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  • 标题:On the Spatio-Temporal Relationship Between MODIS AOD and PM2.5 Particulate Matter Measurements
  • 本地全文:下载
  • 作者:Aaron T. Porter ; Jacob J. Oleson ; Charles O. Stanier
  • 期刊名称:Journal of Data Science
  • 印刷版ISSN:1680-743X
  • 电子版ISSN:1683-8602
  • 出版年度:2014
  • 卷号:12
  • 期号:2
  • 页码:255-275
  • 出版社:Tingmao Publish Company
  • 摘要:Particulate matter smaller than 2.5 microns (PM2:5) is a com-monly measured parameter in ground-based sampling networks designed toassess short and long-term air quality. The measurement techniques forground based PM2:5 are relatively accurate and precise, but monitoring lo-cations are spatially too sparse for many applications. Aerosol Optical Depth(AOD) is a satellite based air quality measurement that can be computedfor more spatial locations, but measures light attenuation by particulatesthroughout in entire air column, not just near the ground. The goal ofthis paper is to better characterize the spatio-temporal relationship betweenthe two measurements. An informative relationship will aid in imputingPM2:5 values for health studies in a way that accounts for the variability inboth sets of measurements, something physics based models cannot do. Weuse a data set of Chicago air quality measurements taken during 2007 and2008 to construct a weekly hierarchical model. We also demonstrate thatAOD measurements and a latent spatio-temporal process aggregated weeklycan be used to aid in the prediction of PM2:5measurements.
  • 关键词:Air Quality; AOD; Bayesian; Hierarchical; Spatio-Temporal
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