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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-429', Anonymous Referee #1, 06 Mar 2022
    • AC1: 'Reply on RC1', Julia Schmale, 19 May 2022
  • RC2: 'Comment on amt-2021-429', Anonymous Referee #2, 20 Mar 2022
    • AC2: 'Reply on RC2', Julia Schmale, 19 May 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Julia Schmale on behalf of the Authors (30 May 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (14 Jun 2022) by Rebecca Washenfelder
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.