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Identifying meteorological drivers of PM 2.5 levels via a Bayesian spatial quantile regression
Author(s) -
Self Stella W.,
McMahan Christopher S.,
Russell Brook T.
Publication year - 2021
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.2669
Subject(s) - quantile regression , environmental science , quantile , air pollution , bayesian probability , regression , regression analysis , particulates , meteorology , human health , atmospheric sciences , econometrics , statistics , climatology , geography , mathematics , environmental health , medicine , chemistry , organic chemistry , geology , ecology , biology
Recently, due to accelerations in urban and industrial development, the health impact of air pollution has become a topic of key concern. Of the various forms of air pollution, fine atmospheric particulate matter (PM 2.5 ; particles less than 2.5 micrometers in diameter) appears to pose the greatest risk to human health. While even moderate levels of PM 2.5 can be detrimental to health, spikes in PM 2.5 to atypically high levels are even more dangerous. These spikes are believed to be associated with regionally specific meteorological factors. To quantify these associations, we develop a Bayesian spatiotemporal quantile regression model to estimate the spatially varying effects of meteorological variables purported to be related to PM 2.5 levels. By adopting a quantile regression model, we are able to examine the entire distribution of PM 2.5 levels; for example, we are able to identify which meteorological drivers are related to abnormally high PM 2.5 levels. Our approach uses penalized splines to model the spatially varying meteorological effects and to account for spatiotemporal dependence. The performance of the methodology is evaluated through extensive numerical studies. We apply our modeling techniques to 5 years of daily PM 2.5 data collected throughout the eastern United States to reveal the effects of various meteorological drivers.