  We use a hybrid deep learning model to predict June-July-August (JJA) daily
maximum 8-h average (MDA8) surface ozone concentrations in the US. A set of
meteorological fields from the ERA-Interim reanalysis as well as monthly mean
NO$_x$ emissions from the Community Emissions Data System (CEDS) inventory are
selected as predictors. Ozone measurements from the US Environmental Protection
Agency (EPA) Air Quality System (AQS) from 1980 to 2009 are used to train the
model, whereas data from 2010 to 2014 are used to evaluate the performance of
the model. The model captures well daily, seasonal and interannual variability
in MDA8 ozone across the US. Feature maps show that the model captures
teleconnections between MDA8 ozone and the meteorological fields, which are
responsible for driving the ozone dynamics. We used the model to evaluate
recent trends in NO$_x$ emissions in the US and found that the trend in the EPA
emission inventory produced the largest negative bias in MDA8 ozone between
2010-2016. The top-down emission trends from the Tropospheric Chemistry
Reanalysis (TCR-2), which is based on satellite observations, produced
predictions in best agreement with observations. In urban regions, the trend in
AQS NO$_2$ observations provided ozone predictions in agreement with
observations, whereas in rural regions the satellite-derived trends produced
the best agreement. In both rural and urban regions the EPA trend resulted in
the largest negative bias in predicted ozone. Our results suggest that the EPA
inventory is overestimating the reductions in NO$_x$ emissions and that the
satellite-derived trend reflects the influence of reductions in NO$_x$
emissions as well as changes in background NO$_x$. Our results demonstrate the
significantly greater predictive capability that the deep learning model
provides over conventional atmospheric chemical transport models for air
quality analyses.
