Bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols measured in the Swiss container during MOSAiC 2019/2020


This dataset contains the bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols (NR-PM1) measured during the MOSAiC expedition from October 2019 to July 2020. These include the mass concentrations of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), chloride (Chl), and organics (Org). The measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern, using a commercial High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS, Aerodyne Research, Inc.). One can refer to existing literature (e.g., DeCarlo et al. (2006) and Canagaratna et al. (2007)) for detailed description, functioning principles and field deployment procedures of the 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. (Submitted), 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). Regular on-site calibrations using monodisperse, number concentration-defined, ammonium nitrate (NH4NO3) and ammonium sulfate ((NH4)2SO4) particles were performed to determine the ionization efficiency of NO3- and relative ionization efficiencies of NH4+ and SO42- (Jimenez et al. (2003) and Allan et al. (2003)). An airbeam correction factor was applied to the dataset, along with a time and composition-dependent collection efficiency (CDCE, Middlebrook et al. (2012)). Several times per month, zero measurements were performed using High-Efficiency Particulate Absorbing (HEPA) filters. These were used to (1) adapt the AMS fragmentation table for air fragmentation patterns (in post processing), and (2) compute the detection limits (DL) for the main aerosol chemical species (SO42-, NO3-, NH4+, Chl, and Org) as 3 times the standard deviation from the mean species concentration during filter measurements. At the instrument's native time resolution of 90 s, the DL are equal to (for Oct-Dec, Mar-May, and Jun-Jul, respectively) 0.017, 0.102, and 0.084 µg/m3 for SO42-, 0.011, 0.069 and 0.152 µg/m3 for NO3-, 0.001, 0.027 and 0.261 µg/m3 for NH4+, 0.055, 0.055 and 0.071 µg/m3 for Chl, and 0.284, 0.718 and 1.029 µg/m3 for Org. A mass closure analysis was performed between the total NR-PM1 calculated from the AMS and aethalometer and the one approximated from the mobility diameter (dm) measured by the Scanning Mobility Particle Sizer (SMPS) located in a neighboring container. The mass closure analysis was performed independently for the three periods (Oct-Dec, Mar-May and Jun-Jul) and yielded the following scaling factors: 3.77, 0.65 and 0.35 for the three respective periods. These scaling factors are not applied by default on the AMS timeseries, and the user is left with the decision to apply them or compute new ones. The following periods were removed from the dataset: when the airbeam correction factor was larger than 2 or smaller than 0, outliers (defined as more than 3 times the standard deviation of half an hour moving average, that constitute < 1 % of the whole dataset), all calibrations, periods when a HEPA filter was placed in front of the instrument, and data non-representative of ambient conditions (e.g. container air).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.Finally, the dataset also contains a quality flag for the ammonium timeseries ("Flag_NH4"), as turbo pump failures rendered ammonium measurements very noisy. A value of 1 indicates a quality assured measurement, while a value of 0 indicates a "bad" measure of NH4+ (basically all data after May 24th 2020).

We encourage data users to refer to Heutte et al. (Submitted) 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.

DOI https://doi.org/10.1594/PANGAEA.961009
Related Identifier https://doi.org/10.1594/PANGAEA.924672
Related Identifier https://doi.org/10.1594/PANGAEA.924678
Related Identifier https://doi.org/10.1594/PANGAEA.924669
Related Identifier https://doi.org/10.1594/PANGAEA.926830
Related Identifier https://doi.org/10.1594/PANGAEA.926911
Related Identifier https://doi.org/10.1029/2002JD002359
Related Identifier https://doi.org/10.5194/amt-15-4195-2022
Related Identifier https://doi.org/10.1002/mas.20115
Related Identifier https://doi.org/10.1038/s41467-022-32872-2
Related Identifier https://doi.org/10.1021/ac061249n
Related Identifier https://doi.org/10.1029/2001JD001213
Related Identifier https://doi.org/10.1080/02786826.2011.620041
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.961009
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; Jokinen, Tuija ORCID logo; Laurila, Tiia; Schmale, Julia ORCID logo
Publisher PANGAEA
Publication Year 2023
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
Language English
Resource Type Dataset
Format text/tab-separated-values
Size 1007790 data points
Discipline Chemistry; Natural Sciences
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