A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates
Lindström, Johan LU
; Szpiro, Adam A.
; Sampson, Paul D.
; Oron, Assaf P.
; Richards, Mark
; Larson, Tim V.
and Sheppard, Lianne
(2014)
In Environmental and Ecological Statistics
21(3).
p.411-433
- Abstract
- The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the... (More)
- The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of in the Los Angeles area during a 10 year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated of approximately at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4648902
- author
- Lindström, Johan
LU
; Szpiro, Adam A.
; Sampson, Paul D.
; Oron, Assaf P.
; Richards, Mark
; Larson, Tim V.
and Sheppard, Lianne
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Air pollution, Cross-validation, NOx, Spatio-temporal data, Unbalanced, data
- in
- Environmental and Ecological Statistics
- volume
- 21
- issue
- 3
- pages
- 411 - 433
- publisher
- Springer
- external identifiers
-
- wos:000340681900002
- pmid:25264424
- scopus:84907596200
- pmid:25264424
- ISSN
- 1352-8505
- DOI
- 10.1007/s10651-013-0261-4
- language
- English
- LU publication?
- yes
- id
- 47c94001-bca6-4eeb-843d-bf544f4445fc (old id 4648902)
- date added to LUP
- 2016-04-01 13:15:58
- date last changed
- 2022-04-14 00:03:06
@article{47c94001-bca6-4eeb-843d-bf544f4445fc,
abstract = {{The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of in the Los Angeles area during a 10 year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated of approximately at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.}},
author = {{Lindström, Johan and Szpiro, Adam A. and Sampson, Paul D. and Oron, Assaf P. and Richards, Mark and Larson, Tim V. and Sheppard, Lianne}},
issn = {{1352-8505}},
keywords = {{Air pollution; Cross-validation; NOx; Spatio-temporal data; Unbalanced; data}},
language = {{eng}},
number = {{3}},
pages = {{411--433}},
publisher = {{Springer}},
series = {{Environmental and Ecological Statistics}},
title = {{A flexible spatio-temporal model for air pollution with spatial and spatio-temporal covariates}},
url = {{http://dx.doi.org/10.1007/s10651-013-0261-4}},
doi = {{10.1007/s10651-013-0261-4}},
volume = {{21}},
year = {{2014}},
}