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Innovative Optical and Scintillation Sensors for Environmental Monitoring
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Innovative Optical and Scintillation Sensors for Environmental Monitoring

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2086

Special Issue Editors

Dr. Giuseppe Gallo

E-Mail Website
Guest Editor
Department of Physics and Astronomy “E. Majorana”, University of Catania, Via Santa Sofia 64, 95123 Catania, Italy
Interests: optical sensors; scintillation sensors; detector design; muography; software development for data analysis; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Domenico Lo Presti

E-Mail Website
Guest Editor
Department of Physics and Astronomy “E. Majorana”, University of Catania, Via Santa Sofia 64, 95123 Catania, Italy
Interests: optical sensors; scintillation sensors; detector design; muography; front-end and read-out electronics design and testing; data acquisition systems

Special Issue Information

Dear Colleagues,

Environmental concerns are one of the trendiest topics of our time, highlighting the need for advanced, accurate, real-time monitoring technologies. This Special Issue aims to serve as a reference point for the latest advances in optical and scintillation sensor technologies that provide a new accuracy and sensitivity standard for detecting environmental changes. A short but growing list of potential applications for these innovative sensors includes the following:

  • Air and water quality monitoring;
  • Pollutant detection;
  • Radiation assessment;
  • Greenhouse gas emission tracking;
  • Soil contamination analysis;
  • Climate change impact studies;
  • Early-warning systems for natural disasters;
  • Biodiversity and ecosystem health monitoring;
  • Oceanographic and marine environment surveillance, and other areas related to environmental protection and public health.

The papers in this issue may focus on a specific aspect of research, such as sensor designs and materials, novel optical detection mechanisms, and state-of-the-art scintillation techniques. At the same time, the impact on real-world applications for more appropriate environmental management should be emphasized.

Dr. Giuseppe Gallo
Dr. Domenico Lo Presti
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 250 words) can be sent to the Editorial Office for assessment.

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

  • optical sensors
  • scintillation sensors
  • environmental monitoring
  • pollutant detection
  • radiation assessment
  • public health

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Published Papers (2 papers)

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Research

15 pages, 10604 KB  
Article
From Light to Energy: Machine Learning Algorithms for Position and Energy Deposition Estimation in Scintillator–SiPM Detectors
by Yoav Simhony, Alex Segal, Ofer Amrani and Erez Etzion
Sensors 2026, 26(1), 101; https://doi.org/10.3390/s26010101 - 23 Dec 2025
Viewed by 909
Abstract
Scintillator-SiPM Particle Detectors (SSPDs) are compact, low-power devices with applications including particle physics, underground tomography, cosmic-ray studies, and space instrumentation. They are based on a prism-shaped scintillator with corner-mounted SiPMs. Previous work has demonstrated that analytic algorithms based on a physical model of [...] Read more.
Scintillator-SiPM Particle Detectors (SSPDs) are compact, low-power devices with applications including particle physics, underground tomography, cosmic-ray studies, and space instrumentation. They are based on a prism-shaped scintillator with corner-mounted SiPMs. Previous work has demonstrated that analytic algorithms based on a physical model of light propagation can reconstruct particle impinging positions and tracks and estimate deposited energy and Linear Energy Transfer (LET) with moderate accuracy. In this study, we enhance this approach by applying machine learning (ML) methods, specifically gradient boosting techniques, to improve the accuracy of spatial location and energy deposition estimation. Using the GEANT4 simulation toolkit, we simulated cosmic muons and energetic oxygen ions traversing an SSPD, and we trained XGBoost and LightGBM models to predict particle impinging positions and deposited energy. Both algorithms outperformed the analytic baseline. We further investigated hybrid strategies, including hybrid boosting and probing. While hybrid boosting provided no significant improvement, probing yielded measurable gains in both position and LET estimation. These results suggest that ML-driven reconstruction provides a powerful enhancement to SSPD performance. Full article
(This article belongs to the Special Issue Innovative Optical and Scintillation Sensors for Environmental Monitoring)
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11 pages, 1433 KB  
Communication
A Study of Dose Rate Probes for the País Vasco Environmental Radioactivity Automatic Network
by Natalia Alegría, Miguel Angel Hernández-Ceballos, Igor Peñalva, Andima Freire and Jose Miguel Muñoz
Sensors 2025, 25(21), 6616; https://doi.org/10.3390/s25216616 - 28 Oct 2025
Viewed by 689
Abstract
There are many types of probes available on the market for measuring ambient dose equivalent rates (ADERs), which makes intercomparison exercises essential to ensure data comparability and reliability. This study evaluated the performance of four widely used and similarly priced probes—the Reuter-Stokes ionization [...] Read more.
There are many types of probes available on the market for measuring ambient dose equivalent rates (ADERs), which makes intercomparison exercises essential to ensure data comparability and reliability. This study evaluated the performance of four widely used and similarly priced probes—the Reuter-Stokes ionization chamber, the RX04L from BITT, the MIRA from ENVINET, and the LB9360 from Berthold. The Reuter-Stokes ionization chamber was also taken as reference. Measurements were continuously conducted in Bilbao, northern Spain, during the period 2017–2021 under background conditions as well as during episodes of heavy rainfall and extreme temperatures. Results show that the BITT proportional counter exhibited the highest consistency with the Reuter-Stokes chamber under all meteorological conditions, and excellent stability even during extreme conditions. The Berthold probe displayed similar trends, but systematically overestimated dose rates, while the Geiger–Müller-based detector showed acceptable agreement under rainfall, but clear instability during temperature extremes. These findings highlight the importance of probe selection in environmental radioactivity networks as well as the use of reliable instruments for integration into modernized radiological surveillance systems. Full article
(This article belongs to the Special Issue Innovative Optical and Scintillation Sensors for Environmental Monitoring)
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