Linked Life Data

19673250

Source:http://linkedlifedata.com/resource/pubmed/id/19673250

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
13
pubmed:dateCreated
2009-8-13
pubmed:abstractText
Land use regression (LUR) is used to map spatial variability in air pollutant concentrations for risk assessment epidemiology, and air quality management. Conventional LUR requires long-term measurements at multiple locations, so application to particulate matter has been limited. Here we use mobile monitoring to characterize spatial variability in black carbon concentrations for LUR modeling. A particle soot absorption photometer in a moving vehicle was used to measure the absorption coefficient (sigma(ap)) during summertime periods of peak afternoon traffic at 39 locations. LUR was used to model the mean and 25th, 50th, 75th, and 90th percentile values of the distribution of 10 s measurements at each location. Model performance (measured by R2) was higher for the 25th and 50th percentiles (0.72 and 0.68, respectively) than for the mean, 75th and 90th percentiles (0.51, 0.55, and 0.54, respectively). Performance was similar to that reported for conventional LUR models of NO2 and NO in this region (116 sites) and better than that for mean sigma(ap) from fixed-location samplers (25 sites). Models of the mean, 75th, and 90th percentiles favored predictors describing truck, rather than total, traffic. This approach is applicable to other urban areas to facilitate the development of LUR models for particulate matter.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0013-936X
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
43
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
4672-8
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Mobile monitoring of particle light absorption coefficient in an urban area as a basis for land use regression.
pubmed:affiliation
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA.
pubmed:publicationType
Journal Article