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Advanced Sensing Techniques for Environmental and Energy Systems
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Advanced Sensing Techniques for Environmental and Energy Systems

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

Deadline for manuscript submissions: 25 February 2026 | Viewed by 3836

Special Issue Editor

Dr. George Efthimiou

E-Mail Website
Guest Editor
Chemical Process and Energy Resources Institute, Thessaloniki 57001, Greece
Interests: examination of the wind flow field and the dispersion of airborne materials in the urban and industrial environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern demand for sustainable and efficient environmental and energy solutions has led to the rapid development of sensor technologies and advanced computational models. The Special Issue "Advanced Sensing and Modeling Techniques for Environmental and Energy Systems" aims to gather original research papers and reviews related to sensors, environmental data, and modeling to enhance energy efficiency, air quality, and climate analysis.

Topics of interest include, but are not limited to:

- Smart sensors and sensor networks for monitoring environmental parameters (pollution, temperature, humidity, wind conditions).

- Integration of sensor data with computational models (e.g., CFD, RANS, LES, machine learning) for extreme value prediction, energy efficiency analysis, and aerodynamics in enclosed and open environments.

- Development of predictive algorithms and methodologies for environmental conditions based on sensor data and advanced computational models.

- Applications of sensors and models in energy systems (wind, solar, biomass, building energy efficiency, smart grids).

- Optimization of ventilation and air management systems in industrial and urban areas through sensors and predictive models.

This Special Issue provides a platform for researchers at the forefront of sensor technology and computational modeling, encouraging new collaborations and applications that can improve energy efficiency, environmental protection, and quality of life.

We invite scientists from diverse fields, including engineering, physics, environmental sciences, computational fluid dynamics, and data analysis, to submit their papers.

Dr. George Efthimiou
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 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

  • smart sensors
  • environmental monitoring
  • computational modeling
  • energy efficiency
  • predictive algorithms
  • air quality management

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

Published Papers (5 papers)

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Research

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15 pages, 2328 KB  
Article
A Control Method for Optimizing the Spectral Ratio Characteristics of LED Lighting to Provide Color Rendering Performance Comparable to Natural Light
by Seung-Teak Oh, Ji-Young Lee and Jae-Hyun Lim
Sensors 2025, 25(24), 7453; https://doi.org/10.3390/s25247453 - 7 Dec 2025
Viewed by 236
Abstract
Light-emitting diode or LED lighting often faces challenges with color rendering due to its unique spectral characteristics compared to natural light. While efforts to enhance the color rendering index (CRI) have typically focused on improving light source elements, there has been less attention [...] Read more.
Light-emitting diode or LED lighting often faces challenges with color rendering due to its unique spectral characteristics compared to natural light. While efforts to enhance the color rendering index (CRI) have typically focused on improving light source elements, there has been less attention on optimizing the software aspects, such as the combination of light sources and spectral composition. Notably, there has been no efficient method proposed specifically for enhancing R9 and R12, which are critical for improving overall color rendering in LED lighting. This paper presents an optimization control method based on the spectral ratios of LED lighting to achieve color rendering similar to natural light. By analyzing the wavelength characteristics of both natural and artificial light, a high CRI light was realized through reinforcement of deficient wavelength bands. Experimental results showed an average CRI of 97, with R9 and R12 values around 93 and 98, respectively, demonstrating that LED technology can achieve color renderings comparable to natural light. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Environmental and Energy Systems)
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22 pages, 5792 KB  
Article
Particle Detection System Analysis in the Stratosphere Using High-Altitude Platforms Based on a MMPP-2 Model
by Mario Eduardo Rivero-Ángeles, Izlian Y. Orea-Flores, Mario Alberto Mendoza-Bárcenas, Iclia Villordo-Jiménez and Edgar Hernan Rosas Espinosa
Sensors 2025, 25(23), 7340; https://doi.org/10.3390/s25237340 - 2 Dec 2025
Viewed by 193
Abstract
Many space missions using High-Altitude Platforms (HAPs) are designed to measure contaminant particles in the stratosphere. However, there is no previous performance analysis of the sensors installed in the HAP in terms of the energy required by the detection system and the efficiency [...] Read more.
Many space missions using High-Altitude Platforms (HAPs) are designed to measure contaminant particles in the stratosphere. However, there is no previous performance analysis of the sensors installed in the HAP in terms of the energy required by the detection system and the efficiency of the experiment. In this regard, it is not possible to assess the number of measurements that may be taken by the mission and the energy that it will consume in advance. Considering that energy resources are extremely limited in these space missions, especially in HAPs attached to hot-air balloons that effectively provide High-Altitude Platforms (HAPs), where the weight of the payload is of major importance to the success of the mission, a previous analysis is required to account for the feasibility and pertinence of the contaminant detection system. Building on this, we propose a mathematical analysis to determine the energy consumption of the measurement system based on the potential trajectories and the particle density. Also, the analysis provides an estimation of the number of particles that can be detected by the experiment in order to determine the performance of the sensor system. The model is based on an MMPP-2 (Markov Modulated Poisson Process with 2 states) model under exponential distribution assumptions, which provides a basic model that can be easily extended to other distributions in future works. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Environmental and Energy Systems)
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15 pages, 3132 KB  
Article
Visibility-Based Calibration of Low-Cost Particulate Matter Sensors: Laboratory Evaluation and Theoretical Analysis
by Ayala Ronen
Sensors 2025, 25(22), 6995; https://doi.org/10.3390/s25226995 - 16 Nov 2025
Viewed by 430
Abstract
Low-cost optical sensors for particulate matter (PM) monitoring, such as the SDS011, are widely used due to their affordability and ease of deployment. However, their accuracy strongly depends on aerosol properties and environmental conditions, necessitating reliable calibration. This study presents a theoretical and [...] Read more.
Low-cost optical sensors for particulate matter (PM) monitoring, such as the SDS011, are widely used due to their affordability and ease of deployment. However, their accuracy strongly depends on aerosol properties and environmental conditions, necessitating reliable calibration. This study presents a theoretical and laboratory evaluation of a practical calibration method based on visibility sensors, which measure atmospheric light extinction and are readily available at many meteorological stations. Experiments were conducted in a controlled aerosol chamber, using SDS011 sensors, visibility sensors (FD70 and SWS250), and gravimetric samplers. The mass extinction coefficient was determined through parallel measurements of visibility and mass concentration, enabling conversion of optical signals into accurate PM values. The calibrated SDS011 sensors demonstrated consistent response with a stable normalization factor (dependent on aerosol type, wavelength, and particle size), allowing their deployment as a spatially distributed sensor network. Comparison with manufacturer calibration revealed substantial deviations due to differences in aerosol optical properties, highlighting the importance of application-specific calibration. The visibility-based approach enables real-time, continuous calibration of low-cost sensors with minimal equipment, offering a scalable solution for PM monitoring in resource-limited or remote environments. The method’s robustness under varying environmental conditions remains to be explored. Nevertheless, the results establish visibility-based calibration as a reliable and accessible framework for enhancing the accuracy of low-cost PM sensing technologies. The method enables scalable calibration with a single gravimetric reference and is suited for future field deployment in resource-limited settings, following additional validation under real atmospheric conditions. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Environmental and Energy Systems)
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23 pages, 5485 KB  
Article
Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning
by Dominic Benintendi, Kevin M. Tennant, Edward M. Sabolsky and Jay Wilhelm
Sensors 2025, 25(15), 4654; https://doi.org/10.3390/s25154654 - 27 Jul 2025
Viewed by 896
Abstract
Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the [...] Read more.
Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 MHz°C. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Environmental and Energy Systems)
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Other

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10 pages, 204 KB  
Perspective
Predicting Extreme Environmental Values with Hybrid Models: A Perspective Across Air Quality, Wind Energy, and Sensor Networks
by George Efthimiou
Sensors 2025, 25(21), 6523; https://doi.org/10.3390/s25216523 - 23 Oct 2025
Viewed by 988
Abstract
This Perspective synthesizes recent (2023–2025) progress in predicting extreme environmental values by combining empirical formulations, physics-based simulation outputs, and sensor-network data. We argue that hybrid approaches—spanning physics-informed machine learning, digital/operational twins, and edge/embedded AI—can deliver faster and more robust maxima estimates than standalone [...] Read more.
This Perspective synthesizes recent (2023–2025) progress in predicting extreme environmental values by combining empirical formulations, physics-based simulation outputs, and sensor-network data. We argue that hybrid approaches—spanning physics-informed machine learning, digital/operational twins, and edge/embedded AI—can deliver faster and more robust maxima estimates than standalone CFD or purely data-driven models, particularly for urban air quality and wind-energy applications. We distill lessons from cross-domain case studies and highlight five open challenges (uncertainty quantification, reproducibility and benchmarks, sensor layout optimization, real-time inference at the edge, and trustworthy model governance). Building on these, we propose a 2025–2030 research agenda: (i) standardized, open benchmarks with sensor–CFD pairs; (ii) physics-informed learners for extremes; (iii) adaptive source-term estimation pipelines; (iv) lightweight inference for embedded sensing; (v) interoperable digital-twin workflows; and (vi) reporting standards for uncertainty and ethics. The goal is a pragmatic path that couples scientific validity with deployability in operational environments. This Perspective is intended for researchers and practitioners in environmental sensing, urban dispersion, and renewable energy who seek actionable, cross-disciplinary directions for the next wave of extreme-value prediction. For instance, in validation studies using CFD-RANS and sensor data, the proposed hybrid models achieved prediction accuracies for peak pollutant concentrations and wind speeds within ~90–95% of high-fidelity simulations, with a computational cost reduction of over 80%. These results underscore the practical viability of the approach for operational use cases such as urban air quality alerts and wind farm micro-siting. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Environmental and Energy Systems)
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