Validation of the Fast Expectation-Maximization Algorithm for Source Apportionment of Organic Compounds Using Nontarget HRMS Fingerprints

Source apportionment of organic pollutants in complex environmental systems remains a major analytical challenge, particularly when dealing with unknown or numerous pollution sources. While high-resolution mass spectrometry (HRMS) offers rich chemical fingerprint data, few computational methods can efficiently and accurately resolve multicomponent sources without prior compositional knowledge. To address this gap, we introduced the fast expectation-maximization (FEM) algorithm that enables rapid, reliable, and unsupervised source attribution using nontarget HRMS data. The FEM algorithm was evaluated under both controlled laboratory conditions, simulating mixtures of municipal wastewater, hospital effluent, and urban runoff, and real case. It demonstrated robust performance, producing accurate contribution estimates (within 0.58–2.61 times theoretical values) even in multisource systems. The method exhibited accuracy comparable to established dilution curve methods and significantly outperformed the widely used SourceTracker tool in both speed and quantitative precision. This work proved the FEM algorithm as a powerful new computational tool for rapid and high-resolution pollution source tracking using the large data set of nontarget HRMS analysis.