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Analyses of School Commuting Data for Exposure Modeling Purposes
Citation:
XUE, J., T. R. MCCURDY, J. M. BURKE, B. Bhaduri, C. Liu, J. Nutaro, AND L. Patterson. Analyses of School Commuting Data for Exposure Modeling Purposes. Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 20(1):69-78, (2009).
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:
Human exposure models often make the simplifying assumption that school children attend school in the same Census tract where they live. This paper analyzes that assumption and provides information on the temporal and spatial distributions associated with school commuting. The data were obtained using Oak Ridge National Laboratory’s LandScan USA population distribution model (Bhaduri et al., 2007) applied to Philadelphia PA. It is a high-resolution model used to allocate individual school-aged children to both a home and school location, and to devise a minimum-time home-to school commuting path (called a trace) between the two locations. LandScan relies heavily on Geographic Information System (GIS) data. With respect to school children attending school in their home Census tract, the vast majority do not in Philadelphia. Our analyses found that: (1) about 32% of the students walk across 2 or more Census tracts going to school and 40% of them walk across 4 or more Census blocks; and, (2) 60% drive across 4 or more Census tracts going to school and 50% drive across 10 or more Census blocks. We also find that: (3) using a five minute commuting time interval—as opposed to the modeled “trace”--results in misclassifying the “actual” path taken in 90% of the Census blocks, 70% of the block groups, and 50% of the tracts; (4) a one-minute time interval is needed to reasonably resolve time spent in the various Census unit designations; and, (5) approximately 50% of both the homes and schools of Philadelphia schoolchildren are located within 160 m of highly-traveled roads, and 64% of the schools are located within 200 m. These findings are very important when modeling school children’s exposures, especially when ascertaining the impacts of near-roadway concentrations on their total daily body burden. Since many school children also travel along these streets and roadways to get to school, a majority of children in Philadelphia are in mobile-source dominated locations most of the day. We hypothesize that exposures of school children in Philadelphia to benzene and particulate matter will be much higher than if home and school locations and 2 commuting paths at a 1-minute time resolution are not explicitly modeled in an exposure assessment. Undertaking such an assessment will be the topic of a future paper.
