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Influence of Human Activity Patterns, particle composition, and residential air exchange rates on modeled distributions of PM 2.5 exposure compared with central-site monitoring data
Citation:
Baxter, L., J. Burke, M. Lunden, B. Turpin, D. Rich, K. Thevenet-Morrison, N. Hodas, AND H. Ozkaynak. Influence of Human Activity Patterns, particle composition, and residential air exchange rates on modeled distributions of PM 2.5 exposure compared with central-site monitoring data. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 23(3):241-247, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Central-site monitors do not account for factors such as outdoor-to-indoor transport and human activity patterns that influence personal exposures to ambient fine-particulate matter (PM2.5). We describe and compare different ambient PM2.5 exposure estimation approaches that incorporate human activity patterns and time-resolved location-specific particle penetration and persistence indoors. Four approaches were used to estimate exposures to ambient PM2.5 for application to the New Jersey Triggering of Myocardial Infarction Study. These include: Tier 1, central-site PM2.5 mass; Tier 2A, the Stochastic Human Exposure and Dose Simulation (SHEDS) model using literature-based air exchange rates (AERs); Tier 2B, the Lawrence Berkeley National Laboratory (LBNL) Aerosol Penetration and Persistence (APP) and Infiltration models; and Tier 3, the SHEDS model where AERs were estimated using the LBNL Infiltration model. Mean exposure estimates from Tier 2A, 2B, and 3 exposure modeling approaches were lower than Tier 1 central-site PM2.5 mass. Tier 2A estimates differed by season but not across the seven monitoring areas. Tier 2B and 3 geographical patterns appeared to be driven by AERs, while seasonal patterns appeared to be due to variations in PM composition and time activity patterns. These model results demonstrate heterogeneity in exposures that are not captured by the central-site monitor.
URLs/Downloads:
BAXTER - ORD-000354 FINAL FINAL.PDF (PDF, NA pp, 384.04 KB, about PDF)Journal of Exposure Science and Environmental Epidemiology
