  This paper applies modern statistical methods in the prediction of the
next-day maximum ozone concentration, as well as the maximum 8-hour-mean ozone
concentration of the next day. The model uses a large number of candidate
features, including the present day's hourly concentration level of various
pollutants, as well as the meteorological variables of the present day's
observation and the future day's forecast values. In order to solve such an
ultra-high dimensional problem, the least absolute shrinkage and selection
operator (Lasso) was applied. The $L_1$ nature of this methodology enables the
automatic feature dimension reduction, and a resultant sparse model. The model
trained by 3-years data demonstrates relatively good prediction accuracy, with
RMSE= 5.63 ppb, MAE= 4.42 ppb for predicting the next-day's maximum $O_3$
concentration, and RMSE= 5.68 ppb, MAE= 4.52 ppb for predicting the next-day's
maximum 8-hour-mean $O_3$ concentration. Our modeling approach is also compared
with several other methods recently applied in the field and demonstrates
superiority in the prediction accuracy.
