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Pragmatic Estimation of a Spatio-Temporal Air Quality Model With Irregular Monitoring Data

Sampson, Paul D. ; Szpiro, Adam A. ; Sheppard, Lianne ; Lindström, Johan LU orcid and Kaufman, Joel D. (2009) In UW Biostatistics Working Paper Series
Abstract
Statistical analyses of the 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 regression models have accounted for spatial correlation structure in combining monitoring data with land-use covariates. The current paper builds on these concepts to address spatio-temporal prediction of ambient concentrations of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) on the basis of a model representing spatially varying seasonal trends and spatial correlation structures. Our hierarchical methodology provides a pragmatic approach that fully exploits regulatory and other supplemental monitoring data which jointly... (More)
Statistical analyses of the 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 regression models have accounted for spatial correlation structure in combining monitoring data with land-use covariates. The current paper builds on these concepts to address spatio-temporal prediction of ambient concentrations of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) on the basis of a model representing spatially varying seasonal trends and spatial correlation structures. Our hierarchical methodology provides a pragmatic approach that fully exploits regulatory and other supplemental monitoring data which jointly define a complex spatio-temporal monitoring design. We explain the elements of the computational approach, including estimation of smoothed empirical orthogonal functions (SEOFs) as basis functions for temporal trend, spatial (“land use”) regression by Partial Least Squares (PLS), modeling of spatio-temporal correlation structure, and generalized universal kriging prediction of ambient exposure for subjects in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) project. Analyses are demonstrated in detail for the South California study area of the MESA Air project using AQS monitoring data from 2000 to 2006 and supplemental MESA Air monitoring data beginning in 2005. Results of application of the modeling and estimation methodology are presented also for five other MESA Air metropolitan study areas across the country with comments on current and future research developments. (Less)
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Working paper/Preprint
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in
UW Biostatistics Working Paper Series
pages
42 pages
publisher
Berkeley Electronic Press
language
English
LU publication?
yes
additional info
Occur in UW Biostatistics Working Paper Series as Working Paper 353. Later published as: Pragmatic Estimation of a Spatio-Temporal Air Quality Model With Irregular Monitoring Data Sampson, Paul D. ; Szpiro, Adam A. ; Sheppard, Lianne ; Lindström, Johan Atmospheric Environment 2011, 45, 6593 (LUPid: 2211597)
id
5b71a2af-43a7-4ca8-910f-47fd47a51139 (old id 4730127)
alternative location
http://www.bepress.com/uwbiostat/paper353
date added to LUP
2016-04-04 12:06:44
date last changed
2020-05-27 10:15:48
@misc{5b71a2af-43a7-4ca8-910f-47fd47a51139,
  abstract     = {{Statistical analyses of the 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 regression models have accounted for spatial correlation structure in combining monitoring data with land-use covariates. The current paper builds on these concepts to address spatio-temporal prediction of ambient concentrations of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) on the basis of a model representing spatially varying seasonal trends and spatial correlation structures. Our hierarchical methodology provides a pragmatic approach that fully exploits regulatory and other supplemental monitoring data which jointly define a complex spatio-temporal monitoring design. We explain the elements of the computational approach, including estimation of smoothed empirical orthogonal functions (SEOFs) as basis functions for temporal trend, spatial (“land use”) regression by Partial Least Squares (PLS), modeling of spatio-temporal correlation structure, and generalized universal kriging prediction of ambient exposure for subjects in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) project. Analyses are demonstrated in detail for the South California study area of the MESA Air project using AQS monitoring data from 2000 to 2006 and supplemental MESA Air monitoring data beginning in 2005. Results of application of the modeling and estimation methodology are presented also for five other MESA Air metropolitan study areas across the country with comments on current and future research developments.}},
  author       = {{Sampson, Paul D. and Szpiro, Adam A. and Sheppard, Lianne and Lindström, Johan and Kaufman, Joel D.}},
  language     = {{eng}},
  note         = {{Working Paper}},
  publisher    = {{Berkeley Electronic Press}},
  series       = {{UW Biostatistics Working Paper Series}},
  title        = {{Pragmatic Estimation of a Spatio-Temporal Air Quality Model With Irregular Monitoring Data}},
  url          = {{http://www.bepress.com/uwbiostat/paper353}},
  year         = {{2009}},
}