GC×GC–HRMS for combustion emissions: A framework for nontarget identification and source apportionment

Combustion processes emit complex mixtures of volatile and semi-volatile organic compounds, many of which remain unresolved by conventional one-dimensional gas chromatography–mass spectrometry. The coupling of comprehensive two-dimensional gas chromatography with high-resolution mass spectrometry (GC×GC–HRMS) has revolutionized the molecular-level characterization of these emissions, offering enhanced chromatographic resolution, improved mass accuracy, and structured chemical fingerprints that can be directly linked to specific sources. This review critically examines the complete analytical framework of GC×GC–HRMS for combustion emission studies, encompassing instrumental optimization, data processing, hierarchical confidence-based compound identification, and chemometric source apportionment. Emphasis is placed on how this technology deconstructs the unresolved complex mixture (UCM), enabling the differentiation of vehicular, biomass burning, and alternative fuel emissions through unique molecular markers and multidimensional data interpretation. Chemometric tools, including PCA, PMF, and PARAFAC, are evaluated for their ability to translate high-dimensional fingerprints into quantitative source contributions, while machine learning (ML) approaches are discussed as emerging solutions to overcome current data processing bottlenecks. Finally, future perspectives highlight the integration of GC×GC–HRMS with complementary analytical platforms and computational toxicology for more holistic environmental monitoring and regulatory decision-making. By merging advanced analytical techniques with intelligent data interpretation, GC×GC–HRMS provides an unprecedented window into the chemical complexity and environmental significance of combustion emissions.