Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS)

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 assessmentsfor 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 EMIto 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.