Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution
Olives, Casey ; Sheppard, Lianne ; Lindström, Johan LU
; Sampson, Paul D
; Kaufman, Joel D
and Szpiro, Adam A
(2014)
In Annals of Applied Statistics
8(4).
p.2509-2537
- Abstract
- There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a exible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict... (More)
- There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a exible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members.
In general, spatio-temporal models are limited in their efficacy for large datasets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of
oxides of nitrogen (NOx) - a pollutant of primary interest in MESA Air - in the Los Angeles metropolitan area via cross-validated R2.
Our findings suggest that use of reduced-rank models can improve computational eciency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4730261
- author
- Olives, Casey
; Sheppard, Lianne
; Lindström, Johan
LU
; Sampson, Paul D
; Kaufman, Joel D
and Szpiro, Adam A
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Annals of Applied Statistics
- volume
- 8
- issue
- 4
- pages
- 2509 - 2537
- publisher
- Institute of Mathematical Statistics
- external identifiers
-
- wos:000347530200028
- scopus:84919461179
- pmid:27014398
- ISSN
- 1932-6157
- DOI
- 10.1214/14-AOAS786
- language
- English
- LU publication?
- yes
- id
- ff860524-a54f-4fea-9c50-a64f7e820d7a (old id 4730261)
- alternative location
- http://www.e-publications.org/ims/submission/AOAS/user/submissionFile/17155?confirm=66f14426
- date added to LUP
- 2016-04-01 11:14:27
- date last changed
- 2022-03-27 23:25:05
@article{ff860524-a54f-4fea-9c50-a64f7e820d7a,
abstract = {{There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a exible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members.<br/><br>
<br/><br>
In general, spatio-temporal models are limited in their efficacy for large datasets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of<br/><br>
oxides of nitrogen (NOx) - a pollutant of primary interest in MESA Air - in the Los Angeles metropolitan area via cross-validated R2.<br/><br>
<br/><br>
Our findings suggest that use of reduced-rank models can improve computational eciency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.}},
author = {{Olives, Casey and Sheppard, Lianne and Lindström, Johan and Sampson, Paul D and Kaufman, Joel D and Szpiro, Adam A}},
issn = {{1932-6157}},
language = {{eng}},
number = {{4}},
pages = {{2509--2537}},
publisher = {{Institute of Mathematical Statistics}},
series = {{Annals of Applied Statistics}},
title = {{Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution}},
url = {{http://dx.doi.org/10.1214/14-AOAS786}},
doi = {{10.1214/14-AOAS786}},
volume = {{8}},
year = {{2014}},
}