Mass concentration of submicron particulate methanesulfonic acid (MSA) measured in the Swiss container during MOSAiC 2019/2020

DOI

This dataset contains the mass concentration of submicron particulate methanesulfonic acid (MSA) measured during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to July 2020. The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using a commercial Aerodyne Research Inc. High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS). The instrument was located behind an automated valve, which switched hourly between a total and an interstitial air inlet, with upper cutoff sizes of 40 and 1 µm respectively (Heutte et al. (2023), Beck et al. (2022), and Dada et al. (2022)). Ambient air was hence sampled alternately every hour from the total and interstitial inlets into an aerodynamic lens with a 1 µm critical orifice and a flow of 0.07 L/min.All data were processed using SQUIRREL v1.65B and PIKA v1.25B within the IGOR Pro v9.00 software. This was done separately for the three distinct periods of available measurements, October to December 2019, March to May, and June to July 2020, as the instrument was each time in a different state (after long down times related to turbo pump failures). A general description of the instrument and of the calibrations for bulk species quantification can be found in Heutte et al. (2023) and the bulk submicron aerosol chemical composition from the AMS can be downloaded at doi:10.1594/PANGAEA.961009.The mass concentration of particulate methanesulfonic acid was determined using the calibrated signal of the CH3SO2+ fragment at the mass-to-charge ratio (m/z) 79 and following the method outlined by Hodshire et al. (2019). The calibration factor applied to the CH3SO2+ ion fragment was equal to 12.1.We applied two corrections. First, the switching valve caused data distortion, observed at every full hour (i.e., when the valve turns and the ambient sampling changes from one inlet to another, there is a brief moment with under-pressure in the inlet lines). Consequently, all data points within ± 2 min of the full hours were removed. Second, during some periods when the inlet switching valve was activated, we observed a difference pattern of mean and standard deviation of the measurements between even and odd hours, most probably caused by a persistent pressure drop in the inlet lines, resulting in a proportional reduction of the concentration measurements. The 1-h arithmetic mean of interstitial inlet measurements and the mean of the two adjacent hours of total inlet measurements were subtracted, and the resulting difference was added as a constant to the data points of the interstitial inlet measurements.This dataset contains a pollution flag ("Flag, pollution") to flag datapoints that were identified as directly influenced by fresh local pollution (e.g., Polarstern exhaust, on-ice diesel generators, skidoos), where a flag equal to 0 indicates clean data and 1 indicates polluted data. The identification method, based on the cosine similarity of the measured mass spectra with a known reference polluted spectrum, is described in Dada et al. (2022). Additionally, a sparse filter with a moving window spanning 60 datapoints (approx. 1h30) was applied to define as entirely polluted periods where more than 60% of the points were already classified as polluted by the cosine similarity method.

We encourage data users to refer to Heutte et al. (2023) for a more detailed description of the data acquisition, data processing and corrections applied.We thank the Laboratory for Atmospheric Chemistry at the Paul Scherrer Institute for providing the instrument and expertise.We extracted 90 sec time resolution positional data from the following datasets: Rex (2020), Haas (2020), Kanzow (2020) and Rex (2021ab).

Identifier
DOI https://doi.org/10.1594/PANGAEA.981399
Related Identifier References https://doi.org/10.1594/PANGAEA.961009
Related Identifier IsDerivedFrom https://doi.org/10.1594/PANGAEA.924672
Related Identifier IsDerivedFrom https://doi.org/10.1594/PANGAEA.924678
Related Identifier IsDerivedFrom https://doi.org/10.1594/PANGAEA.924669
Related Identifier IsDerivedFrom https://doi.org/10.1594/PANGAEA.926830
Related Identifier IsDerivedFrom https://doi.org/10.1594/PANGAEA.926911
Related Identifier References https://doi.org/10.5194/amt-15-4195-2022
Related Identifier References https://doi.org/10.1038/s41467-022-32872-2
Related Identifier References https://doi.org/10.1038/s41597-023-02586-1
Related Identifier References https://doi.org/10.5194/acp-19-3137-2019
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.981399
Provenance
Creator Heutte, Benjamin ORCID logo; Dada, Lubna ORCID logo; Angot, Hélène ORCID logo; Daellenbach, Kaspar R ORCID logo; El Haddad, Imad ORCID logo; Beck, Ivo ORCID logo; Quéléver, Lauriane ORCID logo; Laurila, Tiia; Jokinen, Tuija ORCID logo; Schmale, Julia ORCID logo
Publisher PANGAEA
Publication Year 2025
Funding Reference Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven https://doi.org/10.13039/501100003207 Crossref Funder ID AFMOSAiC-1_00 Multidisciplinary drifting Observatory for the Study of Arctic Climate; Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven https://doi.org/10.13039/501100003207 Crossref Funder ID AWI_PS122_00 Multidisciplinary drifting Observatory for the Study of Arctic Climate / MOSAiC; Horizon 2020 https://doi.org/10.13039/501100007601 Crossref Funder ID 101003826 https://cordis.europa.eu/project/id/101003826 Climate Relevant interactions and feedbacks: the key role of sea ice and Snow in the polar and global climate system; Swiss National Science Foundation https://doi.org/10.13039/501100001711 Crossref Funder ID 188478 https://data.snf.ch/grants/grant/188478 Measurement-Based understanding of the aeRosol budget in the Arctic and its Climate Effects (MBRACE); Swiss Polar Institute https://doi.org/10.13039/501100015594 Crossref Funder ID DIRCR-2018-004 ; United States Department of Energy, Atmospheric Systems Research Program https://doi.org/10.13039/100006132 Crossref Funder ID DE-SC0022046 https://pamspublic.science.energy.gov/WebPAMSExternal/Interface/Common/ViewPublicAbstract.aspx?rv=a2093134-feb9-41c9-b69e-820c5a81d8d2&rtc=24&PRoleId=10 Closing the gap on understudied aerosol-climate processes in the rapidly changing central Arctic
Rights Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 259812 data points
Discipline Earth System Research
Spatial Coverage (2.378W, 78.130S, 136.965E, 88.111N); Arctic Ocean
Temporal Coverage Begin 2019-10-01T00:03:38Z
Temporal Coverage End 2020-07-10T06:14:35Z