On the Spatio-Temporal Relationship Between MODIS AOD and PM2.5 Particulate Matter Measurements

Journal of Data Science

article

Aaron T. Porter¹  Jacob J. Oleson²  Charles O. Stanier³ 

[1] Department of Statistics, The University of Missouri - Columbia;Department of Biostatistics, The University of Iowa.;Department of Chemical and Biochemical Engineering, The University of Iowa.

关键词: Air Quality;    AOD;    Bayesian;   

DOI  :  10.6339/JDS.201404_12(2).0003

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Particulate matter smaller than 2.5 microns (PM2.5) is a com monly measured parameter in ground-based sampling networks designed to assess short and long-term air quality. The measurement techniques for ground 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 computed for more spatial locations, but measures light attenuation by particulates throughout in entire air column, not just near the ground. The goal of this paper is to better characterize the spatio-temporal relationship between the two measurements. An informative relationship will aid in imputing PM2.5 values for health studies in a way that accounts for the variability in both sets of measurements, something physics based models cannot do. We use a data set of Chicago air quality measurements taken during 2007 and 2008 to construct a weekly hierarchical model. We also demonstrate that AOD measurements and a latent spatio-temporal process aggregated weekly can be used to aid in the prediction of PM2.5measurements.

【 授权许可】

CC BY