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Advanced Sensing Technologies for Environmental Applications
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Advanced Sensing Technologies for Environmental Applications

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 3787

Editor

Prof. Dr. Michele Penza

E-Mail Website
Guest Editor
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability, Division of Technologies and Advanced Materials for Sustainable Manufacturing Industry—Brindisi Research Center, Brindisi, Italy
Interests: sensor materials; functional materials; gas sensors; chemical sensors; air-quality sensor systems; sensor technology development; environmental measurements; urban air-quality sensor networks; smart city applications; environmental sustainability; environmental sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor technologies are crucial for environmental monitoring and sustainability applications. Recent progress in sensing technologies includes advanced functional materials, sustainable materials, integrated transducers, AI-enabled sensors, smart sensor systems, multi-sensor data fusion, wireless sensor networks and more for boosting environmental research and innovation. However, most accurate sensors and enhanced data quality are still open questions for environmental sensing.

Networked sensor systems have been largely used for air-quality monitoring, noise monitoring, water monitoring, and soil monitoring in urban and rural environments.

Current trends in environmental technologies explore integrated sensors, mobile sensing, unmanned aerial vehicles (UAVs), satellite monitoring, environmental forecasting and predictive digital models.

Emerging applications of the environmental sensors include remediation, the recovery of critical raw materials, geographic information system (GIS) mapping, environmental digital twin, protection, detection and sustainability. Case studies, new concepts, proofs and trends in environmental sensing are highly welcomed to depict world-class research.

In this Special Issue, we kindly invite front-line scientists to submit original research articles and/or featured reviews on exploring Advanced Sensing Technologies for Environmental Applications.

Potential topics include, but are not limited to, the following:

  • Chemical sensors (toxic gases, volatile organic compounds)
  • Chemo-physical sensors (greenhouse gases, particulate matter)
  • Biochemical sensors (pathogens, pollen, bioagents)
  • Advanced transducing platforms for environmental sensing
  • Wireless environmental sensor networks
  • Sensor systems for air-quality monitoring
  • Sensor systems for noise monitoring
  • Sensor systems for water monitoring
  • Sensor systems for soil monitoring
  • Multi-sensor data fusion and machine learning for environmental sensing
  • AI-enabled sensors for environmental monitoring
  • IoT environmental applications
  • Environment monitoring and big data acquisition
  • Integrated environmental sensing technologies
  • Mobile sensing for environmental applications
  • Unmanned aerial vehicles (UAVs) for environmental monitoring applications
  • Satellite environmental monitoring
  • Environmental forecasting and predictive digital models
  • Sensor technologies for environmental remediation
  • Sensor technologies for recovery of critical raw materials
  • Sensor technologies for geographic information system (GIS) applications
  • Sensor technologies for environmental digital twin
  • Advanced materials for environmental sensing and protection
  • Case studies of environmental detection and sustainability
  • New concepts, proofs and trends in environmental sensing

Prof. Dr. Michele Penza
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-anonymized 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

  • environmental chemical sensors
  • environmental smart sensors
  • environmental sensor systems
  • environmental wireless sensor networks
  • mobile sensing and unmanned aerial vehicles (UAVs)
  • satellite environmental monitoring
  • environmental forecasting
  • sensor technologies for IoT environmental applications
  • sensor technologies for environmental digital twin
  • advanced materials and sustainable materials
  • case studies of environmental sustainability
  • new concepts in environmental sensing

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Published Papers (6 papers)

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24 pages, 29388 KB  
Article
Near-Real Time Monitoring of Active Volcanoes from Space Using SLSTR (Sea and Land Surface Temperature Radiometer) SWIR (Shortwave Infrared) Observations
by Carolina Filizzola, Giuseppe Mazzeo, Nicola Genzano, Carla Pietrapertosa and Francesco Marchese
Sensors 2026, 26(13), 4262; https://doi.org/10.3390/s26134262 - 4 Jul 2026
Viewed by 301
Abstract
The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present [...] Read more.
The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Environmental Applications)
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17 pages, 6180 KB  
Article
Optimized Design and Radiation Error Correction of a Naturally Ventilated Air Temperature Sensor for Atmospheric Environmental Monitoring
by Wei Jin, Qingquan Liu, Wei Dai, Xin Hong, Xilong Cao and Haiwen Sun
Sensors 2026, 26(12), 3853; https://doi.org/10.3390/s26123853 - 17 Jun 2026
Viewed by 283
Abstract
Air temperature measurements in atmospheric environmental monitoring are susceptible to radiation-induced bias under natural ventilation. This study develops a low-power naturally ventilated air temperature sensor and a correction method combining computational fluid dynamics (CFD) with machine learning. The sensor integrates a Pt100 thin-film [...] Read more.
Air temperature measurements in atmospheric environmental monitoring are susceptible to radiation-induced bias under natural ventilation. This study develops a low-power naturally ventilated air temperature sensor and a correction method combining computational fluid dynamics (CFD) with machine learning. The sensor integrates a Pt100 thin-film platinum resistance probe (Heraeus Holding GmbH, Hanau, Germany), symmetric guide plates, and a dual aluminum-plate radiation shield to reduce radiative heating while improving airflow around the probe. A three-dimensional fluid–solid coupled heat-transfer model was established in ANSYS FLUENT 15.0 to optimize guide-plate spacing and inclination angle and quantify the effects of solar radiation, long-wave radiation, scattered radiation, air density, wind speed, solar elevation angle, and surface albedo on radiation error. CFD results identified a guide-plate spacing of 24 mm and an inclination angle of 45° as the preferred parameters. A multilayer perceptron (MLP) model trained with CFD-derived data was validated in field experiments using a Model 076B aspirated radiation shield (Met One Instruments, Inc., Grants Pass, OR, USA) as the reference. The model predicted radiation error with a root mean square error (RMSE) of 0.052 °C, a mean absolute error (MAE) of 0.042 °C, and a correlation coefficient of 0.92. The proposed sensor and correction method provide a low-power and easy-to-maintain approach for reducing radiation-induced bias in naturally ventilated air-temperature measurements, with potential applications in meteorological observation, air-quality monitoring, and agricultural microclimate assessment. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Environmental Applications)
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23 pages, 4967 KB  
Article
LOAC2: The Improved Version of the Light Optical Aerosols Counter for Measurements at Ground Level and Within the Atmosphere Under Balloons
by Jean-Baptiste Renard, Gwenaël Berthet, Matthieu Jeannot, Patrick Jacquet, Benjamin Langerome, Thomas Lecas, Stéphane Chevrier, Emmanuel Briaud, Gilles Chalumeau, Florent Grenard, Benjamin Charpentier, Maylis Gaulin, Slimane Bekki and Jérôme Giacomoni
Sensors 2026, 26(12), 3786; https://doi.org/10.3390/s26123786 - 14 Jun 2026
Viewed by 458
Abstract
The new LOAC2 optical aerosol counter is designed to detect liquid and solid particulates across 19 to 30 size classes within the 0.15–90 µm size range, and to provide their main typology. The instrument can be used at ground level and on all [...] Read more.
The new LOAC2 optical aerosol counter is designed to detect liquid and solid particulates across 19 to 30 size classes within the 0.15–90 µm size range, and to provide their main typology. The instrument can be used at ground level and on all kinds of balloons, including weather balloons, up to an altitude of about 35 km. The measurements are based on principles established for the previous version of LOAC, now incorporating improved electronics and detection geometry. Counting is performed at small scattering angles in the diffraction domain, making it insensitive to the refractive indices and the porosity of the particles, thus allowing a direct relationship between scattered intensity and aerosol size. Typology identification is now performed at three additional scattering angles, where the scattered flux is highly sensitive to the refractive index of the different aerosol families present in the atmosphere. The calibration was conducted using calibrated spherical and irregular grains, as well as different types of solid particles. Several intercomparison sessions with other counters and with reference mass-concentration air quality monitoring stations were carried out indoors, in an atmospheric simulation chamber, and in outdoor ambient air. The agreement between LOAC2 and the other instruments is good, confirming the ability of LOAC2 to be used for scientific studies and for monitoring atmospheric aerosols. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Environmental Applications)
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21 pages, 2774 KB  
Article
Combined Dielectric Spectroscopy and Operando DRIFTS Analysis of Ba-Based NOx Storage Materials for Radio-Frequency-Based NOx Dosimeters
by Daniela Schönauer-Kamin, Fabian Fütterer, Johanna Baumgärtner, Thomas Wöhrl, Gunter Hagen and Ralf Moos
Sensors 2026, 26(10), 3203; https://doi.org/10.3390/s26103203 - 19 May 2026
Viewed by 421
Abstract
This study investigates the dielectric behavior and NOx storage properties of Pt/Ba–Al2O3 NOx storage materials using microwave cavity perturbation, operando DRIFTS, and impedance spectroscopy with respect to their applicability in a radio-frequency-based NOx dosimeter-type sensor. Dielectric losses [...] Read more.
This study investigates the dielectric behavior and NOx storage properties of Pt/Ba–Al2O3 NOx storage materials using microwave cavity perturbation, operando DRIFTS, and impedance spectroscopy with respect to their applicability in a radio-frequency-based NOx dosimeter-type sensor. Dielectric losses (ε″) are identified as the most sensitive indicator of NOx storage, exhibiting a clear linear correlation with both the accumulated NOx dose and the utilization of Ba storage sites. Approximately 35% of the available Ba sites participate in nitrite and nitrate formation, and the absolute dielectric loss response increases proportionally with the Ba content of the NOx storage catalyst. In contrast, the permittivity (ε′) shows only minor changes, which are mainly influenced by temperature. Temperature-dependent experiments reveal stable NOx storage with negligible desorption up to 350 °C, whereas pronounced desorption processes at 400 °C significantly limit the linear dosimeter behavior. Operando DRIFTS measurements on Pt/Ba–Al2O3 functional films confirm temperature-dependent formation of nitrites and nitrates, with nitrates dominating the NOx storage at elevated temperatures. Capacitance measurements show a slight increase during NOx storage, indicating a moderate increase in permittivity. Overall, Pt/Ba–Al2O3 NOx storage materials exhibit a robust, quantitatively interpretable dielectric response that is well suited for radio-frequency-based, dosimeter-type NOx sensing. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Environmental Applications)
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17 pages, 2649 KB  
Article
FRESH: An Autonomous IoT Platform for Multi-Parameter Environmental Sensing and Short-Term Forecasting
by Feiling Pan and James A. Covington
Sensors 2026, 26(10), 3015; https://doi.org/10.3390/s26103015 - 10 May 2026
Viewed by 1010
Abstract
Environmental monitoring systems are often constrained by high cost, limited portability, restricted pollutant coverage, and dependence on fixed infrastructure, which can limit their suitability for distributed real-time sensing. This study presents FRESH, an autonomous Internet of Things (IoT)-based platform for multi-parameter environmental monitoring [...] Read more.
Environmental monitoring systems are often constrained by high cost, limited portability, restricted pollutant coverage, and dependence on fixed infrastructure, which can limit their suitability for distributed real-time sensing. This study presents FRESH, an autonomous Internet of Things (IoT)-based platform for multi-parameter environmental monitoring and short-term forecasting. The system integrates sensors for air quality, thermal conditions, light, acoustics, and weather, together with GSM-based remote data transmission, onboard data logging, and hybrid battery–solar power management. FRESH was deployed across multiple indoor and outdoor locations in Coventry and at the University of Warwick, UK, and operated over a 10-month period to assess practical performance under varied environmental conditions. In addition to continuous environmental sensing, machine learning models were developed to predict short-term changes in selected environmental variables. Across the tested models, the best predictive performance was obtained for several key parameters, including particulate matter (R2 = 0.93), volatile organic compounds (R2 = 0.92), and ozone (R2 = 0.98). The results suggest that FRESH has potential to support portable, multi-parameter environmental monitoring with integrated short-horizon forecasting, providing a basis for further development of distributed sensing and localised early-warning applications. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Environmental Applications)
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12 pages, 1636 KB  
Brief Report
Dynamic Performance Evaluation of Optical and Capacitive Sensors for Atmospheric Water Vapor Monitoring
by Mattia S. Arrigo, Roberto M. R. Di Martino and Marcello Liotta
Sensors 2026, 26(13), 4064; https://doi.org/10.3390/s26134064 - 26 Jun 2026
Viewed by 391
Abstract
Atmospheric water vapor (H2O) is the most significant greenhouse gas; accurately quantifying it is a critical yet challenging task due to its high spatial and temporal variability. This study presents a comparative analysis of the LI-COR LI-7825 (LI-COR Biosciences, Lincoln, Nebraska), [...] Read more.
Atmospheric water vapor (H2O) is the most significant greenhouse gas; accurately quantifying it is a critical yet challenging task due to its high spatial and temporal variability. This study presents a comparative analysis of the LI-COR LI-7825 (LI-COR Biosciences, Lincoln, Nebraska), which utilizes OF-CEAS technology, and the Vaisala HMP7 (Vaisala Oyj, Vantaa, Finland) capacitive probe, which employs HUMICAP ® technology. Before testing the sensors under dynamic conditions, we performed a two-point calibration on the Vaisala HMP7. We then proceeded to evaluate the two technologies using a custom-made setup to put them in series and minimize atmospheric contamination. The results demonstrate a high statistical correlation (R2=0.995), though the Vaisala sensor exhibited systematic offsets at high-humidity levels. To better compare datasets, a low-pass digital filter was used to account for the different T90 values of the instruments. The results show that while spectroscopy provides superior precision and response speed, the environmental resilience of capacitive sensors makes them better suited for field work. The implementation of robust synchronization protocols is essential to develop effective hybrid monitoring networks that balance precision with scalability. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Environmental Applications)
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