Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 14
Articles | Volume 15, issue 14
https://doi.org/10.5194/amt-15-4195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-4195-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 14
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

Data sets

Pollution mask for the continuous corrected particle number concentration data in 1 min resolution, measured in the Swiss aerosol container during MOSAiC 2019/2020 Ivo Beck, Lauriane Quéléver, Tiia Laurila, Tuija Jokinen, Andrea Baccarini, Hélène Angot, and Julia Schmale https://doi.org/10.1594/PANGAEA.941335

Continuous corrected particle number concentration data in 10 sec resolution, measured in the Swiss aerosol container during MOSAiC 2019/2020. Ivo Beck, Lauriane Quéléver, Tiia Laurila, Tuija Jokinen, and Julia Schmale https://doi.org/10.1594/PANGAEA.941886

Continuous raw particle number concentration data in 10 sec resolution, measured in the Swiss aerosol container during MOSAiC 2019/2020 Ivo Beck, Lauriane Quéléver, Tiia Laurila, Tuija Jokinen, and Julia Schmale https://doi.org/10.1594/PANGAEA.941873

Carbon dioxide dry air mole fractions measured in the Swiss container during MOSAiC 2019/2020 Hélène Angot, Ivo Beck, Tuija Jokinen, Tiia Laurila, Lauriane Quéléver, and Julia Schmale https://doi.pangaea.de/10.1594/PANGAEA.944248

Condensation Particle Counter (AOSCPCF) (https://adc.arm.gov/discovery/#/) C. Kuang, C. Salwen, M. Boyer, and A. Singh https://doi.org/10.5439/1046184

Jungfraujoch aerosol number concentrations N. Bukowiecki and U. Baltensperger http://ebas-data.nilu.no/Pages/DataSetList.aspx?key=6316302E6BD54CF7AFBBDE1B71AAB448

Model code and software

Pollution Detection Algorithm (PDA) Ivo Beck, Hélène Angot, Andrea Baccarini, Markus Lampimäki, Matthew Boyer, and Julia Schmale https://doi.org/10.5281/zenodo.5761101

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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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