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Thinking Through Computational Exposure as an Evolving Paradign Shift for Exposure Science: Development and Application of Predictive Models from Big Data
Citation:
Buckley, T. AND R. Zaleski. Thinking Through Computational Exposure as an Evolving Paradign Shift for Exposure Science: Development and Application of Predictive Models from Big Data. ISES, 2014 Annual Meeting, Cincinnati, OH, October 12 - 16, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Symposium Abstract: Exposure science has evolved from a time when the primary focus was on measurements of environmental and biological media and the development of enabling field and laboratory methods. The Total Exposure Assessment Method (TEAM) studies of the 1980s were classic examples of such measurement intensive studies. The measurements from these early years prompted and enabled the development of predictive exposure models. These predictive exposure models have similarly evolved from those that are relatively simple and deterministic considering a single pollutant and a single medium (e.g. SHAPE -- simulation of human activity and pollutant exposure) to those that are stochastic, consider multiple pollutants across multiple media and are computationally sophisticated (e.g. SHEDS-Multimedia). Although measurements will always be at its core, exposure science is entering a new era of research that is much more computationally based than before. We now have: 1) the models that predict the fate and transport of chemicals within and across media; 2) the computational means to process massive data streams using complex algorithms; and 3) unprecedented access to informative data streams that inform and enable model prediction. In this symposium, we will discuss the opportunities and challenges of the emerging and quickly evolving research landscape of exposure science.