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An empirical assessment of Bayesian melding for mapping ozone pollution
Author(s) -
Liu Zhong,
Le Nhu D.,
Zidek James V.
Publication year - 2011
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.1054
Subject(s) - computer science , air quality index , software , bayesian probability , quality (philosophy) , data collection , kriging , data mining , operations research , risk analysis (engineering) , data science , systems engineering , machine learning , artificial intelligence , engineering , meteorology , statistics , mathematics , medicine , philosophy , physics , epistemology , programming language
This paper reviews the Bayesian melding approach while a companion technical report cited in the paper gives technical details about the Gibbs sampling algorithm used to implement the model and software developed for the research reported in this paper and available online can enable the method's use in other applications. This paper critically assesses the use of melding for mapping ozone concentration fields for possible future use in setting regulatory standards. This assessment has two stages. First a simulation study validates the computer code and goes on to investigate properties of the melding approach in situation where the “truth” is known. Then it is critically tested on a ozone mapping application using ozone data from the air quality system (AQS) database and simulated data from the multiscale air quality simulation platform (MAQSIP) chemical transportation model for ozone. In all cases, the melding is testing against Kriging, a simpler and more traditional way of mapping spatial fields. Conclusions and recommendations for future work are provided. Copyright © 2011 John Wiley & Sons, Ltd.