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195 results for “water treatment”
NSW01 Soil water chemistry from porous cup lysimeters on watersheds with different fire treatment
Soil water nitrogen composition is measured using porous cup lysimeters. Measurements include nitrate, ammonia, phosphate, and organic nitrogen and phosphorus. Variables of interest are rainfall patterns, vegetation types, and time since burning.
Knowledge base for NBS for water treatment and stormwater management
<p>Five tables containing:</p> <ol> <li>nbs_catalog.csv: A catalogue of nature-based solutions for wastewater treatment and stormwater management. For each solution there is information on its performance, types of water, cobenefits, barriers and cost.</li> <li>sci_publications.csv: A list of scientific publications focused on one or several technologies of the above catalogue.</li> <li>sci_publications_treatment_details: For solutions for water treatment, a second table containing data about treatment performance extracted from previous scientific publications.</li> <li>description_nbs_catalog.csv: Descriptors for the catalogue.</li> <li>description_sci_publications_treatment_details.csv: Descriptors for the treatment performance data.</li> </ol> <p>The most updated version of each table can be queried from https://snappapi-v2.icradev.cat/</p>
Regional drinking water quality monitoring program: long-term monitoring of water quality in select canals, reservoirs, and treatment plants of the greater Phoenix, Arizona metropolitan area drinking water system, ongoing since 1998
Regional Drinking Water Quality Monitoring Program ================================================== Arizona Statue University (ASU) has been working with regional water providers (Salt River Project (SRP), Central Arizona Project (CAP)) and metropolitan Phoenix cities since 1998 on algae-related issues affecting drinking water supplies, treatment, and distribution. The results have improved the understanding of taste and odor (T&O) occurrence, control, and treatment, improved the understanding of dissolved organic and algae dynamics, and initiated a forum to discuss and address regional water quality issues. The monitoring benefits local Water Treatment Plants (WTPs) by optimizing ongoing operations (i.e., reducing operating costs), improving the quality of municipal water for consumers, facilitating long-term water quality planning, and providing information on potentially future-regulated compounds. ASU has been monitoring water quality in terminal reservoirs (Lake Pleasant, Saguaro Lake, and Bartlett Lake) continuously from 1998 to the present for algae-related constituents (taste and odors, and more recently metals from the upper reservoirs), nutrients, and disinfection by-product precursors (i.e., total and dissolved organic carbon and organic nitrogen). Additional monitoring has been conducted in the SRP and CAP canal systems and in water treatment plants in Phoenix, Tempe and Peoria. During this work the Valley has been in a prolonged drought and recently one above average wet year, and this data provides important baseline data for development of new or expanded WTPs and management of existing WTPs in the future. The current work has improved the understanding of T&O sources and treatment, but additional research and monitoring into the future is necessary. Reservoir monitoring is conducted once per month at Bartlett Lake, Saguaro Lake, and Lake Pleasant, and quarterly at Roosevelt, Apache, and Canyon Lakes. Samples are depth integrated in the epilimnion a
Wastewater Treatment Plant Inflow Rate, Network Water Levels and Rain Rate for Koeln Weiden, Germany
<p>wtp_network_rain.csv contains time series data of measured wastewater treatment plant inflow rates, water levels from multiple locations within the wastewater sewer network and rain rate measured from a rain recorder located at the wastewater treatment plant.</p>
Treatment Trains for Water Reclamation (Dataset)
<p>This dataset lists typical treatment trains (combination of unit processes) from global water reuse and reclamation practices - PDF</p>
Soil water content under rainfall manipulation and nitrogen fertilization treatments at the Jornada Basin LTER site, 2007-2009
This data package contains soil water content data from a precipitation and nitrogen manipulation experiment conducted on the Jornada Experimental Range from 2006-2009. The objective of the study was to understand the interaction of precipitation and nitrogen dynamics on ANPP legacies. Rain-out shelters were used to create 5 levels of precipitation: 80% reduced, 50% reduced, ambient control, 50% increased, and 80% increased precipitation. Half of the plots were randomly assigned to receive ammonium nitrate fertilizer. In a subset of the plots, volumetric soil water content was monitored at shallow (0-5 cm) and deep (30-50 cm) depths. This data set contains the date of collection, block number, plot number, 2007-2008 precipitation treatment, 2009 precipitation treatment, nitrogen treatment, depth of soil probe, sensor voltage, and volumetric water content. Monitoring of soil moisture for this study was discontinued in November 2009. Also available are plant cover data from this study in data package knb-lter-jrn.210278002. For further information and results, see: Throop, H., L. G. Reichmann, O. Sala, and S. Archer. (2012), Response of dominant grass and shrub species to water manipulation: an ecophysical basis for shrub invasion in a Chihuahuan desert grassland. Oecologia 169: 373-383. https://doi.org/10.1007/s00442-011-2217-4 Reichmann, L. G., O. E. Sala, and D. P. C. Peters. (2013), Water controls on nitrogen transformations and stocks in an arid ecosystem. Ecosphere 4(1):11. https://doi.org/10.1890/ES12-00263.1
Pre-dawn water potential of leaves from reduced and enhanced precipitation treatments at the Jornada Basin LTER site, 2007-2008
This data package contains pre-dawn water potential data for leaves collected from mesquite shrubs and black grama grasses at the Jornada Basin LTER site in 2007-2008. The purpose of this study was to quantify ecophysiological performance of shrubs and grasses in the context of precipitation manipulations. One mesquite and one black grama leaf from each plot (n = 20 plots; 5 PPT treatments X 4 replicate plots/treatment) were measured 5 times beginning July 2007 and ending in August 2008. The precipitation treatments consisted of +/-80%, +/-50%, and ambient/control. Leaves were excised from plants and immediately measured for water potential (MPa) with a pressure chamber at least 0.5 h before dawn. The data set is complete.
SBC LTER: Land: Hydrology: Santa Barbara County Flood Control District - Precipitation at Cater Water Treatment Plant (CaterWTP229)
Precipitation was collected by the Santa Barbara County Flood Control District at Cater Water Treatment Plant (CaterWTP229) in the Santa Barbara coastal area. Data are reported hourly, and times reflect the end of the each 1-hour interval. For more information, see https://www.countyofsb.org/pwd/hydrology.sbc
Data from: Testing the heat treatment dose for Agrilus planipennis prepupae using the Humble water bath
<p>The lethal heat treatment dose (time and temperature) for the pre-pupal life stage of <em>Agrilus planipennis</em> Fairmaire (Coleoptera: Buprestidae), emerald ash borer, was determined through an in vitro application using a carefully calibrated heat treatment apparatus. The lethal and sublethal effects of heat on A. planipennis prepupae were assessed through a ramped heat delivery application, simulating industrial kilns and conventional heat chamber operations, for treatments combining target temperatures of 54 °C, 55 °C, and 56 °C, and exposure durations of 0 min (i.e., kiln temperature ramp only), 15 min, or 30 min. Prepupal emerald ash borer larvae did not survive exposure to 56 °C for 15 min or longer, or to 55 °C for 30 min. Sublethal effects were observed for all other treatments. Sublethal effects included delayed development and failure to complete the pupal and adult life stages. The datasets and associated R statistical computing langauge code are deposited here.</p>
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)
Evaluation of point-of-use treatments and biochar to reduce 1,2,3-trichloropropane (TCP) contamination in drinking water
<p>From the 1940s to the 1980s, 1,2,3-trichloropropane (TCP) was widely present as an impurity in soil fumigants to eliminate plant parasitic nematodes. TCP also saw wide usage as a degreaser and solvent in industrial processes. In rural agricultural regions with a history of fumigant use, TCP is a common pollutant in groundwater. As a potent suspected carcinogen (California MCL 5 ng/L), TCP poses a risk to communities reliant on domestic wells. Lacking the populations needed for more centralized water treatment facilities, these communities often use point-of-use (POU) treatment technologies or buy bottled water. In this study, we tested commercially available water pitchers equipped with carbon filters for point-of-use TCP treatment efficacy. As a less costly carbon alternative, we also tested low-cost, locally sourced biochar made from almond shells. Biochar could serve as a sustainable alternative to the current coconut and coal-based carbon feedstocks. Pitcher point-of-use filters removed at or above 98% of TCP in tap water derived from untreated groundwater during their lifetime of use. In our batch studies, almond biochar did absorb TCP at a lower efficiency than commercially available granulated carbon. Ultimately, the study's findings could assist affected communities and households in identifying efficient and cost-effective treatment technologies at the domestic well and household levels.</p>
Fig. 3 in Ornithological Fauna Of The Waste Water Treatment Plants In The Northern Left Bank Ukraine (Chernihiv And Kyiv Regions): Winter Populations And Ecological Structure
Fig. 3. Similarity clusters of bird populations' species composition in winter according to the water treatment facilities' biotopic zones: 1 — zone of water bodies; 2 — dam zone; 3 — technological zone 4 — meadows agricultural zone.
Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
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>
Figure 1 in Discharge compliance at Shanghai port - a case study on discharged ballast water from vessels with Ballast Water Treatment System
Figure 1. Water quality parameters of ballast water samples. Sal.: salinity; Temp.: temperature; DO: dissolved oxygen.
Fig. 3 in Breeding Avifauna Of The Waste Water Treatment Plants, Located In Northern Left-Bank Part Of Ukraine
Fig. 3. Number of birds of ecological groups in zones: 1— landscape-biotopic characteristic; 2 — nests' location.
Dataset: Nutrient Removal in Roadside Stormwater Bioretention Cells Amended with Drinking Water Treatment Residuals
<p>This dataset contains storm volumes and nitrogen and phosphorus concentrations measured at the inflow and outflow of four roadside, lined bioretention cells in a field study conducted in Burlington, Vermont</p> <p>Phosphorus data are published in: Ament, M. R., Roy, E. D., Yuan, Y., & Hurley, S. E. (2022). Phosphorus removal, metals dynamics, and hydraulics in stormwater bioretention systems amended with drinking water treatment residuals. <em>Journal of Sustainable Water in the Built Environment</em>, <em>8</em>(3), 04022003.</p> <p>Nitrogen data are included in a manuscript currently under review.</p> <p> </p> <p> </p>
Evaluation of point-of-use treatments and biochar to reduce 1,2,3-trichloropropane (TCP) contamination in drinking water
Open the record for dataset details and reuse information.
Data from: Testing the heat treatment dose for Agrilus planipennis prepupae using the Humble water bath
Open the record for dataset details and reuse information.
Abandoned Water Treatment Plant
Abandoned water treatment plant From 500 photos, captured october 2020 Location Sinimäe 17 13816 Tallinn 59.443469, 24.902822 Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
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.