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A Flexible Spatio-Temporal Model for Air Pollution: Allowing for Spatio-Temporal Covariates

Lindström, Johan LU ; Szpiro, Adam A; Sampson, Paul D.; Sheppard, Lianne; Oron, Assaf; Richards, Mark and Larson, Tim (2011) In UW Biostatistics Working Paper Series
Abstract
Given the increasing interest in the association between exposure to air pollution and adverse health outcomes, the development of models that provide accurate spatio-temporal predictions of air pollution concentrations at small spatial scales is of great importance when assessing potential health effects of air pollution. The methodology presented here has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. We present a spatio-temporal framework that models and predicts ambient air pollution by combining data from several different monitoring... (More)
Given the increasing interest in the association between exposure to air pollution and adverse health outcomes, the development of models that provide accurate spatio-temporal predictions of air pollution concentrations at small spatial scales is of great importance when assessing potential health effects of air pollution. The methodology presented here has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. We present a spatio-temporal framework that models and predicts ambient air pollution by combining data from several different monitoring networks with the output from deterministic air pollution model(s). The model can accommodate arbitrarily missing observations and allows for a complex spatio-temporal correlation structure.



We apply the model to predict long-term average concentrations of gaseous oxides of nitrogen (NOx) ─ one of the primary pollutants of interest in the MESA Air study ─ during a ten year period in the Los Angeles area, based on measurements from the EPA Air Quality System and MESA Air monitoring. The measurements are augmented by a spatio-temporal covariate based on the output from a source dispersion model for traffic related air pollution (Caline3QHC) and the model is evaluated using cross-validation. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites.



The incorporation of a dispersion model output into the overall prediction model was feasible, but the particular implementation of Caline3QHC used here did not improve predictions in a model that also includes road information. However, excluding the road information the inclusion of model output improves predictions and we find some evidence that the source dispersion model can replace road covariates.



The model presented in this paper has been implemented in an R package, SpatioTemporal, which will be available on CRAN shortly. (Less)
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organization
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Working Paper
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published
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in
UW Biostatistics Working Paper Series
pages
38 pages
publisher
The Berkeley Electronic Press (Bepress)
language
English
LU publication?
yes
id
98608fbd-a7ac-4f21-8590-ccd82fcf4a1e (old id 4730139)
alternative location
http://www.bepress.com/uwbiostat/paper370
date added to LUP
2015-02-20 09:56:31
date last changed
2016-04-16 08:55:42
@misc{98608fbd-a7ac-4f21-8590-ccd82fcf4a1e,
  abstract     = {Given the increasing interest in the association between exposure to air pollution and adverse health outcomes, the development of models that provide accurate spatio-temporal predictions of air pollution concentrations at small spatial scales is of great importance when assessing potential health effects of air pollution. The methodology presented here has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. We present a spatio-temporal framework that models and predicts ambient air pollution by combining data from several different monitoring networks with the output from deterministic air pollution model(s). The model can accommodate arbitrarily missing observations and allows for a complex spatio-temporal correlation structure.<br/><br>
<br/><br>
We apply the model to predict long-term average concentrations of gaseous oxides of nitrogen (NOx) ─ one of the primary pollutants of interest in the MESA Air study ─ during a ten year period in the Los Angeles area, based on measurements from the EPA Air Quality System and MESA Air monitoring. The measurements are augmented by a spatio-temporal covariate based on the output from a source dispersion model for traffic related air pollution (Caline3QHC) and the model is evaluated using cross-validation. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites.<br/><br>
<br/><br>
The incorporation of a dispersion model output into the overall prediction model was feasible, but the particular implementation of Caline3QHC used here did not improve predictions in a model that also includes road information. However, excluding the road information the inclusion of model output improves predictions and we find some evidence that the source dispersion model can replace road covariates.<br/><br>
<br/><br>
The model presented in this paper has been implemented in an R package, SpatioTemporal, which will be available on CRAN shortly.},
  author       = {Lindström, Johan and Szpiro, Adam A and Sampson, Paul D. and Sheppard, Lianne and Oron, Assaf and Richards, Mark and Larson, Tim},
  language     = {eng},
  note         = {Working Paper},
  pages        = {38},
  publisher    = {The Berkeley Electronic Press (Bepress)},
  series       = {UW Biostatistics Working Paper Series},
  title        = {A Flexible Spatio-Temporal Model for Air Pollution: Allowing for Spatio-Temporal Covariates},
  year         = {2011},
}