Abstract
Air pollution epidemiology studies of ambient fine particulate matter (PM(2.5)) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM(2.5) exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM(2.5) using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM(2.5) infiltration model to predict residential AER (Tier 1), infiltration factors (F(inf_home), Tier 2), indoor concentrations (C(in), Tier 3), personal exposure factors (F(pex), Tier 4), and personal exposures (E, Tier 5) for ambient PM(2.5). We applied EMI to predict daily PM(2.5) exposure metrics (Tiers 1-5) for 174 participant-days across the 13 months of DEPS. Individual model predictions were compared to a subset of daily measurements of F(pex) and E (Tiers 4-5) from the DEPS participants. Model-predicted F(pex) and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174 days showed considerable temporal and house-to-house variability of AER, F(inf_home), and C(in) (Tiers 1-3), and person-to-person variability of F(pex) and E (Tiers 4-5). Our study demonstrates the capability of predicting individual-level ambient PM(2.5) exposure metrics for an epidemiological study, in support of improving risk estimation.