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Sensors and Data Analysis Applied in Environmental Monitoring
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Sensors and Data Analysis Applied in Environmental Monitoring

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 14458

Special Issue Editors

Dr. Giuseppe Passarella

E-Mail Website
Guest Editor
National Research Council, Water Research Institute, 70100 Bari, Italy
Interests: characterization of water monitoring information; spatiotemporal processing; water monitoring design and optimization; methods for uncertainty assessment related to water monitoring; methods for defining qualitative–quantitative characteristics of water systems
Special Issues, Collections and Topics in MDPI journals
Dr. Aimé Lay-Ekuakille

E-Mail Website
Guest Editor
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: instrumentation; measurement and sensors; biomedicine; environment; industry; nanotechnology; machine learning; photovoltaic panel aging
Special Issues, Collections and Topics in MDPI journals
Dr. Sabino Maggi

E-Mail Website
Guest Editor
National Research Council, Institute of Atmospheric Pollution Research, 70125 Bari, Italy
Interests: environmental measurements; data analysis; data mining and optimization by evolutionary algorithms; nanotechnology; fabrication and characterization of electrical sensors; sensors physics; metrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global increasing awareness on environmental issues is pushing an amount of regional, national, and transboundary regulation and activities aimed at assessing, preserving, and recovering the qualitative and quantitative state of the natural resources. These measures indicate monitoring as a basic action for any reliable environmental management plan.

Traditional on-site and remote monitoring activities have during the last century provided tons of measured chemical–physical and biological data that, coupled to reliable data analysis methods and ever more powerful computational tools, have allowed us to assess the state of natural systems such as air, surface and groundwater, soil and subsoil, geological complexes, etc. and to significantly improve our scientific and technical knowledge in the fields of natural system monitoring devices and environmental data analysis.

In recent decades, environmental sensors have shrunk in size and cost, while improving their accuracy and reliability. New sensors and mobile technology can facilitate accurate real-time environmental data acquisition and help us to understand how we affect the environment and act to preserve or recover natural systems. These sensors are now readily available and can be installed almost everywhere, even where it would have been impractical to do so just a few years ago. Furthermore, innovative environmental data management, exploration, and comprehension tools have been proposed at the same time, to make data analysis more reliable and sensitive.

Potential topics include but are not limited to:

  • Measuring physical, chemical, and biological properties in field
  • Sensor technology for environmental monitoring
  • Emerging sensors for the environment
  • Development and optimization of environmental monitoring networks
  • Low-power sensor wireless networks
  • Sensor power handling and management
  • Low-cost electronic boards and controllers
  • Open hardware
  • Real time environmental monitoring
  • Environmental data analysis methods and tools
  • Monitoring data representation

Dr. Giuseppe Passarella
Prof. Dr. Aime' Lay-Ekuakille
Dr. Sabino Maggi
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

  • monitoring networks
  • environmental monitoring
  • environmental sensors
  • sensor technology
  • data acquisition and management
  • sensor networks
  • low-cost open hardware
  • environmental data analysis

Published Papers (6 papers)

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Research

21 pages, 2238 KiB  
Article
Soil Moisture Sensor Information Enhanced by Statistical Methods in a Reclaimed Water Irrigation Framework
by Anthony Giorgio, Nicoletta Del Buono, Marco Berardi, Michele Vurro and Gaetano Alessandro Vivaldi
Sensors 2022, 22(20), 8062; https://doi.org/10.3390/s22208062 - 21 Oct 2022
Cited by 2 | Viewed by 1531
Abstract
Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan [...] Read more.
Time series modeling and forecasting play important roles in many practical fields. A good understanding of soil water content and salinity variability and the proper prediction of variations in these variables in response to changes in climate conditions are essential to properly plan water resources and appropriately manage irrigation and fertilization tasks. This paper provides a 48-h forecast of soil water content and salinity in the peculiar context of irrigation with reclaimed water in semi-arid environments. The forecasting was performed based on (i) soil water content and salinity data from 50 cm beneath the soil surface with a time resolution of 15 min, (ii) hourly atmospheric data and (iii) daily irrigation amounts. Exploratory data analysis and data pre-processing phases were performed and then statistical models were constructed for time series forecasting based on the set of available data. The obtained prediction models showed good forecasting accuracy and good interpretability of the results. Full article
(This article belongs to the Special Issue Sensors and Data Analysis Applied in Environmental Monitoring)
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20 pages, 10831 KiB  
Article
An Affordable Streamflow Measurement Technique Based on Delay and Sum Beamforming
by Giuseppe Passarella, Aimé Lay-Ekuakille, John Peter Djungha Okitadiowo, Rita Masciale, Silvia Brigida, Raffaella Matarrese, Ivan Portoghese, Tommaso Isernia and Luciano Blois
Sensors 2022, 22(8), 2843; https://doi.org/10.3390/s22082843 - 7 Apr 2022
Cited by 2 | Viewed by 1661
Abstract
At the local scale, environmental parameters often require monitoring by means of affordable measuring techniques and technologies given they need to be frequently surveyed. Streamflow in riverbeds or in channels is a hydrological variable that needs to be monitored in order to keep [...] Read more.
At the local scale, environmental parameters often require monitoring by means of affordable measuring techniques and technologies given they need to be frequently surveyed. Streamflow in riverbeds or in channels is a hydrological variable that needs to be monitored in order to keep the runoff regimes under control and somehow forecast floods, allowing prevention of damage for people and infrastructure. Moreover, measuring such a variable is always extremely important for the knowledge of the environmental status of connected aquatic ecosystems. This paper presents a new approach to assessing hydrodynamic features related to a given channel by means of a beamforming technique that was applied to video sensing. Different features have been estimated, namely the flow velocity, the temperature, and the riverbed movements. The applied beamforming technique works on a modified sum and delay method, also using the Multiple Signal Classification algorithm (MUSIC), by acting as Synthetic Aperture Radar (SAR) post-processing. The results are very interesting, especially compared to the on-site measured data and encourage the use of affordable video sensors located along the channel or river course for monitoring purposes. The paper also illustrates the use of beamforming measurements to be calibrated by means of conventional techniques with more accurate data. Certainly, the results can be improved; however, they indicate some margins of improvements and updates. As metrics of assessment, a histogram of greyscale/pixels was adopted, taking into account the example of layers and curve plots. They show changes according to the locations where the supporting videos were obtained. Full article
(This article belongs to the Special Issue Sensors and Data Analysis Applied in Environmental Monitoring)
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21 pages, 6086 KiB  
Article
Dissolved Organic Carbon Source Attribution in the Changjiang Outflow Region of the East China Sea
by Xiaoyu Zhang, Yong Du, Zhihua Mao, Lei Bi, Jianyu Chen, Haiyan Jin and Shuchang Ma
Sensors 2021, 21(24), 8450; https://doi.org/10.3390/s21248450 - 17 Dec 2021
Cited by 2 | Viewed by 2461
Abstract
The variable optical properties of chromophoric dissolved organic matter (CDOM) under the complicated dynamic marine environment make it difficult to establish a robust inversion algorithm for quantifying the dissolved organic carbon (DOC). To better understand the main factors affecting the relationship between the [...] Read more.
The variable optical properties of chromophoric dissolved organic matter (CDOM) under the complicated dynamic marine environment make it difficult to establish a robust inversion algorithm for quantifying the dissolved organic carbon (DOC). To better understand the main factors affecting the relationship between the DOC and the CDOM when the Changjiang diluted water (CDW) interacts with the marine currents on the wide continental shelf, we measured the DOC concentration, the absorption, and the fluorescence spectra of the CDOM along the main axis and the northern boundary of the CDW. The sources of DOC and their impacts on the relationship between the optical properties of the DOC and CDOM are discussed. We reached the following conclusions: There are strong positive correlations between the absorptive and fluorescent properties of the DOC and the CDOM as a whole. The dilution of the terrestrial DOC carried by the CDW through mixing with saline sea water is the dominant mechanism controlling the characteristics of the optical properties of the CDOM. CDOM optical properties can be adopted to establish inversion models in retrieving DOC in Changjiang River Estuary. It is concluded that the introduction of extra DOC from different sources is the main factor causing the regional optical complexity leading to the bias of DOC estimation rather than removal mechanism. As whole, the input of polluted water from Huangpujiang River with abnormally high a(355) and Fs(355) will induce the overestimation of DOC. In the main axis of CDW, the impact from autochthonous DOC input to the correlation between DOC and CDOM can be neglected in comparison with conservative dilution procedure. The relationship between the DOC and the CDOM on the northern boundary of the CDW is more complicated, which can be attributed to the continuous input of terrestrial material from the Old Huanghe Delta by the Subei Coastal Current, the input of materials from the Yellow sea by the Yellow Sea Warm Western Coastal Current, and the input of materials from the Changjiang Basin by the CDW. The results of this study suggest that long-term observations of the regional variations in the DOM inputs from multiple sources in the interior of the CDW are essential, which is conducive to assess the degree of impact to the DOC estimation through the CDOM in the East China Sea. Full article
(This article belongs to the Special Issue Sensors and Data Analysis Applied in Environmental Monitoring)
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17 pages, 77531 KiB  
Article
Meteo-Hydrological Sensors within the Lake Maggiore Catchment: System Establishment, Functioning and Data Validation
by Marzia Ciampittiello, Dario Manca, Claudia Dresti, Stefano Grisoni, Andrea Lami and Helmi Saidi
Sensors 2021, 21(24), 8300; https://doi.org/10.3390/s21248300 - 11 Dec 2021
Cited by 3 | Viewed by 2564
Abstract
Climate change and human activities have a strong impact on lakes and their catchments, so to understand ongoing processes it is fundamental to monitor environmental variables with a spatially well-distributed and high frequency network and efficiently share data. An effective sharing and interoperability [...] Read more.
Climate change and human activities have a strong impact on lakes and their catchments, so to understand ongoing processes it is fundamental to monitor environmental variables with a spatially well-distributed and high frequency network and efficiently share data. An effective sharing and interoperability of environmental information between technician and end-user fosters an in-depth knowledge of the territory and its critical environmental issues. In this paper, we present the approaches and the results obtained during the PITAGORA project (Interoperable Technological Platform for Acquisition, Management and Organization of Environmental data, related to the lake basin). PITAGORA was aimed at developing both instruments and data management, including pre-processing and quality control of raw data to ensure that data are findable, accessible, interoperable, and reusable (FAIR principles). The main results show that the developed instrumentation is low-cost, easily implementable and reliable, and can be applied to the measurement of diverse environmental parameters such as meteorological, hydrological, physico-chemical, and geological. The flexibility of the solutions proposed make our system adaptable to different monitoring purposes, research, management, and civil protection. The real time access to environmental information can improve management of a territory and ecosystems, safety of the population, and sustainable socio-economic development. Full article
(This article belongs to the Special Issue Sensors and Data Analysis Applied in Environmental Monitoring)
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11 pages, 1279 KiB  
Communication
Vayu: An Open-Source Toolbox for Visualization and Analysis of Crowd-Sourced Sensor Data
by Sachit Mahajan
Sensors 2021, 21(22), 7726; https://doi.org/10.3390/s21227726 - 20 Nov 2021
Cited by 3 | Viewed by 2433
Abstract
Recent advances in sensor technology and the availability of low-cost and low-power sensors have changed the air quality monitoring paradigm. These sensors are being widely used by scientists and citizens for monitoring air quality at finer spatial-temporal resolution. Such practices are opening up [...] Read more.
Recent advances in sensor technology and the availability of low-cost and low-power sensors have changed the air quality monitoring paradigm. These sensors are being widely used by scientists and citizens for monitoring air quality at finer spatial-temporal resolution. Such practices are opening up opportunities to enhance the traditional monitoring networks, but at the same time, these sensors are producing large data sets that can become overwhelming and challenging when it comes to the scientific tools and skills required to analyze the data. To address this challenge, an open-source, robust, and cross-platform sensor data analysis toolbox called Vayu is developed that allows researchers and citizens to do detailed and reproducible analyses of air quality data. Vayu combines the power of visualization and statistical analysis using a simple and intuitive graphical user interface. Additionally, it offers a comprehensive set of tools for systematic analysis such as data conversion, interpolation, aggregation, and prediction. Even though Vayu was developed with air quality research in mind, it can be used to analyze different kinds of time-series data. Full article
(This article belongs to the Special Issue Sensors and Data Analysis Applied in Environmental Monitoring)
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40 pages, 8910 KiB  
Article
Multivariate Time Series Analysis of Temperatures in the Archaeological Museum of L’Almoina (Valencia, Spain)
by Sandra Ramírez, Manuel Zarzo and Fernando-Juan García-Diego
Sensors 2021, 21(13), 4377; https://doi.org/10.3390/s21134377 - 26 Jun 2021
Cited by 5 | Viewed by 2416
Abstract
An earlier study carried out in 2010 at the archaeological site of L’Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering [...] Read more.
An earlier study carried out in 2010 at the archaeological site of L’Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering part of the remains in the museum. In this study, it was found that the thermal conditions are not homogeneous and vary at different points of the museum and along the year. According to the European Standard EN10829, it is necessary to define a plan for long-term monitoring, elaboration and study of the microclimatic data, in order to preserve the artifacts. With the aforementioned goal of extending the study and offering a tool to monitor the microclimate, a new statistical methodology is proposed. For this propose, during one year (October 2019–October 2020), a set of 27 data-loggers was installed, aimed at recording the temperature inside the museum. By applying principal component analysis and k-means, three different microclimates were established. In order to characterize the differences among the three zones, two statistical techniques were put forward. Firstly, Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was applied to a set of 671 variables extracted from the time series. The second approach consisted of using a random forest algorithm, based on the same functions and variables employed by the first methodology. Both approaches allowed the identification of the main variables that best explain the differences between zones. According to the results, it is possible to establish a representative subset of sensors recommended for the long-term monitoring of temperatures at the museum. The statistical approach proposed here is very effective for discriminant time series analysis and for explaining the differences in microclimate when a net of sensors is installed in historical buildings or museums. Full article
(This article belongs to the Special Issue Sensors and Data Analysis Applied in Environmental Monitoring)
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