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Gas-phase oxygenated organic molecules (OOMs) can contribute significantly to both atmospheric new particle growth and secondary organic aerosol formation. Precursor apportionment of atmospheric OOMs connects them with volatile organic compounds (VOCs). Since atmospheric OOMs are often highly functionalized products of multistep reactions, it is challenging to reveal the complete mapping relationships between OOMs and their precursors. In this study, we demonstrate that the machine learning method is useful in attributing atmospheric OOMs to their precursors using several chemical indicators, such as O/C ratio and H/C ratio. The model is trained and tested using data acquired in controlled laboratory experiments, covering the oxidation products of four main types of VOCs (isoprene, monoterpenes, aliphatics, and aromatics). Then, the model is used for analyzing atmospheric OOMs measured in both urban Beijing and a boreal forest environment in southern Finland. The results suggest that atmospheric OOMs in these two environments can be reasonably assigned to their precursors. Beijing is an anthropogenic VOC dominated environment with ∼64% aromatic and aliphatic OOMs, and the other boreal forested area has ∼76% monoterpene OOMs. This pilot study shows that machine learning can be a promising tool in atmospheric chemistry for connecting the dots. 

Abstract. Oxygenated organic molecules (OOMs) are the crucial intermediates linkingvolatile organic compounds (VOCs) to secondary organic aerosols (SOAs) in theatmosphere, but comprehensive understanding of the characteristics of OOMsand their formation from VOCs is still missing. Ambient observations ofOOMs using recently developed mass spectrometry techniques are stilllimited, especially in polluted urban atmospheres where VOCs and oxidants areextremely variable and complex. Here, we investigate OOMs, measured by anitrate-ion-based chemical ionization mass spectrometer at Nanjing ineastern China, through performing positive matrix factorization on binnedmass spectra (binPMF). The binPMF analysis reveals three factors aboutanthropogenic VOC (AVOC) daytime chemistry, three isoprene-relatedfactors, three factors about biogenic VOC (BVOC) nighttime chemistry, andthree factors about nitrated phenols. All factors are influenced by NOxin different ways and to different extents. Over 1000 non-nitro moleculeshave been identified and then reconstructed from the selected solution ofbinPMF, and about 72 % of the total signals are contributed bynitrogen-containing OOMs, mostly regarded as organic nitrates formed throughperoxy radicals terminated by nitric oxide or nitrate-radical-initiatedoxidations. Moreover, multi-nitrates account for about 24 % of the totalsignals, indicating the significant presence of multiple generations,especially for isoprene (e.g., C5H10O8N2 andC5H9O10N3). Additionally, the distribution of OOMconcentration on the carbon number confirms their precursors are driven by AVOCsmixed with enhanced BVOCs during summer. Our results highlight the decisiverole of NOx in OOM formation in densely populated areas, and we encouragemore studies on the dramatic interactions between anthropogenic and biogenicemissions.