Atmos. Meas. Tech., 14, 2377–2387, 2021 https://doi.org/10.5194/amt-14-2377-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Orbitool: a software tool for analyzing online Orbitrap mass spectrometry data Runlong Cai1;, Yihao Li2,3;, Yohann Clément4, Dandan Li5, Clément Dubois5, Marlène Fabre5, Laurence Besson5, Sebastien Perrier5, Christian George5, Mikael Ehn1, Cheng Huang2, Ping Yi3, Yingge Ma2, and Matthieu Riva5 1Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00014, Finland 2State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China 3School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China 4Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, 69100 Villeurbanne, France 5Univ. Lyon, Université Claude Bernard Lyon 1, CNRS, IRCELYON, 69626, Villeurbanne, France These authors contributed equally to this work. Correspondence: Matthieu Riva ([email protected]) and Yingge Ma ([email protected]) Received: 30 June 2020 – Discussion started: 12 August 2020 Revised: 4 November 2020 – Accepted: 23 November 2020 – Published: 26 March 2021 Abstract. The Orbitrap mass spectrometer has recently been far, which consists of ambient gas-phase measurements in ur- proved to be a powerful instrument to accurately mea- ban Shanghai. These tests showed that Orbitool was able to sure gas-phase and particle-phase organic compounds with automatically assign hundreds of molecular formulae as well a greater mass resolving power than other widely used on- as their isotopes with high accuracy. line mass spectrometers in atmospheric sciences. We develop an open-source software tool (Orbitool, https://orbitrap. catalyse.cnrs.fr, last access: 4 February 2021) to facilitate the analysis of long-term online Orbitrap data. Orbitool can av- 1 Introduction erage long-term data while improving the mass accuracy by Biogenic and anthropogenic sources emit a wide variety re-calibrating each mass spectrum, assign molecular formu- of volatile organic compounds (VOCs) into the atmosphere lae of compounds and their isotopes to measured signals, and (Hallquist et al., 2009; Shrivastava et al., 2017). Once emit- export time series and mass defect plots. The noise reduction ted, VOCs can quickly react with different atmospheric oxi- procedure in Orbitool can separate signal peaks from noise dants (OH radicals, O , NO radicals, or Cl atoms) yielding and reduce the computational and storage expenses. Chem- 3 3 a wide variety of oxidized VOCs (OVOCs) spanning a broad ical ionization Orbitrap data from laboratory experiments range of chemical formulae and, thus, volatilities (Hallquist on ozonolysis of monoterpenes and ambient measurements et al., 2009; Li et al., 2020; Wennberg et al., 2018). OVOCs in urban Shanghai were used to test Orbitool. For the test play a central role in the formation of atmospheric aerosols dataset, the average mass accuracy was improved from < 2 by either condensing onto pre-existing aerosol particles or to < 0:5 ppm by mass calibrating each spectrum. The denois- forming new particles (Hallquist et al., 2009; Jimenez et al., ing procedure removed 97 % of the noise peaks from a spec- 2009; Kirkby et al., 2016; Shrivastava et al., 2017). Gener- trum averaged for 30 min while maintaining the signal peaks, ally, the more oxidized OVOCs are, the lower their volatility substantially helping the automatic assignment of unknown and the greater their probability of partitioning to the par- species. To illustrate the capabilities of Orbitool, we used the ticle phase. However, the quantitative evaluation of the im- most challenging and complex dataset we have collected so pact of aerosols on climate is as yet inadequately constrained Published by Copernicus Publications on behalf of the European Geosciences Union. 2378 R. Cai et al.: Orbitool: a new software tool for Orbitrap due to many factors, including an incomplete understand- continuous online measurements. The commercial software, ing of how VOC oxidation processes contribute to new par- Xcalibur™, which was used in our initial studies (Lee et al., ticle and secondary organic aerosol formation (Glasius and 2020; Riva et al., 2019a, 2020), provides an interface for ba- Goldstein, 2016). Indeed, the gas-phase oxidation of one sin- sic data analysis, e.g., reading single or averaged scans and gle VOC can yield thousands of oxidized products (Glasius exporting the time evolution of selected signal peaks. How- and Goldstein, 2016; Goldstein and Galbally, 2007; Li et al., ever, more complicated data analysis is usually needed for in- 2020; Riva et al., 2019b). As a result, the chemical variety vestigating the complexity and chemical processes occurring of OVOCs poses a major challenge in detecting, quantifying, within the atmosphere. Hence, some customized software and characterizing such a large number and wide variety of tools, e.g., RawQuant (Kovalchik et al., 2018), RawTools organic compounds. (Kovalchik et al., 2019), and DIMSpy (Weber and Zhou, Over the last decade, mass spectrometric techniques have 2019) have been developed to meet different demands. In at- made extensive improvements and are now well suited to de- mospheric sciences, the concentrations of some key chemical tecting a large range of species simultaneously. This is high- species (e.g., peroxy radicals) in a typical atmospheric envi- lighted by the key role of chemical ionization mass spec- ronment are extremely low (< 1 ppt). The measured spectra trometry (CIMS) in improving our understanding of atmo- must be averaged over a long period to decrease the noise spheric chemical composition (Breitenlechner et al., 2017; level so that these low signals can be unambiguously identi- Ehn et al., 2014; Jokinen et al., 2012; Krechmer et al., 2018; fied among noise. This step implies averaging spectra across Lindinger et al., 1998; Yuan et al., 2017). Chemical ioniza- files while maintaining mass accuracy. Although facilitated tion is a soft ionization technique where the analytes are ion- by the high mass resolution of the Orbitrap, a minor propor- ized through a clustering process with the reagent ions and tion of the detected signal peaks still inevitably overlap with undergo only minimal fragmentation. While CIMS provides each other. Therefore, peak fitting is needed to separate the very good sensitivities (i.e., as low as 104 moleculescm−3) overlapped signal peaks. In addition, using a list (i.e., “peak (Jokinen et al., 2012) and is suitable for measuring a wide list”) of possible species obtained in similar atmospheric en- variety of gaseous organic and inorganic compounds, it is vironments reduces the expense of data analysis. The afore- mainly associated with time-of-flight (TOF) mass analyzers. mentioned features have already been realized in existing The mass resolving power of a TOF analyzer typically ranges software tools for analyzing data acquired with CIMS or from hundreds to less than 50 000, and the mass resolving proton-transfer-reaction (PTR) TOF MS, e.g., TofTools (Jun- power of online TOF mass spectrometry (MS) used in at- ninen et al., 2010), Tofware, and other software developed mospheric measurements is only up to 15 000 (Riva et al., to analyze PTR TOF MS datasets (Holzinger, 2015; Müller 2019b). Although often able to distinguish some isobaric et al., 2013). It would seem straightforward to convert the species, these mass resolving powers still limit accurate as- Orbitrap raw data into certain formats and then conduct data signment and quantification of OVOCs in a complex air sam- analysis using these existing software tools. However, due to ple. The existence of multiple overlapping ions yields signif- the high mass resolution of the Orbitrap compared to TOF icant uncertainties (Cubison and Jimenez, 2015; Riva et al., mass analyzers, the computational expense will be high if 2019b; Stark et al., 2015). Computational approaches, in- analyzing Orbitrap data using, for example, TofTools. This is cluding ion deconvolution procedures, are required to partly because the TOF mass spectra are stored on equally spaced resolve this limitation in order to extract the maximum possi- grids (with respect to TOF), with data points on the order ble information content (Cubison and Jimenez, 2015; Meija of 104–105. For comparison, approximately 108 data points and Caruso, 2004; Zhang et al., 2019, 2020). To overcome are needed to characterize a single Orbitrap spectrum using these limitations, we have coupled a high-resolution mass this grid-based data structure. Instead, saving only the sig- spectrometer (Orbitrap; Eliuk and Makarov, 2015) with a nals with their adjacent zeros will reduce the computational chemical ionization source (Riva et al., 2019a, 2020) and and storing expenses by orders of magnitude, and the Orbi- an extractive electrospray ionization inlet (Lee et al., 2020) trap raw data are saved using this data structure. In addition, for online analysis. Similarly, Zuth et al. (2018) have com- TofTools determines the noise level using the equally spaced bined an atmospheric pressure chemical ionization (APCI) data within a certain mass defect range, whereas such infor- interface with an Orbitrap and have shown that such a tech- mation is not recorded in the