The Annals of Applied Statistics

Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Casey Olives, Lianne Sheppard, Johan Lindström, Paul D. Sampson, Joel D. Kaufman, and Adam A. Szpiro

Full-text: Open access

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 flexible 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 data sets 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 ($\mathrm{NO}_{x}$)—a pollutant of primary interest in MESA Air—in the Los Angeles metropolitan area via cross-validated $R^{2}$.

Our findings suggest that use of reduced-rank models can improve computational efficiency 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.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 4 (2014), 2509-2537.

Dates
First available in Project Euclid: 19 December 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1419001753

Digital Object Identifier
doi:10.1214/14-AOAS786

Mathematical Reviews number (MathSciNet)
MR3292507

Zentralblatt MATH identifier
06408788

Keywords
Spatiotemporal modeling reduced-rank air pollution kriging thin plate splines

Citation

Olives, Casey; Sheppard, Lianne; Lindström, Johan; Sampson, Paul D.; Kaufman, Joel D.; Szpiro, Adam A. Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Ann. Appl. Stat. 8 (2014), no. 4, 2509--2537. doi:10.1214/14-AOAS786. https://projecteuclid.org/euclid.aoas/1419001753


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Supplemental materials

  • Supplementary material: Supplement to “Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution”. We provide a detailed derivation of the optimized likelihood, comparisons of the prediction variances, discussion model selection by AIC for the paper “Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution” by Casey Olives, Lianne Sheppard, Johan Lindström, Paul D. Sampson, Joel D. Kaufman and Adam A. Szpiro.