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Title: Active commuting to school in Portuguese adolescents: Using PALMS to detect trips

Authors:
; ORCiD logo; ; ; ORCiD logo; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE), Fuel Cycle Technologies (NE-5)
OSTI Identifier:
1359507
Grant/Contract Number:
UID/DTP/00617/2013; SFRH/BD/70513/2010
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Transport & Health
Additional Journal Information:
Journal Volume: 3; Journal Issue: 3; Related Information: CHORUS Timestamp: 2017-05-28 04:12:30; Journal ID: ISSN 2214-1405
Publisher:
Elsevier
Country of Publication:
Country unknown/Code not available
Language:
English

Citation Formats

Pizarro, Andreia Nogueira, Schipperijn, Jasper, Andersen, Henriette Bondo, Ribeiro, José Carlos, Mota, Jorge, and Santos, Maria Paula. Active commuting to school in Portuguese adolescents: Using PALMS to detect trips. Country unknown/Code not available: N. p., 2016. Web. doi:10.1016/j.jth.2016.02.004.
Pizarro, Andreia Nogueira, Schipperijn, Jasper, Andersen, Henriette Bondo, Ribeiro, José Carlos, Mota, Jorge, & Santos, Maria Paula. Active commuting to school in Portuguese adolescents: Using PALMS to detect trips. Country unknown/Code not available. doi:10.1016/j.jth.2016.02.004.
Pizarro, Andreia Nogueira, Schipperijn, Jasper, Andersen, Henriette Bondo, Ribeiro, José Carlos, Mota, Jorge, and Santos, Maria Paula. 2016. "Active commuting to school in Portuguese adolescents: Using PALMS to detect trips". Country unknown/Code not available. doi:10.1016/j.jth.2016.02.004.
@article{osti_1359507,
title = {Active commuting to school in Portuguese adolescents: Using PALMS to detect trips},
author = {Pizarro, Andreia Nogueira and Schipperijn, Jasper and Andersen, Henriette Bondo and Ribeiro, José Carlos and Mota, Jorge and Santos, Maria Paula},
abstractNote = {},
doi = {10.1016/j.jth.2016.02.004},
journal = {Journal of Transport & Health},
number = 3,
volume = 3,
place = {Country unknown/Code not available},
year = 2016,
month = 9
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.jth.2016.02.004

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  • Numerous socio-environmental studies, including those in public health, utilize population data as one of the essential elements of modeling and analysis. Typically population data are reported by administrative or accounting units. For example, in the US the Census Bureau reports population counts by census blocks, block groups, and tracts. At any resolution, a uniform population distribution is assumed and the population figures and demographic characteristics are typically associated with block (polygon) centroids. In geographic analyses these points are considered representative of the population for census polygons. Traditional spatial modeling approaches commonly include intersection of census data with buffers of influencemore » to quantify target population, using either inclusion-exclusion (of the centroids) or the area weighted population estimation methods. However, it is well understood that uniform population distribution is the weakest assumption and by considering census polygon centroids as representative of population all analytical approaches are very likely to overestimate or underestimate the analytical results. Given that population is spatially restricted by Census accounting units (such as blocks), there often is great uncertainty about spatial distribution of residents within those accounting units. This is particularly appropriate in suburban and rural areas, where the population is dispersed to a greater degree than urban areas. Because of this uncertainty, there is significant potential to misclassify people with respect to their location from pollution sources, and consequently it becomes challenging to determine if certain sub-populations are actually more likely than others to get differential environmental exposure. In this paper, we describe development and utilization of a high resolution demographic data driven approach for modeling and simulation at Oak Ridge National Laboratory.« less
  • 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 onmore » 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.« less
  • The active portion of the Portuguese Bend landslide complex is approximately 3 km/sup 2/ in area and 30-50 m thick. Measured displacement rates range from less than one to greater than 30 mm/day on different parts of the landslide, with total displacements over the last 30 yrs ranging from about 10 to greater than 150 m. Six types of breccia, each locally with a foliated matrix, were recognized in the active landslide complex and are absent outside the landslide complex. Slide-body breccias are of two types, the first formed by extensional fracturing during bulk pure shear at the top ofmore » the landslide (slide-top breccia) and the second by flow of tuffaceous shales and fracture of embedded siliceous shales during simple shear deep in the landslide to the basal decollement (slide-bottom breccias). Slide-margin breccias, also in simple shear, are produced on the lateral margins of individual slide blocks accompanying wrench-fault motion. Other breccias (fault-ramp breccias) are formed during motion over ramps. Colluvial deposits within tension gashes (crack-fill breccias) and at the toe of the slide (slide-toe breccias) represent a fifth breccia type. Diapirs originating from over-pressured zones at the slide base also contain breccia. Recognition of different breccia types in ancient rocks would be difficult, because fabrics in the different types are similar. Foliations are defined by: scaly cleavage, compositional banding and color banding (in shear zones), stretched mud clasts, and aligned hard grains. Foliated breccias are synonymous with melanges. The authors regard the six breccia types described herein as representing the principal types of melange that occur in ancient accretionary settings.« less
  • In-111 oxine labeled white cells were used to diagnose acute inflammatory conditions in 42 children and adolescents, aged 6 weeks to 19 years. In 43 scans where a clinical correlation could be made, the test had a sensitivity of 81% and a specificity of 94%. There were no adverse reactions. For children the dose of In-111 recommended is 10-12 mu Ci/kg body weight to a maximum of 500 mu Ci.