Accurate and rapid organic detection by eliminating hysteresis in bioanode sensor applications

2017 ◽  
Vol 3 (5) ◽  
pp. 905-910 ◽  
Author(s):  
Pengyi Yuan ◽  
Younggy Kim
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Sensor Applications ◽  
Exoelectrogenic Bacteria

Bioanode sensors utilizing exoelectrogenic bacteria can be used for real-time and in situ assessment of water quality.

Flat Flexible Thin Milli-electrode Array for Real-time in situ Water Quality Monitoring in Distribution Systems

2017 ◽  
Vol 2017 (4) ◽  
pp. 5598-5617
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Quality Monitoring

MARINE-EO project: Monitoring and forecast of eutrophication around fish farms, a Maliakos Gulf case (Eastern Mediterranean).

2021 ◽  
Author(s):  
Amandine Declerck ◽  
Matthias Delpey ◽  
Thibaut Voirand ◽  
Ioanna Varkitzi
Keyword(s):  
Water Quality ◽  
High Resolution ◽  
Real Time ◽  
Chlorophyll A ◽  
Satellite Observation ◽  
Risk Level ◽  
Fish Farms ◽  
Ocean Modelling ◽  
Monitoring And Forecasting

<p>Keywords: eutrophication; high resolution ocean modeling ; Chla satellite data ; biogeochemistry</p><p>Maliakos Gulf corresponds to mesotrophic waters that can reach eutrophic conditions and are occasionally subject to Harmful Algal Blooms (HAB) (Varkitzi et al. 2018). At the same time, it is an important fish farming and aquaculture production area. A large issue is thus related to the monitoring and forecasting of the risk of occurrence of algae blooms in the Gulf. For this purpose, the present study couples predictions from a high-resolution numerical ocean model with satellite observation to improve the monitoring and anticipation of threats for the local fish farms induced by occasional eutrophication.</p><p>This solution is developed in the frame of the MARINE-EO project (https://marine-eo.eu/). It combines satellite observation with high-resolution ocean modelling to provide detailed information as a support to fish farms management and operations. It is implemented in an operational platform, which provides continuous information in real time as well as short term predictions. The deployed solution uses CMEMS physical products as an input data and offers to refine this solution in order to provide a local information on site using a downscaling strategy. High resolution satellite products and ocean modelling allow to include the impact of local coastal processes on currents and water quality parameters to provide a proper monitoring and forecasting solution at the scale of a specific fish farm.</p><p>To model specific eutrophication processes, a NPZD (Nutrients-Phytoplankton-Zooplankton-Detritus) biogeochemical model is used. Included in the MOHID Water modelling system, the water quality module (Mateus, 2006) considering 18 properties: nutrients and organic matter (nitrogen, phosphorus and silica biogeochemical cycles), oxygen and organisms (phytoplankton and zooplankton) was deployed in the western Aegean Sea. The simulated chlorophyll a concentrations are used to compute a risk level for the eutrophication occurrence. To complete this indicator, another risk level was based on the eutrophication variation following Primpas et al. (2010) formulation. In addition to model forecasts, ocean color observations from the Sentinel-2 MSI and Landsat-8 OLI sensors are used to provide high resolution chlorophyll a concentrations maps in case of bloom events. The processing chain uses the sixth version of the Quasi-Analytical Algorithm initially developed by Lee et al. (2002) and an empirical relation based on a database built using the HydroLight software to compute chlorophyll a concentration.</p><p>Two past eutrophication events monitored in situ (Varkitzi et al. 2018) were studied to assess the accuracy of the developed tool. Although few in situ data were available on environmental input (as rivers flow and nutrient concentrations), it was possible using statistics to reproduce qualitatively these blooms. Finally, an operational demonstration was conducted during 2 months of the 2020 autumn season, to showcase real time monitoring and predictive perspectives.</p>

High-performance triboelectric nanogenerators for self-powered, in-situ and real-time water quality mapping

Nano Energy ◽  
2019 ◽  
Vol 66 ◽  
pp. 104117 ◽  
Author(s):  
Yu Bai ◽  
Liang Xu ◽  
Chuan He ◽  
Laipan Zhu ◽  
Xiaodan Yang ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
High Performance ◽  
Self Powered ◽  
Triboelectric Nanogenerators ◽  
Water Quality Mapping

Flat flexible thin milli-electrode array for real-time in situ water quality monitoring in distribution systems

2017 ◽  
Vol 3 (5) ◽  
pp. 865-874 ◽  
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Public Health ◽  
Water Quality ◽  
Drinking Water ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Drinking Water Quality ◽  
Quality Monitoring

Drinking water quality along distribution systems is critical for public health.

scholarly journals The aquatic real-time monitoring network; in-situ optical sensors for monitoring the nation's water quality

Fact Sheet ◽  
2011 ◽  
pp. 1-2 ◽  
Author(s):  
Brian A. Pellerin ◽  
Brian A. Bergamaschi ◽  
Peter S. Murdoch ◽  
Bryan D. Downing ◽  
John Franco Saraceno ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Optical Sensors ◽  
Monitoring Network ◽  
Real Time Monitoring

A Case Study: The deployment of a novel in situ fluorimeter for monitoring biological contamination within the urban surface waters of Kolkata, India. 

2021 ◽  
Author(s):  
Bethany Fox ◽  
Robin Thorn ◽  
Tapan Dutta ◽  
Darren Reynolds
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Microbial Activity ◽  
Surface Waters ◽  
Ecosystem Health ◽  
Urban Surface ◽  
Sensing Data ◽  
Biological Contamination ◽  
Pollution Events

<div> <p>With increasing pressures on water resources due to population, industrialization, agriculture, urbanization and climatic changes, improved temporal and spatial understanding of water quality is required. The development of new monitoring parameters, along with new monitoring technologies, are needed to provide real-time insight into the biogeochemical processes that underpin aquatic ecosystem health. Aquatic fluorescent organic matter (AFOM) has recently been explored for its potential to measure underpinning microbial activity within aquatic systems, which are essential in maintaining ecosystem health and function, with specific focus on the utilisation of tryptophan-like fluorescence (TLF or Peak T). <em>In situ</em> real-time portable fluorimeters have been extensively used for the identification and measurement of anthropogenic pollutants, such as polycyclic aromatic hydrocarbons (PAH) and optical brighteners. More recently, this portable fluorescence technology has been adapted for the monitoring and sensing of biological contamination, using microbially derived fluorescence signals (TLF). </p> </div><div> <p>The principal aim of this research was to deploy, for the first time in the field, the VLux TPro sensor (Chelsea Technologies Ltd., UK) and to assess the ability of this novel fluorescence-based sensor to detect the presence of biological contamination and elevation of microbial activity. This sensor has been developed to correct <em>in situ</em> real-time sensing data for optical interferences (caused by high turbidity and absorbance), as well as to provide quantitative fluorescence data by reporting in standardised quinine sulphate units (QSU). The urban surface waters within the city of Kolkata provide an interesting challenge for water quality sensors, allowing exploration of sensor performance in a range of water bodies ranging from turbid river waters to open sewer canals. </p> </div><div> <p>The sensor data collected demonstrates the ability of the VLux to identify waters with high bacterial loads using Peak T fluorescence. Moderate and weak positive correlations are seen for Peak T and <em>E. coli</em> or total coliform counts, R2 = 0.55 and 0.38 respectively. However, a strong significant correlation is identified between Peak T and the total bacterial cell counts (R2 = 0.75). This demonstrates that Peak T should not be used as a species-specific enumerator in complex surface water matrices. It does, however, demonstrate the ability of the VLux to successfully measure optically corrected and quantitative Peak T fluorescence in QSU. Therefore, data regarding the activity and fast-acting dynamics of freshwater bacterial communities, in response to pollution events, can now be reliably sensed and collected. This was demonstrated by the elevated Peak T fluorescence intensity observed when biologically contaminated water entered the main river channel, enabling identification of contamination hotspots. Sensing data has been further validated by laboratory analysis of spot samples confirming the significant correlations between Peak T and bacteria and nutrient concentrations. Further field-based research is required to determine the feasibility of long-term catchment scale sensor deployment as part of a sensing network, for the monitoring of biological activity and pollution events in freshwaters.  </p> <p> </p> </div><div> <p><strong>Acknowledgements:</strong> This research was supported by the NERC-DST Indo-UK Water Quality Programme NE/R003106/1 and DST/TM/INDO-UK/2K17/30. We would also like to acknowledge Chelsea Technologies Ltd. for ongoing VLux TPro sensor support.</p> </div>

Ulyssys Water Quality Viewer: a satellite-based global near real time water quality visualization

2020 ◽  
Author(s):  
András Zlinszky ◽  
Gergely Padányi-Gulyás
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Real Time ◽  
Suspended Sediment ◽  
Satellite Imagery ◽  
Quality Information ◽  
Custom Script ◽  
Chlorophyll Concentrations ◽  
Sentinel 2

<p>Sampling-based water quality monitoring networks are inherently spatially sparse. In locations or times where no in-situ water quality data are available, satellite imagery is an essential source of information. Satellite remote sensing can provide high spatial or temporal resolution imagery and has provided a breakthrough for oceanography, but so far, applications for coastal and inland water were limited by data resolution. Recently established satellite systems provide significant advances: Sentinel-2 delivers imagery with 20 m resolution, suitable for viewing even small rivers and ponds. Sentinel-3 delivers daily imagery with 300 m pixel size, which for lakes and coastal seas allows tracking water quality processes at the speed they happen. Information on suspended sediment and chlorophyll concentrations in water can be derived from optical images using simple calculations. The accuracy of these operations will vary across locations and can only be assessed through calibration and validation with in situ data. In absence of such data for all lakes globally, UWQV is based on a small set of algorithms that have been verified on several optically complex water systems to have a close to linear correlation with chlorophyll or suspended sediment concentration. Suspended sediment visualization is based on radiances observed in the 620 or 700 nm spectral bands, while chlorophyll visualization uses fluorescence-based indicators: Fluorescence Line Height, Reflectance Line Height and Maximum Chlorophyll Index. Since remote sensing based chlorophyll retrieval in sediment-laden waters with low transparency is hardly possible, for such cases chlorophyll concentrations are not visualized. The viewer runs as a Custom Script in the Sentinel-Hub EO Browser, which is a global, near real-time satellite data viewing and algorithm testing framework. The Javascript code is open source and enables users to easily tune visualization parameters and select different algorithms for cloud and water masking and chlorophyll and suspended sediment visualization.<br>Wherever in-situ water quality measurements are available, UWQV contributes significant added value by complementing water sample or instrument-based data, providing a map view or even a timelapse of maps; by providing an early warning system for water quality deterioration; by supporting optimization of sampling times and locations based on spatially and temporally explicit information, and  enabling cross-validating water quality information from different sources to reduce uncertainty or identify implausible measurements. Additionally, data-driven spatially explicit models can be verified and tuned based on similarity of their output to situations observed on satellite imagery.<br>UWQV is has all the advantages and drawbacks of a global solution: it will never be more accurate than a locally tuned water quality remote sensing algorithm; however, we hope that it will encourage water quality authorities and stakeholders to initiate the development of locally optimized satellite-based monitoring. By providing easy to read visualizations in a framework accessible to the general public, UWQV can democratize water quality information and raise public awareness of water quality processes and problems.</p><p>The first version of the algorithm is available in the Sentinel-Hub Custom Script Repository under the following link: https://github.com/sentinel-hub/custom-scripts/tree/master/sentinel-2/ulyssys_water_quality_viewer</p><p>An interactive test example of the visualization can be accessed here: tinyurl.com/UWQV-example</p>

Real-time in-situ measurement of haemoglobin in wastewater

2009 ◽  
Vol 60 (7) ◽  
pp. 1683-1689 ◽  
Author(s):  
L. Sutherland-Stacey ◽  
R. Dexter ◽  
B. McWilliams ◽  
K. Watson
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Wastewater Treatment Plants ◽  
Quality Parameters ◽  
High Temporal Resolution ◽  
Grab Sampling ◽  
Time Resolved ◽  
Treatment Processes ◽  
Haemoglobin Measurement

The meat processing industry generates large volumes of relatively high load wastewater. In New Zealand and Australia this wastewater is often pre-treated on site and then discharged to environmental waters or municipal sewers. Owing to the limited number of water quality parameters which can be measured in real-time it is often difficult for industry to optimise treatment processes or public bodies to monitor for water-quality compliance. Abattoir wastewater is often observed to be red in colour, owing to the presence of haemoglobin. Measurement of visible light absorption spectra of wastewater grab samples has for some time provided information about blood concentration. However such grab sampling techniques are piecemeal and cannot provide instantaneous time resolved signals which are required for process control or comprehensive monitoring. In this work an in-situ UV/VIS spectrometer is used to continuously determine the concentration of haemoglobin in wastewater arriving for treatment at two different Wastewater Treatment Plants (WWTPs). The data is of high temporal resolution- data recorded at the distant WWTPs allows for identification process events, such as the end of shift wash downs.

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