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Sensors for Water Monitoring
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Sensors for Water Monitoring

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 21936

Special Issue Editor

Prof. Dr. Fiona Regan

E-Mail Website
Guest Editor
DCU Water Institute, School of Chemical Sciences, Dublin City University, Dublin 9, D09 E432 Dublin, Ireland
Interests: optical sensing; analytes (nutrients, metals, E. coli, pharmaceuticals, emerging contaminants); biosensors; centrifugal microfluidic sensors; antifouling for sensors, autonomous systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, the most popular sensors for water monitoring applications include temperature, conductivity, turbidity, colour and pH. During recent decades, devices have become smaller, more rugged and stable, leading to more reliable systems. In recent years, major advances have taken place in the measurement of species such as trace metals, nutrients (nitrate, nitrite, phosphate, ammonia), and E. coli using electrochemical and optical techniques. The convergence of material science, engineering, integration and the demand for new analyte-specific sensors poses a significant challenge, and researchers are delivering exciting new developments.

This Special Issue “Sensors for Water Monitoring” will highlight developments and improvements to sensors and sensing technologies. There is potential for scientists to demonstrate how information supplied from reliable sensors is necessary for the growth of "big" data analytics, creating opportunities for novel applications and alternative measurements.

In particular, this Special Issue invites novel work on demonstrations of existing sensors in real applications, the development of novel chemical and biosensors for water quality, low cost sensors, autonomous systems for long-term deployment, sensors for challenging parameters like bacteria, toxins, emerging contaminants or other topics that demonstrate the challenge and huge opportunities for new research and development in sensors, engineering, integration and implementation in real world challenges.

Prof. Dr. Fiona Regan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

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16 pages, 4240 KiB  
Article
A Data-Based Framework for Identifying a Source Location of a Contaminant Spill in a River System with Random Measurement Errors
by Jun Hyeong Kim, Mi Lim Lee and Chuljin Park
Sensors 2019, 19(15), 3378; https://doi.org/10.3390/s19153378 - 01 Aug 2019
Cited by 3 | Viewed by 2386
Abstract
This study addresses the problem of identifying the source location of a contaminant spill in a river system when a sensor network returns observations containing random measurement errors. To solve this problem, we suggest a new framework comprising three main steps: (i) spill [...] Read more.
This study addresses the problem of identifying the source location of a contaminant spill in a river system when a sensor network returns observations containing random measurement errors. To solve this problem, we suggest a new framework comprising three main steps: (i) spill detection, (ii) data preprocessing, and (iii) source identification. Specifically, we applied a statistical process control chart to detect a contaminant spill with measurement errors while keeping the false alarm rate at less than or equal to a user-specified value. After detecting a spill, we generated a nonlinear regression model to estimate a breakthrough curve of the observations and derive a characteristic vector of the estimated curve. Using the characteristic vector as an input, a random forest model was constructed with the sensor raising the first alarm. The model provides output values between 0 and 1 to represent the possibility of each candidate location being the true spill source. These possibility values allow users to identify strong candidate locations for the spill. The accuracy of our framework was tested on part of the Altamaha River system in Georgia, USA. Full article
(This article belongs to the Special Issue Sensors for Water Monitoring)
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22 pages, 4635 KiB  
Article
A Low-Cost Smart Sensor Network for Catchment Monitoring
by Dian Zhang, Brendan Heery, Maria O’Neil, Suzanne Little, Noel E. O’Connor and Fiona Regan
Sensors 2019, 19(10), 2278; https://doi.org/10.3390/s19102278 - 17 May 2019
Cited by 14 | Viewed by 5209
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Sensors for Water Monitoring)
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15 pages, 3879 KiB  
Article
Study on an Online Detection Method for Ground Water Quality and Instrument Design
by Xiushan Wu, Renyuan Tong, Yanjie Wang, Congli Mei and Qing Li
Sensors 2019, 19(9), 2153; https://doi.org/10.3390/s19092153 - 09 May 2019
Cited by 19 | Viewed by 3580
Abstract
The online measurement of ground water quality, as one important area of water resource protection, can provide real-time measured water quality parameters and send out warning information in a timely manner when the water resource is polluted. Based on ultraviolet (UV) spectrophotometry, a [...] Read more.
The online measurement of ground water quality, as one important area of water resource protection, can provide real-time measured water quality parameters and send out warning information in a timely manner when the water resource is polluted. Based on ultraviolet (UV) spectrophotometry, a remote online measurement method is proposed and used to measure the ground water quality parameters chemical oxygen demand (COD), total organic carbon (TOC), nitrate nitrogen (NO3–N), and turbidity (TURB). The principle of UV spectrophotometry and the data processing method are discussed in detail, the correlated mathematical modeling of COD and TOC is given, and a confirmatory experiment is carried out. Turbidity-compensated mathematical modeling is proposed to improve the COD measurement accuracy and a confirmatory experiment is finished with turbidity that ranges from 0 to 100 NTU (Nephelometric Turbidity Unit). The development of a measurement instrument to detect the ground water COD, TOC, NO3–N, and TURB is accomplished; the test experiments are completed according to the standard specification of China’s technical requirement for water quality online automatic monitoring of UV, and the absolute measuring errors of COD, TOC, and NO3–N are smaller than 5.0%, while that of TURB is smaller than 5.4%, which meets the requirements for the online measurement of ground water quality. Full article
(This article belongs to the Special Issue Sensors for Water Monitoring)
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13 pages, 1869 KiB  
Article
Three-Stage Single-Chambered Microbial Fuel Cell Biosensor Inoculated with Exiguobacterium aestuarii YC211 for Continuous Chromium (VI) Measurement
by Li-Chun Wu, Guey-Horng Wang, Teh-Hua Tsai, Shih-Yu Lo, Chiu-Yu Cheng and Ying-Chien Chung
Sensors 2019, 19(6), 1418; https://doi.org/10.3390/s19061418 - 22 Mar 2019
Cited by 11 | Viewed by 2498
Abstract
Chromium (VI) [Cr(VI)] compounds display high toxic, mutagenic, and carcinogenic potential. Biological analysis techniques (e.g., such as enzyme-based or cell-based sensors) have been developed to measure Cr(VI); however, these biological elements are sensitive to the environment, limited to measuring trace Cr(VI), and require [...] Read more.
Chromium (VI) [Cr(VI)] compounds display high toxic, mutagenic, and carcinogenic potential. Biological analysis techniques (e.g., such as enzyme-based or cell-based sensors) have been developed to measure Cr(VI); however, these biological elements are sensitive to the environment, limited to measuring trace Cr(VI), and require deployment offsite. In this study, a three-stage single-chambered microbial fuel cell (SCMFC) biosensor inoculated with Exiguobacterium aestuarii YC211 was developed for in situ, real-time, and continuous Cr(VI) measurement. A negative linear relationship was observed between the Cr(VI) concentration (5–30 mg/L) and the voltage output using an SCMFC at 2-min liquid retention time. The theoretical Cr(VI) measurement range of the system could be extended to 5–90 mg/L by connecting three separate SCMFCs in series. The three-stage SCMFC biosensor could accurately measure Cr(VI) concentrations in actual tannery wastewater with low deviations (<7%). After treating the wastewater with the SCMFC, the original inoculated E. aestuarii remained dominant (>92.5%), according to the next-generation sequencing analysis. The stable bacterial community present in the SCMFC favored the reliable performance of the SCMFC biosensor. Thus, the three-stage SCMFC biosensor has potential as an early warning device with wide dynamic range for in situ, real-time, and continuous Cr(VI) measurement of tannery wastewater. Full article
(This article belongs to the Special Issue Sensors for Water Monitoring)
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Review

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17 pages, 2112 KiB  
Review
Point-of-Care Strategies for Detection of Waterborne Pathogens
by Sandeep Kumar, Monika Nehra, Jyotsana Mehta, Neeraj Dilbaghi, Giovanna Marrazza and Ajeet Kaushik
Sensors 2019, 19(20), 4476; https://doi.org/10.3390/s19204476 - 16 Oct 2019
Cited by 55 | Viewed by 7360
Abstract
Waterborne diseases that originated due to pathogen microorganisms are emerging as a serious global health concern. Therefore, rapid, accurate, and specific detection of these microorganisms (i.e., bacteria, viruses, protozoa, and parasitic pathogens) in water resources has become a requirement of water quality assessment. [...] Read more.
Waterborne diseases that originated due to pathogen microorganisms are emerging as a serious global health concern. Therefore, rapid, accurate, and specific detection of these microorganisms (i.e., bacteria, viruses, protozoa, and parasitic pathogens) in water resources has become a requirement of water quality assessment. Significant research has been conducted to develop rapid, efficient, scalable, and affordable sensing techniques to detect biological contaminants. State-of-the-art technology-assisted smart sensors have improved features (high sensitivity and very low detection limit) and can perform in a real-time manner. However, there is still a need to promote this area of research, keeping global aspects and demand in mind. Keeping this view, this article was designed carefully and critically to explore sensing technologies developed for the detection of biological contaminants. Advancements using paper-based assays, microfluidic platforms, and lateral flow devices are discussed in this report. The emerging recent trends, mainly point-of-care (POC) technologies, of water safety analysis are also discussed here, along with challenges and future prospective applications of these smart sensing technologies for water health diagnostics. Full article
(This article belongs to the Special Issue Sensors for Water Monitoring)
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