Articles | Volume 22, issue 3
Research article
08 Feb 2022
Research article |  | 08 Feb 2022

Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets

Pak Lun Fung, Martha A. Zaidan, Jarkko V. Niemi, Erkka Saukko, Hilkka Timonen, Anu Kousa, Joel Kuula, Topi Rönkkö, Ari Karppinen, Sasu Tarkoma, Markku Kulmala, Tuukka Petäjä, and Tareq Hussein


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on acp-2021-427', Santtu Mikkonen, 06 Jul 2021
    • AC1: 'Reply on CC1', Pak Lun Fung, 11 Nov 2021
  • RC1: 'Comment on acp-2021-427', Anonymous Referee #1, 08 Sep 2021
  • RC2: 'Comment on acp-2021-427', Anonymous Referee #2, 23 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Pak Lun Fung on behalf of the Authors (11 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (01 Dec 2021) by Manabu Shiraiwa
RR by Anonymous Referee #1 (19 Dec 2021)
ED: Publish subject to minor revisions (review by editor) (03 Jan 2022) by Manabu Shiraiwa
AR by Pak Lun Fung on behalf of the Authors (04 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (06 Jan 2022) by Manabu Shiraiwa
Short summary
We developed an input-adaptive mixed-effects model, which was automatised to select the best combination of input variables, including up to three fixed effect variables and three time indictors as random effect variables. We tested the model to estimate lung-deposited surface area (LDSA), which correlates well with human health. The results show the inclusion of time indicators improved the sensitivity and the accuracy of the model so that it could serve as a network of virtual sensors.
Final-revised paper