aipred: A Flexible R Package Implementing Methods for Predicting Air Pollution. Fine particulate matter (PM2.5) 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 (PM2.5) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of PM2.5 provide the necessary evidence base for lowering the safety standards and inform regulatory policy. However, ambient monitors of PM2.5 (as well as monitors for other pollutants) are sparsely located across the U.S., and therefore studies based only on the levels of PM2.5 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 PM2.5, NO2, 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 PM2.5. 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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References in zbMATH (referenced in 1 article , 1 standard article )
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- M. Benjamin Sabath, Qian Di, Danielle Braun, Francesca Dominici, Christine Choirat: aipred: A Flexible R Package Implementing Methods for Predicting Air Pollution (2018) arXiv