Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-9015-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-9015-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Dec 2022
Model description paper |
| 16 Dec 2022
| 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
CORRESPONDING AUTHOR
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Yifeng Wang
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Petros Vasilakos
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Cesunica E. Ivey
Department of Chemical and Environmental Engineering, University of
California, Riverside, Riverside, CA 92521, USA
now at: Department of Civil and Environmental Engineering,
University of California, Berkeley, Berkeley, CA 94720, USA
Khanh Do
Department of Chemical and Environmental Engineering, University of
California, Riverside, Riverside, CA 92521, USA
Center for Environmental Research and Technology, University of
California, Riverside, Riverside, CA 92521,
USA
Armistead G. Russell
School of Civil and Environmental Engineering, Georgia Institute of
Technology, Atlanta, GA 30332, USA
Related authors
No articles found.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Short summary
Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
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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.
While the national ambient air quality standard of ozone is based on the 3-year average of the...
