Accurately detecting atmospheric carbon dioxide is a vital part of responding to the global greenhouse effect. Conventional off-axis integral cavity detection systems are computationally intensive and susceptible to environmental factors. This study deploys an Extreme Learning Machine model incorporating a cascaded integrator comb (CIC) filter into the off-axis integrating cavity. It is shown that appropriate parameters can effectively improve the performance of the instrument in terms of lower detection limit, accuracy, and root mean square deviation. The proposed method is incorporated successfully into a monitoring station situated near an industrial area for detecting atmospheric carbon dioxide (CO₂) concentration daily.

As the signature gas released before coal combustion, methane’s telemetry accuracy is susceptible to environmental influences, which is a difficult problem that needs to be solved. This article provides a detailed exposition on the application of Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology in the field of gas monitoring, with particular emphasis on its advantages in coal safety detection. The coal combustion experiment is conducted to determine the required performance indexes. Through the temperature compensation algorithm and normalized signals, the impact of factors such as ambient temperature and environmental noise is reduced, effectively improving the signal-to-noise ratio and accuracy. The experiments demonstrate that this system effectively lowers the detection limit for methane while improving measurement accuracy, thereby providing robust support for the early warning of coal spontaneous combustion.