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Spatial Variation and Land Use Regression Modeling of the Oxidative Potential of Fine Particles

Spatial Variation and Land Use Regression Modeling of the Oxidative Potential of Fine Particles

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4629740

Spatial Variation and Land Use Regression Modeling of the Oxidative Potential of Fine Particles

About this item

Full title

Spatial Variation and Land Use Regression Modeling of the Oxidative Potential of Fine Particles

Publisher

United States: National Institute of Environmental Health Sciences

Journal title

Environmental health perspectives, 2015-11, Vol.123 (11), p.1187-1192

Language

English

Formats

Publication information

Publisher

United States: National Institute of Environmental Health Sciences

More information

Scope and Contents

Contents

Oxidative potential (OP) has been suggested to be a more health-relevant metric than particulate matter (PM) mass. Land use regression (LUR) models can estimate long-term exposure to air pollution in epidemiological studies, but few have been developed for OP.
We aimed to characterize the spatial contrasts of two OP methods and to develop and evaluate LUR models to assess long-term exposure to the OP of PM2.5.
Three 2-week PM2.5 samples were collected at 10 regional background, 12 urban background, and 18 street sites spread over the Netherlands/Belgium in 1 year and analyzed for OP using electron spin resonance (OP(ESR)) and dithiothreitol (OP(DTT)). LUR models were developed using temporally adjusted annual averages and a range of land-use and traffic-related GIS variables.
Street/urban background site ratio was 1.2 for OP(DTT) and 1.4 for OP(ESR), whereas regional/urban background ratio was 0.8 for both. OP(ESR) correlated moderately with OP(DTT) (R2 = 0.35). The LUR models included estimated regional background OP, local traffic, and large-scale urbanity with explained variance (R2) of 0.60 for OP(DTT) and 0.67 for OP(ESR). OP(DTT) and OP(ESR) model predictions were moderately correlated (R2 = 0.44). OP model predictions were moderately to highly correlated with predictions from a previously published PM2.5 model (R2 = 0.37-0.52), and highly correlated with predictions from previously published models of traffic components (R2 > 0.50).
LUR models explained a large fraction of the spatial variation of the two OP metrics. The moderate correlations among the predictions of OP(DTT), OP(ESR), and PM2.5 models offer the potential to investigate which metric is the strongest predictor of health effects.
Yang A, Wang M, Eeftens M, Beelen R, Dons E, Leseman DL, Brunekreef B, Cassee FR, Janssen NA, Hoek G. 2015. Spatial variation and land use regression modeling of the oxidative potential of fine particles. Environ Health Perspect 123:1187-1192; http://dx.doi.org/10.1289/ehp.1408916....

Alternative Titles

Full title

Spatial Variation and Land Use Regression Modeling of the Oxidative Potential of Fine Particles

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4629740

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_4629740

Other Identifiers

ISSN

0091-6765

E-ISSN

1552-9924

DOI

10.1289/ehp.1408916

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