The concentration of trace gases in the atmospheric environment is extremely low, but it has a great impact on the living environment of organisms. Photoacoustic spectroscopy has attracted extensive attention in the field of trace gas detection because of its high sensitivity, good selectivity, and fast response. As the core of a photoacoustic detection setup, the photoacoustic cell has a significant impact on detection performance. To improve detection sensitivity, a sphere-tube coupled photoacoustic cell (STPAC) was developed, which was mainly composed of a diffuse-reflective sphere and an acoustic resonance tube. Modulated light was reflected multiple times in the sphere to increase optical path, and photoacoustic (PA) signals were further amplified by the tube. Based on STPAC, a PA gas detection setup was built with a laser diode (LD) at 450 nm as the light source. The experimental results showed that the minimum detection limit (noise equivalent concentration, NEC) of NO₂ was ~0.7 parts per billion (ppb). Compared with the T-type PA cell (TPAC) in which the modulated light passed through the sphere, the signal-to-noise ratio of STPAC was increased by an order of magnitude at the same concentration of the NO₂ sample.

Hyperspectral imaging for gas plume identification holds significant potential for applications in industrial emission control and environmental monitoring, including critical needs in livestock farm environments. Conventional pixel-by-pixel spectral identification methods primarily rely on spectral information, often overlooking the rich spatial distribution features inherent in hyperspectral images. This oversight can lead to challenges such as inaccurate identification or leakage along the edge regions of gas plumes and false positives from isolated pixels in non-gas areas. While infrared imaging for gas plumes offers a new perspective by leveraging multi-frame image variations to locate plumes, these methods typically lack spectral discriminability. To address these limitations, we draw inspiration from the multi-frame analysis framework of infrared imaging and propose a novel hyperspectral gas plume identification method based on image background modeling and suppression. Our approach begins by employing background modeling and suppression techniques to accurately detect the primary gas plume region. Subsequently, a representative spectrum is extracted from this identified plume region for precise gas identification. To further enhance the identification accuracy, especially in the challenging plume edge regions, a spatial-spectral combined judgment operator is applied as a post-processing step. The effectiveness of the method was validated through experiments using both simulated and real-world measured data from ammonia and methanol gas releases. We compare its performance against classical methods and an ablation version of our model. Results consistently demonstrate that our method effectively detects low-concentration, thin, and diffuse gas plumes, offering a more robust and accurate solution for hyperspectral gas plume identification with strong applicability to real-world industrial and livestock farm scenarios.