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Air Pollution Exposure Model for Individuals (EMI) in Health Studies
Citation:
Breen, M., J. Burke, S. Batterman, A. Vette, G. Norris, C. Godwin, M. Landis, C. Croghan, Brad Schultz, AND M. Breen. Air Pollution Exposure Model for Individuals (EMI) in Health Studies. Presented at 2014 Annual Meeting of the Society of Toxicology, Phoenix, AR, March 23 - 27, 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:
In health studies, traffic-related air pollution is associated with adverse respiratory effects. Due to cost and participant burden of personal measurements, health studies often estimate exposures using local ambient air monitors. Since outdoor levels do not necessarily reflect personal exposures, we developed the Exposure Model for Individuals (EMI) in health studies. A critical aspect of EMI is estimation of the air exchange rate (AER) for individual homes where people spend most of their time. The AER, which is the airflow into and out of buildings, can substantially impact indoor air pollutant concentrations and resulting occupant exposures. Our goal was to evaluate and apply an AER model to predict residential AER for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), which is examining traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We developed an AER model to predict AER from building characteristics related to air leakage; local airport temperatures and wind speeds; and open windows. Cross validation was used with a subset of NEXUS homes (N=24) with daily AER measured on five consecutive days during fall 2010 and spring 2011. Individual predicted and measured AER closely matched with median absolute differences of 36% and 24% for the fall and spring, respectively. The model was then applied to predict hourly AER for all NEXUS homes (N=193) during the study (Jan. 2010 - Dec. 2012). The AER predictions show (1) substantial house-to-house (spatial) variations (0.1 – 3.5 h-1) from building leakage differences; (2) slow oscillations from seasonal temperature changes; and (3) large transients from wind speed fluctuations. This study demonstrates the ability to predict spatiotemporal variability of residential AER in support of improving health study exposure assessments.