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Sensors
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Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection
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Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3742

Special Issue Editors

Prof. Dr. Zbigniew Otremba

E-Mail Website
Guest Editor
Department of Physics, Gdynia Maritime University, 81-225 Gdynia, Poland
Interests: physics; oceanology; oils; environment protection; ocean optics; environmental impact assessment; environmental pollution; environmental management; environmental monitoring; water analysis
Dr. Emilia Baszanowska

E-Mail Website
Guest Editor
Department of Physics, Gdynia Maritime University, 81-225 Gdynia, Poland
Interests: atomic physics; oceanography

Special Issue Information

Dear Colleagues,

Anthropogenic pressure, combined with fluctuations in the global climate, affects the functioning of the marine environment. It is important to take care of its health, and the related durability of its productivity. Information about the changes of biological, physical and chemical parameters of marine areas resulting from their use is highly desirable. The possibility of early detection of alien substances and energies in water masses is also important. To meet such needs requires the improvement of sensors for environmental parameters changes, the creation of theoretical foundations for the functioning of sensors, as well as the automation of collecting, processing, and sharing information from sensor systems for use in national and global environmental management. In this Special Issue, original research articles and reviews are welcome.

Potential topics include, but are not limited to:

  • Materials dedicated to the construction of marine sensors;
  • Transmission of the signal from underwater sensors;
  • Autonomic sensors;
  • Sensing the sea surface dynamics;
  • Sensors for solar radiation in the seawater column;
  • Intelligent sensors for marine applications;
  • Machine learning in the development of sensor signals;
  • Sensing and identifying sound sources;
  • Multi-sensor data processing;
  • Spatially integrated sensing.

Prof. Dr. Zbigniew Otremba
Dr. Emilia Baszanowska
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

  • ocean health
  • weather parameters
  • climate change
  • detecting of marine pollution
  • marine large scale constructions
  • alien energies and substances

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

16 pages, 3514 KiB  
Article
Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations
by Elena Arce, Andrés Suárez-García, José Antonio López-Vázquez and Rosa Devesa-Rey
Sensors 2024, 24(8), 2454; https://doi.org/10.3390/s24082454 - 11 Apr 2024
Viewed by 614
Abstract
Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to [...] Read more.
Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel’s dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations. Full article
(This article belongs to the Special Issue Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection)
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Figure 1

Figure 1
<p>Tabarca patrol moored in port.</p> Full article ">Figure 2
<p>Patrol model in SketchUp. “Obra viva” is underwater hull; “Obra muerta” is upper works.</p> Full article ">Figure 3
<p>Simulation panel from TRNSYS.</p> Full article ">Figure 4
<p>Temperature sensors in the engine room and storeroom.</p> Full article ">Figure 5
<p>(<b>left</b>) Electric Imp shield (<b>right</b>) final assembly.</p> Full article ">Figure 6
<p>Distribution of the shields inside the shell and complete weather station.</p> Full article ">Figure 7
<p>Violin plot for weather variables.</p> Full article ">Figure 8
<p>Monthly heating and cooling demands in kWh (hourly accounts) based on onsite weather data.</p> Full article ">
17 pages, 3481 KiB  
Article
Monitoring Bioindication of Plankton through the Analysis of the Fourier Spectra of the Underwater Digital Holographic Sensor Data
by Victor Dyomin, Alexandra Davydova, Nikolay Kirillov, Oksana Kondratova, Yuri Morgalev, Sergey Morgalev, Tamara Morgaleva and Igor Polovtsev
Sensors 2024, 24(7), 2370; https://doi.org/10.3390/s24072370 - 8 Apr 2024
Viewed by 625
Abstract
The study presents a bioindication complex and a technology of the experiment based on a submersible digital holographic camera with advanced monitoring capabilities for the study of plankton and its behavioral characteristics in situ. Additional mechanical and software options expand the capabilities of [...] Read more.
The study presents a bioindication complex and a technology of the experiment based on a submersible digital holographic camera with advanced monitoring capabilities for the study of plankton and its behavioral characteristics in situ. Additional mechanical and software options expand the capabilities of the digital holographic camera, thus making it possible to adapt the depth of the holographing scene to the parameters of the plankton habitat, perform automatic registration of the “zero” frame and automatic calibration, and carry out natural experiments with plankton photostimulation. The paper considers the results of a long-term digital holographic experiment on the biotesting of the water area in Arctic latitudes. It shows additional possibilities arising during the spectral processing of long time series of plankton parameters obtained during monitoring measurements by a submersible digital holographic camera. In particular, information on the rhythmic components of the ecosystem and behavioral characteristics of plankton, which can be used as a marker of the ecosystem well-being disturbance, is thus obtained. Full article
(This article belongs to the Special Issue Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection)
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Figure 1

Figure 1
<p>DHC scheme: 1—laser diodes with fiber output, 2—optical multiplexer, 3—beam expander, 4—lenses, 5—portholes, 6—prisms forming the working volume, marked red, 7—selective filter, 8—CCD/CMOS camera.</p> Full article ">Figure 2
<p>Holographic images of plankton particles located in different parts of the DHC working volume ((<b>a</b>,<b>b</b>)—particles in part I (<a href="#sensors-24-02370-f003" class="html-fig">Figure 3</a>b), (<b>c</b>,<b>d</b>)—in part II (<a href="#sensors-24-02370-f003" class="html-fig">Figure 3</a>b), (<b>e</b>,<b>f</b>)—in part IV (<a href="#sensors-24-02370-f003" class="html-fig">Figure 3</a>b)), obtained using the DHC in the natural conditions of the Arctic expedition. Size of the scale ruler in all images—500 µm.</p> Full article ">Figure 3
<p>(<b>a</b>) General layout of the DHC for plankton monitoring, (<b>b</b>) layout of nodes. 1—laser diode, 2—beam expander, 3—portholes, 4—CMOS camera, 5—optical system for optical radiation receiving, 6—mirror-prism system to form a measuring channel in the medium (working volume), 7—calibers, 8—replaceable rods, 9—lighting module, 10—recording module. The red color marks the working volume (studied medium volume). I, II, III, IV—parts of the working volume separated by prisms.</p> Full article ">Figure 4
<p>Moorage place of the DHC FOCL probe at Dalnye Zelentsy, geographical coordinates N 69°7′7.9″ E 36°4′10.6″: DHC FOCL probe before submergence (<b>a</b>), site layout plan and installation diagram (<b>b</b>), DHC FOCL probe fixed at the moorage place (<b>c</b>). 1—operator’s post at the onshore research station, 2—FOCL winch, 3—pontoon platform, 4—DHC FOCL probe, 5—bottom station.</p> Full article ">Figure 5
<p>Time series of zooplankton concentration in the DHC working volume (<b>a</b>) and the Fourier amplitude spectrum (<b>b</b>) of this series during the moorage period used for analysis in this work. The green line corresponds to the frequency of the circadian rhythm (~1/24 h<sup>−1</sup>). The red circle in (<b>a</b>) shows the measurements obtained during the introduction of the indicative pollutant. (<b>b</b>) shows the ultradian rhythm range—1, diurnal rhythm range—2, seasonal rhythm range—3.</p> Full article ">Figure 6
<p>Time series of measurements of environmental factors at the moorage place, accompanying the measurements of plankton. The red circle indicates the contamination period.</p> Full article ">Figure 7
<p>Circadian rhythm of plankton concentration in the DHC working volume for the time series shown in <a href="#sensors-24-02370-f005" class="html-fig">Figure 5</a>a. Green line—interpolation of the circadian rhythm amplitude, the daily readings—black dots and red dots during a rhythm failure under the influence of an indicator impurity. Red circle—time of indicator impurity introduction into biocenosis. Vertical green dashes—manifestations of the circadian rhythm in the daily Fourier spectrum of the time series of plankton concentration in the DHC working volume.</p> Full article ">
10 pages, 7482 KiB  
Communication
A Global Seawater Density Distribution Model Using a Convolutional Neural Network
by Qin Liu, Liyan Li, Yan Zhou, Shiwen Zhang, Yuliang Liu and Xinwei Wang
Sensors 2024, 24(6), 1972; https://doi.org/10.3390/s24061972 - 20 Mar 2024
Viewed by 786
Abstract
Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce [...] Read more.
Seawater density is an important physical property in oceanography that affects the accuracy of calculations such as gravity fields and tidal potentials and the calibration of acoustic and optical oceanographic sensors. In related studies, constant density values are frequently used, which can introduce significant errors. Therefore, this study employs a basic convolutional neural network model to construct a comprehensive model showing the seawater density distribution across the globe. The model takes into account depth, latitude, longitude, and month as inputs. Numerous real seawater datasets were used to train the model, and it has been shown that the model has an absolute mean error and root mean square error of less than 1 kg/m3 in 99% of the test set samples. The model effectively demonstrates the influence of input parameters on the distribution of seawater density. In this paper, we present a newly developed global model for distributing seawater density which is both comprehensive and accurate, surpassing previous models. The utilization of the model presented in this paper for estimating seawater density can minimize errors in theoretical ocean models and serve as a foundation for designing and analyzing ocean exploration systems. Full article
(This article belongs to the Special Issue Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection)
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Figure 1

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<p>An illustration of the architecture of the CNN.</p> Full article ">Figure 2
<p>Activation functions used in the model.</p> Full article ">Figure 3
<p>Model training convergence curves. The learning rate is 0.001 in (<b>a</b>) and 0.0002 in (<b>b</b>).</p> Full article ">Figure 4
<p>Error distribution of the test set. (<b>a</b>) MAE; (<b>b</b>) RMSE; (<b>c</b>) MAXE. The gray area represents the land and is determined by the output <span class="html-italic">in_ocean</span> = 0 of <span class="html-italic">gsw_SA_from_SP</span> in TEOS-10.</p> Full article ">Figure 5
<p>Error curves of the test set. (<b>a</b>) Error of depth and (<b>b</b>) error of month.</p> Full article ">Figure 6
<p>The model’s seawater density output varies with depth. There are density profiles (<b>a</b>) at 0° N, (<b>b</b>) at −30° N and (<b>c</b>) at −60° N respectively. The legend clarifies that the letters ‘A’, ‘I’, and ‘P’ correspond to the Atlantic, Indian, and Pacific oceans, respectively. The month of June is considered winter in the southern hemisphere.</p> Full article ">Figure 7
<p>Annual mean sea water density distribution at different depths. “Annual” refers to the average of the results of the model calculations from January to December.</p> Full article ">Figure 8
<p>Monthly mean surface density distribution in different seasons.</p> Full article ">
13 pages, 1929 KiB  
Article
Practical Considerations for Laser-Induced Graphene Pressure Sensors Used in Marine Applications
by Tessa Van Volkenburg, Daniel Ayoub, Andrea Alemán Reyes, Zhiyong Xia and Leslie Hamilton
Sensors 2023, 23(22), 9044; https://doi.org/10.3390/s23229044 - 8 Nov 2023
Viewed by 1022
Abstract
Small, low-power, and inexpensive marine depth sensors are of interest for a myriad of applications from maritime security to environmental monitoring. Recently, laser-induced graphene (LIG) piezoresistive pressure sensors have been proposed given their rapid fabrication and large dynamic range. In this work, the [...] Read more.
Small, low-power, and inexpensive marine depth sensors are of interest for a myriad of applications from maritime security to environmental monitoring. Recently, laser-induced graphene (LIG) piezoresistive pressure sensors have been proposed given their rapid fabrication and large dynamic range. In this work, the practicality of LIG integration into fieldable deep ocean (1 km) depth sensors in bulk is explored. Initially, a design of experiments (DOEs) approach evaluated laser engraver fabrication parameters such as line length, line width, laser speed, and laser power on resultant resistances of LIG traces. Next, uniaxial compression and thermal testing at relevant ocean pressures up to 10.3 MPa and temperatures between 0 and 25 °C evaluated the piezoresistive response of replicate sensors and determined the individual characterization of each, which is necessary. Additionally, bare LIG sensors showed larger resistance changes with temperature (ΔR ≈ 30 kΩ) than pressure (ΔR ≈ 1–15 kΩ), indicating that conformal coatings are required to both thermally insulate and electrically isolate traces from surrounding seawater. Sensors encapsulated with two dip-coated layers of 5 wt% polydimethylsiloxane (PDMS) silicone and submerged in water baths from 0 to 25 °C showed significant thermal dampening (ΔR ≈ 0.3 kΩ), indicating a path forward for the continued development of LIG/PDMS composite structures. This work presents both the promises and limitations of LIG piezoresistive depth sensors and recommends further research to validate this platform for global deployment. Full article
(This article belongs to the Special Issue Sensor Technologies for Ocean Environments: Impact Assessment, Monitoring and Protection)
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Figure 1

Figure 1
<p>Testing repeatability of LIG recipe (S = % laser speed, P = % laser power) fabrication. (<b>a</b>) Schematic of a test swatch with different line lengths (5, 10, and 15 mm) and widths (1, 2, and 3 mm), enabling the calculation of average resistivity for each recipe. (<b>b</b>) Comparison of laser resistivity with each on separate test swatches (print bed re-leveled each time). SEM images of LIG samples with top-down and cross-sectional (inset) views (<b>c</b>) the lowest laser fluence (S65P45) and (<b>d</b>) the highest laser fluence (S55P60).</p> Full article ">Figure 2
<p>Instron compression testing of LIG sensors during repeated presses. LIG traces were assembled into (<b>a</b>) LIG piezoresistive sensors for testing using the (<b>b</b>) experimental setup of sensor compressed between two steel blocks. Recovery of LIG porous structure is shown after six cycles for (<b>c</b>) the lowest laser fluence and (<b>d</b>) highest laser fluence.</p> Full article ">Figure 3
<p>Piezoresistive response of LIG sensors in compression. Resistance change is shown for increasing load on three samples with three subsequent presses each.</p> Full article ">Figure 4
<p>Piezoresistive response of LIG sensors with increasing load and decreasing temperature. Temperature changes are more significant than resistance changes.</p> Full article ">Figure 5
<p>Thermal response of conformally coated LIG sensors shown with the (<b>a</b>) experimental setup with chiller, jacketed beaker, DC power source, voltage divider, and automatic temperature and resistance logging over time for (<b>b</b>) thick 10 wt% PDMS silicone layers, 2× dip coat of 5 wt% PDMS silicone, 1× dip coat of 5 wt% PDMS silicone, and vapor deposited 0.1 mm parylene-c layer.</p> Full article ">
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