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4 results for “Oxidation-reduction potential”
Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, oxidation-reduction potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - Ma
Time series of high-frequency profiles of depth, temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, turbidity, pH, oxidation-reduction potential, photosynthetically active radiation, colored dissolved organic matter, phycocyanin, phycoerythrin, and descent rate for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025
Depth profiles of water biogeochemical properties were collected with SeaBird Electronics (SBE) Conductivity, Temperature, and Depth (CTD) profilers from 2013-2025 at five drinking water reservoirs in southwestern Virginia, USA. The study reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of CTD depth profiles measured at the deepest site of each reservoir adjacent to the dam as well as other upstream reservoir sites. The profiles were collected approximately fortnightly in the spring months, weekly in the summer and early autumn, and monthly in the late autumn and winter. Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir were sampled every year in the dataset (2013-2025); Spring Hollow Reservoir was only sampled 2013-2017 and 2019; and Gatewood Reservoir was only sampled in 2016. Data availability differs across years due to additional sensors that have been added or replaced over time. From 2013-2016, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor and an ECO FLNTU sensor for turbidity and chlorophyll. From 2017-2025, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor, an ECO FLNTU sensor for turbidity and chlorophyll, a PAR-LOG ICSW sensor for photosynthetically active radiation, and a SBE 27 pH and ORP (oxidation-reduction potential) sensor. In 2022 and 2023, profiles were also taken with an additional CTD equipped with an SBE 43 Dissolved Oxygen sensor; an ECO Triplet Scattering Fluorescence sensor for
SALTEx soil oxidation-reduction (redox) potential measurements from the GCE LTER Seawater Addition Long-Term Experiment (SALTEx) between July 2016 and March 2017
SALTEx (Seawater Addition Long-Term Experiment) is a field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. The SALTEx experiment was initiated in 2012 and consists of 31 field plots , each 2.5 m on a side. There are three treatments (Press, Pulse, and Fresh) and two types of controls (with and without sides), each consisting of six replicates. The Press treatment plots receive regular (4 times each week) additions of a mixture of seawater and fresh river water. Pulse plots receive the same mixture of seawater and river water during September and October, which is historically a time of low flow in the river when natural saltwater intrusion occurs. The Fresh treatment plots receive regular additions of fresh river water. Treatment water is added during low tide to facilitate its infiltration into the soil, and all plots are inundated by astronomical tides at high tide. Response measurements include porewater chemistry, specifically concentrations of chloride, sulfate, sulfide, dissolved organic carbon (DOC), ammonium-N, nitrate/nitrite-N, dissolved reactive phosphorus, total phosphorus, total nitrogen, organic nitrogen, carbon:nitrogen ratio, organic-carbon:organic-nitrogen ratio, and pH.
Data for: Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors
<p>Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89 to 91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.</p>
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