A Novel Analytical Framework for Simultaneous Background Correction and Chemical Characterization in Aerosol Molecular Composition Online Mass Spectrometry

Online molecular characterization of atmospheric aerosols using advanced mass spectrometry techniques has revolutionized our understanding of aerosol chemistry but faces significant challenges from instrumental background. Two powerful techniques, Filter Inlet for Gases and AEROsols coupled to Chemical Ionization Mass Spectrometry (FIGAERO-CIMS) and Extractive Electrospray Ionization Time-of-Flight Mass Spectrometry (EESI-TOF), particularly suffer from memory effects and system contamination. These background interferences mask atmospheric signals and necessitate intensive maintenance, including ion source and sampling system cleaning, thereby limiting long-term field applications. Here, we present the SMART-PMF framework (<b>S</b>ynchronous <b>M</b>ass-Spectral <b>A</b>nalysis and Background <b>R</b>emoval <b>T</b>reatment) that applies positive matrix factorization directly to near-raw mass spectral data, utilizing distinct temporal patterns to separate atmospheric signals from instrumental background. Applied to FIGAERO-CIMS and EESI-TOF data, this approach successfully extracts atmospheric signals, reduces maintenance needs, and enables simultaneous background correction and atmospheric source apportionment. By simplifying spectral complexity via factor-specific separation and chemical characterization, SMART-PMF enhances the accuracy of peak fitting and molecular formula assignments. Validation against colocated Aerosol Mass Spectrometer (AMS) measurements confirms reliable background correction and methodological complementarity, offering robust support for long-term, molecular-level aerosol measurements. This systematic approach has broad applicability for addressing background interference across diverse online mass spectrometry applications.