Fecha de publicación: 02/12/2024 Fuente: PubMed "swarm" ACS Sens. 2024 Dec 2. doi: 10.1021/acssensors.4c02405. Online ahead of print.ABSTRACTSulfur 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.PMID:39621866 | DOI:10.1021/acssensors.4c02405