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

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
fourth highest 8 h 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), multivariate adaptive regression splines, random forest, and
support vector regression, to develop observation-based relationships
between the fourth 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.
