The long-term health effects of coarse particular matter (PM10-2.5) are challenging to assess because of a limited understanding of the spatial variation in PM10-2.5 mass and its chemical components. We conducted a spatially intensive field study and developed spatial prediction models for PM10-2.5 mass and four selected species (copper, zinc, phosphorus, and silicon) in three American cities. PM10-2.5 snapshot campaigns were conducted in Chicago, Illinois; St. Paul, Minnesota; and

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... , North Carolina, in 2009 for the Multi-Ethnic Study of Atherosclerosis and Coarse Airborne Particulate Matter (MESA Coarse). In each city, samples were collected simultaneously outside the homes of approximately 40 participants over 2 weeks in the winter and/or summer. City-specific and combined prediction models were developed using land use regression (LUR) and universal kriging (UK). Model performance was evaluated by cross-validation (CV). PM10-2.5 mass and species varied within and between cities in a manner that was predictable by geographic covariates. City-specific LUR models generally performed well for total mass (CV R2, 0.41-0.68), copper (CV R2, 0.51-0.86), phosphorus (CV R2, 0.50-0.76), silicon (CV R2, 0.48-0.93), and zinc (CV R2, 0.36-0.73). Models pooled across all cities inconsistently captured within-city variability. Little difference was observed between the performance of LUR and UK models in predicting concentrations. Characterization of fine-scale spatial variability of these often heterogeneous pollutants using geographic covariates should reduce exposure misclassification and increase the power of epidemiological studies investigating the long-term health impacts of PM10-2.5.