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Microenvironment Tracker (MicroTrac)
Citation:
Breen, M. Microenvironment Tracker (MicroTrac). U.S. EPA Office of Research and Development, Washington, DC, EPA/600/B-16/207, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Epidemiologic studies have shown associations between air pollution concentrations measured at central-site ambient monitors and adverse health outcomes. Using central-site concentrations as exposure surrogates, however, can lead to exposure errors due to time spent in various indoor and outdoor microenvironments (ME) with pollutant concentrations that can be substantially different from central-site concentrations. These exposure errors can introduce bias and incorrect confidence intervals in health effect estimates, which diminish the power of such studies to establish correct conclusions about the exposure and health effects association. The significance of this issue was highlighted in the National Research Council (NRC) Report “Research Priorities for Airborne Particulate Matter”, which recommends that EPA address exposure error in health studies. To address this limitation, we developed MicroTrac, an automated classification model that estimates time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from personal global positioning system (GPS) data and geocoded boundaries of buildings (e.g., home, work, school). MicroTrac has several innovative design features: (1) using GPS signal quality to account for GPS signal loss inside certain buildings, (2) spatial buffering of building boundaries to account for the spatial inaccuracy of the GPS device, and (3) temporal buffering of GPS position to account for GPS signal reflections that occur from nearby structures. Disseminating MicroTrac to external researchers will help support additional applications of MicroTrac. To further disseminate MicroTrac, we developed a User Guide and a standalone program that requires no specialized software.