  Tropospheric ozone is one of six criteria pollutants regulated by the US EPA,
and has been linked to respiratory and cardiovascular endpoints and adverse
effects on vegetation and ecosystems. Regional photochemical models have been
developed to study the impacts of emission reductions on ozone levels. The
standard approach is to run the deterministic model under new emission levels
and attribute the change in ozone concentration to the emission control
strategy. However, running the deterministic model requires substantial
computing time, and this approach does not provide a measure of uncertainty for
the change in ozone levels. Recently, a reduced form model (RFM) has been
proposed to approximate the complex model as a simple function of a few
relevant inputs. In this paper, we develop a new statistical approach to make
full use of the RFM to study the effects of various control strategies on the
probability and magnitude of extreme ozone events. We fuse the model output
with monitoring data to calibrate the RFM by modeling the conditional
distribution of monitoring data given the RFM using a combination of flexible
semiparametric quantile regression for the center of the distribution where
data are abundant and a parametric extreme value distribution for the tail
where data are sparse. Selected parameters in the conditional distribution are
allowed to vary by the RFM value and the spatial location. Also, due to the
simplicity of the RFM, we are able to embed the RFM in our Bayesian
hierarchical framework to obtain a full posterior for the model input
parameters, and propagate this uncertainty to the estimation of the effects of
the control strategies. We use the new framework to evaluate three potential
control strategies, and find that reducing mobile-source emissions has a larger
impact than reducing point-source emissions or a combination of several
emission sources.
