title: Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort.
abstract: 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.
authors.affiliation: Institute of Health and Environment, Seoul National University, Seoul, Korea.|Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
authors.forename: Sun-Young
authors.initials: SY
authors.lastname: Kim
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.|Department of Biostatistics, University of Washington, Seattle, Washington, USA.
authors.forename: Lianne
authors.initials: L
authors.lastname: Sheppard
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, USA.|Department of Mathematics and Statistics, Winona State University, Winona, Minnesota, USA.
authors.forename: Silas
authors.initials: S
authors.lastname: Bergen
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington, USA.
authors.forename: Adam A
authors.initials: AA
authors.lastname: Szpiro
authors.affiliation: Department of Statistics, University of Washington, Seattle, Washington, USA.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Sampson
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.|Department of Medicine, University of Washington, Seattle, Washington, USA.|Department of Epidemiology, University of Washington, Seattle, Washington, USA.
authors.forename: Joel D
authors.initials: JD
authors.lastname: Kaufman
authors.affiliation: Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
authors.forename: Sverre
authors.initials: S
authors.lastname: Vedal
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D050197:Atherosclerosis; D002244:Carbon / Q000032:analysis; D002947:Cities; D015331:Cohort Studies; D004781:Environmental Exposure / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods* / Q000592:standards*; D005006:Ethnicity; D006801:Humans; D010316:Particle Size; D052638:Particulate Matter; D015203:Reproducibility of Results; D012825:Silicon / Q000032:analysis; D062211:Spatio-Temporal Analysis; D013455:Sulfur / Q000032:analysis; D014481:United States
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D013455:Sulfur; D002244:Carbon; D012825:Silicon