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https://doi.org/10.5194/egusphere-2022-396
https://doi.org/10.5194/egusphere-2022-396
28 Jul 2022
 | 28 Jul 2022

Predicting peak daily maximum 8-hour ozone, and linkages to emissions and meteorology, in Southern California using machine learning methods

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

Abstract. The growing abundance of data is conducive to using numerical methods to relate air quality, meteorology, and emissions to address which factors impact pollutant concentrations. Often, it is the extreme values that are of interest for health and regulatory purposes (e.g., the National Ambient Air Quality Standard for ozone uses the annual, maximum, daily 4th highest, 8-hour average (MDA8) ozone), though such values are the most challenging to predict using empirical models. We developed four different computational models, including the Generalized Additive Model (GAM), the Multivariate Adaptive Regression Splines, the Random Forest, and the Support Vector Regression, to develop observation-based relationships between the 4th highest MDA8 ozone in the South Coast Air Basin and precursor emissions, meteorological factors, and large-scale climate patterns. All models had similar predictive performance, though the GAM showed a relatively higher R2 value (0.96) with a lower root mean square error and mean bias.

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Journal article(s) based on this preprint

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
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022,https://doi.org/10.5194/gmd-15-9015-2022, 2022
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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While the national ambient air quality standard of ozone is based on the 3-year average of the...
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