Research Advance Publication
Characterizing Spatial Patterns of Airborne Coarse Particulate (PM10-2.5) Mass and Chemical Components in Three Cities: The Multi-Ethnic Study of Atherosclerosis
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Citation: Zhang K, Larson TV, Gassett A, Szpiro AA, Daviglus M, Burke GL, Kaufman JD, Adar SD. Characterizing Spatial Patterns of Airborne Coarse Particulate (PM10-2.5) Mass and Chemical Components in Three Cities: The Multi-Ethnic Study of Atherosclerosis. Environ Health Perspect; http://dx.doi.org/10.1289/ehp.1307287.
Received: 28 June 2013
Accepted: 14 March 2014
Advance Publication: 18 March 2014
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Background: The long-term health effects of coarse particular matter (PM10-2.5) are challenging to assess due to 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 during 2-week periods 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 to 0.68), copper (CV R2, 0.51 to 0.86), phosphorus (CV R2, 0.50 to 0.76), silicon (CV R2, 0.48 to 0.93) and zinc (CV R2, 0.36 to 0.73). Models pooled across all cities performed inconsistently at capturing 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.
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