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Optical Sensors for Water Monitoring
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Optical Sensors for Water Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 8093

Special Issue Editors

Dr. Klaus Koren

E-Mail Website
Guest Editor
Aarhus University Centre for Water Technology (WATEC); Department of Bioscience, Aarhus Universitet, 8000 Aarhus, Denmark
Interests: chemical sensors; optical sensors; oxygen sensors; environmental sensing; nanoparticles; bioanalytics
Dr. Silvia E. Zieger

E-Mail Website
Guest Editor
Aarhus University Centre for Water Technology (WATEC); Department of Bioscience, Aarhus Universitet, 8000 Aarhus, Denmark
Interests: environmental sensing; chemical sensors; optical sensors; multiparametric sensing; data analysis; biosensors

Special Issue Information

Dear Colleagues,

Water not only covers two-thirds of the Earth’s surface, it is also the basis for all life on this planet. Anthropogenic contamination and climate change, among other factors, are threatening the quality and availability of this important resource. The identification of relevant types of hazards at the appropriate temporal and spatial scale is crucial to detect their sources and origin, understand the processes governing their magnitude and distribution, and to evaluate their risks and consequences for preventing economic losses.

In order to monitor water quality in all sorts of aquatic environments in real-time, novel sensors are needed. Optical sensors have the potential to fulfill this need due to their high sensitivity and selectivity. The heterogeneity of aquatic environments (e.g., fresh water, marine) and concentration levels of various analytes found within the respective environments are challenging and demand continuous development.

In this Special Issue, we aim to bring together the latest developments within the field. We welcome all submissions dealing with optical sensing in aquatic environments including, but not limited to:

  • Marine or brackish waters;
  • Fresh water;
  • Waste water;
  • Drinking water.

In terms of analytes, we aim to cover the full spectrum that can be monitored using optical sensing technology, including:

  • Nutrients (e.g., NH4+, nitrate, phosphorous);
  • Ionic species;
  • Organic contaminants (e.g., pesticides, antibiotics);
  • Biological species (e.g., bacteria, toxic algae)

Dr. Klaus Koren
Dr. Silvia E. Zieger
Guest Editors

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.

Keywords

  • aquatic environments
  • optical sensing
  • luminescence based sensors
  • chemical monitoring
  • label-free sensors

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Published Papers (2 papers)

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Research

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18 pages, 7797 KiB  
Article
Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data
by Lifei Wei, Yu Zhang, Can Huang, Zhengxiang Wang, Qingbin Huang, Feng Yin, Yue Guo and Liqin Cao
Sensors 2020, 20(5), 1345; https://doi.org/10.3390/s20051345 - 29 Feb 2020
Cited by 14 | Viewed by 3378
Abstract
The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality [...] Read more.
The sustainable development of water resources is always emphasized in China, and a set of perfect standards for the division of inland water environment quality have been established to monitor water quality. However, most of the 24 indicators that determine the water quality level in the standards are non-optically active parameters. The weak optical characteristics make it difficult to find significant correlations between the single parameters and the remote sensing imagery. In addition, traditional on-site testing methods have been unable to meet the increasingly extensive water-quality monitoring requirements. Based on the above questions, it’s meaningful that the supervised classification process of a detail-preserving smoothing classifier based on conditional random field (CRF) and Landsat-8 data was proposed in the two study areas around Wuhan and Huangshi in Hubei Province. The random forest classifier was selected to model the association potential of the CRF. The results (the first study area: OA = 89.50%, Kappa = 0.841; the second study area: OA = 90.35%, Kappa = 0.868) showed that the water-quality monitoring based on CRF model is feasible, and this approach can provide a reference for water-quality mapping of inland lakes. In the future, it may only require a small amount of on-site sampling to achieve the identification of the water quality levels of inland lakes across a large area of China. Full article
(This article belongs to the Special Issue Optical Sensors for Water Monitoring)
►▼ Show Figures

Figure 1

Figure 1
<p>Study areas: (<b>a</b>) the Wuhan study area; (<b>b</b>) the Huangshi study area.</p> Full article ">Figure 2
<p>Percentage of lakes in each water quality level: (<b>a</b>) Wuhan; (<b>b</b>) Huangshi.</p> Full article ">Figure 3
<p>The datasets of the two study areas. (<b>a</b>) False-color composite and water vector data for the Wuhan study area. (<b>b</b>) ground-truth map in the Wuhan study area. (<b>c</b>) False-color composite and water vector data for the Huangshi study area. (<b>d</b>) ground-truth map in the Huangshi study area.</p> Full article ">Figure 3 Cont.
<p>The datasets of the two study areas. (<b>a</b>) False-color composite and water vector data for the Wuhan study area. (<b>b</b>) ground-truth map in the Wuhan study area. (<b>c</b>) False-color composite and water vector data for the Huangshi study area. (<b>d</b>) ground-truth map in the Huangshi study area.</p> Full article ">Figure 4
<p>The classification result of the water quality levels of the lakes for the Wuhan dataset: (<b>a1</b>–<b>a5</b>) DT; (<b>b1</b>–<b>b5</b>) DNN; (<b>c1</b>–<b>c5</b>) RF; (<b>d1</b>–<b>d5</b>) RF-CRF; (1) Class II Niushan Lake; (2) Class III East Lake; (3) Class IV Wu Lake; (4) Class V Tangxun Lake; (5) Class VI South Lake and Yezhi Lake.</p> Full article ">Figure 5
<p>The classification result of the RF-CRF model for the Wuhan dataset.</p> Full article ">Figure 6
<p>The classification result maps of the water quality levels for the Huangshi dataset: (<b>a1</b>–<b>a4</b>) DT; (<b>b1</b>–<b>b4</b>) DNN; (<b>c1</b>–<b>c4</b>) RF; (<b>d1</b>–<b>d4</b>) RF-CRF; (1) Class III Taibai Lake; (2) Class IV Daye Lake; (3) Class V Baoan Lake; (4) Class VI Haikou Lake.</p> Full article ">Figure 7
<p>The classification result of the RF-CRF model for the Huangshi dataset.</p> Full article ">

Other

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11 pages, 2504 KiB  
Letter
Bacterial Respiration Used as a Proxy to Evaluate the Bacterial Load in Cooling Towers
by Stepan Toman, Bruno Kiilerich, Ian P.G. Marshall and Klaus Koren
Sensors 2020, 20(21), 6398; https://doi.org/10.3390/s20216398 - 9 Nov 2020
Viewed by 3575
Abstract
Evaporative cooling towers to dissipate excess process heat are essential installations in a variety of industries. The constantly moist environment enables substantial microbial growth, causing both operative challenges (e.g., biocorrosion) as well as health risks due to the potential aerosolization of pathogens. Currently, [...] Read more.
Evaporative cooling towers to dissipate excess process heat are essential installations in a variety of industries. The constantly moist environment enables substantial microbial growth, causing both operative challenges (e.g., biocorrosion) as well as health risks due to the potential aerosolization of pathogens. Currently, bacterial levels are monitored using rather slow and infrequent sampling and cultivation approaches. In this study, we describe the use of metabolic activity, namely oxygen respiration, as an alternative measure of bacterial load within cooling tower waters. This method is based on optical oxygen sensors that enable an accurate measurement of oxygen consumption within a closed volume. We show that oxygen consumption correlates with currently used cultivation-based methods (R2 = 0.9648). The limit of detection (LOD) for respiration-based bacterial quantification was found to be equal to 1.16 × 104 colony forming units (CFU)/mL. Contrary to the cultivation method, this approach enables faster assessment of the bacterial load with a measurement time of just 30 min compared to 48 h needed for cultivation-based measurements. Furthermore, this approach has the potential to be integrated and automated. Therefore, this method could contribute to more robust and reliable monitoring of bacterial contamination within cooling towers and subsequently increase operational stability and reduce health risks. Full article
(This article belongs to the Special Issue Optical Sensors for Water Monitoring)
►▼ Show Figures

Figure 1

Figure 1
<p>Sketch of an evaporative cooling tower with a water reservoir, fill material and heat exchanger. Microbial communities can thrive within the humid environment, both in planktonic form as well as in biofilms on all surfaces.</p> Full article ">Figure 2
<p>(<b>A</b>): Picture of the measurement vial with the glass magnet (1), rubber stopper (2), thin glass capillary (3), temperature probe (4) and O<sub>2</sub> sensor spot with the respective connector (5). (<b>B</b>): Three samples and one control measured at the same time within a water bath kept at 22 °C.</p> Full article ">Figure 3
<p>(<b>A</b>): Principal Coordinates Analysis based on Bray–Curtis distances between ASV relative abundances. The two most significant principal components are shown, axes annotated with percentage of variation explained. (<b>B</b>): Heatmap showing mean percentage abundances for all sample types for the 21 most abundant genera in the dataset. Rows labeled with names ending in “_ASVXX” show ASVs with undefined genera (only defined up to the family level) which are nonetheless more abundant than all genera in the top 21. vE6 is an uncultured family-level group within the Chlamydiales.</p> Full article ">Figure 4
<p>Comparison of the decrease in O<sub>2</sub> concentration over time of a pure sample compared to a sample with added glucose and LB medium. The respective respiration rates were determined via linear regression and are displayed within the graph.</p> Full article ">Figure 5
<p>Examples of measured oxygen respiration of an undiluted sample, a 1 + 1 diluted sample and a 1 + 2 diluted sample compared to the negative control.</p> Full article ">Figure 6
<p>Correlation between the numbers of colony forming units (CFUs) in samples measured by plate counts and the oxygen consumption rates measured by the optode system. Data points represent means with the respective standard deviations for each sample (n = 5 for the undiluted sample, 2 for 1 + 1 and 1 + 2 dilutions and 3 for the negative control). The linear regression shown by the blue dotted line has an R<sup>2</sup> = 0.9648.</p> Full article ">
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