BACKGROUND: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. OBJECTIVES: We developed a unified modeling approach for predicting fine particulate matter, @entity7459 , @entity28600 of @entity1568 , and black @entity505 (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of @entity731 and Air Pollution (MESA Air). METHODS: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and @entity1 ' homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations. RESULTS: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92. CONCLUSIONS: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated @entity1 -specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies. Citation: Keller JP, Olives C, Kim SY, Sheppard L, @entity1726 , Szpiro AA, Oron AP, Lindstr  m J, Vedal S, Kaufman JD. 2015. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi-Ethnic Study of @entity731 and Air Pollution. Environ Health Perspect 123:301-309;   http://dx.doi.org/10.1289/ehp.1408145.

A Unified Spatiotemporal Modeling Approach for Predicting Concentrations of Multiple Air Pollutants in the Multi-Ethnic Study of XXXX and Air Pollution.

@entity1 :: ('9606', 'Species') :: ['participants', 'participant'] @entity7459 :: ('MESH:D009585', 'Chemical') :: ['nitrogen dioxide'] @entity1568 :: ('MESH:D009584', 'Chemical') :: ['nitrogen'] @entity1726 :: ('OMIM:168600', 'Disease') :: ['Sampson PD'] @entity28600 :: ('MESH:D010087', 'Chemical') :: ['oxides'] @entity505 :: ('MESH:D002244', 'Chemical') :: ['carbon'] @entity731 :: ('MESH:D050197', 'Disease') :: ['Atherosclerosis']
@entity731 :: (MESH:D050197,Disease) :: ['Atherosclerosis']
