  Issues regarding air quality and related health concerns have prompted this
study, which develops an accurate and computationally fast, efficient hybrid
modeling system that combines numerical modeling and machine learning for
forecasting concentrations of surface ozone. Currently available numerical
modeling systems for air quality predictions (e.g., CMAQ, NCEP EMP) can
forecast 24 to 48 hours in advance. In this study, we develop a modeling system
based on a convolutional neural network (CNN) model that is not only fast but
covers a temporal period of two weeks with a resolution as small as a single
hour for 255 stations. The CNN model uses forecasted meteorology from the
Weather Research and Forecasting model (processed by the Meteorology-Chemistry
Interface Processor), forecasted air quality from the Community Multi-scale Air
Quality Model (CMAQ), and previous 24-hour concentrations of various measurable
air quality parameters as inputs and predicts the following 14-day hourly
surface ozone concentrations. The model achieves an average accuracy of 0.91 in
terms of the index of agreement for the first day and 0.78 for the fourteenth
day while the average index of agreement for one day ahead prediction from the
CMAQ is 0.77. Through this study, we intend to amalgamate the best features of
numerical modeling (i.e., fine spatial resolution) and a deep neural network
(i.e., computation speed and accuracy) to achieve more accurate spatio-temporal
predictions of hourly ozone concentrations. Although the primary purpose of
this study is the prediction of hourly ozone concentrations, the system can be
extended to various other pollutants.
