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Multivariate air pollution prediction modeling with partial missingness
Author(s) -
Boaz R.M.,
Lawson A.B.,
Pearce J.L.
Publication year - 2019
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.2592
Subject(s) - multivariate statistics , missing data , pollutant , air pollution , air quality index , environmental science , computer science , pollution , bayesian probability , multivariate analysis , statistics , data mining , meteorology , machine learning , artificial intelligence , mathematics , geography , ecology , chemistry , organic chemistry , biology
Missing observations from air pollution monitoring networks has posed a longstanding problem for health investigators of air pollution. Growing interest in mixtures of air pollutants has further complicated this problem, as many new challenges have arisen that require development of novel methods. The objective of this study is to develop a methodology for multivariate prediction of air pollution. We focus specifically on tackling different forms of missing data such as spatial (sparse sites), outcome (pollutants not measured at some sites), and temporal (varieties of interrupted time series). To address these challenges, we develop a novel multivariate fusion framework, which leverages the observed interpollutant correlation structure to reduce error in the simultaneous prediction of multiple air pollutants. Our joint fusion model employs predictions from the Environmental Protection Agency's Community Multiscale Air Quality model along with spatiotemporal error terms. We have implemented our models on both simulated data and a case study in South Carolina for eight pollutants over a 28‐day period in June 2006. We found that our model, which uses a multivariate correlated error in a Bayesian framework, showed promising predictive accuracy particularly for gaseous pollutants.