Real-time in-situ measurement of haemoglobin in wastewater

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

Proceedings of the Water Environment Federation ◽

10.2175/193864717822157432 ◽

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

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Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽

10.3390/w13111547 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1547

Author(s):

Jian Sha ◽

Xue Li ◽

Man Zhang ◽

Zhong-Liang Wang

Keyword(s):

Neural Network ◽

Water Quality ◽

Deep Learning ◽

Real Time ◽

Best Management Practices ◽

Input Data ◽

Management Practices ◽

Quality Parameters ◽

Water Quality Parameters ◽

Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

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Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

Wireless Communications and Mobile Computing ◽

10.1155/2021/9403963 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Wei Chen ◽

Xiao Hao ◽

JianRong Lu ◽

Kui Yan ◽

Jin Liu ◽

...

Keyword(s):

Water Quality ◽

Water Temperature ◽

Real Time ◽

Monitoring System ◽

Water Environment ◽

Quality Parameters ◽

Water Quality Parameters ◽

Environment Monitoring ◽

Long Distance ◽

Internet Of Things Technology

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real-time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than -81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

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THE CONTRIBUTION OF GIS TO DISPLAY AND ANALYZE THE WATER QUALITY DATA COLLECTED BY A WIRELESS SENSOR NETWORK: CASE OF BOUREGREG CATCHMENT, MOROCCO

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w4-331-2017 ◽

2017 ◽

Vol IV-4/W4 ◽

pp. 331-334

Author(s):

S. Boubakri ◽

H. Rhinane

Keyword(s):

Water Quality ◽

Wireless Sensor Networks ◽

Wireless Sensor Network ◽

Geographic Information Systems ◽

Real Time ◽

Quality Parameters ◽

Wireless Sensor ◽

Quality Data ◽

Catchment Scale ◽

Water Quality Data

The monitoring of water quality is, in most cases, managed in the laboratory and not on real time bases. Besides this process being lengthy, it doesn’t provide the required specifications to describe the evolution of the quality parameters that are of interest. This study presents the integration of Geographic Information Systems (GIS) with wireless sensor networks (WSN) aiming to create a system able to detect the parameters like temperature, salinity and conductivity in a Moroccan catchment scale and transmit information to the support station. This Information is displayed and evaluated in a GIS using maps and spatial dashboard to monitor the water quality in real time.

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Evaluasi Lahan Pembudidayaan Rumput Laut di Perairan Kampung Sakabu, Pulau Salawati, Kabupaten Raja Ampat

e-Journal BUDIDAYA PERAIRAN ◽

10.35800/bdp.2.3.2014.5705 ◽

2014 ◽

Vol 2 (3) ◽

Author(s):

Wihelmina Dimara ◽

Edwin D Ngangi ◽

Lukas L.J.J Mondoringin

Keyword(s):

Water Quality ◽

Quality Parameters ◽

Water Quality Parameters ◽

Environmental Condition ◽

Protection Factor ◽

Environment Factors ◽

Research Results ◽

Bottom Substrate ◽

Seaweed Culture

The objective of this research was to evaluate the suitability of several environment factors and water quality parameters for development of seaweed culture in Kampung Sakabu. The research was conducted through observation at three stations while protection factor and bottom substrate of waters were observed visually. Water quality parameters including pH, salinity, current rate, temperature were measured in situ and the compared to Standard Water Quality Citeria by Bakosurtanal 1996. Research results were divided into three suitability categories namely 1) very suitable, 2) suitable, and 3) less suitable. In general, environmental condition and water quatily in Kampung Sakabu were categorized as suitable to very suitable. This results indicated that waters of Kampung Sakabu was very potential for development of seaweed culture. Keywords: Kampung Sakabu, seaweeds, area suitability, water quality

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

10.5194/egusphere-egu21-9961 ◽

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

Keywords: eutrophication; high resolution ocean modeling ; Chla satellite data ; biogeochemistryMaliakos 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.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.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.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.

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Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

Remote Sensing ◽

10.3390/rs12101586 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1586

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

Rodrigo Sepúlveda ◽

Sergio I. Martinez-Martinez ◽

Markus Disse

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Water Quality ◽

Water Quality Monitoring ◽

Temporal Variations ◽

Quality Parameters ◽

Parameters Estimation ◽

Learning Approaches ◽

Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

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Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes

Remote Sensing ◽

10.3390/rs12111745 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1745

Author(s):

Michael Seidel ◽

Christopher Hutengs ◽

Felix Oertel ◽

Daniel Schwefel ◽

András Jung ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Water Column ◽

Hyperspectral Imaging ◽

Quality Parameters ◽

Optically Active ◽

Freshwater Lakes ◽

Active Substances ◽

Optically Active Substances

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.

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Time-resolved in situ monitoring of photocatalytic reactions by probe electrospray ionization mass spectrometry

The Analyst ◽

10.1039/d0an00305k ◽

2020 ◽

Vol 145 (9) ◽

pp. 3313-3319

Author(s):

Zhongbao Han ◽

Xiaoyu Gu ◽

Shirong Wang ◽

Liyan Liu ◽

Ying Wang ◽

...

Keyword(s):

Mass Spectrometry ◽

Electrospray Ionization ◽

Real Time ◽

Electrospray Ionization Mass Spectrometry ◽

In Situ Monitoring ◽

Ionization Mass ◽

Time Resolved ◽

Photocatalytic Reactions ◽

Probe Electrospray Ionization

We report the application of PESI-MS to in situ monitoring of photocatalytic reactions of cationic dyes in suspensions in real-time.

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Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

Journal of Unmanned Vehicle Systems ◽

10.1139/juvs-2019-0020 ◽

2020 ◽

Vol 8 (3) ◽

pp. 172-185

Author(s):

Juan G. Arango ◽

Brandon K. Holzbauer-Schweitzer ◽

Robert W. Nairn ◽

Robert C. Knox

Keyword(s):

Water Quality ◽

Near Infrared ◽

Quality Parameters ◽

Water Quality Parameters ◽

Quality Data ◽

Electromagnetic Spectrum ◽

Water Quality Data ◽

Ground Control Points ◽

Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

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