Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Nov;123(11):1187-92.
doi: 10.1289/ehp.1408916. Epub 2015 Apr 3.

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

Aileen Yang  1 , Meng Wang, Marloes Eeftens, Rob Beelen, Evi Dons, Daan L A C Leseman, Bert Brunekreef, Flemming R Cassee, Nicole A H Janssen, Gerard Hoek
Affiliations

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

Aileen Yang et al. Environ Health Perspect. 2015 Nov.

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare they have no actual or potential competing financial interests.

Figures

Figure 1
Figure 1
Adjusted annual average of OPESR (left) and OPDTT (right) by site type. Median, mean, and 25th and 75th percentiles are shown in the box, whiskers indicate minimum and maximum values, and individual outliers are shown as points. n = 40 sites.
Figure 2
Figure 2
Relationship among measured annual average of OPDTT, OPESR, Cu (ng/m3), Fe (ng/m3), NO2 (μg/m3), PM2.5 mass concentration (μg/m3), and PM2.5 absorbance (abs; 10–5/m) by site type; n = 40. The correlation coefficients (R2) are presented in Table 2.
See this image and copyright information in PMC

References

    1. Ayres JG, Borm P, Cassee FR, Castranova V, Donaldson K, Ghio A, et al. 2008Evaluating the toxicity of airborne particulate matter and nanoparticles by measuring oxidative stress potential—a workshop report and consensus statement. Inhal Toxicol 2075–99.; 10.1080/08958370701665517 - DOI - PubMed
    1. Beelen R, Hoek G, Vienneau D, Eeftens M, Dimakopoulou K, Pedeli X, et al. 2013Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe—the ESCAPE project. Atmos Environ 7210–23.; 10.1016/j.atmosenv.2013.02.037 - DOI
    1. Boogaard H, Janssen NAH, Fischer PH, Kos GPA, Weijers EP, Cassee FR, et al. 2011Contrasts in oxidative potential and other particulate matter characteristics collected near major streets and background locations. Environ Health Perspect 120185–191.; 10.1289/ehp.1103667 - DOI - PMC - PubMed
    1. Borm PJA, Kelly F, Künzli N, Schins RPF, Donaldson K.2007Oxidant generation by particulate matter: from biologically effective dose to a promising, novel metric. Occup Environ Med 6473–74.; 10.1136/oem.2006.029090 - DOI - PMC - PubMed
    1. Brunekreef B, Holgate ST. Air pollution and health. Lancet. 2002;360:1233–1242. - PubMed

Publication types

LinkOut - more resources