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,2 Aaron van Donkelaar,3 Randall V. Martin,3,4 Richard T. Burnett,5 and Michael Jerrett6
1Geospatial Development Department, Risk Management Solutions Inc., Newark, California, USA; 2Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; 3Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada; 4The Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA; 5Population Studies Division, Health Canada, Ottawa, Ontario, Canada; 6Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
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.
Key words: air pollution, chronic exposure, geostatistics, PM2.5, remote sensing.
Environ Health Perspect 120:1727–1732 (2012). http://dx.doi.org/10.1289/ehp.1205006 [Online 2 October 2012]
Address correspondence to M. Jerrett, Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, 50 University Hall, Berkeley, CA 94720-7360 USA. Telephone: (510) 642-3960. Fax: (510) 642-5815. E-mail: firstname.lastname@example.org
Supplemental Material is available online (http://dx.doi.org/10.1289/ehp.1205006).
This research was funded by Health Canada (grant HC-4500209) and the U.S. Centers for Disease Control and Prevention (grant 200-2010-37394). S.‑J.L. is now employed by Geospatial Development Department, Risk Management Solutions Inc., Newark, California, but this work was performed while he worked at UC Berkeley.
The authors declare they have no actual or potential competing financial interests.
Received 1 January 2012; Accepted 2 October 2012; Online 2 October 2012.
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