Articles | Volume 15, issue 14
https://doi.org/10.5194/amt-15-4195-2022
https://doi.org/10.5194/amt-15-4195-2022
Research article
 | 
20 Jul 2022
Research article |  | 20 Jul 2022

Automated identification of local contamination in remote atmospheric composition time series

Ivo Beck, Hélène Angot, Andrea Baccarini, Lubna Dada, Lauriane Quéléver, Tuija Jokinen, Tiia Laurila, Markus Lampimäki, Nicolas Bukowiecki, Matthew Boyer, Xianda Gong, Martin Gysel-Beer, Tuukka Petäjä, Jian Wang, and Julia Schmale

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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung: Polar Research and Supply Vessel POLARSTERN Operated by the Alfred-Wegener-Institute, Journal of large-scale research facilities, 3, 119, https://doi.org/10.17815/jlsrf-3-163, 2017. 
Alroe, J., Cravigan, L. T., Miljevic, B., Johnson, G. R., Selleck, P., Humphries, R. S., Keywood, M. D., Chambers, S. D., Williams, A. G., and Ristovski, Z. D.: Marine productivity and synoptic meteorology drive summer-time variability in Southern Ocean aerosols, Atmos. Chem. Phys., 20, 8047–8062, https://doi.org/10.5194/acp-20-8047-2020, 2020. 
Angot, H., Beck, I., Jokinen, T., Laurila, T., Quéléver, L., and Schmale, J.: Carbon dioxide dry air mole fractions measured in the Swiss container during MOSAiC 2019/2020, PANGAEA [data set], https://doi.pangaea.de/10.1594/PANGAEA.944248, in review, 2022a. 
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Short summary
We present the pollution detection algorithm (PDA), a new method to identify local primary pollution in remote atmospheric aerosol and trace gas time series. The PDA identifies periods of contaminated data and relies only on the target dataset itself; i.e., it is independent of ancillary data such as meteorological variables. The parameters of all pollution identification steps are adjustable so that the PDA can be tuned to different locations and situations. It is available as open-access code.
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