Modeling individual exposures to ambient PM_2.5 in the diabetes and the environment panel study (DEPS)

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.