  Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants
regulated by the Environmental Protection Agency in the United States. There is
strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of
mortality and hospitalization. Large scale epidemiological studies on the
health effects of PM$_{2.5}$ provide the necessary evidence base for lowering
the safety standards and inform regulatory policy. However, ambient monitors of
PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located
across the U.S., and therefore studies based only on the levels of PM$_{2.5}$
measured from the monitors would inevitably exclude large amounts of the
population. One approach to resolving this issue has been developing models to
predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological,
and land use data. This process typically relies developing a prediction model
that relies on large amounts of input data and is highly computationally
intensive to predict levels of air pollution in unmonitored areas. We have
developed a flexible R package that allows for environmental health researchers
to design and train spatio-temporal models capable of predicting multiple
pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data
platform, to achieve both performance and scalability when used in conjunction
with cloud or cluster computing systems.
