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Air pollution exposure modeling of individuals for epidemiology studies
Citation:
Breen, M. Air pollution exposure modeling of individuals for epidemiology studies. International Workshop: Novel Approaches to Research on Environment and Health, Washington, DC, October 09 - 11, 2018.
Impact/Purpose:
To better understand people's contact with air pollutants, and their potential for adverse health effects, it is important to estimate time spent in different locations and the air pollutant concentrations in those locations. To address this, we developed and evaluated the Exposure Model for Individuals (EMI). The capability of EMI could help reduce uncertainty of ambient pollutant exposure metrics used in epidemiology studies in support of improving health risk estimates.
Description:
Air pollution health studies often use outdoor concentrations from a central-site monitor as exposure surrogates. To improve exposure assessments, we previously developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient air pollutants using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance air pollutant infiltration model to predict residential AER (Tier 1), infiltration factors (Finf, Tier 2), indoor concentrations (Cin, Tier 3), personal exposure factors (Fpex, Tier 4), and personal exposures (E, Tier 5) for ambient air pollutants. In this study, we extended EMI by including: (1) an air quality model to predict hourly census-block outdoor concentrations for four pollutants (PM, NOX, CO, EC), (2) a GPS-based microenvironment tracker (MicroTrac) model to predict time spent by individuals in various microenvironments. We predicted daily exposure metrics (Tiers 1-5) for the 15 participants across 10 consecutive weeks in a cohort health study in central North Carolina called Coronary Artery Disease and Environmental Exposure (CADEE). Our modeled predictions for a total of 708 participant-days showed substantial house-to-house and temporal variability of AER, Finf, and Cin (Tiers 1-3); and subject-to-subject variability of Fpex and E (Tiers 4-5) for the four pollutants. The capability of EMI could help reduce uncertainty of ambient pollutant exposure metrics used in health studies, such as CADEE, in support of improving health risk estimates.