Articles | Volume 15, issue 24
Model description paper
16 Dec 2022
Model description paper |  | 16 Dec 2022

Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0)

Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-396', William Stockwell, 20 Sep 2022
  • RC2: 'Comment on egusphere-2022-396', Anonymous Referee #2, 01 Nov 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ziqi Gao on behalf of the Authors (24 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (02 Dec 2022) by Volker Grewe
AR by Ziqi Gao on behalf of the Authors (02 Dec 2022)  Manuscript 
Short summary
While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.