Advanced GC-MS Chemosensing Combined with Atomistic Modeling: A Synergistic Approach for Environmental Water Analysis

Gas chromatography–mass spectrometry (GC-MS) plays a crucial role in analyzing complex water samples due to its high sensitivity, selectivity, and robustness. Recent developments have transformed GC-MS into a powerful chemosensor platform, capable of generating detailed chemical fingerprints for targeted or untargeted environmental analysis. This review highlights the integration of GC-MS with atomistic modeling approaches, including quantum chemical calculations and molecular simulations, to enhance the interpretation of mass spectra and support the identification of emerging contaminants and transformation products. These computational tools offer mechanistic insight into fragmentation pathways, molecular reactivity, and pollutant behavior in aqueous environments. Emphasis is placed on recent trends that couple GC-MS with machine learning, advanced sample preparation, and simulation-based spectrum prediction, forming a synergistic analytical framework for advanced water contaminant profiling. The review concludes by addressing current challenges and outlining future perspectives in combining experimental and theoretical tools for intelligent environmental monitoring.