Search for a command to run...
Accurate characterization of greenhouse gas (CH4, N2O) and atmospheric pollutant (NH3) plumes is essential for quantifying point-source emission rates and understanding regional carbon-nitrogen cycles. However, current plume analysis workflows face significant bottlenecks. At the algorithmic level, plume identification, background subtraction, and feature extraction rely heavily on subjective manual expertise, hindering standardized and high-throughput outputs. At the data fidelity level, conventional closed-path systems suffer from signal desynchronization caused by the sorption kinetics of polar molecules (e.g., NH3) within sampling line, creating uncertainty in multi-species correlation analysis and source apportionment and resulting real-time decision-making during field campaigns.To overcome these limitations, this study proposes an automated multi-species plume analysis framework driven by Generative AI. The innovation lies in an end-to-end mapping architecture that autonomously transforms multi-dimensional raw observation sequences into structured scientific insights. It integrates advanced recognition algorithms for plume signal stripping and high-precision emission rate inversion by fusing synchronized 3D wind fields, geospatial coordinates, and solar radiation data. Analytical performance is further enabled by high-fidelity input data acquired from a self-developed open-path quantum cascade laser (OP-QCL) spectrometer, which delivers inherently synchronized 10 Hz multi-species signals with inherent physical synchronization. This work eliminates the need for complex pre-processing of sampling artifacts at the hardware level, thereby increasing the efficiency of high-level feature extraction.Field validation demonstrates that this AI-driven workflow achieves a paradigm shift in processing efficiency, reducing data interpretation time from hours to minutes. In industrial and wastewater treatment scenarios, the system captured transient fluctuations of CH4 (up to 7539 ppb) and NH3 (background increments of ~37 ppb). Leveraging the high temporal coherence between species (r = 0.62, p < 0.01; lag,±1 s), the AI successfully extracted representative source fingerprints (CH4/NH3 ratio ≈10), with inversion robustness verified through controlled release experiments. Notably, the real-time feedback supports an adaptive sampling strategy, enabling dynamic path adjustments during mobile monitoring to ensure high-fidelity capture of stochastic emission events. This integrated, intelligent framework fills a critical gap in real-time plume capturing and provides a robust digital toolset for industrial emission regulation and the realization of carbon-neutral goals.