Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data
Sampson, Paul D. ; Szpiro, Adam A. ; Sheppard, Lianne ; Lindström, Johan LU
and Kaufman, Joel D.
(2011)
In Atmospheric Environment
45(36).
p.6593-6606
- Abstract
- Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a... (More)
- Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation. (C) 2011 Elsevier Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2211597
- author
- Sampson, Paul D.
; Szpiro, Adam A.
; Sheppard, Lianne
; Lindström, Johan
LU
and Kaufman, Joel D.
- organization
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Exposure estimation, Hierarchical model, Nonstationary spatial, covariance, Partial least squares, Particulate matter, Universal kriging
- in
- Atmospheric Environment
- volume
- 45
- issue
- 36
- pages
- 6593 - 6606
- publisher
- Elsevier
- external identifiers
-
- wos:000296220200011
- scopus:79959891530
- ISSN
- 1352-2310
- DOI
- 10.1016/j.atmosenv.2011.04.073
- language
- English
- LU publication?
- yes
- id
- b4a138c3-6ce9-47cf-a165-48b758a14315 (old id 2211597)
- date added to LUP
- 2016-04-01 14:37:27
- date last changed
- 2022-04-22 04:18:49
@article{b4a138c3-6ce9-47cf-a165-48b758a14315,
abstract = {{Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in "land use" regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation. (C) 2011 Elsevier Ltd. All rights reserved.}},
author = {{Sampson, Paul D. and Szpiro, Adam A. and Sheppard, Lianne and Lindström, Johan and Kaufman, Joel D.}},
issn = {{1352-2310}},
keywords = {{Exposure estimation; Hierarchical model; Nonstationary spatial; covariance; Partial least squares; Particulate matter; Universal kriging}},
language = {{eng}},
number = {{36}},
pages = {{6593--6606}},
publisher = {{Elsevier}},
series = {{Atmospheric Environment}},
title = {{Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data}},
url = {{http://dx.doi.org/10.1016/j.atmosenv.2011.04.073}},
doi = {{10.1016/j.atmosenv.2011.04.073}},
volume = {{45}},
year = {{2011}},
}