Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5Concentrations across the Continental United States | Zendy

SeungJae Lee | Zendy; Marc L. Serre | Zendy; Aaron van Donkelaar | Zendy; Randall V. Martin | Zendy; Richard T. Burnett | Zendy; Michael Jerrett | Zendy

Open Access

Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM_2.5Concentrations across the Continental United States

Author(s) -

SeungJae Lee,

Marc L. Serre,

Aaron van Donkelaar,

Randall V. Martin,

Richard T. Burnett,

Michael Jerrett

Publication year - 2012

Publication title -

environmental health perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.257

H-Index - 282

eISSN - 1552-9924

pISSN - 0091-6765

DOI - 10.1289/ehp.1205006

Subject(s) - kriging , remote sensing , interpolation (computer graphics) , environmental science , satellite , sampling (signal processing) , multivariate interpolation , geostatistics , term (time) , spatial variability , computer science , statistics , geography , mathematics , machine learning , animation , computer graphics (images) , physics , quantum mechanics , filter (signal processing) , aerospace engineering , bilinear interpolation , engineering , computer vision

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.

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