Science Inventory

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

Citation:

Breen, M., Y. Xu, A. Schneider, R. Williams, AND R. Devlin. Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS). SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 626:807-816, (2018).

Impact/Purpose:

The goal of this study was to improve PM2.5 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 PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Our study demonstrates the capability of predicting individual-level ambient PM2.5 exposure metrics for an epidemiological study to help reduce the uncertainty of using outdoor concentrations as exposure surrogates, in support of improving risk estimation.

Description:

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 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 PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Finf_home, Tier 2), indoor concentrations (Cin, Tier 3), personal exposure factors (Fpex, Tier 4), and personal exposures (E, Tier 5) for ambient PM2.5. We applied EMI to predict daily PM2.5 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 Fpex and E (Tiers 4–5) from the DEPS participants. Model-predicted Fpex 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, Finf_home, and Cin (Tiers 1–3), and person-to-person variability of Fpex and E (Tiers 4–5). Our study demonstrates the capability of predicting individual-level ambient PM2.5 exposure metrics for an epidemiological study, in support of improving risk estimation.

URLs/Downloads:

https://doi.org/10.1016/j.scitotenv.2018.01.139   Exit

Record Details:

Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Product Published Date: 06/01/2018
Record Last Revised: 02/28/2018
OMB Category: Other
Record ID: 339813

Organization:

U.S. ENVIRONMENTAL PROTECTION AGENCY

OFFICE OF RESEARCH AND DEVELOPMENT

NATIONAL EXPOSURE RESEARCH LABORATORY

COMPUTATIONAL EXPOSURE DIVISION

HUMAN EXPOSURE & DOSE MODELING BRANCH