southern Utopia Planitia. The MarSCoDe onboard the Zhurong rover can obtain mineralogical information on the Martian surface materials by using SWIR spectra. However, the exact types of hydrated phases are not clear in the existing literature. Thus we combined the SWIR spectra and laboratory database of binary mixtures to further constrain mineral types and their contents. Our results reveal that allophane may be the source of hydration features in the SWIR spectra of the Tianwen-1 landing site. This is of great significance to the geological interpretation and process of the landing site.</span></p>
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