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Abstract. Aerosol mass spectrometry has proved to be a powerful tool to measure submicron particulate composition with high time resolution. Factor analysis of mass spectra (MS) collected worldwide by aerosol mass spectrometer (AMS) demonstrates that submicron organic aerosol (OA) is usually composed of several major components, such as oxygenated (OOA), hydrocarbon-like (HOA), biomass burning (BBOA), and other primary OA. In order to help interpretation of component MS from factor analysis of ambient OA datasets, AMS measurements of different primary sources is required for comparison. Such work, however, has been very scarce in the literature, especially for high resolution MS (HR-MS) measurements, which performs improved characterization by separating the ions of different elemental composition at each m/z in comparison with unit mass resolution MS (UMR-MS) measurements. In this study, primary emissions from four types of Chinese cooking (CC) and six types of biomass burning (BB) were simulated systematically and measured using an Aerodyne High-Resolution Time-of-Flight AMS (HR-ToF-AMS). The MS of the CC emissions show high similarity, with m/z 41 and m/z 55 being the highest signals; the MS of the BB emissions also show high similarity, with m/z 29 and m/z 43 being the highest signals. The MS difference between the CC and BB emissions is much bigger than that between different CC (or BB) types, especially for the HR-MS. The O/C ratio of OA ranges from 0.08 to 0.13 for the CC emissions and from 0.18 to 0.26 for the BB emissions. The UMR ions of m/z 43, m/z 44, m/z 57, and m/z 60, usually used as tracers in AMS measurements, were examined for their HR-MS characteristics in the CC and BB emissions. In addition, the MS of the CC and BB emissions are also compared with component MS from factor analysis of ambient OA datasets observed in China, as well as with other AMS measurements of primary sources in the literature. The MS signatures of cooking and biomass burning emissions revealed in this study can be used as important reference for factor analysis of ambient OA datasets, especially for the relevant studies in East Asia.
He et al. (Mon,) studied this question.