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Combining numerical model output and particulate data using Bayesian space–time modeling
Author(s) -
McMillan Nancy J.,
Holland David M.,
Morara Michele,
Feng Jingyu
Publication year - 2010
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.984
Subject(s) - cmaq , markov chain monte carlo , bayesian probability , computer science , kriging , data mining , bayesian inference , statistics , air quality index , algorithm , mathematics , machine learning , meteorology , geography
Abstract Over the past few years, Bayesian models for combining output from numerical models and air monitoring data have been applied to environmental data sets to improve spatial prediction. This paper develops a new hierarchical Bayesian model (HBM) for fine particulate matter (PM 2.5 ) that combines U. S. EPA Federal Reference Method (FRM) PM 2.5 monitoring data and Community Multi‐scale Air Quality (CMAQ) numerical model output. The model is specified in a Bayesian framework and fitted using Markov Chain Monte Carlo (MCMC) techniques. We find that the statistical model combining monitoring data and CMAQ output provides reliable information about the true underlying PM 2.5 process over time and space. We base these conclusions on results of a validation exercise in which independent monitoring data were compared with predicted values from the HBM and predictions from a standard kriging model based solely on the monitoring data. Copyright © 2009 John Wiley & Sons, Ltd.