Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-573-2023
https://doi.org/10.5194/gmd-16-573-2023
Model description paper
 | 
26 Jan 2023
Model description paper |  | 26 Jan 2023

The AirGAM 2022r1 air quality trend and prediction model

Sam-Erik Walker, Sverre Solberg, Philipp Schneider, and Cristina Guerreiro

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-70', Anonymous Referee #1, 04 Jun 2022
  • RC2: 'Comment on gmd-2022-70', Anonymous Referee #2, 18 Jun 2022
  • AC1: 'Comment on gmd-2022-70', Sam-Erik Walker, 27 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sam-Erik Walker on behalf of the Authors (25 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (11 Oct 2022) by Havala Pye
RR by Anonymous Referee #1 (20 Oct 2022)
ED: Publish subject to minor revisions (review by editor) (21 Oct 2022) by Havala Pye
AR by Sam-Erik Walker on behalf of the Authors (31 Oct 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (01 Nov 2022) by Havala Pye

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Sam-Erik Walker on behalf of the Authors (10 Jan 2023)   Author's adjustment   Manuscript
EA: Adjustments approved (13 Jan 2023) by Havala Pye
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Short summary
We have developed a statistical model for estimating trends in the daily air quality observations of NO2, O3, PM10 and PM2.5, adjusting for trends and short-term variations in meteorology. The model is general and may also be used for prediction purposes, including forecasting. It has been applied in a recent comprehensive study in Europe. Significant declines are shown for the pollutants from 2005 to 2019, mainly due to reductions in emissions not attributable to changes in meteorology.