Dataset Information

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

ABSTRACT:

Background

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.

Objectives

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.

Methods

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.

Results

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).

Conclusion

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.

Citation

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.

SUBMITTER: Yang A

PROVIDER: S-EPMC4629740 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Yang Aileen A Wang Meng M Eeftens Marloes M Beelen Rob R Dons Evi E Leseman Daan L A C DL Brunekreef Bert B Cassee Flemming R FR Janssen Nicole A H NA Hoek Gerard G

Environmental health perspectives 20150403 11

<h4>Background</h4>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.<h4>Objectives</h4>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.<h4>Methods</h4>Three 2-week PM2.5 samples were co ...[more]

PMID: 25840153

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Project description:Results from studies on residential health effects of livestock farming are inconsistent, potentially due to simple exposure proxies used (e.g., livestock density). Accuracy of these proxies compared with measured exposure concentrations is unknown.We aimed to assess spatial variation of endotoxin in PM10 (particulate matter ?10?m) at residential level in a livestock-dense area, compare simple livestock exposure proxies to measured endotoxin concentrations, and evaluate whether land-use regression (LUR) can be used to explain spatial variation of endotoxin.The study area (3,000?km2) was located in Netherlands. Ambient PM10 was collected at 61 residential sites representing a variety of surrounding livestock-related characteristics. Three to four 2-wk averaged samples were collected at each site. A local reference site was used for temporal variation adjustment. Samples were analyzed for PM10 mass by weighing and for endotoxin by using the limulus amebocyte lysate assay. Three LUR models were developed, first a model based on general livestock-related GIS predictors only, followed by models that also considered species-specific predictors and farm type-specific predictors.Variation in concentrations measured between sites was substantial for endotoxin and more limited for PM10 (coefficient of variation: 43%, 8%, respectively); spatial patterns differed considerably. Simple exposure proxies were associated with endotoxin concentrations although spatial variation explained was modest (R2<26%). LUR models using a combination of animal-specific livestock-related characteristics performed markedly better, with up to 64% explained spatial variation.The considerable spatial variation of ambient endotoxin concentrations measured in a livestock-dense area can largely be explained by LUR modeling based on livestock-related characteristics. Application of endotoxin LUR models seems promising for residential exposure estimation within health studies. https://doi.org/10.1289/EHP2252.

Dataset Information

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

Background

Objectives

Methods

Results

Conclusion

Citation

Publications

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

Similar Datasets

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