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

DOI

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.

Identifier
DOI https://doi.org/10.1594/PANGAEA.961120
Related Identifier References https://doi.org/10.5194/amt-15-4195-2022
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 IsVariantFormOf https://doi.org/10.1594/PANGAEA.961118
Related Identifier IsVariantFormOf https://doi.org/10.1594/PANGAEA.961011
Related Identifier References https://doi.org/10.5281/zenodo.5761101
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.961120
Provenance
Creator Beck, Ivo ORCID logo; Quéléver, Lauriane ORCID logo; Laurila, Tiia; Jokinen, Tuija ORCID logo; Baccarini, Andrea (ORCID: 0000-0003-4614-247X); Angot, Hélène ORCID logo; 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
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 1037481 data points
Discipline Earth System Research
Spatial Coverage (-176.209W, 78.112S, 174.435E, 90.000N); Arctic Ocean; North Greenland Sea
Temporal Coverage Begin 2019-10-01T00:00:00Z
Temporal Coverage End 2020-09-30T23:59:00Z