• Michelle L Bell and HEI Health Review Committee.
• School of Forestry & Environmental Studies and School of Public Health, Yale University, New Haven, Connecticut 06511, USA. michelle.bell@yale.edu
• Res Rep Health Eff Inst. 2012 Jan 1 (161): 5-38.

AbstractWhile numerous studies have demonstrated that shortterm exposure to particulate matter (PM*) is associated with adverse health effects, the characteristics of PM that cause harm are not well understood, and PM toxicity may vary by its chemical composition. This study investigates whether spatial and temporal patterns in PM health effect estimates based on total mass can be explained by spatial and temporal heterogeneity in the chemical composition of the particles. A database of 52 chemical components of PM with an aerodynamic diameter < or = 2.5 pm (PM2.5) was constructed for 187 U.S. counties, for 2000 through 2005, based on data from U.S. Environmental Protection Agency (U.S. EPA) monitoring networks. Components that covary with PM2.5 total mass and/or are large contributors to PM2.5, total mass were identified using actual and seasonally detrended data. Using Bayesian hierarchical modeling, seasonal and temporal variation in PM2.5 and the risk of total, cardiovascular, and respiratory hospital admissions were investigated for persons > or = 65 years in 202 U.S. counties for 1999 through 2005. Seasonal variation was investigated using three model structures with different underlying assumptions about the relationship between PM2.5 and hospitalizations. The findings of this study indicate higher effects in winter for both causes of hospitalization, and higher effects in the Northeast for cardiovascular admissions, although 53% of the counties were in this region. Higher PM2.5 effect estimates for cardiovascular or respiratory hospitalizations were observed in seasons and counties with a higher PM2.5 content of nickel (Ni), vanadium (V), or EC. Mortality effect estimates for PM with an aerodynamic diameter < or = 10 pm (PM10) were higher in seasons and counties with higher PM2.5 Ni content. The association between the Ni content of PM2.5 and effect estimates for cardiovascular hospitalization was robust to adjustment by EC, V, or both EC and V. An interquartile range (IQR) increase in the fraction of PM2.5 that is Ni was associated with a 14.9% (PI, 3.4-26.4) increase in the relative rates of cardiovascular hospital admissions associated with PM2.5 total mass adjusted for EC and V. No associations were observed between PM total mass health effect estimates and community-level variables for socioeconomic status, racial composition, or urbanicity. Communities with a higher prevalence of central AC had lower PM2.5 effect estimates for cardiovascular hospital admissions. The findings of this study indicate strong spatial and temporal variation in the chemical composition of the particle mixture and in the regional and seasonal variation in health effect estimates for PM2.5 total mass. The chemical composition of particles partially explained the heterogeneity of effect estimates. Observed associations could be related to the components themselves, to other components, or to a combination of components that share similar sources. The findings do not exclude the possibility that other components or characteristics of PM are harmful. The limitations of this study include the use of community-level aggregated data for exposure and for the variables used to investigate alternate hypotheses. Also, particle components and chemical forms (e.g., ammonium sulfate) not measured in the U.S. EPA database were not included. PM10 results in particular should be viewed with caution as the time frame of measurement and PM size fraction are different for the chemical composition and health effects data. A better understanding of the particular chemical components or sources that are most harmful to health can help decision-makers develop more targeted air pollution regulations and can aid in understanding the biological mechanisms by which air pollution-related health effects occur, thereby informing future research.

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