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Background: 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. Objectives: 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. Methods: PM10–2.5 snapshot campaigns were conducted in Chicago, Illinois; St. Paul, Minnesota; and Winston-Salem, 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). Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]