Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution
Author
Summary, in English
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.
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.
Department/s
Publishing year
2014
Language
English
Pages
2509-2537
Publication/Series
Annals of Applied Statistics
Volume
8
Issue
4
Links
Document type
Journal article
Publisher
Institute of Mathematical Statistics
Topic
- Probability Theory and Statistics
Status
Published
ISBN/ISSN/Other
- ISSN: 1932-6157