Premium
A data fusion approach for spatial analysis of speciated PM 2.5 across time
Author(s) -
Rundel Colin W.,
Schliep Erin M.,
Gelfand Alan E.,
Holland David M.
Publication year - 2015
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.2369
Subject(s) - statistics , scale (ratio) , covariate , component (thermodynamics) , observational error , environmental data , environmental science , computer science , econometrics , mathematics , geography , cartography , ecology , physics , biology , thermodynamics
PM 2.5 exposure is linked to a number of adverse health effects such as lung cancer and cardiovascular disease. However, PM 2.5 is a complex mixture of different species whose composition varies substantially in both space and time. An open question is how these constituent species contribute to the overall negative health outcomes seen from PM 2.5 exposure. To this end, the Environmental Protection Agency as well as other federal, state, and local organization monitor total PM 2.5 along with its primary species on a national scale. From an epidemiological perspective, there is a need to develop effective methods that will allow for the spatially and temporally sparse observations to be used to predict exposures for locations across the entire United States. Toward this objective, we have collected data from three separate monitoring station networks as well as output from a deterministic atmospheric computer model. We introduce a novel multi‐level speciated PM 2.5 model, which captures the following features: (1) it fuses data from three monitoring networks; (2) it simultaneously models each of the five primary components of PM 2.5 from each network along with the computer model output; (3) it introduces species and network level measurement error models as well as total PM 2.5 measurement error models, all varying around the respective latent true levels; (4) it incorporates an unobserved “other” species component as well as a sum constraint such that the total is physically consistent (i.e., total must be equal to the sum of the primary species and “other”), which is not always the case with the observed data. Copyright © 2015 John Wiley & Sons, Ltd.