Automatic and precise identification of volatile organic compounds from gas chromatography in prolonged atmospheric monitoring

Long-term continuous monitoring of volatile organic compounds (VOCs) is pivotal for climate change research, air quality assessment, pollution source identification, and public health early warning systems. Prolonged VOC monitoring is routinely implemented by gas chromatographs. However, accurate identification of target contaminants heavily relies on time-consuming and error-prone manual processes conducted by professional personnel due to complex chromatograms and anomalous patterns. This study proposes an artificial intelligence-based model, ResGRU, for the automated and precise identification of VOCs in a chromatograph. By taking real data from a monitoring site in Shanghai, the model achieved a mean absolute error of 0.0144 min for retention time localization, which is 2.76 to 38.19 times smaller compared to conventional machine learning or deep learning models by previous reports. Moreover, it achieves precise recognition of subtle chromatographic peaks and exceptional adaptability to abnormal chromatograms. Notably, the vast majority of these weak peaks are attributed to olefinic compounds, which exhibit exceptionally high ozone formation potential. In addition, cross-transfer verification of data from four monitoring sites in Shanghai, Hubei, and Jiangsu, China further proved the robust transferability of this model. This work provides a novel methodology for precise analysis of GC data, enabling deeper exploration of the mechanisms behind VOCs pollution over extended temporal scales.