Greenhouse Gas Detection using AI Powered Infrared Sensors
DOI:
https://doi.org/10.46610/JOARES.2025.v11i02.005Keywords:
Carbon nanotube emitters, Deep Neural Networks (DNN), hyperspectral imaging, Longwave Infrared (LWIR), Metal-Organic Frameworks (MOFs), Near-Infrared (NIR), Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS), Shortwave infrared (SWIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS)Abstract
The stability of the global climate is seriously threatened by the growing buildup of Greenhouse Gases (GHGs), especially carbon dioxide (CO₂) and methane (CH₄). Because it is colorless and odorless, methane is particularly hard to detect, even though it has a global warming potential that is more than 30 times that of CO₂ over a 100-year period. Traditional detection techniques, like satellite remote sensing, point sensors, and manual inspection, frequently have poor sensitivity, high latency, and limited spatial resolution when it comes to identifying diffuse or low-concentration leaks. This study suggests an AI-powered, multimodal detection framework that combines real-time data processing, nanophotonic sensor improvements, and advanced infrared (IR) spectroscopy in order to overcome these constraints.
To increase sensitivity, selectivity, and energy efficiency, the system makes use of the near-infrared (NIR), shortwave infrared (SWIR), and Longwave Infrared (LWIR) bands, which are supported by materials like Metal-Organic Frameworks (MOFs), Carbon Nanotubes (CNTs), and quantum dots. For low-latency performance, embedded FPGA hardware is used for signal processing. Principal Component Analysis (PCA) in conjunction with deep neural networks facilitates the quantification of CO₂ from spectral data, while AI models such as Vision Transformers (e.g., Gasformer) and temporal networks (e.g., GLRNet) are used to segment methane plumes in thermal imagery. Further advancements in system miniaturization and deployability include CMOS-compatible imagers and micro-LED-based illumination sources, enabling compact, low-power, and scalable integration for real-world applications. By combining interpretable, AI-driven analytics with nanophotonic sensor design, this work offers a unified and scalable solution for future GHG monitoring.
References
L. A. Zimmerman and J. P. Kerekes, “Comparison of Methane Detection Using Shortwave and Longwave Infrared Hyperspectral Sensors Under Varying Environmental Conditions,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 2517–2531, Jan. 2023, doi: https://doi.org/10.1109/jstars.2023.3247246.
B. Rouet-Leduc and C. Hulbert, “Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer,” Nature Communications, vol. 15, no. 1, May 2024, doi: https://doi.org/10.1038/s41467-024-47754-y.
E. Huang, L. Chen, T. Lv, and X. Cao, “GLRNet: Gas Leak Recognition via Temporal Difference in Infrared Video,” Lecture Notes in Computer Science, pp. 515–520, 2022, doi: https://doi.org/10.1007/978-3-031-20503-3_41.
T. T. Sarker, M. G. Embaby, K. R. Ahmed, and A. AbuGhazaleh, “Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, pp. 5489–5497, Jun. 2024, doi: https://doi.org/10.1109/cvprw63382.2024.00558.
P. Narkhede, R. Walambe, S. Mandaokar, P. Chandel, K. Kotecha, and G. Ghinea, “Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion,” Applied System Innovation, vol. 4, no. 1, p. 3, Jan. 2021, doi: https://doi.org/10.3390/asi4010003.
J. Wang, Y. Chuai, P. Li, and G. Lin, “Near-Infrared Off-Axis Integrated Cavity Output Spectroscopic Dual Greenhouse Gas Sensor Based on FPGA for in situ Application,” IEEE access, vol. 10, pp. 93901–93909, Jan. 2022, doi: https://doi.org/10.1109/access.2022.3203701.
L. Wang, tao hong, W. Cui, and M. Jiang, “GPR68 inhibited nucleus or ER to exosome non-protein based circadian exercise based on Thalamus big data,” Research Square (Research Square), May 2023, doi: https://doi.org/10.21203/rs.3.rs-2906322/v1.
C. Hao et al., “Greenhouse Gas Detection Based on Infrared Nanophotonic Devices,” IEEE Open Journal of Nanotechnology, vol. 4, pp. 10–22, Jan. 2023, doi: https://doi.org/10.1109/ojnano.2022.3233485.
M. A. Z. Chowdhury and M. A. Oehlschlaeger, “Deep Learning for Gas Sensing via Infrared Spectroscopy,” Sensors, vol. 24, no. 6, p. 1873, Mar. 2024, doi: https://doi.org/10.3390/s24061873.
L. Chen and Z. Yang, “Research on Gas Detection Algorithm Based on Reconstruction of Background Infrared Radiation,” Photonics, vol. 12, no. 6, pp. 570–570, Jun. 2025, doi: https://doi.org/10.3390/photonics12060570.
I. Cho, K. Lee, Y. C. Sim, J. Jeong, “Deep-learning-based gas identification by time-variant illumination of a single micro-LED-embedded gas sensor,” Light: Science & Applications, vol. 12, no. 1, Apr. 2023, doi: https://doi.org/10.1038/s41377-023-01120-7.
