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Statistical Properties of Longitudinal Time-Activity Data for Use in Human Exposure Modeling
Citation:
ISAACS, K. K., T. R. MCCURDY, G. Glen, M. Nyswander, A. ERRICKSON, S. Forbes, S. E. GRAHAM, L. McCurdy, L. Smith, N. S. TULVE, AND D. A. VALLERO. Statistical Properties of Longitudinal Time-Activity Data for Use in Human Exposure Modeling. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 23(3):328-336, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) 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:
Understanding the longitudinal properties of the time spent in different locations and activities is important in characterizing human exposure to pollutants. The results of a four-season longitudinal time-activity diary study in eight working adults are presented, with the goal of improving the parameterization of human activity algorithms in EPA’s exposure modeling efforts. Despite the longitudinal, multi-season nature of the study, participant non-compliance with the protocol over time did not play a major role in data collection. The diversity (D)—a ranked intraclass correlation coefficient (ICC)— and lag-one autocorrelation (A) statistics of study participants are presented for time spent in outdoor, motor vehicle, residential, and other-indoor locations. Day-type (workday versus non-workday, and weekday versus weekend), season, temperature, and gender differences in the time spent in selected locations and activities are described, and D & A statistics are presented. The overall D and ICC values ranged from approximately 0.08–0.26, while the mean population rank A values ranged from approximately 0.19–0.36. These statistics indicate that intra-individual variability exceeds explained inter-individual variability, and low day-to-day correlations among locations. Most exposure models do not address these behavioral characteristics, and thus underestimate population exposure distributions and subsequent health risks associated with environmental exposures.
URLs/Downloads:
Statistical Properties of Longitudinal Time-Activity Data for Use in Human Exposure Modeling (PDF, NA pp, 200 KB, about PDF)Journal of Exposure Science and Environmental Epidemiology
