Pollution mask for the continuous corrected particle number concentration data in 1 min time resolution measured in the Swiss aerosol container using a whole air inlet during MOSAiC 2019/2020

• Main Authors: Beck, Ivo, Quéléver, Lauriane, Laurila, Tiia, Jokinen, Tuija, Baccarini, Andrea, Angot, Hélène, Schmale, Julia
• Format: Dataset
• Language: English
• Published: PANGAEA 2023
• Subjects: aerosol; aerosol number concentration; Arctic aerosol; Arctic Ocean; Condensation particle counter; Condensation particle counter (CPC); TSI; 3776; CPC; DATE/TIME; Event label; Flag; LATITUDE; LONGITUDE; MOSAiC; MOSAiC_ATMOS; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Particle number; Polarstern; Pollution detection algorithm; PS122/1; PS122/1_1-79; PS122/2; PS122/2_14-36; PS122/3; PS122/3_28-32; PS122/4; PS122/4_43-24; PS122/5; PS122/5_58-26; Arctic; Greenland; Beck; Greenland Sea; North Greenland
• Online Access: https://doi.pangaea.de/10.1594/PANGAEA.961120; https://doi.org/10.1594/PANGAEA.961120

This dataset contains particle number concentrations and a pollution flag in 1 min time resolution. It is derived by the pollution detection algorithm (PDA, doi:10.5281/zenodo.5761101) based on the corrected particle number concentration data of the CPC3776 measured during the year-long MOSAiC expedition from October 2019 to September 2020. With pollution, we refer to emission from the exhaust of the ship stack, snow groomers, diesel generators, ship vents, helicopters and other (primary pollution, not circulated, nor transported). Pollution hence reflects locally emitted particles and trace gases, which are not representative of the central Arctic ambient concentrations. The PDA identifies and flags periods of polluted data in the particle number concentration dataset five steps. The first and most important step identifies polluted periods based on the gradient (time-derivative) of a concentration over time. If this gradient exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median and a sparse data filter (optional). The detailed methodology of the derivation of the pollution flag is described in Beck et al. (2022).