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Smart Gas Sensor Applications in Environmental Change Monitoring

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 17853

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Special Issue Editor

Dr. Kai Xu

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Guest Editor

School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
Interests: low-dimensional materials; gas sensing; optoelectronic devices; micro–nano fabrications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global warming and climate change have become serious environmental threats in the last decade. Air pollution due to rapid modernization and urbanization is the major cause of environmental deterioration. The emission of sulfur dioxide (SO₂) and nitrogen oxides (NO_x), for instance, can be directly linked to the evolution of acid rain. Greenhouse gasses, including carbon dioxide (CO₂), methane (CH₄), and NO_x, are the main driver of global warming. Thus, the continuous monitoring and control of such pollutants are imperative to prevent environmental disasters.

This has prompted efforts to find new and user-friendly techniques for the monitoring of gasses hazardous to the environment and human health, which has led to the development of key technologies for their rapid, selective, sensitive, and efficient detection as well as that of chemical vapors and explosives. Given the boom of the Internet of Things (IoT), the next generation of gas sensors is expected to be massively deployed into dense network systems with low cost, low power consumption, and long-term stability. In addition, to achieve continuous monitoring, gas sensors may also need to demonstrate a high tolerance to environmental variables such as temperature, humidity, and pressure.

This Special Issue aims to provide a comprehensive collection of the latest advances in gas sensors based on various materials and an outlook on gas sensors in environmental monitoring. We invite you to submit short communications, full research articles, and timely reviews focusing on advanced gas sensing techniques.

Dr. Kai Xu
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

gas sensors
environmental monitoring
nanomaterials
metal oxides
metal sulfides
pollutants
semiconductors

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Published Papers (7 papers)

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Research

Jump to: Review

16 pages, 2953 KB

Open AccessArticle

Drone-Based Statistical Detection of Methane Anomalies Around Abandoned Oil and Gas Well Sites

by William Hoyt Thomas and Caixia Wang

Sensors 2026, 26(7), 2205; https://doi.org/10.3390/s26072205 - 2 Apr 2026

Viewed by 424

Abstract

Abandoned oil and gas wells pose significant risks to human health and the environment by emitting air pollutants, contaminating groundwater, and leaving behind hazardous debris. In the United States, approximately 3.9 million documented wells vary widely in the accuracy of their recorded locations and plugging status, creating major challenges for detection, mapping, and remediation. Existing well detection methods show some promise but often lose effectiveness under complex conditions, such as vegetation occlusion or construction without metal components. In this study, we propose a drone-based approach equipped with a highly sensitive methane sensor to identify statistical anomalies in methane concentrations around abandoned oil and gas well sites. To address the noisy and variable nature of environmental sensor data, statistical methods were developed that enable reliable anomaly detection under field conditions. Controlled release experiments with known emission points validated the method’s ability to statistically detect methane anomalies that may indicate nearby emission sources. We further tested the approach at a field site containing three abandoned wells with known locations and sparse emission profiles. The results demonstrate that the proposed drone-based sensing method can serve as a rapid survey approach to identify areas with elevated methane signals around well sites, helping to reduce the scope of the ground survey area, and supporting prioritization of follow-up ground investigations. This approach provides a practical means to support targeted monitoring and prioritization of remediation efforts, while supporting the future development of source attribution and localization methods. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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16 pages, 503 KB

Open AccessArticle

Detection of Hydraulic Oil-Polluted Soil Using a Low-Cost Electronic Nose with Sample Heating

by Piotr Borowik, Przemysław Pluta, Rafał Tarakowski and Tomasz Oszako

Sensors 2026, 26(4), 1154; https://doi.org/10.3390/s26041154 - 11 Feb 2026

Cited by 1 | Viewed by 1681

Abstract

Monitoring soil contamination from petroleum products is vital for protecting human health and the environment. In forestry, hydraulic oil spills frequently result from leaks in equipment such as harvesters. This study evaluates a custom-built, inexpensive electronic nose, equipped with a Figaro TGS gas sensor array, for discriminating between pristine and contaminated soil samples. Two oil types and three pollution intensities were analyzed. The constructed electronic nose applied two sensor operation modes: (i) response to change of sensor operation condition from clean air to target odors and (ii) response to sensor heater temperature modulation. Classification was performed using Random Forest and Support Vector Machine (SVM) algorithms, and Linear Discriminant Analysis (LDA) was used to explore multidimensional data patterns. The sensor heater temperature modulation mode provided superior classification performance. Measurements at room temperature achieved an accuracy of 97%, clearly outperforming measurements on samples heated to 60 °C (75%). While the system successfully identified biodegradable oil contamination, standard mineral oil was more challenging to detect. Among the sensors tested, TGS 2602 was the most effective. These findings indicate that portable electronic noses can provide a statistically robust and cost-effective tool for assessing the severity of soil pollution. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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20 pages, 60830 KB

Open AccessArticle

Wildfire Early Warning System Based on a Smart CO₂ Sensors Network

by Alessio De Rango, Luca Furnari, Fabio Cortale, Alfonso Senatore and Giuseppe Mendicino

Sensors 2025, 25(7), 2012; https://doi.org/10.3390/s25072012 - 23 Mar 2025

Cited by 4 | Viewed by 6630

Abstract

Climate change exacerbates wildfire risks in regions like the Mediterranean, where rising temperatures and prolonged droughts create ideal fire conditions. Adapting to this scenario requires implementing advanced risk management strategies that leverage cutting-edge technologies. Wildfire early warning systems are crucial tools for detecting fires at an early stage, helping prevent potential future damage. This paper proposes a smart CO₂ sensor network-based early warning system, relying on a platform that enables the connection, management, and processing of data from the devices through the cloud. The wildfire early warning system was tested in a real controlled experiment, in which 44 sensors were deployed in strategically selected locations at varying distances from the fire. To enhance early detection, three Artificial Intelligence (AI) models were developed using AutoEncoders (AEs) and Long-Short-Term Memory (LSTM), and these were compared to a simple threshold-based (NO-AI) model. All AI models, especially the LSTM-based model, were able to extract more valuable information from the CO₂ records, activating up to 56% more sensors than the NO-AI model in less time and tracking potential fire front propagation based on wind patterns. Therefore, the system not only improves early fire detection models but also effectively supports firefighting operations. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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17 pages, 6467 KB

Open AccessArticle

Enhanced N-Butanol Sensing Performance of Cr-Doped CeO₂ Nanomaterials

by Yanping Chen, Haoyang Xu, Jing Ren, Guangfeng Zhang and Yonghui Jia

Sensors 2025, 25(4), 1208; https://doi.org/10.3390/s25041208 - 16 Feb 2025

Cited by 7 | Viewed by 1407

Abstract

The Cr-doped CeO₂ nanomaterials were prepared by a simple hydrothermal method. Morphological analysis revealed that Cr doping altered the morphology and size of the CeO₂ particles. Gas sensing tests results showed that Cr/Ce-2 has the highest response (R_a/R_g = 15.6 @ 10 ppm), which was 12.58 times higher than that of the pure CeO₂ sensor. Furthermore, the optimal operating temperature was reduced from 210 °C to 170 °C. The Cr/Ce-2 sensor also displayed outstanding repeatability and gas selectivity. The improved gas sensing performance of the Cr-doped CeO₂ sensor can be attributed to its smaller grain size and higher porosity compared to pure CeO₂. In addition, oxygen vacancies played a pivotal role in improving the gas-sensing performance. The present work provides a new CeO₂-based gas-sensitive material for the detection of n-butanol. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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15 pages, 8392 KB

Open AccessArticle

Sensitivity Analysis of an Optical Interferometric Surface Stress Ethanol Gas Sensor with a Freestanding Nanosheet

by Ryusei Sogame, Yong-Joon Choi, Toshihiko Noda, Kazuaki Sawada and Kazuhiro Takahashi

Sensors 2024, 24(24), 8055; https://doi.org/10.3390/s24248055 - 17 Dec 2024

Cited by 2 | Viewed by 1646

Abstract

Ethanol (EtOH) gas detection has garnered considerable attention owing to its wide range of applications in industries such as food, pharmaceuticals, medical diagnostics, and fuel management. The development of highly sensitive EtOH-gas sensors has become a focus of research. This study proposes an optical interferometric surface stress sensor for detecting EtOH gas. The sensor incorporates a 100 nm-thick freestanding membrane of Parylene C and gas-sensitive polymethylmethacrylate (PMMA) fabricated within a microcavity on a Si substrate. The results showed that reducing the thickness of the freestanding Parylene C membrane is essential for achieving higher sensitivity. Previously, a 100-nm-thick membrane transfer onto microcavities was achieved using a surfactant-assisted release technique. However, polymerization inhibition caused by the surfactant presented challenges in forming ultrathin membranes of several tens of nanometers. In this study, we employed a surfactant-free release technique using a hydrophilic natural oxide layer to successfully form a 14-nm-thick freestanding Parylene C membrane. In contrast, the optimum thickness of the gas-adsorbed PMMA membrane was approximately 295 nm. Moreover, we demonstrated that this thinner membrane improved EtOH gas detection sensitivity by a factor of eight compared with our previously reported sensor. Thus, this study advances the field of nanoscale materials and sensor technology. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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Review

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39 pages, 2975 KB

Open AccessReview

Digital Technologies and Machine Learning in Environmental Hazard Monitoring: A Synthesis of Evidence for Floods, Air Pollution, Earthquakes, and Fires

by Jacek Lukasz Wilk-Jakubowski, Artur Kuchcinski, Grzegorz Kazimierz Wilk-Jakubowski, Andrzej Palej and Lukasz Pawlik

Sensors 2026, 26(3), 893; https://doi.org/10.3390/s26030893 - 29 Jan 2026

Viewed by 677

Abstract

This review synthesizes the state of the art on the integration of digital technologies, particularly machine learning, the Internet of Things (IoT), and advanced image processing techniques, for enhanced hazard monitoring. Focusing on air pollution, earthquakes, floods, and fires, we analyze articles selected from Scopus published between 2015 and 2024. This study classifies the selected articles based on hazard type, digital technology application, geographical location, and research methodology. We assess the effectiveness of various approaches in improving the accuracy and efficiency of hazard detection, monitoring, and prediction. The review highlights the growing trend of leveraging multi-sensor data fusion, deep learning models, and IoT-enabled systems for real-time monitoring and early warning. Furthermore, we identify key challenges and future directions in the development of robust and scalable hazard monitoring systems, emphasizing the importance of data-driven solutions for sustainable environmental management and disaster resilience. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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49 pages, 517 KB

Open AccessReview

A Comprehensive Review of Data-Driven Techniques for Air Pollution Concentration Forecasting

by Jaroslaw Bernacki and Rafał Scherer

Sensors 2025, 25(19), 6044; https://doi.org/10.3390/s25196044 - 1 Oct 2025

Cited by 5 | Viewed by 4358

Abstract

Air quality is crucial for public health and the environment, which makes it important to both monitor and forecast the level of pollution. Polluted air, containing harmful substances such as particulate matter, nitrogen oxides, or ozone, can lead to serious respiratory and circulatory diseases, especially in people at risk. Air quality forecasting allows for early warning of smog episodes and taking actions to reduce pollutant emissions. In this article, we review air pollutant concentration forecasting methods, analyzing both classical statistical approaches and modern techniques based on artificial intelligence, including deep models, neural networks, and machine learning, as well as advanced sensing technologies. This work aims to present the current state of research and identify the most promising directions of development in air quality modeling, which can contribute to more effective health and environmental protection. According to the reviewed literature, deep learning–based models, particularly hybrid and attention-driven architectures, emerge as the most promising approaches, while persistent challenges such as data quality, interpretability, and integration of heterogeneous sensing systems define the open issues for future research. Full article

(This article belongs to the Special Issue Smart Gas Sensor Applications in Environmental Change Monitoring)

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