2003 Progress Report: A Source-Oriented Evaluation of the Combined Effects of Fine Particles and CopollutantsEPA Grant Number: R827997
Title: A Source-Oriented Evaluation of the Combined Effects of Fine Particles and Copollutants
Investigators: Ito, Kazuhiko , Thurston, George D. , Xue, Nan
Current Investigators: Ito, Kazuhiko , Thurston, George D. , Xue, Nan , Lall, Ramona , DeLeon, Samantha
Institution: New York University
Current Institution: New York University School of Medicine , Albert Einstein College of Medicine
EPA Project Officer: Chung, Serena
Project Period: February 18, 2000 through February 17, 2004 (Extended to February 17, 2006)
Project Period Covered by this Report: February 18, 2002 through February 17, 2003
Project Amount: $478,522
RFA: Airborne Particulate Matter Health Effects (1999) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air , Human Health , Particulate Matter
The objective of this research project is to apply novel approaches to estimate the combined effects of size-specific particulate matter (PM) air pollution (i.e., PM2.5) and their copollutants in major U.S. cities where source types, levels of PM copollutants, and weather patterns vary considerably. The alternative approaches to evaluate the health impacts of PM are needed because the current (prevailing) regression approach does not address possible source-specific toxicity of PM, or its interactive toxicity with gaseous copollutants that may or may not come from the same source. PM is a chemically nonspecific pollutant, and may originate or be derived from different emission source types. Thus, its toxicity should vary depending on its chemical composition and perhaps on the presence of any gaseous copollutants. In time-series analyses of the acute effects of PM, the prevailing approach to dealing with gaseous copollutants is to treat them as confounders, and to include them simultaneously in regression models. Such an approach can not only lead to misleading conclusions in "identifying" the causal pollutant (e.g., when pollutants are correlated and have varying extents of exposure error), but also cannot address the likely combined effects of PM and gaseous copollutants. The regulatory implications of these limitations in the prevailing regression approach could be serious, because the wrong sources may be identified for regulatory control. The expected reduction in risk (i.e., benefits) also may not be optimized, depending on the extent to which the PM and the gaseous copollutants share the same source types. The null hypothesis to be tested is that the PM effect size estimate is constant for all source types, regardless of composition or the presence of gaseous copollutants.
In Year 3 of the project, large databases containing PM2.5 speciation data, gaseous pollutants, weather data, and elderly hospital admissions have been assembled (2002 hospital admissions data are being obtained). The health effects analyses of the PM2.5 speciation data await the completion of the 2002 hospital data processing, but the characterization of the speciation data set for epidemiological application has been completed for the New York City (NYC) data, and a manuscript has been submitted. In addition, the associations between PM2.5/gaseous pollutants and cardiovascular and respiratory elderly hospital admissions in NYC have been investigated for the study period 1999-2001. A manuscript of the results is in preparation. The following paragraphs summarize these results.
The newly available PM2.5 chemical speciation data are useful for source-oriented evaluations of PM health effects. However, there are several issues that need to be considered in the analysis and interpretation of these data. One major issue is a monitor's representation of regional, subregional, and local air pollution exposures to the population in a city or metropolitan area. Because health outcomes in time-series air pollution epidemiological studies are aggregated over a wide geographical area, regional PM pollution may have smaller errors in exposure estimates than more spatially varying local pollution. We examined this issue using newly available PM2.5 speciation data from three monitors (a few miles apart) in NYC during 2001-2002. The strongest temporal correlations across the three monitors were found for the individual PM components that are related to secondary aerosols (e.g., S, NH4). The estimated source-apportioned PM2.5 mass using Absolute Principal Component Analysis showed positive correlations (approximately 0.8) across the three monitors for four major source types (secondary aerosols, soil, traffic, and oil burning/incineration). However, the size of the estimated mean PM2.5 mass contributions from each source types varied more across the three monitors, especially for locally influenced source types such as traffic. Thus, the implication of these results to the health effects analysis is that the size of risk estimates may be biased somewhat, but the strength of associations may be robust to the choice of monitor in this case.
We examined associations between PM2.5/gaseous pollutants (NO2, CO, SO2, and O3) and the cardiovascular and respiratory elderly hospital admissions in NYC for the period 1999-2001. The relationship among PM2.5, gaseous pollutants, and weather variables were first examined using cross-correlation functions across seasons after removing long-term trend and seasonal cycles. The associations between air pollution and the elderly hospital admissions were examined in four different ways: (1) single-pollutant model; (2) two-pollutant model with or without interaction terms; (3) single-pollutant model stratified with the level of a copollutant; and (4) model with factor-analysis derived composite pollution indices. The Poisson Generalized Linear Model was used to estimate pollution effects, adjusting for temporal trends, day-of-week, and temperature (quintile indicators with lags). The temporal relationships between PM2.5 and NO2, SO2, or CO were relatively unchanged across seasons with moderate to high (0.5 to 0.8) correlation. The short-term correlation between O3 and PM2.5 was positive in the warm season, but negative in the cold season, apparently reflecting the association between cold sunny clear days and relatively higher O3 in winter. In single-pollutant models, pneumonia admissions were positively significantly associated with PM2.5 and CO; chronic obstructive pulmaonarypulmonary disease admissions were associated with CO; ischemic heart disease admissions were associated with NO2 and CO; and, heart failure admissions were associated with PM2.5, NO2, and CO. Two-pollutant models generally resulted in attenuation of the risk estimates for one of the pollutants, while the other remained significant without improving model fit. In stratified analysis, PM2.5 risk estimates were larger for high CO days than for low CO days for both pneumonia and heart failure admissions. The factor-analysis derived components that represented the common variation of PM2.5, NO2, and CO were not as effective predictors of these health outcomes as the individual pollutants. Overall, the pollution mixture that includes PM2.5, NO2, and CO was associated with cardiovascular and respiratory elderly hospital admissions in NYC.
Characterization of the PM2.5 speciation data similar to that in NYC will be conducted for the Los Angeles and Pittsburgh data. Both mortality (up to 2001) and elderly hospital admissions data (up to 2002) will be conducted for NYC, Chicago, Santa Clara County (CA), Pittsburgh, and Philadelphia.