Sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) are chemical indicators of crop straw combustion as well as significant atmospheric pollutants. It is challenging to promptly detect natural “wildfires” during agricultural production, which often lead to uncontrollable and substantial economic losses. Moreover, both “wildfires” and artificial “straw burning” practices pose severe threats to the ecological environment and human health. Consequently, developing sensors capable of rapid and high-precision quantitative analysis of SO₂/NO₂ is essential and urgent for detecting early fires in agricultural activities. Here, we demonstrate an incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) sensing system utilizing a 366 nm ultraviolet light emitting diode, designed for real-time, high-precision monitoring of SO₂ and NO₂ and is used for early fire detection validation. The optical resonant cavity is constructed within a 60 mm cage system mechanical structure, achieving a maximum optical path length of nearly 2 km with a length of ∼460 mm. The output light carrying information about the species and concentration of the analyte molecules is coupled into the miniaturized grating spectrometer via a fiber, and continuous spectral fitting and concentration inversion are performed on the computer. We propose a spectral analysis and concentration inversion model based on an improved particle swarm optimization-support vector machine (IPSO-SVM) algorithm. By discrimination of the absorption spectral characteristics of SO₂/NO₂, we achieve superior prediction accuracy. Experimental results indicate that the detection limits of SO₂ and NO₂ under the optimized averaging time are 77.5 parts per billion by volume (ppbv) and 0.037 ppbv, respectively. The field deployment of the sensor in scenarios such as continuous outdoor air pollution monitoring, in situ combustion feature identification, and early fire mobile detection has demonstrated the superior reliability and sensitivity of this sensor system.

中文翻译：

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用于农业火灾早期识别的 ppb 级 SO2/NO2 双气体传感器系统的开发和现场部署


二氧化硫 （SO₂） 和二氧化氮 （NO₂） 是农作物秸秆燃烧的化学指标，也是重要的大气污染物。在农业生产过程中及时发现自然“野火”是一项挑战，这通常会导致无法控制的重大经济损失。此外，“野火”和人工“焚烧秸秆”行为都对生态环境和人类健康构成严重威胁。因此，开发能够快速、高精度地定量分析 SO₂/NO₂ 的传感器对于检测农业活动的早期火灾至关重要且紧迫。在这里，我们展示了一种利用 366 nm 紫外发光二极管的非相干宽带腔增强吸收光谱 （IBBCEAS） 传感系统，该系统设计用于实时、高精度地监测 SO₂ 和 NO₂，并用于早期火灾探测验证。光学谐振腔构造在 60 mm 笼式系统机械结构内，最大光程长度接近 2 km，长度为 ∼460 mm。携带有关分析物分子种类和浓度信息的输出光通过光纤耦合到微型光栅光谱仪中，并在计算机上进行连续光谱拟合和浓度反转。我们提出了一种基于改进的粒子群优化-支持向量机 （IPSO-SVM） 算法的光谱分析和浓度反演模型。通过区分 SO₂/NO₂ 的吸收光谱特性，我们实现了卓越的预测精度。 实验结果表明，在优化平均时间内，SO₂ 和 NO₂ 的检出限分别为十亿分之 77.5 ppbv （ppbv） 和 0.037 ppbv。该传感器在室外空气污染连续监测、原位燃烧特征识别、火灾早期移动探测等场景中的现场部署，证明了该传感器系统卓越的可靠性和灵敏度。