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Singh Y, Walingo T. Smart Water Quality Monitoring with IoT Wireless Sensor Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:2871. [PMID: 38732981 PMCID: PMC11086156 DOI: 10.3390/s24092871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/03/2024] [Accepted: 01/11/2024] [Indexed: 05/13/2024]
Abstract
Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.
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Affiliation(s)
- Yurav Singh
- Discipline of Electrical Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4000, South Africa
| | - Tom Walingo
- Discipline of Electrical Electronic and Computer Engineering, University of KwaZulu-Natal, Durban 4000, South Africa
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2
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Briciu-Burghina C, Power S, Delgado A, Regan F. Sensors for Coastal and Ocean Monitoring. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:451-469. [PMID: 37314875 DOI: 10.1146/annurev-anchem-091922-085746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In situ water monitoring sensors are critical to gain an understanding of ocean biochemistry and ecosystem health. They enable the collection of high-frequency data and capture ecosystem spatial and temporal changes, which in turn facilitate long-term global predictions. They are used as decision support tools in emergency situations and for risk mitigation, pollution source tracking, and regulatory monitoring. Advanced sensing platforms exist to support various monitoring needs together with state-of-the-art power and communication capabilities. To be fit-for-purpose, sensors must withstand the challenging marine environment and provide data at an acceptable cost. Significant technological advancements have catalyzed the development of new and improved sensors for coastal and oceanographic applications. Sensors are becoming smaller, smarter, more cost-effective, and increasingly specialized and diversified. This article, therefore, provides a review of the state-of-the art oceanographic and coastal sensors. Progress in sensor development is discussed in terms of performance and the key strategies used for achieving robustness, marine rating, cost reduction, and antifouling protection.
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Affiliation(s)
| | - Sean Power
- DCU Water Institute, School of Chemical Sciences, Dublin City University, Dublin, Ireland;
| | - Adrian Delgado
- DCU Water Institute, School of Chemical Sciences, Dublin City University, Dublin, Ireland;
| | - Fiona Regan
- DCU Water Institute, School of Chemical Sciences, Dublin City University, Dublin, Ireland;
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Paepae T, Bokoro PN, Kyamakya K. Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:1061. [PMID: 36772100 PMCID: PMC9920320 DOI: 10.3390/s23031061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
To better control eutrophication, reliable and accurate information on phosphorus and nitrogen loading is desired. However, the high-frequency monitoring of these variables is economically impractical. This necessitates using virtual sensing to predict them by utilizing easily measurable variables as inputs. While the predictive performance of these data-driven, virtual-sensor models depends on the use of adequate training samples (in quality and quantity), the procurement and operational cost of nitrogen and phosphorus sensors make it impractical to acquire sufficient samples. For this reason, the variational autoencoder, which is one of the most prominent methods in generative models, was utilized in the present work for generating synthetic data. The generation capacity of the model was verified using water-quality data from two tributaries of the River Thames in the United Kingdom. Compared to the current state of the art, our novel data augmentation-including proper experimental settings or hyperparameter optimization-improved the root mean squared errors by 23-63%, with the most significant improvements observed when up to three predictors were used. In comparing the predictive algorithms' performances (in terms of the predictive accuracy and computational cost), k-nearest neighbors and extremely randomized trees were the best-performing algorithms on average.
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Affiliation(s)
- Thulane Paepae
- Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Doornfontein 2028, South Africa
| | - Pitshou N. Bokoro
- Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Doornfontein 2028, South Africa
| | - Kyandoghere Kyamakya
- Institute for Smart Systems Technologies, Transportation Informatics, Alpen-Adria Universität Klagenfurt, 9020 Klagenfurt, Austria
- Faculté Polytechnique, Université de Kinshasa, P.O. Box 127, Kinshasa XI, Democratic Republic of the Congo
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Fulton SG, Stegen JC, Kaufman MH, Dowd J, Thompson A. Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare? PLoS One 2023; 18:e0285092. [PMID: 37141332 PMCID: PMC10159144 DOI: 10.1371/journal.pone.0285092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 04/15/2023] [Indexed: 05/06/2023] Open
Abstract
Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.
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Affiliation(s)
- Stephanie G Fulton
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
- Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia, United States of America
| | - James C Stegen
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
- School of the Environment, Washington State University, Pullman, Washington, United States of America
| | - Matthew H Kaufman
- Pacific Northwest National Laboratory, Richland, Washington, United States of America
| | - John Dowd
- Geology Department, University of Georgia, Athens, Georgia, United States of America
| | - Aaron Thompson
- Department of Crop and Soil Sciences, University of Georgia, Athens, Georgia, United States of America
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Bogomolov A, Evseeva A, Ignatiev E, Korneev V. New approaches to data processing and analysis in optical sensing. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Butt MA, Voronkov GS, Grakhova EP, Kutluyarov RV, Kazanskiy NL, Khonina SN. Environmental Monitoring: A Comprehensive Review on Optical Waveguide and Fiber-Based Sensors. BIOSENSORS 2022; 12:bios12111038. [PMID: 36421155 PMCID: PMC9688474 DOI: 10.3390/bios12111038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 05/31/2023]
Abstract
Globally, there is active development of photonic sensors incorporating multidisciplinary research. The ultimate objective is to develop small, low-cost, sensitive, selective, quick, durable, remote-controllable sensors that are resistant to electromagnetic interference. Different photonic sensor designs and advances in photonic frameworks have shown the possibility to realize these capabilities. In this review paper, the latest developments in the field of optical waveguide and fiber-based sensors which can serve for environmental monitoring are discussed. Several important topics such as toxic gas, water quality, indoor environment, and natural disaster monitoring are reviewed.
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Affiliation(s)
| | - Grigory S Voronkov
- Ufa University of Science and Technology, Z. Validi St. 32, 450076 Ufa, Russia
| | | | - Ruslan V Kutluyarov
- Ufa University of Science and Technology, Z. Validi St. 32, 450076 Ufa, Russia
| | - Nikolay L Kazanskiy
- Samara National Research University, 443086 Samara, Russia
- IPSI RAS-Branch of the FSRC "Crystallography and Photonics" RAS, 443001 Samara, Russia
| | - Svetlana N Khonina
- Samara National Research University, 443086 Samara, Russia
- IPSI RAS-Branch of the FSRC "Crystallography and Photonics" RAS, 443001 Samara, Russia
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Zainurin SN, Wan Ismail WZ, Mahamud SNI, Ismail I, Jamaludin J, Ariffin KNZ, Wan Ahmad Kamil WM. Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14080. [PMID: 36360992 PMCID: PMC9653618 DOI: 10.3390/ijerph192114080] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/06/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.
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Affiliation(s)
- Siti Nadhirah Zainurin
- Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
| | - Wan Zakiah Wan Ismail
- Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
| | - Siti Nurul Iman Mahamud
- TF AMD Microelectronics Sdn Bhd, Kawasan Perindustrian Bayan Lepas, Bayan Lepas 11900, Pulau Pinang, Malaysia
| | - Irneza Ismail
- Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
| | - Juliza Jamaludin
- Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
| | - Khairul Nabilah Zainul Ariffin
- Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
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Paepae T, Bokoro PN, Kyamakya K. A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2022; 22:7338. [PMID: 36236438 PMCID: PMC9572788 DOI: 10.3390/s22197338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/20/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
Harmful cyanobacterial bloom (HCB) is problematic for drinking water treatment, and some of its strains can produce toxins that significantly affect human health. To better control eutrophication and HCB, catchment managers need to continuously keep track of nitrogen (N) and phosphorus (P) in the water bodies. However, the high-frequency monitoring of these water quality indicators is not economical. In these cases, machine learning techniques may serve as viable alternatives since they can learn directly from the available surrogate data. In the present work, a random forest, extremely randomized trees (ET), extreme gradient boosting, k-nearest neighbors, a light gradient boosting machine, and bagging regressor-based virtual sensors were used to predict N and P in two catchments with contrasting land uses. The effect of data scaling and missing value imputation were also assessed, while the Shapley additive explanations were used to rank feature importance. A specification book, sensitivity analysis, and best practices for developing virtual sensors are discussed. Results show that ET, MinMax scaler, and a multivariate imputer were the best predictive model, scaler, and imputer, respectively. The highest predictive performance, reported in terms of R2, was 97% in the rural catchment and 82% in an urban catchment.
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Affiliation(s)
- Thulane Paepae
- Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Doornfontein 2028, South Africa
| | - Pitshou N. Bokoro
- Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Doornfontein 2028, South Africa
| | - Kyandoghere Kyamakya
- Institute for Smart Systems Technologies, Transportation Informatics, Alpen-Adria Universität Klagenfurt, 9020 Klagenfurt, Austria
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9
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Convolutional Neural Network for Measurement of Suspended Solids and Turbidity. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The great potential of the convolutional neural networks (CNNs) provides novel and alternative ways to monitor important parameters with high accuracy. In this study, we developed a soft sensor model for dynamic processes based on a CNN for the measurement of suspended solids and turbidity from a single image of the liquid sample to be measured by using a commercial smartphone camera (Android or IOS system) and light-emitting diode (LED) illumination. For this, an image dataset of liquid samples illuminated with white, red, green, and blue LED light was taken and used to train a CNN and fit a multiple linear regression (MLR) by using different color lighting, we evaluated which color gives more accurate information about the concentration of suspended particles in the sample. We implemented a pre-trained AlexNet model, and an MLR to estimate total suspended solids (TSS), and turbidity values in liquid samples based on suspended particles. The proposed technique obtained high goodness of fit (R2 = 0.99). The best performance was achieved using white light, with an accuracy of 98.24% and 97.20% for TSS and turbidity, respectively, with an operational range of 0–800 mgL−1, and 0–306 NTU. This system was designed for aquaculture environments and tested with both commercial fish feed and paprika. This motivates further research with different aquatic environments such as river water, domestic and industrial wastewater, and potable water, among others.
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10
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Advances in Technological Research for Online and In Situ Water Quality Monitoring—A Review. SUSTAINABILITY 2022. [DOI: 10.3390/su14095059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Monitoring water quality is an essential tool for the control of pollutants and pathogens that can cause damage to the environment and human health. However, water quality analysis is usually performed in laboratory environments, often with the use of high-cost equipment and qualified professionals. With the progress of nanotechnology and the advance in engineering materials, several studies have shown, in recent years, the development of technologies aimed at monitoring water quality, with the ability to reduce the costs of analysis and accelerate the achievement of results for management and decision-making. In this work, a review was carried out on several low-cost developed technologies and applied in situ for water quality monitoring. Thus, new alternative technologies for the main physical (color, temperature, and turbidity), chemical (chlorine, fluorine, phosphorus, metals, nitrogen, dissolved oxygen, pH, and oxidation–reduction potential), and biological (total coliforms, Escherichia coli, algae, and cyanobacteria) water quality parameters were described. It was observed that there has been an increase in the number of publications related to the topic in recent years, mainly since 2012, with 641 studies being published in 2021. The main new technologies developed are based on optical or electrochemical sensors, however, due to the recent development of these technologies, more robust analyses and evaluations in real conditions are essential to guarantee the precision and repeatability of the methods, especially when it is desirable to compare the values with government regulatory standards.
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Kumar A, Al-Jumaili A, Bazaka O, Ivanova EP, Levchenko I, Bazaka K, Jacob MV. Functional nanomaterials, synergisms, and biomimicry for environmentally benign marine antifouling technology. MATERIALS HORIZONS 2021; 8:3201-3238. [PMID: 34726218 DOI: 10.1039/d1mh01103k] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Marine biofouling remains one of the key challenges for maritime industries, both for seafaring and stationary structures. Currently used biocide-based approaches suffer from significant drawbacks, coming at a significant cost to the environment into which the biocides are released, whereas novel environmentally friendly approaches are often difficult to translate from lab bench to commercial scale. In this article, current biocide-based strategies and their adverse environmental effects are briefly outlined, showing significant gaps that could be addressed through advanced materials engineering. Current research towards the use of natural antifouling products and strategies based on physio-chemical properties is then reviewed, focusing on the recent progress and promising novel developments in the field of environmentally benign marine antifouling technologies based on advanced nanocomposites, synergistic effects and biomimetic approaches are discussed and their benefits and potential drawbacks are compared to existing techniques.
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Affiliation(s)
- Avishek Kumar
- Electronics Materials Lab, College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia.
| | - Ahmed Al-Jumaili
- Electronics Materials Lab, College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia.
- Medical Physics Department, College of Medical Sciences Techniques, The University of Mashreq, Baghdad, Iraq
| | - Olha Bazaka
- School of Science, RMIT University, PO Box 2476, Melbourne, VIC 3001, Australia
| | - Elena P Ivanova
- School of Science, RMIT University, PO Box 2476, Melbourne, VIC 3001, Australia
| | - Igor Levchenko
- Plasma Sources and Application Centre, NIE, Nanyang Technological University, 637616, Singapore
| | - Kateryna Bazaka
- Electronics Materials Lab, College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia.
- Faculty of Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
- School of Engineering, The Australian National University, Canberra, ACT 2601, Australia
| | - Mohan V Jacob
- Electronics Materials Lab, College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia.
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Development of an Optical Method to Monitor Nitrification in Drinking Water. SENSORS 2021; 21:s21227525. [PMID: 34833600 PMCID: PMC8618176 DOI: 10.3390/s21227525] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022]
Abstract
Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH2Cl) residual. The decay of monochloramine releases ammonia (NH3), which is converted to nitrite (NO2-) and nitrate (NO3-) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L-1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.
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Paepae T, Bokoro PN, Kyamakya K. From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art. SENSORS (BASEL, SWITZERLAND) 2021; 21:6971. [PMID: 34770278 PMCID: PMC8587795 DOI: 10.3390/s21216971] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/17/2021] [Accepted: 10/17/2021] [Indexed: 12/17/2022]
Abstract
Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.
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Affiliation(s)
- Thulane Paepae
- Department of Mathematics and Applied Mathematics, University of Johannesburg, Doornfontein 2028, South Africa;
| | - Pitshou N. Bokoro
- Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Doornfontein 2028, South Africa
| | - Kyandoghere Kyamakya
- Institute for Smart Systems Technologies, Transportation Informatics Group, Alpen-Adria Universität Klagenfurt, 9020 Klagenfurt, Austria;
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How does the Internet of Things (IoT) help in microalgae biorefinery? Biotechnol Adv 2021; 54:107819. [PMID: 34454007 DOI: 10.1016/j.biotechadv.2021.107819] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 07/27/2021] [Accepted: 08/22/2021] [Indexed: 12/14/2022]
Abstract
Microalgae biorefinery is a platform for the conversion of microalgal biomass into a variety of value-added products, such as biofuels, bio-based chemicals, biomaterials, and bioactive substances. Commercialization and industrialization of microalgae biorefinery heavily rely on the capability and efficiency of large-scale cultivation of microalgae. Thus, there is an urgent need for novel technologies that can be used to monitor, automatically control, and precisely predict microalgae production. In light of this, innovative applications of the Internet of things (IoT) technologies in microalgae biorefinery have attracted tremendous research efforts. IoT has potential applications in a microalgae biorefinery for the automatic control of microalgae cultivation, monitoring and manipulation of microalgal cultivation parameters, optimization of microalgae productivity, identification of toxic algae species, screening of target microalgae species, classification of microalgae species, and viability detection of microalgal cells. In this critical review, cutting-edge IoT technologies that could be adopted to microalgae biorefinery in the upstream and downstream processing are described comprehensively. The current advances of the integration of IoT with microalgae biorefinery are presented. What this review discussed includes automation, sensors, lab-on-chip, and machine learning, which are the main constituent elements and advanced technologies of IoT. Specifically, future research directions are discussed with special emphasis on the development of sensors, the application of microfluidic technology, robotized microalgae, high-throughput platforms, deep learning, and other innovative techniques. This review could contribute greatly to the novelty and relevance in the field of IoT-based microalgae biorefinery to develop smarter, safer, cleaner, greener, and economically efficient techniques for exhaustive energy recovery during the biorefinery process.
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Mathematical Modelling of Biosensing Platforms Applied for Environmental Monitoring. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9030050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In recent years, mathematical modelling has known an overwhelming integration in different scientific fields. In general, modelling is used to obtain new insights and achieve more quantitative and qualitative information about systems by programming language, manipulating matrices, creating algorithms and tracing functions and data. Researchers have been inspired by these techniques to explore several methods to solve many problems with high precision. In this direction, simulation and modelling have been employed for the development of sensitive and selective detection tools in different fields including environmental control. Emerging pollutants such as pesticides, heavy metals and pharmaceuticals are contaminating water resources, thus threatening wildlife. As a consequence, various biosensors using modelling have been reported in the literature for efficient environmental monitoring. In this review paper, the recent biosensors inspired by modelling and applied for environmental monitoring will be overviewed. Moreover, the level of success and the analytical performances of each modelling-biosensor will be discussed. Finally, current challenges in this field will be highlighted.
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Pattnaik BS, Pattanayak AS, Udgata SK, Panda AK. Machine learning based soft sensor model for BOD estimation using intelligence at edge. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-020-00259-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractReal-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.
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17
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Wastewater Quality Estimation Through Spectrophotometry-Based Statistical Models. SENSORS 2020; 20:s20195631. [PMID: 33019750 PMCID: PMC7582758 DOI: 10.3390/s20195631] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/28/2020] [Accepted: 09/29/2020] [Indexed: 12/02/2022]
Abstract
Local administrations are increasingly demanding real-time continuous monitoring of pollution in the sanitation system to improve and optimize its operation, to comply with EU environmental policies and to reach European Green Deal targets. The present work shows a full-scale Wastewater Treatment Plant field-sampling campaign to estimate COD, BOD5, TSS, P, TN and NO3−N in both influent and effluent, in the absence of pre-treatment or chemicals addition to the samples, resulting in a reduction of the duration and cost of analysis. Different regression models were developed to estimate the pollution load of sewage systems from the spectral response of wastewater samples measured at 380–700 nm through multivariate linear regressions and machine learning genetic algorithms. The tests carried out concluded that the models calculated by means of genetic algorithms can estimate the levels of five of the pollutants under study (COD, BOD5, TSS, TN and NO3−N), including both raw and treated wastewater, with an error rate below 4%. In the case of the multilinear regression models, these are limited to raw water and the estimate is limited to COD and TSS, with less than a 0.5% error rate.
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18
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Mao F, Khamis K, Clark J, Krause S, Buytaert W, Ochoa-Tocachi BF, Hannah DM. Moving beyond the Technology: A Socio-technical Roadmap for Low-Cost Water Sensor Network Applications. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:9145-9158. [PMID: 32628837 DOI: 10.1021/acs.est.9b07125] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this paper, we critically review the current state-of-the-art for sensor network applications and approaches that have developed in response to the recent rise of low-cost technologies. We specifically focus on water-related low-cost sensor networks, and conceptualize them as socio-technical systems that can address resource management challenges and opportunities at three scales of resolution: (1) technologies, (2) users and scenarios, and (3) society and communities. Building this argument, first we identify a general structure for building low-cost sensor networks by assembling technical components across configuration levels. Second, we identify four application categories, namely operational monitoring, scientific research, system optimization, and community development, each of which has different technical and nontechnical configurations that determine how, where, by whom, and for what purpose low-cost sensor networks are used. Third, we discuss the governance factors (e.g., stakeholders and users, networks sustainability and maintenance, application scenarios, and integrated design) and emerging technical opportunities that we argue need to be considered to maximize the added value and long-term societal impact of the next generation of sensor network applications. We conclude that consideration of the full range of socio-technical issues is essential to realize the full potential of sensor network technologies for society and the environment.
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Affiliation(s)
- Feng Mao
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, U.K
| | - Kieran Khamis
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, U.K
| | - Julian Clark
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, U.K
| | - Stefan Krause
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, U.K
| | - Wouter Buytaert
- Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, U.K
- Grantham Institute - Climate Change and the Environment, Imperial College London, London SW7 2AZ, U.K
| | - Boris F Ochoa-Tocachi
- Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, U.K
- Grantham Institute - Climate Change and the Environment, Imperial College London, London SW7 2AZ, U.K
- Regional Initiative for Hydrological Monitoring of Andean Ecosystems, Lima, Peru
| | - David M Hannah
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, U.K
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19
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Performing Calibration of Transmittance by Single RGB-LED within the Visible Spectrum. SENSORS 2020; 20:s20123492. [PMID: 32575743 PMCID: PMC7349540 DOI: 10.3390/s20123492] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 06/02/2020] [Accepted: 06/17/2020] [Indexed: 12/04/2022]
Abstract
Spectrophotometry has proven to be an effective non-invasive technique for the characterization of the pollution load of sewer systems, enabling compliance with new environmental protection regulations. This type of equipment has costs and an energy consumption which make it difficult to place it inside a sewer network for real-time and massive monitoring. These shortcomings are mainly due to the use of incandescent lamps to generate the working spectrum as they often require the use of optical elements, such as diffraction gratings, to work. The search for viable alternatives to incandescent lamps is key to the development of portable equipment that is cheaper and with a lower consumption that can be used in different points of the sewer network. This research work achieved the following results in terms of the measured samples: First, the development a calibration procedure that enables the use of RGB-LED technology as a viable alternative to incandescent lamps, within the range of 510 to 645 nm, with high accuracy. Secondly, demonstration of a simple method to model the transmittance value of a specific wavelength without the need for optical elements, achieving a cost-effective equipment. Thirdly, it provides a simple method to obtain the transmittance based on the combination of RGB colors. Finally its viability is demonstrated for the spectral analysis of wastewater.
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20
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Yaroshenko I, Kirsanov D, Marjanovic M, Lieberzeit PA, Korostynska O, Mason A, Frau I, Legin A. Real-Time Water Quality Monitoring with Chemical Sensors. SENSORS 2020; 20:s20123432. [PMID: 32560552 PMCID: PMC7349867 DOI: 10.3390/s20123432] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/12/2020] [Accepted: 06/14/2020] [Indexed: 02/07/2023]
Abstract
Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described.
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Affiliation(s)
- Irina Yaroshenko
- Institute of Chemistry, St. Petersburg State University, Mendeleev Center, Universitetskaya nab. 7/9, 199034 St. Petersburg, Russia; (I.Y.); (A.L.)
| | - Dmitry Kirsanov
- Institute of Chemistry, St. Petersburg State University, Mendeleev Center, Universitetskaya nab. 7/9, 199034 St. Petersburg, Russia; (I.Y.); (A.L.)
- Correspondence: ; Tel.: +7-921-333-1246
| | - Monika Marjanovic
- Faculty for Chemistry, Department of Physical Chemistry, University of Vienna, Waehringer Strasse 42, 1090 Vienna, Austria; (M.M.); (P.A.L.)
| | - Peter A. Lieberzeit
- Faculty for Chemistry, Department of Physical Chemistry, University of Vienna, Waehringer Strasse 42, 1090 Vienna, Austria; (M.M.); (P.A.L.)
| | - Olga Korostynska
- Faculty of Technology, Art and Design, Department of Mechanical, Electronic and Chemical Engineering, Oslo Metropolitan University, 0166 Oslo, Norway;
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway;
| | - Alex Mason
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway;
- Animalia AS, Norwegian Meat and Poultry Research Centre, P.O. Box 396, 0513 Økern, Oslo, Norway
- Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK;
| | - Ilaria Frau
- Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK;
| | - Andrey Legin
- Institute of Chemistry, St. Petersburg State University, Mendeleev Center, Universitetskaya nab. 7/9, 199034 St. Petersburg, Russia; (I.Y.); (A.L.)
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21
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Elmas S, Pospisilova A, Sekulska AA, Vasilev V, Nann T, Thornton S, Priest C. Photometric Sensing of Active Chlorine, Total Chlorine, and pH on a Microfluidic Chip for Online Swimming Pool Monitoring. SENSORS 2020; 20:s20113099. [PMID: 32486236 PMCID: PMC7308966 DOI: 10.3390/s20113099] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 05/27/2020] [Accepted: 05/28/2020] [Indexed: 12/20/2022]
Abstract
A microfluidic sensor was studied for the photometric detection of active chlorine, total chlorine, and pH in swimming pool samples. The sensor consisted of a four-layer borosilicate glass chip, containing a microchannel network and a 2.2 mm path length, 1.7 mL optical cell. The chip was optimised to measure the bleaching of methyl orange and spectral changes in phenol red for quantitative chlorine (active and total) and pH measurements that were suited to swimming pool monitoring. Reagent consumption (60 mL per measurement) was minimised to allow for maintenance-free operation over a nominal summer season (3 months) with minimal waste. The chip was tested using samples from 12 domestic, public, and commercial swimming pools (indoor and outdoor), with results that compare favourably with commercial products (test strips and the N,N'-diethyl-p-phenylenediamine (DPD) method), precision pH electrodes, and iodometric titration.
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Affiliation(s)
- Sait Elmas
- Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia; (S.E.); (A.P.); (A.A.S.); (V.V.); (T.N.)
- Institute for Nanoscale Science & Technology, College of Science & Engineering, Flinders University, Sturt Road, Bedford Park, SA 5042, Australia
| | - Aneta Pospisilova
- Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia; (S.E.); (A.P.); (A.A.S.); (V.V.); (T.N.)
| | - Aneta Anna Sekulska
- Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia; (S.E.); (A.P.); (A.A.S.); (V.V.); (T.N.)
| | - Vasil Vasilev
- Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia; (S.E.); (A.P.); (A.A.S.); (V.V.); (T.N.)
| | - Thomas Nann
- Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia; (S.E.); (A.P.); (A.A.S.); (V.V.); (T.N.)
- School of Mathematical and Physical Sciences, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia
| | - Stephen Thornton
- Tekelek Australia Pty Ltd., 95A Bedford St, Gillman, SA 5013, Australia;
| | - Craig Priest
- Future Industries Institute, University of South Australia, Mawson Lakes, SA 5095, Australia; (S.E.); (A.P.); (A.A.S.); (V.V.); (T.N.)
- School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia
- Correspondence:
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22
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Zhu Y, Cao P, Liu S, Zheng Y, Huang C. Development of a New Method for Turbidity Measurement Using Two NIR Digital Cameras. ACS OMEGA 2020; 5:5421-5428. [PMID: 32201833 PMCID: PMC7081420 DOI: 10.1021/acsomega.9b04488] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 02/20/2020] [Indexed: 06/10/2023]
Abstract
This paper proposes a new method of using two NIR digital cameras to measure water turbidity accurately and quickly. A measuring device based on an NIR camera and image processing software is designed. Two NIR cameras collect scattered and transmitted images when the NIR light is passing through the turbid solution. The average RGB values of 400 pixels in the central region of the image are obtained and converted into CIE Lab color space values. The water turbidity was measured by the functional relationship between turbidity and the corresponding color components (R, G, B, L, a, b, and grayscale). The results of comparison with a commercial turbidimeter show that this method has a high accuracy for the determination of standard solution with wider linear range and is consistent with the turbidimeter results for the measurement of real samples, which verifies the feasibility of this method.
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Affiliation(s)
- Yuanyang Zhu
- College
of Computer Science and Technology, Huaibei
Normal University, Huaibei 235000, China
| | - Pingping Cao
- College
of Computer Science and Technology, Huaibei
Normal University, Huaibei 235000, China
| | - Sheng Liu
- College
of Computer Science and Technology, Huaibei
Normal University, Huaibei 235000, China
| | - Ying Zheng
- College
of Computer Science and Technology, Huaibei
Normal University, Huaibei 235000, China
| | - Chaoqun Huang
- Anhui
Province Key Laboratory of Medical Physics and Technology, Center
of Medical Physics and Technology, Hefei
Institutes of Physical Science, Chinese Academy of Science, Hefei 230031, China
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23
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Zolfaghari K, Wilkes G, Bird S, Ellis D, Pintar KDM, Gottschall N, McNairn H, Lapen DR. Chlorophyll-a, dissolved organic carbon, turbidity and other variables of ecological importance in river basins in southern Ontario and British Columbia, Canada. ENVIRONMENTAL MONITORING AND ASSESSMENT 2019; 192:67. [PMID: 31879802 DOI: 10.1007/s10661-019-7800-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 06/03/2019] [Indexed: 06/10/2023]
Abstract
Optical sensing of chlorophyll-a (chl-a), turbidity, and fluorescent dissolved organic matter (fDOM) is often used to characterize the quality of water. There are many site-specific factors and environmental conditions that can affect optically sensed readings; notwithstanding the comparative implication of different procedures used to measure these properties in the laboratory. In this study, we measured these water quality properties using standard laboratory methods, and in the field using optical sensors (sonde-based) at water quality monitoring sites located in four watersheds in Canada. The overall objective of this work was to explore the relationships among sonde-based and standard laboratory measurements of the aforementioned water properties, and evaluate associations among these eco-hydrological properties and land use, environmental, and ancillary water quality variables such as dissolved organic carbon (DOC) and total suspended solids (TSS). Differences among sonde versus laboratory relationships for chl-a suggest such relationships are impacted by laboratory methods and/or site specific conditions. Data mining analysis indicated that interactive site-specific factors predominately impacting chl-a values across sites were specific conductivity and turbidity (variables with positive global associations with chl-a). The overall linear regression predicting DOC from fDOM was relatively strong (R2 = 0.77). However, slope differences in the watershed-specific models suggest laboratory DOC versus fDOM relationships could be impacted by unknown localized water quality properties affecting fDOM readings, and/or the different standard laboratory methods used to estimate DOC. Artificial neural network analyses (ANN) indicated that higher relative chl-a concentrations were associated with low to no tree cover around sample sites and higher daily rainfall in the watersheds examined. Response surfaces derived from ANN indicated that chl-a concentrations were higher where combined agricultural and urban land uses were relatively higher.
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Affiliation(s)
- K Zolfaghari
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - G Wilkes
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - S Bird
- Fluvial Systems Research Inc., White Rock, BC, Canada
| | - D Ellis
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | | | - N Gottschall
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - H McNairn
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - D R Lapen
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada.
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24
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Development of Microalgae Biosensor Chip by Incorporating Microarray Oxygen Sensor for Pesticides Sensing. BIOSENSORS-BASEL 2019; 9:bios9040133. [PMID: 31726653 PMCID: PMC6956216 DOI: 10.3390/bios9040133] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/08/2019] [Accepted: 11/08/2019] [Indexed: 01/24/2023]
Abstract
A microalgae (Pseudokirchneriella subcapitata) biosensor chip for pesticide sensing has been developed by attaching the immobilized microalgae biofilm pon the microarray dye spots (size 100 μm and pitch 200 μm). The dye spots (ruthenium complex) were printed upon SO3-modified glass slides using a polydimethylsiloxane (PDMS) stamp and a microcontact printer (μCP). Emitted fluorescence intensity (FI) variance due to photosynthetic activity (O2 production) of microalgae was monitored by an inverted fluorescent microscope and inhibition of the oxygen generation rate was calculated based on the FI responses both before and after injection of pesticide sample. The calibration curves, as the inhibition of oxygen generation rate (%) due to photosynthetic activity inhibition by the pesticides, depicted that among the 6 tested pesticides, the biosensor showed good sensitivity for 4 pesticides (diuron, simetryn, simazine, and atrazine) but was insensitive for mefenacet and pendimethalin. The detection limits were 1 ppb for diuron and 10 ppb for simetryn, simazine, and atrazine. The simple and low-cost nature of sensing of the developed biosensor sensor chip has apparently created opportunities for regular water quality monitoring, where pesticides are an important concern.
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25
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Assessment of Antifouling Potential of Novel Transparent Sol Gel Coatings for Application in the Marine Environment. Molecules 2019; 24:molecules24162983. [PMID: 31426449 PMCID: PMC6719174 DOI: 10.3390/molecules24162983] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 08/11/2019] [Accepted: 08/14/2019] [Indexed: 11/17/2022] Open
Abstract
In recent years, there has become a growing need for the development of antifouling technology for application in the marine environment. The accumulation of large quantities of biomass on these surfaces cause substantial economic burdens within the marine industry, or adversely impact the performance of sensor technologies. Here, we present a study of transparent coatings with potential for applications on sensors or devices with optical windows. The focus of the study is on the abundance and diversity of biofouling organisms that accumulate on glass panels coated with novel transparent or opaque organically modified silicate (ORMOSIL) coatings. The diatom assessment was used to determine the effectiveness of the coatings against biofouling. Test panels were deployed in a marine environment in Galway Bay for durations of nine and thirteen months to examine differences in biofilm formation in both microfouling and macrofouling conditions. The most effective coating is one which consists of precursor, tetraethyl orthosilicate (HC006) that has a water contact angle > 100, without significant roughness (43.52 nm). However, improved roughness and wettability of a second coating, diethoxydimethylsilane (DMDEOS), showed real promise in relation to macrofouling reduction.
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26
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Gillett D, Marchiori A. A Low-Cost Continuous Turbidity Monitor. SENSORS 2019; 19:s19143039. [PMID: 31295890 PMCID: PMC6679002 DOI: 10.3390/s19143039] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 06/24/2019] [Accepted: 07/08/2019] [Indexed: 11/16/2022]
Abstract
Turbidity describes the cloudiness, or clarity, of a liquid. It is a principal indicator of water quality, sensitive to any suspended solids present. Prior work has identified the lack of low-cost turbidity monitoring as a significant hurdle to overcome to improve water quality in many domains, especially in the developing world. Low-cost hand-held benchtop meters have been proposed. This work adapts and verifies the technology for continuous monitoring. Lab tests show the low-cost continuous monitor can achieve 1 nephelometric turbidity unit (NTU) accuracy in the range 0–100 NTU and costs approximately 64 USD in components to construct. This level of accuracy yields useful and actionable data about water quality and may be sufficient in certain applications where cost is a primary constraint. A 38-day continuous monitoring trial, including a step change in turbidity, showed promising results with a median error of 0.45 and 1.40 NTU for two different monitors. However, some noise was present in the readings resulting in a standard deviation of 1.90 and 6.55 NTU, respectively. The cause was primarily attributed to ambient light and bubbles in the piping. By controlling these noise sources, we believe the low-cost continuous turbidity monitor could be a useful tool in multiple domains.
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Affiliation(s)
- David Gillett
- Department of Chemical Engineering, Bucknell University, Lewisburg, PA 17837, USA
| | - Alan Marchiori
- Department of Computer Science, Bucknell University, Lewisburg, PA 17837, USA.
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27
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Nayeem H, Syed A, Khan MZA. Low Cost Wavelength Specific Water Quality Measurement Technique. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:1175-1178. [PMID: 31946103 DOI: 10.1109/embc.2019.8857381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Optical sensing for chemical analysis is emerging as it provides advantages such as good sensitivity, selectivity, electromagnetic immunity, etc. This work presents a low-cost, robust and easy to use technique for measurement of bulk water property changes, specifically pH, total dissolved solids (TDS), and turbidity. The designed multi-wavelength sensing mechanism is capable of measuring the absorption of light emitted by three different LEDs after passing through water. The optical responses obtained using this mechanism are then related to parameter changes of water for quality measurement. The results show that measurements for pH, TDS, and turbidity have a linear regression coefficient of 0.9691, 0.9729 and 0.76 respectively. By utilizing narrowband light sources of characteristic wavelengths for the target parameters, a compact and portable device can be designed for rapid measurements. This can work as a replacement of spectrophotometers for parameter specific measurements of water quality and a low cost prototype (costing ~ 20 $) for the same has been demonstrated.
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28
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Designing the National Network for Automatic Monitoring of Water Quality Parameters in Greece. WATER 2019. [DOI: 10.3390/w11061310] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Water quality indices that describe the status of water are commonly used in freshwater vulnerability assessment. The design of river water quality monitoring programs has always been a complex process and despite the numerous methodologies employed by experts, there is still no generally accepted, holistic and practical approach to support all the phases and elements related. Here, a Geographical Information System (GIS)-based multicriteria decision analysis approach was adopted so as to contribute to the design of the national network for monitoring of water quality parameters in Greece that will additionally fulfill the urgent needs for an operational, real-time monitoring of the water resources. During this cost-effective and easily applied procedure the high priority areas were defined by taking into consideration the most important conditioning factors that impose pressures on rivers and the special conditions that increase the need for monitoring locally. The areas of increased need for automatic monitoring of water quality parameters are highlighted and the output map is validated. The sites in high priority areas are proposed for the installation of automatic monitoring stations and the installation and maintenance budget is presented. Finally, the proposed network is contrasted with the current automatic monitoring network in Greece.
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29
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Zhang D, Heery B, O'Neil M, Little S, O'Connor NE, Regan F. A Low-Cost Smart Sensor Network for Catchment Monitoring. SENSORS 2019; 19:s19102278. [PMID: 31108837 PMCID: PMC6567359 DOI: 10.3390/s19102278] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 11/16/2022]
Abstract
Understanding hydrological processes in large, open areas, such as catchments, and further modelling these processes are still open research questions. The system proposed in this work provides an automatic end-to-end pipeline from data collection to information extraction that can potentially assist hydrologists to better understand the hydrological processes using a data-driven approach. In this work, the performance of a low-cost off-the-shelf self contained sensor unit, which was originally designed and used to monitor liquid levels, such as AdBlue, fuel, lubricants etc., in a sealed tank environment, is first examined. This process validates that the sensor does provide accurate water level information for open water level monitoring tasks. Utilising the dataset collected from eight sensor units, an end-to-end pipeline of automating the data collection, data processing and information extraction processes is proposed. Within the pipeline, a data-driven anomaly detection method that automatically extracts rapid changes in measurement trends at a catchment scale. The lag-time of the test site (Dodder catchment Dublin, Ireland) is also analyzed. Subsequently, the water level response in the catchment due to storm events during the 27 month deployment period is illustrated. To support reproducible and collaborative research, the collected dataset and the source code of this work will be publicly available for research purposes.
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Affiliation(s)
- Dian Zhang
- Insight Centre for Data Analytics, Dublin City University, Dublin D9, Ireland.
- Water Institute, Dublin City University, Dublin D9, Ireland.
| | - Brendan Heery
- Water Institute, Dublin City University, Dublin D9, Ireland.
| | - Maria O'Neil
- Water Institute, Dublin City University, Dublin D9, Ireland.
| | - Suzanne Little
- Insight Centre for Data Analytics, Dublin City University, Dublin D9, Ireland.
| | - Noel E O'Connor
- Insight Centre for Data Analytics, Dublin City University, Dublin D9, Ireland.
- Water Institute, Dublin City University, Dublin D9, Ireland.
| | - Fiona Regan
- Water Institute, Dublin City University, Dublin D9, Ireland.
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Siepi M, Oliva R, Petraccone L, Del Vecchio P, Ricca E, Isticato R, Lanzilli M, Maglio O, Lombardi A, Leone L, Notomista E, Donadio G. Fluorescent peptide dH3w: A sensor for environmental monitoring of mercury (II). PLoS One 2018; 13:e0204164. [PMID: 30303991 PMCID: PMC6179210 DOI: 10.1371/journal.pone.0204164] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 09/03/2018] [Indexed: 01/06/2023] Open
Abstract
Heavy metals are hazardous environmental contaminants, often highly toxic even at extremely low concentrations. Monitoring their presence in environmental samples is an important but complex task that has attracted the attention of many research groups. We have previously developed a fluorescent peptidyl sensor, dH3w, for monitoring Zn2+ in living cells. This probe, designed on the base on the internal repeats of the human histidine rich glycoprotein, shows a turn on response to Zn2+ and a turn off response to Cu2+. Other heavy metals (Mn2+, Fe2+, Ni2+, Co2+, Pb2+ and Cd2+) do not interfere with the detection of Zn2+ and Cu2+. Here we report that dH3w has an affinity for Hg2+ considerably higher than that for Zn2+ or Cu2+, therefore the strong fluorescence of the Zn2+/dH3w complex is quenched when it is exposed to aqueous solutions of Hg2+, allowing the detection of sub-micromolar levels of Hg2+. Fluorescence of the Zn2+/dH3w complex is also quenched by Cu2+ whereas other heavy metals (Mn2+, Fe2+, Ni2+, Co2+, Cd2+, Pb2+, Sn2+ and Cr3+) have no effect. The high affinity and selectivity suggest that dH3w and the Zn2+/dH3w complex are suited as fluorescent sensor for the detection of Hg2+ and Cu2+ in environmental as well as biological samples.
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Affiliation(s)
- Marialuisa Siepi
- Department of Biology University of Naples Federico II, Naples, Italy
| | - Rosario Oliva
- Department of Chemical Sciences University of Naples Federico II, Naples, Italy
| | - Luigi Petraccone
- Department of Chemical Sciences University of Naples Federico II, Naples, Italy
| | - Pompea Del Vecchio
- Department of Chemical Sciences University of Naples Federico II, Naples, Italy
| | - Ezio Ricca
- Department of Biology University of Naples Federico II, Naples, Italy
| | - Rachele Isticato
- Department of Biology University of Naples Federico II, Naples, Italy
| | | | - Ornella Maglio
- Department of Chemical Sciences University of Naples Federico II, Naples, Italy
- IBB, CNR, Naples, Italy
| | - Angela Lombardi
- Department of Chemical Sciences University of Naples Federico II, Naples, Italy
| | - Linda Leone
- Department of Chemical Sciences University of Naples Federico II, Naples, Italy
| | - Eugenio Notomista
- Department of Biology University of Naples Federico II, Naples, Italy
| | - Giuliana Donadio
- Department of Biology University of Naples Federico II, Naples, Italy
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Chlorine Soft Sensor Based on Extreme Learning Machine for Water Quality Monitoring. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018. [DOI: 10.1007/s13369-018-3253-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Burke TA, Cascio WE, Costa DL, Deener K, Fontaine TD, Fulk FA, Jackson LE, Munns WR, Orme-Zavaleta J, Slimak MW, Zartarian VG. Rethinking Environmental Protection: Meeting the Challenges of a Changing World. ENVIRONMENTAL HEALTH PERSPECTIVES 2017; 125:A43-A49. [PMID: 28248180 PMCID: PMC5332174 DOI: 10.1289/ehp1465] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
From climate change to hydraulic fracturing, and from drinking water safety to wildfires, environmental challenges are changing. The United States has made substantial environmental protection progress based on media-specific and single pollutant risk-based frameworks. However, today’s environmental problems are increasingly complex and new scientific approaches and tools are needed to achieve sustainable solutions to protect the environment and public health. In this article, we present examples of today’s environmental challenges and offer an integrated systems approach to address them. We provide a strategic framework and recommendations for advancing the application of science for protecting the environment and public health. We posit that addressing 21st century challenges requires transdisciplinary and systems approaches, new data sources, and stakeholder partnerships. To address these challenges, we outline a process driven by problem formulation with the following steps: a) formulate the problem holistically, b) gather and synthesize diverse information, c) develop and assess options, and d) implement sustainable solutions. This process will require new skills and education in systems science, with an emphasis on science translation. A systems-based approach can transcend media- and receptor-specific bounds, integrate diverse information, and recognize the inextricable link between ecology and human health.
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Affiliation(s)
| | | | | | - Kacee Deener
- Address correspondence to K. Deener, Ronald Reagan Bldg., 1300 Pennsylvania Ave., N.W. Room 41207, Washington, DC 20004 USA. Telephone: (202) 564-1990. E-mail:
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