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Title: Analysis of School Commuting Data for Exposure Modeling Purposes

Abstract

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. Our GIS analyses found that in Philadelphia: (1) about 32% of the students walk across 2 or more census tracts and 40% of them walk across 4 or more census blocks; (2) 60% drive across 4 or more census tracts going to school and 50% drive across 10 or more census blocks; (3) five-minute commuting time intervals result in misclassification as high as 90% for census blocks, 70% for block groups, and 50% for census tracts; (4) a one-minute time interval is needed to reasonably resolve time spent in the various census unit designations; (5)more » approximately 50% of both schoolchildren s homes and schools 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 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.« less

Authors:
 [1];  [2];  [2];  [3];  [3];  [3];  [3]
  1. U.S. Environmental Protection Agency
  2. U.S. Environmental Protection Agency, Raleigh, North Carolina
  3. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
965837
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Journal Of Exposure Science And Environmental Epidemiology
Additional Journal Information:
Journal Volume: 20; Journal Issue: 1; Journal ID: ISSN 1559--0631
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CHILDREN; POPULATION DYNAMICS; EDUCATIONAL FACILITIES; SPATIAL DISTRIBUTION; MATHEMATICAL MODELS; BENZENE; PARTICULATES; ENVIRONMENTAL EXPOSURE

Citation Formats

Xue, Jianping, McCurdy, Thomas, Burke, Janet, Bhaduri, Budhendra L, Liu, Cheng, Nutaro, James J, and Patterson, Lauren A. Analysis of School Commuting Data for Exposure Modeling Purposes. United States: N. p., 2010. Web. doi:10.1038/jes.2009.3.
Xue, Jianping, McCurdy, Thomas, Burke, Janet, Bhaduri, Budhendra L, Liu, Cheng, Nutaro, James J, & Patterson, Lauren A. Analysis of School Commuting Data for Exposure Modeling Purposes. United States. https://doi.org/10.1038/jes.2009.3
Xue, Jianping, McCurdy, Thomas, Burke, Janet, Bhaduri, Budhendra L, Liu, Cheng, Nutaro, James J, and Patterson, Lauren A. 2010. "Analysis of School Commuting Data for Exposure Modeling Purposes". United States. https://doi.org/10.1038/jes.2009.3.
@article{osti_965837,
title = {Analysis of School Commuting Data for Exposure Modeling Purposes},
author = {Xue, Jianping and McCurdy, Thomas and Burke, Janet and Bhaduri, Budhendra L and Liu, Cheng and Nutaro, James J and Patterson, Lauren A},
abstractNote = {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. Our GIS analyses found that in Philadelphia: (1) about 32% of the students walk across 2 or more census tracts and 40% of them walk across 4 or more census blocks; (2) 60% drive across 4 or more census tracts going to school and 50% drive across 10 or more census blocks; (3) five-minute commuting time intervals result in misclassification as high as 90% for census blocks, 70% for block groups, and 50% for census tracts; (4) a one-minute time interval is needed to reasonably resolve time spent in the various census unit designations; (5) approximately 50% of both schoolchildren s homes and schools 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 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.},
doi = {10.1038/jes.2009.3},
url = {https://www.osti.gov/biblio/965837}, journal = {Journal Of Exposure Science And Environmental Epidemiology},
issn = {1559--0631},
number = 1,
volume = 20,
place = {United States},
year = {Fri Jan 01 00:00:00 EST 2010},
month = {Fri Jan 01 00:00:00 EST 2010}
}