2005 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.
Current Investigators: Ito, Kazuhiko , DeLeon, Samantha , Lall, Ramona , Thurston, George D. , Xue, Nan
Institution: New York University
Current Institution: New York University , 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, 2004 through February 17, 2005
Project Amount: $478,522
RFA: Airborne Particulate Matter Health Effects (1999) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air , Health Effects , Particulate Matter
The objective of this 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 co-pollutants in major U.S. cities where source types, levels of PM co-pollutants, 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 co-pollutants that may or may not come from the same source. PM is a chemically non-specific 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 co-pollutants. In time-series analyses of the acute effects of PM, the prevailing approach to dealing with gaseous co-pollutants 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 co-pollutants. 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) may also not be optimized, depending on the extent to which the PM and the gaseous co-pollutants 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 co-pollutants.
During 2003-2004, progress was made in the following tasks: (1) application of alternative source apportionment techniques to the 2001-2002 PM2.5 speciation data in New York City; (2) analysis of 1999-2001 PM2.5, gaseous pollutants and elderly hospital admissions in New York City, Los Angeles, Chicago, Philadelphia, and Pittsburgh; (3) preliminary examination of the source-apportioned 2001-2002 PM2.5 data and elderly hospital admissions in New York City. The following paragraphs briefly summarize our findings.
In addition to the examination of monitor-to-monitor correlations of PM2.5 chemical species and application of absolute principal component analysis (APCA) that we described in our previous progress report, we also applied Positive Matrix Factorization (PMF) to the 2001-2002 New York City data and published the results (Ito, et al., 2004). We compared the mean source contributions and temporal correlations of the source-apportioned PM2.5 for each of the identified sources from three PM2.5 speciation monitors. We identified four major source/pollution types: (1) secondary (largely regional) aerosols, (2) soil, (3) traffic-related, and (4) residual oil burning/incineration, in each of the three monitors. The estimated source-apportioned PM2.5 mass showed generally the highest monitor-to-monitor correlation for the secondary aerosol factor (r range: 0.72 to 0.93). The correlation for the more localized traffic-related factor was more variable (r range: 0.26 to 0.95). The estimated mean PM2.5 mass contributions by source/pollution type across the monitors varied least for the secondary aerosol factor. The extent of variability in the source-apportioned PM2.5 mass by the monitor was comparable to that of the difference resulting from the two source-apportionment techniques used (i.e., APCA and PMF). The implication of the results of our study is that a source-oriented evaluation of PM health effects needs to take into consideration the uncertainty associated with spatial representativeness of the species measured at a single monitor.
We conducted a preliminary examination of the association between source-apportioned PM2.5 and elderly hospital admissions using our published source-apportioned PM2.5 in New York City, 2001-2002. We used the source-apportioned PM2.5 mass concentrations from the Queens College monitor as analyzed in our initial source apportionment using both PMF and APCA and examined their associations with chronic obstructive pulmonary disease (COPD) admissions for the entire city as well as for the borough of Queens. Poisson GLM was used to estimate percent excess risks per 5th-to-95th percentile increment of source-apportioned mass concentrations adjusting for day-of-week, seasonal trends, and weather. The two estimated major source contributors to PM2.5 were “secondary sulfate” (as identified by S and NH4 and the estimated mean of 6.0 µg/m3 by PMF and 6.8 µg/m3 by APCA) and “traffic” (as identified by EC and OC and the estimated mean of 5.1 µg/m3 by PMF and 5.5 µg/m3 by APCA). The results suggest that the PM2.5 apportioned to secondary sulfate shows the largest excess risk estimates (~ 20% per 20 µg/m3) and highest consistency between the two geographic categories and the two source apportionment techniques. In contrast, the risk estimates for “traffic”-related PM2.5 were less consistent across the areas as well as across the source-apportionment methods. The “soil”-related PM2.5 showed consistent null estimates regardless of the geographic categories or the methods. Future source-apportionment analyses also will need to consider traffic-related gaseous pollutants (CO and NO2), because the factor-analysis based approaches that examined only trace elements and EC/OC appear to show less clearly defined traffic-related PM components.
We examined associations between PM2.5/gaseous pollutants (NO2, CO, SO2, and O3) and the cardiovascular and respiratory elderly hospital admissions in New York City, Los Angeles, Chicago, Philadelphia, and Pittsburgh for the period 1999-2001. The results for New York City were presented at a meeting in summer 2004 (Ito, et al., 2004 [abstract]). The relationship among PM2.5, gaseous pollutants, and weather variables were first examined using cross-correlation functions (CCF) across seasons after removing long-term and seasonal cycles. The hospital admission outcomes analyzed include: total respiratory, total cardiovascular, pneumonia, COPD, ischemic heart diseases, dysrhythmias, heart failure, and stroke. The associations between air pollution and the elderly hospital admissions were examined in four ways: (1) single pollutant model; (2) two-pollutant model with or without interaction terms; (3) single pollutant model stratified with the level of a co-pollutant; and (4) model with factor-analysis derived composite pollution indices. Poisson Generalized Linear Model (GLM) was used to estimate pollution effects, adjusting for temporal trends, day-of-week, and weather variables. Several alternative weather models also were applied in sensitivity analyses. The temporal relationships between PM2.5 and NO2, SO2, or CO were relatively unchanged across seasons with moderate to high (0.4 to 0.8) correlation. The short-term correlation between O3 and PM2.5 was positive in warm seasons but negative in cold seasons. Generally, NO2 or CO was more significantly associated with cardiovascular/respiratory elderly hospital admissions than PM2.5 in these cities. Most consistent associations were found between NO2/CO (and PM2.5 in New York City and Philadelphia) and the total cardiovascular and heart failure admissions. The excess risk estimates per 5th-to-95th percentile increment of pollution ranged from approximately 5 to 15 percent. There was no strong evidence for synergistic effects among these pollutants. Overall, the pollution mixture that includes PM2.5, NO2, and CO, likely traffic-related air pollution, was associated with elderly hospital admissions.
We plan to: (1) finalize the analyses of PM2.5/gaseous pollution and elderly hospital admission data with 2002 data; (2) conduct analyses of PM2.5/gaseous pollution and mortality data for the period 1999-2002; and (3) analyze PM2.5 speciation data for their associations with hospital admissions and mortality in the cities that collected “sufficient” data (New York City, Philadelphia, and Pittsburgh).
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other project views:||All 12 publications||5 publications in selected types||All 5 journal articles|
||Ito K, Xue N, Thurston G. Spatial variation of PM2.5 chemical species and source-apportioned mass concentrations in New York City. Atmospheric Environment 2004;38(31):5269-5282.||
Supplemental Keywords:ambient air, air pollution, particulates, PM2.5, PM10, particulate matter, fine particles, copollutants, health effects, epidemiology, source apportionment, human health, modeling,, RFA, Health, Scientific Discipline, Air, Geographic Area, Waste, particulate matter, air toxics, Environmental Chemistry, Health Risk Assessment, Epidemiology, State, chemical mixtures, Risk Assessments, Susceptibility/Sensitive Population/Genetic Susceptibility, genetic susceptability, tropospheric ozone, Atmospheric Sciences, Ecology and Ecosystems, ambient air quality, copollutant exposures, PM10, co-factors, air pollutants, fine particles, human health effects, PM 2.5, toxicology, stratospheric ozone, Pennsylvania, ambient air, exposure, air pollution, Washington (WA), chronic health effects, human exposure, Illinois (IL), particulate exposure, fine particle sources, gaseous pollutants, Texas (TX), mortality studies, copollutanats, elderly, epidemiological studies, hospital admissions, PM, mortality, California (CA), human health risk, PA, New York (NY), Michigan (MI)