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Comparative Study
. 2012 Dec;120(12):1727-32.
doi: 10.1289/ehp.1205006. Epub 2012 Oct 2.

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States

Seung-Jae Lee  1 , Marc L Serre, Aaron van Donkelaar, Randall V Martin, Richard T Burnett, Michael Jerrett
Affiliations
Comparative Study

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States

Seung-Jae Lee et al. Environ Health Perspect. 2012 Dec.

Abstract

Background: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data.

Objective: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation.

Methods: We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals.

Results: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates.

Conclusions: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

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Conflict of interest statement

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

Figures

Figure 1
Figure 1
Monitoring stations for the U.S. EPA’s PM2.5 measurements. Training data for estimation were obtained from 1,315 sites. Data for validation were obtained from 147 randomly selected validation sites.
Figure 2
Figure 2
Periodicity shift in time across the United States and mean trend models fitting the shift. Map of the United States indicating the month of the year when the monthly average PM2.5 concentration was highest (A); circles indicate individual monitoring sites. PM2.5 measurements and corresponding CSTM and SSTM for a single monitoring site in the western (B) and in the eastern United States (C); the sites are indicated by black circles in (A).
Figure 3
Figure 3
Percent change in MSE from RS to KC shown as a function of the distance between the validation point and its closest measurement site. The curve indicates a second order polynomial regression model that fits the MSE changes.
Figure 4
Figure 4
Average PM2.5 exposure estimates at 10-km gridded locations for 2001–2006 based on (A) RS (integrated remote sensing-meteorology model), (B) KC (monitor-based model), and (C) a combination of RS and KC.
See this image and copyright information in PMC

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References

    1. Bell ML, Dominici F, Ebisu K, Zeger SL, Samet M. Spatial and temporal variation in PM2.5 chemical composition in the United States for health effects studies. Environ Health Perspect. 2007;15:989–995. - PMC - PubMed
    1. BMElib. The Bayesian Maximum Entropy Software for Space/Time Geostatistics, and Temporal GIS Data Integration. 2012. Available: http://www.unc.edu/depts/case/BMELIB [accessed 31 October 2012]
    1. Brauer M, Amann M, Burnett RT, Cohen A, Dentener F, Ezzati M, et al. 2011Exposure assessment for the estimation of the global burden of disease attributable to outdoor air pollution. Environ Sci Tech doi:10.1021/es2025752 [Online 8 December 2011]. - DOI - PMC - PubMed
    1. Christakos G. Mineola, NY: Dover Publications; 1992. Random Field Models in Earth Sciences.
    1. Christakos G. Modern Spatiotemporal Geostatistics. New York:Oxford University Press 2000

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