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Modeling Air Pollution Exposure Metrics for the Diabetes and Environment Panel Study (DEPS)
Citation:
Breen, M., Y. Xu, A. Schneider, R. Williams, AND R. Devlin. Modeling Air Pollution Exposure Metrics for the Diabetes and Environment Panel Study (DEPS). 2016 Annual International Society of Exposure Science Meeting, Utrecht, The Netherlands, October 09 - 13, 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:
Air pollution health studies of fine particulate matter (PM) often use outdoor concentrations as exposure surrogates. To improve exposure assessments, we developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM 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 PM. In this study, we applied EMI to predict daily PM exposure metrics (Tiers 1-5) for the 21 participants in a cohort health study in central North Carolina called Diabetes and Environment Panel Study (DEPS). Using literature-reported parameters for the PM infiltration model, individual predictions were compared to 76 daily measurements of Fpex based on ratio of personal to home-outdoor sulfate concentrations from the 21 participants. Median difference between measured and modeled Fpex was 14% (25th and 75th percentiles of 7% and 34%, respectively). Using EMI, we predicted house-to-house and temporal variability of AER, Finf, and Cin (Tiers 1-3); and person-to-person variability of Fpex and E (Tiers 4-5). The capability of EMI could help reduce uncertainty of ambient PM exposure metrics used in health studies, such as DEPS, in support of improving health risk estimates.
