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

In the context of monitoring water quality in natural ecosystems in real time, on-line data quality control is a very important issue for effective system surveillance and for optimizing maintenance of the monitoring network. This paper presents some applications of recursive state-parameter estimation algorithms to real-time detection of signal drift in high-frequency observations. Two continuous-discrete recursive estimation schemes, namely the Extended Kalman Filter and the Recursive Prediction Error algorithm, were applied to assuring the quality of the dissolved oxygen (DO) time series, as obtained from the Lagoon of Venice (Italy) during August 2002, through the real-time monitoring network of the Magistrato alle Acque (the Venice Water Authority). Results demonstrate the effectiveness of the methodology in early detection of a probable drift in the DO signal. Comparison of these results with those obtained from the application of a related recursive scheme (a Dynamic Linear Regression procedure) suggests the strong benefits of approaching the problem of on-line data quality control with several (not merely a single) independent such estimation methods.

Download Full-text

In-situ techniques for monitoring drinking water quality

10.5194/egusphere-egu21-12109 ◽

2021 ◽

Author(s):

Inge Elfferich ◽

Elizabeth Bagshaw ◽

Rupert Perkins ◽

Peter Kille ◽

Sophie Straiton ◽

...

Keyword(s):

Water Quality ◽

Drinking Water ◽

Real Time ◽

Early Warning ◽

Drinking Water Quality ◽

In Situ Monitoring ◽

Real Time Monitoring ◽

Early Assessment ◽

Water Taste

<p>Efficient management of drinking water quality is critical for the water supply, so effective monitoring of supply and storage systems is a priority. This project aims to predict the presence of Taste and Odour (T&O) compounds in drinking water reservoirs, using molecular analyses and smart in-situ monitoring systems. The most common T&O compounds, Geosmin and 2-MIB, are secondary metabolites that can be produced in waterbodies by cyanobacteria and actinomycetes and impact drinking water taste and odour. Although there is no evidence of related health risks, they can be perceived by humans at very low concentrations (5-10 ng/L) and the treatment process to remove them from drinking water is costly. Early assessment of T&O risk is crucial, but currently requires time-consuming and costly sampling as well as laboratory analysis which prevents real-time monitoring and a timely management response.</p><p>Cyanobacterial species responsible for T&O production can be monitored with eDNA techniques and potentially provide an early warning of T&O episodes. Moreover, detection of the genes that are responsible for T&O production within the DNA of the freshwater community can help to speed up analysis. We show that qPCR methods can target the Geosmin synthase gene (geoA) and that this correlates significantly with Geosmin concentrations >15 ng/L. Alternatively, in-situ sensors that can be deployed remotely and transmit data, can provide real-time monitoring for early warning and potentially predictive capacity. Commercially available sensors do not currently exist for T&O compounds, but they do for many other water quality parameters. We consider the analytes that could be effective for T&O warning systems, using a Welsh reservoir as an exemplar case. Assessment of nutrient dynamics suggests N and P ratios are critical, hence we evaluate the sensors that are available for these compounds and associated environmental controls on their behaviour. We present recommendations for the design of an in-situ monitoring programme and introduce the planned tests that will evaluate it.</p>

Download Full-text

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

Download Full-text

Real-time monitoring of high-gravity corn mash fermentation using in situ raman spectroscopy

Biotechnology and Bioengineering ◽

10.1002/bit.24849 ◽

2013 ◽

Vol 110 (6) ◽

pp. 1654-1662 ◽

Cited By ~ 18

Author(s):

Steven R. Gray ◽

Steven W. Peretti ◽

H. Henry Lamb

Keyword(s):

Raman Spectroscopy ◽

Real Time ◽

High Gravity ◽

Real Time Monitoring ◽

In Situ Raman Spectroscopy ◽

In Situ Raman ◽

Corn Mash

Download Full-text

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.

Download Full-text