Journal
JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY
Volume 26, Issue 5, Pages 520-528Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/jes.2016.29
Keywords
empirical/statistical models; epidemiology; exposure modeling; particulate matter
Funding
- National Particle Component Toxicity (NPACT) - Health Effects Institute (HEI) (Health Effects Institute) [4749-RFA05]
- Multi-Ethnic Study of Atherosclerosis and Air Pollution by the U.S. Environmental Protection Agency (EPA) Science to Achieve Results program (STAR) [RD 831697]
- National Institute of Environmental Health Sciences (NIEHS) [T32ES015459, P50 ES015915]
- U.S. EPA [RD 83479601, CR-834077101-0]
- National Research Foundation of Korea (Basic Science Research Program through the National Research Foundation of Korea - Ministry of Education) [2013R1A6A3A04059017]
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Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R-2 was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R(2)s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies.
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