aipred: A Flexible R Package Implementing Methods for Predicting Air Pollution
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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.
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