Characterizing spatial patterns of airborne coarse particulate (PM10-2.5) mass and chemical components in three cities: The multi-ethnic study of atherosclerosis

Zhang, K; Larson, TV; Gassett, A; Szpiro, AA; Daviglus, M; Burke, GL; Kaufman, JD; Adar, SD

HERO ID

2347426

Reference Type

Journal Article

Year

2014

Language

English

PMID

24642481

HERO ID 2347426
In Press No
Year 2014
Title Characterizing spatial patterns of airborne coarse particulate (PM10-2.5) mass and chemical components in three cities: The multi-ethnic study of atherosclerosis
Authors Zhang, K; Larson, TV; Gassett, A; Szpiro, AA; Daviglus, M; Burke, GL; Kaufman, JD; Adar, SD
Journal Environmental Health Perspectives
Volume 122
Issue 8
Page Numbers 823-830
Abstract <strong>BACKGROUND: </strong>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.<br /><br /><strong>OBJECTIVES: </strong>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.<br /><br /><strong>METHODS: </strong>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).<br /><br /><strong>RESULTS: </strong>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 R(2), 0.41 to 0.68), copper (CV R(2), 0.51 to 0.86), phosphorus (CV R(2), 0.50 to 0.76), silicon (CV R(2), 0.48 to 0.93) and zinc (CV R(2), 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.<br /><br /><strong>CONCLUSIONS: </strong>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.
Doi 10.1289/ehp.1307287
Pmid 24642481
Wosid WOS:000341713800017
Is Certified Translation No
Dupe Override No
Is Public Yes
Language Text English
Relationship(s)