文章基本信息

标题：Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM_2.5 Concentrations across the Continental United States
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作者：Seung-Jae Lee ; Marc L. Serre ; Aaron van Donkelaar 等
期刊名称：Environmental Health Perspectives
印刷版ISSN：0091-6765
电子版ISSN：1552-9924
出版年度：2012
卷号：120
期号：12
页码：1727-1732
DOI：10.1289/ehp.1205006
出版社：OCR Subscription Services Inc
摘要：Ba c k g r o u n d: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data.oB j e c t i v e: We evaluated and compared the predictive capabilities of remote sensing and geostatis-tical interpolation.Me t h o d s: We developed a space–time geostatistical kriging model to predict PM2.5over the conti-nental United States and compared resulting predictions to estimates derived from satellite retrievals.re s u l t s: 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 com-bines the kriging and satellite-based PM2.5estimates.co n c l u s i o n s: 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 esti-mates that perform nearly as well
关键词：air pollution; chronic exposure; geostatistics; PM;2.5; remote sensing