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Environ Health Perspect. 2014 Aug;122(8):823-30. doi: 10.1289/ehp.1307287. Epub 2014 Mar 18.

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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  • 1Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas, USA.

Erratum in

  • Environ Health Perspect. 2014 Aug;122(8):A205.

Abstract

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.

PMID:
24642481
[PubMed - indexed for MEDLINE]
PMCID:
PMC4123025
Free PMC Article
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