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

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). This dataset contains a pollution flag in 1 min time resolution and the corresponding particle number concentration data. The data columns include Event, Time, Latitude, Longitude, Particle number concentration and a pollution flag to indicate polluted periods (0=not polluted, 1=polluted). The pollution flag is derived from the Pollution Detection Algorithm (PDA), a python-based open access script to automatically detect contamination in remote atmospheric time series Beck et al. (2022). The following parameters were used in the PDA script to derive this pollution flag:• a= 0.35 cm-3s-1• m = 0.58 s-1• avg_time = 60 s• upper_threshold: 104 cm-3• lower_threshold: 60 cm-3• neighboring points filter: Yes/on• median deviation factor: 1.4• sparse window: 30• sparse threshold: 6Remark_1: The corrected particle number concentration may still contain some minor artefacts and a critical review of the data by an expert is required. The pollution flag is based on the aforementioned parameters. If needed, the PDA can be tuned to be stricter. The decision whether a single data point is affected by pollution is up to the user and requires an expert review.Remark_2: This pollution mask can be applied to other particle and trace measurements obtained during MOSAiC. Please see Beck et al. (2022), for a detailed discussion.