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162 results for “Water quality data”
The Jefferson Project 2021 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
AquaMatch Chlorophyll a Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Chlorophyll a Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“v2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make-up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat chlorophyll a dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we can not verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 6, 1970, to June 20, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resources Research, 5
AquaMatch Dissolved Organic Carbon Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Dissolved Organic Carbon Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make-up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat dissolved organic carbon dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we cannot verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 5, 1970, to June 27, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water
Missouri reservoir water quality data from the Statewide Lake Assessment Program (SLAP), the Lakes of Missouri Volunteer Program (LMVP), and the Reservoir Observer Student Scientists (ROSS) program
This dataset of limnological water quality data continues from Jones et al., 2024, starting in 2017 until 2021. It is from 195 reservoirs, the majority of which are in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: areal pigment absorption coefficient, alkalinity, alpha (light utilization efficiency P-E parameter), ammonium (NH4), ammonium-debt, anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, seston d13C, seston d15N, dissolved turbidity, dissolved organic carbon, Ek (light saturation P-E parameter), FVFM (maximum quantum yield of PSII for photochemistry), gross primary production, microcystin, nitrate & nitrite (NO3), nitrate-debt, particulate nitrogen, particulate phosphorus, phosphorus-debt, pheophytin, particulate carbon, particulate inorganic matter, particulate organic matter, phycocyanin (PHYCO), saxitoxin, Secchi disk depth, silica, soluble reactive phosphorus, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. Most of the data come from the Statewide Lake Assessment Project (SLAP) and the Lakes of Missouri Volunteer Program (LMVP) funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete dep
Missouri reservoir water quality data (2022 - current) from the Statewide Lake Assessment Program (SLAP)
This dataset of limnological water quality data continues from North et al., 2025, starting in 2022 until present. The data is from reservoirs, primarily within the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Water quality parameters analyzed in the MU Limnology Lab during this time frame include: ammonium (NH4), anatoxin, chlorophyll a (corrected and uncorrected for pheophytins), chloride, cylindrospermopsin, dissolved organic carbon, microcystin, nitrate & nitrite (NO3), pheophytin, particulate inorganic matter, particulate organic matter, phycocyanin, saxitoxin, Secchi disk depth, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), total nitrogen (TN), total phosphorus (TP), total suspended solids, and urea. Most of the samples were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of samples were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few samples were taken from shorelines and drinking water treatment intake pipes. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. This data represents duplicate or triplicate water samples collected from either the water surface, integrated over the depth of the epilimnion, or from discrete depths in the hypolimnion.
The Jefferson Project 2022 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2022 water quality data from two vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2022. These vertical profiler stations are named VP_HarrisBay and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
Water Quality Data (Extensive) from the Taylor Slough, just outside Everglades National Park (FCE), from August 1998 to December 2006
Water quality samples are being collected using ISCO autosamplers at all wetland sites (that is, all sites except TS/Ph-9, 10, and 11). The autosamplers contain 24 1L bottles. Water is sampled by programming the autosamplers to take composite samples once every 3 days. These samples are a composite of four 250mL subsamples drawn every 18 hours (a sampling scheme that captures a dawn, noon, dusk, and midnight sample in every three day composite). Starting in December 2006 for Sites SRS1d, SRS2, SRS3, and June 2007 for TS/Ph1a, TS/Ph2, and TS/Ph3 - the 3 day composite samples are now combined in a 2 liter bottle upon returning to the lab to form 6 day composite sample. Samples are retrieved every 3-4 weeks. Retrieval of the composite samples may result in a composite sample which is less than three or six days. The recorded date for each composite sample indicates the end date of the sample interval. Samples are analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. When sites are visited to collect these samples, we also collect a grab sample that is immediately put on ice. A portion of these grab samples are filtered through a Whatman GF/F filters (0.7 um) immediately upon return to the lab, and the filtered samples are analyzed for inorganic nutrients such as NO2-, NO3-, NH4+, SRP, and DOC. The unfiltered fraction of these grab samples is analyzed for TP, TN, and TOC (TOC is no longer analyzed starting in August 2005 for all sites). We use these montly grab samples to generate relationships between TP and SRP, and between TN and NO2- + NO3- + NH4+. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques. TP is analyzed with a modified Solorzano and Sharp (1980) technique. TN is measured with an Antec TN analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with a YSI conductivity meter. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwat
North Temperate Lakes LTER: Multiparameter Water Quality Data -- CFL Pier, Lake Mendota.
This is data from a SUNA V2 nitrate sensor and a YSI EXO 2 sonde instrumented with water temperature, dissolved oxygen, pH, chlorophyll, phycocyanin, conductivity, turbidity, and fDOM sensors. The sensors are located at the lake end of the pier serving the Center for Limnology on the UW-Madison campus. The YSI sonde is fixed on the pier with sensors nominally at 0.5 meters depth, while the SUNA is suspended below the pier at one meter depth in the open water season. In the winter season, the SUNA is placed in a cage on the lake bottom close to shore with the sensor 18cm off the bottom. The depth of the sensors will vary with lake level over the season. The water depth at the lake end of the pier is normally about 3 meters. YSI sonde data are sampled once per minute. Hourly and daily averages are provided as separate CSV files. The SUNA sample rate varies. In the winter (under the ice) it relies on single battery charge, so the wiper is deactivated and the sensor samples every 1-2 hours. Daily averages of SUNA are also provided as a separate CSV. The YSI sonde is deployed only during the ice-free season coinciding with the placement of the pier. Sensors are cleaned and maintained roughly every two weeks. Number of sites: 1. Location lat/long: 43.07758, -89.40297
Data and R-Scripts for "Quality and timing of crowd-based water level class observations"
<p>This are the data and the R-scripts used for the manuscript "Quality and timing of crowd-based water level class observations" accepted for publication in the journal Hydrological Processes in July 2020 as a Scientific Briefing. To run the code, just run the R-script with the name "RunThisForResults.R". Results will be written to the "Figures" and the "Results" folder.</p>
Water quality and diarrhoea bibliometric data and visualisation
<p>This repository contains bibliometric data and its visualisations:</p> <p>Bibliometric data</p> <ul> <li>Keyword: "water quality" AND diarrhoea</li> <li>Database: Scopus</li> <li>Date taken: 28 June 2017</li> <li>Formats: bib, csv, ris</li> <li>Reference manager: Jabref and Zotero</li> </ul> <p>Visualisations</p> <ul> <li>Tools: VosViewer (http://VosViewer.com)</li> <li>Tool's citation: Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. (paper, preprint, supplementary material) (http://dx.doi.org/10.1007/s11192-009-0146-3)</li> <li>Mindmap of analysis procedures using Freeplane https://www.freeplane.org/wiki/index.php/Main_Page</li> </ul>
UK shale gas air and water quality data
<p>Datasets for UK coal bed methane compositions (Airth field), shale gas composition from Bowland shale operations and produced water composition from UK Airth field.</p>
Monthly lake water quality data (May-September 2022) following the Greenwood Fire in northeastern Minnesota v1.0
<p>This repository contains lake water quality responses to the 2021 Greenwood Fire in Superior National Forest, Minnesota, USA (near Isabella, MN in northeastern MN). Thirty lakes (15 burned watershed, 15 control) were sampled monthly from May-September 2022 along various fire disturbance gradients (e.g., % watershed burned) and in relation to hydrologic connectivity (i.e., drainage vs. isolated lakes). Much of the non-water quality data we used came from published sources, which are described and referenced below. This repository also contains R code used to analyze and visualize data, as well as some output figures.</p>
Southwest United States Wetland Water Quality and Macroinvertebrate Data 2018-2022
Water quality and macroinvertebrate data were collected between 2018 and 2020 from 14 different wetland and riparian sites spanning across West Texas, New Mexico, and Arizona. Water quality data such as Cl, SO4, and conductivity were collected as well as nutrients such as NO3, PO4, and Total Dissolved Nitrogen. Macroinvertebrate data were collected from all sites during the summer months (June, July, and August).
Petit-lac-Saint-François surface water quality monitoring data
Water Quality samples were collected at Lake Inlet, Outlet, and In-Lake sites between October 2009 and September 2020. Water Samples were collected on a weekly, same-day-of-the-week basis, between 10 a.m. and noon, year-round. Field sampling was conducted by the same technician during the entire period to ensure method consistency and sampling frequency was maintained throughout, except for the winter of 2017, during which sampling was suspended. Epilimnion samples were collected using a swing sampler with a wide neck, polyethylene bottle (Nasco Sampling, Madison, WI, USA), and composited in an acid-washed, opaque, 4-L polyethylene bottle pre-conditioned with lake water. Four grab samples were taken to account for spatial heterogeneity that can be substantial within distances of a few meters, particularly when surface phytoplankton blooms are present. The grab samples were collected by tilting the swing sampler bottle approximately 45 degrees and allowing it to fill as it was submerged 8 to 15 cm below the surface. During periods of ice cover, access holes were drilled or cut through the ice to collect samples. For dissolved parameters, samples were filtered on the same day as collection. Quality control measures, including field, transport, and laboratory blanks, consisting of HPLC grade water and preservative where required, as well as split samples for interlaboratory comparisons, were included quarterly in the sampling program along with regular samples. These were used to establish practicable detection limits and to monitor for levels of contaminants to which field samples might be exposed.
Caloosahatchee River Estuary Optical Water Quality Data (May 2008 - May 2020)
This dataset is an aggregated dataset from various sources including the South Florida Water Management District and Lee County (Florida) Environmental Lab and includes parameters of optical water quality. Optical water quality samples were collected throughout the Caloosahatchee River Estuary from the head of the estuary to its outlet in San Carlos Bay and the Gulf of Mexico. Parameters within the dataset include light attenuation coefficient, Secchi disk depth, chlorophyll-a concentration, turbidity, total suspended solid concentration, color, water temperature, specific conductivity, and salinity. Samples were collected from various agencies from May 2008 to May 2020. Other water quality parameters were also collected and some of the monitoring continued to present but is not presented here.
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
FISHPASS ASSESSMENT PLAN LONG-TERM MONITORING OF HYDROLOGIC AND WATER QUALITY DATA
The Great Lakes Fishery Commissions’ (GLFC) FishPass project seeks to reconnect the waterscape for only desired species (i.e., selective passage) by integrating a multitude of existing and novel passage techniques and technologies. The probability of a fish passing through a sorting system is dependent on environmental conditions and a fish’s motivation ─ its internal state in relation to environmental stimuli. While fish decision making abilities introduce complexity to the sorting operations, they also provide an opportunity to exploit behavioral tendencies and abilities to achieve selective sorting. The FishPass Assessment Plan details a monitoring program aimed at quantifying fish movement and sorting capabilities associated with both individual mechanisms and integrated sorting systems. The results of the monitoring program will be used to inform future adjustments to the selection of techniques and technologies and their configuration to optimize passage of desirable species while blocking and/or removing undesirable species. A key component to the Assessment Plan is the long-term monitoring of abiotic variables in and around FishPass. This data set contains the hydrologic (e.g., river discharge, water level) and water quality data (e.g., temperature, specific conductivity, conductivity, and turbidity) collected at mostly static stations throughout the Boardman/Ottaway River. The dataset is updated annually. These data are collected until the initiation and/or substantial completion of the FishPass structure. Collection of this type of data are expected to continue after FishPass construction completion but modifications to the extent and location of monitoring stations are anticipated. As a result, a new dataset will be updated in the future containing all long term hydrologic and water quality monitoring post construction. R. Swanson, GLFC Assessment Biologist, is primarily responsible for maintaining the monitoring equipment, data retrieval, quality assuran
High-frequency water quality data for three urban streams in Boston, MA (USA), 2021-2022
This dataset contains high-frequency water quality data for three urban stream locations in the great Boston, Massachusetts metropolitan area. Multiparameter sondes with sensors to measure temperature, pH, specific conductivity, optical dissolved oxygen (DO), turbidity, colored dissolved organic matter (CDOM), and optical brighteners (OB) were deployed from 23 November 2021 to 20 December 2022. Data were collected at 15-minute intervals.
AquaMatch Secchi Disk Depth Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Secchi Disk Depth Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“V2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat Secchi Disk Depth dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we can not verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 10, 1970, to July 1, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resources Res
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